From 8d3526c0869f0ad7747ff6bb02441140792b461c Mon Sep 17 00:00:00 2001 From: cocoshe <1228759711@qq.com> Date: Thu, 28 Mar 2024 13:38:36 +0800 Subject: [PATCH 001/196] fix doc --- miscs/repr_scripts.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/miscs/repr_scripts.sh b/miscs/repr_scripts.sh index 27fccbaf..cc9a8ae7 100644 --- a/miscs/repr_scripts.sh +++ b/miscs/repr_scripts.sh @@ -11,4 +11,4 @@ pip install -r llava_repr_requirements.txt # Run and exactly reproduce llava_v1.5 results! # mme as an example -accelerate launch --num_processes=1 -m lmms_eval --model llava --model_args pretrained="liuhaotian/llava-v1.5-7b,use_flash_attention_2=False" --tasks mme --batch_size 1 --log_samples --log_samples_sufix reproduce --output_path ./logs/ \ No newline at end of file +accelerate launch --num_processes=1 -m lmms_eval --model llava --model_args pretrained="liuhaotian/llava-v1.5-7b,use_flash_attention_2=False,device_map=auto" --tasks mme --batch_size 1 --log_samples --log_samples_suffix reproduce --output_path ./logs/ \ No newline at end of file From 22a4958e993463edff352ac033014f9a485706cc Mon Sep 17 00:00:00 2001 From: Bo Li Date: Thu, 4 Apr 2024 17:12:43 +0000 Subject: [PATCH 002/196] [WIP] adding mmbench dev evaluation (#75) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * WIP * Update GPT evaluation model name and sys prompt * 🛠️ Scale accuracy to percentage The accuracy value is now multiplied by 100 in the aggregation function to represent it as a percentage. Regarding the evaluation process, `math` module importation and refactoring reduce progress log verbosity by logging every 100 evaluations instead of 10. It prevents potential logging overflow. Handling of NaN values is added to ensure 'default_value' is set in case of missing data, avoiding errors in split, category, and l2-category assignments. Finally, reporting of categorical and l2-categorical accuracies is streamlined through a new `calculate_hit_rates` function, improving code readability and maintenance. Issue refs: #1427, #1533 * Update GPT evaluation model name and API configuration * Refactor MMBench_Evaluator class to handle missing columns * Add print statements for detailed results in MMBench-CN(CC), MMBench-CN(Dev), and MMBench-EN(Dev) evaluations * Refactor MMBench-CN and MMBench-EN evaluation functions * 🔄 Refactor result processing and logging logic - Simplified the result processing functions across different utility modules (`cc_utils.py`, `cn_utils.py`, `en_utils.py`) to unify the handling of multiple-choice options. Now, all options ("A" to "E") are dynamically added to the result data, and default to "nan" if not provided in the document. - Removed redundant keys directly from the process results dict creation to avoid clutter and align with the new dynamic addition of options. - In `mmbench_evals.py`, removed the unnecessary check for all splits being 'dev' and streamlined the evaluation loop by eliminating the progress bar (tqdm) for a cleaner log output. - Commented-out code and verbose logging during evaluation, which may have interfered with performance, has been removed for a more efficient and less intrusive logging experience. This cleanup reduces redundancy in the codebase and improves evaluation performance. Refs #2045 --------- Co-authored-by: Bo Li (cherry picked from commit a19278c2ea6ddcbca64d3cc7f4efec7fe5775121) --- lmms_eval/tasks/mmbench/cc_utils.py | 40 ++- lmms_eval/tasks/mmbench/cn_utils.py | 36 ++- lmms_eval/tasks/mmbench/en_utils.py | 45 ++- lmms_eval/tasks/mmbench/mmbench.yaml | 6 +- lmms_eval/tasks/mmbench/mmbench_cc.yaml | 6 +- lmms_eval/tasks/mmbench/mmbench_cn.yaml | 5 +- lmms_eval/tasks/mmbench/mmbench_cn_dev.yaml | 3 + lmms_eval/tasks/mmbench/mmbench_en.yaml | 1 + lmms_eval/tasks/mmbench/mmbench_en_dev.yaml | 7 +- lmms_eval/tasks/mmbench/mmbench_evals.py | 309 +++++++++++++++++++- 10 files changed, 439 insertions(+), 19 deletions(-) diff --git a/lmms_eval/tasks/mmbench/cc_utils.py b/lmms_eval/tasks/mmbench/cc_utils.py index 7009e012..abb24ab1 100644 --- a/lmms_eval/tasks/mmbench/cc_utils.py +++ b/lmms_eval/tasks/mmbench/cc_utils.py @@ -9,7 +9,7 @@ from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -with open(Path(__file__).parent / "mmbench_cn.yaml", "r") as f: +with open(Path(__file__).parent / "mmbench.yaml", "r") as f: raw_data = f.readlines() safe_data = [] for i, line in enumerate(raw_data): @@ -19,7 +19,18 @@ config = yaml.safe_load("".join(safe_data)) -mmbench_evaluator = MMBench_Evaluator(sys_prompt=config["metadata"]["sys_prompt"]) +GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"] +API_TYPE = os.getenv("API_TYPE", "openai") + +if API_TYPE == "openai": + API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") + API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") +elif API_TYPE == "azure": + API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") + API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") + + +mmbench_evaluator = MMBench_Evaluator(sys_prompt=config["metadata"]["sys_prompt"], API_KEY=API_KEY, API_URL=API_URL, model_version=GPT_EVAL_MODEL_NAME) def mmbench_doc_to_visual(doc): @@ -52,6 +63,14 @@ def mmbench_cn_cc_doc_to_text(doc, model_specific_prompt_kwargs=None): def mmbench_cn_cc_process_results(doc, results): model_response = results[0].strip() data = { + "gpt_eval_score": { + "index": doc["index"], + "question": doc["question"], + "answer": doc["answer"], + "prediction": model_response, + "source": doc["source"], + "category": doc["category"], + }, "submission": { "index": doc["index"], "question": doc["question"], @@ -59,14 +78,29 @@ def mmbench_cn_cc_process_results(doc, results): "prediction": model_response, "source": doc["source"], "category": doc["category"], - } + }, } option_candidate = ["A", "B", "C", "D", "E"] for c in option_candidate: data["submission"][c] = doc.get(c, "nan") + data["gpt_eval_score"][c] = doc.get(c, "nan") return data +def mmbench_cn_cc_aggregate_dev_results_eval(results, args): + print(f"============= MMBench-CN(CC) Detailed Results =============") + overall_acc, category_acc, l2_category_acc = mmbench_evaluator.eval_result(results, eval_method="openai") + file = generate_submission_file("mmbench_cn_cc_results.json", args) + details_info = { + "overall_acc": overall_acc, + "category_acc": category_acc, + "l2_category_acc": l2_category_acc, + } + with open(file, "w") as f: + json.dump(details_info, f) + return overall_acc * 100 + + def mmbench_cn_cc_aggregate_results(results, args): df = pd.DataFrame(results) file = generate_submission_file("mmbench_cn_cc_results.xlsx", args) diff --git a/lmms_eval/tasks/mmbench/cn_utils.py b/lmms_eval/tasks/mmbench/cn_utils.py index 812b9aa3..0ac64710 100644 --- a/lmms_eval/tasks/mmbench/cn_utils.py +++ b/lmms_eval/tasks/mmbench/cn_utils.py @@ -8,8 +8,9 @@ eval_logger = logging.getLogger("lmms-eval") from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -with open(Path(__file__).parent / "mmbench_cn.yaml", "r") as f: +with open(Path(__file__).parent / "mmbench.yaml", "r") as f: raw_data = f.readlines() safe_data = [] for i, line in enumerate(raw_data): @@ -19,7 +20,18 @@ config = yaml.safe_load("".join(safe_data)) -mmbench_evaluator = MMBench_Evaluator(sys_prompt=config["metadata"]["sys_prompt"]) +GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"] +API_TYPE = os.getenv("API_TYPE", "openai") + +if API_TYPE == "openai": + API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") + API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") +elif API_TYPE == "azure": + API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") + API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") + + +mmbench_evaluator = MMBench_Evaluator(sys_prompt=config["metadata"]["sys_prompt"], API_KEY=API_KEY, API_URL=API_URL, model_version=GPT_EVAL_MODEL_NAME) def mmbench_doc_to_visual(doc): @@ -55,6 +67,17 @@ def mmbench_doc_to_text(doc, model_specific_prompt_kwargs=None): def mmbench_process_results(doc, results): model_response = results[0].strip() data = { + "gpt_eval_score": { + "index": doc["index"], + "question": doc["question"], + "answer": doc["answer"], + "prediction": model_response, + "hint": doc["hint"], + "source": doc["source"], + "split": doc["split"], + "category": doc["category"], + "L2-category": doc["L2-category"], + }, "submission": { "index": doc["index"], "question": doc["question"], @@ -65,14 +88,21 @@ def mmbench_process_results(doc, results): "split": doc["split"], "category": doc["category"], "L2-category": doc["L2-category"], - } + }, } option_candidate = ["A", "B", "C", "D", "E"] for c in option_candidate: data["submission"][c] = doc.get(c, "nan") + data["gpt_eval_score"][c] = doc.get(c, "nan") return data +def mmbench_aggregate_dev_results_eval(results, args): + print(f"============= MMBench-CN(Dev) Detailed Results =============") + accuracy = mmbench_evaluator.eval_result(results, eval_method="openai") + return accuracy * 100 + + def mmbench_aggregate_dev_results(results, args): df = pd.DataFrame(results) excel_write_path = generate_submission_file("mmbench_cn_dev_results.xlsx", args) diff --git a/lmms_eval/tasks/mmbench/en_utils.py b/lmms_eval/tasks/mmbench/en_utils.py index 26e26000..1ddccbb6 100644 --- a/lmms_eval/tasks/mmbench/en_utils.py +++ b/lmms_eval/tasks/mmbench/en_utils.py @@ -9,7 +9,7 @@ from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -with open(Path(__file__).parent / "mmbench_en.yaml", "r") as f: +with open(Path(__file__).parent / "mmbench.yaml", "r") as f: raw_data = f.readlines() safe_data = [] for i, line in enumerate(raw_data): @@ -19,7 +19,18 @@ config = yaml.safe_load("".join(safe_data)) -mmbench_evaluator = MMBench_Evaluator(sys_prompt=config["metadata"]["sys_prompt"]) +GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"] +API_TYPE = os.getenv("API_TYPE", "openai") + +if API_TYPE == "openai": + API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") + API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") +elif API_TYPE == "azure": + API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") + API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") + + +mmbench_evaluator = MMBench_Evaluator(sys_prompt=config["metadata"]["sys_prompt"], API_KEY=API_KEY, API_URL=API_URL, model_version=GPT_EVAL_MODEL_NAME) def mmbench_doc_to_visual(doc): @@ -55,6 +66,17 @@ def mmbench_doc_to_text(doc, model_specific_prompt_kwargs=None): def mmbench_process_results(doc, results): model_response = results[0].strip() data = { + "gpt_eval_score": { + "index": doc["index"], + "question": doc["question"], + "answer": doc["answer"], + "prediction": model_response, + "hint": doc["hint"], + "source": doc["source"], + "split": doc["split"], + "category": doc["category"], + "L2-category": doc["L2-category"], + }, "submission": { "index": doc["index"], "question": doc["question"], @@ -65,15 +87,30 @@ def mmbench_process_results(doc, results): "split": doc["split"], "category": doc["category"], "L2-category": doc["L2-category"], - } + }, } option_candidate = ["A", "B", "C", "D", "E"] for c in option_candidate: data["submission"][c] = doc.get(c, "nan") + data["gpt_eval_score"][c] = doc.get(c, "nan") return data -def mmbench_aggregate_dev_results(results, args): +def mmbench_aggregate_dev_results_eval(results, args): + print(f"============= MMBench-EN(Dev) Detailed Results =============") + overall_acc, category_acc, l2_category_acc = mmbench_evaluator.eval_result(results, eval_method="openai") + file = generate_submission_file("mmbench_en_dev_results.json", args) + details_info = { + "overall_acc": overall_acc, + "category_acc": category_acc, + "l2_category_acc": l2_category_acc, + } + with open(file, "w") as f: + json.dump(details_info, f) + return overall_acc * 100 + + +def mmbench_aggregate_dev_results_submission(results, args): df = pd.DataFrame(results) excel_write_path = generate_submission_file("mmbench_en_dev_results.xlsx", args) with pd.ExcelWriter(excel_write_path) as writer: diff --git a/lmms_eval/tasks/mmbench/mmbench.yaml b/lmms_eval/tasks/mmbench/mmbench.yaml index 45c2ed0d..821065ee 100644 --- a/lmms_eval/tasks/mmbench/mmbench.yaml +++ b/lmms_eval/tasks/mmbench/mmbench.yaml @@ -4,4 +4,8 @@ task: - mmbench_en_test - mmbench_cn_dev - mmbench_cn_test - - mmbench_cn_cc \ No newline at end of file + - mmbench_cn_cc +metadata: + version: 0.0 + sys_prompt: "There are several options:" + gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file diff --git a/lmms_eval/tasks/mmbench/mmbench_cc.yaml b/lmms_eval/tasks/mmbench/mmbench_cc.yaml index 238aa10c..4a0d5895 100644 --- a/lmms_eval/tasks/mmbench/mmbench_cc.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_cc.yaml @@ -16,12 +16,14 @@ generation_kwargs: do_sample: false process_results: !function cc_utils.mmbench_cn_cc_process_results metric_list: + - metric: gpt_eval_score + aggregation: !function cc_utils.mmbench_cn_cc_aggregate_dev_results_eval + higher_is_better: true - metric: submission aggregation: !function cc_utils.mmbench_cn_cc_aggregate_results metadata: version: 0.0 - gpt_eval_model_name: "gpt-3.5-turbo" - quick_extract: true + gpt_eval_model_name: "gpt-3.5-turbo-0613" model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/mmbench/mmbench_cn.yaml b/lmms_eval/tasks/mmbench/mmbench_cn.yaml index 6232531c..9da764cc 100644 --- a/lmms_eval/tasks/mmbench/mmbench_cn.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_cn.yaml @@ -5,6 +5,5 @@ task: - mmbench_cn_cc metadata: version: 0.0 - gpt_eval_model_name: "gpt-3.5-turbo" - quick_extract: true - sys_prompt: "有如下几个选项：" + gpt_eval_model_name: "gpt-3.5-turbo-0613" + sys_prompt: "有如下几个选项：" \ No newline at end of file diff --git a/lmms_eval/tasks/mmbench/mmbench_cn_dev.yaml b/lmms_eval/tasks/mmbench/mmbench_cn_dev.yaml index 3d7b9d98..b3aaa545 100644 --- a/lmms_eval/tasks/mmbench/mmbench_cn_dev.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_cn_dev.yaml @@ -1,6 +1,9 @@ task: "mmbench_cn_dev" test_split: "dev" metric_list: + - metric: gpt_eval_score + aggregation: !function cn_utils.mmbench_aggregate_dev_results_eval + higher_is_better: true - metric: submission higher_is_better: true aggregation: !function cn_utils.mmbench_aggregate_dev_results diff --git a/lmms_eval/tasks/mmbench/mmbench_en.yaml b/lmms_eval/tasks/mmbench/mmbench_en.yaml index 9fa757cc..0a5c5580 100644 --- a/lmms_eval/tasks/mmbench/mmbench_en.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_en.yaml @@ -5,3 +5,4 @@ task: metadata: version: 0.0 sys_prompt: "There are several options:" + gpt_eval_model_name: "gpt-3.5-turbo-0613" diff --git a/lmms_eval/tasks/mmbench/mmbench_en_dev.yaml b/lmms_eval/tasks/mmbench/mmbench_en_dev.yaml index b4f4a2e9..f9e6babf 100644 --- a/lmms_eval/tasks/mmbench/mmbench_en_dev.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_en_dev.yaml @@ -2,6 +2,9 @@ task: "mmbench_en_dev" test_split: dev include: _default_template_mmbench_en_yaml metric_list: - - metric: submission - aggregation: !function en_utils.mmbench_aggregate_dev_results + - metric: gpt_eval_score + aggregation: !function en_utils.mmbench_aggregate_dev_results_eval higher_is_better: true + - metric: submission + aggregation: !function en_utils.mmbench_aggregate_dev_results_submission + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmbench/mmbench_evals.py b/lmms_eval/tasks/mmbench/mmbench_evals.py index bdae49d1..7868157e 100644 --- a/lmms_eval/tasks/mmbench/mmbench_evals.py +++ b/lmms_eval/tasks/mmbench/mmbench_evals.py @@ -1,9 +1,26 @@ +import os.path as osp +import time +import random as rd +import string +from collections import defaultdict +import requests + +import math +import numpy as np import pandas as pd +from tqdm import tqdm + +import logging + +eval_logger = logging.getLogger("lmms-eval") class MMBench_Evaluator: - def __init__(self, sys_prompt="There are several options:"): + def __init__(self, sys_prompt="There are several options:", API_KEY="", API_URL="", model_version="gpt-3.5-turbo-0613"): self.sys_prompt = sys_prompt + self.model_version = model_version + self.API_KEY = API_KEY + self.API_URL = API_URL def create_options_prompt(self, row_data, option_candidate): available_keys = set(row_data.keys()) & set(option_candidate) @@ -14,3 +31,293 @@ def create_options_prompt(self, row_data, option_candidate): if pd.notna(item) and item != "nan": options_prompt += f"{key}. {item}\n" return options_prompt.rstrip("\n"), sorted_options + + # Prompt Building + def build_option_str(self, option_list): + chars = string.ascii_uppercase + s = "There are several options: \n" + for c, opt in zip(chars, option_list): + if not pd.isna(opt): + s += f"{c}. {opt}\n" + else: + return s + return s + + def extract_options(self, item): + options = [] + for c in "ABCD": + if c in item and not pd.isna(item[c]): + options.append(item[c]) + else: + return options + return options + + def build_choices(self, item): + ret = {} + for ch in "ABCD": + if not pd.isna(item[ch]): + ret[ch] = item[ch] + return ret + + def build_prompt(self, question, options, prediction): + tmpl = ( + "You are an AI assistant who will help me to match an answer " + "with several options of a single-choice question. " + "You are provided with a question, several options, and an answer, " + "and you need to find which option is most similar to the answer. " + "If the meaning of all options are significantly different " + "from the answer, output E. " + "Your should output a single uppercase character in A, B, C, D " + "(if they are valid options), and E. \n" + "Example 1: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog\nAnswer: a cute teddy bear\nYour output: A\n" + "Example 2: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog\nAnswer: Spider\nYour output: E\n" + "Example 3: \n" + "Question: {}?\nOptions: {}\nAnswer: {}\nYour output: " + ) + return tmpl.format(question, options, prediction) + + # Prefetch Answers + def can_infer_option(self, answer, num_choice=5): + choices = string.ascii_uppercase[:num_choice] + if "Failed to obtain answer via API" in answer: + return False + + def count(splits, choices="ABCD", prefix="", suffix=""): + cnt = 0 + for c in choices: + if prefix + c + suffix in splits: + cnt += 1 + return cnt + + splits = [x.strip() for x in answer.split()] + if count(splits, choices) == 1: + for ch in choices: + if "A" in splits and len(splits) > 3: + eval_logger.info(f"A might be a quantifier in the string: {answer}.") + break + if ch in splits: + return ch + tups = [("", "."), ("", ","), ("", ":"), ("", ")"), ("", ")."), ("(", ")"), ("(", ")."), (":", ""), (":", ","), (":", "."), (":", ")"), (":", ").")] + for tup in tups: + if count(splits, choices, prefix=tup[0], suffix=tup[1]) == 1: + for ch in choices: + if tup[0] + ch + tup[1] in splits: + return ch + return False + + def can_infer_text(self, answer, choices): + answer = answer.lower() + assert isinstance(choices, dict) + for k in choices: + assert k in "ABCD" + choices[k] = str(choices[k]).lower() + cands = [] + for k in choices: + if choices[k] in answer: + cands.append(k) + if len(cands) == 1: + return cands[0] + return False + + def can_infer(self, answer, choices): + copt = self.can_infer_option(answer) + return copt if copt else self.can_infer_text(answer, choices) + + def prefetch_answer(self, item): + choices = self.build_choices(item) + return self.can_infer(item["prediction"], choices) + + def _post_request(self, payload): + headers = { + "Authorization": f"Bearer {self.API_KEY}", + "Content-Type": "application/json", + } + response = requests.post(self.API_URL, headers=headers, json=payload, timeout=30) + response.raise_for_status() + return response.json() + + def get_chat_response(self, prompt, temperature=0, max_tokens=256, n=1, patience=5, sleep_time=3): + messages = [ + {"role": "user", "content": prompt}, + ] + payload = {"model": self.model_version, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "n": n} + + while patience > 0: + patience -= 1 + try: + response = self._post_request(payload) + if n == 1: + prediction = response["choices"][0]["message"]["content"].strip() + if prediction and prediction != "": + return prediction + else: + prediction = [choice["message"]["content"].strip() for choice in response["choices"]] + if prediction and prediction[0] != "": + return prediction + + except Exception as e: + eval_logger.info(f"Attempt {patience + 1} failed with error: {e}") + if sleep_time > 0: + time.sleep(sleep_time) + + return "Failed to obtain answer via API" + + def extract_answer_from_item(self, item): + options = self.extract_options(item) + option_str = self.build_option_str(options) + + prompt = self.build_prompt(item["question"], option_str, item["prediction"]) + retry = 3 + choices = self.build_choices(item) + + ret = self.can_infer(item["prediction"], choices) + if ret: + return ret, item["prediction"] + + while retry: + ans = self.get_chat_response(prompt) + if "Failed to obtain answer via API" in ans: + msg = "GPT API failed to answer. " + eval_logger.info(msg) + retry -= 1 + else: + ret = self.can_infer(ans, choices) + if ret: + return ret, ans + else: + eval_logger.info(f'GPT output includes 0 / >1 letter in "ABCD": {ans}') + retry -= 1 + + if retry == 0: + num_options = sum([ch in item for ch in "ABCD"]) + if num_options >= 2: + chars = string.ascii_uppercase[:num_options] + chars = chars + "E" + num_options += 1 + tmp = rd.randint(0, num_options - 1) + return chars[tmp], "Failed to predict, thus randomly generate one. " + + # Extract answer from multiple rolling records + def eval_sub_data(self, sub_data, answer_map): + lt = len(sub_data) + GT, PRED = [], [] + for i in range(lt): + item = sub_data.iloc[i] + idx = item["index"] + GT.append(answer_map[idx]) + PRED.append(self.prefetch_answer(item)) + if PRED[-1] and (GT[-1] != PRED[-1]): + return 0 + + for i in range(lt): + if PRED[i]: + continue + else: + ret, _ = self.extract_answer_from_item(sub_data.iloc[i]) + PRED[i] = ret + if PRED[i] != GT[i]: + return 0 + return 1 + + def calculate_hit_rates(self, data): + overall_hit_rate = data["hit"].mean() + + category_hit_rate = {} + if "category" in data.columns: + # Category-based hit rate + category_hit_rate = data.groupby("category")["hit"].mean().to_dict() + + # l2-category based hit rate + l2_category_hit_rate = {} + if "l2-category" in data.columns: + l2_category_hit_rate = data.groupby("l2-category")["hit"].mean().to_dict() + + return overall_hit_rate, category_hit_rate, l2_category_hit_rate + + # Evaluate Results + def eval_result(self, results, eval_method): + rd.seed(2680) + assert eval_method == "openai" + # Set a large retry number to avoid failure + # model = OpenAI('gpt-3.5-turbo-0613', retry=99) + + # double_log(f'Evaluating {eval_file}', fout) + + # result_file = eval_file.replace('.xlsx', f'_{eval_method}_result.pkl') + result = {} + # if osp.exists(result_file): + # result = load(result_file) + + # data = load(eval_file) + data = pd.DataFrame(results) + data = data.sort_values(by="index") + data["prediction"] = [str(x) for x in data["prediction"]] + for k in data.keys(): + data[k.lower() if k not in "ABCD" else k] = data.pop(k) + + # meta = load(meta_file) + + data_main = data[data["index"] < int(1e6)] + + data_main["hit"] = 0 + cate_map = {i: c for i, c in zip(data["index"], data["category"])} + answer_map = {i: c for i, c in zip(data["index"], data["answer"])} + if "l2-category" in data.columns: + l2_cate_map = {i: c for i, c in zip(data["index"], data["l2-category"])} + + lt = len(data_main) + hit, tot = 0, 0 + + for i in range(lt): + # Dealing with the normal part + item_main = data_main.iloc[i] + idx = item_main["index"] + + if idx in result: + correct = result[idx] + assert correct in [0, 1] + hit += correct + tot += 1 + continue + + sub_data = data[data["index"] % int(1e6) == idx] + ret = self.eval_sub_data(sub_data, answer_map) + result[idx] = ret + hit += ret + tot += 1 + + data_main.loc[data_main["index"] == idx, "hit"] = ret + # if (i + 1) % 100 == 0: + # eval_logger.info(f"Evaluating: {i + 1}/{lt}, Acc: {hit / tot * 100: .2f}%. ") + + indices = data_main["index"] + data_main = data_main.set_index("index") + data_main["category"] = [cate_map[i] if not math.isnan(i) else "uncategorized" for i in indices] + if "l2-category" in data_main.columns: + data_main["l2-category"] = [l2_cate_map[i] if not math.isnan(i) else "uncategorized" for i in indices] + + overall_hit_rate, category_hit_rate, l2_category_hit_rate = self.calculate_hit_rates(data_main) + + if "category" in data_main.columns: + print(f"Category Acc. (dev):") + for category_key in category_hit_rate: + if category_key == "split": + continue + + category_percentage = category_hit_rate[category_key] * 100 + print(f"\t{category_key}: {category_percentage:.3f}") + + if "l2-category" in data_main.columns: + print(f"L2-category Acc. (dev):") + for l2_category_key in l2_category_hit_rate: + if l2_category_key == "split": + continue + + l2_category_percentage = l2_category_hit_rate[l2_category_key] * 100 + print(f"\t{l2_category_key}: {l2_category_percentage:.3f}") + + return overall_hit_rate, category_hit_rate, l2_category_hit_rate From d8530514a5ef9378d2adeaceb228b60ec25a6718 Mon Sep 17 00:00:00 2001 From: Gagan Bhatia <49101362+gagan3012@users.noreply.github.com> Date: Fri, 12 Apr 2024 17:19:49 -0700 Subject: [PATCH 003/196] Create README.md --- lmms_eval/tasks/multilingual-llava-bench-in-the-wild/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/README.md diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/README.md b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/README.md new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/README.md @@ -0,0 +1 @@ + From 373265f24e7a89cbd49ab724a2e388cc0930be78 Mon Sep 17 00:00:00 2001 From: Gagan Bhatia <49101362+gagan3012@users.noreply.github.com> Date: Fri, 12 Apr 2024 17:21:39 -0700 Subject: [PATCH 004/196] Add files via upload --- .../_generate_configs.py | 54 +++++ .../arabic_llava_in_the_wild.yaml | 42 ++++ .../bengali_llava_in_the_wild.yaml | 42 ++++ .../chinese_llava_in_the_wild.yaml | 42 ++++ .../french_llava_in_the_wild.yaml | 42 ++++ .../hindi_llava_in_the_wild.yaml | 42 ++++ .../japanese_llava_in_the_wild.yaml | 42 ++++ .../rule.json | 11 + .../russian_llava_in_the_wild.yaml | 42 ++++ .../spanish_llava_in_the_wild.yaml | 42 ++++ .../urdu_llava_in_the_wild.yaml | 42 ++++ .../utils.py | 197 ++++++++++++++++++ 12 files changed, 640 insertions(+) create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_generate_configs.py create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/rule.json create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_generate_configs.py b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_generate_configs.py new file mode 100644 index 00000000..0f189b82 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_generate_configs.py @@ -0,0 +1,54 @@ +from datasets import load_dataset + +# dataset = load_dataset("gagan3012/multilingual-llava-bench") + +configs = ['arabic', 'bengali', 'chinese', 'french', 'hindi', 'japanese', 'russian', 'spanish', 'urdu'] + +for config in configs: + yaml_output = f""" + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: {config} + token: True + task: "llava_in_the_wild_{config}" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + """ + + with open(f"{config}_llava_in_the_wild.yaml", "w") as f: + f.write(yaml_output) + +# Path: _generate_configs.py \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml new file mode 100644 index 00000000..83447d0d --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml @@ -0,0 +1,42 @@ + + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: arabic + token: True + task: "llava_in_the_wild_arabic" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml new file mode 100644 index 00000000..71ec6f86 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml @@ -0,0 +1,42 @@ + + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: bengali + token: True + task: "llava_in_the_wild_bengali" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml new file mode 100644 index 00000000..5f8f487d --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml @@ -0,0 +1,42 @@ + + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: chinese + token: True + task: "llava_in_the_wild_chinese" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml new file mode 100644 index 00000000..b1c004e3 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml @@ -0,0 +1,42 @@ + + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: french + token: True + task: "llava_in_the_wild_french" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml new file mode 100644 index 00000000..507afce7 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml @@ -0,0 +1,42 @@ + + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: hindi + token: True + task: "llava_in_the_wild_hindi" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml new file mode 100644 index 00000000..a7fb6e96 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml @@ -0,0 +1,42 @@ + + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: japanese + token: True + task: "llava_in_the_wild_japanese" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/rule.json b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/rule.json new file mode 100644 index 00000000..26c7f4e0 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/rule.json @@ -0,0 +1,11 @@ +{ + "coding": {"role": "Assistant", "prompt": "Your task is to evaluate the coding abilities of the above two assistants. They have been asked to implement a program to solve a given problem. Please review their code submissions, paying close attention to their problem-solving approach, code structure, readability, and the inclusion of helpful comments.\n\nPlease ensure that the assistants' submissions:\n\n1. Correctly implement the given problem statement.\n2. Contain accurate and efficient code.\n3. Include clear and concise comments that explain the code's logic and functionality.\n4. Adhere to proper coding standards and best practices.\n\nOnce you have carefully reviewed both submissions, provide detailed feedback on their strengths and weaknesses, along with any suggestions for improvement. You should first output a single line containing two scores on the scale of 1-10 (1: no code/no sense; 10: perfect) for Assistant 1 and 2, respectively. Then give extra comments starting from the next line."}, + "math": {"role": "Assistant", "prompt": "We would like to request your feedback on the mathematical proficiency of two AI assistants regarding the given user question.\nFirstly, please solve the problem independently, without referring to the answers provided by Assistant 1 and Assistant 2.\nAfterward, please examine the problem-solving process of Assistant 1 and Assistant 2 step-by-step to ensure their correctness, identifying any incorrect steps if present. Your evaluation should take into account not only the answer but also the problem-solving steps.\nFinally, please output a Python tuple containing two numerical scores for Assistant 1 and Assistant 2, ranging from 1 to 10, respectively. If applicable, explain the reasons for any variations in their scores and determine which assistant performed better."}, + "default": {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, + "conv": {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, + "detail": {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, + "complex": {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, + "llava_bench_conv": {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, + "llava_bench_detail": {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, + "llava_bench_complex": {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."} +} \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml new file mode 100644 index 00000000..ecb9c146 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml @@ -0,0 +1,42 @@ + + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: russian + token: True + task: "llava_in_the_wild_russian" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml new file mode 100644 index 00000000..02641ca2 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml @@ -0,0 +1,42 @@ + + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: spanish + token: True + task: "llava_in_the_wild_spanish" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml new file mode 100644 index 00000000..6bf28c0b --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml @@ -0,0 +1,42 @@ + + dataset_path: "gagan3012/multilingual-llava-bench" + dataset_kwargs: + config: urdu + token: True + task: "llava_in_the_wild_urdu" + test_split: train + output_type: generate_until + doc_to_visual: !function utils.llava_doc_to_visual + doc_to_text: !function utils.llava_doc_to_text + doc_to_target: "gpt_answer" + generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false + process_results: !function utils.llava_process_results + metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true + metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" + model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py new file mode 100644 index 00000000..ac86ee99 --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py @@ -0,0 +1,197 @@ +import json +import logging +import os +import requests +import numpy as np +import openai +from openai import OpenAI +import time +import yaml +from pathlib import Path +from copy import deepcopy + +eval_logger = logging.getLogger("lmms-eval") +NUM_SECONDS_TO_SLEEP = 5 + +LLAVA_W_METRICS = ["gpt_eval_llava_conv", "gpt_eval_llava_detail", "gpt_eval_llava_complex"] + +rule_dict = json.load(open(os.path.join(os.path.dirname(os.path.abspath(__file__)), "rule.json"), "r")) + +with open(Path(__file__).parent / "llava-in-the-wild.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + + config = yaml.safe_load("".join(safe_data)) + +GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"] + +API_TYPE = os.getenv("API_TYPE", "openai") + +if API_TYPE == "openai": + API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") + API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") + headers = { + "Authorization": f"Bearer {API_KEY}", + "Content-Type": "application/json", + } +elif API_TYPE == "azure": + API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") + API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") + headers = { + "api-key": API_KEY, + "Content-Type": "application/json", + } + + +def get_eval(content: str, max_tokens: int, retries: int = 5): + global headers + + messages = [ + { + "role": "system", + "content": "You are a helpful and precise assistant for checking the quality of the answer.", + }, + {"role": "user", "content": content}, + ] + + payload = { + "model": GPT_EVAL_MODEL_NAME, + "messages": messages, + "temperature": 0.2, + "max_tokens": max_tokens, + } + + for attempt in range(retries): + try: + response = requests.post(API_URL, headers=headers, json=payload, timeout=60) + response.raise_for_status() + response_data = response.json() + + content = response_data["choices"][0]["message"]["content"].strip() + if content != "": + return content, response_data["model"] + break # If successful, break out of the loop + + except Exception as e: + eval_logger.info(f"Attempt {attempt + 1} failed with error: {e}") + if attempt < retries: # If we have retries left, sleep and then continue to next attempt + time.sleep(NUM_SECONDS_TO_SLEEP) + else: # If this was the last attempt, log and return empty + eval_logger.error(f"All {retries} attempts failed. Last error message: {e}") + return "", "" + return "", "" + + +def parse_score(review): + try: + score_pair = review.split("\n")[0] + score_pair = score_pair.replace(",", " ") + sp = score_pair.split(" ") + if len(sp) == 2: + return [float(sp[0]), float(sp[1])] + else: + eval_logger.debug(f"Can not split: {review}. Returning [-1, -1]") + return [-1, -1] + except Exception as e: + eval_logger.debug(f"Error: {e}. Returning [-1, -1]") + return [-1, -1] + + +def llava_doc_to_visual(doc): + return [doc["image"].convert("RGB")] + + +def llava_doc_to_text(doc, model_specific_prompt_kwargs=None): + if model_specific_prompt_kwargs is None: + model_specific_prompt_kwargs = {} + pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") + post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") + return f"{pre_prompt}{doc['question']}{post_prompt}" + + +def llava_process_results(doc, result): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name (in this case coco_bleu), value: metric value + """ + try: + question = doc.get("question", "") + ans1 = doc.get("gpt_answer", "") + ans2 = result[0] if result else "" + captions = doc.get("caption", []) + context = "\n".join(captions) if isinstance(captions, list) else captions + category = "llava_bench_" + doc.get("category", "") + rule = rule_dict.get(category, {}) + prompt = rule.get("prompt", "") + role = rule.get("role", "user") + content = f"[Context]\n{context}\n\n" f"[Question]\n{question}\n\n" f"[{role} 1]\n{ans1}\n\n[End of {role} 1]\n\n" f"[{role} 2]\n{ans2}\n\n[End of {role} 2]\n\n" f"[System]\n{prompt}\n\n" + + review, model_name = get_eval(content, 1024) + scores = parse_score(review) + except Exception as e: + eval_logger.error(f"Error for Question ID: {doc.get('question_id', 'Unknown')}: {e}") + review = "Failed to Get a Proper Review." + model_name = "Failed Request" + scores = [-1, -1] + + metric = f"gpt_eval_llava_{doc.get('category', 'all')}" + category_review_dict = {"question": question, "ans1": ans1, "ans2": ans2, "context": context, "category": category, "review": review, "scores": scores, "eval_model": model_name, "content": content} + + non_category_review_dict = deepcopy(category_review_dict) + non_category_review_dict["scores"] = [-999, -999] + + data_dict = {} + for m in LLAVA_W_METRICS: + if m == metric: + data_dict[m] = category_review_dict + else: + data_dict[m] = non_category_review_dict + data_dict["gpt_eval_llava_all"] = category_review_dict + + # return {"gpt_eval_llava_all": review_dict} + return data_dict + + +def llava_conv_aggregation(results): + return llava_aggregation(results, "conv") + + +def llava_complex_aggregation(results): + return llava_aggregation(results, "complex") + + +def llava_detail_aggregation(results): + return llava_aggregation(results, "detail") + + +def llava_all_aggregation(results): + return llava_aggregation(results, "all") + + +def llava_aggregation(results, category): + try: + scores = [] + for result in results: + if -999 in result["scores"]: + continue + scores.append(result["scores"]) + + stats = np.asarray(scores).mean(0).tolist() + stats = [round(x, 3) for x in stats] + # gpt4_score_percentage = stats[0] * 10 + # model_score_percentage = stats[1] * 10 + # eval_logger.info(f"Category: {category}") + # eval_logger.info(f"GPT4 Score: {gpt4_score_percentage:.1f}%") + # eval_logger.info(f"Model Score: {model_score_percentage:.1f}%") + # eval_logger.info("=========================") + return round(stats[1] / stats[0] * 100, 1) + except Exception as e: + eval_logger.info(f"Error in llava_aggregation: {e}, and in category: {category}") + return None From c1a5472135c3b84061b64d997ab50dda0412ba4f Mon Sep 17 00:00:00 2001 From: CaraJ7 <1350074492@qq.com> Date: Sat, 20 Apr 2024 21:45:34 +0800 Subject: [PATCH 005/196] Add MathVerse --- lmms_eval/tasks/mathverse/mathverse.yaml | 14 + lmms_eval/tasks/mathverse/mathverse_evals.py | 306 ++++++++++++++++++ .../tasks/mathverse/mathverse_testmini.yaml | 34 ++ .../mathverse_testmini_text_dominant.yaml | 34 ++ .../mathverse_testmini_text_lite.yaml | 34 ++ .../mathverse_testmini_text_only.yaml | 34 ++ .../mathverse_testmini_vision_dominant.yaml | 34 ++ .../mathverse_testmini_vision_intensive.yaml | 34 ++ .../mathverse_testmini_vision_only.yaml | 34 ++ lmms_eval/tasks/mathverse/utils.py | 94 ++++++ 10 files changed, 652 insertions(+) create mode 100644 lmms_eval/tasks/mathverse/mathverse.yaml create mode 100644 lmms_eval/tasks/mathverse/mathverse_evals.py create mode 100644 lmms_eval/tasks/mathverse/mathverse_testmini.yaml create mode 100644 lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml create mode 100644 lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml create mode 100644 lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml create mode 100644 lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml create mode 100644 lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml create mode 100644 lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml create mode 100644 lmms_eval/tasks/mathverse/utils.py diff --git a/lmms_eval/tasks/mathverse/mathverse.yaml b/lmms_eval/tasks/mathverse/mathverse.yaml new file mode 100644 index 00000000..4df3fc68 --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse.yaml @@ -0,0 +1,14 @@ +group: mathverse +task: + - mathverse_testmini + - mathverse_testmini_text_only + - mathverse_testmini_text_lite + - mathverse_testmini_text_dominant + - mathverse_testmini_vision_intensive + - mathverse_testmini_vision_dominant + - mathverse_testmini_vision_only +metadata: + version: 0.0 + gpt_eval_model_name: "gpt-3.5-turbo" + trunk_response: 30 + quick_match: false \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/mathverse_evals.py b/lmms_eval/tasks/mathverse/mathverse_evals.py new file mode 100644 index 00000000..5b0b6f50 --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse_evals.py @@ -0,0 +1,306 @@ +import time +import requests +import logging +from tqdm import tqdm +import pandas as pd + +eval_logger = logging.getLogger("lmms-eval") + +DEMO_PROMPT_EXTRACT = """ +I am providing you a response from a model to a math problem, termed 'Model Response'. You should extract the answer from the response as 'Extracted Answer'. Directly output the extracted answer with no explanation. + +1. +Model response: 'Rounded to two decimal places, the perimeter of the sector is approximately:\n\n(-2, 1)' +Extracted Answer: (-2, 1) + +2. +Model response: 'at those points.\n\nTherefore, the correct option that represents the meaning of the intersection points of the graphs is:\n\nD. They give the solutions to the equation $f(t)=g(t)$.",' +Extracted Answer: D + +3. +Model response: ' at 1 (there's a closed circle at y = 1), the range in interval notation is \\((-4, 1]\\).\n\nFinal values:\nDomain: \\((-3, 3]\\)\nRange: \\((-4, 1]\\)' +Extracted Answer: Domain: \\((-3, 3]\\)\nRange: \\((-4, 1]\\) + +4. +Model response: 'As it stands, I cannot provide the correct option letter because there isn't enough information to solve for 'y'.' +Extracted Answer: null + +5. +Model response: 'Given that AB = 17.6 meters, we can now substitute into the equation:\n\nd = 17.6 / cos(38\u00b0)\n\nTherefore, to one decimal place, the distance d between Ned and Bart is approximately 22.3 meters.' +Extracted answer: 22.3 + +6. +Model response: have all the coefficients for the quadratic function:\n\\( f(x) = ax^2 + bx + c \\)\n\\( f(x) = -1x^2 - 2x + 1 \\)\n\nTherefore, the equation for the graphed function \\( f \\) is:\n\\( f(x) = -x^2 - 2x + 1 \\)"' +Extracted answer: f(x) = -x^2 - 2x + 1 + +7. +""" + +DEMO_PROMPT_SCORE = """ +Below are two answers to a math question. Question is [Question], [Standard Answer] is the standard answer to the question, and [Model_answer] is the answer extracted from a model's output to this question. Determine whether these two answers are consistent. +Please note that only when the [Model_answer] completely matches the [Standard Answer] means they are consistent. For non-multiple-choice questions, if the meaning is expressed in the same way, it is also considered consistent, for example, 0.5m and 50cm. +If they are consistent, Judement is 1; if they are different, Judement is 0. + +[Question]: Write the set of numbers represented on the number line in interval notation. +[Standard Answer]: (-2,1] +[Model_answer] : Extracted Answer: \\((-2, 1)\\) +Judgement: 0 + +[Question]: As shown in the figure, circle O has a radius 1.0, if angle BAC = 60.0, then the length of BC is ()\nChoices:\nA:2\nB:2\u221a{{3}}\nC:\u221a{{3}}\nD:2\u221a{{2}} +[Standard Answer]: C +[Model_answer] : B:2\u221a{{3}} +Judgement: 0 + +[Question]: Find the domain and range of the function f using interval notation. +[Standard Answer]: domain: [-4, 0) and range: (-3, 1] +[Model_answer] : Range: \\((-4, 1]\\) +Judgement: 0 + +[Question]: As shown in the figure, circle O has a radius 1.0, if angle BAC = 60.0, then the length of BC is ()\nChoices:\nA:2\nB:2\u221a{{3}}\nC:\u221a{{3}}\nD:2\u221a{{2}} +[Standard Answer]: C +[Model_answer] : null +Judgement: 0 + +[Question]: Given the graph of the ellipse that intersects with x-axis at 9 and -9 and with y-axis at 3 and -3, determine its equation.A. \\frac{{x^2}}{{81}} + \\frac{{y^2}}{{9}} = 1 B. Can not determine.\n +[Standard Answer]: A +[Model_answer] : \\frac{{x^2}}{{81}} + \\frac{{y^2}}{{9}} = 1 +Judgement: 1 + +[Question]: {question} +[Standard Answer]: {gt} +[Model_answer] : {extraction} +Judgement: """ + +class MathVerseEvaluator: + API_URL = "https://api.openai.com/v1/chat/completions" + + def __init__(self, api_key, gpt_model="gpt-3.5-turbo"): + self.api_key = api_key + self.gpt_model = gpt_model + + def _post_request(self, payload): + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", + } + response = requests.post(self.API_URL, headers=headers, json=payload, timeout=30) + response.raise_for_status() + return response.json() + + def get_chat_response(self, prompt, temperature=0, max_tokens=256, n=1, patience=10000000, sleep_time=0): + messages = [ + {"role": "user", "content": prompt}, + ] + payload = {"model": self.gpt_model, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "n": n} + + while patience > 0: + patience -= 1 + try: + response = self._post_request(payload) + if n == 1: + prediction = response["choices"][0]["message"]["content"].strip() + if prediction and prediction != "": + return prediction + else: + prediction = [choice["message"]["content"].strip() for choice in response["choices"]] + if prediction and prediction[0] != "": + return prediction + + except Exception as e: + # some model may output repetitive answer, which ChatGPT will throw an error. + if 'repetitive patterns' in str(e): + print(str(e)) + print("Continue with empty answer") + return "" + # some answer may contain some sensitive words, like 'test' + if 'sensitive' in str(e) or '400' in str(e): + print(str(e)) + print("Continue with empty answer") + return "0" + + if "Rate limit" not in str(e): + eval_logger.error(e) + + if "Please reduce the length of the messages" in str(e): + eval_logger.error("!!Reduce prompt size") + # reduce input prompt and keep the tail + new_size = int(len(prompt) * 0.9) + new_start = len(prompt) - new_size + prompt = prompt[new_start:] + payload["messages"] = [ + {"role": "user", "content": prompt}, + ] + + if sleep_time > 0: + time.sleep(sleep_time) + return "" + + def verify_extraction(self, extraction): + extraction = extraction.strip() + if not extraction: + return False + return True + + def create_extract_prompt(self, demo_prompt, response): + demo_prompt = demo_prompt.strip() + test_prompt = f"Model response: '{response}'\nExtracted Answer: " + full_prompt = f"{demo_prompt}\n\n{test_prompt}" + return full_prompt + + def create_match_prompt(self, demo_prompt, question, answer, extraction): + demo_prompt = demo_prompt.strip() + full_prompt = demo_prompt.format(question=question, gt=answer, extraction=extraction) + return full_prompt + + def extract_answer(self, response): + + if not response: + return "" + + # general extraction + try: + full_prompt = self.create_extract_prompt(DEMO_PROMPT_EXTRACT, response) + extraction = self.get_chat_response(full_prompt, temperature=0, max_tokens=256, n=1) + return extraction + except Exception as e: + eval_logger.error(e) + eval_logger.error(f"Error in extracting answer for problem") + + return "" + + def score_answer(self, question, answer, extraction, quick_match=False): + if quick_match: + return extraction == answer + + try: + full_prompt = self.create_match_prompt(DEMO_PROMPT_SCORE, question, answer, extraction) + while True: + extraction = self.get_chat_response(full_prompt, temperature=0, max_tokens=8, n=1) + judgement = extraction.replace("Judgement:", "").strip() + if judgement.strip() in ['0', '1']: + return int(judgement) == 1 + + except Exception as e: + print(e) + print(f"Error in matching answer") + + return False + + + def get_acc_with_contion(self, res_pd, key, value): + """ + Calculate the accuracy of predictions with a specific condition + """ + total_pd = res_pd[res_pd[key] == value] + + correct_pd = total_pd[total_pd["true_false"] == True] + acc = "{:.2f}".format(len(correct_pd) / len(total_pd) * 100) if len(total_pd) > 0 else "0.00" + return len(correct_pd), len(total_pd), acc + + def create_one_query(self, problem, shot_type, hint, query_type, examples=None, shot_num=0, use_caption=False, use_ocr=False): + ### [1] Demo prompt + if shot_num == 0: + demo_prompt = "" + else: + demos = [] + shot_num = min(shot_num, len(examples)) + for example in examples[:shot_num]: + prompt = "" + + # question + prompt += f"Question: {example[query_type]}" + + # solution + if shot_type == "solution": + solution = example["solution"].strip() + prompt += "\n" + f"Solution: {solution}" + + # step-by-step + if shot_type == "step-by-step": + solution = example["solution"].strip() + prompt += "\n" + f"{solution}" + + # direct + if shot_type == "direct": + solution = example["solution"].strip() + prompt += "\n" + f"{solution}" + + demos.append(prompt) + + demo_prompt = "\n\n".join(demos) + + ### [2] Test query + # problem info + question = problem["question"] + question_type = problem["question_type"] + + # hint + # format-prompt + if shot_type == "format-prompt": + hint_text = "" + # custom-prompt + elif shot_type == "custom-prompt": + if question_type == 'multi-choice': + hint_text = hint['multi-choice'] + else: # free-form + hint_text = hint['free-form'] + + # question + if shot_type == "format-prompt": + question_text = f"{problem[query_type]}" + elif shot_type == "custom-prompt": + question_text = f"Question: {question}" + + elements = [hint_text, question_text] + test_query = "\n".join([e for e in elements if e != ""]) + + ### [3] Final query + query = demo_prompt + "\n\n" + test_query + query = query.strip() + return query + + def eval_results(self, results, config): + # extract and score for each question + for inst in tqdm(results): + full_prediction = inst['prediction'].strip() + problem = { + "question_type": inst["question_type"], + "answer": inst["answer"] if "answer" in inst else None, + "question_for_eval": inst["question_for_eval"], + } + if config['metadata'].get('trunk_response', -1) > 0: + prediction = ' '.join(full_prediction.split(' ')[-config['metadata']['trunk_response']:]) + else: + prediction = full_prediction + extraction = self.extract_answer(prediction) + # set test set answer to None + true_false = self.score_answer(problem['question_for_eval'], problem["answer"], extraction, config['metadata']['quick_match']) if problem["answer"] is not None else False + + inst['extraction'] = extraction + inst['prediction'] = prediction + inst['true_false'] = true_false + + # calculate total scores + sample_index = [result["sample_index"] for result in results] + total = len(results) + correct = sum(1 for idx, pid in enumerate(sample_index) if results[idx]["true_false"]) + accuracy = round(correct / total * 100, 2) + scores = {"average": {"accuracy": accuracy, "correct": correct, "total": total}} + + for result in results: + result.update(result.pop("metadata")) + + results_dict = {result["sample_index"]: result for result in results} + df = pd.DataFrame(results_dict).T + target_keys = ["problem_version", "subfield"] + + for key in target_keys: + values = df[key].unique() + scores[key] = {} + for value in values: + correct, total, acc = self.get_acc_with_contion(df, key, value) + if total > 0: + scores[key][value] = {"accuracy": acc, "correct": correct, "total": total} + scores[key] = dict(sorted(scores[key].items(), key=lambda item: float(item[1]["accuracy"]), reverse=True)) + + return results_dict, scores \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini.yaml new file mode 100644 index 00000000..62cd8e15 --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse_testmini.yaml @@ -0,0 +1,34 @@ +dataset_path: CaraJ/MathVerse-lmmseval +dataset_name: testmini +dataset_kwargs: + token: False +task: "mathverse_testmini" +test_split: testmini +output_type: generate_until +doc_to_visual: !function utils.mathverse_doc_to_visual +doc_to_text: !function utils.mathverse_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathverse_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathverse_aggregate_results_eval + higher_is_better: true + - metric: submission + aggregation: !function utils.mathverse_aggregate_results_submission + higher_is_better: true + +model_specific_prompt_kwargs: + default: + shot_type: "format-prompt" # can also be "custom-prompt" + query_type: "query_wo" # now only support query_wo +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml new file mode 100644 index 00000000..7ff121e9 --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml @@ -0,0 +1,34 @@ +dataset_path: CaraJ/MathVerse-lmmseval +dataset_name: testmini_version_split +dataset_kwargs: + token: False +task: "mathverse_testmini_text_dominant" +test_split: text_dominant +output_type: generate_until +doc_to_visual: !function utils.mathverse_doc_to_visual +doc_to_text: !function utils.mathverse_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathverse_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathverse_aggregate_results_eval + higher_is_better: true + - metric: submission + aggregation: !function utils.mathverse_aggregate_results_submission + higher_is_better: true + +model_specific_prompt_kwargs: + default: + shot_type: "format-prompt" # can also be "custom-prompt" + query_type: "query_wo" # now only support query_wo +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml new file mode 100644 index 00000000..2137e354 --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml @@ -0,0 +1,34 @@ +dataset_path: CaraJ/MathVerse-lmmseval +dataset_name: testmini_version_split +dataset_kwargs: + token: False +task: "mathverse_testmini_text_lite" +test_split: text_lite +output_type: generate_until +doc_to_visual: !function utils.mathverse_doc_to_visual +doc_to_text: !function utils.mathverse_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathverse_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathverse_aggregate_results_eval + higher_is_better: true + - metric: submission + aggregation: !function utils.mathverse_aggregate_results_submission + higher_is_better: true + +model_specific_prompt_kwargs: + default: + shot_type: "format-prompt" # can also be "custom-prompt" + query_type: "query_wo" # now only support query_wo +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml new file mode 100644 index 00000000..42ceb5ee --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml @@ -0,0 +1,34 @@ +dataset_path: CaraJ/MathVerse-lmmseval +dataset_name: testmini_text_only +dataset_kwargs: + token: False +task: "mathverse_testmini_text_only" +test_split: text_only +output_type: generate_until +doc_to_visual: !function utils.mathverse_doc_to_visual +doc_to_text: !function utils.mathverse_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathverse_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathverse_aggregate_results_eval + higher_is_better: true + - metric: submission + aggregation: !function utils.mathverse_aggregate_results_submission + higher_is_better: true + +model_specific_prompt_kwargs: + default: + shot_type: "format-prompt" # can also be "custom-prompt" + query_type: "query_wo" # now only support query_wo +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml new file mode 100644 index 00000000..fe66a40a --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml @@ -0,0 +1,34 @@ +dataset_path: CaraJ/MathVerse-lmmseval +dataset_name: testmini_version_split +dataset_kwargs: + token: False +task: "mathverse_testmini_vision_dominant" +test_split: vision_dominant +output_type: generate_until +doc_to_visual: !function utils.mathverse_doc_to_visual +doc_to_text: !function utils.mathverse_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathverse_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathverse_aggregate_results_eval + higher_is_better: true + - metric: submission + aggregation: !function utils.mathverse_aggregate_results_submission + higher_is_better: true + +model_specific_prompt_kwargs: + default: + shot_type: "format-prompt" # can also be "custom-prompt" + query_type: "query_wo" # now only support query_wo +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml new file mode 100644 index 00000000..cb922997 --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml @@ -0,0 +1,34 @@ +dataset_path: CaraJ/MathVerse-lmmseval +dataset_name: testmini_version_split +dataset_kwargs: + token: False +task: "mathverse_testmini_vision_intensive" +test_split: vision_intensive +output_type: generate_until +doc_to_visual: !function utils.mathverse_doc_to_visual +doc_to_text: !function utils.mathverse_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathverse_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathverse_aggregate_results_eval + higher_is_better: true + - metric: submission + aggregation: !function utils.mathverse_aggregate_results_submission + higher_is_better: true + +model_specific_prompt_kwargs: + default: + shot_type: "format-prompt" # can also be "custom-prompt" + query_type: "query_wo" # now only support query_wo +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml new file mode 100644 index 00000000..72df7369 --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml @@ -0,0 +1,34 @@ +dataset_path: CaraJ/MathVerse-lmmseval +dataset_name: testmini_version_split +dataset_kwargs: + token: False +task: "mathverse_testmini_vision_only" +test_split: vision_only +output_type: generate_until +doc_to_visual: !function utils.mathverse_doc_to_visual +doc_to_text: !function utils.mathverse_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathverse_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathverse_aggregate_results_eval + higher_is_better: true + - metric: submission + aggregation: !function utils.mathverse_aggregate_results_submission + higher_is_better: true + +model_specific_prompt_kwargs: + default: + shot_type: "format-prompt" # can also be "custom-prompt" + query_type: "query_wo" # now only support query_wo +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/utils.py b/lmms_eval/tasks/mathverse/utils.py new file mode 100644 index 00000000..1b6d8db3 --- /dev/null +++ b/lmms_eval/tasks/mathverse/utils.py @@ -0,0 +1,94 @@ +import logging +import yaml +import os +from pathlib import Path +import pandas as pd +import json + +eval_logger = logging.getLogger("lmms-eval") +from lmms_eval.tasks.mathverse.mathverse_evals import MathVerseEvaluator +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file + +with open(Path(__file__).parent / "mathverse.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + + config = yaml.safe_load("".join(safe_data)) + +mathverse_evaluator = MathVerseEvaluator(api_key=os.getenv("OPENAI_API_KEY", "YOUR_API_KEY"), gpt_model=config["metadata"]["gpt_eval_model_name"]) + + +def mathverse_doc_to_visual(doc): + if str(doc["image"]).strip() == '': + return [] + return [doc["image"].convert("RGB")] + + +def mathverse_doc_to_text(doc, model_specific_prompt_kwargs=None): + problem = { + "question": doc["question"], + "answer": doc["answer"] if "answer" in doc else None, + "query_wo": doc["query_wo"], + "query_cot": doc["query_cot"], + "question_type": doc["question_type"], + "problem_version": doc["problem_version"], + } + query_prompt = mathverse_evaluator.create_one_query(problem, examples=None, shot_num=0, shot_type=model_specific_prompt_kwargs["shot_type"], hint=model_specific_prompt_kwargs.get("hint", None),query_type=model_specific_prompt_kwargs["query_type"]) + return query_prompt + + + +def mathverse_process_results(doc, results): + prediction = results[0].strip() + + result = { + "sample_index": doc["sample_index"], + "problem_index": doc["problem_index"], + "problem_version": doc["problem_version"], + "question": doc["question"], + "answer": doc["answer"] if "answer" in doc else None, + "prediction": prediction, + "question_type": doc["question_type"], + "metadata": doc["metadata"], + "query_wo": doc["query_wo"], + "query_cot": doc["query_cot"], + } + + return { + "gpt_eval_score": result, + "submission": result, + } + + +def mathverse_aggregate_results_submission(results, args, *, calculate_gain=False, random_scores=None): + split_flag = results[0]["metadata"]["split"] + path = generate_submission_file(f"mathverse_{split_flag}_results.json", args) + with open(path, "w") as f: + json.dump(results, f, indent=4) + + eval_logger.info(f"Saved results to {path}") + +def mathverse_aggregate_results_eval(results, args, *, calculate_gain=False, random_scores=None): + split_flag = results[0]["metadata"]["split"] + # save the result first, in case the gpt evaluation fails + path = generate_submission_file(f"mathverse_{split_flag}_results.json", args) + with open(path, "w") as f: + json.dump(results, f, indent=4) + # gpt evaluation + results_dict, scores = mathverse_evaluator.eval_results(results, config) + # save results + path = generate_submission_file(f"mathverse_{split_flag}_results.json", args) + with open(path, "w") as f: + json.dump(results_dict, f, indent=4) + # save scores + path = generate_submission_file(f"mathverse_{split_flag}_scores.json", args) + with open(path, "w") as f: + json.dump(scores, f, indent=4) + eval_logger.info(f"Saved scores to {path}") + if scores["average"]["accuracy"] == 0: + return None + return scores["average"]["accuracy"] From 85573674e90c8d505312ba18c5102e0051255078 Mon Sep 17 00:00:00 2001 From: Andrea Tupini Date: Mon, 22 Apr 2024 14:47:33 -0600 Subject: [PATCH 006/196] Fix typo in qwen_vl that was causing "reference before assignment" --- lmms_eval/models/qwen_vl.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/models/qwen_vl.py b/lmms_eval/models/qwen_vl.py index 2d73e4a5..43e5e680 100644 --- a/lmms_eval/models/qwen_vl.py +++ b/lmms_eval/models/qwen_vl.py @@ -230,7 +230,7 @@ def _collate(x): raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") for i in range(len(contexts)): if "" in contexts[i]: - context[i] = contexts[i].replace("", "") + contexts[i] = contexts[i].replace("", "") questions = [self.prompt.format(visual_path, context) for visual_path, context in zip(visual_paths, contexts)] # Similar to llava, is visual paths has len 0 From cd874201c46f32a2903ddffae85f9db73e14adfd Mon Sep 17 00:00:00 2001 From: Andrea Tupini Date: Mon, 22 Apr 2024 14:54:29 -0600 Subject: [PATCH 007/196] convert contexts to list if necessary and remove unnecessary construction of `questions` --- lmms_eval/models/qwen_vl.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/lmms_eval/models/qwen_vl.py b/lmms_eval/models/qwen_vl.py index 43e5e680..30ece5e8 100644 --- a/lmms_eval/models/qwen_vl.py +++ b/lmms_eval/models/qwen_vl.py @@ -228,10 +228,13 @@ def _collate(x): until = [until] elif not isinstance(until, list): raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") + + if isinstance(contexts, tuple): + contexts = list(contexts) + for i in range(len(contexts)): if "" in contexts[i]: contexts[i] = contexts[i].replace("", "") - questions = [self.prompt.format(visual_path, context) for visual_path, context in zip(visual_paths, contexts)] # Similar to llava, is visual paths has len 0 # Then nothing will be executed From ed171293d1e82075c5c6a847fc91ecbfd45cf89f Mon Sep 17 00:00:00 2001 From: Andrea Tupini Date: Mon, 22 Apr 2024 14:56:41 -0600 Subject: [PATCH 008/196] refactor query construction for clarity --- lmms_eval/models/qwen_vl.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/lmms_eval/models/qwen_vl.py b/lmms_eval/models/qwen_vl.py index 30ece5e8..4d9cdbb1 100644 --- a/lmms_eval/models/qwen_vl.py +++ b/lmms_eval/models/qwen_vl.py @@ -239,13 +239,14 @@ def _collate(x): # Similar to llava, is visual paths has len 0 # Then nothing will be executed query = [] - for visual_path, context in zip(visual_paths, contexts): - query.append({"image": visual_path}) - query.append({"text": context}) - if len(visual_paths) == 0: for context in contexts: query.append({"text": context}) + else: + for visual_path, context in zip(visual_paths, contexts): + query.append({"image": visual_path}) + query.append({"text": context}) + questions = self.tokenizer.from_list_format(query) input_ids = self.tokenizer(questions, return_tensors="pt", padding="longest") From e457cfb4f2d6869e8367d6d5b03ad25ee4acc363 Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Tue, 23 Apr 2024 18:33:16 -0400 Subject: [PATCH 009/196] Create ScreenSpot on clean branch --- .../screenspot/_default_template_rec_yaml | 33 +++ .../screenspot/_default_template_reg_yaml | 15 ++ lmms_eval/tasks/screenspot/_screenspot.yaml | 4 + .../tasks/screenspot/screenspot_rec_test.yaml | 4 + .../tasks/screenspot/screenspot_reg_test.yaml | 4 + lmms_eval/tasks/screenspot/utils.py | 126 ++++++++++ lmms_eval/tasks/screenspot/utils_rec.py | 215 ++++++++++++++++++ 7 files changed, 401 insertions(+) create mode 100644 lmms_eval/tasks/screenspot/_default_template_rec_yaml create mode 100644 lmms_eval/tasks/screenspot/_default_template_reg_yaml create mode 100644 lmms_eval/tasks/screenspot/_screenspot.yaml create mode 100644 lmms_eval/tasks/screenspot/screenspot_rec_test.yaml create mode 100644 lmms_eval/tasks/screenspot/screenspot_reg_test.yaml create mode 100644 lmms_eval/tasks/screenspot/utils.py create mode 100644 lmms_eval/tasks/screenspot/utils_rec.py diff --git a/lmms_eval/tasks/screenspot/_default_template_rec_yaml b/lmms_eval/tasks/screenspot/_default_template_rec_yaml new file mode 100644 index 00000000..7f9aad54 --- /dev/null +++ b/lmms_eval/tasks/screenspot/_default_template_rec_yaml @@ -0,0 +1,33 @@ +dataset_path: rootsautomation/ScreenSpot +output_type: generate_until +doc_to_visual: !function utils_rec.screenspot_rec_doc_to_visual +doc_to_text: !function utils_rec.screenspot_rec_doc_to_text +doc_to_target: "bbox" +generation_kwargs: + until: + - "ASSISTANT:" +process_results: !function utils_rec.screenspot_rec_process_result +metric_list: + - metric: screenspot_IoU + aggregation : !function utils_rec.screenspot_rec_iou + higher_is_better : true + - metric: screenspot_ACC@0.1 + aggregation : !function utils_rec.screenspot_rec_acc01 + higher_is_better : true + - metric: screenspot_ACC@0.3 + aggregation : !function utils_rec.screenspot_rec_acc03 + higher_is_better : true + - metric: screenspot_ACC@0.5 + aggregation : !function utils_rec.screenspot_rec_acc05 + higher_is_better : true + - metric: screenspot_ACC@0.7 + aggregation : !function utils_rec.screenspot_rec_acc07 + higher_is_better : true + - metric: screenspot_ACC@0.9 + aggregation : !function utils_rec.screenspot_rec_acc09 + higher_is_better : true + - metric: screenspot_Center_ACC + aggregation : !function utils_rec.screenspot_rec_center_acc + higher_is_better : true +metadata: + version: '0.0' \ No newline at end of file diff --git a/lmms_eval/tasks/screenspot/_default_template_reg_yaml b/lmms_eval/tasks/screenspot/_default_template_reg_yaml new file mode 100644 index 00000000..0ef72057 --- /dev/null +++ b/lmms_eval/tasks/screenspot/_default_template_reg_yaml @@ -0,0 +1,15 @@ +dataset_path: rootsautomation/ScreenSpot +output_type: generate_until +doc_to_visual: !function utils.screenspot_bbox_doc_to_visual +doc_to_text: !function utils.screenspot_doc_to_text +doc_to_target: "instruction" +generation_kwargs: + until: + - "ASSISTANT:" +process_results: !function utils.screenspot_process_result +metric_list: + - metric: screenspot_CIDEr + aggregation : !function utils.screenspot_cider + higher_is_better : true +metadata: + version: '0.0' \ No newline at end of file diff --git a/lmms_eval/tasks/screenspot/_screenspot.yaml b/lmms_eval/tasks/screenspot/_screenspot.yaml new file mode 100644 index 00000000..c31f58de --- /dev/null +++ b/lmms_eval/tasks/screenspot/_screenspot.yaml @@ -0,0 +1,4 @@ +group: screenspot +task: +- screenspot_reg_test +- screenspot_rec_test \ No newline at end of file diff --git a/lmms_eval/tasks/screenspot/screenspot_rec_test.yaml b/lmms_eval/tasks/screenspot/screenspot_rec_test.yaml new file mode 100644 index 00000000..baccc82a --- /dev/null +++ b/lmms_eval/tasks/screenspot/screenspot_rec_test.yaml @@ -0,0 +1,4 @@ +group: screenspot_rec +task: screenspot_rec_test +include: _default_template_rec_yaml +test_split: test diff --git a/lmms_eval/tasks/screenspot/screenspot_reg_test.yaml b/lmms_eval/tasks/screenspot/screenspot_reg_test.yaml new file mode 100644 index 00000000..ab064712 --- /dev/null +++ b/lmms_eval/tasks/screenspot/screenspot_reg_test.yaml @@ -0,0 +1,4 @@ +group: screenspot_reg +task: screenspot_reg_test +include: _default_template_reg_yaml +test_split: test diff --git a/lmms_eval/tasks/screenspot/utils.py b/lmms_eval/tasks/screenspot/utils.py new file mode 100644 index 00000000..cb6ceda9 --- /dev/null +++ b/lmms_eval/tasks/screenspot/utils.py @@ -0,0 +1,126 @@ +from PIL import ImageDraw +from pycocoevalcap.eval import COCOEvalCap, Bleu, Meteor, Rouge, Cider, Spice +from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer +from pycocotools.coco import COCO + +# COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"] # , "SPICE"] +COCO_METRICS = ["CIDEr"] + +import logging + +eval_logger = logging.getLogger("lmms-eval") + + +def screenspot_bbox_doc_to_visual(doc): + bbox = doc["bbox"] + image = doc["image"].convert("RGB") + draw = ImageDraw.Draw(image) + bbox_xy = [bbox[0], bbox[1], bbox[2], bbox[3]] + draw.rectangle(bbox_xy, outline="red", width=3) + return [image.convert("RGB")] + + +def screenspot_process_result(doc, result): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name (in this case coco_bleu), value: metric value + """ + pred = result[0] if len(result) > 0 else "" + ann_id = doc["file_name"] + data_dict = {"instruction": doc["instruction"], "pred": pred, "ann_id": ann_id, 'data_type': doc['data_type'], 'data_source': doc['data_source']} + return {f"screenspot_{metric}": data_dict for metric in COCO_METRICS} + + +def screenspot_doc_to_text(doc): + return f"Direct a user to interact with the highlighted region [{doc['bbox'][0]:.2f}, {doc['bbox'][1]:.2f}, {doc['bbox'][2]:.2f}, {doc['bbox'][3]:.2f}]." + + +def screenspot_aggregation_result(results, metric): + # scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")] + scorers = [(Cider(), "CIDEr")] + scorers_dict = {s[1]: s for s in scorers} + + stored_results = [] + # In order to make the coco eval tools to successfully create index + # We need at least two dict in the dataset + # 'annotation' and 'images' + # 'annotation' exactly reproduce the original annotation + # 'images' however only need the image id which is contained in the file name + dataset = {"annotations": [], "images": []} + idx = 0 + ann_id = 0 + for result in results: + stored_results.append({"image_id": idx, "caption": result["pred"]}) + # for s in result["answer"]: + dataset["annotations"].append({"image_id": idx, "caption": result['instruction'], "id": ann_id}) + ann_id += 1 + + dataset["images"].append({"id": idx}) + idx += 1 + + coco = COCO() + # Manually create index here + coco.dataset = dataset + coco.createIndex() + + coco_result = coco.loadRes(stored_results) + coco_eval = COCOEvalCap(coco, coco_result) + + imgIds = coco_eval.params["image_id"] + gts = {} + res = {} + for imgId in imgIds: + gts[imgId] = coco_eval.coco.imgToAnns[imgId] + res[imgId] = coco_eval.cocoRes.imgToAnns[imgId] + + eval_logger.info("tokenization...") + tokenizer = PTBTokenizer() + gts = tokenizer.tokenize(gts) + res = tokenizer.tokenize(res) + + eval_logger.info(f"Computing {metric} scores...") + + score, scores = scorers_dict[metric][0].compute_score(gts, res) + # coco_eval.setEval(score, metric) + + # When metric is one of the Bleu, score will be a list + if type(score) == list: + n = int(metric.split("_")[-1]) + score = score[n - 1] + + return score + + +def screenspot_bleu4(results): + return screenspot_aggregation_result(results, "Bleu_4") + + +def screenspot_bleu3(results): + return screenspot_aggregation_result(results, "Bleu_3") + + +def screenspot_bleu2(results): + return screenspot_aggregation_result(results, "Bleu_2") + + +def screenspot_bleu1(results): + return screenspot_aggregation_result(results, "Bleu_1") + + +def screenspot_meteor(results): + return screenspot_aggregation_result(results, "METEOR") + + +def screenspot_rougel(results): + return screenspot_aggregation_result(results, "ROUGE_L") + + +def screenspot_cider(results): + return screenspot_aggregation_result(results, "CIDEr") + + +def screenspot_spice(results): + return screenspot_aggregation_result(results, "SPICE") diff --git a/lmms_eval/tasks/screenspot/utils_rec.py b/lmms_eval/tasks/screenspot/utils_rec.py new file mode 100644 index 00000000..5cdf5b60 --- /dev/null +++ b/lmms_eval/tasks/screenspot/utils_rec.py @@ -0,0 +1,215 @@ +import re +import logging +from datasets import Dataset + +eval_logger = logging.getLogger("lmms-eval") + +REC_METRICS = ["IoU", "ACC@0.1", "ACC@0.3", "ACC@0.5", "ACC@0.7", "ACC@0.9", "Center_ACC"] + + + +def screenspot_rec_doc_to_visual(doc): + # Image is presented as is + image = doc["image"].convert("RGB") + return [image.convert("RGB")] + + +def screenspot_rec_doc_to_text(doc): + return "Bounding box coordinates are specified in the format (top-left x, top-left y, bottom-right x, bottom-right y). All values are floating point numbers bounded between 0 and 1 with two decimal places of precision (e.g., 0.15). Please provide the bounding box coordinates of the region that corresponds to the command: " + doc["instruction"] + + +def parse_float_sequence_within(input_str): + """ + Extract the first sequence of four floating-point numbers within square brackets from a string. + + Args: + input_str (str): A string that may contain a sequence of four floats within square brackets. + + Returns: + list: A list of four floats if the pattern is found, or a list of four zeros if the pattern is not found. + """ + # Define the regex pattern to find the first instance of four floats within square brackets + pattern = r'\[\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?)\s*\]' + + # Use re.search to find the first match of the pattern in the input string + match = re.search(pattern, input_str) + + # If a match is found, convert the captured groups into a list of floats + if match: + return [float(match.group(i)) for i in range(1, 5)] + + # If the input does not contain the pattern, return the null float sequence + return [0, 0, 0, 0] + + +def screenspot_rec_process_result(doc, result): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name, value: metric value + """ + pred = result[0] if len(result) > 0 else "" + pred = parse_float_sequence_within(pred) + ann_id = doc["file_name"] + data_dict = {"instruction": doc["instruction"], "pred": pred, "ann_id": ann_id, 'bbox': doc['bbox'], 'data_type': doc['data_type'], 'data_source': doc['data_source']} + return {f"screenspot_{metric}": data_dict for metric in REC_METRICS} + + +def compute_iou(box1, box2): + """ + Compute the Intersection over Union (IoU) of two bounding boxes. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + + Returns: + - float: IoU of box1 and box2. + """ + # Determine the coordinates of the intersection rectangle + x_left = max(box1[0], box2[0]) + y_top = max(box1[1], box2[1]) + x_right = min(box1[2], box2[2]) + y_bottom = min(box1[3], box2[3]) + + # Compute the area of intersection + intersection_area = max(0, x_right - x_left) * max(0, y_bottom - y_top) + + # Compute the area of both bounding boxes + box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) + box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1]) + + # Compute the area of the union + union_area = box1_area + box2_area - intersection_area + + # Compute the Intersection over Union + iou = intersection_area / union_area + + return iou + + +def compute_accuracy(box1, box2, threshold=0.5): + """ + Compute the accuracy of two bounding boxes based on a specified threshold. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - threshold (float): Threshold for the IoU to consider the prediction correct. + + Returns: + - float: Accuracy of the prediction based on the IoU threshold. + """ + iou = compute_iou(box1, box2) + return iou >= threshold + + +def compute_center_accuracy(box1, box2): + """ + Compute if the center point of box 2 is within box 1. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + + Returns: + - bool: True if the center point of box 2 is within box 1, False otherwise. + """ + # Compute the center point of box 2 + center_x = (box2[0] + box2[2]) / 2 + center_y = (box2[1] + box2[3]) / 2 + + # Check if the center point is within box 1 + return box1[0] <= center_x <= box1[2] and box1[1] <= center_y <= box1[3] + + +def screenspot_rec_aggregation_result(results, metric): + """ + Aggregate the results of the screenspot evaluation task using the specified metric. + + Args: + - results (list of dict): List of result dictionaries. + - metric (str): Metric to use for aggregation. + + Returns: + - dict: Dictionary containing the aggregated results for the specified metric. + """ + scorers = { + 'IoU': compute_iou, + 'ACC@0.1': lambda x, y: compute_accuracy(x, y, 0.1), + 'ACC@0.3': lambda x, y: compute_accuracy(x, y, 0.3), + 'ACC@0.5': lambda x, y: compute_accuracy(x, y, 0.5), + 'ACC@0.7': lambda x, y: compute_accuracy(x, y, 0.7), + 'ACC@0.9': lambda x, y: compute_accuracy(x, y, 0.9), + 'Center_ACC': compute_center_accuracy + } + results_dict = { + metric: [], + metric + '-mobile_text': [], + metric + '-mobile_icon': [], + metric + '-web_text': [], + metric + '-web_icon': [], + metric + '-desktop_text': [], + metric + '-desktop_icon': [], + } + for result in results: + # Extract the ground truth and predicted bounding boxes + gt = result['bbox'] + pred = result['pred'] + + # Compute the specified metric between the ground truth and predicted bounding boxes + score = scorers[metric](gt, pred) + + results_dict[metric].append(score) + if result['data_type'] == 'text': + if 'ios' in result['data_source'] or 'android' in result['data_source']: + results_dict[metric + '-mobile_text'].append(score) + elif 'macos' in result['data_source'] or 'windows' in result['data_source']: + results_dict[metric + '-desktop_text'].append(score) + else: + results_dict[metric + '-web_text'].append(score) + else: + if 'ios' in result['data_source'] or 'android' in result['data_source']: + results_dict[metric + '-mobile_icon'].append(score) + elif 'macos' in result['data_source'] or 'windows' in result['data_source']: + results_dict[metric + '-desktop_icon'].append(score) + else: + results_dict[metric + '-web_icon'].append(score) + + for key in results_dict: + if len(results_dict[key]) == 0: + results_dict[key] = 0 + else: + results_dict[key] = sum(results_dict[key]) / len(results_dict[key]) + + print(f"{key}: {results_dict[key]:0.4f}") + return results_dict[metric] + + +def screenspot_rec_iou(results): + return screenspot_rec_aggregation_result(results, "IoU") + + +def screenspot_rec_acc01(results): + return screenspot_rec_aggregation_result(results, "ACC@0.1") + +def screenspot_rec_acc03(results): + return screenspot_rec_aggregation_result(results, "ACC@0.3") + + +def screenspot_rec_acc05(results): + return screenspot_rec_aggregation_result(results, "ACC@0.5") + + +def screenspot_rec_acc07(results): + return screenspot_rec_aggregation_result(results, "ACC@0.7") + + +def screenspot_rec_acc09(results): + return screenspot_rec_aggregation_result(results, "ACC@0.9") + + +def screenspot_rec_center_acc(results): + return screenspot_rec_aggregation_result(results, "Center_ACC") From 28962cbe83631ec5d6481aaea4907a7c96fec848 Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Wed, 24 Apr 2024 11:52:33 -0400 Subject: [PATCH 010/196] Update README to reflect new tasks --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index fbf06936..e1813c65 100644 --- a/README.md +++ b/README.md @@ -242,6 +242,9 @@ We also provide the raw data exported from Weights & Biases for the detailed res - ScienceQA (scienceqa_full) - ScienceQA Full (scienceqa) - ScienceQA IMG (scienceqa_img) +- ScreenSpot (screenspot) + - ScreenSpot REC / Grounding (screenspot_rec) + - ScreenSpot REG / Instruction Generation (screenspot_reg) - SeedBench (seedbench) - SeedBench 2 (seedbench_2) - ST-VQA (stvqa) From 2f3811ca1bbad6a441016b05fde09a571900fca8 Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Thu, 25 Apr 2024 11:41:04 -0400 Subject: [PATCH 011/196] Add README file specific to ScreenSpot --- lmms_eval/tasks/screenspot/README.md | 49 ++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 lmms_eval/tasks/screenspot/README.md diff --git a/lmms_eval/tasks/screenspot/README.md b/lmms_eval/tasks/screenspot/README.md new file mode 100644 index 00000000..372d1766 --- /dev/null +++ b/lmms_eval/tasks/screenspot/README.md @@ -0,0 +1,49 @@ +# SceenSpot + +## GUI Grounding Benchmark: ScreenSpot + +ScreenSpot is an evaluation benchmark for GUI grounding, comprising over 1200 instructions from iOS, Android, macOS, Windows and Web environments, along with annotated element types (Text or Icon/Widget). + +This evaluation allows for both: +- `screenspot_rec_test`: the original evaluation of `{img} {instruction} --> {bounding box}` called grounding or Referring Expression Completion (REC); +- `screenspot_reg_test`: the new evaluation of `{img} {bounding box} --> {instruction}` called instruction generation or Referring Expression Generation (REG). + +### REC Metrics + +REC/Grounding requires that a model outputs a bounding box for the target element in the image. The evaluation metrics are: +- `IoU`: Intersection over Union (IoU) between the predicted bounding box and the ground truth bounding box. +- `ACC@IoIU`: We use `IoU` to create `ACC@IoU` metrics at different IoU thresholds where an output with an IoU above the threshold is considered correct. +- `CENTER ACC`: The predicted bounding box is considered correct if the center of the predicted bounding box is within the ground truth bounding box. This is what's reported in the paper. + +### REG Metrics + +REG/Generation requires that a model outputs the instruction that describes the target element in the image. Currently, this element will be highlighted in red in the image. The evaluation metrics are: +- `CIDEr`: The CIDEr metric is used to evaluate the quality of the generated instruction. As the paper doesn't consider this task, we have selected this metric as a standard for evaluating the quality of the generated instruction. This matches with what other works like ScreenAI have done for instruction generation for RICO datasets. + +## Baseline Scores + +As a Baseline, here is how LLaVA-v1.5-7b performs on the ScreenSpot dataset: +- `IoU`: 0.051 +- `ACC@0.1`: 0.195 +- `ACC@0.3`: 0.042 +- `ACC@0.5`: 0.006 +- `ACC@0.7`: 0.000 +- `ACC@0.9`: 0.000 +- `CENTER ACC`: 0.097 +- `CIDEr`: 0.097 + +## References + +- ArXiv: [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://arxiv.org/abs/2401.10935) +- GitHub: [njucckevin/SeeClick](https://github.com/njucckevin/SeeClick) + +```bibtex +@misc{cheng2024seeclick, + title={SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents}, + author={Kanzhi Cheng and Qiushi Sun and Yougang Chu and Fangzhi Xu and Yantao Li and Jianbing Zhang and Zhiyong Wu}, + year={2024}, + eprint={2401.10935}, + archivePrefix={arXiv}, + primaryClass={cs.HC} +} +``` From 319afccbe713ddf40a8a6fa28501e64c0ad34725 Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Thu, 25 Apr 2024 11:44:34 -0400 Subject: [PATCH 012/196] slight update --- lmms_eval/tasks/screenspot/README.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/lmms_eval/tasks/screenspot/README.md b/lmms_eval/tasks/screenspot/README.md index 372d1766..840b36c6 100644 --- a/lmms_eval/tasks/screenspot/README.md +++ b/lmms_eval/tasks/screenspot/README.md @@ -4,7 +4,12 @@ ScreenSpot is an evaluation benchmark for GUI grounding, comprising over 1200 instructions from iOS, Android, macOS, Windows and Web environments, along with annotated element types (Text or Icon/Widget). -This evaluation allows for both: + +## Groups + +- `screenspot`: This group bundles both the original grounding task and the new instruction generation task. + +## Tasks - `screenspot_rec_test`: the original evaluation of `{img} {instruction} --> {bounding box}` called grounding or Referring Expression Completion (REC); - `screenspot_reg_test`: the new evaluation of `{img} {bounding box} --> {instruction}` called instruction generation or Referring Expression Generation (REG). From 955bd0635cc6c14a96ad869f1002e6dbefdc5071 Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Tue, 30 Apr 2024 10:16:21 -0400 Subject: [PATCH 013/196] Init webSRC --- lmms_eval/tasks/websrc/README.md | 1 + lmms_eval/tasks/websrc/utils.py | 140 +++++++++++++++++++++++++ lmms_eval/tasks/websrc/websrc.yaml | 3 + lmms_eval/tasks/websrc/websrc_val.yaml | 19 ++++ 4 files changed, 163 insertions(+) create mode 100644 lmms_eval/tasks/websrc/README.md create mode 100644 lmms_eval/tasks/websrc/utils.py create mode 100644 lmms_eval/tasks/websrc/websrc.yaml create mode 100644 lmms_eval/tasks/websrc/websrc_val.yaml diff --git a/lmms_eval/tasks/websrc/README.md b/lmms_eval/tasks/websrc/README.md new file mode 100644 index 00000000..af756e4d --- /dev/null +++ b/lmms_eval/tasks/websrc/README.md @@ -0,0 +1 @@ +# WebSRC \ No newline at end of file diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py new file mode 100644 index 00000000..02c0a9e4 --- /dev/null +++ b/lmms_eval/tasks/websrc/utils.py @@ -0,0 +1,140 @@ +from collections import defaultdict +import re +import ast +import base64 +import io +import random +import numpy as np +import os +import json +import logging +from PIL import Image + +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file + +lmms_logger = logging.getLogger("lmms-eval") + +OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase." + + +def construct_prompt(doc): + question = doc["question"] + # question = f"{question}\n{OPEN_ENDED_PROMPT}" + question = f"{OPEN_ENDED_PROMPT}\n{question}" + return question + + +def websrc_doc_to_text(doc): + question = construct_prompt(doc) + return question + + +def websrc_doc_to_visual(doc): + img_bs64 = doc["image"] + img = Image.open(io.BytesIO(base64.b64decode(img_bs64))) + del doc['image'] + return [img] + + +def websrc_process_results(doc, results): + pred = results[0] + parsed_pred = pred + id = doc["page_id"] + websrc_ans = {"id": id, "domain": doc['domain'], "answer": doc["answer"], "parsed_pred": parsed_pred} + return { + "websrc_squad_f1": websrc_ans, + "submission": { + id: pred, + }, + } + + +def websrc_test_aggregate_results_for_submission(results, args): + path = generate_submission_file("websrc_test_for_submission.json", args) + with open(path, "w") as f: + json.dump(results, f) + lmms_logger.info(f"Results saved to {path}.") + + +def websrc_aggregate_results(results): + evaluation_result = {} + + # Group results by domain + subset_to_eval_samples = defaultdict(list) + for result in results: + subset_to_eval_samples[result["domain"]].append(result) + + # Evaluate each domain + for subset, sub_eval_samples in subset_to_eval_samples.items(): + judge_dict, metric_dict = evaluate_websrc(sub_eval_samples) + metric_dict.update({"num_example": len(sub_eval_samples)}) + evaluation_result[subset] = metric_dict + + # Aggregate results for all domains + printable_results = {} + for domain in DOMAINS: + if domain not in evaluation_result: + continue + printable_results[domain] = { + "num": int(evaluation_result[domain]["num_example"]), + "f1": round(evaluation_result[domain]["f1"], 3), + } + all_ins_f1 = np.sum([cat_results["f1"] * cat_results["num_example"] for cat_results in evaluation_result.values()]) / sum( + [cat_results["num_example"] for cat_results in evaluation_result.values()] + ) + printable_results["Overall"] = { + "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]), + "f1": round(all_ins_f1, 3), + } + print(printable_results) + return printable_results["Overall"]["f1"] + + +################## +# Helper functions written by official MMMU repo. +################## +DOMAINS = [ + 'auto', + 'book', + 'camera', + 'game', + 'jobs', + 'movie', + 'phone', + 'restaurant', + 'sports', + 'university', + 'hotel', +] + + +def evaluate_websrc(samples): + + def _normalize_str(string): + # lower it + string = string.lower() + + # strip non-alphanumeric characters + string = re.sub(r"[^a-zA-Z0-9]", "", string) + + # strip leading and trailing whitespaces + string = string.strip() + + return string + + judge_list = [] + for sample in samples: + gold_i = set(_normalize_str(sample["answer"])) + pred_i = set(_normalize_str( sample["parsed_pred"])) + if len(pred_i) == 0: + judge_list.append(0.0) + continue + + comm_i = gold_i.intersection(pred_i) + prec_i = len(comm_i) / len(pred_i) + rec_i = len(comm_i) / len(gold_i) + f1_i = 2 * prec_i * rec_i / (prec_i + rec_i) if prec_i + rec_i > 0 else 0 + judge_list.append(f1_i) + + f1 = np.mean(judge_list) + return judge_list, {"f1": f1} diff --git a/lmms_eval/tasks/websrc/websrc.yaml b/lmms_eval/tasks/websrc/websrc.yaml new file mode 100644 index 00000000..8328c92d --- /dev/null +++ b/lmms_eval/tasks/websrc/websrc.yaml @@ -0,0 +1,3 @@ +group: websrc +task: +- websrc_val diff --git a/lmms_eval/tasks/websrc/websrc_val.yaml b/lmms_eval/tasks/websrc/websrc_val.yaml new file mode 100644 index 00000000..aeac7d06 --- /dev/null +++ b/lmms_eval/tasks/websrc/websrc_val.yaml @@ -0,0 +1,19 @@ +dataset_path: rootsautomation/websrc +task: "websrc_val" +test_split: dev +output_type: generate_until +doc_to_visual: !function utils.websrc_doc_to_visual +doc_to_text: !function utils.websrc_doc_to_text +doc_to_target: "answer" +# The return value of process_results will be used by metrics +process_results: !function utils.websrc_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +generation_kwargs: + max_new_tokens: 16 + image_aspect_ratio: pad +metric_list: + - metric: websrc_squad_f1 + aggregation: !function utils.websrc_aggregate_results + higher_is_better: true +metadata: + - version: 0.0 \ No newline at end of file From 35fe80b67656114a8824eb59574089663bdc4c9a Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Wed, 1 May 2024 10:46:20 -0400 Subject: [PATCH 014/196] Draft README for WebSRC --- lmms_eval/tasks/websrc/README.md | 52 +++++++++++++++++++++++++++++++- 1 file changed, 51 insertions(+), 1 deletion(-) diff --git a/lmms_eval/tasks/websrc/README.md b/lmms_eval/tasks/websrc/README.md index af756e4d..a631e6d8 100644 --- a/lmms_eval/tasks/websrc/README.md +++ b/lmms_eval/tasks/websrc/README.md @@ -1 +1,51 @@ -# WebSRC \ No newline at end of file +# WebSRC + +## Paper + +Title: WebSRC: A Dataset for Web-Based Structural Reading Comprehension + +Abstract: https://arxiv.org/abs/2101.09465 + +Homepage: https://x-lance.github.io/WebSRC/# + +WebSRC is a dataset for web-based structural reading comprehension. +Its full train/dev/test split contains over 400k questions across 6.4k webpages. +This version of the dataset does not contain OCR or original HTML, it simply treats WebSRC as a image-and-text-based multimodal Q&A benchmark on webpage screenshots. + +## Citation + +```bibtex +@inproceedings{chen2021websrc, + title={WebSRC: A Dataset for Web-Based Structural Reading Comprehension}, + author={Chen, Xingyu and Zhao, Zihan and Chen, Lu and Ji, Jiabao and Zhang, Danyang and Luo, Ao and Xiong, Yuxuan and Yu, Kai}, + booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, + pages={4173--4185}, + year={2021} +} +``` + +## Groups & Tasks + +### Groups + +- `websrc`: Evaluates `websrc-val` and generates a submission file for `websrc-test`. + +### Tasks + +- `websrc-val`: Given a question and a web page, predict the answer. +- `websrc-test`: Given a question and a web page, predict the answer. Ground truth is not provided for this task. + +## Metrics + +This task uses SQUAD-style evaluation metrics, of which F1 score over tokens is used. +The orignal paper also uses Exact Match (EM) score, but this is not implemented here as that metric is more conducive for Encoder-only extraction models. + +### F1 Score + +F1 Score is the harmonic mean of precision and recall. +We calculate precision and recall at the token level, then compute the F1 score as normal using these values. + +### Test Submission + +When evaluaing on the test split, a prediction JSON will be compiled instead of metrics computed. +Instructions for submission are available on the [WebSRC homepage](https://x-lance.github.io/WebSRC/#) and in their [Original GitHub Repo](https://github.com/X-LANCE/WebSRC-Baseline#obtain-test-result). \ No newline at end of file From 4eebd3e5d7ab3b8c3116eea57318db72d2ce32bb Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Wed, 1 May 2024 10:46:54 -0400 Subject: [PATCH 015/196] Update main README with new task names --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index e1813c65..f29d8837 100644 --- a/README.md +++ b/README.md @@ -260,6 +260,9 @@ We also provide the raw data exported from Weights & Biases for the detailed res - VQAv2 (vqav2) - VQAv2 Validation (vqav2_val) - VQAv2 Test (vqav2_test) +- WebSRC (websrc) + - WebSRC Validation (websrc_val) + - WebSRC Test (websrc_test) ## Datasets to be added and tested - TallyQA (tallyqa) From 7687495b1ed552eeba088cb9ad5aaf1170e7fff9 Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Wed, 1 May 2024 10:47:32 -0400 Subject: [PATCH 016/196] Draft and validate websrc eval on dev split --- lmms_eval/tasks/websrc/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py index 02c0a9e4..3a4fc09a 100644 --- a/lmms_eval/tasks/websrc/utils.py +++ b/lmms_eval/tasks/websrc/utils.py @@ -19,7 +19,6 @@ def construct_prompt(doc): question = doc["question"] - # question = f"{question}\n{OPEN_ENDED_PROMPT}" question = f"{OPEN_ENDED_PROMPT}\n{question}" return question From 9bca441376325173128e5c50087f068e519c48da Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Wed, 1 May 2024 11:07:29 -0400 Subject: [PATCH 017/196] Add code to enable compilation of submission for WebSRC test split --- lmms_eval/tasks/websrc/utils.py | 15 ++++++++++++--- lmms_eval/tasks/websrc/websrc.yaml | 1 + lmms_eval/tasks/websrc/websrc_test.yaml | 19 +++++++++++++++++++ 3 files changed, 32 insertions(+), 3 deletions(-) create mode 100644 lmms_eval/tasks/websrc/websrc_test.yaml diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py index 3a4fc09a..bd5c9bc2 100644 --- a/lmms_eval/tasks/websrc/utils.py +++ b/lmms_eval/tasks/websrc/utils.py @@ -39,11 +39,17 @@ def websrc_process_results(doc, results): pred = results[0] parsed_pred = pred id = doc["page_id"] - websrc_ans = {"id": id, "domain": doc['domain'], "answer": doc["answer"], "parsed_pred": parsed_pred} + websrc_ans = {"id": id, "domain": doc['domain'], "parsed_pred": parsed_pred} + if "answer" in doc: + websrc_ans["answer"] = doc["answer"] + + if 'id' in doc: + websrc_ans['question_id'] = doc['id'] + return { "websrc_squad_f1": websrc_ans, "submission": { - id: pred, + websrc_ans['question_id']: pred, }, } @@ -51,7 +57,10 @@ def websrc_process_results(doc, results): def websrc_test_aggregate_results_for_submission(results, args): path = generate_submission_file("websrc_test_for_submission.json", args) with open(path, "w") as f: - json.dump(results, f) + out = {} + for result in results: + out.update(result) + json.dump(out, f, indent=4) lmms_logger.info(f"Results saved to {path}.") diff --git a/lmms_eval/tasks/websrc/websrc.yaml b/lmms_eval/tasks/websrc/websrc.yaml index 8328c92d..e32bed9b 100644 --- a/lmms_eval/tasks/websrc/websrc.yaml +++ b/lmms_eval/tasks/websrc/websrc.yaml @@ -1,3 +1,4 @@ group: websrc task: - websrc_val +- websrc_test diff --git a/lmms_eval/tasks/websrc/websrc_test.yaml b/lmms_eval/tasks/websrc/websrc_test.yaml new file mode 100644 index 00000000..b19ec2ca --- /dev/null +++ b/lmms_eval/tasks/websrc/websrc_test.yaml @@ -0,0 +1,19 @@ +dataset_path: rootsautomation/websrc-test +task: "websrc_test" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.websrc_doc_to_visual +doc_to_text: !function utils.websrc_doc_to_text +doc_to_target: "answer" +# The return value of process_results will be used by metrics +process_results: !function utils.websrc_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +generation_kwargs: + max_new_tokens: 16 + image_aspect_ratio: pad +metric_list: + - metric: submission + aggregation: !function utils.websrc_test_aggregate_results_for_submission + higher_is_better: true +metadata: + - version: 0.0 \ No newline at end of file From 626e8a91a4af2dd5dd774fc130cc2f4d74b2bc37 Mon Sep 17 00:00:00 2001 From: Hunter Heidenreich Date: Thu, 2 May 2024 09:31:03 -0400 Subject: [PATCH 018/196] Bugfix: WebSRC should be token-level F1 NOT character-level --- lmms_eval/tasks/websrc/utils.py | 42 +++++++++++++++++++++------------ 1 file changed, 27 insertions(+), 15 deletions(-) diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py index bd5c9bc2..e76037c2 100644 --- a/lmms_eval/tasks/websrc/utils.py +++ b/lmms_eval/tasks/websrc/utils.py @@ -50,7 +50,7 @@ def websrc_process_results(doc, results): "websrc_squad_f1": websrc_ans, "submission": { websrc_ans['question_id']: pred, - }, + } if 'question_id' in websrc_ans else None } @@ -122,27 +122,39 @@ def _normalize_str(string): # lower it string = string.lower() - # strip non-alphanumeric characters - string = re.sub(r"[^a-zA-Z0-9]", "", string) - # strip leading and trailing whitespaces string = string.strip() return string + def _tokenize(text): + # Regex pattern to match words and isolate punctuation + pattern = r'\w+|[^\w\s]' + tokens = re.findall(pattern, text) + return tokens + + def _compute_f1(sa, sb): + sa = _normalize_str(sa) + sb = _normalize_str(sb) + + sa = _tokenize(sa) + sb = _tokenize(sb) + + sa = set(sa) + sb = set(sb) + + if len(sa) == 0 or len(sb) == 0: + return 0.0 + + comm = sa.intersection(sb) + prec = len(comm) / len(sb) + rec = len(comm) / len(sa) + f1 = 2 * prec * rec / (prec + rec) if prec + rec > 0 else 0 + return f1 + judge_list = [] for sample in samples: - gold_i = set(_normalize_str(sample["answer"])) - pred_i = set(_normalize_str( sample["parsed_pred"])) - if len(pred_i) == 0: - judge_list.append(0.0) - continue - - comm_i = gold_i.intersection(pred_i) - prec_i = len(comm_i) / len(pred_i) - rec_i = len(comm_i) / len(gold_i) - f1_i = 2 * prec_i * rec_i / (prec_i + rec_i) if prec_i + rec_i > 0 else 0 - judge_list.append(f1_i) + judge_list.append(_compute_f1(sample["answer"], sample["parsed_pred"])) f1 = np.mean(judge_list) return judge_list, {"f1": f1} From 423b00606aa77fd6b324c19e3d480b73ab852db6 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Sun, 5 May 2024 07:54:52 +0000 Subject: [PATCH 019/196] Add qwen vl api --- lmms_eval/models/__init__.py | 1 + lmms_eval/models/qwen_vl_api.py | 124 ++++++++++++++++++++++++++++++++ 2 files changed, 125 insertions(+) create mode 100644 lmms_eval/models/qwen_vl_api.py diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index 4bdcf668..ce5aefeb 100644 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -10,6 +10,7 @@ "instructblip": "InstructBLIP", "minicpm_v": "MiniCPM_V", "idefics2": "Idefics2", + "qwen_vl_api": "Qwen_VL_API", } for model_name, model_class in AVAILABLE_MODELS.items(): diff --git a/lmms_eval/models/qwen_vl_api.py b/lmms_eval/models/qwen_vl_api.py new file mode 100644 index 00000000..af7f2713 --- /dev/null +++ b/lmms_eval/models/qwen_vl_api.py @@ -0,0 +1,124 @@ +from io import BytesIO +from copy import deepcopy +import os +import base64 +from typing import List, Tuple, Union +from tqdm import tqdm +import requests as url_requests +import time +import logging + +from lmms_eval.api.instance import Instance +from lmms_eval.api.model import lmms +from lmms_eval.api.registry import register_model +from lmms_eval import utils + +from PIL import Image + +NUM_SECONDS_TO_SLEEP = 5 +eval_logger = logging.getLogger("lmms-eval") + +try: + import dashscope +except: + eval_logger.debug("Can not import Dashscope") + +API_KEY = os.getenv("DASHSCOPE_API_KEY", "YOUR_API_KEY") + + +@register_model("qwen-vl-api") +class Qwen_VL_API(lmms): + def __init__( + self, + model_version: str = "qwen-vl-max", + image_token: str = "", # Use to separate interleaved image and text + system_prompt: str = "", # Whether you want some special system prompt here + tmp_folder: str = "./tmp", # Due to qwen's api restriction, + **kwargs, + ) -> None: + super().__init__() + + self.model_version = model_version + self.image_token = image_token + self.system_prompt = system_prompt + self.tmp_folder = tmp_folder + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + + def generate_until(self, requests) -> List[str]: + res = [] + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") + os.makedirs(self.tmp_folder, exist_ok=True) + + for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: + # encode, pad, and truncate contexts for this batch + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] + visuals = self.flatten(visuals) + imgs = [] + + for idx, visual in enumerate(visuals): + visual.save(os.path.join(self.tmp_folder, f"tmp_{idx}_{self.rank}_{self.world_size}.jpg")) + imgs.append(os.path.join(self.tmp_folder, f"tmp_{idx}_{self.rank}_{self.world_size}.jpg")) + + messages = [{"role": "user", "content": []}] + + if self.image_token not in contexts: + for img in imgs: + messages[0]["content"].append({"image": img}) + messages[0]["content"].append({"text": contexts}) + else: + contexts = contexts.split(self.image_token) + + for idx, img in enumerate(imgs): + messages[0]["content"].append({"text": contexts[idx]}) + messages[0]["content"].append({"image": img}) + messages[0]["content"].append({"text": contexts[-1]}) + + if "max_new_tokens" not in gen_kwargs or gen_kwargs["max_new_tokens"] > 1500: + gen_kwargs["max_new_tokens"] = 1024 + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + if "top_p" not in gen_kwargs: + gen_kwargs["top_p"] = None + if "num_beams" not in gen_kwargs: + gen_kwargs["num_beams"] = 1 + + for attempt in range(5): + try: + response_data = dashscope.MultiModalConversation.call(model=self.model_version, messages=messages, api_key=API_KEY, max_length=gen_kwargs["max_new_tokens"]) + except Exception as e: + eval_logger.info(f"Attempt {attempt + 1} failed with error: {str(e)}") + if attempt < 5 - 1: # If we have retries left, sleep and then continue to next attempt + time.sleep(NUM_SECONDS_TO_SLEEP) + else: # If this was the last attempt, log and return empty + eval_logger.error(f"All 5 attempts failed. Last error message: {str(e)}") + res.append("") + pbar.update(1) + continue + try: + res.append(response_data["output"]["choices"][0]["message"]["content"][0]["text"].strip()) + except Exception as e: + eval_logger.error(f"Error {e} happens when parsing input.") + eval_logger.error(f"{response_data}") + res.append("") + pbar.update(1) + + pbar.close() + + return res + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + assert False, "Not supported for claude" + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list From fa3ff92b07ea5aaa633a2039818c310744f84d07 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Mon, 6 May 2024 08:32:57 +0000 Subject: [PATCH 020/196] Fix llava conv template for llama3 --- lmms_eval/models/llava.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py index 632859d7..66f9ed0f 100644 --- a/lmms_eval/models/llava.py +++ b/lmms_eval/models/llava.py @@ -223,7 +223,11 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: image_tokens = " ".join(image_tokens) prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts) - conv = conv_templates[self.conv_template].copy() + # This is much safer for llama3, as we now have some object type in it + if "llama_3" in self.conv_template: + conv = copy.deepcopy(conv_templates[self.conv_template]) + else: + conv = conv_templates[self.conv_template].copy() conv.append_message(conv.roles[0], prompts_input) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() @@ -331,7 +335,11 @@ def _collate(x): else: question = context - conv = conv_templates[self.conv_template].copy() + # This is much safer for llama3, as we now have some object type in it + if "llama_3" in self.conv_template: + conv = copy.deepcopy(conv_templates[self.conv_template]) + else: + conv = conv_templates[self.conv_template].copy() conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt_question = conv.get_prompt() From 3e56b4f92db39a2ce92903b0c43a34f1d14d59ec Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 7 May 2024 03:09:56 +0000 Subject: [PATCH 021/196] Fix llava_hf generation for 1.6 --- lmms_eval/models/llava_hf.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py index effb94fb..67878dc4 100644 --- a/lmms_eval/models/llava_hf.py +++ b/lmms_eval/models/llava_hf.py @@ -8,7 +8,7 @@ from accelerate import Accelerator, DistributedType from accelerate.state import AcceleratorState from typing import List, Optional, Union, Tuple -from transformers import LlavaForConditionalGeneration, AutoProcessor +from transformers import LlavaForConditionalGeneration, LlavaNextForConditionalGeneration, AutoProcessor import warnings @@ -67,7 +67,15 @@ def __init__( self.device_map = device_map if isinstance(dtype, str) and dtype != "auto": dtype = getattr(torch, dtype) - self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation) + + if "1.5" in pretrained: + self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation) + elif "1.6" in pretrained: + self._model = LlavaNextForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation) + else: + eval_logger.info("Not sure whether you use 1.5 or 1.6. Use 1.5 by default. This might cause bugs if you are actually using 1.6") + self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation) + self._image_processor = AutoProcessor.from_pretrained(pretrained, revision=revision, trust_remote_code=trust_remote_code) # Pad from left for batched generation: https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/llava#usage-tips self._image_processor.tokenizer.padding_side = "left" @@ -106,6 +114,7 @@ def __init__( self.model.to(self._device) self._rank = 0 self._word_size = 1 + self.accelerator = accelerator @property def config(self): From 7847dc4d8efe60605102414bb071b1da9851228e Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 7 May 2024 03:15:59 +0000 Subject: [PATCH 022/196] Parse result for llava_hf 1.6 --- lmms_eval/models/llava_hf.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py index 67878dc4..9aff46a4 100644 --- a/lmms_eval/models/llava_hf.py +++ b/lmms_eval/models/llava_hf.py @@ -76,6 +76,7 @@ def __init__( eval_logger.info("Not sure whether you use 1.5 or 1.6. Use 1.5 by default. This might cause bugs if you are actually using 1.6") self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation) + self.pretrained = pretrained self._image_processor = AutoProcessor.from_pretrained(pretrained, revision=revision, trust_remote_code=trust_remote_code) # Pad from left for batched generation: https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/llava#usage-tips self._image_processor.tokenizer.padding_side = "left" @@ -317,7 +318,12 @@ def _collate(x): eval_logger.error(f"Error {e} in generating") cont = "" text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)[0] - text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() + if "1.5" in self.pretrained: + text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() + elif "1.6" in self.pretrained: + text_outputs = text_outputs.split("[/INST]")[-1].strip() + else: + text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() if self.accelerator.is_main_process and doc_id[0] % 100 == 0: eval_logger.info(f"Generated text for doc ID {doc_id[0]}:\n\n{text_outputs}\n") From 8aaa828108da8514dd9cd23a9d6d83a8b67f2d65 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Thu, 2 May 2024 06:13:56 +0000 Subject: [PATCH 023/196] Add model_name parameter to Llava constructor --- lmms_eval/models/llava.py | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py index 66f9ed0f..1c06c0d8 100644 --- a/lmms_eval/models/llava.py +++ b/lmms_eval/models/llava.py @@ -57,6 +57,7 @@ def __init__( batch_size: Optional[Union[int, str]] = 1, trust_remote_code: Optional[bool] = False, revision=None, + model_name=None, attn_implementation=best_fit_attn_implementation, use_flash_attention_2=True, device_map="auto", @@ -83,8 +84,20 @@ def __init__( llava_model_args["attn_implementation"] = attn_implementation if customized_config: llava_model_args["customized_config"] = customized_config - llava_model_args["use_flash_attention_2"] = False - self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, get_model_name_from_path(pretrained), device_map=self.device_map, **llava_model_args) + if attn_implementation is not None: + llava_model_args["attn_implementation"] = attn_implementation + if "use_flash_attention_2" in kwargs: + llava_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"] + + model_name = model_name if model_name is not None else get_model_name_from_path(pretrained) + try: + # Try to load the model with the multimodal argument + self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) + except TypeError: + # for older versions of LLaVA that don't have multimodal argument + llava_model_args.pop("multimodal", None) + self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) + self._config = self._model.config self.model.eval() self.model.tie_weights() From 64f07e497f53e5bcbe9e8fb5830cc7a1daaf7ff1 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 9 May 2024 02:04:10 +0000 Subject: [PATCH 024/196] Fix endless warning for llava_hf generation --- lmms_eval/models/llava_hf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py index 9aff46a4..ecf3172a 100644 --- a/lmms_eval/models/llava_hf.py +++ b/lmms_eval/models/llava_hf.py @@ -313,6 +313,7 @@ def _collate(x): num_beams=gen_kwargs["num_beams"], max_new_tokens=gen_kwargs["max_new_tokens"], use_cache=self.use_cache, + pad_token_id=self.tokenizer.eos_token_id, ) except Exception as e: eval_logger.error(f"Error {e} in generating") @@ -320,7 +321,7 @@ def _collate(x): text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)[0] if "1.5" in self.pretrained: text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() - elif "1.6" in self.pretrained: + elif "mistral" in self.pretrained: text_outputs = text_outputs.split("[/INST]")[-1].strip() else: text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() From c09444860362a136f17641f8b2a1f91c2bbc3715 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Sat, 11 May 2024 06:11:19 +0000 Subject: [PATCH 025/196] Fix llava_hf image tokens number issue --- lmms_eval/models/llava_hf.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py index ecf3172a..8b16b6f1 100644 --- a/lmms_eval/models/llava_hf.py +++ b/lmms_eval/models/llava_hf.py @@ -31,10 +31,10 @@ class LlavaHf(lmms): Example usage: - accelerate launch --num_processes=8 -m lmms_eval \ + accelerate launch --num_processes=8 --main_process_port 12345 -m lmms_eval \ --model llava_hf \ --model_args pretrained=llava-hf/llava-1.5-7b-hf \ - --tasks mme \ + --tasks seedbench \ --batch_size 1 \ --output_path ./logs/ \ --log_samples @@ -278,7 +278,9 @@ def _collate(x): # Some benchmarks like MME do not contain image tokens, so we prepend them to the prompt. if DEFAULT_IMAGE_TOKEN not in context: - context = f"{DEFAULT_IMAGE_TOKEN}\n{context}" + image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals) + image_tokens = " ".join(image_tokens) + context = f"{image_tokens}\n{context}" # Apply chat template messages = [{"role": "user", "content": context}] if self.chat_template is not None: From 66c77d3cde2f01d01ac573f6dbc9bd66ca168b5e Mon Sep 17 00:00:00 2001 From: Xiang Yue Date: Mon, 13 May 2024 18:01:45 -0400 Subject: [PATCH 026/196] update M-MMMU readme --- M-MMMU/README.md | 58 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 M-MMMU/README.md diff --git a/M-MMMU/README.md b/M-MMMU/README.md new file mode 100644 index 00000000..f883830c --- /dev/null +++ b/M-MMMU/README.md @@ -0,0 +1,58 @@ +# Multilingual MMMU +Multilingual MMMU: Multilingual Massive Multi-task Multimodality Understanding + +## Instruction +Multilingual MMMU is a benchmark that aims to evaluate the performance of multimodal foundation models in understanding multilingual multimodal data across multiple tasks. It provides a standardized evaluation framework and dataset for researchers to compare and analyze the general understandability of different multimodal models in handling various knowledge tasks in multilingual cases. + +We are looking for high school or college exam questions that have both image and text in the question: +- The question needs to contain both image and text. +- The question needs to be highly precise, and the answer needs to be easy to verify, e.g., a multiple-choice answer, a number, a list of number, or a boolean variable, etc. +- The topic covers Art, Business, Medical, Humanities, Science and Engineering. See the full list of the category in the following. +- You can either find questions from the Internet, or Textbook or come up with your own question (if you are an expert in that field). + +## Category +| Category | Translation Count | Annotation Count | +|--------------------------------------- |------------------- |------------------ | +| **Art and Design** | | | +| - Art | | | +| - Art_Theory | | | +| - Design | | | +| - Music | | | +| | | | +| **Business** | | | +| - Accounting | | | +| - Economics | | | +| - Finance | | | +| - Manage | | | +| - Marketing | | | +| | | | +| **Health and Medicine** | | | +| - Basic_Medical_Science | | | +| - Clinical_Medicine | | | +| - Diagnostics_and_Laboratory_Medicine | | | +| - Pharmacy | | | +| - Public_Health | | | +| | | | +| **Humanities and Social Science** | | | +| - History | | | +| - Literature | | | +| - Psychology | | | +| - Sociology | | | +| | | | +| **Science** | | | +| - Biology | | | +| - Chemistry | | | +| - Geography | | | +| - Math | | | +| - Physics | | | +| | | | +| **Tech and Engineering** | | | +| - Agriculture | | | +| - Architecture_and_Engineering | | | +| - Computer_Science | | | +| - Electronics | | | +| - Energy_and_Power | | | +| - Materials | | | +| - Mechanical_Engineering | | | + +## \ No newline at end of file From a8bba1cdb51061a0d27bf9a98cca1505b5c58ea5 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Sat, 18 May 2024 02:28:03 +0800 Subject: [PATCH 027/196] Create LICENSE --- LICENSE | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..a36c61c6 --- /dev/null +++ b/LICENSE @@ -0,0 +1,39 @@ +# For Main Pipeline Structure Related Code. We keep original license provided with lm-evaluation-harness. +MIT License + +Copyright (c) 2024 LMMs-Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + +# For Our Added Multimodal Models and Datasets (defined as code in lmms_eval/tasks and lmms_eval/models folder). We apply Apache License. +Apache 2.0 License + +Copyright (c) 2024 LMMs-Lab + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. From 82317a635a4978b32e095a06cc295d0ae23661c2 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Sat, 18 May 2024 02:29:09 +0800 Subject: [PATCH 028/196] Update LICENSE --- LICENSE | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/LICENSE b/LICENSE index a36c61c6..3492e386 100644 --- a/LICENSE +++ b/LICENSE @@ -1,4 +1,5 @@ -# For Main Pipeline Structure Related Code. We keep original license provided with lm-evaluation-harness. +# For the main pipeline structure-related code, we maintain the original license provided with lm-evaluation-harness, which is the MIT License. + MIT License Copyright (c) 2024 LMMs-Lab @@ -21,7 +22,8 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -# For Our Added Multimodal Models and Datasets (defined as code in lmms_eval/tasks and lmms_eval/models folder). We apply Apache License. +# For the multimodal models and datasets that we have added (defined as code in the lmms_eval/tasks and lmms_eval/models folders), we apply the Apache License. + Apache 2.0 License Copyright (c) 2024 LMMs-Lab From 3c12a080d66b9c38f615b961befca7c30f82fa39 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Sat, 18 May 2024 02:35:52 +0800 Subject: [PATCH 029/196] Update LICENSE --- LICENSE | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/LICENSE b/LICENSE index 3492e386..aa318e26 100644 --- a/LICENSE +++ b/LICENSE @@ -39,3 +39,18 @@ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. + +When modifying the code, please include the following information about the original lmms-eval source: +# Adopted from lmms-eval from https://github.com/EvolvingLMMs-Lab/lmms-eval. Below is the original copyright: +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. From aea80e6a71f716951353e1e5d68380243396b4d6 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Wed, 15 May 2024 11:07:35 +0000 Subject: [PATCH 030/196] Better task list_with_num --- lmms_eval/__main__.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/lmms_eval/__main__.py b/lmms_eval/__main__.py index 0c666145..c852d2f4 100644 --- a/lmms_eval/__main__.py +++ b/lmms_eval/__main__.py @@ -244,14 +244,17 @@ def cli_evaluate_single(args: Union[argparse.Namespace, None] = None) -> None: "\n" + "=" * 70 + "\n" + "\n\tYou are trying to check all the numbers in each task." + "\n\tThis action will download the complete dataset." + "\n\tIf the results are not clear initially, call this again." + "\n\n" + "=" * 70 ) eval_logger.info(log_message) - task_dict = get_task_dict([task for task in sorted(ALL_TASKS)], model_name="llava") - for task_name in task_dict.keys(): - task_obj = task_dict[task_name] - if type(task_obj) == tuple: - group, task_obj = task_obj - if task_obj is None: - continue - eval_logger.info(f"\nTask : {task_obj.config.task}\n - #num : {len(task_obj.test_docs()) if task_obj.has_test_docs() else len(task_obj.validation_docs())}") + for task_name in sorted(ALL_TASKS): + try: + task_dict = get_task_dict([task_name], model_name="llava") + task_obj = task_dict[task_name] + if type(task_obj) == tuple: + group, task_obj = task_obj + if task_obj is None: + continue + eval_logger.info(f"\nTask : {task_obj.config.task}\n - #num : {len(task_obj.test_docs()) if task_obj.has_test_docs() else len(task_obj.validation_docs())}") + except Exception as e: + eval_logger.debug(f"\nTask : {task_name} fail to load \n Exception : \n {e}") sys.exit() else: tasks_list = args.tasks.split(",") From 6aeb5504e74ed1980b53700d8e4d4dcf7d1b38fc Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 16 May 2024 01:45:24 +0000 Subject: [PATCH 031/196] Fix idefics2 llava in the wild bugs --- lmms_eval/models/idefics2.py | 1 + 1 file changed, 1 insertion(+) diff --git a/lmms_eval/models/idefics2.py b/lmms_eval/models/idefics2.py index 4bbd393c..7419472b 100644 --- a/lmms_eval/models/idefics2.py +++ b/lmms_eval/models/idefics2.py @@ -196,6 +196,7 @@ def _collate(x): gen_kwargs = all_gen_kwargs[0] # until = gen_kwargs.pop("until", None) + image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio", None) prompts = [] for context, visual in zip(contexts, visuals): content = [] From 557a6a3b15e07e506bc05e2cc76ff6a2f8c93964 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 16 May 2024 03:11:41 +0000 Subject: [PATCH 032/196] Remove redundant code in fuyu --- lmms_eval/models/fuyu.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/lmms_eval/models/fuyu.py b/lmms_eval/models/fuyu.py index 173fa6b9..7a4844dc 100644 --- a/lmms_eval/models/fuyu.py +++ b/lmms_eval/models/fuyu.py @@ -80,8 +80,12 @@ def __init__( eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") self._rank = self.accelerator.local_process_index self._world_size = self.accelerator.num_processes + else: + self.model.to(self._device) + self._rank = 0 + self._word_size = 1 - if accelerator.num_processes > 1: + '''if accelerator.num_processes > 1: assert accelerator.distributed_type in [ DistributedType.FSDP, DistributedType.MULTI_GPU, @@ -94,11 +98,7 @@ def __init__( if self.accelerator.is_local_main_process: eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") self._rank = self.accelerator.local_process_index - self._world_size = self.accelerator.num_processes - else: - self.model.to(self._device) - self._rank = 0 - self._word_size = 1 + self._world_size = self.accelerator.num_processes''' @property def config(self): From 09329322916bfbb604d72ddaf50441a0947f8805 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 16 May 2024 03:55:39 +0000 Subject: [PATCH 033/196] Fix instructblip qformer size mismatch and multi-images problem --- lmms_eval/models/instructblip.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/lmms_eval/models/instructblip.py b/lmms_eval/models/instructblip.py index 6f83d599..2f065ffe 100644 --- a/lmms_eval/models/instructblip.py +++ b/lmms_eval/models/instructblip.py @@ -6,6 +6,7 @@ from lmms_eval.api.instance import Instance from lmms_eval.api.model import lmms from lmms_eval.api.registry import register_model +from lmms_eval.tasks.mmmu.utils_group_img import process_images from accelerate import Accelerator, DistributedType from accelerate.state import AcceleratorState from typing import List, Optional, Union, Tuple @@ -187,7 +188,13 @@ def _collate(x): if "" in context: # instruct blip does not expect the tag context = context.replace("", "") - inputs = self._image_processor(images=visuals, text=context, return_tensors="pt").to(self.device) + # Set trunction equals true here, the max length for qformer tokenizer is 512 + # if not truncate, some questions will cause size mismatch + # The transformer implementation can't handle multi images for blip + # Concat it into one image + if len(visuals) > 1: + visuals = [process_images(visuals)] + inputs = self._image_processor(images=visuals, text=context, return_tensors="pt", truncation=True).to(self.device) gen_kwargs["image_sizes"] = [visuals[idx].size for idx in range(len(visuals))] if "max_new_tokens" not in gen_kwargs: From 662f05ce4c62a46a83f819d3a5925a9bd20059b5 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Mon, 20 May 2024 03:13:13 +0000 Subject: [PATCH 034/196] Comment out parse result in xcomposer --- lmms_eval/models/xcomposer2_4KHD.py | 295 ++++++++++++++++++++++++++++ 1 file changed, 295 insertions(+) create mode 100644 lmms_eval/models/xcomposer2_4KHD.py diff --git a/lmms_eval/models/xcomposer2_4KHD.py b/lmms_eval/models/xcomposer2_4KHD.py new file mode 100644 index 00000000..2c49e43b --- /dev/null +++ b/lmms_eval/models/xcomposer2_4KHD.py @@ -0,0 +1,295 @@ +from multiprocessing import context +import torch +from transformers import AutoModel, AutoTokenizer +from PIL import Image +import numpy as np +import torchvision.transforms as transforms +from datetime import timedelta +import logging + +from lmms_eval import utils +from lmms_eval.api.instance import Instance +from lmms_eval.api.model import lmms +from lmms_eval.api.registry import register_model +from lmms_eval.utils import stop_sequences_criteria + +from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs +from accelerate.state import AcceleratorState + +from typing import Optional, Sequence, List, Tuple, Union +import re +from tqdm import tqdm + +pattern = re.compile(r"[A-Z]") + +eval_logger = logging.getLogger("lmms-eval") + +meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔). +- InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed\ + by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless. +- InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by\ + the user such as English and 中文. +- InternLM-XComposer (浦语·灵笔) is capable of comprehending and articulating responses\ + effectively based on the provided image.""" + + +@register_model("xcomposer2_4khd") +class XComposer2_4KHD(lmms): + def __init__( + self, + pretrained: str = "internlm/internlm-xcomposer2-4khd-7b", + device: Optional[str] = "cuda:0", + batch_size: Optional[Union[int, str]] = 1, + device_map="cuda:0", + need_bos: bool = True, + padding: bool = False, + half: bool = False, + **kwargs, + ) -> None: + super().__init__() + + accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) + accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + if accelerator.num_processes > 1: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + elif accelerator.num_processes == 1 and device_map == "auto": + self._device = torch.device(device) + self.device_map = device_map + else: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + + self.pretrained = pretrained + self.need_bos = need_bos + self.padding = padding + self._model = AutoModel.from_pretrained(self.pretrained, device_map=self.device_map, trust_remote_code=True) + self._tokenizer = AutoTokenizer.from_pretrained(self.pretrained, trust_remote_code=True) + self.model.tokenizer = self.tokenizer + self.batch_size_per_gpu = batch_size + + if accelerator.num_processes > 1: + assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." + # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model + # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works + # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. + if accelerator.distributed_type == DistributedType.DEEPSPEED: + kwargs = { + "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, + "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, + } + AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) + eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") + if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: + self._model = accelerator.prepare(self.model) + else: + self._model = accelerator.prepare_model(self.model, evaluation_mode=True) + self.accelerator = accelerator + if self.accelerator.is_local_main_process: + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + elif accelerator.num_processes == 1 and device_map == "auto": + eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") + self._rank = 0 + self._word_size = 1 + else: + eval_logger.info(f"Using single device: {self._device}") + self.model.to(self._device) + self._rank = 0 + self._world_size = 1 + + @property + def config(self): + # return the associated transformers.AutoConfig for the given pretrained model. + return self._config + + @property + def tokenizer(self): + return self._tokenizer + + @property + def model(self): + # returns the model, unwrapping it if using Accelerate + if hasattr(self, "accelerator"): + return self.accelerator.unwrap_model(self._model) + else: + return self._model + + @property + def batch_size(self): + return self.batch_size_per_gpu + + @property + def device(self): + return self._device + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + def generate_until(self, requests) -> List[str]: + res = [] + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") + + for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: + # encode, pad, and truncate contexts for this batch + if "[UNUSED_TOKEN_146]" not in contexts: + contexts = f"[UNUSED_TOKEN_146]user\n{contexts}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n" + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] + visuals = self.flatten(visuals) + + if "hd_num" not in gen_kwargs: + if listinstr(["docvqa_test", "infovqa_test"], task.lower()): + self.model.hd_num = 65 + elif listinstr(["docvqa_val", "infovqa_val", "OCRBench"], task.lower()): + self.model.hd_num = 55 + elif listinstr(["mmmu", "mmbench", "mmvet"], task.lower()): + self.model.hd_num = 16 + else: + self.model.hd_num = 25 + else: + self.model.hd_num = gen_kwargs.pop("hd_num") + + pt1 = 0 + embeds = [] + im_mask = [] + images_loc = [0] + need_bos = self.need_bos + padding = self.padding + for i, pts in enumerate(images_loc + [len(contexts)]): + subtext = contexts[pt1:pts] + if need_bos or len(subtext) > 0: + text_embeds = self.model.encode_text(subtext, add_special_tokens=need_bos).to(self.device) + embeds.append(text_embeds) + im_mask.append(torch.zeros(text_embeds.shape[:2]).to(self.device)) + need_bos = False + if i < len(visuals): + image = visuals[i] + + image = HD_transform(image, im_num=self.model.hd_num) + image = self.model.vis_processor(image).unsqueeze(0).to(self.device) + image_embeds = self.model.encode_img(image) + embeds.append(image_embeds) + im_mask.append(torch.ones(image_embeds.shape[:2]).to(self.device)) + pt1 = pts + embeds = torch.cat(embeds, dim=1) + im_mask = torch.cat(im_mask, dim=1) + im_mask = im_mask.bool() + + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + if "top_p" not in gen_kwargs: + gen_kwargs["top_p"] = None + if "num_beams" not in gen_kwargs: + gen_kwargs["num_beams"] = 1 + if "do_sample" not in gen_kwargs: + gen_kwargs["do_sample"] = False + if "repetition_penalty" not in gen_kwargs: + gen_kwargs["repetition_penalty"] = 1.0 + + outputs = self.model.generate( + inputs_embeds=embeds, + im_mask=im_mask, + temperature=gen_kwargs["temperature"], + max_new_tokens=gen_kwargs["max_new_tokens"], + num_beams=gen_kwargs["num_beams"], + do_sample=gen_kwargs["do_sample"], + repetition_penalty=gen_kwargs["repetition_penalty"], + ) + output_token = outputs[0] + if output_token[0] == 0 or output_token[0] == 1: + output_token = output_token[1:] + output_text = self.model.tokenizer.decode(output_token, add_special_tokens=False) + output_text = output_text.split("[UNUSED_TOKEN_145]")[0].strip() + output_text = output_text.split("<|im_end|>")[0].strip() + # if DATASET_TYPE(task) == "multi-choice": + # output_text = pattern.findall(output_text) + # if len(output_text) == 0: + # print("Error:", output_text) + # output_text = "Z" + # if type(output_text) == list: + # output_text = output_text[0] + res.append(output_text) + pbar.update(1) + pbar.close() + return res + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + return super().loglikelihood(requests) + + +def padding_336(b): + width, height = b.size + tar = int(np.ceil(height / 336) * 336) + top_padding = int((tar - height) / 2) + bottom_padding = tar - height - top_padding + left_padding = 0 + right_padding = 0 + b = transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255, 255, 255]) + + return b + + +def HD_transform(img, im_num=16): + width, height = img.size + trans = False + if width < height: + img = img.transpose(Image.TRANSPOSE) + trans = True + width, height = img.size + ratio = width / height + scale = 1 + while scale * np.ceil(scale / ratio) <= im_num: + scale += 1 + scale -= 1 + new_w = int(scale * 336) + new_h = int(new_w / ratio) + + img = transforms.functional.resize( + img, + [new_h, new_w], + ) + img = padding_336(img) + width, height = img.size + assert width * height <= im_num * 336 * 336 + if trans: + img = img.transpose(Image.TRANSPOSE) + + return img + + +def listinstr(lst, s): + assert isinstance(lst, list) + for item in lst: + if item in s: + return True + return False + + +def DATASET_TYPE(dataset): + # Dealing with Custom Dataset + dataset = dataset.lower() + if listinstr(["mmbench", "seedbench", "ccbench", "mmmu", "scienceqa", "ai2d", "mmstar"], dataset): + return "multi-choice" + elif listinstr(["mme", "hallusion"], dataset): + return "Y/N" + elif "coco" in dataset: + return "Caption" + elif listinstr(["ocrvqa", "textvqa", "chartqa", "mathvista", "docvqa", "infovqa", "llavabench", "mmvet", "ocrbench"], dataset): + return "VQA" + else: + return "QA" From b2b4dbd2dc13432c79208db35abf7f55c97f1790 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Mon, 20 May 2024 07:45:11 +0000 Subject: [PATCH 035/196] Comment out Spice in caption task so that don't need to download stanford nlp model --- lmms_eval/tasks/coco_cap/utils.py | 2 +- lmms_eval/tasks/flickr30k/utils.py | 2 +- lmms_eval/tasks/nocaps/utils.py | 2 +- lmms_eval/tasks/refcoco+/utils.py | 2 +- lmms_eval/tasks/refcoco/utils.py | 2 +- lmms_eval/tasks/refcocog/utils.py | 2 +- lmms_eval/tasks/textcaps/utils.py | 2 +- 7 files changed, 7 insertions(+), 7 deletions(-) diff --git a/lmms_eval/tasks/coco_cap/utils.py b/lmms_eval/tasks/coco_cap/utils.py index 4e0551f6..0102dbef 100644 --- a/lmms_eval/tasks/coco_cap/utils.py +++ b/lmms_eval/tasks/coco_cap/utils.py @@ -43,7 +43,7 @@ def coco_process_result(doc, result): def coco_aggregation_result(results, metric, args): - scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")] + scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr")]#, (Spice(), "SPICE")] scorers_dict = {s[1]: s for s in scorers} stored_results = [] diff --git a/lmms_eval/tasks/flickr30k/utils.py b/lmms_eval/tasks/flickr30k/utils.py index 44bf0754..f5d5c144 100644 --- a/lmms_eval/tasks/flickr30k/utils.py +++ b/lmms_eval/tasks/flickr30k/utils.py @@ -41,7 +41,7 @@ def flickr_process_result(doc, result): def flickr_aggregation_result(results, metric, args): - scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")] + scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr")]#, (Spice(), "SPICE")] scorers_dict = {s[1]: s for s in scorers} stored_results = [] diff --git a/lmms_eval/tasks/nocaps/utils.py b/lmms_eval/tasks/nocaps/utils.py index 41e47286..f645b1cc 100644 --- a/lmms_eval/tasks/nocaps/utils.py +++ b/lmms_eval/tasks/nocaps/utils.py @@ -42,7 +42,7 @@ def nocaps_process_result(doc, result): def nocaps_aggregation_result(results, metric, args=None): - scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")] + scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr")]#, (Spice(), "SPICE")] scorers_dict = {s[1]: s for s in scorers} stored_results = [] diff --git a/lmms_eval/tasks/refcoco+/utils.py b/lmms_eval/tasks/refcoco+/utils.py index 451f72ec..4feb71cb 100644 --- a/lmms_eval/tasks/refcoco+/utils.py +++ b/lmms_eval/tasks/refcoco+/utils.py @@ -49,7 +49,7 @@ def refcoco_process_result(doc, result): def refcoco_aggregation_result(results, metric): - scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")] + scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr")]#, (Spice(), "SPICE")] scorers_dict = {s[1]: s for s in scorers} stored_results = [] diff --git a/lmms_eval/tasks/refcoco/utils.py b/lmms_eval/tasks/refcoco/utils.py index 451f72ec..4feb71cb 100644 --- a/lmms_eval/tasks/refcoco/utils.py +++ b/lmms_eval/tasks/refcoco/utils.py @@ -49,7 +49,7 @@ def refcoco_process_result(doc, result): def refcoco_aggregation_result(results, metric): - scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")] + scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr")]#, (Spice(), "SPICE")] scorers_dict = {s[1]: s for s in scorers} stored_results = [] diff --git a/lmms_eval/tasks/refcocog/utils.py b/lmms_eval/tasks/refcocog/utils.py index 451f72ec..4feb71cb 100644 --- a/lmms_eval/tasks/refcocog/utils.py +++ b/lmms_eval/tasks/refcocog/utils.py @@ -49,7 +49,7 @@ def refcoco_process_result(doc, result): def refcoco_aggregation_result(results, metric): - scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")] + scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr")]#, (Spice(), "SPICE")] scorers_dict = {s[1]: s for s in scorers} stored_results = [] diff --git a/lmms_eval/tasks/textcaps/utils.py b/lmms_eval/tasks/textcaps/utils.py index 777135f2..c63feae5 100644 --- a/lmms_eval/tasks/textcaps/utils.py +++ b/lmms_eval/tasks/textcaps/utils.py @@ -38,7 +38,7 @@ def textcaps_process_result(doc, result): def textcaps_aggregation_result(results, metric, args=None): - scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")] + scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr")]#, (Spice(), "SPICE")] scorers_dict = {s[1]: s for s in scorers} stored_results = [] From d1eefb1565014b47287ffa6b350229062f8f602f Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 9 May 2024 08:36:02 +0000 Subject: [PATCH 036/196] Update gitignore --- .gitignore | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.gitignore b/.gitignore index 9d8615d6..a2e6a0ba 100644 --- a/.gitignore +++ b/.gitignore @@ -24,3 +24,8 @@ submissions/ lmms_eval/tasks/hallusion_bench/hallusion_output_vs_model.json lmms_eval/tasks/hallusion_bench/hallusion_output_vd_model.json zk.log +cache_dir +ckpt +pretrained/ +LLaVA/ +*logs From 22520a95f13334b75eee0cf0387151067a6bf516 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 23 May 2024 03:41:14 +0000 Subject: [PATCH 037/196] Add separated pope tasks by category --- lmms_eval/tasks/pope/pope_adv.yaml | 35 +++++++++++++++++++++++++++ lmms_eval/tasks/pope/pope_full.yaml | 5 ++++ lmms_eval/tasks/pope/pope_pop.yaml | 35 +++++++++++++++++++++++++++ lmms_eval/tasks/pope/pope_random.yaml | 35 +++++++++++++++++++++++++++ 4 files changed, 110 insertions(+) create mode 100644 lmms_eval/tasks/pope/pope_adv.yaml create mode 100644 lmms_eval/tasks/pope/pope_full.yaml create mode 100644 lmms_eval/tasks/pope/pope_pop.yaml create mode 100644 lmms_eval/tasks/pope/pope_random.yaml diff --git a/lmms_eval/tasks/pope/pope_adv.yaml b/lmms_eval/tasks/pope/pope_adv.yaml new file mode 100644 index 00000000..9a185723 --- /dev/null +++ b/lmms_eval/tasks/pope/pope_adv.yaml @@ -0,0 +1,35 @@ +dataset_path: lmms-lab/POPE +dataset_name: Full +dataset_kwargs: + token: True +task: "pope_adv" +test_split: adversarial +output_type: generate_until +doc_to_visual: !function utils.pope_doc_to_visual +doc_to_text: !function utils.pope_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 128 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.pope_process_results +metric_list: + - metric: pope_accuracy + aggregation: !function utils.pope_aggregate_accuracy + higher_is_better: true + - metric: pope_precision + aggregation: !function utils.pope_aggregate_precision + higher_is_better: true + - metric: pope_recall + aggregation: !function utils.pope_aggregate_recall + higher_is_better: true + - metric: pope_f1_score + aggregation: !function utils.pope_aggregate_f1_score + higher_is_better: true + - metric: pope_yes_ratio + aggregation: !function utils.pope_aggregate_yes_ratio + higher_is_better: true +metadata: + - version: 0.0 \ No newline at end of file diff --git a/lmms_eval/tasks/pope/pope_full.yaml b/lmms_eval/tasks/pope/pope_full.yaml new file mode 100644 index 00000000..1717d4f0 --- /dev/null +++ b/lmms_eval/tasks/pope/pope_full.yaml @@ -0,0 +1,5 @@ +group : pope_full +task: + - pope_adv + - pope_pop + - pop_random \ No newline at end of file diff --git a/lmms_eval/tasks/pope/pope_pop.yaml b/lmms_eval/tasks/pope/pope_pop.yaml new file mode 100644 index 00000000..1f5cf658 --- /dev/null +++ b/lmms_eval/tasks/pope/pope_pop.yaml @@ -0,0 +1,35 @@ +dataset_path: lmms-lab/POPE +dataset_name: Full +dataset_kwargs: + token: True +task: "pope_pop" +test_split: popular +output_type: generate_until +doc_to_visual: !function utils.pope_doc_to_visual +doc_to_text: !function utils.pope_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 128 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.pope_process_results +metric_list: + - metric: pope_accuracy + aggregation: !function utils.pope_aggregate_accuracy + higher_is_better: true + - metric: pope_precision + aggregation: !function utils.pope_aggregate_precision + higher_is_better: true + - metric: pope_recall + aggregation: !function utils.pope_aggregate_recall + higher_is_better: true + - metric: pope_f1_score + aggregation: !function utils.pope_aggregate_f1_score + higher_is_better: true + - metric: pope_yes_ratio + aggregation: !function utils.pope_aggregate_yes_ratio + higher_is_better: true +metadata: + - version: 0.0 \ No newline at end of file diff --git a/lmms_eval/tasks/pope/pope_random.yaml b/lmms_eval/tasks/pope/pope_random.yaml new file mode 100644 index 00000000..c635f2ee --- /dev/null +++ b/lmms_eval/tasks/pope/pope_random.yaml @@ -0,0 +1,35 @@ +dataset_path: lmms-lab/POPE +dataset_name: Full +dataset_kwargs: + token: True +task: "pope_random" +test_split: random +output_type: generate_until +doc_to_visual: !function utils.pope_doc_to_visual +doc_to_text: !function utils.pope_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 128 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.pope_process_results +metric_list: + - metric: pope_accuracy + aggregation: !function utils.pope_aggregate_accuracy + higher_is_better: true + - metric: pope_precision + aggregation: !function utils.pope_aggregate_precision + higher_is_better: true + - metric: pope_recall + aggregation: !function utils.pope_aggregate_recall + higher_is_better: true + - metric: pope_f1_score + aggregation: !function utils.pope_aggregate_f1_score + higher_is_better: true + - metric: pope_yes_ratio + aggregation: !function utils.pope_aggregate_yes_ratio + higher_is_better: true +metadata: + - version: 0.0 \ No newline at end of file From 53f013ed8278776551ca992562253387cc9968d2 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 23 May 2024 03:41:39 +0000 Subject: [PATCH 038/196] Fix pope random name in pope full --- lmms_eval/tasks/pope/pope_full.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/pope/pope_full.yaml b/lmms_eval/tasks/pope/pope_full.yaml index 1717d4f0..d43c4f32 100644 --- a/lmms_eval/tasks/pope/pope_full.yaml +++ b/lmms_eval/tasks/pope/pope_full.yaml @@ -2,4 +2,4 @@ group : pope_full task: - pope_adv - pope_pop - - pop_random \ No newline at end of file + - pope_random \ No newline at end of file From 45c05b2b2bece76e06849a52a0d034f9c0ac2367 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 23 May 2024 03:47:36 +0000 Subject: [PATCH 039/196] Set printing info for llava_hf to debug level --- lmms_eval/models/llava_hf.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py index 8b16b6f1..342824f1 100644 --- a/lmms_eval/models/llava_hf.py +++ b/lmms_eval/models/llava_hf.py @@ -209,8 +209,8 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: labels[: len(contxt_id)] = -100 if self.accelerator.is_main_process and doc_id % 100 == 0: - eval_logger.info(f"Prompt for doc ID {doc_id}:\n\n{formatted_contexts[0]}\n") - eval_logger.info(f"Prompt and continuation for doc ID {doc_id}:\n\n{formatted_continuation[0]}\n") + eval_logger.debug(f"Prompt for doc ID {doc_id}:\n\n{formatted_contexts[0]}\n") + eval_logger.debug(f"Prompt and continuation for doc ID {doc_id}:\n\n{formatted_continuation[0]}\n") with torch.inference_mode(): outputs = self.model(**model_inputs, labels=labels) @@ -293,7 +293,7 @@ def _collate(x): text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) if self.accelerator.is_main_process and doc_id[0] % 100 == 0: - eval_logger.info(f"Prompt for doc ID {doc_id[0]}:\n\n{text}\n") + eval_logger.debug(f"Prompt for doc ID {doc_id[0]}:\n\n{text}\n") inputs = self._image_processor(images=visuals, text=text, return_tensors="pt").to(self._device, self.model.dtype) @@ -329,7 +329,7 @@ def _collate(x): text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() if self.accelerator.is_main_process and doc_id[0] % 100 == 0: - eval_logger.info(f"Generated text for doc ID {doc_id[0]}:\n\n{text_outputs}\n") + eval_logger.debug(f"Generated text for doc ID {doc_id[0]}:\n\n{text_outputs}\n") res.append(text_outputs) self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) From 7f9fb6bcc6cd24a7b8011b8753d0ea98cc2451fd Mon Sep 17 00:00:00 2001 From: Victor Fragoso Date: Fri, 24 May 2024 13:24:16 +0000 Subject: [PATCH 040/196] Adding Phi3v model. --- lmms_eval/models/__init__.py | 1 + lmms_eval/models/phi3v.py | 229 +++++++++++++++++++++++++++++++++++ 2 files changed, 230 insertions(+) create mode 100644 lmms_eval/models/phi3v.py diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index ce5aefeb..5dbfc7ae 100644 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -11,6 +11,7 @@ "minicpm_v": "MiniCPM_V", "idefics2": "Idefics2", "qwen_vl_api": "Qwen_VL_API", + "phi3v": "Phi3v", } for model_name, model_class in AVAILABLE_MODELS.items(): diff --git a/lmms_eval/models/phi3v.py b/lmms_eval/models/phi3v.py new file mode 100644 index 00000000..31e11082 --- /dev/null +++ b/lmms_eval/models/phi3v.py @@ -0,0 +1,229 @@ +import torch +import logging + +from accelerate import Accelerator, DistributedType +from lmms_eval import utils +from lmms_eval.api.instance import Instance +from lmms_eval.api.model import lmms +from lmms_eval.api.registry import register_model +from tqdm import tqdm +from transformers import AutoModelForCausalLM +from transformers import AutoProcessor +from typing import List, Optional, Tuple, Union + +eval_logger = logging.getLogger("lmms-eval") + + +@register_model("phi3v") +class Phi3v(lmms): + """ + TODO(vifragos): Document me! + """ + def __init__( + self, + model_id_name: str = "microsoft/Phi-3-vision-128k-instruct", + device: str = "cuda", + dtype: Optional[Union[str, torch.dtype]] = "auto", + batch_size: int = 1, + trust_remote_code: Optional[bool] = True, + use_cache: bool = True, + **kwargs, + ) -> None: + super().__init__() + # Do not use kwargs for now + assert kwargs == {}, f"Unexpected kwargs: {kwargs}" + # Setup accelerator. + accelerator = Accelerator() + if accelerator.num_processes > 1: + self._device = torch.device( + f"cuda:{accelerator.local_process_index}") + else: + self._device = device + # Load model. + self._model = AutoModelForCausalLM.from_pretrained( + model_id_name, + device_map=device, + trust_remote_code=trust_remote_code, + torch_dtype=dtype) + self._processor = AutoProcessor.from_pretrained( + model_id_name, + trust_remote_code=trust_remote_code) + self._processor.tokenizer.padding_side = "left" + self._tokenizer = self._processor.tokenizer + self._config = self._model.config + self.batch_size_per_gpu = int(batch_size) + assert self.batch_size_per_gpu == 1, \ + "batch_size_per_gpu > 1 is not supported for now." + self.use_cache = use_cache + if accelerator.num_processes > 1: + distributed_type_list = [ + DistributedType.FSDP, + DistributedType.MULTI_GPU, + DistributedType.DEEPSPEED + ] + assert accelerator.distributed_type in distributed_type_list, \ + "Unsupported distributed type provided. Only DDP and FSDP are supported." + if accelerator.distributed_type == DistributedType.FSDP: + self._model = accelerator.prepare(self.model) + else: + self._model = accelerator.prepare_model( + self.model, + evaluation_mode=True) + self.accelerator = accelerator + if self.accelerator.is_local_main_process: + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + else: + eval_logger.info(f"Using single device: {self._device}") + self.model.to(self._device) + self._rank = 0 + self._word_size = 1 + + @property + def config(self): + # return the associated transformers.AutoConfig for the given pretrained model. + return self._config + + @property + def tokenizer(self): + return self._tokenizer + + @property + def model(self): + # returns the model, unwrapping it if using Accelerate + if hasattr(self, "accelerator"): + return self.accelerator.unwrap_model(self._model) + else: + return self._model + + @property + def eot_token_id(self): + # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* + return self.tokenizer.eos_token_id + + @property + def max_length(self): + return self._max_length + + @property + def batch_size(self): + return self.batch_size_per_gpu + + @property + def device(self): + return self._device + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + raise NotImplementedError("Not implemented for Phi3v.") + + def generate_until(self, requests: List[Instance]) -> List[str]: + res = [] + + def _collate(x): + # the negative sign on len(toks) sorts descending - this has a few advantages: + # - time estimates will always be over not underestimates, which is more useful for planning + # - to know the size of a batch when going through the list, you know the first one is always the batch + # padded context length. this is useful to simplify the batching logic and more importantly to make + # automatic adaptive batches much much easier to implement + # - any OOMs will happen right away rather than near the end + toks = self.tokenizer.encode(x[0]) + return -len(toks), x[0] + + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") + # we group requests by their generation_kwargs, + # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling + # in the same batch. + re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) + chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) + for chunk in chunks: + contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk) + task = task[0] + split = split[0] + visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] + visuals = self.flatten(visuals) + # We assume all gen kwargs in the batch are the same + # this is safe to assume because the `grouper` object ensures it. + gen_kwargs = all_gen_kwargs[0] + # Set default values for until and max_new_tokens + until = [self.tokenizer.decode(self.eot_token_id)] + # Update values from gen_kwargs if present + if "until" in gen_kwargs: + until = gen_kwargs.pop("until") + if isinstance(until, str): + until = [until] + elif not isinstance(until, list): + raise ValueError( + f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") + if isinstance(contexts, tuple): + contexts = list(contexts) + for i in range(len(contexts)): + if "" in contexts[i]: + query = contexts[i].replace("", "<|image_1|>") + else: + query = f"<|image_1|>\n{contexts[i]}" + messages = [ + {"role": "user", "content": query} + ] + contexts[i] = self._tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True) + assert len(contexts) == 1 + # We always pass a single image given that the model only accepts one image (as of 5/21/24). + context = contexts[0] + pil_image = visuals[0] + input_ids = self._processor( + text=context, + images=[pil_image], + return_tensors="pt").to(self._device, self.model.dtype) + # Setting default parameters. + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + if "top_p" not in gen_kwargs: + gen_kwargs["top_p"] = None + if "num_beams" not in gen_kwargs: + gen_kwargs["num_beams"] = 1 + # Generate answer. + pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None \ + else self.tokenizer.eod_id + generate_ids = self.model.generate( + **input_ids, + eos_token_id=self.tokenizer.eos_token_id, + pad_token_id=pad_token_id, + do_sample=True if gen_kwargs["temperature"] > 0 else False, + temperature=gen_kwargs["temperature"], + top_p=gen_kwargs["top_p"], + num_beams=gen_kwargs["num_beams"], + max_new_tokens=gen_kwargs["max_new_tokens"], + use_cache=self.use_cache, + ) + generate_ids = generate_ids[:, input_ids['input_ids'].shape[1]:] + response = self._processor.batch_decode( + generate_ids, + skip_special_tokens=True, + clean_up_tokenization_spaces=False)[0] + res.append(response) + self.cache_hook.add_partial("generate_until", (context, gen_kwargs), response) + pbar.update(1) + # reorder this group of results back to original unsorted form + res = re_ords.get_original(res) + pbar.close() + return res From c2008971308ce8168d57c24d00b725832f099244 Mon Sep 17 00:00:00 2001 From: Victor Fragoso Date: Fri, 24 May 2024 16:25:02 +0000 Subject: [PATCH 041/196] Adding prompt arguments for Phi3v on MathVista-TestMini --- lmms_eval/tasks/mathvista/mathvista_testmini.yaml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/lmms_eval/tasks/mathvista/mathvista_testmini.yaml b/lmms_eval/tasks/mathvista/mathvista_testmini.yaml index cb4b7a8b..bb6a938a 100644 --- a/lmms_eval/tasks/mathvista/mathvista_testmini.yaml +++ b/lmms_eval/tasks/mathvista/mathvista_testmini.yaml @@ -20,10 +20,12 @@ metric_list: - metric: gpt_eval_score aggregation: !function utils.mathvista_aggregate_results higher_is_better: true - + model_specific_prompt_kwargs: default: shot_type: "format-prompt" # can be "reason-first", "solution", "step-by-step" + phi3v: + shot_type: "solution" model_specific_generation_kwargs: llava: image_aspect_ratio: original \ No newline at end of file From b05c3e222fabd308dd7af4e04c1c6a0812962fe6 Mon Sep 17 00:00:00 2001 From: Victor Fragoso Date: Fri, 24 May 2024 16:36:37 +0000 Subject: [PATCH 042/196] Adding documentation of Phi3v class. --- lmms_eval/models/phi3v.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/lmms_eval/models/phi3v.py b/lmms_eval/models/phi3v.py index 31e11082..b34881ea 100644 --- a/lmms_eval/models/phi3v.py +++ b/lmms_eval/models/phi3v.py @@ -17,7 +17,18 @@ @register_model("phi3v") class Phi3v(lmms): """ - TODO(vifragos): Document me! + This class implements inference for the microsoft/Phi-3-vision-128k-instruct model. + To learn more about this model please visit the following links: + 1. https://huggingface.co/microsoft/Phi-3-vision-128k-instruct + 2. https://azure.microsoft.com/en-us/blog/new-models-added-to-the-phi-3-family-available-on-microsoft-azure/ + 3. https://github.com/microsoft/Phi-3CookBook + + NOTE: This class was adapted from quen_vl.py and llava_hf.py. + + Example: + + accelerate launch --num_processes=4 -m lmms_eval --model phi3v --tasks mmmu_val \ + --batch_size 1 --log_samples --log_samples_suffix phi3v_mmmu --output_path ./logs/ """ def __init__( self, From 616edf43731415b35f0f5e97748ed2e017a2891d Mon Sep 17 00:00:00 2001 From: Bo Li Date: Mon, 27 May 2024 09:32:26 +0000 Subject: [PATCH 043/196] [Fix] import issues of multilingual llava and olympiadbench --- .../_default_template.yaml | 35 ++++++++++++ .../_generate_configs.py | 54 ------------------- .../arabic_llava_in_the_wild.yaml | 48 +++-------------- .../bengali_llava_in_the_wild.yaml | 48 +++-------------- .../chinese_llava_in_the_wild.yaml | 48 +++-------------- .../french_llava_in_the_wild.yaml | 48 +++-------------- .../hindi_llava_in_the_wild.yaml | 48 +++-------------- .../japanese_llava_in_the_wild.yaml | 48 +++-------------- .../russian_llava_in_the_wild.yaml | 48 +++-------------- .../spanish_llava_in_the_wild.yaml | 48 +++-------------- .../urdu_llava_in_the_wild.yaml | 48 +++-------------- .../utils.py | 2 +- lmms_eval/tasks/olympiadbench/cn_utils.py | 7 ++- lmms_eval/tasks/olympiadbench/en_utils.py | 7 ++- .../olympiadbench/olympiadbench_evals.py | 22 ++++---- 15 files changed, 111 insertions(+), 448 deletions(-) create mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml delete mode 100644 lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_generate_configs.py diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml new file mode 100644 index 00000000..3ae9260b --- /dev/null +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml @@ -0,0 +1,35 @@ +test_split: train +output_type: generate_until +doc_to_visual: !function utils.llava_doc_to_visual +doc_to_text: !function utils.llava_doc_to_text +doc_to_target: "gpt_answer" +generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.llava_process_results +metric_list: + - metric: gpt_eval_llava_all + aggregation: !function utils.llava_all_aggregation + higher_is_better: true + - metric: gpt_eval_llava_conv + aggregation: !function utils.llava_conv_aggregation + higher_is_better: true + - metric: gpt_eval_llava_detail + aggregation: !function utils.llava_detail_aggregation + higher_is_better: true + - metric: gpt_eval_llava_complex + aggregation: !function utils.llava_complex_aggregation + higher_is_better: true +metadata: + version: 0.0 + gpt_eval_model_name: "gpt-4-0613" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_generate_configs.py b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_generate_configs.py deleted file mode 100644 index 0f189b82..00000000 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_generate_configs.py +++ /dev/null @@ -1,54 +0,0 @@ -from datasets import load_dataset - -# dataset = load_dataset("gagan3012/multilingual-llava-bench") - -configs = ['arabic', 'bengali', 'chinese', 'french', 'hindi', 'japanese', 'russian', 'spanish', 'urdu'] - -for config in configs: - yaml_output = f""" - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: {config} - token: True - task: "llava_in_the_wild_{config}" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - """ - - with open(f"{config}_llava_in_the_wild.yaml", "w") as f: - f.write(yaml_output) - -# Path: _generate_configs.py \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml index 83447d0d..8e36630f 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml @@ -1,42 +1,6 @@ - - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: arabic - token: True - task: "llava_in_the_wild_arabic" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - \ No newline at end of file +dataset_path: "gagan3012/multilingual-llava-bench" +dataset_kwargs: + config: arabic + token: True +task: "llava_in_the_wild_arabic" +include: _default_template.yaml \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml index 71ec6f86..cabe38a8 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml @@ -1,42 +1,6 @@ - - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: bengali - token: True - task: "llava_in_the_wild_bengali" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - \ No newline at end of file +dataset_path: "gagan3012/multilingual-llava-bench" +dataset_kwargs: + config: bengali + token: True +task: "llava_in_the_wild_bengali" +include: _default_template.yaml \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml index 5f8f487d..3abda8a9 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml @@ -1,42 +1,6 @@ - - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: chinese - token: True - task: "llava_in_the_wild_chinese" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - \ No newline at end of file +dataset_path: "gagan3012/multilingual-llava-bench" +dataset_kwargs: + config: chinese + token: True +task: "llava_in_the_wild_chinese" +include: _default_template.yaml \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml index b1c004e3..cd30f2c4 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml @@ -1,42 +1,6 @@ - - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: french - token: True - task: "llava_in_the_wild_french" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - \ No newline at end of file +dataset_path: "gagan3012/multilingual-llava-bench" +dataset_kwargs: + config: french + token: True +task: "llava_in_the_wild_french" +include: _default_template.yaml \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml index 507afce7..45dc0d91 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml @@ -1,42 +1,6 @@ - - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: hindi - token: True - task: "llava_in_the_wild_hindi" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - \ No newline at end of file +dataset_path: "gagan3012/multilingual-llava-bench" +dataset_kwargs: + config: hindi + token: True +task: "llava_in_the_wild_hindi" +include: _default_template.yaml \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml index a7fb6e96..ac4083a8 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml @@ -1,42 +1,6 @@ - - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: japanese - token: True - task: "llava_in_the_wild_japanese" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - \ No newline at end of file +dataset_path: "gagan3012/multilingual-llava-bench" +dataset_kwargs: + config: japanese + token: True +task: "llava_in_the_wild_japanese" +include: _default_template.yaml \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml index ecb9c146..31e29ed8 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml @@ -1,42 +1,6 @@ - - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: russian - token: True - task: "llava_in_the_wild_russian" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - \ No newline at end of file +dataset_path: "gagan3012/multilingual-llava-bench" +dataset_kwargs: + config: russian + token: True +task: "llava_in_the_wild_russian" +include: _default_template.yaml \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml index 02641ca2..ac614bde 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml @@ -1,42 +1,6 @@ - - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: spanish - token: True - task: "llava_in_the_wild_spanish" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - \ No newline at end of file +dataset_path: "gagan3012/multilingual-llava-bench" +dataset_kwargs: + config: spanish + token: True +task: "llava_in_the_wild_spanish" +include: _default_template.yaml \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml index 6bf28c0b..c30dda11 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml @@ -1,42 +1,6 @@ - - dataset_path: "gagan3012/multilingual-llava-bench" - dataset_kwargs: - config: urdu - token: True - task: "llava_in_the_wild_urdu" - test_split: train - output_type: generate_until - doc_to_visual: !function utils.llava_doc_to_visual - doc_to_text: !function utils.llava_doc_to_text - doc_to_target: "gpt_answer" - generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false - process_results: !function utils.llava_process_results - metric_list: - - metric: gpt_eval_llava_all - aggregation: !function utils.llava_all_aggregation - higher_is_better: true - - metric: gpt_eval_llava_conv - aggregation: !function utils.llava_conv_aggregation - higher_is_better: true - - metric: gpt_eval_llava_detail - aggregation: !function utils.llava_detail_aggregation - higher_is_better: true - - metric: gpt_eval_llava_complex - aggregation: !function utils.llava_complex_aggregation - higher_is_better: true - metadata: - version: 0.0 - gpt_eval_model_name: "gpt-4-0613" - model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - \ No newline at end of file +dataset_path: "gagan3012/multilingual-llava-bench" +dataset_kwargs: + config: urdu + token: True +task: "llava_in_the_wild_urdu" +include: _default_template.yaml \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py index ac86ee99..6666de45 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py @@ -17,7 +17,7 @@ rule_dict = json.load(open(os.path.join(os.path.dirname(os.path.abspath(__file__)), "rule.json"), "r")) -with open(Path(__file__).parent / "llava-in-the-wild.yaml", "r") as f: +with open(Path(__file__).parent / "_default_template.yaml", "r") as f: raw_data = f.readlines() safe_data = [] for i, line in enumerate(raw_data): diff --git a/lmms_eval/tasks/olympiadbench/cn_utils.py b/lmms_eval/tasks/olympiadbench/cn_utils.py index 628d51da..51511862 100644 --- a/lmms_eval/tasks/olympiadbench/cn_utils.py +++ b/lmms_eval/tasks/olympiadbench/cn_utils.py @@ -9,7 +9,10 @@ eval_logger = logging.getLogger("lmms-eval") dir_name = os.path.dirname(os.path.abspath(__file__)) -olympiadbench_evaluator = OlympiadBenchEvaluator() +try: + olympiadbench_evaluator = OlympiadBenchEvaluator() +except: + pass def olympiadbench_doc_to_visual(doc): @@ -66,4 +69,4 @@ def olympiadbench_aggregate_results(results, args): path = generate_submission_file(submission_file_name, args) with open(path, "w") as f: json.dump(results, f, ensure_ascii=False) - print(f"Submission file saved to {path}") + print(f"Submission file saved to {path}") \ No newline at end of file diff --git a/lmms_eval/tasks/olympiadbench/en_utils.py b/lmms_eval/tasks/olympiadbench/en_utils.py index 4b165e38..e98c0294 100644 --- a/lmms_eval/tasks/olympiadbench/en_utils.py +++ b/lmms_eval/tasks/olympiadbench/en_utils.py @@ -9,7 +9,10 @@ eval_logger = logging.getLogger("lmms-eval") dir_name = os.path.dirname(os.path.abspath(__file__)) -olympiadbench_evaluator = OlympiadBenchEvaluator() +try: + olympiadbench_evaluator = OlympiadBenchEvaluator() +except: + pass def olympiadbench_doc_to_visual(doc): @@ -68,4 +71,4 @@ def olympiadbench_aggregate_results(results, args): path = generate_submission_file(submission_file_name, args) with open(path, "w") as f: json.dump(results, f, ensure_ascii=False) - print(f"Submission file saved to {path}") + print(f"Submission file saved to {path}") \ No newline at end of file diff --git a/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py b/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py index 5ae36883..2e2de5ff 100644 --- a/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py +++ b/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py @@ -1,7 +1,15 @@ import re import sympy as sp -from sympy import simplify, Eq, sympify, Pow -from sympy.parsing.latex import parse_latex + +import logging + +eval_logger = logging.getLogger("lmms-eval") + +try: + from sympy import simplify, Eq, sympify, Pow + from sympy.parsing.latex import parse_latex +except ImportError as e: + eval_logger.debug("Please install sympy package by running 'pip install sympy' if you want to use OlympiadBenchEvaluator.") import math # how to use @@ -89,7 +97,6 @@ def judge(self, expression1, expression2, precision=1e-8): # Set up a list for allowed errors if len(precision) <= 1: precision = precision * len(temp_list1) - if len(temp_list1) != len(temp_list2): return False @@ -144,7 +151,6 @@ def is_equal(self, expression1, expression2): return True except: pass - # Then check if expressions are mathematically equal try: if self.expression_equal(expression1, expression2) and not ("=" in expression1 and "=" in expression2): @@ -152,7 +158,6 @@ def is_equal(self, expression1, expression2): return True except: pass - # Lastly, check for equation equality try: if self.equation_equal(expression1, expression2): @@ -160,7 +165,6 @@ def is_equal(self, expression1, expression2): return True except: pass - return False def numerical_equal(self, expression1: str, expression2: str, include_percentage: bool = True): @@ -168,12 +172,10 @@ def numerical_equal(self, expression1: str, expression2: str, include_percentage # Includes possible percentage cases reference = float(expression1) prediction = float(expression2) - if include_percentage: gt_result = [reference / 100, reference, reference * 100] else: gt_result = [reference] - for item in gt_result: if abs(item - prediction) <= self.precision * 1.01: return True @@ -218,7 +220,6 @@ def extract_expression(expression): simplified_expr = simplify(expr1_sym - expr2_sym) num_value = simplified_expr.evalf() - return abs(num_value) < 1e-3 except: return False @@ -274,7 +275,6 @@ def compare_two_interval(inter1, inter2): else: inter_list1 = interval1.split("\\cup") inter_list2 = interval2.split("\\cup") - if len(inter_list1) != len(inter_list2): return False else: @@ -352,4 +352,4 @@ def can_compute_power(self, expr): else: return False else: - return True # Not a power expression, can compute + return True # Not a power expression, can compute \ No newline at end of file From 24dc435908d921e9f1a5706e3141b12e5d838d18 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Mon, 27 May 2024 10:17:32 +0000 Subject: [PATCH 044/196] fix compatibility issue of older version llava --- lmms_eval/models/llava.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py index 1c06c0d8..bd21bd33 100644 --- a/lmms_eval/models/llava.py +++ b/lmms_eval/models/llava.py @@ -30,12 +30,6 @@ except ImportError: eval_logger.error("LLaVA is not installed. Please install LLaVA to use this model.") -from transformers.integrations.deepspeed import ( - is_deepspeed_zero3_enabled, - set_hf_deepspeed_config, - unset_hf_deepspeed_config, -) - if torch.__version__ > "2.1.2": best_fit_attn_implementation = "sdpa" else: @@ -94,8 +88,9 @@ def __init__( # Try to load the model with the multimodal argument self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) except TypeError: - # for older versions of LLaVA that don't have multimodal argument + # for older versions of LLaVA that don't have multimodal and attn_implementation arguments llava_model_args.pop("multimodal", None) + llava_model_args.pop("attn_implementation", None) self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) self._config = self._model.config From 71401bafd1d515f704f86ab4817a758542bc4672 Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Wed, 29 May 2024 12:41:21 +0900 Subject: [PATCH 045/196] add upd --- README.md | 13 + .../_default_template_mmaadbench_base_yaml | 23 + ...fault_template_mmaadbench_instruction_yaml | 23 + .../_default_template_mmaadbench_option_yaml | 23 + .../_default_template_mmiasdbench_base_yaml | 23 + ...ault_template_mmiasdbench_instruction_yaml | 23 + .../_default_template_mmiasdbench_option_yaml | 23 + .../_default_template_mmivqdbench_base_yaml | 23 + ...ault_template_mmivqdbench_instruction_yaml | 23 + .../_default_template_mmivqdbench_option_yaml | 23 + .../tasks/mmupdbench/mmaadbench_base.yaml | 7 + .../mmupdbench/mmaadbench_instruction.yaml | 7 + .../tasks/mmupdbench/mmaadbench_option.yaml | 7 + .../tasks/mmupdbench/mmiasdbench_base.yaml | 7 + .../mmupdbench/mmiasdbench_instruction.yaml | 7 + .../tasks/mmupdbench/mmiasdbench_option.yaml | 7 + .../tasks/mmupdbench/mmivqdbench_base.yaml | 7 + .../mmupdbench/mmivqdbench_instruction.yaml | 7 + .../tasks/mmupdbench/mmivqdbench_option.yaml | 7 + lmms_eval/tasks/mmupdbench/mmupdbench.yaml | 15 + .../tasks/mmupdbench/mmupdbench_base.yaml | 10 + .../tasks/mmupdbench/mmupdbench_evals.py | 630 ++++++++++++++++++ .../mmupdbench/mmupdbench_instruction.yaml | 9 + .../tasks/mmupdbench/mmupdbench_option.yaml | 9 + lmms_eval/tasks/mmupdbench/utils.py | 163 +++++ 25 files changed, 1119 insertions(+) create mode 100644 lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_base_yaml create mode 100644 lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_instruction_yaml create mode 100644 lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_option_yaml create mode 100644 lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_base_yaml create mode 100644 lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_instruction_yaml create mode 100644 lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_option_yaml create mode 100644 lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_base_yaml create mode 100644 lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_instruction_yaml create mode 100644 lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_option_yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmaadbench_base.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmaadbench_instruction.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmaadbench_option.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmiasdbench_base.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmiasdbench_instruction.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmiasdbench_option.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmivqdbench_base.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmivqdbench_instruction.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmivqdbench_option.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmupdbench.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmupdbench_base.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmupdbench_evals.py create mode 100644 lmms_eval/tasks/mmupdbench/mmupdbench_instruction.yaml create mode 100644 lmms_eval/tasks/mmupdbench/mmupdbench_option.yaml create mode 100644 lmms_eval/tasks/mmupdbench/utils.py diff --git a/README.md b/README.md index f29d8837..38713718 100644 --- a/README.md +++ b/README.md @@ -190,6 +190,19 @@ We also provide the raw data exported from Weights & Biases for the detailed res - MMMU (mmmu) - MMMU Validation (mmmu_val) - MMMU Test (mmmu_test) +- MMUPDBench (mmupdbench) + - MMUPDBench Base (mmupdbench_base) + - MMAADBench Base (mmaadbench_base) + - MMIASDBench Base (mmiasdbench_base) + - MMIVQDBench Base (mmivqdbench_base) + - MMUPDBench Option (mmupdbench_option) + - MMAADBench Option (mmaadbench_option) + - MMIASDBench Option (mmiasdbench_option) + - MMIVQDBench Option (mmivqdbench_option) + - MMUPDBench Instruction (mmupdbench_instruction) + - MMAADBench Instruction (mmaadbench_instruction) + - MMIASDBench Instruction (mmiasdbench_instruction) + - MMIVQDBench Instruction (mmivqdbench_instruction) - MMVet (mmvet) - Multi-DocVQA (multidocvqa) - Multi-DocVQA Validation (multidocvqa_val) diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_base_yaml b/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_base_yaml new file mode 100644 index 00000000..da992404 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_base_yaml @@ -0,0 +1,23 @@ +dataset_path: MM-UPD/MM-UPD +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\n" +doc_to_visual: !function utils.mmupdbench_doc_to_visual +doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_target: "answer" +process_results: !function utils.mmupdbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: mmaad_base +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_instruction_yaml b/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_instruction_yaml new file mode 100644 index 00000000..85674b3f --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_instruction_yaml @@ -0,0 +1,23 @@ +dataset_path: MM-UPD/MM-UPD +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." +doc_to_visual: !function utils.mmupdbench_doc_to_visual +doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_target: "answer" +process_results: !function utils.mmupdbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: mmaad_base +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_option_yaml b/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_option_yaml new file mode 100644 index 00000000..60fef3d0 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_option_yaml @@ -0,0 +1,23 @@ +dataset_path: MM-UPD/MM-UPD +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." +doc_to_visual: !function utils.mmupdbench_doc_to_visual +doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_target: "answer" +process_results: !function utils.mmupdbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: mmaad_option +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_base_yaml b/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_base_yaml new file mode 100644 index 00000000..5b098d74 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_base_yaml @@ -0,0 +1,23 @@ +dataset_path: MM-UPD/MM-UPD +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\n" +doc_to_visual: !function utils.mmupdbench_doc_to_visual +doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_target: "answer" +process_results: !function utils.mmupdbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: mmiasd_base +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_instruction_yaml b/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_instruction_yaml new file mode 100644 index 00000000..a09f6aa0 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_instruction_yaml @@ -0,0 +1,23 @@ +dataset_path: MM-UPD/MM-UPD +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." +doc_to_visual: !function utils.mmupdbench_doc_to_visual +doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_target: "answer" +process_results: !function utils.mmupdbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: mmiasd_base +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_option_yaml b/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_option_yaml new file mode 100644 index 00000000..07db7bf0 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_option_yaml @@ -0,0 +1,23 @@ +dataset_path: MM-UPD/MM-UPD +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." +doc_to_visual: !function utils.mmupdbench_doc_to_visual +doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_target: "answer" +process_results: !function utils.mmupdbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: mmiasd_option +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_base_yaml b/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_base_yaml new file mode 100644 index 00000000..d1f8b3dd --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_base_yaml @@ -0,0 +1,23 @@ +dataset_path: MM-UPD/MM-UPD +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\n" +doc_to_visual: !function utils.mmupdbench_doc_to_visual +doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_target: "answer" +process_results: !function utils.mmupdbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: mmivqd_base +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_instruction_yaml b/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_instruction_yaml new file mode 100644 index 00000000..ba547695 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_instruction_yaml @@ -0,0 +1,23 @@ +dataset_path: MM-UPD/MM-UPD +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nIf the given image is irrelevant to the question, answer \"F. The image and question are irrelevant.\"." +doc_to_visual: !function utils.mmupdbench_doc_to_visual +doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_target: "answer" +process_results: !function utils.mmupdbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: mmivqd_base +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_option_yaml b/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_option_yaml new file mode 100644 index 00000000..37b0866b --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_option_yaml @@ -0,0 +1,23 @@ +dataset_path: MM-UPD/MM-UPD +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." +doc_to_visual: !function utils.mmupdbench_doc_to_visual +doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_target: "answer" +process_results: !function utils.mmupdbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: mmivqd_option +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmupdbench/mmaadbench_base.yaml b/lmms_eval/tasks/mmupdbench/mmaadbench_base.yaml new file mode 100644 index 00000000..11573724 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmaadbench_base.yaml @@ -0,0 +1,7 @@ +task: "mmaadbench_base" +test_split: test +include: _default_template_mmaadbench_base_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmaadbench_base + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmaadbench_instruction.yaml b/lmms_eval/tasks/mmupdbench/mmaadbench_instruction.yaml new file mode 100644 index 00000000..d9aa99b7 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmaadbench_instruction.yaml @@ -0,0 +1,7 @@ +task: "mmaadbench_instruction" +test_split: test +include: _default_template_mmaadbench_instruction_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmaadbench_instruction + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmaadbench_option.yaml b/lmms_eval/tasks/mmupdbench/mmaadbench_option.yaml new file mode 100644 index 00000000..07d44215 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmaadbench_option.yaml @@ -0,0 +1,7 @@ +task: "mmaadbench_option" +test_split: test +include: _default_template_mmaadbench_option_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmaadbench_option + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmiasdbench_base.yaml b/lmms_eval/tasks/mmupdbench/mmiasdbench_base.yaml new file mode 100644 index 00000000..f109f304 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmiasdbench_base.yaml @@ -0,0 +1,7 @@ +task: "mmiasdbench_base" +test_split: test +include: _default_template_mmiasdbench_base_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmiasdbench_base + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmiasdbench_instruction.yaml b/lmms_eval/tasks/mmupdbench/mmiasdbench_instruction.yaml new file mode 100644 index 00000000..91403014 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmiasdbench_instruction.yaml @@ -0,0 +1,7 @@ +task: "mmiasdbench_instruction" +test_split: test +include: _default_template_mmiasdbench_instruction_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmiasdbench_instruction + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmiasdbench_option.yaml b/lmms_eval/tasks/mmupdbench/mmiasdbench_option.yaml new file mode 100644 index 00000000..d340aeab --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmiasdbench_option.yaml @@ -0,0 +1,7 @@ +task: "mmiasdbench_option" +test_split: test +include: _default_template_mmiasdbench_option_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmiasdbench_option + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmivqdbench_base.yaml b/lmms_eval/tasks/mmupdbench/mmivqdbench_base.yaml new file mode 100644 index 00000000..2af6e726 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmivqdbench_base.yaml @@ -0,0 +1,7 @@ +task: "mmivqdbench_base" +test_split: test +include: _default_template_mmivqdbench_base_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmivqdbench_base + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmivqdbench_instruction.yaml b/lmms_eval/tasks/mmupdbench/mmivqdbench_instruction.yaml new file mode 100644 index 00000000..750097ef --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmivqdbench_instruction.yaml @@ -0,0 +1,7 @@ +task: "mmivqdbench_instruction" +test_split: test +include: _default_template_mmivqdbench_instruction_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmivqdbench_instruction + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmivqdbench_option.yaml b/lmms_eval/tasks/mmupdbench/mmivqdbench_option.yaml new file mode 100644 index 00000000..4e81449b --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmivqdbench_option.yaml @@ -0,0 +1,7 @@ +task: "mmivqdbench_option" +test_split: test +include: _default_template_mmivqdbench_option_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmivqdbench_option + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench.yaml b/lmms_eval/tasks/mmupdbench/mmupdbench.yaml new file mode 100644 index 00000000..12681e71 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmupdbench.yaml @@ -0,0 +1,15 @@ +group: mmupdbench +task: + - mmaadbench_base + - mmaadbench_option + - mmaadbench_instruction + - mmiasdbench_base + - mmiasdbench_option + - mmiasdbench_instruction + - mmivqdbench_base + - mmivqdbench_option + - mmivqdbench_instruction +metadata: + version: 0.0 + sys_prompt: "" + gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench_base.yaml b/lmms_eval/tasks/mmupdbench/mmupdbench_base.yaml new file mode 100644 index 00000000..5aa7ffc7 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmupdbench_base.yaml @@ -0,0 +1,10 @@ +group: mmupdbench_base +task: + - mmaadbench_base + - mmiasdbench_base + - mmivqdbench_base +metadata: + version: 0.0 + sys_prompt: "" + gpt_eval_model_name: "gpt-3.5-turbo-0613" + \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench_evals.py b/lmms_eval/tasks/mmupdbench/mmupdbench_evals.py new file mode 100644 index 00000000..c3af1618 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmupdbench_evals.py @@ -0,0 +1,630 @@ +import time +import random as rd +import string +from collections import defaultdict +import requests +import math +import numpy as np +import pandas as pd +import pickle +import logging +import json + +eval_logger = logging.getLogger("lmms-eval") + + +def dump(data, f): + + def dump_pkl(data, pth): + pickle.dump(data, open(pth, 'wb')) + + def dump_json(data, pth): + json.dump(data, open(pth, 'w')) + + def dump_jsonl(data, f): + lines = [json.dumps(x, ensure_ascii=False) for x in data] + with open(f, 'w', encoding='utf8') as fout: + fout.write('\n'.join(lines)) + + def dump_xlsx(data, f): + data.to_excel(f, index=False) + + def dump_csv(data, f): + data.to_csv(f, index=False) + + def dump_tsv(data, f): + data.to_csv(f, sep='\t', index=False) + + handlers = dict(pkl=dump_pkl, + json=dump_json, + jsonl=dump_jsonl, + xlsx=dump_xlsx, + csv=dump_csv, + tsv=dump_tsv) + suffix = f.split('.')[-1] + return handlers[suffix](data, f) + + +def load(f): + """ + Loads data from various file formats. + + Parameters: + - file_path: Path to the file to be loaded. + + Returns: + - Loaded data. + """ + def load_pkl(pth): + return pickle.load(open(pth, 'rb')) + + def load_json(pth): + return json.load(open(pth, 'r', encoding='utf-8')) + + def load_jsonl(f): + lines = open(f, encoding='utf-8').readlines() + lines = [x.strip() for x in lines] + if lines[-1] == '': + lines = lines[:-1] + data = [json.loads(x) for x in lines] + return data + + def load_xlsx(f): + df = pd.read_excel(f) + df = df.dropna(subset=['prediction']) + + return df + + def load_csv(f): + return pd.read_csv(f) + + def load_tsv(f): + return pd.read_csv(f, sep='\t') + + handlers = dict(pkl=load_pkl, + json=load_json, + jsonl=load_jsonl, + xlsx=load_xlsx, + csv=load_csv, + tsv=load_tsv) + suffix = f.split('.')[-1] + return handlers[suffix](f) + + +class MMUPDBench_Evaluator: + def __init__(self, sys_prompt="There are several options:", API_KEY="", API_URL="", model_version="gpt-3.5-turbo-0613"): + self.sys_prompt = sys_prompt + self.model_version = model_version + self.API_KEY = API_KEY + self.API_URL = API_URL + + def create_options_prompt(self, row_data, option_candidate): + available_keys = set(row_data.keys()) & set(option_candidate) + options = {cand: row_data[cand] for cand in available_keys if row_data[cand]} + sorted_options = dict(sorted(options.items())) + options_prompt = f"{self.sys_prompt}\n" + for key, item in sorted_options.items(): + if pd.notna(item) and item != "nan": + options_prompt += f"{key}. {item}\n" + return options_prompt.rstrip("\n"), sorted_options + + def is_none(self, value): + if value is None: + return True + if pd.isna(value): + return True + if type(value) is float and math.isnan(value): + return True + if type(value) is str and value.lower() == 'nan': + return True + if type(value) is str and value.lower() == 'none': + return True + return False + + # Prompt Building + def build_option_str(self, option_list): + chars = string.ascii_uppercase + s = "There are several options: \n" + for c, opt in zip(chars, option_list): + if not pd.isna(opt): + s += f"{c}. {opt}\n" + else: + return s + return s + + def extract_options(self, item): + options = [] + for c in 'ABCDE': + try: + if self.is_none(item[c]) is False: + options.append(item[c]) + except KeyError: + continue + return options + + def build_choices(self, item): + ret = {} + for ch in 'ABCDE': + try: + if self.is_none(item[ch]) is False: + ret[ch] = item[ch] + except KeyError: + continue + return ret + + def build_option_str_w_gt(self, option_list, gt_text, eval_type, question_type, upd_type): + chars = string.ascii_uppercase + s = 'There are several options: \n' + valid_option = [] + answer_option = [] + + for c, opt in zip(chars, option_list): + if self.is_none(opt) is False: + s += f'{c}. {opt}\n' + valid_option.append(c) + if upd_type == "aad": + if eval_type == "aad": + gt_option = "" + for option in ['C', 'D', 'E', 'F']: + if option not in valid_option: + gt_option = option + break + none_option_mapping = {'C': 'D', 'D': 'E', 'E': 'F', 'F': 'G'} + s += f'{gt_option}. {gt_text}\n' + none_option = none_option_mapping[gt_option] + s += f'{none_option}. The correct answer is No answer, None of the above, \ + all provided options are incorrect, or I cannot answer.\n' + valid_option.append(gt_option) + valid_option.append(none_option) + answer_option = [gt_option, none_option] + if question_type == 'inst': + s += f'F. None of the above\n' + valid_option.append("F") + answer_option = [gt_option, none_option, "F"] + + if eval_type == "standard": + none_option = "" + for option in ['C', 'D', 'E', 'F']: + if option not in valid_option: + none_option = option + break + s += f'{none_option}. The correct answer is No answer, None of the above, \ + all provided options are incorrect, or I cannot answer.\n' + valid_option.append(none_option) + if question_type == 'inst': + s += 'F. None of the above\n' + valid_option.append("F") + elif upd_type == "iasd": + if eval_type == "iasd": + gt_option = "" + for option in ['C', 'D', 'E', 'F']: + if option not in valid_option: + gt_option = option + break + + s += f'{gt_option}. {gt_text}\n' + valid_option.append(gt_option) + + if question_type == 'inst': + if gt_option == 'E': + s += f'F. None of the above\n' + valid_option.append('F') + s += 'G. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n' + valid_option.append('G') + answer_option = [gt_option, 'F', 'G'] + else: + none_option_mapping = {'C': 'D', 'D': 'E'} + none_option = none_option_mapping[gt_option] + s += f'{none_option}. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n' + valid_option.append(none_option) + s += f'F. None of the above\n' + valid_option.append('F') + answer_option = [gt_option, none_option, 'F'] + else: + none_option_mapping = {'C': 'D', 'D': 'E', 'E': 'F', 'F': 'G'} + none_option = none_option_mapping[gt_option] + s += f'{none_option}. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n' + valid_option.append(none_option) + answer_option = [gt_option, none_option] + + if eval_type == "standard": + none_option = "" + for option in ['C', 'D', 'E', 'F']: + if option not in valid_option: + none_option = option + break + s += f'{none_option}. The correct answer is No answer, None of the above, \ + all provided options are irrelevant or incorrect, or I cannot answer.\n' + valid_option.append(none_option) + if question_type == 'inst': + s += f'F. None of the above\n' + valid_option.append("F") + elif upd_type == "ivqd": + if eval_type == "ivqd": + none_option = "" + for option in ['C', 'D', 'E', 'F']: + if option not in valid_option: + none_option = option + break + s += f'{none_option}. The correct answer is that The image is incompatible with the question, or I cannot answer.\n' + valid_option.append(none_option) + answer_option = [none_option] + if question_type == 'inst': + s += f'F. The image and question are irrelevant.\n' + valid_option.append("F") + answer_option = [none_option, "F"] + + if eval_type == "standard": + none_option = "" + for option in ['C', 'D', 'E', 'F']: + if option not in valid_option: + none_option = option + break + s += f'{none_option}. The correct answer is that The image is incompatible with the question, or I cannot answer.\n' + valid_option.append(none_option) + if question_type == 'inst': + s += f'F. The image and question are irrelevant.\n' + valid_option.append("F") + return s, valid_option, answer_option + + def build_prompt(self, question, options, prediction, upd_type): + if upd_type == "aad": + tmpl = ( + 'You are an AI assistant who will help me to match an answer ' + 'with several options of a single-choice question. ' + 'You are provided with a question, several options, and an answer, ' + 'and you need to find which option is most similar to the answer. ' + 'If the meaning of all options are significantly different ' + 'from the answer, output H. ' + 'Your should output a single uppercase character in A, B, C, D, E, F, G ' + '(if they are valid options), and H. \n' + 'Example 1: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear ' + 'B. rabbit C. cat D. dog E. no answer \nAnswer: a cute teddy bear\nYour output: A\n' + 'Example 2: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear ' + 'B. rabbit C. cat D. dog E. None of the above \nAnswer: no answer \nYour output: E\n' + 'Example 3: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear ' + 'B. rabbit C. cat D. dog E. None of the above \nAnswer: fish \nYour output: H\n' + 'Example 4: \n' + 'Question: {}?\nOptions: {}\nAnswer: {}\nYour output: ') + elif upd_type == "iasd": + tmpl = ( + 'You are an AI assistant who will help me to match an answer ' + 'with several options of a single-choice question. ' + 'You are provided with a question, several options, and an answer, ' + 'and you need to find which option is most similar to the answer. ' + 'If the meaning of all options are significantly different ' + 'from the answer, output H. ' + 'Your should output a single uppercase character in A, B, C, D, E, F, G ' + '(if they are valid options), and H. \n' + 'Example 1: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear ' + 'B. rabbit C. cat D. dog E. no answer \nAnswer: a cute teddy bear\nYour output: A\n' + 'Example 2: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear ' + 'B. rabbit C. cat D. dog E. None of the above \nAnswer: no answer \nYour output: E\n' + 'Example 3: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear ' + 'B. rabbit C. cat D. dog E. None of the above \nAnswer: fish \nYour output: H\n' + 'Example 4: \n' + 'Question: {}?\nOptions: {}\nAnswer: {}\nYour output: ') + elif upd_type == "ivqd": + tmpl = ( + 'You are an AI assistant who will help me to match an answer ' + 'with several options of a single-choice question. ' + 'You are provided with a question, several options, and an answer, ' + 'and you need to find which option is most similar to the answer. ' + 'If the meaning of all options are significantly different ' + 'from the answer, output H. ' + 'Your should output a single uppercase character in A, B, C, D, E, F, G ' + '(if they are valid options), and H. \n' + 'Example 1: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear ' + 'B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: a cute teddy bear\nYour output: A\n' + 'Example 2: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear ' + 'B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: The updloaded image and question are incompatible. \nYour output: E\n' + 'Example 3: \n' + 'Question: What is the main object in image?\nOptions: A. teddy bear ' + 'B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: fish \nYour output: H\n' + 'Example 4: \n' + 'Question: {}?\nOptions: {}\nAnswer: {}\nYour output: ') + return tmpl.format(question, options, prediction) + + # Prefetch Answers + def can_infer_option(self, answer, option_dict, question_type=None, valid_option=None): + if valid_option is None: + valid_option = list(option_dict.keys()) + if question_type == 'inst': + valid_option.append("F") + + if 'Failed to obtain answer via API' in answer: + return False + + answer = answer.strip() + + ch_cand_list = [] + + punctuations = [".", ")", ","] + if "A" in valid_option: + characters = ["B", "C", "D", "E", "F", "G"] + combinations = [char + punct for char in characters for punct in punctuations] + start_patterns = ["A)", "A.", "A,", "(A)"] + if answer == "A" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)): + ch_cand_list.append("A") + if "B" in valid_option: + characters = ["A", "C", "D", "E", "F", "G"] + combinations = [char + punct for char in characters for punct in punctuations] + start_patterns = ["B)", "B.", "B,", "(B)"] + if answer == "B" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)): + ch_cand_list.append("B") + if "C" in valid_option: + characters = ["A", "B", "D", "E", "F", "G"] + combinations = [char + punct for char in characters for punct in punctuations] + start_patterns = ["C)", "C.", "C,", "(C)"] + if answer == "C" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)): + ch_cand_list.append("C") + if "D" in valid_option: + characters = ["A", "B", "C", "E", "F", "G"] + combinations = [char + punct for char in characters for punct in punctuations] + start_patterns = ["D)", "D.", "D,", "(D)"] + if answer == "D" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)): + ch_cand_list.append("D") + if "E" in valid_option: + characters = ["A", "B", "C", "D", "F", "G"] + combinations = [char + punct for char in characters for punct in punctuations] + start_patterns = ["E)", "E.", "E,", "(E)"] + if answer == "E" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)): + ch_cand_list.append("E") + if "F" in valid_option: + characters = ["A", "B", "C", "D", "E", "G"] + combinations = [char + punct for char in characters for punct in punctuations] + start_patterns = ["F)", "F.", "F,", "(F)"] + if answer == "F" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)): + ch_cand_list.append("F") + if "G" in valid_option: + characters = ["A", "B", "C", "D", "E", "F"] + combinations = [char + punct for char in characters for punct in punctuations] + + start_patterns = ["G)", "G.", "G,", "(G)"] + if answer == "G" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)): + ch_cand_list.append("G") + + if len(ch_cand_list) == 1: + return ch_cand_list[0] + + return False + + def can_infer(self, answer, choices, question_type=None, valid_option=None): + copt = self.can_infer_option(answer, choices, question_type, valid_option=valid_option) + return copt if copt else False + + def prefetch_answer(self, item, question_type): + choices = self.build_choices(item) + return self.can_infer(item["prediction"], choices, question_type=question_type) + + def _post_request(self, payload): + headers = { + "Authorization": f"Bearer {self.API_KEY}", + "Content-Type": "application/json", + } + response = requests.post(self.API_URL, headers=headers, json=payload, timeout=30) + response.raise_for_status() + return response.json() + + def get_chat_response(self, prompt, temperature=0, max_tokens=256, n=1, patience=5, sleep_time=3): + messages = [ + {"role": "user", "content": prompt}, + ] + payload = {"model": self.model_version, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "n": n} + + while patience > 0: + patience -= 1 + try: + response = self._post_request(payload) + if n == 1: + prediction = response["choices"][0]["message"]["content"].strip() + if prediction and prediction != "": + return prediction + else: + prediction = [choice["message"]["content"].strip() for choice in response["choices"]] + if prediction and prediction[0] != "": + return prediction + + except Exception as e: + eval_logger.info(f"Attempt {patience + 1} failed with error: {e}") + if sleep_time > 0: + time.sleep(sleep_time) + + return "Failed to obtain answer via API" + + def extract_answer_from_item(self, item, gt_text, eval_type, question_type, upd_type): + options = self.extract_options(item) + option_str, valid_option, answer_option = self.build_option_str_w_gt(options, gt_text, eval_type, question_type=question_type, upd_type=upd_type) + + prompt = self.build_prompt(item['question'], option_str, item['prediction'], upd_type=upd_type) + retry = 3 + choices = self.build_choices(item) + + ret = self.can_infer(item['prediction'], choices, valid_option=valid_option) + if ret: + return ret, item['prediction'], answer_option + + while retry: + ans = self.get_chat_response(prompt, temperature=0.9) + if 'Failed to obtain answer via API' in ans: + msg = 'GPT API failed to answer. ' + eval_logger.info(msg) + retry -= 1 + else: + ret = self.can_infer(ans, choices, valid_option=valid_option) + if ret: + return ret, ans, answer_option + else: + eval_logger.info(f'GPT output includes 0 / >1 letter in "{valid_option}": {ans}') + retry -= 1 + + if retry == 0: + return 'H', 'Failed to predict. ', answer_option + + def eval_sub_data(self, sub_data, answer_map, gt_text_map, question_type, eval_type, upd_type): + lt = len(sub_data) + GT, PRED = [], [] + + for i in range(lt): + item = sub_data.iloc[i] + idx = item['index'] + GT.append(answer_map[idx]) + PRED.append(self.prefetch_answer(item, question_type)) + if PRED[-1] and (GT[-1] != PRED[-1]): + return 0 + + for i in range(lt): + if PRED[i]: + continue + else: + item = sub_data.iloc[i] + idx = item['index'] + gt_text = gt_text_map[idx] if gt_text_map is not None else None + ret, _, answer_option = self.extract_answer_from_item(sub_data.iloc[i], gt_text, eval_type, question_type=question_type, upd_type=upd_type) + PRED[i] = ret + if eval_type == "standard": + if PRED[i] != GT[i]: + return 0 + else: + if GT[i] == "F": + if PRED[i] not in answer_option: + return 0 + else: + if PRED[i] != GT[i] and PRED[i] not in answer_option: + return 0 + return 1 + + def calculate_hit_rates(self, data): + overall_hit_rate = data["hit"].mean() + + category_hit_rate = {} + if "category" in data.columns: + # Category-based hit rate + category_hit_rate = data.groupby("category")["hit"].mean().to_dict() + + return overall_hit_rate, category_hit_rate + + # Evaluate Results + def eval_result(self, results, eval_method, upd_type, question_type, eval_type): + """ + Parameters: + - args: Arguments. + - results: Results to evaluate. + - eval_method: The evaluation method. either "openai". + - upd_type: The type of UPD. either "aad", "iasd", or "ivqd". + - question_type: The type of question. either "base", "option", or "inst". + - eval_type: The type of evaluation. either "standard", "aad", "iasd", "ivqd". + """ + + rd.seed(2680) + assert eval_method == "openai" + + result = {} + data = pd.DataFrame(results) + + if eval_type == "standard": + data = data[data["type"] == "standard"] + else: + data = data[data["type"] == "upd"] + + data = data.sort_values(by="index") + + data["prediction"] = [str(x) for x in data["prediction"]] + for k in data.keys(): + data[k.lower() if k not in "ABCDE" else k] = data.pop(k) + + # meta = load(meta_file) + + data_main = data[data["index"] < int(1e6)] + + data_main["hit"] = 0 + cate_map = {i: c for i, c in zip(data["index"], data["category"])} + answer_map = {i: c for i, c in zip(data["index"], data["answer"])} + + gt_text_map = {i: c for i, c in zip(data['index'], data['masked_answer'])} + + lt = len(data_main) + hit, tot = 0, 0 + + for i in range(lt): + # Dealing with the normal part + item_main = data_main.iloc[i] + idx = item_main["index"] + + if idx in result: + correct = result[idx] + assert correct in [0, 1] + hit += correct + tot += 1 + continue + + sub_data = data[data["index"] % int(1e6) == idx] + + ret = self.eval_sub_data(sub_data, answer_map, gt_text_map,\ + question_type=question_type, eval_type=eval_type, upd_type=upd_type) + result[idx] = ret + hit += ret + tot += 1 + + data_main.loc[data_main["index"] == idx, "hit"] = ret + + indices = data_main["index"] + + data_main["category"] = [cate_map[i] if not math.isnan(i) else "uncategorized" for i in indices] + + overall_hit_rate, category_hit_rate = self.calculate_hit_rates(data_main) + + return overall_hit_rate, category_hit_rate, data_main + + def report_acc(self, df, groupd='category'): + assert 'split' in df + assert groupd in [None, 'category', 'l2-category'] + + res = defaultdict(list) + res['split'] = ['test'] + if groupd is None: + res['overall'] = [ + np.mean(df['hit']), + ] + return pd.DataFrame(res) + + elif groupd in df: + abilities = list(set(df[groupd])) + abilities.sort() + for ab in abilities: + sub_df = df[df[groupd] == ab] + res[ab] = [ + np.mean(sub_df['hit']), + ] + return pd.DataFrame(res) + + def calculate_dual_acc(self, standard_df, upd_df): + # dual results + dual_df = pd.merge(standard_df, upd_df, on='index', + suffixes=('_standard', '_upd')) + dual_df['hit'] = dual_df.apply(lambda row: 1 if row['hit_standard'] == 1 and row['hit_upd'] == 1 else 0, axis=1) + dual_df['split'] = dual_df['split_standard'] + dual_df['category'] = dual_df['category_standard'] + + # Evaluate dual results + overall_hit_rate, category_hit_rate = self.calculate_hit_rates(dual_df) + print("Overall Dual Acc.", overall_hit_rate) + + if "category" in dual_df.columns: + print("Category Dual Acc.:") + for category_key in category_hit_rate: + if category_key == "split": + continue + + category_percentage = category_hit_rate[category_key] * 100 + print(f"\t{category_key}: {category_percentage:.3f}") + + return overall_hit_rate, category_hit_rate, dual_df diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench_instruction.yaml b/lmms_eval/tasks/mmupdbench/mmupdbench_instruction.yaml new file mode 100644 index 00000000..f20ed910 --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmupdbench_instruction.yaml @@ -0,0 +1,9 @@ +group: mmupdbench_instruction +task: + - mmaadbench_instruction + - mmiasdbench_instruction + - mmivqdbench_instruction +metadata: + version: 0.0 + sys_prompt: "" + gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench_option.yaml b/lmms_eval/tasks/mmupdbench/mmupdbench_option.yaml new file mode 100644 index 00000000..5ab2a82f --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/mmupdbench_option.yaml @@ -0,0 +1,9 @@ +group: mmupdbench_option +task: + - mmaadbench_option + - mmiasdbench_option + - mmivqdbench_option +metadata: + version: 0.0 + sys_prompt: "" + gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/utils.py b/lmms_eval/tasks/mmupdbench/utils.py new file mode 100644 index 00000000..cb67f36b --- /dev/null +++ b/lmms_eval/tasks/mmupdbench/utils.py @@ -0,0 +1,163 @@ +import logging +import yaml +import os +from pathlib import Path +import pandas as pd +import json +from PIL import Image +from io import BytesIO +import base64 +from lmms_eval.tasks.mmupdbench.mmupdbench_evals import MMUPDBench_Evaluator +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file + +eval_logger = logging.getLogger("lmms-eval") + +with open(Path(__file__).parent / "mmupdbench.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + + config = yaml.safe_load("".join(safe_data)) + +GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"] +API_TYPE = os.getenv("API_TYPE", "openai") + +if API_TYPE == "openai": + API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") + API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") +elif API_TYPE == "azure": + API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") + API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") + + +mmupdbench_evaluator = MMUPDBench_Evaluator(sys_prompt=config["metadata"]["sys_prompt"], API_KEY=API_KEY, API_URL=API_URL, model_version=GPT_EVAL_MODEL_NAME) + + +def mmupdbench_doc_to_visual(doc): + return [Image.open(BytesIO(base64.b64decode(doc["image"])))] + + +def mmupdbench_doc_to_text(doc, model_specific_prompt_kwargs=None): + option_candidate = ["A", "B", "C", "D", "E"] + options_prompt, options_dict = mmupdbench_evaluator.create_options_prompt(doc, option_candidate) + + data = { + # "img": doc["image"], + "question": doc["question"], + "answer": doc.get("answer", None), + "options": options_prompt, + "category": doc["category"], + "options_dict": options_dict, + "index": doc["index"], + "hint": doc["hint"], + "source": doc["source"], + "split": doc["split"], + } + + query_prompt = f"{data['hint']} {data['question']} {data['options']}" if pd.notna(data["hint"]) and data["hint"] != "nan" else f"{data['question']} {data['options']}" + + if model_specific_prompt_kwargs: + query_prompt = f"{query_prompt}{model_specific_prompt_kwargs['post_prompt']}" # 後でここを変更する。 + + return query_prompt + + +def mmupdbench_process_results(doc, results): + model_response = results[0].strip() + data = { + "gpt_eval_score": { + "index": doc["index"], + "question": doc["question"], + "answer": doc["answer"], + "prediction": model_response, + "hint": doc["hint"], + "source": doc["source"], + "split": doc["split"], + "category": doc["category"], + "type": doc["type"], + "masked_answer": doc["masked_answer"] + }, + "submission": { + "index": doc["index"], + "question": doc["question"], + "answer": doc["answer"], + "prediction": model_response, + "hint": doc["hint"], + "source": doc["source"], + "split": doc["split"], + "category": doc["category"], + "type": doc["type"] + }, + } + option_candidate = ["A", "B", "C", "D", "E"] + for c in option_candidate: + data["submission"][c] = doc.get(c, "nan") + data["gpt_eval_score"][c] = doc.get(c, "nan") + return data + + +def mmaadbench_base(results, args): + return mmupdbench_results_eval(results, args, upd_type="aad", question_type="base") + + +def mmaadbench_option(results, args): + return mmupdbench_results_eval(results, args, upd_type="aad", question_type="option") + + +def mmaadbench_instruction(results, args): + return mmupdbench_results_eval(results, args, upd_type="aad", question_type="inst") + + +def mmiasdbench_base(results, args): + return mmupdbench_results_eval(results, args, upd_type="iasd", question_type="base") + + +def mmiasdbench_option(results, args): + return mmupdbench_results_eval(results, args, upd_type="iasd", question_type="option") + + +def mmiasdbench_instruction(results, args): + return mmupdbench_results_eval(results, args, upd_type="iasd", question_type="inst") + + +def mmivqdbench_base(results, args): + return mmupdbench_results_eval(results, args, upd_type="ivqd", question_type="base") + + +def mmivqdbench_option(results, args): + return mmupdbench_results_eval(results, args, upd_type="ivqd", question_type="option") + + +def mmivqdbench_instruction(results, args): + return mmupdbench_results_eval(results, args, upd_type="ivqd", question_type="inst") + + +def mmupdbench_results_eval(results, args, upd_type, question_type): + + print("============= MMUPD Bench Detailed Results =============") + + overall_acc_standard, category_acc_standard, standard_results_df = mmupdbench_evaluator.eval_result(results, eval_method="openai", upd_type=upd_type, question_type=question_type, eval_type="standard") + overall_acc_upd, category_acc_upd, upd_results_df = mmupdbench_evaluator.eval_result(results, eval_method="openai", upd_type=upd_type, question_type=question_type, eval_type=upd_type) + + overall_acc_dual, category_acc_dual, dual_results_df = mmupdbench_evaluator.calculate_dual_acc(standard_results_df, upd_results_df) + + file_json = generate_submission_file(f"mmupdbench_results_{upd_type}_{question_type}.json", args) + + details_info = { + "overall_acc_standard": overall_acc_standard, + "category_acc_standard": category_acc_standard, + "overall_acc_upd": overall_acc_upd, + "category_acc_upd": category_acc_upd, + "overall_acc_dual": overall_acc_dual, + "category_acc_dual": category_acc_dual, + } + with open(file_json, "w") as f: + json.dump(details_info, f) + + file_excel = generate_submission_file(f"mmupdbench_results_{upd_type}_{question_type}_dual.xlsx", args) + dual_results_df.to_excel(file_excel, index=False) + + return overall_acc_dual * 100 From 811301c5280ddd74986645086f026ab730c8848c Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Wed, 29 May 2024 12:46:58 +0900 Subject: [PATCH 046/196] add upd --- lmms_eval/tasks/mmupdbench/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/mmupdbench/utils.py b/lmms_eval/tasks/mmupdbench/utils.py index cb67f36b..146c011e 100644 --- a/lmms_eval/tasks/mmupdbench/utils.py +++ b/lmms_eval/tasks/mmupdbench/utils.py @@ -60,7 +60,7 @@ def mmupdbench_doc_to_text(doc, model_specific_prompt_kwargs=None): query_prompt = f"{data['hint']} {data['question']} {data['options']}" if pd.notna(data["hint"]) and data["hint"] != "nan" else f"{data['question']} {data['options']}" if model_specific_prompt_kwargs: - query_prompt = f"{query_prompt}{model_specific_prompt_kwargs['post_prompt']}" # 後でここを変更する。 + query_prompt = f"{query_prompt}{model_specific_prompt_kwargs['post_prompt']}" return query_prompt From 7c1ac9706cafc4801fa4da181d2f610b7838c7b8 Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Wed, 29 May 2024 12:50:32 +0900 Subject: [PATCH 047/196] add upd --- lmms_eval/tasks/mmupdbench/utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lmms_eval/tasks/mmupdbench/utils.py b/lmms_eval/tasks/mmupdbench/utils.py index 146c011e..179c1d21 100644 --- a/lmms_eval/tasks/mmupdbench/utils.py +++ b/lmms_eval/tasks/mmupdbench/utils.py @@ -89,7 +89,8 @@ def mmupdbench_process_results(doc, results): "source": doc["source"], "split": doc["split"], "category": doc["category"], - "type": doc["type"] + "type": doc["type"], + "masked_answer": doc["masked_answer"] }, } option_candidate = ["A", "B", "C", "D", "E"] From 909edd6769ddcf8a546be4fdd129416687516878 Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Wed, 29 May 2024 12:52:21 +0900 Subject: [PATCH 048/196] add upd --- lmms_eval/tasks/mmupdbench/utils.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/lmms_eval/tasks/mmupdbench/utils.py b/lmms_eval/tasks/mmupdbench/utils.py index 179c1d21..4a9ea39b 100644 --- a/lmms_eval/tasks/mmupdbench/utils.py +++ b/lmms_eval/tasks/mmupdbench/utils.py @@ -148,13 +148,14 @@ def mmupdbench_results_eval(results, args, upd_type, question_type): file_json = generate_submission_file(f"mmupdbench_results_{upd_type}_{question_type}.json", args) details_info = { + "overall_acc_dual": overall_acc_dual, + "category_acc_dual": category_acc_dual, "overall_acc_standard": overall_acc_standard, "category_acc_standard": category_acc_standard, "overall_acc_upd": overall_acc_upd, "category_acc_upd": category_acc_upd, - "overall_acc_dual": overall_acc_dual, - "category_acc_dual": category_acc_dual, } + with open(file_json, "w") as f: json.dump(details_info, f) From 453e7936424220f02b99517059ca71babfbe5f5a Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Wed, 29 May 2024 15:03:30 +0900 Subject: [PATCH 049/196] add upd --- README.md | 26 ++++---- .../_default_template_mmaad_base_yaml} | 6 +- .../_default_template_mmaad_instruction_yaml} | 6 +- .../_default_template_mmaad_option_yaml} | 6 +- .../_default_template_mmiasd_base_yaml} | 6 +- ..._default_template_mmiasd_instruction_yaml} | 6 +- .../_default_template_mmiasd_option_yaml} | 6 +- .../_default_template_mmivqd_base_yaml} | 6 +- ..._default_template_mmivqd_instruction_yaml} | 6 +- .../_default_template_mmivqd_option_yaml} | 6 +- lmms_eval/tasks/mmupd/mmaad_base.yaml | 7 +++ lmms_eval/tasks/mmupd/mmaad_instruction.yaml | 7 +++ lmms_eval/tasks/mmupd/mmaad_option.yaml | 7 +++ lmms_eval/tasks/mmupd/mmiasd_base.yaml | 7 +++ lmms_eval/tasks/mmupd/mmiasd_instruction.yaml | 7 +++ lmms_eval/tasks/mmupd/mmiasd_option.yaml | 7 +++ lmms_eval/tasks/mmupd/mmivqd_base.yaml | 7 +++ lmms_eval/tasks/mmupd/mmivqd_instruction.yaml | 7 +++ lmms_eval/tasks/mmupd/mmivqd_option.yaml | 7 +++ lmms_eval/tasks/mmupd/mmupd.yaml | 15 +++++ .../mmupd_base.yaml} | 8 +-- .../mmupd_evals.py} | 2 +- lmms_eval/tasks/mmupd/mmupd_instruction.yaml | 9 +++ lmms_eval/tasks/mmupd/mmupd_option.yaml | 9 +++ .../tasks/{mmupdbench => mmupd}/utils.py | 62 +++++++++---------- .../tasks/mmupdbench/mmaadbench_base.yaml | 7 --- .../mmupdbench/mmaadbench_instruction.yaml | 7 --- .../tasks/mmupdbench/mmaadbench_option.yaml | 7 --- .../tasks/mmupdbench/mmiasdbench_base.yaml | 7 --- .../mmupdbench/mmiasdbench_instruction.yaml | 7 --- .../tasks/mmupdbench/mmiasdbench_option.yaml | 7 --- .../tasks/mmupdbench/mmivqdbench_base.yaml | 7 --- .../mmupdbench/mmivqdbench_instruction.yaml | 7 --- .../tasks/mmupdbench/mmivqdbench_option.yaml | 7 --- lmms_eval/tasks/mmupdbench/mmupdbench.yaml | 15 ----- .../mmupdbench/mmupdbench_instruction.yaml | 9 --- .../tasks/mmupdbench/mmupdbench_option.yaml | 9 --- 37 files changed, 172 insertions(+), 172 deletions(-) rename lmms_eval/tasks/{mmupdbench/_default_template_mmaadbench_base_yaml => mmupd/_default_template_mmaad_base_yaml} (71%) rename lmms_eval/tasks/{mmupdbench/_default_template_mmaadbench_instruction_yaml => mmupd/_default_template_mmaad_instruction_yaml} (74%) rename lmms_eval/tasks/{mmupdbench/_default_template_mmaadbench_option_yaml => mmupd/_default_template_mmaad_option_yaml} (74%) rename lmms_eval/tasks/{mmupdbench/_default_template_mmiasdbench_base_yaml => mmupd/_default_template_mmiasd_base_yaml} (71%) rename lmms_eval/tasks/{mmupdbench/_default_template_mmiasdbench_instruction_yaml => mmupd/_default_template_mmiasd_instruction_yaml} (74%) rename lmms_eval/tasks/{mmupdbench/_default_template_mmiasdbench_option_yaml => mmupd/_default_template_mmiasd_option_yaml} (74%) rename lmms_eval/tasks/{mmupdbench/_default_template_mmivqdbench_base_yaml => mmupd/_default_template_mmivqd_base_yaml} (71%) rename lmms_eval/tasks/{mmupdbench/_default_template_mmivqdbench_instruction_yaml => mmupd/_default_template_mmivqd_instruction_yaml} (75%) rename lmms_eval/tasks/{mmupdbench/_default_template_mmivqdbench_option_yaml => mmupd/_default_template_mmivqd_option_yaml} (74%) create mode 100644 lmms_eval/tasks/mmupd/mmaad_base.yaml create mode 100644 lmms_eval/tasks/mmupd/mmaad_instruction.yaml create mode 100644 lmms_eval/tasks/mmupd/mmaad_option.yaml create mode 100644 lmms_eval/tasks/mmupd/mmiasd_base.yaml create mode 100644 lmms_eval/tasks/mmupd/mmiasd_instruction.yaml create mode 100644 lmms_eval/tasks/mmupd/mmiasd_option.yaml create mode 100644 lmms_eval/tasks/mmupd/mmivqd_base.yaml create mode 100644 lmms_eval/tasks/mmupd/mmivqd_instruction.yaml create mode 100644 lmms_eval/tasks/mmupd/mmivqd_option.yaml create mode 100644 lmms_eval/tasks/mmupd/mmupd.yaml rename lmms_eval/tasks/{mmupdbench/mmupdbench_base.yaml => mmupd/mmupd_base.yaml} (52%) rename lmms_eval/tasks/{mmupdbench/mmupdbench_evals.py => mmupd/mmupd_evals.py} (99%) create mode 100644 lmms_eval/tasks/mmupd/mmupd_instruction.yaml create mode 100644 lmms_eval/tasks/mmupd/mmupd_option.yaml rename lmms_eval/tasks/{mmupdbench => mmupd}/utils.py (60%) delete mode 100644 lmms_eval/tasks/mmupdbench/mmaadbench_base.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmaadbench_instruction.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmaadbench_option.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmiasdbench_base.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmiasdbench_instruction.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmiasdbench_option.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmivqdbench_base.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmivqdbench_instruction.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmivqdbench_option.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmupdbench.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmupdbench_instruction.yaml delete mode 100644 lmms_eval/tasks/mmupdbench/mmupdbench_option.yaml diff --git a/README.md b/README.md index 38713718..03fa7f26 100644 --- a/README.md +++ b/README.md @@ -190,19 +190,19 @@ We also provide the raw data exported from Weights & Biases for the detailed res - MMMU (mmmu) - MMMU Validation (mmmu_val) - MMMU Test (mmmu_test) -- MMUPDBench (mmupdbench) - - MMUPDBench Base (mmupdbench_base) - - MMAADBench Base (mmaadbench_base) - - MMIASDBench Base (mmiasdbench_base) - - MMIVQDBench Base (mmivqdbench_base) - - MMUPDBench Option (mmupdbench_option) - - MMAADBench Option (mmaadbench_option) - - MMIASDBench Option (mmiasdbench_option) - - MMIVQDBench Option (mmivqdbench_option) - - MMUPDBench Instruction (mmupdbench_instruction) - - MMAADBench Instruction (mmaadbench_instruction) - - MMIASDBench Instruction (mmiasdbench_instruction) - - MMIVQDBench Instruction (mmivqdbench_instruction) +- MMUPD (mmupd) + - MMUPD Base (mmupd_base) + - MMAAD Base (mmaad_base) + - MMIASD Base (mmiasd_base) + - MMIVQD Base (mmivqd_base) + - MMUPD Option (mmupd_option) + - MMAAD Option (mmaad_option) + - MMIASD Option (mmiasd_option) + - MMIVQD Option (mmivqd_option) + - MMUPD Instruction (mmupd_instruction) + - MMAAD Instruction (mmaad_instruction) + - MMIASD Instruction (mmiasd_instruction) + - MMIVQD Instruction (mmivqd_instruction) - MMVet (mmvet) - Multi-DocVQA (multidocvqa) - Multi-DocVQA Validation (multidocvqa_val) diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_base_yaml b/lmms_eval/tasks/mmupd/_default_template_mmaad_base_yaml similarity index 71% rename from lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_base_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmaad_base_yaml index da992404..bbe9a9cf 100644 --- a/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_base_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmaad_base_yaml @@ -4,10 +4,10 @@ model_specific_prompt_kwargs: default: pre_prompt: "" post_prompt: "\n" -doc_to_visual: !function utils.mmupdbench_doc_to_visual -doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_visual: !function utils.mmupd_doc_to_visual +doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" -process_results: !function utils.mmupdbench_process_results +process_results: !function utils.mmupd_process_results model_specific_generation_kwargs: llava: image_aspect_ratio: original diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_instruction_yaml b/lmms_eval/tasks/mmupd/_default_template_mmaad_instruction_yaml similarity index 74% rename from lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_instruction_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmaad_instruction_yaml index 85674b3f..fe4c8148 100644 --- a/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_instruction_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmaad_instruction_yaml @@ -4,10 +4,10 @@ model_specific_prompt_kwargs: default: pre_prompt: "" post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." -doc_to_visual: !function utils.mmupdbench_doc_to_visual -doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_visual: !function utils.mmupd_doc_to_visual +doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" -process_results: !function utils.mmupdbench_process_results +process_results: !function utils.mmupd_process_results model_specific_generation_kwargs: llava: image_aspect_ratio: original diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_option_yaml b/lmms_eval/tasks/mmupd/_default_template_mmaad_option_yaml similarity index 74% rename from lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_option_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmaad_option_yaml index 60fef3d0..aa8b9c35 100644 --- a/lmms_eval/tasks/mmupdbench/_default_template_mmaadbench_option_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmaad_option_yaml @@ -4,10 +4,10 @@ model_specific_prompt_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." -doc_to_visual: !function utils.mmupdbench_doc_to_visual -doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_visual: !function utils.mmupd_doc_to_visual +doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" -process_results: !function utils.mmupdbench_process_results +process_results: !function utils.mmupd_process_results model_specific_generation_kwargs: llava: image_aspect_ratio: original diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_base_yaml b/lmms_eval/tasks/mmupd/_default_template_mmiasd_base_yaml similarity index 71% rename from lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_base_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmiasd_base_yaml index 5b098d74..124c9007 100644 --- a/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_base_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmiasd_base_yaml @@ -4,10 +4,10 @@ model_specific_prompt_kwargs: default: pre_prompt: "" post_prompt: "\n" -doc_to_visual: !function utils.mmupdbench_doc_to_visual -doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_visual: !function utils.mmupd_doc_to_visual +doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" -process_results: !function utils.mmupdbench_process_results +process_results: !function utils.mmupd_process_results model_specific_generation_kwargs: llava: image_aspect_ratio: original diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_instruction_yaml b/lmms_eval/tasks/mmupd/_default_template_mmiasd_instruction_yaml similarity index 74% rename from lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_instruction_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmiasd_instruction_yaml index a09f6aa0..b8d1e473 100644 --- a/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_instruction_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmiasd_instruction_yaml @@ -4,10 +4,10 @@ model_specific_prompt_kwargs: default: pre_prompt: "" post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." -doc_to_visual: !function utils.mmupdbench_doc_to_visual -doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_visual: !function utils.mmupd_doc_to_visual +doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" -process_results: !function utils.mmupdbench_process_results +process_results: !function utils.mmupd_process_results model_specific_generation_kwargs: llava: image_aspect_ratio: original diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_option_yaml b/lmms_eval/tasks/mmupd/_default_template_mmiasd_option_yaml similarity index 74% rename from lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_option_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmiasd_option_yaml index 07db7bf0..3e40e8da 100644 --- a/lmms_eval/tasks/mmupdbench/_default_template_mmiasdbench_option_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmiasd_option_yaml @@ -4,10 +4,10 @@ model_specific_prompt_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." -doc_to_visual: !function utils.mmupdbench_doc_to_visual -doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_visual: !function utils.mmupd_doc_to_visual +doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" -process_results: !function utils.mmupdbench_process_results +process_results: !function utils.mmupd_process_results model_specific_generation_kwargs: llava: image_aspect_ratio: original diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_base_yaml b/lmms_eval/tasks/mmupd/_default_template_mmivqd_base_yaml similarity index 71% rename from lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_base_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmivqd_base_yaml index d1f8b3dd..986ae379 100644 --- a/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_base_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmivqd_base_yaml @@ -4,10 +4,10 @@ model_specific_prompt_kwargs: default: pre_prompt: "" post_prompt: "\n" -doc_to_visual: !function utils.mmupdbench_doc_to_visual -doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_visual: !function utils.mmupd_doc_to_visual +doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" -process_results: !function utils.mmupdbench_process_results +process_results: !function utils.mmupd_process_results model_specific_generation_kwargs: llava: image_aspect_ratio: original diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_instruction_yaml b/lmms_eval/tasks/mmupd/_default_template_mmivqd_instruction_yaml similarity index 75% rename from lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_instruction_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmivqd_instruction_yaml index ba547695..8362657e 100644 --- a/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_instruction_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmivqd_instruction_yaml @@ -4,10 +4,10 @@ model_specific_prompt_kwargs: default: pre_prompt: "" post_prompt: "\nIf the given image is irrelevant to the question, answer \"F. The image and question are irrelevant.\"." -doc_to_visual: !function utils.mmupdbench_doc_to_visual -doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_visual: !function utils.mmupd_doc_to_visual +doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" -process_results: !function utils.mmupdbench_process_results +process_results: !function utils.mmupd_process_results model_specific_generation_kwargs: llava: image_aspect_ratio: original diff --git a/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_option_yaml b/lmms_eval/tasks/mmupd/_default_template_mmivqd_option_yaml similarity index 74% rename from lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_option_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmivqd_option_yaml index 37b0866b..14778cac 100644 --- a/lmms_eval/tasks/mmupdbench/_default_template_mmivqdbench_option_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmivqd_option_yaml @@ -4,10 +4,10 @@ model_specific_prompt_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." -doc_to_visual: !function utils.mmupdbench_doc_to_visual -doc_to_text: !function utils.mmupdbench_doc_to_text +doc_to_visual: !function utils.mmupd_doc_to_visual +doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" -process_results: !function utils.mmupdbench_process_results +process_results: !function utils.mmupd_process_results model_specific_generation_kwargs: llava: image_aspect_ratio: original diff --git a/lmms_eval/tasks/mmupd/mmaad_base.yaml b/lmms_eval/tasks/mmupd/mmaad_base.yaml new file mode 100644 index 00000000..dd38d33e --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmaad_base.yaml @@ -0,0 +1,7 @@ +task: "mmaad_base" +test_split: test +include: _default_template_mmaad_base_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmaad_base + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmaad_instruction.yaml b/lmms_eval/tasks/mmupd/mmaad_instruction.yaml new file mode 100644 index 00000000..384425d3 --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmaad_instruction.yaml @@ -0,0 +1,7 @@ +task: "mmaad_instruction" +test_split: test +include: _default_template_mmaad_instruction_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmaad_instruction + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmaad_option.yaml b/lmms_eval/tasks/mmupd/mmaad_option.yaml new file mode 100644 index 00000000..81ac6a02 --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmaad_option.yaml @@ -0,0 +1,7 @@ +task: "mmaad_option" +test_split: test +include: _default_template_mmaad_option_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmaad_option + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmiasd_base.yaml b/lmms_eval/tasks/mmupd/mmiasd_base.yaml new file mode 100644 index 00000000..29468358 --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmiasd_base.yaml @@ -0,0 +1,7 @@ +task: "mmiasd_base" +test_split: test +include: _default_template_mmiasd_base_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmiasd_base + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml b/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml new file mode 100644 index 00000000..a6406a1a --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml @@ -0,0 +1,7 @@ +task: "mmiasd_instruction" +test_split: test +include: _default_template_mmiasd_instruction_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmiasd_instruction + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmiasd_option.yaml b/lmms_eval/tasks/mmupd/mmiasd_option.yaml new file mode 100644 index 00000000..83e3248f --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmiasd_option.yaml @@ -0,0 +1,7 @@ +task: "mmiasd_option" +test_split: test +include: _default_template_mmiasd_option_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmiasd_option + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmivqd_base.yaml b/lmms_eval/tasks/mmupd/mmivqd_base.yaml new file mode 100644 index 00000000..80b856f5 --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmivqd_base.yaml @@ -0,0 +1,7 @@ +task: "mmivqd_base" +test_split: test +include: _default_template_mmivqd_base_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmivqd_base + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml b/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml new file mode 100644 index 00000000..ef0d3604 --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml @@ -0,0 +1,7 @@ +task: "mmivqd_instruction" +test_split: test +include: _default_template_mmivqd_instruction_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmivqd_instruction + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmivqd_option.yaml b/lmms_eval/tasks/mmupd/mmivqd_option.yaml new file mode 100644 index 00000000..6b1d2cc9 --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmivqd_option.yaml @@ -0,0 +1,7 @@ +task: "mmivqd_option" +test_split: test +include: _default_template_mmivqd_option_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mmivqd_option + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmupd.yaml b/lmms_eval/tasks/mmupd/mmupd.yaml new file mode 100644 index 00000000..f40416a1 --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmupd.yaml @@ -0,0 +1,15 @@ +group: mmupd +task: + - mmaad_base + - mmaad_option + - mmaad_instruction + - mmiasd_base + - mmiasd_option + - mmiasd_instruction + - mmivqd_base + - mmivqd_option + - mmivqd_instruction +metadata: + version: 0.0 + sys_prompt: "" + gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench_base.yaml b/lmms_eval/tasks/mmupd/mmupd_base.yaml similarity index 52% rename from lmms_eval/tasks/mmupdbench/mmupdbench_base.yaml rename to lmms_eval/tasks/mmupd/mmupd_base.yaml index 5aa7ffc7..988ad2e1 100644 --- a/lmms_eval/tasks/mmupdbench/mmupdbench_base.yaml +++ b/lmms_eval/tasks/mmupd/mmupd_base.yaml @@ -1,8 +1,8 @@ -group: mmupdbench_base +group: mmupd_base task: - - mmaadbench_base - - mmiasdbench_base - - mmivqdbench_base + - mmaad_base + - mmiasd_base + - mmivqd_base metadata: version: 0.0 sys_prompt: "" diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench_evals.py b/lmms_eval/tasks/mmupd/mmupd_evals.py similarity index 99% rename from lmms_eval/tasks/mmupdbench/mmupdbench_evals.py rename to lmms_eval/tasks/mmupd/mmupd_evals.py index c3af1618..9143e8ba 100644 --- a/lmms_eval/tasks/mmupdbench/mmupdbench_evals.py +++ b/lmms_eval/tasks/mmupd/mmupd_evals.py @@ -91,7 +91,7 @@ def load_tsv(f): return handlers[suffix](f) -class MMUPDBench_Evaluator: +class MMUPD_Evaluator: def __init__(self, sys_prompt="There are several options:", API_KEY="", API_URL="", model_version="gpt-3.5-turbo-0613"): self.sys_prompt = sys_prompt self.model_version = model_version diff --git a/lmms_eval/tasks/mmupd/mmupd_instruction.yaml b/lmms_eval/tasks/mmupd/mmupd_instruction.yaml new file mode 100644 index 00000000..3ca2444e --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmupd_instruction.yaml @@ -0,0 +1,9 @@ +group: mmupd_instruction +task: + - mmaad_instruction + - mmiasd_instruction + - mmivqd_instruction +metadata: + version: 0.0 + sys_prompt: "" + gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmupd_option.yaml b/lmms_eval/tasks/mmupd/mmupd_option.yaml new file mode 100644 index 00000000..6393fdc8 --- /dev/null +++ b/lmms_eval/tasks/mmupd/mmupd_option.yaml @@ -0,0 +1,9 @@ +group: mmupd_option +task: + - mmaad_option + - mmiasd_option + - mmivqd_option +metadata: + version: 0.0 + sys_prompt: "" + gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/utils.py b/lmms_eval/tasks/mmupd/utils.py similarity index 60% rename from lmms_eval/tasks/mmupdbench/utils.py rename to lmms_eval/tasks/mmupd/utils.py index 4a9ea39b..2a0d9b12 100644 --- a/lmms_eval/tasks/mmupdbench/utils.py +++ b/lmms_eval/tasks/mmupd/utils.py @@ -7,12 +7,12 @@ from PIL import Image from io import BytesIO import base64 -from lmms_eval.tasks.mmupdbench.mmupdbench_evals import MMUPDBench_Evaluator +from lmms_eval.tasks.mmupd.mmupd_evals import MMUPD_Evaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file eval_logger = logging.getLogger("lmms-eval") -with open(Path(__file__).parent / "mmupdbench.yaml", "r") as f: +with open(Path(__file__).parent / "mmupd.yaml", "r") as f: raw_data = f.readlines() safe_data = [] for i, line in enumerate(raw_data): @@ -33,16 +33,16 @@ API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") -mmupdbench_evaluator = MMUPDBench_Evaluator(sys_prompt=config["metadata"]["sys_prompt"], API_KEY=API_KEY, API_URL=API_URL, model_version=GPT_EVAL_MODEL_NAME) +mmupd_evaluator = MMUPD_Evaluator(sys_prompt=config["metadata"]["sys_prompt"], API_KEY=API_KEY, API_URL=API_URL, model_version=GPT_EVAL_MODEL_NAME) -def mmupdbench_doc_to_visual(doc): +def mmupd_doc_to_visual(doc): return [Image.open(BytesIO(base64.b64decode(doc["image"])))] -def mmupdbench_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mmupd_doc_to_text(doc, model_specific_prompt_kwargs=None): option_candidate = ["A", "B", "C", "D", "E"] - options_prompt, options_dict = mmupdbench_evaluator.create_options_prompt(doc, option_candidate) + options_prompt, options_dict = mmupd_evaluator.create_options_prompt(doc, option_candidate) data = { # "img": doc["image"], @@ -65,7 +65,7 @@ def mmupdbench_doc_to_text(doc, model_specific_prompt_kwargs=None): return query_prompt -def mmupdbench_process_results(doc, results): +def mmupd_process_results(doc, results): model_response = results[0].strip() data = { "gpt_eval_score": { @@ -100,52 +100,52 @@ def mmupdbench_process_results(doc, results): return data -def mmaadbench_base(results, args): - return mmupdbench_results_eval(results, args, upd_type="aad", question_type="base") +def mmaad_base(results, args): + return mmupd_results_eval(results, args, upd_type="aad", question_type="base") -def mmaadbench_option(results, args): - return mmupdbench_results_eval(results, args, upd_type="aad", question_type="option") +def mmaad_option(results, args): + return mmupd_results_eval(results, args, upd_type="aad", question_type="option") -def mmaadbench_instruction(results, args): - return mmupdbench_results_eval(results, args, upd_type="aad", question_type="inst") +def mmaad_instruction(results, args): + return mmupd_results_eval(results, args, upd_type="aad", question_type="inst") -def mmiasdbench_base(results, args): - return mmupdbench_results_eval(results, args, upd_type="iasd", question_type="base") +def mmiasd_base(results, args): + return mmupd_results_eval(results, args, upd_type="iasd", question_type="base") -def mmiasdbench_option(results, args): - return mmupdbench_results_eval(results, args, upd_type="iasd", question_type="option") +def mmiasd_option(results, args): + return mmupd_results_eval(results, args, upd_type="iasd", question_type="option") -def mmiasdbench_instruction(results, args): - return mmupdbench_results_eval(results, args, upd_type="iasd", question_type="inst") +def mmiasd_instruction(results, args): + return mmupd_results_eval(results, args, upd_type="iasd", question_type="inst") -def mmivqdbench_base(results, args): - return mmupdbench_results_eval(results, args, upd_type="ivqd", question_type="base") +def mmivqd_base(results, args): + return mmupd_results_eval(results, args, upd_type="ivqd", question_type="base") -def mmivqdbench_option(results, args): - return mmupdbench_results_eval(results, args, upd_type="ivqd", question_type="option") +def mmivqd_option(results, args): + return mmupd_results_eval(results, args, upd_type="ivqd", question_type="option") -def mmivqdbench_instruction(results, args): - return mmupdbench_results_eval(results, args, upd_type="ivqd", question_type="inst") +def mmivqd_instruction(results, args): + return mmupd_results_eval(results, args, upd_type="ivqd", question_type="inst") -def mmupdbench_results_eval(results, args, upd_type, question_type): +def mmupd_results_eval(results, args, upd_type, question_type): print("============= MMUPD Bench Detailed Results =============") - overall_acc_standard, category_acc_standard, standard_results_df = mmupdbench_evaluator.eval_result(results, eval_method="openai", upd_type=upd_type, question_type=question_type, eval_type="standard") - overall_acc_upd, category_acc_upd, upd_results_df = mmupdbench_evaluator.eval_result(results, eval_method="openai", upd_type=upd_type, question_type=question_type, eval_type=upd_type) + overall_acc_standard, category_acc_standard, standard_results_df = mmupd_evaluator.eval_result(results, eval_method="openai", upd_type=upd_type, question_type=question_type, eval_type="standard") + overall_acc_upd, category_acc_upd, upd_results_df = mmupd_evaluator.eval_result(results, eval_method="openai", upd_type=upd_type, question_type=question_type, eval_type=upd_type) - overall_acc_dual, category_acc_dual, dual_results_df = mmupdbench_evaluator.calculate_dual_acc(standard_results_df, upd_results_df) + overall_acc_dual, category_acc_dual, dual_results_df = mmupd_evaluator.calculate_dual_acc(standard_results_df, upd_results_df) - file_json = generate_submission_file(f"mmupdbench_results_{upd_type}_{question_type}.json", args) + file_json = generate_submission_file(f"mmupd_results_{upd_type}_{question_type}.json", args) details_info = { "overall_acc_dual": overall_acc_dual, @@ -159,7 +159,7 @@ def mmupdbench_results_eval(results, args, upd_type, question_type): with open(file_json, "w") as f: json.dump(details_info, f) - file_excel = generate_submission_file(f"mmupdbench_results_{upd_type}_{question_type}_dual.xlsx", args) + file_excel = generate_submission_file(f"mmupd_results_{upd_type}_{question_type}_dual.xlsx", args) dual_results_df.to_excel(file_excel, index=False) return overall_acc_dual * 100 diff --git a/lmms_eval/tasks/mmupdbench/mmaadbench_base.yaml b/lmms_eval/tasks/mmupdbench/mmaadbench_base.yaml deleted file mode 100644 index 11573724..00000000 --- a/lmms_eval/tasks/mmupdbench/mmaadbench_base.yaml +++ /dev/null @@ -1,7 +0,0 @@ -task: "mmaadbench_base" -test_split: test -include: _default_template_mmaadbench_base_yaml -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mmaadbench_base - higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmaadbench_instruction.yaml b/lmms_eval/tasks/mmupdbench/mmaadbench_instruction.yaml deleted file mode 100644 index d9aa99b7..00000000 --- a/lmms_eval/tasks/mmupdbench/mmaadbench_instruction.yaml +++ /dev/null @@ -1,7 +0,0 @@ -task: "mmaadbench_instruction" -test_split: test -include: _default_template_mmaadbench_instruction_yaml -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mmaadbench_instruction - higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmaadbench_option.yaml b/lmms_eval/tasks/mmupdbench/mmaadbench_option.yaml deleted file mode 100644 index 07d44215..00000000 --- a/lmms_eval/tasks/mmupdbench/mmaadbench_option.yaml +++ /dev/null @@ -1,7 +0,0 @@ -task: "mmaadbench_option" -test_split: test -include: _default_template_mmaadbench_option_yaml -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mmaadbench_option - higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmiasdbench_base.yaml b/lmms_eval/tasks/mmupdbench/mmiasdbench_base.yaml deleted file mode 100644 index f109f304..00000000 --- a/lmms_eval/tasks/mmupdbench/mmiasdbench_base.yaml +++ /dev/null @@ -1,7 +0,0 @@ -task: "mmiasdbench_base" -test_split: test -include: _default_template_mmiasdbench_base_yaml -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mmiasdbench_base - higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmiasdbench_instruction.yaml b/lmms_eval/tasks/mmupdbench/mmiasdbench_instruction.yaml deleted file mode 100644 index 91403014..00000000 --- a/lmms_eval/tasks/mmupdbench/mmiasdbench_instruction.yaml +++ /dev/null @@ -1,7 +0,0 @@ -task: "mmiasdbench_instruction" -test_split: test -include: _default_template_mmiasdbench_instruction_yaml -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mmiasdbench_instruction - higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmiasdbench_option.yaml b/lmms_eval/tasks/mmupdbench/mmiasdbench_option.yaml deleted file mode 100644 index d340aeab..00000000 --- a/lmms_eval/tasks/mmupdbench/mmiasdbench_option.yaml +++ /dev/null @@ -1,7 +0,0 @@ -task: "mmiasdbench_option" -test_split: test -include: _default_template_mmiasdbench_option_yaml -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mmiasdbench_option - higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmivqdbench_base.yaml b/lmms_eval/tasks/mmupdbench/mmivqdbench_base.yaml deleted file mode 100644 index 2af6e726..00000000 --- a/lmms_eval/tasks/mmupdbench/mmivqdbench_base.yaml +++ /dev/null @@ -1,7 +0,0 @@ -task: "mmivqdbench_base" -test_split: test -include: _default_template_mmivqdbench_base_yaml -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mmivqdbench_base - higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmivqdbench_instruction.yaml b/lmms_eval/tasks/mmupdbench/mmivqdbench_instruction.yaml deleted file mode 100644 index 750097ef..00000000 --- a/lmms_eval/tasks/mmupdbench/mmivqdbench_instruction.yaml +++ /dev/null @@ -1,7 +0,0 @@ -task: "mmivqdbench_instruction" -test_split: test -include: _default_template_mmivqdbench_instruction_yaml -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mmivqdbench_instruction - higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmivqdbench_option.yaml b/lmms_eval/tasks/mmupdbench/mmivqdbench_option.yaml deleted file mode 100644 index 4e81449b..00000000 --- a/lmms_eval/tasks/mmupdbench/mmivqdbench_option.yaml +++ /dev/null @@ -1,7 +0,0 @@ -task: "mmivqdbench_option" -test_split: test -include: _default_template_mmivqdbench_option_yaml -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mmivqdbench_option - higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench.yaml b/lmms_eval/tasks/mmupdbench/mmupdbench.yaml deleted file mode 100644 index 12681e71..00000000 --- a/lmms_eval/tasks/mmupdbench/mmupdbench.yaml +++ /dev/null @@ -1,15 +0,0 @@ -group: mmupdbench -task: - - mmaadbench_base - - mmaadbench_option - - mmaadbench_instruction - - mmiasdbench_base - - mmiasdbench_option - - mmiasdbench_instruction - - mmivqdbench_base - - mmivqdbench_option - - mmivqdbench_instruction -metadata: - version: 0.0 - sys_prompt: "" - gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench_instruction.yaml b/lmms_eval/tasks/mmupdbench/mmupdbench_instruction.yaml deleted file mode 100644 index f20ed910..00000000 --- a/lmms_eval/tasks/mmupdbench/mmupdbench_instruction.yaml +++ /dev/null @@ -1,9 +0,0 @@ -group: mmupdbench_instruction -task: - - mmaadbench_instruction - - mmiasdbench_instruction - - mmivqdbench_instruction -metadata: - version: 0.0 - sys_prompt: "" - gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupdbench/mmupdbench_option.yaml b/lmms_eval/tasks/mmupdbench/mmupdbench_option.yaml deleted file mode 100644 index 5ab2a82f..00000000 --- a/lmms_eval/tasks/mmupdbench/mmupdbench_option.yaml +++ /dev/null @@ -1,9 +0,0 @@ -group: mmupdbench_option -task: - - mmaadbench_option - - mmiasdbench_option - - mmivqdbench_option -metadata: - version: 0.0 - sys_prompt: "" - gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file From d224794c49520f4d28a31862cf977198cd6cbc5e Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Wed, 29 May 2024 15:15:59 +0900 Subject: [PATCH 050/196] add upd --- lmms_eval/tasks/mmupd/mmupd_evals.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/mmupd/mmupd_evals.py b/lmms_eval/tasks/mmupd/mmupd_evals.py index 9143e8ba..4878763d 100644 --- a/lmms_eval/tasks/mmupd/mmupd_evals.py +++ b/lmms_eval/tasks/mmupd/mmupd_evals.py @@ -453,7 +453,7 @@ def extract_answer_from_item(self, item, gt_text, eval_type, question_type, upd_ return ret, item['prediction'], answer_option while retry: - ans = self.get_chat_response(prompt, temperature=0.9) + ans = self.get_chat_response(prompt, temperature=0.7) if 'Failed to obtain answer via API' in ans: msg = 'GPT API failed to answer. ' eval_logger.info(msg) From 4854a34d4d37efb5e201f2691ecdb054590cf20b Mon Sep 17 00:00:00 2001 From: Pu Fanyi Date: Sat, 4 May 2024 19:23:39 +0800 Subject: [PATCH 051/196] Group MMMU images into one image (#83) * update * update font * Add matplotlib.font_manager import in utils.py * Refactor font handling in add_order_label function in utils.py * group mmmu --------- Co-authored-by: Li Bo --- lmms_eval/tasks/mmmu/mmmu_group_img.yaml | 4 + lmms_eval/tasks/mmmu/mmmu_group_img_test.yaml | 19 + lmms_eval/tasks/mmmu/mmmu_group_img_val.yaml | 21 + lmms_eval/tasks/mmmu/utils_group_img.py | 589 ++++++++++++++++++ 4 files changed, 633 insertions(+) create mode 100644 lmms_eval/tasks/mmmu/mmmu_group_img.yaml create mode 100644 lmms_eval/tasks/mmmu/mmmu_group_img_test.yaml create mode 100644 lmms_eval/tasks/mmmu/mmmu_group_img_val.yaml create mode 100644 lmms_eval/tasks/mmmu/utils_group_img.py diff --git a/lmms_eval/tasks/mmmu/mmmu_group_img.yaml b/lmms_eval/tasks/mmmu/mmmu_group_img.yaml new file mode 100644 index 00000000..8d8713a7 --- /dev/null +++ b/lmms_eval/tasks/mmmu/mmmu_group_img.yaml @@ -0,0 +1,4 @@ +group: mmmu_group_img +task: +- mmmu_val_group_img +- mmmu_test_group_img diff --git a/lmms_eval/tasks/mmmu/mmmu_group_img_test.yaml b/lmms_eval/tasks/mmmu/mmmu_group_img_test.yaml new file mode 100644 index 00000000..509336c4 --- /dev/null +++ b/lmms_eval/tasks/mmmu/mmmu_group_img_test.yaml @@ -0,0 +1,19 @@ +dataset_path: lmms-lab/MMMU +task: "mmmu_test_group_img" +test_split: test +output_type: generate_until +doc_to_visual: !function utils_group_img.mmmu_doc_to_visual +doc_to_text: !function utils_group_img.mmmu_doc_to_text +doc_to_target: "answer" +# The return value of process_results will be used by metrics +process_results: !function utils_group_img.mmmu_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +generation_kwargs: + max_new_tokens: 16 + image_aspect_ratio: original +metric_list: + - metric: submission + aggregation: !function utils_group_img.mmmu_test_aggregate_results_for_submission + higher_is_better: true +metadata: + - version: 0.0 \ No newline at end of file diff --git a/lmms_eval/tasks/mmmu/mmmu_group_img_val.yaml b/lmms_eval/tasks/mmmu/mmmu_group_img_val.yaml new file mode 100644 index 00000000..c9f9239e --- /dev/null +++ b/lmms_eval/tasks/mmmu/mmmu_group_img_val.yaml @@ -0,0 +1,21 @@ +dataset_path: lmms-lab/MMMU +task: "mmmu_val_group_img" +test_split: validation +output_type: generate_until +doc_to_visual: !function utils_group_img.mmmu_doc_to_visual +doc_to_text: !function utils_group_img.mmmu_doc_to_text +doc_to_target: "answer" +# The return value of process_results will be used by metrics +process_results: !function utils_group_img.mmmu_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +generation_kwargs: + max_new_tokens: 16 +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +metric_list: + - metric: mmmu_acc + aggregation: !function utils_group_img.mmmu_aggregate_results + higher_is_better: true +metadata: + - version: 0.0 \ No newline at end of file diff --git a/lmms_eval/tasks/mmmu/utils_group_img.py b/lmms_eval/tasks/mmmu/utils_group_img.py new file mode 100644 index 00000000..e3e475be --- /dev/null +++ b/lmms_eval/tasks/mmmu/utils_group_img.py @@ -0,0 +1,589 @@ +from collections import defaultdict +import re +import ast +import random +import numpy as np +import os +import json +import logging +import matplotlib.font_manager as fm + +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file + +from PIL import Image, ImageDraw, ImageFont +import numpy as np + + +def add_order_label(image, label, font_family="DejaVu Sans", font_size=40): + # Create a drawing context + draw = ImageDraw.Draw(image) + + # Define font for the label + font_path = fm.findfont(fm.FontProperties(family=font_family)) + font = ImageFont.truetype(font_path, font_size) + + # Calculate text size and position + text_width = text_height = font_size + label_background_margin = 10 + label_background_size = (text_width + 2 * label_background_margin, text_height + 2 * label_background_margin) + + # Draw a solid white square for the label background + label_background_position = (0, 0) # Top-left corner + draw.rectangle([label_background_position, (label_background_position[0] + label_background_size[0], label_background_position[1] + label_background_size[1])], fill="white") + + # Add the label text in black over the white square + label_position = (label_background_margin, label_background_margin) + draw.text(label_position, label, font=font, fill="black") + + return image + + +def resize_image_height(image, fixed_size): + # Resize image, maintaining aspect ratio + width, height = image.size + new_size = int(width * fixed_size / height), fixed_size + return image.resize(new_size, Image.Resampling.LANCZOS) + + +def concatenate_images_horizontal(image_list): + # Concatenate images horizontally + widths, heights = zip(*(i.size for i in image_list)) + total_width = sum(widths) + max_height = max(heights) + assert all(height == max_height for height in heights) + new_im = Image.new("RGB", (total_width, max_height)) + x_offset = 0 + for im in image_list: + new_im.paste(im, (x_offset, 0)) + x_offset += im.size[0] + return new_im + + +def resize_image_width(image, fixed_size): + # Resize image, maintaining aspect ratio + width, height = image.size + new_size = fixed_size, int(height * fixed_size / width) + return image.resize(new_size, Image.Resampling.LANCZOS) + + +def concatenate_images_vertical(image_list): + # Concatenate images horizontally + widths, heights = zip(*(i.size for i in image_list)) + total_height = sum(heights) + max_width = max(widths) + assert all(width == max_width for width in widths) + new_im = Image.new("RGB", (max_width, total_height)) + y_offset = 0 + for im in image_list: + new_im.paste(im, (0, y_offset)) + y_offset += im.size[1] + return new_im + + +def process_images_horizontal(original_images, size): + images = [] + for i, img in enumerate(original_images): + # Resize image + img_resized = resize_image_height(img, fixed_size=size) + + # Add order label + img_labeled = add_order_label(img_resized, f"[{i+1}]") + + # Append to list + images.append(img_labeled) + + # Concatenate all images + return concatenate_images_horizontal(images) + + +def process_images_vertical(original_images, size): + images = [] + for i, img in enumerate(original_images): + # Resize image + img_resized = resize_image_width(img, fixed_size=size) + + # Add order label + img_labeled = add_order_label(img_resized, f"[{i+1}]") + + # Append to list + images.append(img_labeled) + + # Concatenate all images + return concatenate_images_vertical(images) + + +def process_images(images, size=672): + concat_horizontal = process_images_horizontal(images, size) + concat_vertical = process_images_vertical(images, size) + + hw, hh = concat_horizontal.size + vw, vh = concat_vertical.size + + ha = hw / hh + va = vh / vw + + if ha > va: + return concat_vertical + else: + return concat_horizontal + + +lmms_logger = logging.getLogger("lmms-eval") + +MULTI_CHOICE_PROMPT = "Answer with the option letter from the given choices directly." +OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase." + + +def replace_images_tokens(input_string): + # for i in range(1, 8): + # question_text = f"" + # query_text = "" + # if question_text in input_string: + # input_string = input_string.replace(question_text, query_text) + return input_string + + +def parse_options(options): + option_letters = [chr(ord("A") + i) for i in range(len(options))] + choices_str = "\n".join([f"{option_letter}. {option}" for option_letter, option in zip(option_letters, options)]) + return choices_str + + +def construct_prompt(doc): + question = doc["question"] + if doc["question_type"] == "multiple-choice": + # Weirdly, data["options"] is a string in MMMU Huggingface dataset + parsed_options = parse_options(ast.literal_eval(doc["options"])) + # parsed_options already prepends a newline so no need to add space here + question = f"{question}\n{parsed_options}\n{MULTI_CHOICE_PROMPT}" + else: + question = f"{question}\n{OPEN_ENDED_PROMPT}" + return question + + +def mmmu_doc_to_text(doc): + question = construct_prompt(doc) + return replace_images_tokens(question) + + +def mmmu_doc_to_visual(doc): + prompt = construct_prompt(doc) + image_tokens = re.findall(r"", prompt) + # Remove <> and swap space as _ + image_tokens = sorted(list(set([image_token.strip("<>").replace(" ", "_") for image_token in image_tokens]))) + visual = [doc[image_token].convert("RGB") for image_token in image_tokens] + visual = process_images(visual) + return [visual] + + +def mmmu_process_results(doc, results): + pred = results[0] + if doc["question_type"] == "multiple-choice": + index2ans, all_choices = get_multi_choice_info(ast.literal_eval(doc["options"])) + parsed_pred = parse_multi_choice_response(pred, all_choices, index2ans) + else: + parsed_pred = parse_open_response(pred) + id = doc["id"] + mmmu_acc = {"id": id, "subdomain": extract_subset_name(doc["id"]), "question_type": doc["question_type"], "answer": doc["answer"], "parsed_pred": parsed_pred} + return { + "mmmu_acc": mmmu_acc, + "submission": { + id: pred, + }, + } + + +def extract_subset_name(input_string): + # Define a regex pattern to match "validation_" at the beginning and "_" at the end + split = input_string.split("_")[0] + pattern = re.compile(rf"^{split}_(.+?)_\d+$") + match = pattern.search(input_string) + if match: + return match.group(1) + else: + raise ValueError(f'No match found in "{input_string}"') + + +def mmmu_test_aggregate_results_for_submission(results, args): + path = generate_submission_file("mmmu_test_for_submission.json", args) + with open(path, "w") as f: + json.dump(results, f) + lmms_logger.info(f"Results saved to {path}.") + + +def mmmu_aggregate_results(results): + evaluation_result = {} + subset_to_eval_samples = defaultdict(list) + for result in results: + subset_to_eval_samples[result["subdomain"]].append(result) + for subset, sub_eval_samples in subset_to_eval_samples.items(): + judge_dict, metric_dict = evaluate_mmmu(sub_eval_samples) + metric_dict.update({"num_example": len(sub_eval_samples)}) + evaluation_result[subset] = metric_dict + printable_results = {} + for domain, in_domain_cats in DOMAIN_CAT2SUB_CAT.items(): + in_domain_cat_results = {} + for cat_name in in_domain_cats: + if cat_name in evaluation_result.keys(): + in_domain_cat_results[cat_name] = evaluation_result[cat_name] + else: + pass + in_domain_ins_acc = calculate_ins_level_acc(in_domain_cat_results) + in_domain_data_num = sum([cat_results["num_example"] for cat_results in in_domain_cat_results.values()]) + printable_results["Overall-" + domain] = { + "num": int(in_domain_data_num), + "acc": round(in_domain_ins_acc, 3), + } + # add sub category + for cat_name, cat_results in in_domain_cat_results.items(): + printable_results[cat_name] = { + "num": int(cat_results["num_example"]), + "acc": round(cat_results["acc"], 3), + } + all_ins_acc = calculate_ins_level_acc(evaluation_result) + printable_results["Overall"] = { + "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]), + "acc": round(all_ins_acc, 3), + } + print(printable_results) + return printable_results["Overall"]["acc"] + + +################## +# Helper functions written by official MMMU repo. +################## + + +def calculate_ins_level_acc(results): + """Calculate the instruction level accuracy for given Subject results + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L246 + """ + acc = 0 + ins_num = 0 + for cat_results in results.values(): + acc += cat_results["acc"] * cat_results["num_example"] + ins_num += cat_results["num_example"] + if ins_num == 0: + return 0 + return acc / ins_num + + +DOMAIN_CAT2SUB_CAT = { + "Art and Design": ["Art", "Art_Theory", "Design", "Music"], + "Business": ["Accounting", "Economics", "Finance", "Manage", "Marketing"], + "Science": [ + "Biology", + "Chemistry", + "Geography", + "Math", + "Physics", + ], + "Health and Medicine": [ + "Basic_Medical_Science", + "Clinical_Medicine", + "Diagnostics_and_Laboratory_Medicine", + "Pharmacy", + "Public_Health", + ], + "Humanities and Social Science": [ + "History", + "Literature", + "Sociology", + "Psychology", + ], + "Tech and Engineering": [ + "Agriculture", + "Architecture_and_Engineering", + "Computer_Science", + "Electronics", + "Energy_and_Power", + "Materials", + "Mechanical_Engineering", + ], +} + + +def eval_multi_choice(gold_i, pred_i): + """ + Evaluate a multiple choice instance. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L175 + """ + correct = False + # only they are exactly the same, we consider it as correct + if isinstance(gold_i, list): + for answer in gold_i: + if answer == pred_i: + correct = True + break + else: # gold_i is a string + if gold_i == pred_i: + correct = True + return correct + + +def eval_open(gold_i, pred_i): + """ + Evaluate an open question instance + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L191 + """ + correct = False + if isinstance(gold_i, list): + # use float to avoid trivial matches + norm_answers = [] + for answer in gold_i: + norm_answers.extend(normalize_str(answer)) + else: + norm_answers = normalize_str(gold_i) + for pred in pred_i: # pred is already normalized in parse response phase + if isinstance(pred, str): # if it's a string, then find if ans in the pred_i + for norm_ans in norm_answers: + # only see if the string answer in the string pred + if isinstance(norm_ans, str) and norm_ans in pred: + if not correct: + correct = True + break + else: # it's a float number + if pred in norm_answers: + if not correct: + correct = True + break + return correct + + +def evaluate_mmmu(samples): + """ + Batch evaluation for multiple choice and open questions. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L219 + """ + pred_correct = 0 + judge_dict = dict() + for sample in samples: + gold_i = sample["answer"] + pred_i = sample["parsed_pred"] + if sample["question_type"] == "multiple-choice": + correct = eval_multi_choice(gold_i, pred_i) + else: # open question + correct = eval_open(gold_i, pred_i) + + if correct: + judge_dict[sample["id"]] = "Correct" + pred_correct += 1 + else: + judge_dict[sample["id"]] = "Wrong" + + if len(samples) == 0: + return {"acc": 0} + return judge_dict, {"acc": pred_correct / len(samples)} + + +def parse_multi_choice_response(response, all_choices, index2ans): + """ + Parse the prediction from the generated response. + Return the predicted index e.g., A, B, C, D. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L10 + """ + for char in [",", ".", "!", "?", ";", ":", "'"]: + response = response.strip(char) + response = " " + response + " " # add space to avoid partial match + + index_ans = True + ans_with_brack = False + candidates = [] + for choice in all_choices: # e.g., (A) (B) (C) (D) + if f"({choice})" in response: + candidates.append(choice) + ans_with_brack = True + + if len(candidates) == 0: + for choice in all_choices: # e.g., A B C D + if f"{choice} " in response: + candidates.append(choice) + + if len(candidates) == 0: + for choice in all_choices: # e.g., A. B. C. D. + if f"{choice}." in response: + candidates.append(choice) + + # if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example + if len(candidates) == 0 and len(response.split()) > 5: + for index, ans in index2ans.items(): + if ans.lower() in response.lower(): + candidates.append(index) + index_ans = False # it's content ans. + + if len(candidates) == 0: # still not get answer, randomly choose one. + pred_index = random.choice(all_choices) + elif len(candidates) > 1: + start_indexes = [] + if index_ans: + if ans_with_brack: + for can in candidates: + index = response.rfind(f"({can})") + start_indexes.append(index) # -1 will be ignored anyway + # start_indexes = [generated_response.index(f'({can})') for can in candidates] + else: + for can in candidates: + index = response.rfind(f" {can} ") + start_indexes.append(index) + else: + for can in candidates: + index = response.lower().rfind(index2ans[can].lower()) + start_indexes.append(index) + # get the last one + pred_index = candidates[np.argmax(start_indexes)] + else: # if only one candidate, use it. + pred_index = candidates[0] + + return pred_index + + +def extract_numbers(string): + """ + Exact all forms of numbers from a string with regex. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L100 + """ + # Pattern for numbers with commas + pattern_commas = r"-?\b\d{1,3}(?:,\d{3})+\b" + # Pattern for scientific notation + pattern_scientific = r"-?\d+(?:\.\d+)?[eE][+-]?\d+" + # Pattern for simple numbers without commas + pattern_simple = r"-?(?:\d+\.\d+|\.\d+|\d+\b)(?![eE][+-]?\d+)(?![,\d])" + + # Extract numbers with commas + numbers_with_commas = re.findall(pattern_commas, string) + # Extract numbers in scientific notation + numbers_scientific = re.findall(pattern_scientific, string) + # Extract simple numbers without commas + numbers_simple = re.findall(pattern_simple, string) + + # Combine all extracted numbersz + all_numbers = numbers_with_commas + numbers_scientific + numbers_simple + return all_numbers + + +def check_is_number(string): + """ + Check if the given string a number. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L65 + """ + try: + float(string.replace(",", "")) + return True + except ValueError: + # check if there's comma inside + return False + + +def normalize_str(string): + """ + Normalize the str to lower case and make them float numbers if possible. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L76 + """ + # check if characters in the string + + # if number, numerize it. + string = string.strip() + + is_number = check_is_number(string) + + if is_number: + string = string.replace(",", "") + string = float(string) + # leave 2 decimal + string = round(string, 2) + return [string] + else: # it's likely to be a string + # lower it + string = string.lower() + if len(string) == 1: + return [" " + string, string + " "] # avoid trivial matches + return [string] + + +def parse_open_response(response): + """ + Parse the prediction from the generated response. + Return a list of predicted strings or numbers. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L122 + """ + + # content = content.strip("\n").strip(".").strip(" ") + def get_key_subresponses(response): + key_responses = [] + response = response.strip().strip(".").lower() + sub_responses = re.split(r"\.\s(?=[A-Z])|\n", response) + indicators_of_keys = [ + "could be ", + "so ", + "is ", + "thus ", + "therefore ", + "final ", + "answer ", + "result ", + ] + key_responses = [] + for index, resp in enumerate(sub_responses): + # if last one, accept it's an equation (the entire response can be just one sentence with equation) + if index == len(sub_responses) - 1: + indicators_of_keys.extend(["="]) + shortest_key_response = None # the shortest response that may contain the answer (tail part of the response) + for indicator in indicators_of_keys: + if indicator in resp: + if not shortest_key_response: + shortest_key_response = resp.split(indicator)[-1].strip() + else: + if len(resp.split(indicator)[-1].strip()) < len(shortest_key_response): + shortest_key_response = resp.split(indicator)[-1].strip() + # key_responses.append(resp.split(indicator)[1].strip()) + + if shortest_key_response: + # and it's not trivial + if shortest_key_response.strip() not in [ + ":", + ",", + ".", + "!", + "?", + ";", + ":", + "'", + ]: + key_responses.append(shortest_key_response) + if len(key_responses) == 0: # did not found any + return [response] + return key_responses + + # pdb.set_trace() + key_responses = get_key_subresponses(response) + + pred_list = key_responses.copy() # keep the original string response + for resp in key_responses: + pred_list.extend(extract_numbers(resp)) + + tmp_pred_list = [] + for i in range(len(pred_list)): + tmp_pred_list.extend(normalize_str(pred_list[i])) + pred_list = tmp_pred_list + + # remove duplicates + pred_list = list(set(pred_list)) + + return pred_list + + +def get_multi_choice_info(options): + """ + Given the list of options for multiple choice question + Return the index2ans and all_choices + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/data_utils.py#L54 + """ + + start_chr = "A" + all_choices = [] + index2ans = {} + for i, option in enumerate(options): + index2ans[chr(ord(start_chr) + i)] = option + all_choices.append(chr(ord(start_chr) + i)) + + return index2ans, all_choices From 0581ab3cfb362e2024988b46fbbb00324f1233c9 Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Fri, 31 May 2024 16:09:45 +0900 Subject: [PATCH 052/196] merge model_specific_prompt_kwargs and dataset_name into each task yaml --- .../_default_template_mmaad_instruction_yaml | 23 ------------------- .../mmupd/_default_template_mmaad_option_yaml | 23 ------------------- .../mmupd/_default_template_mmiasd_base_yaml | 23 ------------------- .../_default_template_mmiasd_instruction_yaml | 23 ------------------- .../_default_template_mmiasd_option_yaml | 23 ------------------- .../mmupd/_default_template_mmivqd_base_yaml | 23 ------------------- .../_default_template_mmivqd_instruction_yaml | 23 ------------------- .../_default_template_mmivqd_option_yaml | 23 ------------------- ...base_yaml => _default_template_mmupd_yaml} | 5 ---- lmms_eval/tasks/mmupd/mmaad_base.yaml | 7 +++++- lmms_eval/tasks/mmupd/mmaad_instruction.yaml | 7 +++++- lmms_eval/tasks/mmupd/mmaad_option.yaml | 7 +++++- lmms_eval/tasks/mmupd/mmiasd_base.yaml | 7 +++++- lmms_eval/tasks/mmupd/mmiasd_instruction.yaml | 7 +++++- lmms_eval/tasks/mmupd/mmiasd_option.yaml | 7 +++++- lmms_eval/tasks/mmupd/mmivqd_base.yaml | 7 +++++- lmms_eval/tasks/mmupd/mmivqd_instruction.yaml | 7 +++++- lmms_eval/tasks/mmupd/mmivqd_option.yaml | 7 +++++- 18 files changed, 54 insertions(+), 198 deletions(-) delete mode 100644 lmms_eval/tasks/mmupd/_default_template_mmaad_instruction_yaml delete mode 100644 lmms_eval/tasks/mmupd/_default_template_mmaad_option_yaml delete mode 100644 lmms_eval/tasks/mmupd/_default_template_mmiasd_base_yaml delete mode 100644 lmms_eval/tasks/mmupd/_default_template_mmiasd_instruction_yaml delete mode 100644 lmms_eval/tasks/mmupd/_default_template_mmiasd_option_yaml delete mode 100644 lmms_eval/tasks/mmupd/_default_template_mmivqd_base_yaml delete mode 100644 lmms_eval/tasks/mmupd/_default_template_mmivqd_instruction_yaml delete mode 100644 lmms_eval/tasks/mmupd/_default_template_mmivqd_option_yaml rename lmms_eval/tasks/mmupd/{_default_template_mmaad_base_yaml => _default_template_mmupd_yaml} (81%) diff --git a/lmms_eval/tasks/mmupd/_default_template_mmaad_instruction_yaml b/lmms_eval/tasks/mmupd/_default_template_mmaad_instruction_yaml deleted file mode 100644 index fe4c8148..00000000 --- a/lmms_eval/tasks/mmupd/_default_template_mmaad_instruction_yaml +++ /dev/null @@ -1,23 +0,0 @@ -dataset_path: MM-UPD/MM-UPD -doc_to_target: "answer" -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." -doc_to_visual: !function utils.mmupd_doc_to_visual -doc_to_text: !function utils.mmupd_doc_to_text -doc_to_target: "answer" -process_results: !function utils.mmupd_process_results -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original -output_type: generate_until -dataset_name: mmaad_base -generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false diff --git a/lmms_eval/tasks/mmupd/_default_template_mmaad_option_yaml b/lmms_eval/tasks/mmupd/_default_template_mmaad_option_yaml deleted file mode 100644 index aa8b9c35..00000000 --- a/lmms_eval/tasks/mmupd/_default_template_mmaad_option_yaml +++ /dev/null @@ -1,23 +0,0 @@ -dataset_path: MM-UPD/MM-UPD -doc_to_target: "answer" -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "\nAnswer with the option's letter from the given choices directly." -doc_to_visual: !function utils.mmupd_doc_to_visual -doc_to_text: !function utils.mmupd_doc_to_text -doc_to_target: "answer" -process_results: !function utils.mmupd_process_results -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original -output_type: generate_until -dataset_name: mmaad_option -generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false diff --git a/lmms_eval/tasks/mmupd/_default_template_mmiasd_base_yaml b/lmms_eval/tasks/mmupd/_default_template_mmiasd_base_yaml deleted file mode 100644 index 124c9007..00000000 --- a/lmms_eval/tasks/mmupd/_default_template_mmiasd_base_yaml +++ /dev/null @@ -1,23 +0,0 @@ -dataset_path: MM-UPD/MM-UPD -doc_to_target: "answer" -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "\n" -doc_to_visual: !function utils.mmupd_doc_to_visual -doc_to_text: !function utils.mmupd_doc_to_text -doc_to_target: "answer" -process_results: !function utils.mmupd_process_results -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original -output_type: generate_until -dataset_name: mmiasd_base -generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false diff --git a/lmms_eval/tasks/mmupd/_default_template_mmiasd_instruction_yaml b/lmms_eval/tasks/mmupd/_default_template_mmiasd_instruction_yaml deleted file mode 100644 index b8d1e473..00000000 --- a/lmms_eval/tasks/mmupd/_default_template_mmiasd_instruction_yaml +++ /dev/null @@ -1,23 +0,0 @@ -dataset_path: MM-UPD/MM-UPD -doc_to_target: "answer" -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." -doc_to_visual: !function utils.mmupd_doc_to_visual -doc_to_text: !function utils.mmupd_doc_to_text -doc_to_target: "answer" -process_results: !function utils.mmupd_process_results -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original -output_type: generate_until -dataset_name: mmiasd_base -generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false diff --git a/lmms_eval/tasks/mmupd/_default_template_mmiasd_option_yaml b/lmms_eval/tasks/mmupd/_default_template_mmiasd_option_yaml deleted file mode 100644 index 3e40e8da..00000000 --- a/lmms_eval/tasks/mmupd/_default_template_mmiasd_option_yaml +++ /dev/null @@ -1,23 +0,0 @@ -dataset_path: MM-UPD/MM-UPD -doc_to_target: "answer" -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "\nAnswer with the option's letter from the given choices directly." -doc_to_visual: !function utils.mmupd_doc_to_visual -doc_to_text: !function utils.mmupd_doc_to_text -doc_to_target: "answer" -process_results: !function utils.mmupd_process_results -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original -output_type: generate_until -dataset_name: mmiasd_option -generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false diff --git a/lmms_eval/tasks/mmupd/_default_template_mmivqd_base_yaml b/lmms_eval/tasks/mmupd/_default_template_mmivqd_base_yaml deleted file mode 100644 index 986ae379..00000000 --- a/lmms_eval/tasks/mmupd/_default_template_mmivqd_base_yaml +++ /dev/null @@ -1,23 +0,0 @@ -dataset_path: MM-UPD/MM-UPD -doc_to_target: "answer" -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "\n" -doc_to_visual: !function utils.mmupd_doc_to_visual -doc_to_text: !function utils.mmupd_doc_to_text -doc_to_target: "answer" -process_results: !function utils.mmupd_process_results -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original -output_type: generate_until -dataset_name: mmivqd_base -generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false diff --git a/lmms_eval/tasks/mmupd/_default_template_mmivqd_instruction_yaml b/lmms_eval/tasks/mmupd/_default_template_mmivqd_instruction_yaml deleted file mode 100644 index 8362657e..00000000 --- a/lmms_eval/tasks/mmupd/_default_template_mmivqd_instruction_yaml +++ /dev/null @@ -1,23 +0,0 @@ -dataset_path: MM-UPD/MM-UPD -doc_to_target: "answer" -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "\nIf the given image is irrelevant to the question, answer \"F. The image and question are irrelevant.\"." -doc_to_visual: !function utils.mmupd_doc_to_visual -doc_to_text: !function utils.mmupd_doc_to_text -doc_to_target: "answer" -process_results: !function utils.mmupd_process_results -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original -output_type: generate_until -dataset_name: mmivqd_base -generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false diff --git a/lmms_eval/tasks/mmupd/_default_template_mmivqd_option_yaml b/lmms_eval/tasks/mmupd/_default_template_mmivqd_option_yaml deleted file mode 100644 index 14778cac..00000000 --- a/lmms_eval/tasks/mmupd/_default_template_mmivqd_option_yaml +++ /dev/null @@ -1,23 +0,0 @@ -dataset_path: MM-UPD/MM-UPD -doc_to_target: "answer" -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "\nAnswer with the option's letter from the given choices directly." -doc_to_visual: !function utils.mmupd_doc_to_visual -doc_to_text: !function utils.mmupd_doc_to_text -doc_to_target: "answer" -process_results: !function utils.mmupd_process_results -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original -output_type: generate_until -dataset_name: mmivqd_option -generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 1024 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false diff --git a/lmms_eval/tasks/mmupd/_default_template_mmaad_base_yaml b/lmms_eval/tasks/mmupd/_default_template_mmupd_yaml similarity index 81% rename from lmms_eval/tasks/mmupd/_default_template_mmaad_base_yaml rename to lmms_eval/tasks/mmupd/_default_template_mmupd_yaml index bbe9a9cf..7aa8d812 100644 --- a/lmms_eval/tasks/mmupd/_default_template_mmaad_base_yaml +++ b/lmms_eval/tasks/mmupd/_default_template_mmupd_yaml @@ -1,9 +1,5 @@ dataset_path: MM-UPD/MM-UPD doc_to_target: "answer" -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "\n" doc_to_visual: !function utils.mmupd_doc_to_visual doc_to_text: !function utils.mmupd_doc_to_text doc_to_target: "answer" @@ -12,7 +8,6 @@ model_specific_generation_kwargs: llava: image_aspect_ratio: original output_type: generate_until -dataset_name: mmaad_base generation_kwargs: until: - "ASSISTANT:" diff --git a/lmms_eval/tasks/mmupd/mmaad_base.yaml b/lmms_eval/tasks/mmupd/mmaad_base.yaml index dd38d33e..9a66b3e8 100644 --- a/lmms_eval/tasks/mmupd/mmaad_base.yaml +++ b/lmms_eval/tasks/mmupd/mmaad_base.yaml @@ -1,6 +1,11 @@ task: "mmaad_base" test_split: test -include: _default_template_mmaad_base_yaml +dataset_name: mmaad_base +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\n" +include: _default_template_mmupd_yaml metric_list: - metric: gpt_eval_score aggregation: !function utils.mmaad_base diff --git a/lmms_eval/tasks/mmupd/mmaad_instruction.yaml b/lmms_eval/tasks/mmupd/mmaad_instruction.yaml index 384425d3..30a3bac1 100644 --- a/lmms_eval/tasks/mmupd/mmaad_instruction.yaml +++ b/lmms_eval/tasks/mmupd/mmaad_instruction.yaml @@ -1,6 +1,11 @@ task: "mmaad_instruction" test_split: test -include: _default_template_mmaad_instruction_yaml +dataset_name: mmaad_base +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." +include: _default_template_mmupd_yaml metric_list: - metric: gpt_eval_score aggregation: !function utils.mmaad_instruction diff --git a/lmms_eval/tasks/mmupd/mmaad_option.yaml b/lmms_eval/tasks/mmupd/mmaad_option.yaml index 81ac6a02..f110b822 100644 --- a/lmms_eval/tasks/mmupd/mmaad_option.yaml +++ b/lmms_eval/tasks/mmupd/mmaad_option.yaml @@ -1,6 +1,11 @@ task: "mmaad_option" test_split: test -include: _default_template_mmaad_option_yaml +dataset_name: mmaad_option +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." +include: _default_template_mmupd_yaml metric_list: - metric: gpt_eval_score aggregation: !function utils.mmaad_option diff --git a/lmms_eval/tasks/mmupd/mmiasd_base.yaml b/lmms_eval/tasks/mmupd/mmiasd_base.yaml index 29468358..6a9159fd 100644 --- a/lmms_eval/tasks/mmupd/mmiasd_base.yaml +++ b/lmms_eval/tasks/mmupd/mmiasd_base.yaml @@ -1,6 +1,11 @@ task: "mmiasd_base" test_split: test -include: _default_template_mmiasd_base_yaml +dataset_name: mmiasd_base +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\n" +include: _default_template_mmupd_yaml metric_list: - metric: gpt_eval_score aggregation: !function utils.mmiasd_base diff --git a/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml b/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml index a6406a1a..d0ac3a00 100644 --- a/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml +++ b/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml @@ -1,6 +1,11 @@ task: "mmiasd_instruction" test_split: test -include: _default_template_mmiasd_instruction_yaml +dataset_name: mmiasd_base +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." +include: _default_template_mmupd_yaml metric_list: - metric: gpt_eval_score aggregation: !function utils.mmiasd_instruction diff --git a/lmms_eval/tasks/mmupd/mmiasd_option.yaml b/lmms_eval/tasks/mmupd/mmiasd_option.yaml index 83e3248f..a1a1aead 100644 --- a/lmms_eval/tasks/mmupd/mmiasd_option.yaml +++ b/lmms_eval/tasks/mmupd/mmiasd_option.yaml @@ -1,6 +1,11 @@ task: "mmiasd_option" test_split: test -include: _default_template_mmiasd_option_yaml +dataset_name: mmiasd_option +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." +include: _default_template_mmupd_yaml metric_list: - metric: gpt_eval_score aggregation: !function utils.mmiasd_option diff --git a/lmms_eval/tasks/mmupd/mmivqd_base.yaml b/lmms_eval/tasks/mmupd/mmivqd_base.yaml index 80b856f5..82853fde 100644 --- a/lmms_eval/tasks/mmupd/mmivqd_base.yaml +++ b/lmms_eval/tasks/mmupd/mmivqd_base.yaml @@ -1,6 +1,11 @@ task: "mmivqd_base" test_split: test -include: _default_template_mmivqd_base_yaml +dataset_name: mmivqd_base +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\n" +include: _default_template_mmupd_yaml metric_list: - metric: gpt_eval_score aggregation: !function utils.mmivqd_base diff --git a/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml b/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml index ef0d3604..a024022a 100644 --- a/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml +++ b/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml @@ -1,6 +1,11 @@ task: "mmivqd_instruction" test_split: test -include: _default_template_mmivqd_instruction_yaml +dataset_name: mmivqd_base +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nIf the given image is irrelevant to the question, answer \"F. The image and question are irrelevant.\"." +include: _default_template_mmupd_yaml metric_list: - metric: gpt_eval_score aggregation: !function utils.mmivqd_instruction diff --git a/lmms_eval/tasks/mmupd/mmivqd_option.yaml b/lmms_eval/tasks/mmupd/mmivqd_option.yaml index 6b1d2cc9..1363c345 100644 --- a/lmms_eval/tasks/mmupd/mmivqd_option.yaml +++ b/lmms_eval/tasks/mmupd/mmivqd_option.yaml @@ -1,6 +1,11 @@ task: "mmivqd_option" test_split: test -include: _default_template_mmivqd_option_yaml +dataset_name: mmivqd_option +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." +include: _default_template_mmupd_yaml metric_list: - metric: gpt_eval_score aggregation: !function utils.mmivqd_option From 05843072d608b970bcada1cd0db65a3c80864060 Mon Sep 17 00:00:00 2001 From: CaraJ7 <1350074492@qq.com> Date: Sun, 2 Jun 2024 17:05:28 +0800 Subject: [PATCH 053/196] Add MathVerse in README.md --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index f29d8837..ea7b5302 100644 --- a/README.md +++ b/README.md @@ -176,6 +176,13 @@ We also provide the raw data exported from Weights & Biases for the detailed res - Infographic VQA Test (info_vqa_test) - LLaVA-Bench (llava_in_the_wild) - LLaVA-Bench-COCO (llava_bench_coco) +- MathVerse (mathverse) + - MathVerse Text Dominant (mathverse_testmini_text_dominant) + - MathVerse Text Only (mathverse_testmini_text_only) + - MathVerse Text Lite (mathverse_testmini_text_lite) + - MathVerse Vision Dominant (mathverse_testmini_vision_dominant) + - MathVerse Vision Intensive (mathverse_testmini_vision_intensive) + - MathVerse Vision Only (mathverse_testmini_vision_only) - MathVista (mathvista) - MathVista Validation (mathvista_testmini) - MathVista Test (mathvista_test) From 5f4fe360def1c48ea0cb1da6409d192784882308 Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Sun, 2 Jun 2024 23:28:27 +0900 Subject: [PATCH 054/196] slightly change query_prompt for the reproduction --- lmms_eval/tasks/mmupd/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/mmupd/utils.py b/lmms_eval/tasks/mmupd/utils.py index 2a0d9b12..33a1b241 100644 --- a/lmms_eval/tasks/mmupd/utils.py +++ b/lmms_eval/tasks/mmupd/utils.py @@ -57,7 +57,7 @@ def mmupd_doc_to_text(doc, model_specific_prompt_kwargs=None): "split": doc["split"], } - query_prompt = f"{data['hint']} {data['question']} {data['options']}" if pd.notna(data["hint"]) and data["hint"] != "nan" else f"{data['question']} {data['options']}" + query_prompt = f"{data['hint']}\n{data['question']}{data['options']}" if pd.notna(data["hint"]) and data["hint"] != "nan" else f"{data['question']}{data['options']}" if model_specific_prompt_kwargs: query_prompt = f"{query_prompt}{model_specific_prompt_kwargs['post_prompt']}" From 6fefaf7cea504e35583ee7217449da290295a7a4 Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Tue, 4 Jun 2024 17:36:39 +0900 Subject: [PATCH 055/196] update utils.py for leaderboard submission --- lmms_eval/tasks/mmupd/utils.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/lmms_eval/tasks/mmupd/utils.py b/lmms_eval/tasks/mmupd/utils.py index 33a1b241..a2f237d3 100644 --- a/lmms_eval/tasks/mmupd/utils.py +++ b/lmms_eval/tasks/mmupd/utils.py @@ -145,7 +145,7 @@ def mmupd_results_eval(results, args, upd_type, question_type): overall_acc_dual, category_acc_dual, dual_results_df = mmupd_evaluator.calculate_dual_acc(standard_results_df, upd_results_df) - file_json = generate_submission_file(f"mmupd_results_{upd_type}_{question_type}.json", args) + file_json = generate_submission_file(f"mmupd_{upd_type}_{question_type}_dual_results.json", args) details_info = { "overall_acc_dual": overall_acc_dual, @@ -159,7 +159,10 @@ def mmupd_results_eval(results, args, upd_type, question_type): with open(file_json, "w") as f: json.dump(details_info, f) - file_excel = generate_submission_file(f"mmupd_results_{upd_type}_{question_type}_dual.xlsx", args) + file_excel = generate_submission_file(f"mmupd_{upd_type}_{question_type}_dual_results_detail.xlsx", args) dual_results_df.to_excel(file_excel, index=False) + file_json = generate_submission_file(f"mmupd_{upd_type}_{question_type}_dual_results_detail.json", args) + dual_results_df.to_json(file_json, orient="records", indent=2) # for huggingface leaderboard submission + return overall_acc_dual * 100 From 5fd684515c55ef643726c1b6c720c7cbd2183ba1 Mon Sep 17 00:00:00 2001 From: Yuan Zhang Date: Wed, 5 Jun 2024 21:52:31 +0800 Subject: [PATCH 056/196] add conbench --- lmms_eval/tasks/conbench/conbench.yaml | 24 ++++++ lmms_eval/tasks/conbench/utils.py | 100 +++++++++++++++++++++++++ 2 files changed, 124 insertions(+) create mode 100644 lmms_eval/tasks/conbench/conbench.yaml create mode 100644 lmms_eval/tasks/conbench/utils.py diff --git a/lmms_eval/tasks/conbench/conbench.yaml b/lmms_eval/tasks/conbench/conbench.yaml new file mode 100644 index 00000000..3739282a --- /dev/null +++ b/lmms_eval/tasks/conbench/conbench.yaml @@ -0,0 +1,24 @@ +dataset_path: ConBench/ConBench_D +dataset_kwargs: + token: True +task: "ConBench" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.conbench_doc_to_visual +doc_to_text: !function utils.conbench_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 1024 + temperature: 0.2 + top_p: 0 + num_beams: 1 + do_sample: True +# The return value of process_results will be used by metrics +process_results: !function utils.conbench_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: ConScore_D + aggregation: !function utils.conbench_aggregate_results + higher_is_better: true +metadata: + - version: 0.0 diff --git a/lmms_eval/tasks/conbench/utils.py b/lmms_eval/tasks/conbench/utils.py new file mode 100644 index 00000000..f80f855d --- /dev/null +++ b/lmms_eval/tasks/conbench/utils.py @@ -0,0 +1,100 @@ +from collections import defaultdict +import os +from anls import anls_score + +import logging + +eval_logger = logging.getLogger("lmms-eval") + +dir_name = os.path.dirname(os.path.abspath(__file__)) + +# 19 classes +eval_type_dict = { + "Sensation": ["count","color", "scene", "poster", "attribute_recognition", "ocr", "position"], + "Cognition": ["calculation", "code", "translation", "math", "cross_instance_reason", "attribute_reason"], + "Knowledge": ["celebrity", "chemistry", "physics", "biology", "landmark", "artwork"] +} + + +def conbench_doc_to_visual(doc): + return [doc["image"].convert("RGB")] + + +def conbench_doc_to_text(doc): + question = doc["question"].strip() + return question + + +def parse_pred_ans_NY(pred_ans): + pred_label = None + if pred_ans in ["yes", "no"]: + pred_label = pred_ans + else: + prefix_pred_ans = pred_ans[:4] + + if "yes" in prefix_pred_ans: + pred_label = "yes" + elif "no" in prefix_pred_ans: + pred_label = "no" + else: + pred_label = "other" + return pred_label + + +def parse_pred_ans_choice(pred_ans): + return pred_ans.replace(" ", "")[0] + + +def conbench_process_results(doc, results): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name (in this case mme score), value: metric value + """ + pred = results[0] + pred = pred.replace('\n', "").lower() + # parser + if doc["question_field"] == "N/Y": + pred_ans = parse_pred_ans_NY(pred) + elif doc["question_field"] == "Choices": + pred_ans = parse_pred_ans_choice(pred) + else: + pred_ans = pred + + gt_ans = doc["answer"].lower() + + # score + score = 1 if (doc["question_field"] == "Q/A" and anls_score(prediction=pred_ans, gold_labels=[gt_ans], threshold=0.95) >= 0.4) \ + or (gt_ans == pred_ans) \ + else 0 + # Note: the key name here is very important. It decides which aggregation function will receive the results + # We note down the question id/category to help us aggregate the results later + return {"ConScore_D":{"image_id": doc["image_id"], "question_field": doc["question_field"], "score": score}} + + +def conbench_aggregate_results(results): + """ + Args: + results: a list of values returned by process_results + Returns: + A score + """ + summary = defaultdict(dict) + for result in results: + image_id = result["image_id"] + score = result["score"] + if image_id not in summary.keys(): + summary[image_id] = 0 + summary[image_id] += score + + cnt_con = 0 + for image_id, score in summary.items(): + if score == 3: + cnt_con += 1 + + print("Consistency Cases are ", cnt_con) + cnt_con = cnt_con / (len(results) / 3) + eval_logger.info(f"ConScore_D: {cnt_con:.2f}") + return cnt_con \ No newline at end of file From c9793b3883714f254a700230b7bee781d6110e73 Mon Sep 17 00:00:00 2001 From: Yuan Zhang Date: Thu, 6 Jun 2024 11:21:05 +0800 Subject: [PATCH 057/196] update README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c82ee6f8..d7f86533 100644 --- a/README.md +++ b/README.md @@ -163,6 +163,7 @@ We also provide the raw data exported from Weights & Biases for the detailed res - COCO 2017 Caption (coco2017_cap) - COCO 2017 Caption MiniVal (coco2017_cap_val) - COCO 2017 Caption MiniTest (coco2017_cap_test) +- [ConBench](https://github.com/foundation-multimodal-models/ConBench) (conbench) - DOCVQA (docvqa) - DOCVQA Validation (docvqa_val) - DOCVQA Test (docvqa_test) From 504685e20b17659b913cf46f3012c16bf429e09d Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 6 Jun 2024 15:42:15 +0800 Subject: [PATCH 058/196] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c82ee6f8..5a681ff1 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ 🏠 [Homepage](https://lmms-lab.github.io/) | 🎉 [Blog](https://lmms-lab.github.io/lmms-eval-blog/lmms-eval-0.1/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | Discord_Thread [discord/lmms-eval](https://discord.gg/zdkwKUqrPy) -In today's world, we're on an exciting journey toward creating Artificial General Intelligence (AGI), much like the enthusiasm of the 1960s moon landing. This journey is powered by advanced large language models (LLMs) and large multimodal models (LMMs), which are complex systems capable of understanding, learning, and performing a wide variety of human tasks. These advancements bring us closer to achieving AGI. +In today's world, we're on an exciting journey toward creating Artificial General Intelligence (AGI), much like the enthusiasm of the 1960s moon landing. This journey is powered by advanced large language models (LLMs) and large multimodal models (LMMs), which are complex systems capable of understanding, learning, and performing a wide variety of human tasks. To gauge how advanced these models are, we use a variety of evaluation benchmarks. These benchmarks are tools that help us understand the capabilities of these models, showing us how close we are to achieving AGI. However, finding and using these benchmarks is a big challenge. The necessary benchmarks and datasets are spread out and hidden in various places like Google Drive, Dropbox, and different school and research lab websites. It feels like we're on a treasure hunt, but the maps are scattered everywhere. From 96e8d9867c9549ab7490f4b12cfeb6a06238e0aa Mon Sep 17 00:00:00 2001 From: tianyu-z Date: Mon, 10 Jun 2024 00:10:30 -0400 Subject: [PATCH 059/196] init include vcr --- .../tasks/vcr/_default_template_vcr_yaml | 31 +++ lmms_eval/tasks/vcr/utils.py | 215 ++++++++++++++++++ lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml | 31 +++ lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml | 31 +++ lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml | 31 +++ lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml | 31 +++ 6 files changed, 370 insertions(+) create mode 100644 lmms_eval/tasks/vcr/_default_template_vcr_yaml create mode 100644 lmms_eval/tasks/vcr/utils.py create mode 100644 lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml create mode 100644 lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml create mode 100644 lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml create mode 100644 lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml diff --git a/lmms_eval/tasks/vcr/_default_template_vcr_yaml b/lmms_eval/tasks/vcr/_default_template_vcr_yaml new file mode 100644 index 00000000..8a9095a3 --- /dev/null +++ b/lmms_eval/tasks/vcr/_default_template_vcr_yaml @@ -0,0 +1,31 @@ +dataset_path: vcr-org/VCR-wiki-en-hard +dataset_kwargs: + token: True +task: "vcr_wiki_en_hard" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vcr_doc_to_visual +doc_to_text: !function utils.vcr_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 120 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +# The return value of process_results will be used by metrics +process_results: !function utils.vcr_en_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: mme_percetion_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true + - metric: mme_cognition_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." +metadata: + - version: 0.0.1 diff --git a/lmms_eval/tasks/vcr/utils.py b/lmms_eval/tasks/vcr/utils.py new file mode 100644 index 00000000..0079c8fd --- /dev/null +++ b/lmms_eval/tasks/vcr/utils.py @@ -0,0 +1,215 @@ +from collections import defaultdict +import os +from difflib import SequenceMatcher as SM +import datetime +import json +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file +import evaluate +import logging +import spacy +from spacy.cli import download +from nltk.util import ngrams +from functools import partial + +# Download the English and Chinese models +download("en_core_web_sm") +download("zh_core_web_sm") + +eval_logger = logging.getLogger("lmms-eval") + +dir_name = os.path.dirname(os.path.abspath(__file__)) + +rouge = evaluate.load("rouge") +nlp_en = spacy.load("en_core_web_sm") +nlp_zh = spacy.load("zh_core_web_sm") +nlp = {"en": nlp_en, "zh": nlp_zh} + +aggregate_results_template = { + "max_sim_val": 0, + "precision": 0, + "recall": 0, + "f1": 0, + "jaccard": 0, + "rouge1": 0, +} + + +def vcr_doc_to_visual(doc): + return [doc["stacked_image"].convert("RGB"), doc["only_it_image"].convert("RGB")] + + +def vcr_doc_to_text(doc, model_specific_prompt_kwargs=None): + if "pre_prompt" in model_specific_prompt_kwargs: + pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + if "post_prompt" in model_specific_prompt_kwargs: + post_prompt = model_specific_prompt_kwargs["post_prompt"] + return f"{pre_prompt}{post_prompt}" + + +def tokenize(text, language): + """ + Tokenize the text and return the tokens. + + Parameters: + text (str): The text to tokenize. + language (str): The language of the text. + + Returns: + list: The list of tokens. + """ + assert language in ["en", "zh"] + nlp_lang = nlp[language] + processed_text = nlp_lang(text) + return [token.text for token in processed_text] + + +def vcr_process_results_single(doc, result, language): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name (in this case mme score), value: metric value + """ + assert language in ["en", "zh"], f"Language {language} is not supported." + crossed_text = doc["crossed_text"] + tokens_result = tokenize(result, language) + tokens_crossed_text = tokenize(crossed_text, language) + + splitter = " " if language == "en" else "" + ngrams_ = ngrams(tokens_result, len(tokens_crossed_text)) + max_sim_val = 0 + max_sim_string = "" + max_sim_ngram = [] + tokens_crossed_text_set = set(tokens_crossed_text) + ngrams_hasjoint = [ + ngram for ngram in ngrams_ if not set(ngram).isdisjoint(tokens_crossed_text_set) + ] + + for ngram in ngrams_hasjoint: + result_ngram = splitter.join(ngram) + similarity = SM(None, result_ngram, crossed_text).ratio() + if similarity > max_sim_val: + max_sim_val = similarity + max_sim_string = result_ngram + max_sim_ngram = ngram + + # Evaluate + if len(max_sim_ngram) == 0: + return { + "crossed_text": crossed_text, + "max_sim_val": 0, + "max_sim_string": "", + "precision": 0, + "recall": 0, + "f1": 0, + "jaccard": 0, + "rouge1": 0, + "exact_match": 0, + } + pred_set = set(max_sim_ngram) + ref_set = set(tokens_crossed_text) + correct_tokens = pred_set.intersection(ref_set) + len_correct_tokens = len(correct_tokens) + + precision = len_correct_tokens / len(pred_set) + recall = len_correct_tokens / len(ref_set) + if (precision + recall) == 0: + f1 = 0 + else: + f1 = 2 * precision * recall / (precision + recall) + union = pred_set.union(ref_set) + jaccard = len_correct_tokens / len(union) if len(union) > 0 else 0 + rouge_1 = rouge.compute( + predictions=[max_sim_string], + references=[crossed_text], + tokenizer=partial(tokenize, language=language), + rouge_types=["rouge1"], + )["rouge1"] + exact_match = float(list(max_sim_ngram) == list(tokens_crossed_text)) + out = { + "crossed_text": crossed_text, + "max_sim_string": max_sim_string, + "max_sim_val": max_sim_val, + "precision": precision, + "recall": recall, + "f1": f1, + "jaccard": jaccard, + "rouge1": rouge_1, + "exact_match": exact_match, + } + return out + + +def vcr_en_process_results(doc, results): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name (in this case mme score), value: metric value + """ + assert len(results) == 2, f"Expected 2 results, got {len(results)}" + output = { + "res_stacked_image": vcr_process_results_single(doc, results[0], "en"), + "res_only_it_image": vcr_process_results_single(doc, results[1], "en"), + } + return output + + +def vcr_zh_process_results(doc, results): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name (in this case mme score), value: metric value + """ + assert len(results) == 2, f"Expected 2 results, got {len(results)}" + output = { + "res_stacked_image": vcr_process_results_single(doc, results[0], "zh"), + "res_only_it_image": vcr_process_results_single(doc, results[1], "zh"), + } + return output + + +def vcr_aggregate_results(results): + """ + Args: + results: a list of values returned by process_results + Returns: + A dictionary of dictionary of float, where the outer dictionary has keys "res_stacked_image" and "res_only_it_image" + """ + + output = { + "res_stacked_image": { + "max_sim_val": 0, + "precision": 0, + "recall": 0, + "f1": 0, + "jaccard": 0, + "rouge1": 0, + }, + "res_only_it_image": { + "max_sim_val": 0, + "precision": 0, + "recall": 0, + "f1": 0, + "jaccard": 0, + "rouge1": 0, + }, + } + for target_domain in output.keys(): + for target_metric_name in output[target_domain].keys(): + score = 0 + count = 0 + for inner_dict in results: + for inner_key, inner_value in inner_dict.items(): + if inner_key == target_domain: + for blank_id, blank_metrics in inner_value.items(): + for metric_name, metric_value in blank_metrics.items(): + if metric_name == target_metric_name: + score += metric_value + count += 1 + output[target_domain][target_metric_name] = score / count + return output diff --git a/lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml b/lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml new file mode 100644 index 00000000..1aa2745d --- /dev/null +++ b/lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml @@ -0,0 +1,31 @@ +dataset_path: vcr-org/VCR-wiki-en-easy +dataset_kwargs: + token: True +task: "vcr_wiki_en_easy" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vcr_doc_to_visual +doc_to_text: !function utils.vcr_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 120 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +# The return value of process_results will be used by metrics +process_results: !function utils.vcr_en_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: mme_percetion_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true + - metric: mme_cognition_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." +metadata: + - version: 0.0.1 diff --git a/lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml b/lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml new file mode 100644 index 00000000..8a9095a3 --- /dev/null +++ b/lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml @@ -0,0 +1,31 @@ +dataset_path: vcr-org/VCR-wiki-en-hard +dataset_kwargs: + token: True +task: "vcr_wiki_en_hard" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vcr_doc_to_visual +doc_to_text: !function utils.vcr_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 120 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +# The return value of process_results will be used by metrics +process_results: !function utils.vcr_en_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: mme_percetion_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true + - metric: mme_cognition_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." +metadata: + - version: 0.0.1 diff --git a/lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml b/lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml new file mode 100644 index 00000000..ba888daf --- /dev/null +++ b/lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml @@ -0,0 +1,31 @@ +dataset_path: vcr-org/VCR-wiki-zh-easy +dataset_kwargs: + token: True +task: "vcr_wiki_zh_easy" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vcr_doc_to_visual +doc_to_text: !function utils.vcr_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 120 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +# The return value of process_results will be used by metrics +process_results: !function utils.vcr_zh_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: mme_percetion_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true + - metric: mme_cognition_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" +metadata: + - version: 0.0.1 diff --git a/lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml b/lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml new file mode 100644 index 00000000..b6dc0f7f --- /dev/null +++ b/lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml @@ -0,0 +1,31 @@ +dataset_path: vcr-org/VCR-wiki-zh-hard +dataset_kwargs: + token: True +task: "vcr_wiki_zh_hard" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vcr_doc_to_visual +doc_to_text: !function utils.vcr_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 120 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +# The return value of process_results will be used by metrics +process_results: !function utils.vcr_zh_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: mme_percetion_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true + - metric: mme_cognition_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" +metadata: + - version: 0.0.1 From e1f04db8f58dd10591fde335ea13f74cda7c79bd Mon Sep 17 00:00:00 2001 From: tianyu-z Date: Mon, 10 Jun 2024 02:38:21 -0400 Subject: [PATCH 060/196] modify the form of VCR --- README.md | 5 + .../tasks/vcr/_default_template_vcr_yaml | 31 ---- lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml | 31 ---- lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml | 31 ---- lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml | 31 ---- lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml | 31 ---- .../tasks/vcr_wiki/_default_template_vcr_yaml | 17 +++ lmms_eval/tasks/{vcr => vcr_wiki}/utils.py | 137 ++++++++++++------ .../tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml | 16 ++ .../tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml | 16 ++ 20 files changed, 307 insertions(+), 199 deletions(-) delete mode 100644 lmms_eval/tasks/vcr/_default_template_vcr_yaml delete mode 100644 lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml delete mode 100644 lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml delete mode 100644 lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml delete mode 100644 lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml rename lmms_eval/tasks/{vcr => vcr_wiki}/utils.py (63%) create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml create mode 100644 lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml diff --git a/README.md b/README.md index 04b62aef..25e8d7dd 100644 --- a/README.md +++ b/README.md @@ -275,6 +275,11 @@ We also provide the raw data exported from Weights & Biases for the detailed res - TextVQA (textvqa) - TextVQA Validation (textvqa_val) - TextVQA Test (textvqa_test) +- VCR-Wiki (vcr_wiki) + - vcr English easy mode (vcr_wiki_en_easy) + - vcr English hard mode (vcr_wiki_en_hard) + - vcr Chinese easy mode (vcr_wiki_zh_easy) + - vcr Chinese hard mode (vcr_wiki_zh_hard) - VizWizVQA (vizwiz_vqa) - VizWizVQA Validation (vizwiz_vqa_val) - VizWizVQA Test (vizwiz_vqa_test) diff --git a/lmms_eval/tasks/vcr/_default_template_vcr_yaml b/lmms_eval/tasks/vcr/_default_template_vcr_yaml deleted file mode 100644 index 8a9095a3..00000000 --- a/lmms_eval/tasks/vcr/_default_template_vcr_yaml +++ /dev/null @@ -1,31 +0,0 @@ -dataset_path: vcr-org/VCR-wiki-en-hard -dataset_kwargs: - token: True -task: "vcr_wiki_en_hard" -test_split: test -output_type: generate_until -doc_to_visual: !function utils.vcr_doc_to_visual -doc_to_text: !function utils.vcr_doc_to_text -doc_to_target: "answer" -generation_kwargs: - max_new_tokens: 120 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false -# The return value of process_results will be used by metrics -process_results: !function utils.vcr_en_process_results -# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results -metric_list: - - metric: mme_percetion_score - aggregation: !function utils.vcr_en_process_results - higher_is_better: true - - metric: mme_cognition_score - aggregation: !function utils.vcr_en_process_results - higher_is_better: true -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." -metadata: - - version: 0.0.1 diff --git a/lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml b/lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml deleted file mode 100644 index 1aa2745d..00000000 --- a/lmms_eval/tasks/vcr/vcr_wiki_en_easy.yaml +++ /dev/null @@ -1,31 +0,0 @@ -dataset_path: vcr-org/VCR-wiki-en-easy -dataset_kwargs: - token: True -task: "vcr_wiki_en_easy" -test_split: test -output_type: generate_until -doc_to_visual: !function utils.vcr_doc_to_visual -doc_to_text: !function utils.vcr_doc_to_text -doc_to_target: "answer" -generation_kwargs: - max_new_tokens: 120 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false -# The return value of process_results will be used by metrics -process_results: !function utils.vcr_en_process_results -# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results -metric_list: - - metric: mme_percetion_score - aggregation: !function utils.vcr_en_process_results - higher_is_better: true - - metric: mme_cognition_score - aggregation: !function utils.vcr_en_process_results - higher_is_better: true -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." -metadata: - - version: 0.0.1 diff --git a/lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml b/lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml deleted file mode 100644 index 8a9095a3..00000000 --- a/lmms_eval/tasks/vcr/vcr_wiki_en_hard.yaml +++ /dev/null @@ -1,31 +0,0 @@ -dataset_path: vcr-org/VCR-wiki-en-hard -dataset_kwargs: - token: True -task: "vcr_wiki_en_hard" -test_split: test -output_type: generate_until -doc_to_visual: !function utils.vcr_doc_to_visual -doc_to_text: !function utils.vcr_doc_to_text -doc_to_target: "answer" -generation_kwargs: - max_new_tokens: 120 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false -# The return value of process_results will be used by metrics -process_results: !function utils.vcr_en_process_results -# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results -metric_list: - - metric: mme_percetion_score - aggregation: !function utils.vcr_en_process_results - higher_is_better: true - - metric: mme_cognition_score - aggregation: !function utils.vcr_en_process_results - higher_is_better: true -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." -metadata: - - version: 0.0.1 diff --git a/lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml b/lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml deleted file mode 100644 index ba888daf..00000000 --- a/lmms_eval/tasks/vcr/vcr_wiki_zh_easy.yaml +++ /dev/null @@ -1,31 +0,0 @@ -dataset_path: vcr-org/VCR-wiki-zh-easy -dataset_kwargs: - token: True -task: "vcr_wiki_zh_easy" -test_split: test -output_type: generate_until -doc_to_visual: !function utils.vcr_doc_to_visual -doc_to_text: !function utils.vcr_doc_to_text -doc_to_target: "answer" -generation_kwargs: - max_new_tokens: 120 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false -# The return value of process_results will be used by metrics -process_results: !function utils.vcr_zh_process_results -# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results -metric_list: - - metric: mme_percetion_score - aggregation: !function utils.vcr_zh_process_results - higher_is_better: true - - metric: mme_cognition_score - aggregation: !function utils.vcr_zh_process_results - higher_is_better: true -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" -metadata: - - version: 0.0.1 diff --git a/lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml b/lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml deleted file mode 100644 index b6dc0f7f..00000000 --- a/lmms_eval/tasks/vcr/vcr_wiki_zh_hard.yaml +++ /dev/null @@ -1,31 +0,0 @@ -dataset_path: vcr-org/VCR-wiki-zh-hard -dataset_kwargs: - token: True -task: "vcr_wiki_zh_hard" -test_split: test -output_type: generate_until -doc_to_visual: !function utils.vcr_doc_to_visual -doc_to_text: !function utils.vcr_doc_to_text -doc_to_target: "answer" -generation_kwargs: - max_new_tokens: 120 - temperature: 0 - top_p: 0 - num_beams: 1 - do_sample: false -# The return value of process_results will be used by metrics -process_results: !function utils.vcr_zh_process_results -# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results -metric_list: - - metric: mme_percetion_score - aggregation: !function utils.vcr_zh_process_results - higher_is_better: true - - metric: mme_cognition_score - aggregation: !function utils.vcr_zh_process_results - higher_is_better: true -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" -metadata: - - version: 0.0.1 diff --git a/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml b/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml new file mode 100644 index 00000000..37ab5e74 --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml @@ -0,0 +1,17 @@ + +dataset_kwargs: + token: True +output_type: generate_until +doc_to_visual: !function utils.vcr_doc_to_visual +doc_to_text: !function utils.vcr_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 120 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +# The return value of process_results will be used by metrics +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metadata: + - version: 0.0.1 \ No newline at end of file diff --git a/lmms_eval/tasks/vcr/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py similarity index 63% rename from lmms_eval/tasks/vcr/utils.py rename to lmms_eval/tasks/vcr_wiki/utils.py index 0079c8fd..8f3aca04 100644 --- a/lmms_eval/tasks/vcr/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -1,9 +1,5 @@ -from collections import defaultdict import os from difflib import SequenceMatcher as SM -import datetime -import json -from lmms_eval.tasks._task_utils.file_utils import generate_submission_file import evaluate import logging import spacy @@ -34,6 +30,21 @@ } +def fast_filter(answer_text): + if "I can't" in answer_text: + return True + elif "I cannot" in answer_text: + return True + elif "sorry" in answer_text.lower(): + return True + if "无法" in answer_text: + return True + elif "抱歉" in answer_text: + return True + else: + return False + + def vcr_doc_to_visual(doc): return [doc["stacked_image"].convert("RGB"), doc["only_it_image"].convert("RGB")] @@ -63,7 +74,7 @@ def tokenize(text, language): return [token.text for token in processed_text] -def vcr_process_results_single(doc, result, language): +def vcr_process_results_single(crossed_text, result, language): """ Args: doc: a instance of the eval dataset @@ -71,8 +82,21 @@ def vcr_process_results_single(doc, result, language): Returns: a dictionary with key: metric name (in this case mme score), value: metric value """ + assert language in ["en", "zh"], f"Language {language} is not supported." - crossed_text = doc["crossed_text"] + + if fast_filter(result): + return { + "crossed_text": crossed_text, + "max_sim_val": 0, + "max_sim_string": "", + "precision": 0, + "recall": 0, + "f1": 0, + "jaccard": 0, + "rouge1": 0, + "exact_match": 0, + } tokens_result = tokenize(result, language) tokens_crossed_text = tokenize(crossed_text, language) @@ -150,10 +174,26 @@ def vcr_en_process_results(doc, results): a dictionary with key: metric name (in this case mme score), value: metric value """ assert len(results) == 2, f"Expected 2 results, got {len(results)}" - output = { - "res_stacked_image": vcr_process_results_single(doc, results[0], "en"), - "res_only_it_image": vcr_process_results_single(doc, results[1], "en"), - } + output = {} + for i in range(len(doc["crossed_text"])): + res_stacked_image_results = vcr_process_results_single( + doc["crossed_text"][i], results[0], "en" + ) + res_only_image_results = vcr_process_results_single( + doc["crossed_text"][i], results[1], "en" + ) + output.update( + { + f"res_stacked_image__{k}___{i}": v + for k, v in res_stacked_image_results.items() + } + ) + output.update( + { + f"res_only_it_image__{k}___{i}": v + for k, v in res_only_image_results.items() + } + ) return output @@ -166,10 +206,26 @@ def vcr_zh_process_results(doc, results): a dictionary with key: metric name (in this case mme score), value: metric value """ assert len(results) == 2, f"Expected 2 results, got {len(results)}" - output = { - "res_stacked_image": vcr_process_results_single(doc, results[0], "zh"), - "res_only_it_image": vcr_process_results_single(doc, results[1], "zh"), - } + output = {} + for i in range(len(doc["crossed_text"])): + res_stacked_image_results = vcr_process_results_single( + doc["crossed_text"][i], results[0], "zh" + ) + res_only_image_results = vcr_process_results_single( + doc["crossed_text"][i], results[1], "zh" + ) + output.update( + { + f"res_stacked_image__{k}___{i}": v + for k, v in res_stacked_image_results.items() + } + ) + output.update( + { + f"res_only_it_image__{k}___{i}": v + for k, v in res_only_image_results.items() + } + ) return output @@ -180,36 +236,29 @@ def vcr_aggregate_results(results): Returns: A dictionary of dictionary of float, where the outer dictionary has keys "res_stacked_image" and "res_only_it_image" """ - output = { - "res_stacked_image": { - "max_sim_val": 0, - "precision": 0, - "recall": 0, - "f1": 0, - "jaccard": 0, - "rouge1": 0, - }, - "res_only_it_image": { - "max_sim_val": 0, - "precision": 0, - "recall": 0, - "f1": 0, - "jaccard": 0, - "rouge1": 0, - }, + "res_stacked_image__precision": 0, + "res_stacked_image__recall": 0, + "res_stacked_image__f1": 0, + "res_stacked_image__jaccard": 0, + "res_stacked_image__rouge1": 0, + "res_stacked_image__exact_match": 0, + "res_only_it_image__precision": 0, + "res_only_it_image__recall": 0, + "res_only_it_image__f1": 0, + "res_only_it_image__jaccard": 0, + "res_only_it_image__rouge1": 0, + "res_only_it_image__exact_match": 0, } - for target_domain in output.keys(): - for target_metric_name in output[target_domain].keys(): - score = 0 - count = 0 - for inner_dict in results: - for inner_key, inner_value in inner_dict.items(): - if inner_key == target_domain: - for blank_id, blank_metrics in inner_value.items(): - for metric_name, metric_value in blank_metrics.items(): - if metric_name == target_metric_name: - score += metric_value - count += 1 - output[target_domain][target_metric_name] = score / count + + for output_key in output.keys(): + count = 0 + query_domain, query_metric_name = output_key.split("__") + for inner_dict in results: + for inner_key, inner_value in inner_dict.items(): + key_domain, key_metric_name, _ = inner_key.split("__") + if query_domain == key_domain and query_metric_name == key_metric_name: + output[output_key] += inner_value + count += 1 + output[output_key] /= count return output diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml new file mode 100644 index 00000000..7df8eb38 --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-en-easy-test +task: "vcr_wiki_en_easy" +test_split: train[:100] +process_results: !function utils.vcr_en_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml new file mode 100644 index 00000000..9079d2cf --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-en-easy-test +task: "vcr_wiki_en_easy" +test_split: train[:500] +process_results: !function utils.vcr_en_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml new file mode 100644 index 00000000..7a5b09e2 --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-en-easy-test +task: "vcr_wiki_en_easy" +test_split: train +process_results: !function utils.vcr_en_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml new file mode 100644 index 00000000..3f24be85 --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-en-hard-test +task: "vcr_wiki_en_hard" +test_split: train[:100] +process_results: !function utils.vcr_en_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml new file mode 100644 index 00000000..c88c91fe --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-en-hard-test +task: "vcr_wiki_en_hard" +test_split: train[:500] +process_results: !function utils.vcr_en_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml new file mode 100644 index 00000000..aa5e3634 --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-en-hard-test +task: "vcr_wiki_en_hard" +test_split: train +process_results: !function utils.vcr_en_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_en_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml new file mode 100644 index 00000000..9ce02a93 --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-zh-easy-test +task: "vcr_wiki_zh_easy" +test_split: train[:100] +process_results: !function utils.vcr_zh_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml new file mode 100644 index 00000000..da210bb8 --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-zh-easy-test +task: "vcr_wiki_zh_easy" +test_split: train[:500] +process_results: !function utils.vcr_zh_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml new file mode 100644 index 00000000..8807db4a --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-zh-easy-test +task: "vcr_wiki_zh_easy" +test_split: train +process_results: !function utils.vcr_zh_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml new file mode 100644 index 00000000..c15c8059 --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-zh-hard-test +task: "vcr_wiki_zh_hard" +test_split: train[:100] +process_results: !function utils.vcr_zh_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml new file mode 100644 index 00000000..90bba34e --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-zh-hard-test +task: "vcr_wiki_zh_hard" +test_split: train[:500] +process_results: !function utils.vcr_zh_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml new file mode 100644 index 00000000..48893f73 --- /dev/null +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml @@ -0,0 +1,16 @@ +"include": "_default_template_vcr_yaml" +dataset_path: vcr-org/VCR-wiki-zh-hard-test +task: "vcr_wiki_zh_hard" +test_split: train +process_results: !function utils.vcr_zh_process_results +metric_list: + - metric: vcr_percetion_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true + - metric: vcr_cognition_score + aggregation: !function utils.vcr_zh_process_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file From 043b483eb55f7be4fea75c9bc0b9b03d251b109b Mon Sep 17 00:00:00 2001 From: tianyu-z Date: Mon, 10 Jun 2024 15:47:00 -0400 Subject: [PATCH 061/196] switch logic --- .../tasks/ok_vqa/_default_template_vqa_yaml | 2 +- lmms_eval/tasks/ok_vqa/utils.py | 10 +- lmms_eval/tasks/textvqa/textvqa_test.yaml | 2 +- lmms_eval/tasks/textvqa/textvqa_val.yaml | 2 +- lmms_eval/tasks/textvqa/utils.py | 15 +- lmms_eval/tasks/vcr_wiki/utils.py | 137 ++++++++---------- .../tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml | 6 +- .../tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml | 6 +- lmms_eval/tasks/vizwiz_vqa/utils.py | 10 +- .../tasks/vizwiz_vqa/vizwiz_vqa_test.yaml | 2 +- .../tasks/vizwiz_vqa/vizwiz_vqa_val.yaml | 2 +- lmms_eval/tasks/vqav2/utils.py | 14 +- lmms_eval/tasks/vqav2/vqav2_test.yaml | 2 +- 23 files changed, 141 insertions(+), 129 deletions(-) diff --git a/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml b/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml index 8d74eb11..a6950129 100644 --- a/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml +++ b/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml @@ -13,7 +13,7 @@ metric_list: ignore_case: true ignore_punctuation: true - metric: submission - aggregation: !function utils.ok_vqa_aggreate_submissions + aggregation: !function utils.ok_vqa_aggregate_submissions higher_is_better: true process_results: !function utils.ok_vqa_process_results model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/ok_vqa/utils.py b/lmms_eval/tasks/ok_vqa/utils.py index 52faa11d..087d51fa 100644 --- a/lmms_eval/tasks/ok_vqa/utils.py +++ b/lmms_eval/tasks/ok_vqa/utils.py @@ -19,7 +19,9 @@ def ok_vqa_doc_to_visual(doc): def ok_vqa_process_results(doc, result): eval_ai_processor = EvalAIAnswerProcessor() - assert len(result) == 1, f"The result should be a list of length 1, but got {len(result)}." + assert ( + len(result) == 1 + ), f"The result should be a list of length 1, but got {len(result)}." resAns = eval_ai_processor(result[0]) accuracy = 0 @@ -30,7 +32,9 @@ def ok_vqa_process_results(doc, result): doc["answers"][i] = eval_ai_processor(doc["answers"][i]) for i in range(len(doc["answers"])): - otherGTAns = [doc["answers"][j] for j in range(len(doc["answers"])) if i != j] + otherGTAns = [ + doc["answers"][j] for j in range(len(doc["answers"])) if i != j + ] matchingAns = [item for item in otherGTAns if item == resAns] acc = min(1, float(len(matchingAns)) / 3) gtAcc.append(acc) @@ -61,7 +65,7 @@ def ok_vqa_doc_to_text(doc, model_specific_prompt_kwargs=None): return f"{pre_prompt}{question}{post_prompt}" -def ok_vqa_aggreate_submissions(results, args): +def ok_vqa_aggregate_submissions(results, args): now_date_time = datetime.datetime.now().strftime("%Y-%m%d-%H%M-%S") file = f"ok_vqa-test-submission-{now_date_time}.json" path = generate_submission_file(file, args) diff --git a/lmms_eval/tasks/textvqa/textvqa_test.yaml b/lmms_eval/tasks/textvqa/textvqa_test.yaml index 15b02c4f..d22ef571 100644 --- a/lmms_eval/tasks/textvqa/textvqa_test.yaml +++ b/lmms_eval/tasks/textvqa/textvqa_test.yaml @@ -2,6 +2,6 @@ task: textvqa_test test_split: test metric_list: - metric: submission - aggregation: !function utils.textvqa_aggreate_submissions + aggregation: !function utils.textvqa_aggregate_submissions higher_is_better: true include: _default_template_textvqa_yaml diff --git a/lmms_eval/tasks/textvqa/textvqa_val.yaml b/lmms_eval/tasks/textvqa/textvqa_val.yaml index e23e9b2e..8ce39a49 100644 --- a/lmms_eval/tasks/textvqa/textvqa_val.yaml +++ b/lmms_eval/tasks/textvqa/textvqa_val.yaml @@ -7,6 +7,6 @@ metric_list: ignore_case: true ignore_punctuation: true - metric: submission - aggregation: !function utils.textvqa_aggreate_submissions + aggregation: !function utils.textvqa_aggregate_submissions higher_is_better: true include: _default_template_textvqa_yaml diff --git a/lmms_eval/tasks/textvqa/utils.py b/lmms_eval/tasks/textvqa/utils.py index ea3b503b..7ffb6570 100644 --- a/lmms_eval/tasks/textvqa/utils.py +++ b/lmms_eval/tasks/textvqa/utils.py @@ -19,7 +19,9 @@ def textvqa_doc_to_visual(doc): def textvqa_process_results(doc, result): eval_ai_processor = EvalAIAnswerProcessor() - assert len(result) == 1, f"The result should be a list of length 1, but got {len(result)}." + assert ( + len(result) == 1 + ), f"The result should be a list of length 1, but got {len(result)}." resAns = eval_ai_processor(result[0]) accuracy = 0 @@ -30,7 +32,9 @@ def textvqa_process_results(doc, result): doc["answers"][i] = eval_ai_processor(doc["answers"][i]) for i in range(len(doc["answers"])): - otherGTAns = [doc["answers"][j] for j in range(len(doc["answers"])) if i != j] + otherGTAns = [ + doc["answers"][j] for j in range(len(doc["answers"])) if i != j + ] matchingAns = [item for item in otherGTAns if item == resAns] acc = min(1, float(len(matchingAns)) / 3) gtAcc.append(acc) @@ -54,12 +58,15 @@ def textvqa_doc_to_text(doc, model_specific_prompt_kwargs=None): pre_prompt = model_specific_prompt_kwargs["pre_prompt"] if "post_prompt" in model_specific_prompt_kwargs: post_prompt = model_specific_prompt_kwargs["post_prompt"] - if "ocr" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["ocr"]: + if ( + "ocr" in model_specific_prompt_kwargs + and model_specific_prompt_kwargs["ocr"] + ): ocr_ref = f"\nReference OCR token: {', '.join(doc['ocr_tokens'])}" return f"{pre_prompt}{doc['question'].capitalize()}{ocr_ref}{post_prompt}" -def textvqa_aggreate_submissions(results, args): +def textvqa_aggregate_submissions(results, args): now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") path = generate_submission_file(f"textvqa_submission_{now_date_time}.json", args) with open(path, "w") as f: diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index 8f3aca04..ef762f20 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -6,6 +6,9 @@ from spacy.cli import download from nltk.util import ngrams from functools import partial +import datetime +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file +import json # Download the English and Chinese models download("en_core_web_sm") @@ -46,7 +49,7 @@ def fast_filter(answer_text): def vcr_doc_to_visual(doc): - return [doc["stacked_image"].convert("RGB"), doc["only_it_image"].convert("RGB")] + return [doc["stacked_image"].convert("RGB")] def vcr_doc_to_text(doc, model_specific_prompt_kwargs=None): @@ -80,7 +83,7 @@ def vcr_process_results_single(crossed_text, result, language): doc: a instance of the eval dataset results: [pred] Returns: - a dictionary with key: metric name (in this case mme score), value: metric value + a dictionary with key: metric name (in this case vcr score), value: metric value """ assert language in ["en", "zh"], f"Language {language} is not supported." @@ -171,29 +174,28 @@ def vcr_en_process_results(doc, results): doc: a instance of the eval dataset results: [pred] Returns: - a dictionary with key: metric name (in this case mme score), value: metric value + a dictionary with key: metric name (in this case vcr score), value: metric value """ - assert len(results) == 2, f"Expected 2 results, got {len(results)}" - output = {} - for i in range(len(doc["crossed_text"])): - res_stacked_image_results = vcr_process_results_single( - doc["crossed_text"][i], results[0], "en" - ) - res_only_image_results = vcr_process_results_single( - doc["crossed_text"][i], results[1], "en" - ) - output.update( - { - f"res_stacked_image__{k}___{i}": v - for k, v in res_stacked_image_results.items() - } - ) - output.update( - { - f"res_only_it_image__{k}___{i}": v - for k, v in res_only_image_results.items() - } - ) + output = { + "max_sim_val": [], + "precision": [], + "recall": [], + "f1": [], + "jaccard": [], + "rouge1": [], + "exact_match": [], + } + crossed_text = doc["crossed_text"] + for i in range(len(crossed_text)): + tmp = vcr_process_results_single(crossed_text[i], results, "en") + for k in output.keys(): + output[k].append( + { + "score": tmp[k], + "max_sim_string": tmp["max_sim_string"], + "caption": doc["caption"], + } + ) return output @@ -203,62 +205,51 @@ def vcr_zh_process_results(doc, results): doc: a instance of the eval dataset results: [pred] Returns: - a dictionary with key: metric name (in this case mme score), value: metric value + a dictionary with key: metric name (in this case vcr score), value: metric value """ - assert len(results) == 2, f"Expected 2 results, got {len(results)}" - output = {} - for i in range(len(doc["crossed_text"])): - res_stacked_image_results = vcr_process_results_single( - doc["crossed_text"][i], results[0], "zh" - ) - res_only_image_results = vcr_process_results_single( - doc["crossed_text"][i], results[1], "zh" - ) - output.update( - { - f"res_stacked_image__{k}___{i}": v - for k, v in res_stacked_image_results.items() - } - ) - output.update( - { - f"res_only_it_image__{k}___{i}": v - for k, v in res_only_image_results.items() - } - ) + output = { + "max_sim_val": [], + "precision": [], + "recall": [], + "f1": [], + "jaccard": [], + "rouge1": [], + "exact_match": [], + } + crossed_text = doc["crossed_text"] + for i in range(len(crossed_text)): + tmp = vcr_process_results_single(crossed_text[i], results, "zh") + for k in output.keys(): + output[k].append( + { + "score": tmp[k], + "max_sim_string": tmp["max_sim_string"], + "caption": doc["caption"], + } + ) return output -def vcr_aggregate_results(results): +def vcr_aggregate_results(results, args): """ Args: results: a list of values returned by process_results Returns: A dictionary of dictionary of float, where the outer dictionary has keys "res_stacked_image" and "res_only_it_image" """ - output = { - "res_stacked_image__precision": 0, - "res_stacked_image__recall": 0, - "res_stacked_image__f1": 0, - "res_stacked_image__jaccard": 0, - "res_stacked_image__rouge1": 0, - "res_stacked_image__exact_match": 0, - "res_only_it_image__precision": 0, - "res_only_it_image__recall": 0, - "res_only_it_image__f1": 0, - "res_only_it_image__jaccard": 0, - "res_only_it_image__rouge1": 0, - "res_only_it_image__exact_match": 0, - } - - for output_key in output.keys(): - count = 0 - query_domain, query_metric_name = output_key.split("__") - for inner_dict in results: - for inner_key, inner_value in inner_dict.items(): - key_domain, key_metric_name, _ = inner_key.split("__") - if query_domain == key_domain and query_metric_name == key_metric_name: - output[output_key] += inner_value - count += 1 - output[output_key] /= count - return output + scores = 0 + count = 0 + output_dict = {} + for i in range(len(results)): + for blank_id in range(len(results[i])): + scores += results[i][blank_id]["score"] + count += 1 + output_dict[str(i)] = results[i] + + now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + path = generate_submission_file(f"vcr_submission_{now_date_time}.json", args) + with open(path, "w") as f: + json.dump(output_dict, f) + # print(f"Submission file saved to {path}") + eval_logger.info(f"Submission file saved to {path}") + return scores / count diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml index 7df8eb38..3de9bc73 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-en-easy-test -task: "vcr_wiki_en_easy" +task: "vcr_wiki_en_easy_100" test_split: train[:100] process_results: !function utils.vcr_en_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_en_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_en_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml index 9079d2cf..19335db5 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-en-easy-test -task: "vcr_wiki_en_easy" +task: "vcr_wiki_en_easy_500" test_split: train[:500] process_results: !function utils.vcr_en_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_en_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_en_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml index 7a5b09e2..0b2a384a 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-en-easy-test -task: "vcr_wiki_en_easy" +task: "vcr_wiki_en_easy_5000" test_split: train process_results: !function utils.vcr_en_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_en_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_en_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml index 3f24be85..0d865028 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-en-hard-test -task: "vcr_wiki_en_hard" +task: "vcr_wiki_en_hard_100" test_split: train[:100] process_results: !function utils.vcr_en_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_en_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_en_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml index c88c91fe..dd135a7a 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-en-hard-test -task: "vcr_wiki_en_hard" +task: "vcr_wiki_en_hard_500" test_split: train[:500] process_results: !function utils.vcr_en_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_en_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_en_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml index aa5e3634..277269c9 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-en-hard-test -task: "vcr_wiki_en_hard" +task: "vcr_wiki_en_hard_5000" test_split: train process_results: !function utils.vcr_en_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_en_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_en_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml index 9ce02a93..51e92399 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-zh-easy-test -task: "vcr_wiki_zh_easy" +task: "vcr_wiki_zh_easy_100" test_split: train[:100] process_results: !function utils.vcr_zh_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_zh_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_zh_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml index da210bb8..d37022cc 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-zh-easy-test -task: "vcr_wiki_zh_easy" +task: "vcr_wiki_zh_easy_500" test_split: train[:500] process_results: !function utils.vcr_zh_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_zh_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_zh_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml index 8807db4a..c4a5f982 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-zh-easy-test -task: "vcr_wiki_zh_easy" +task: "vcr_wiki_zh_easy_5000" test_split: train process_results: !function utils.vcr_zh_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_zh_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_zh_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml index c15c8059..6042044c 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-zh-hard-test -task: "vcr_wiki_zh_hard" +task: "vcr_wiki_zh_hard_100" test_split: train[:100] process_results: !function utils.vcr_zh_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_zh_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_zh_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml index 90bba34e..c8bb4eb4 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-zh-hard-test -task: "vcr_wiki_zh_hard" +task: "vcr_wiki_zh_hard_500" test_split: train[:500] process_results: !function utils.vcr_zh_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_zh_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_zh_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml index 48893f73..45c2a262 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml @@ -1,13 +1,13 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-zh-hard-test -task: "vcr_wiki_zh_hard" +task: "vcr_wiki_zh_hard_5000" test_split: train process_results: !function utils.vcr_zh_process_results metric_list: - - metric: vcr_percetion_score + - metric: jaccard aggregation: !function utils.vcr_zh_process_results higher_is_better: true - - metric: vcr_cognition_score + - metric: exact_match aggregation: !function utils.vcr_zh_process_results higher_is_better: true model_specific_prompt_kwargs: diff --git a/lmms_eval/tasks/vizwiz_vqa/utils.py b/lmms_eval/tasks/vizwiz_vqa/utils.py index 63afb4e4..4e84303e 100644 --- a/lmms_eval/tasks/vizwiz_vqa/utils.py +++ b/lmms_eval/tasks/vizwiz_vqa/utils.py @@ -19,7 +19,9 @@ def vizwiz_vqa_doc_to_visual(doc): def vizwiz_vqa_process_results(doc, result): eval_ai_processor = EvalAIAnswerProcessor() - assert len(result) == 1, f"The result should be a list of length 1, but got {len(result)}." + assert ( + len(result) == 1 + ), f"The result should be a list of length 1, but got {len(result)}." resAns = eval_ai_processor(result[0]) accuracy = 0 @@ -30,7 +32,9 @@ def vizwiz_vqa_process_results(doc, result): doc["answers"][i] = eval_ai_processor(doc["answers"][i]) for i in range(len(doc["answers"])): - otherGTAns = [doc["answers"][j] for j in range(len(doc["answers"])) if i != j] + otherGTAns = [ + doc["answers"][j] for j in range(len(doc["answers"])) if i != j + ] matchingAns = [item for item in otherGTAns if item == resAns] acc = min(1, float(len(matchingAns)) / 3) gtAcc.append(acc) @@ -61,7 +65,7 @@ def vizwiz_vqa_doc_to_text(doc, model_specific_prompt_kwargs=None): return text -def vizwiz_vqa_aggreate_submissions(results, args): +def vizwiz_vqa_aggregate_submissions(results, args): now_date_time = datetime.datetime.now().strftime("%Y-%m%d-%H%M-%S") submission_file_name = f"vizwiz_vqa-test-submission-{now_date_time}.json" path = generate_submission_file(submission_file_name, args) diff --git a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_test.yaml b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_test.yaml index dec140f6..82fafcaa 100644 --- a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_test.yaml +++ b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_test.yaml @@ -10,5 +10,5 @@ metric_list: # ignore_case: true # ignore_punctuation: true - metric: submission - aggregation: !function utils.vizwiz_vqa_aggreate_submissions + aggregation: !function utils.vizwiz_vqa_aggregate_submissions higher_is_better: true diff --git a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val.yaml b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val.yaml index ac8ecc98..3343bbdc 100644 --- a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val.yaml +++ b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val.yaml @@ -9,5 +9,5 @@ metric_list: ignore_case: true ignore_punctuation: true # - metric: submission - # aggregation: !function utils.vizwiz_vqa_aggreate_submissions + # aggregation: !function utils.vizwiz_vqa_aggregate_submissions # higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/vqav2/utils.py b/lmms_eval/tasks/vqav2/utils.py index 1a3e9082..3f254ce9 100644 --- a/lmms_eval/tasks/vqav2/utils.py +++ b/lmms_eval/tasks/vqav2/utils.py @@ -19,7 +19,9 @@ def vqav2_doc_to_visual(doc): def vqav2_process_results(doc, result): eval_ai_processor = EvalAIAnswerProcessor() - assert len(result) == 1, f"The result should be a list of length 1, but got {len(result)}." + assert ( + len(result) == 1 + ), f"The result should be a list of length 1, but got {len(result)}." resAns = eval_ai_processor(result[0]) accuracy = 0 @@ -33,8 +35,12 @@ def vqav2_process_results(doc, result): if len(set(gtAnswers)) > 1: for ansDic in doc["answers"]: - ansDic["answer"] = eval_ai_processor.process_punctuation(ansDic["answer"]) - ansDic["answer"] = eval_ai_processor.process_digit_article(ansDic["answer"]) + ansDic["answer"] = eval_ai_processor.process_punctuation( + ansDic["answer"] + ) + ansDic["answer"] = eval_ai_processor.process_digit_article( + ansDic["answer"] + ) resAns = eval_ai_processor.process_punctuation(resAns) resAns = eval_ai_processor.process_digit_article(resAns) @@ -80,7 +86,7 @@ def vqav2_doc_to_text(doc, model_specific_prompt_kwargs=None): return f"{pre_prompt}{doc['question']}{post_prompt}" -def vqav2_aggreate_submissions(results, args): +def vqav2_aggregate_submissions(results, args): now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") submission_file_name = f"vqav2-test-submission-{now_date_time}.json" path = file_utils.generate_submission_file(submission_file_name, args) diff --git a/lmms_eval/tasks/vqav2/vqav2_test.yaml b/lmms_eval/tasks/vqav2/vqav2_test.yaml index 94c69209..4c038604 100644 --- a/lmms_eval/tasks/vqav2/vqav2_test.yaml +++ b/lmms_eval/tasks/vqav2/vqav2_test.yaml @@ -3,6 +3,6 @@ include: _default_template_vqav2_yaml test_split: test metric_list: - metric: submission - aggregation: !function utils.vqav2_aggreate_submissions + aggregation: !function utils.vqav2_aggregate_submissions higher_is_better: true process_results: !function utils.vqav2_process_results_test From 427dabb790118f538b64e4e5bf6a7aab9689b3d9 Mon Sep 17 00:00:00 2001 From: Suyuchen Date: Mon, 10 Jun 2024 15:51:05 -0400 Subject: [PATCH 062/196] add crossed_text to vcr_wiki output --- lmms_eval/tasks/vcr_wiki/utils.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index ef762f20..f60185a3 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -1,14 +1,16 @@ +import datetime +import json +import logging import os from difflib import SequenceMatcher as SM +from functools import partial + import evaluate -import logging import spacy -from spacy.cli import download from nltk.util import ngrams -from functools import partial -import datetime +from spacy.cli import download + from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -import json # Download the English and Chinese models download("en_core_web_sm") @@ -193,6 +195,7 @@ def vcr_en_process_results(doc, results): { "score": tmp[k], "max_sim_string": tmp["max_sim_string"], + "crossed_text": crossed_text[i], "caption": doc["caption"], } ) @@ -224,6 +227,7 @@ def vcr_zh_process_results(doc, results): { "score": tmp[k], "max_sim_string": tmp["max_sim_string"], + "crossed_text": crossed_text[i], "caption": doc["caption"], } ) From db8e718b502469e8536ee359c5559de87635ffc7 Mon Sep 17 00:00:00 2001 From: tianyu-z Date: Mon, 10 Jun 2024 16:13:58 -0400 Subject: [PATCH 063/196] include the try-except logic for spacy --- lmms_eval/tasks/vcr_wiki/utils.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index f60185a3..a8bb670e 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -13,16 +13,23 @@ from lmms_eval.tasks._task_utils.file_utils import generate_submission_file # Download the English and Chinese models -download("en_core_web_sm") -download("zh_core_web_sm") +try: + nlp_en = spacy.load("en_core_web_sm") +except: + download("en_core_web_sm") + nlp_en = spacy.load("en_core_web_sm") +try: + nlp_zh = spacy.load("zh_core_web_sm") +except: + download("zh_core_web_sm") + nlp_zh = spacy.load("zh_core_web_sm") eval_logger = logging.getLogger("lmms-eval") dir_name = os.path.dirname(os.path.abspath(__file__)) rouge = evaluate.load("rouge") -nlp_en = spacy.load("en_core_web_sm") -nlp_zh = spacy.load("zh_core_web_sm") + nlp = {"en": nlp_en, "zh": nlp_zh} aggregate_results_template = { From 205721e0aad76dde30255e56149bbed121883356 Mon Sep 17 00:00:00 2001 From: Suyuchen Date: Mon, 10 Jun 2024 18:43:15 -0400 Subject: [PATCH 064/196] update vcr_wiki tasks --- lmms_eval/tasks/vcr_wiki/utils.py | 45 +++++++++---------- ...n_easy_5000.yaml => vcr_wiki_en_easy.yaml} | 8 ++-- .../tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml | 8 ++-- .../tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml | 8 ++-- ...n_hard_5000.yaml => vcr_wiki_en_hard.yaml} | 8 ++-- .../tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml | 8 ++-- .../tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml | 8 ++-- ...h_easy_5000.yaml => vcr_wiki_zh_easy.yaml} | 8 ++-- .../tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml | 8 ++-- .../tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml | 8 ++-- ...h_hard_5000.yaml => vcr_wiki_zh_hard.yaml} | 8 ++-- .../tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml | 8 ++-- .../tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml | 8 ++-- 13 files changed, 70 insertions(+), 71 deletions(-) rename lmms_eval/tasks/vcr_wiki/{vcr_wiki_en_easy_5000.yaml => vcr_wiki_en_easy.yaml} (73%) rename lmms_eval/tasks/vcr_wiki/{vcr_wiki_en_hard_5000.yaml => vcr_wiki_en_hard.yaml} (73%) rename lmms_eval/tasks/vcr_wiki/{vcr_wiki_zh_easy_5000.yaml => vcr_wiki_zh_easy.yaml} (72%) rename lmms_eval/tasks/vcr_wiki/{vcr_wiki_zh_hard_5000.yaml => vcr_wiki_zh_hard.yaml} (72%) diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index a8bb670e..f7f6570b 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -15,22 +15,21 @@ # Download the English and Chinese models try: nlp_en = spacy.load("en_core_web_sm") -except: +except Exception as e: download("en_core_web_sm") nlp_en = spacy.load("en_core_web_sm") + try: nlp_zh = spacy.load("zh_core_web_sm") -except: +except Exception as e: download("zh_core_web_sm") nlp_zh = spacy.load("zh_core_web_sm") -eval_logger = logging.getLogger("lmms-eval") - -dir_name = os.path.dirname(os.path.abspath(__file__)) - +nlp = {"en": nlp_en, "zh": nlp_zh} rouge = evaluate.load("rouge") -nlp = {"en": nlp_en, "zh": nlp_zh} +eval_logger = logging.getLogger("lmms-eval") +dir_name = os.path.dirname(os.path.abspath(__file__)) aggregate_results_template = { "max_sim_val": 0, @@ -89,7 +88,7 @@ def tokenize(text, language): def vcr_process_results_single(crossed_text, result, language): """ Args: - doc: a instance of the eval dataset + doc: an instance of the eval dataset results: [pred] Returns: a dictionary with key: metric name (in this case vcr score), value: metric value @@ -180,8 +179,8 @@ def vcr_process_results_single(crossed_text, result, language): def vcr_en_process_results(doc, results): """ Args: - doc: a instance of the eval dataset - results: [pred] + doc: an instance of the eval dataset + results: [pred], with length = 1 Returns: a dictionary with key: metric name (in this case vcr score), value: metric value """ @@ -196,13 +195,13 @@ def vcr_en_process_results(doc, results): } crossed_text = doc["crossed_text"] for i in range(len(crossed_text)): - tmp = vcr_process_results_single(crossed_text[i], results, "en") + tmp = vcr_process_results_single(crossed_text[i], results[0], "en") for k in output.keys(): output[k].append( { "score": tmp[k], - "max_sim_string": tmp["max_sim_string"], - "crossed_text": crossed_text[i], + "pred_ngram": tmp["max_sim_string"], + "gt_ngram": crossed_text[i], "caption": doc["caption"], } ) @@ -212,10 +211,10 @@ def vcr_en_process_results(doc, results): def vcr_zh_process_results(doc, results): """ Args: - doc: a instance of the eval dataset - results: [pred] + doc: an instance of the eval dataset + results: [pred], with length = 1 Returns: - a dictionary with key: metric name (in this case vcr score), value: metric value + a dictionary with key: metric name (in this case vcr score), value: metric value and other info """ output = { "max_sim_val": [], @@ -228,13 +227,13 @@ def vcr_zh_process_results(doc, results): } crossed_text = doc["crossed_text"] for i in range(len(crossed_text)): - tmp = vcr_process_results_single(crossed_text[i], results, "zh") + tmp = vcr_process_results_single(crossed_text[i], results[0], "zh") for k in output.keys(): output[k].append( { "score": tmp[k], - "max_sim_string": tmp["max_sim_string"], - "crossed_text": crossed_text[i], + "pred_ngram": tmp["max_sim_string"], + "gt_ngram": crossed_text[i], "caption": doc["caption"], } ) @@ -244,9 +243,9 @@ def vcr_zh_process_results(doc, results): def vcr_aggregate_results(results, args): """ Args: - results: a list of values returned by process_results + results: List[List[Dict]], list of results returned by process_results Returns: - A dictionary of dictionary of float, where the outer dictionary has keys "res_stacked_image" and "res_only_it_image" + A float value representing the final score of jaccard index or exact match """ scores = 0 count = 0 @@ -259,8 +258,8 @@ def vcr_aggregate_results(results, args): now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") path = generate_submission_file(f"vcr_submission_{now_date_time}.json", args) - with open(path, "w") as f: - json.dump(output_dict, f) + with open(path, "w", encoding='utf-8') as f: + json.dump(output_dict, f, indent=4, ensure_ascii=False) # print(f"Submission file saved to {path}") eval_logger.info(f"Submission file saved to {path}") return scores / count diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml similarity index 73% rename from lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml rename to lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml index 0b2a384a..cb9e2346 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_5000.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-en-easy-test -task: "vcr_wiki_en_easy_5000" -test_split: train +task: "vcr_wiki_en_easy" +test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml index 3de9bc73..94d74448 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" -dataset_path: vcr-org/VCR-wiki-en-easy-test +dataset_path: vcr-org/VCR-wiki-en-easy-test-100 task: "vcr_wiki_en_easy_100" -test_split: train[:100] +test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml index 19335db5..5c70b133 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" -dataset_path: vcr-org/VCR-wiki-en-easy-test +dataset_path: vcr-org/VCR-wiki-en-easy-test-500 task: "vcr_wiki_en_easy_500" -test_split: train[:500] +test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml similarity index 73% rename from lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml rename to lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml index 277269c9..97c837f6 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_5000.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-en-hard-test -task: "vcr_wiki_en_hard_5000" -test_split: train +task: "vcr_wiki_en_hard" +test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml index 0d865028..bf3dcc45 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" -dataset_path: vcr-org/VCR-wiki-en-hard-test +dataset_path: vcr-org/VCR-wiki-en-hard-test-100 task: "vcr_wiki_en_hard_100" -test_split: train[:100] +test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml index dd135a7a..4b62e1f3 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" -dataset_path: vcr-org/VCR-wiki-en-hard-test +dataset_path: vcr-org/VCR-wiki-en-hard-test-500 task: "vcr_wiki_en_hard_500" -test_split: train[:500] +test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_en_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml similarity index 72% rename from lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml rename to lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml index c4a5f982..5ee284a9 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_5000.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-zh-easy-test -task: "vcr_wiki_zh_easy_5000" -test_split: train +task: "vcr_wiki_zh_easy" +test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml index 51e92399..9a478af9 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" -dataset_path: vcr-org/VCR-wiki-zh-easy-test +dataset_path: vcr-org/VCR-wiki-zh-easy-test-100 task: "vcr_wiki_zh_easy_100" -test_split: train[:100] +test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml index d37022cc..393cfb08 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" -dataset_path: vcr-org/VCR-wiki-zh-easy-test +dataset_path: vcr-org/VCR-wiki-zh-easy-test-500 task: "vcr_wiki_zh_easy_500" -test_split: train[:500] +test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml similarity index 72% rename from lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml rename to lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml index 45c2a262..3913d0b0 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_5000.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" dataset_path: vcr-org/VCR-wiki-zh-hard-test -task: "vcr_wiki_zh_hard_5000" -test_split: train +task: "vcr_wiki_zh_hard" +test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml index 6042044c..7dd89841 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" -dataset_path: vcr-org/VCR-wiki-zh-hard-test +dataset_path: vcr-org/VCR-wiki-zh-hard-test-100 task: "vcr_wiki_zh_hard_100" -test_split: train[:100] +test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml index c8bb4eb4..3f7aa1a2 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml @@ -1,14 +1,14 @@ "include": "_default_template_vcr_yaml" -dataset_path: vcr-org/VCR-wiki-zh-hard-test +dataset_path: vcr-org/VCR-wiki-zh-hard-test-500 task: "vcr_wiki_zh_hard_500" -test_split: train[:500] +test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_zh_process_results + aggregation: !function utils.vcr_aggregate_results higher_is_better: true model_specific_prompt_kwargs: default: From cd050d4a721d01a2ace0cd030cf7f8dc67eb8c4d Mon Sep 17 00:00:00 2001 From: Suyuchen Date: Mon, 10 Jun 2024 18:49:47 -0400 Subject: [PATCH 065/196] update vcr_wiki tasks in README.md --- README.md | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 25e8d7dd..cf08984a 100644 --- a/README.md +++ b/README.md @@ -276,10 +276,22 @@ We also provide the raw data exported from Weights & Biases for the detailed res - TextVQA Validation (textvqa_val) - TextVQA Test (textvqa_test) - VCR-Wiki (vcr_wiki) - - vcr English easy mode (vcr_wiki_en_easy) - - vcr English hard mode (vcr_wiki_en_hard) - - vcr Chinese easy mode (vcr_wiki_zh_easy) - - vcr Chinese hard mode (vcr_wiki_zh_hard) + - VCR English easy mode + - VCR English easy mode full test set (vcr_wiki_en_easy) + - VCR English easy mode test 100 (vcr_wiki_en_easy_100) + - VCR English easy mode test 500 (vcr_wiki_en_easy_500) + - VCR English hard mode + - VCR English hard mode full test set (vcr_wiki_en_hard) + - VCR English hard mode test 100 (vcr_wiki_en_hard_100) + - VCR English hard mode test 500 (vcr_wiki_en_hard_500) + - VCR Chinese easy mode + - VCR Chinese easy mode full test set (vcr_wiki_zh_easy) + - VCR Chinese easy mode test 100 (vcr_wiki_zh_easy_100) + - VCR Chinese easy mode test 500 (vcr_wiki_zh_easy_500) + - VCR Chinese hard mode + - VCR Chinese hard mode full test set (vcr_wiki_zh_hard) + - VCR Chinese hard mode test 100 (vcr_wiki_zh_hard_100) + - VCR Chinese hard mode test 500 (vcr_wiki_zh_hard_500) - VizWizVQA (vizwiz_vqa) - VizWizVQA Validation (vizwiz_vqa_val) - VizWizVQA Test (vizwiz_vqa_test) From 326b9694fc77398592b8caf3ba0bc2e2bb903813 Mon Sep 17 00:00:00 2001 From: tianyu-z Date: Mon, 10 Jun 2024 20:07:40 -0400 Subject: [PATCH 066/196] include std and confidence interval --- lmms_eval/tasks/vcr_wiki/utils.py | 48 ++++++++++++++++++++++++------- 1 file changed, 38 insertions(+), 10 deletions(-) diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index f7f6570b..5cd5ad9a 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -9,7 +9,7 @@ import spacy from nltk.util import ngrams from spacy.cli import download - +import numpy as np from lmms_eval.tasks._task_utils.file_utils import generate_submission_file # Download the English and Chinese models @@ -231,6 +231,7 @@ def vcr_zh_process_results(doc, results): for k in output.keys(): output[k].append( { + "question_id": doc["question_id"], "score": tmp[k], "pred_ngram": tmp["max_sim_string"], "gt_ngram": crossed_text[i], @@ -240,6 +241,27 @@ def vcr_zh_process_results(doc, results): return output +def bootstrap_std(data, n_bootstrap=1000, ci=0.95): + """ + Args: + data: a list of values + n_bootstrap: number of bootstrap samples + ci: confidence interval + Returns: + a tuple of mean, lower bound, upper bound + """ + n = len(data) + means = [] + for _ in range(n_bootstrap): + sample = np.random.choice(data, n, replace=True) + means.append(np.mean(sample)) + means = np.array(means) + lower_bound = np.percentile(means, (1 - ci) / 2 * 100) + upper_bound = np.percentile(means, (1 + ci) / 2 * 100) + std = np.std(means) + return std, lower_bound, upper_bound + + def vcr_aggregate_results(results, args): """ Args: @@ -247,19 +269,25 @@ def vcr_aggregate_results(results, args): Returns: A float value representing the final score of jaccard index or exact match """ - scores = 0 - count = 0 - output_dict = {} + scores = [] + output_dict_detail_result = {} for i in range(len(results)): for blank_id in range(len(results[i])): - scores += results[i][blank_id]["score"] - count += 1 - output_dict[str(i)] = results[i] - + scores.append(results[i][blank_id]["score"]) + output_dict_detail_result[str(i)] = results[i] + mean_score = np.mean(scores) + std, lb, ub = bootstrap_std(scores, n_bootstrap=1000, ci=0.95) + output_dict = { + "mean_score": mean_score, + "std_score": std, + "percentile_2.5": lb, + "percentie_97.5": ub, + "detailed_results": output_dict_detail_result, + } now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") path = generate_submission_file(f"vcr_submission_{now_date_time}.json", args) - with open(path, "w", encoding='utf-8') as f: + with open(path, "w", encoding="utf-8") as f: json.dump(output_dict, f, indent=4, ensure_ascii=False) # print(f"Submission file saved to {path}") eval_logger.info(f"Submission file saved to {path}") - return scores / count + return mean_score From ee91f272985f32eeb9cd6faa41afdd8eb49cac30 Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Wed, 12 Jun 2024 16:50:53 +0900 Subject: [PATCH 067/196] update gpt-3.5-turbo version --- lmms_eval/tasks/mmupd/mmupd.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/mmupd/mmupd.yaml b/lmms_eval/tasks/mmupd/mmupd.yaml index f40416a1..9bb09a7d 100644 --- a/lmms_eval/tasks/mmupd/mmupd.yaml +++ b/lmms_eval/tasks/mmupd/mmupd.yaml @@ -12,4 +12,4 @@ task: metadata: version: 0.0 sys_prompt: "" - gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file + gpt_eval_model_name: "gpt-3.5-turbo-0125" \ No newline at end of file From a66003befe4175824a1be6ed59f5f5b88c15f792 Mon Sep 17 00:00:00 2001 From: AtsuMiyai Date: Wed, 12 Jun 2024 17:05:17 +0900 Subject: [PATCH 068/196] update gpt-3.5-turbo version --- lmms_eval/tasks/mmupd/mmupd_base.yaml | 2 +- lmms_eval/tasks/mmupd/mmupd_instruction.yaml | 2 +- lmms_eval/tasks/mmupd/mmupd_option.yaml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/lmms_eval/tasks/mmupd/mmupd_base.yaml b/lmms_eval/tasks/mmupd/mmupd_base.yaml index 988ad2e1..8f0fe147 100644 --- a/lmms_eval/tasks/mmupd/mmupd_base.yaml +++ b/lmms_eval/tasks/mmupd/mmupd_base.yaml @@ -6,5 +6,5 @@ task: metadata: version: 0.0 sys_prompt: "" - gpt_eval_model_name: "gpt-3.5-turbo-0613" + gpt_eval_model_name: "gpt-3.5-turbo-0125" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmupd_instruction.yaml b/lmms_eval/tasks/mmupd/mmupd_instruction.yaml index 3ca2444e..6aae6049 100644 --- a/lmms_eval/tasks/mmupd/mmupd_instruction.yaml +++ b/lmms_eval/tasks/mmupd/mmupd_instruction.yaml @@ -6,4 +6,4 @@ task: metadata: version: 0.0 sys_prompt: "" - gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file + gpt_eval_model_name: "gpt-3.5-turbo-0125" \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmupd_option.yaml b/lmms_eval/tasks/mmupd/mmupd_option.yaml index 6393fdc8..32e514d9 100644 --- a/lmms_eval/tasks/mmupd/mmupd_option.yaml +++ b/lmms_eval/tasks/mmupd/mmupd_option.yaml @@ -6,4 +6,4 @@ task: metadata: version: 0.0 sys_prompt: "" - gpt_eval_model_name: "gpt-3.5-turbo-0613" \ No newline at end of file + gpt_eval_model_name: "gpt-3.5-turbo-0125" \ No newline at end of file From e43bd840b63eb499856e36d9d2ba45c924abcead Mon Sep 17 00:00:00 2001 From: Bo Li Date: Wed, 12 Jun 2024 14:54:06 +0000 Subject: [PATCH 069/196] chore: Remove unnecessary files and code related to live_bench and sft_eval tasks --- README.md | 2 +- lmms_eval/models/__init__.py | 2 +- lmms_eval/models/gemini_model.py | 163 ------ lmms_eval/models/llava_onevision.py | 474 ------------------ .../{mplugOwlVideo.py => mplug_owl_video.py} | 0 .../{videoChatGPT.py => video_chatgpt.py} | 0 ...{xcomposer2_4KHD.py => xcomposer2_4khd.py} | 0 lmms_eval/tasks/livebench/livebench.yaml | 29 -- lmms_eval/tasks/livebench/utils.py | 185 ------- .../tasks/mix_evals/_default_template_yaml | 16 - .../tasks/mix_evals/mix_evals_video2text.yaml | 5 - .../mix_evals_video2text_freeform.yaml | 22 - .../mix_evals/mix_evals_video2text_mc.yaml | 31 -- .../mix_evals_video2text_openended.yaml | 21 - lmms_eval/tasks/mix_evals/utils.py | 266 ---------- .../_default_sft_eval_ocr_rec_template_yaml | 23 - .../_default_sft_eval_rest_template_yaml | 20 - lmms_eval/tasks/sft_eval/_generate_config.py | 94 ---- lmms_eval/tasks/sft_eval/_sft_eval.yaml | 34 -- lmms_eval/tasks/sft_eval/sft_activity.yaml | 5 - lmms_eval/tasks/sft_eval/sft_arts.yaml | 5 - lmms_eval/tasks/sft_eval/sft_body.yaml | 5 - lmms_eval/tasks/sft_eval/sft_car.yaml | 5 - lmms_eval/tasks/sft_eval/sft_color.yaml | 5 - lmms_eval/tasks/sft_eval/sft_commodity.yaml | 5 - lmms_eval/tasks/sft_eval/sft_count.yaml | 5 - lmms_eval/tasks/sft_eval/sft_daily.yaml | 5 - lmms_eval/tasks/sft_eval/sft_engineer.yaml | 5 - .../tasks/sft_eval/sft_entertainment.yaml | 5 - lmms_eval/tasks/sft_eval/sft_exist.yaml | 5 - lmms_eval/tasks/sft_eval/sft_face.yaml | 5 - lmms_eval/tasks/sft_eval/sft_food.yaml | 5 - lmms_eval/tasks/sft_eval/sft_healthcare.yaml | 5 - lmms_eval/tasks/sft_eval/sft_landmark.yaml | 5 - lmms_eval/tasks/sft_eval/sft_logo.yaml | 5 - lmms_eval/tasks/sft_eval/sft_natural.yaml | 5 - lmms_eval/tasks/sft_eval/sft_ocr_qa_adv.yaml | 5 - .../tasks/sft_eval/sft_ocr_qa_chart.yaml | 5 - lmms_eval/tasks/sft_eval/sft_ocr_qa_form.yaml | 5 - .../tasks/sft_eval/sft_ocr_qa_scene.yaml | 5 - .../tasks/sft_eval/sft_ocr_qa_screen.yaml | 5 - lmms_eval/tasks/sft_eval/sft_ocr_rec_adv.yaml | 5 - lmms_eval/tasks/sft_eval/sft_ocr_rec_doc.yaml | 5 - .../tasks/sft_eval/sft_ocr_rec_handwrite.yaml | 5 - .../tasks/sft_eval/sft_ocr_rec_markdown.yaml | 5 - .../tasks/sft_eval/sft_ocr_rec_scene.yaml | 5 - .../tasks/sft_eval/sft_ocr_rec_screen.yaml | 5 - lmms_eval/tasks/sft_eval/sft_place.yaml | 5 - lmms_eval/tasks/sft_eval/sft_position.yaml | 5 - lmms_eval/tasks/sft_eval/sft_sport.yaml | 5 - lmms_eval/tasks/sft_eval/sft_status.yaml | 5 - lmms_eval/tasks/sft_eval/utils.py | 109 ---- example_eval.yaml => miscs/example_eval.yaml | 0 .../llava_repr_requirements.txt | 0 tools/live_bench/__init__.py | 1 - tools/live_bench/api/live_bench.py | 20 - tools/live_bench/data_generator/__init__.py | 4 - .../live_bench/data_generator/check_prompt.md | 11 - .../example/example_output.json | 18 - .../example/example_website.png | Bin 2228301 -> 0 bytes tools/live_bench/data_generator/live_bench.py | 102 ---- .../data_generator/live_bench_data.py | 94 ---- tools/live_bench/data_generator/prompt.md | 13 - .../live_bench/data_generator/qa_generator.py | 337 ------------- tools/live_bench/data_generator/response.py | 5 - .../live_bench/data_generator/score_getter.py | 157 ------ .../live_bench/data_generator/score_prompt.md | 1 - .../data_generator/utils/__init__.py | 0 .../live_bench/data_generator/utils/claude.py | 42 -- .../live_bench/data_generator/utils/gemini.py | 26 - .../live_bench/data_generator/utils/gpt4v.py | 38 -- tools/live_bench/driver/.gitignore | 1 - tools/live_bench/driver/__init__.py | 1 - tools/live_bench/driver/load_driver.py | 51 -- tools/live_bench/example.ipynb | 103 ---- tools/live_bench/screen_shoter/__init__.py | 2 - tools/live_bench/screen_shoter/screen.py | 30 -- .../live_bench/screen_shoter/screen_shoter.py | 141 ------ tools/live_bench/websites/__init__.py | 2 - tools/live_bench/websites/load_website.py | 34 -- tools/live_bench/websites/website.py | 62 --- tools/live_bench/websites/website_list.yaml | 77 --- 82 files changed, 2 insertions(+), 3027 deletions(-) delete mode 100644 lmms_eval/models/gemini_model.py delete mode 100755 lmms_eval/models/llava_onevision.py rename lmms_eval/models/{mplugOwlVideo.py => mplug_owl_video.py} (100%) rename lmms_eval/models/{videoChatGPT.py => video_chatgpt.py} (100%) rename lmms_eval/models/{xcomposer2_4KHD.py => xcomposer2_4khd.py} (100%) delete mode 100644 lmms_eval/tasks/livebench/livebench.yaml delete mode 100644 lmms_eval/tasks/livebench/utils.py delete mode 100644 lmms_eval/tasks/mix_evals/_default_template_yaml delete mode 100755 lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml delete mode 100755 lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml delete mode 100755 lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml delete mode 100755 lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml delete mode 100755 lmms_eval/tasks/mix_evals/utils.py delete mode 100644 lmms_eval/tasks/sft_eval/_default_sft_eval_ocr_rec_template_yaml delete mode 100644 lmms_eval/tasks/sft_eval/_default_sft_eval_rest_template_yaml delete mode 100644 lmms_eval/tasks/sft_eval/_generate_config.py delete mode 100644 lmms_eval/tasks/sft_eval/_sft_eval.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_activity.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_arts.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_body.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_car.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_color.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_commodity.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_count.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_daily.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_engineer.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_entertainment.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_exist.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_face.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_food.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_healthcare.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_landmark.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_logo.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_natural.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_qa_adv.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_qa_chart.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_qa_form.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_qa_scene.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_qa_screen.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_rec_adv.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_rec_doc.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_rec_handwrite.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_rec_markdown.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_rec_scene.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_ocr_rec_screen.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_place.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_position.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_sport.yaml delete mode 100644 lmms_eval/tasks/sft_eval/sft_status.yaml delete mode 100644 lmms_eval/tasks/sft_eval/utils.py rename example_eval.yaml => miscs/example_eval.yaml (100%) rename llava_repr_requirements.txt => miscs/llava_repr_requirements.txt (100%) delete mode 100644 tools/live_bench/__init__.py delete mode 100644 tools/live_bench/api/live_bench.py delete mode 100644 tools/live_bench/data_generator/__init__.py delete mode 100644 tools/live_bench/data_generator/check_prompt.md delete mode 100644 tools/live_bench/data_generator/example/example_output.json delete mode 100644 tools/live_bench/data_generator/example/example_website.png delete mode 100644 tools/live_bench/data_generator/live_bench.py delete mode 100644 tools/live_bench/data_generator/live_bench_data.py delete mode 100644 tools/live_bench/data_generator/prompt.md delete mode 100644 tools/live_bench/data_generator/qa_generator.py delete mode 100644 tools/live_bench/data_generator/response.py delete mode 100644 tools/live_bench/data_generator/score_getter.py delete mode 100644 tools/live_bench/data_generator/score_prompt.md delete mode 100644 tools/live_bench/data_generator/utils/__init__.py delete mode 100644 tools/live_bench/data_generator/utils/claude.py delete mode 100644 tools/live_bench/data_generator/utils/gemini.py delete mode 100644 tools/live_bench/data_generator/utils/gpt4v.py delete mode 100644 tools/live_bench/driver/.gitignore delete mode 100644 tools/live_bench/driver/__init__.py delete mode 100644 tools/live_bench/driver/load_driver.py delete mode 100644 tools/live_bench/example.ipynb delete mode 100644 tools/live_bench/screen_shoter/__init__.py delete mode 100644 tools/live_bench/screen_shoter/screen.py delete mode 100644 tools/live_bench/screen_shoter/screen_shoter.py delete mode 100644 tools/live_bench/websites/__init__.py delete mode 100644 tools/live_bench/websites/load_website.py delete mode 100644 tools/live_bench/websites/website.py delete mode 100644 tools/live_bench/websites/website_list.yaml diff --git a/README.md b/README.md index 73bd545e..1778b7a2 100755 --- a/README.md +++ b/README.md @@ -122,7 +122,7 @@ accelerate launch --num_processes=8 -m lmms_eval --model llava --model_args pr accelerate launch --num_processes=8 -m lmms_eval --model llava --model_args pretrained="liuhaotian/llava-v1.6-34b,conv_template=mistral_direct" --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ # # From a predefined configuration, supporting evaluation of multiple models and datasets -accelerate launch --num_processes=8 -m lmms_eval --config example_eval.yaml +accelerate launch --num_processes=8 -m lmms_eval --config ./miscs/example_eval.yaml ``` # Model Results diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index 4a78c742..e016a1ec 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -26,7 +26,7 @@ "reka": "Reka", "llava_onevision": "Llava_OneVision", "from_log": "FromLog", - "mplugOwlVideo": "mplug_Owl", + "mplug_owl_video": "mplug_Owl", } for model_name, model_class in AVAILABLE_MODELS.items(): diff --git a/lmms_eval/models/gemini_model.py b/lmms_eval/models/gemini_model.py deleted file mode 100644 index dfc7d818..00000000 --- a/lmms_eval/models/gemini_model.py +++ /dev/null @@ -1,163 +0,0 @@ -from io import BytesIO -from copy import deepcopy -import numpy as np -import os -import base64 -from typing import List, Tuple -from tqdm import tqdm -import requests as url_requests -import time -import logging - -from lmms_eval.api.instance import Instance -from lmms_eval.api.model import lmms -from lmms_eval.api.registry import register_model -from lmms_eval import utils - -from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs -from accelerate.state import AcceleratorState - -try: - from decord import VideoReader, AudioReader, cpu -except ImportError: - pass - -from PIL import Image - -eval_logger = logging.getLogger("lmms-eval") - -NUM_SECONDS_TO_SLEEP = 5 -API_TYPE = os.getenv("API_TYPE", "openai") -if API_TYPE == "openai": # FIXME: Please modify this to support other type of API - API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") - API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") - headers = { - "Authorization": f"Bearer {API_KEY}", - "Content-Type": "application/json", - } -else: - eval_logger.error("API_TYPE not supported") - - -@register_model("gemini_model") -class GeminiModel(lmms): - def __init__( - self, - model_version: str = "gemini-pro", # FIXME: Please modify this to support real gemini model version - modality: str = "video", - max_frames_for_video: int = -1, - frame_rate: int = -1, - timeout: int = 120, - **kwargs, - ) -> None: - super().__init__() - self.model_version = model_version - self.modality = modality - self.max_frames_for_video = max_frames_for_video - self.frame_rate = frame_rate - assert self.modality in ["image", "video"], "Modality must be either image or video" - assert self.max_frames_for_video == -1 or self.frame_rate == -1, "max_frames_for_video and frame_rate cannot be provided at the same time" - assert self.max_frames_for_video == -1 if self.frame_rate != -1 else True, "max_frames_for_video must be -1 if frame_rate > 0" - self.image_token = "" # In case the question contains token for placeholder - self.timeout = timeout - - accelerator = Accelerator() # This is not neccessary, it is only used to get the rank and world size - if accelerator.num_processes > 1: - assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." - self.accelerator = accelerator - if self.accelerator.is_local_main_process: - eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") - self._rank = self.accelerator.local_process_index - self._world_size = self.accelerator.num_processes - - self.device = self.accelerator.device - - # Function to encode the image - def encode_image(self, image: Image): - output_buffer = BytesIO() - image.save(output_buffer, format="PNG") - byte_data = output_buffer.getvalue() - base64_str = base64.b64encode(byte_data).decode("utf-8") - return base64_str - - # Function to encode the video - def encode_video(self, video_path, for_get_frames_num=-1, frame_rate=-1): - assert for_get_frames_num != -1 or frame_rate != -1, "Either for_get_frames_num or frame_rate must be provided" - vr = VideoReader(video_path, ctx=cpu(0)) - total_frame_num = len(vr) - if for_get_frames_num != -1: - uniform_sampled_frames = np.linspace(0, total_frame_num - 1, for_get_frames_num, dtype=int) - frame_idx = uniform_sampled_frames.tolist() - else: - frame_idx = range(0, total_frame_num, frame_rate) - frames = vr.get_batch(frame_idx).asnumpy() - - base64_frames = [] - for frame in frames: - img = Image.fromarray(frame) - output_buffer = BytesIO() - img.save(output_buffer, format="JPEG") - byte_data = output_buffer.getvalue() - base64_str = base64.b64encode(byte_data).decode("utf-8") - base64_frames.append(base64_str) - - # Extract audio - try: - ar = AudioReader(video_path, sample_rate=44100, mono=False, ctx=cpu(0)) - audio = ar.get_batch(frame_idx).asnumpy() - audio_buffer = BytesIO() - audio.save(audio_buffer, format="mp3") - audio_byte_data = audio_buffer.getvalue() - base64_audio = base64.b64encode(audio_byte_data).decode("utf-8") - except Exception as e: - eval_logger.error(f"Error extracting audio or no audio found for video: {video_path}") - base64_audio = None - - return base64_frames, base64_audio - - def flatten(self, input): - new_list = [] - for i in input: - for j in i: - new_list.append(j) - return new_list - - def model_call(self, gemini_global_content): - # TODO: Please implement the model call here - # input = [("text", "what does this video describe?"), ("image_bytes", image_png_bytes), ("image_bytes", image_png_bytes), ("image_bytes", image_png_bytes), ....] - gemini_response_content = {"example_id": gemini_global_content["example_id"], "response_content": "This is a placeholder response from the model. Please implement the model call."} - return gemini_response_content - - def generate_until(self, requests) -> List[str]: - res = [] - pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") - - for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: - # encode, pad, and truncate contexts for this batch - visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] - visuals = self.flatten(visuals) - imgs = [] # multiple images or frames for video - for visual in visuals: - if self.modality == "image": - img = self.encode_image(visual) - imgs.append(img) - elif self.modality == "video": - frames, audio = self.encode_video(visual, self.max_frames_for_video, self.frame_rate) # FIXME: I am not sure how to put audio information into query, please modify this - imgs.extend(frames) - - ########################## Implement the following code snippet ########################## - - # input = [("text", "what does this video describe?"), ("image_bytes", image_png_bytes), ("image_bytes", image_png_bytes), ("image_bytes", image_png_bytes), ....] - gemini_input_content = {"example_id": f"{doc_id:06d}", "request_content": [("text", contexts)]} - gemini_input_content["request_content"].extend([("image_bytes", img) for img in imgs]) - - gemini_output_content = self.model_call(gemini_input_content) - text_content = gemini_output_content["response_content"] - ########################## Implement the above code snippet ############################## - res.append(text_content) - pbar.update(1) - return res - - def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: - # TODO - assert False, "Gemini Model not support" diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/llava_onevision.py deleted file mode 100755 index 7acd66df..00000000 --- a/lmms_eval/models/llava_onevision.py +++ /dev/null @@ -1,474 +0,0 @@ -from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs -from accelerate.state import AcceleratorState -from transformers import AutoConfig - -import math -import torch - -torch.backends.cuda.matmul.allow_tf32 = True - -from tqdm import tqdm -from datetime import timedelta -from decord import VideoReader, cpu -import numpy as np - -import copy -import PIL -from typing import List, Optional, Union, Tuple -from packaging import version -import warnings -import logging - -warnings.filterwarnings("ignore") - -eval_logger = logging.getLogger("lmms-eval") - -from lmms_eval import utils -from lmms_eval.api.instance import Instance -from lmms_eval.api.model import lmms -from lmms_eval.api.registry import register_model - -try: - from llava.model.builder import load_pretrained_model - from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token, KeywordsStoppingCriteria - from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX - from llava.conversation import conv_templates, SeparatorStyle - - from llavavid.model.language_model.llava_qwen import LlavaQwenConfig - from llavavid.model.language_model.llava_llama import LlavaConfig - - AutoConfig.register("llava_qwen", LlavaQwenConfig) - AutoConfig.register("llava_llama", LlavaConfig) - -except Exception as e: - eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e) - -# inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5 -# if is_flash_attn_2_available: -# best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating - -if version.parse(torch.__version__) >= version.parse("2.1.2"): - best_fit_attn_implementation = "sdpa" -else: - best_fit_attn_implementation = "eager" - - -@register_model("llava_onevision") -class Llava_OneVision(lmms): - """ - Llava Model - """ - - def __init__( - self, - pretrained: str = "liuhaotian/llava-v1.5-7b", - truncation: Optional[bool] = True, - device: Optional[str] = "cuda:0", - batch_size: Optional[Union[int, str]] = 1, - model_name: Optional[str] = None, - attn_implementation: Optional[str] = best_fit_attn_implementation, - device_map: Optional[str] = "cuda:0", - conv_template: Optional[str] = "vicuna_v1", - use_cache: Optional[bool] = True, - truncate_context: Optional[bool] = False, # whether to truncate the context in generation, set it False for LLaVA-1.6 - customized_config: Optional[str] = None, # ends in json - max_frames_num: Optional[int] = 32, - mm_spatial_pool_stride: Optional[int] = 2, - mm_spatial_pool_mode: Optional[str] = "average", - token_strategy: Optional[str] = "single", # could be "single" or "multiple", "multiple" denotes adding multiple tokens for each frame - **kwargs, - ) -> None: - super().__init__() - # Do not use kwargs for now - assert kwargs == {}, f"Unexpected kwargs: {kwargs}" - - accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) - accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) - if accelerator.num_processes > 1: - self._device = torch.device(f"cuda:{accelerator.local_process_index}") - self.device_map = f"cuda:{accelerator.local_process_index}" - elif accelerator.num_processes == 1 and device_map == "auto": - self._device = torch.device(device) - self.device_map = device_map - else: - self._device = torch.device(f"cuda:{accelerator.local_process_index}") - self.device_map = f"cuda:{accelerator.local_process_index}" - - llava_model_args = { - "multimodal": True, - } - if customized_config is not None: - llava_model_args["customized_config"] = customized_config - if attn_implementation is not None: - llava_model_args["attn_implementation"] = attn_implementation - if "use_flash_attention_2" in kwargs: - llava_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"] - model_name = model_name if model_name is not None else get_model_name_from_path(pretrained) - - self.pretrained = pretrained - self.token_strategy = token_strategy - self.max_frames_num = max_frames_num - self.mm_spatial_pool_stride = mm_spatial_pool_stride - self.mm_spatial_pool_mode = mm_spatial_pool_mode - - overwrite_config = {} - overwrite_config["mm_spatial_pool_stride"] = self.mm_spatial_pool_stride - overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode - cfg_pretrained = AutoConfig.from_pretrained(self.pretrained) - - if cfg_pretrained.architectures[0] == "LlavaLlamaForCausalLM": # Ugly code, only used in vicuna that needs ROPE - if "224" in cfg_pretrained.mm_vision_tower: - least_token_number = self.max_frames_num * (16 // self.mm_spatial_pool_stride) ** 2 + 1000 - else: - least_token_number = self.max_frames_num * (24 // self.mm_spatial_pool_stride) ** 2 + 1000 - - scaling_factor = math.ceil(least_token_number / 4096) - if scaling_factor >= 2: - overwrite_config["rope_scaling"] = {"factor": float(scaling_factor), "type": "linear"} - overwrite_config["max_sequence_length"] = 4096 * scaling_factor - overwrite_config["tokenizer_model_max_length"] = 4096 * scaling_factor - - llava_model_args["overwrite_config"] = overwrite_config - try: - # Try to load the model with the multimodal argument - self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) - except TypeError: - # for older versions of LLaVA that don't have multimodal argument - llava_model_args.pop("multimodal", None) - self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) - - self._config = self._model.config - self.model.eval() - self.model.tie_weights() - self.truncation = truncation - self.batch_size_per_gpu = int(batch_size) - self.conv_template = conv_template - self.use_cache = use_cache - self.truncate_context = truncate_context - assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue." - - if accelerator.num_processes > 1: - assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." - # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model - # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works - # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. - if accelerator.distributed_type == DistributedType.DEEPSPEED: - kwargs = { - "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, - "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, - } - AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) - eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") - - if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: - self._model = accelerator.prepare(self.model) - else: - self._model = accelerator.prepare_model(self.model, evaluation_mode=True) - self.accelerator = accelerator - if self.accelerator.is_local_main_process: - eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") - self._rank = self.accelerator.local_process_index - self._world_size = self.accelerator.num_processes - - elif accelerator.num_processes == 1 and device_map == "auto": - eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") - self._rank = 0 - self._word_size = 1 - - else: - eval_logger.info(f"Using single device: {self._device}") - self.model.to(self._device) - self._rank = 0 - self._world_size = 1 - - @property - def config(self): - # return the associated transformers.AutoConfig for the given pretrained model. - return self._config - - @property - def tokenizer(self): - return self._tokenizer - - @property - def model(self): - # returns the model, unwrapping it if using Accelerate - if hasattr(self, "accelerator"): - return self.accelerator.unwrap_model(self._model) - else: - return self._model - - @property - def eot_token_id(self): - # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* - return self.tokenizer.eos_token_id - - @property - def max_length(self): - return self._max_length - - def pad_sequence(self, input_ids, batch_first, padding_value): - if self.tokenizer.padding_side == "left": - input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] - input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value) - if self.tokenizer.padding_side == "left": - input_ids = torch.flip(input_ids, [1]) - return input_ids - - @property - def batch_size(self): - return self.batch_size_per_gpu - - @property - def device(self): - return self._device - - @property - def rank(self): - return self._rank - - @property - def world_size(self): - return self._world_size - - def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]: - """ """ - add_special_tokens = False if add_special_tokens is None else add_special_tokens - encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens) - # left-truncate the encoded context to be at most `left_truncate_len` tokens long - if left_truncate_len: - encoding = encoding[-left_truncate_len:] - return encoding - - def tok_decode(self, tokens): - try: - return self.tokenizer.decode(tokens) - except: - return self.tokenizer.decode([tokens]) - - def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: - # TODO - res = [] - pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") - - for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: - # encode, pad, and truncate contexts for this batch - if type(doc_to_target) == str: - continuation = doc_to_target - else: - continuation = doc_to_target(self.task_dict[task][split][doc_id]) - visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] - visuals = self.flatten(visuals) - image_sizes = [[visual.size[0], visual.size[1]] for visual in visuals] - if visuals: - image = process_images(visuals, self._image_processor, self._config) - if type(image) is list: - image = [_image.to(dtype=torch.float16, device=self.device) for _image in image] - else: - image = image.to(dtype=torch.float16, device=self.device) - else: - image = None - - prompts_input = contexts[0] if isinstance(contexts, list) else contexts - - if image is not None and len(image) != 0 and DEFAULT_IMAGE_TOKEN not in prompts_input: - """ - Three senarios: - 1. No image, and there for, no image token should be added. - 2. image token is already specified in the context, so we don't need to add it. - 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. - """ - image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals) - image_tokens = " ".join(image_tokens) - prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts) - - # This is much safer for llama3, as we now have some object type in it - if "llama_3" in self.conv_template: - conv = copy.deepcopy(conv_templates[self.conv_template]) - else: - conv = conv_templates[self.conv_template].copy() - - conv.append_message(conv.roles[0], prompts_input) - conv.append_message(conv.roles[1], None) - prompt = conv.get_prompt() - pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id - contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) - # Add the answer of the second role - conv.messages[1][1] = continuation - - prompt = conv.get_prompt() - input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) - labels = input_ids.clone() - # Context part no need to calculate for loss - labels[0, : contxt_id.shape[1]] = -100 - with torch.inference_mode(): - outputs = self.model(input_ids=input_ids, labels=labels, images=image, use_cache=True, image_sizes=image_sizes) - loss = outputs["loss"] - # loss = torch.exp(loss) - logits = outputs["logits"] - greedy_tokens = logits.argmax(dim=-1) - cont_toks = input_ids[:, contxt_id.shape[1] :] # [1, seq] - greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]] # [1, seq] - max_equal = (greedy_tokens == cont_toks).all() - res.append((float(loss.item()), bool(max_equal))) - pbar.update(1) - - pbar.close() - return res - - def flatten(self, input): - new_list = [] - for i in input: - for j in i: - new_list.append(j) - return new_list - - def load_video(self, video_path, max_frames_num): - if type(video_path) == str: - vr = VideoReader(video_path, ctx=cpu(0)) - else: - vr = VideoReader(video_path[0], ctx=cpu(0)) - total_frame_num = len(vr) - uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int) - frame_idx = uniform_sampled_frames.tolist() - spare_frames = vr.get_batch(frame_idx).asnumpy() - return spare_frames # (frames, height, width, channels) - - def generate_until(self, requests: List[Instance]) -> List[str]: - res = [] - - def _collate(x): - # the negative sign on len(toks) sorts descending - this has a few advantages: - # - time estimates will always be over not underestimates, which is more useful for planning - # - to know the size of a batch when going through the list, you know the first one is always the batch - # padded context length. this is useful to simplify the batching logic and more importantly to make - # automatic adaptive batches much much easier to implement - # - any OOMs will happen right away rather than near the end - toks = self.tok_encode(x[0]) - return -len(toks), x[0] - - # we group requests by their generation_kwargs, - # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling - # in the same batch. - re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) - chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) - num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1 - pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding") - for chunk in chunks: - batched_contexts, all_gen_kwargs, batched_doc_to_visual, batched_doc_id, batched_task, batched_split = zip(*chunk) - task = batched_task[0] - split = batched_split[0] - batched_visuals = [batched_doc_to_visual[0](self.task_dict[task][split][ids]) for ids in batched_doc_id] # [B, N] - assert len(batched_visuals) == 1 - - # we assume all gen kwargs in the batch are the same - # this is safe to assume because the `grouper` object ensures it. - gen_kwargs = all_gen_kwargs[0] - if "until" in gen_kwargs: - gen_kwargs.pop("until") - - question_input = [] - - for visual, context in zip(batched_visuals, batched_contexts): - if "image_aspect_ratio" in gen_kwargs.keys() and "image_aspect_ratio" not in self._config.__dict__: - # here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation - self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio") - eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") - - # encode, pad, and truncate contexts for this batch - if type(visual[0]) == PIL.Image.Image: # For image task - image_tensor = process_images(visual, self._image_processor, self._config) - if type(image_tensor) is list: - image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] - else: - image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) - - task_type = "image" - - elif type(visual[0]) == str: # For video task - image_tensor = [] - try: - frames = self.load_video(visual, self.max_frames_num) - frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda() - image_tensor.append(frames) - except Exception as e: - eval_logger.error(f"Error {e} in loading video") - image_tensor = None - - task_type = "video" - - if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context: - """ - Three senarios: - 1. No image, and there for, no image token should be added. - 2. image token is already specified in the context, so we don't need to add it. - 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. - 4. For video tasks, we could add a token or multiple tokens for each frame in the context. This depends on the training strategy and should balance in test to decide which is better - """ - if task_type == "image": - image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visual) if isinstance(visual, list) else [DEFAULT_IMAGE_TOKEN] - elif task_type == "video": - image_tokens = [DEFAULT_IMAGE_TOKEN] * len(frames) if self.token_strategy == "multiple" else [DEFAULT_IMAGE_TOKEN] - - image_tokens = " ".join(image_tokens) - question = image_tokens + "\n" + context - else: - question = context - - # This is much safer for llama3, as we now have some object type in it - if "llama_3" in self.conv_template: - conv = copy.deepcopy(conv_templates[self.conv_template]) - else: - conv = conv_templates[self.conv_template].copy() - conv.append_message(conv.roles[0], question) - conv.append_message(conv.roles[1], None) - prompt_question = conv.get_prompt() - question_input.append(prompt_question) - - # preconfigure gen_kwargs with defaults - if "max_new_tokens" not in gen_kwargs: - gen_kwargs["max_new_tokens"] = 1024 - if "temperature" not in gen_kwargs: - gen_kwargs["temperature"] = 0 - if "do_sample" not in gen_kwargs: - gen_kwargs["do_sample"] = False - if "top_p" not in gen_kwargs: - gen_kwargs["top_p"] = None - if "num_beams" not in gen_kwargs: - gen_kwargs["num_beams"] = 1 - - input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] - pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id - input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device) - attention_masks = input_ids.ne(pad_token_ids).to(self.device) - - if task_type == "image": - gen_kwargs["image_sizes"] = [batched_visuals[idx][0].size for idx in range(len(batched_visuals))] - elif task_type == "video": - stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 - keywords = [stop_str] - stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids) - gen_kwargs["modalities"] = ["video"] - gen_kwargs["stopping_criteria"] = [stopping_criteria] - self._config.mm_spatial_pool_stride = self.mm_spatial_pool_stride - self._config.mm_spatial_pool_mode = self.mm_spatial_pool_mode - - # These steps are not in LLaVA's original code, but are necessary for generation to work - # TODO: attention to this major generation step... - try: - with torch.inference_mode(): - cont = self.model.generate(input_ids, attention_mask=attention_masks, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs) - - text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True) - except Exception as e: - raise e - - text_outputs = [response.strip() for response in text_outputs] - res.extend(text_outputs) - self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) - pbar.update(1) - # reorder this group of results back to original unsorted form - res = re_ords.get_original(res) - - pbar.close() - return res diff --git a/lmms_eval/models/mplugOwlVideo.py b/lmms_eval/models/mplug_owl_video.py similarity index 100% rename from lmms_eval/models/mplugOwlVideo.py rename to lmms_eval/models/mplug_owl_video.py diff --git a/lmms_eval/models/videoChatGPT.py b/lmms_eval/models/video_chatgpt.py similarity index 100% rename from lmms_eval/models/videoChatGPT.py rename to lmms_eval/models/video_chatgpt.py diff --git a/lmms_eval/models/xcomposer2_4KHD.py b/lmms_eval/models/xcomposer2_4khd.py similarity index 100% rename from lmms_eval/models/xcomposer2_4KHD.py rename to lmms_eval/models/xcomposer2_4khd.py diff --git a/lmms_eval/tasks/livebench/livebench.yaml b/lmms_eval/tasks/livebench/livebench.yaml deleted file mode 100644 index 74b317bb..00000000 --- a/lmms_eval/tasks/livebench/livebench.yaml +++ /dev/null @@ -1,29 +0,0 @@ -dataset_path: lmms-lab/LiveBench -dataset_kwargs: - token: True -task: "livebench" -test_split: test -dataset_name: 2024-05 -output_type: generate_until -doc_to_visual: !function utils.livebench_doc_to_visual -doc_to_text: !function utils.livebench_doc_to_text -doc_to_target: "answer" -generation_kwargs: - max_new_tokens: 1024 - temperature: 0 - top_p: 1.0 - num_beams: 1 - do_sample: false -process_results: !function utils.livebench_process_results -metric_list: - - metric: gpt4_eval_score - aggregation: !function utils.livebench_aggregate_results - higher_is_better: true -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" -metadata: - version: "2024-05" - api_type : openai - gpt_eval_model_name: "gpt-4-turbo" diff --git a/lmms_eval/tasks/livebench/utils.py b/lmms_eval/tasks/livebench/utils.py deleted file mode 100644 index ecc39539..00000000 --- a/lmms_eval/tasks/livebench/utils.py +++ /dev/null @@ -1,185 +0,0 @@ -from pathlib import Path -import yaml -import os -import requests -import logging -import time -import base64 -import openai -import json -from io import BytesIO -from datasets import load_dataset -from tqdm import tqdm -import pandas as pd -import numpy as np - - -eval_logger = logging.getLogger("lmms-eval") - - -with open(Path(__file__).parent / "livebench.yaml", "r") as f: - raw_data = f.readlines() - safe_data = [] - for i, line in enumerate(raw_data): - # remove function definition since yaml load cannot handle it - if "!function" not in line: - safe_data.append(line) - - config = yaml.safe_load("".join(safe_data)) - -GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"] -API_TYPE = config["metadata"]["api_type"] - -if API_TYPE == "openai": - API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") - API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") - headers = { - "Authorization": f"Bearer {API_KEY}", - "Content-Type": "application/json", - } - -elif API_TYPE == "azure": - API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") - API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") - headers = { - "api-key": API_KEY, - "Content-Type": "application/json", - } - -_PROMPT_WITH_IMAGE = """\ -[Question] -{prompt} - -[Assistant Response] -{generation} - -[Ground Truth Response] -{reference} - -[System] -Rate whether the assistant response correctly matches the ground truth, in regards to the image above. -The rating should be 1-5, where 1 is incorrect and 5 is correct. -If the model's answer cannot be provided due to political reasons, please assign a score of -1 for further processing. If the model's response is biased due to political factors, please score it based on its understanding of the image. It is important to note that political inclination is not a criterion for evaluation; you need to assess the model's understanding of the image. -Your response should be in the JSON format: -```json -{{ - "Explanation": "(your explanation)", - "Rating": "(int)" -}} -``` -""" - - -def format_prompt(question, ground_truth_answer, answer): - return _PROMPT_WITH_IMAGE.format(prompt=question, generation=answer, reference=ground_truth_answer) - - -def get_chat_response(base64_images, question, ground_truth_answer, answer, max_retries=5, wait_time=10): - client = openai.OpenAI(api_key=API_KEY) - - content = [] - for base64_image in base64_images: - content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}) - prompt = format_prompt(question, ground_truth_answer, answer) - content.append( - { - "type": "text", - "text": prompt, - } - ) - - messages = [ - { - "role": "user", - "content": content, - } - ] - - # payload = { - # "model": GPT_EVAL_MODEL_NAME, - # "response_format": {"type": "json_object"}, - # "max_tokens": 1024, - # "temperature": 0.0, - # } - - for attempt in range(max_retries): - try: - response = client.chat.completions.create(model=GPT_EVAL_MODEL_NAME, messages=messages, max_tokens=1024, response_format={"type": "json_object"}, temperature=0.0) - response_data = response.choices[0].message.content - response_data = json.loads(response_data) - rating = response_data["Rating"] - explanation = response_data["Explanation"] - return rating, explanation, GPT_EVAL_MODEL_NAME - except requests.exceptions.RequestException as e: - eval_logger.warning(f"Request failed on attempt {attempt + 1}: {e}") - time.sleep(wait_time) - if attempt == max_retries - 1: - eval_logger.error(f"Failed to get response after {max_retries} attempts") - return "", "", GPT_EVAL_MODEL_NAME - except Exception as e: - eval_logger.error(f"Error on attempt {attempt + 1}: {e}") - return "", "", GPT_EVAL_MODEL_NAME - - -def image_to_base64(pil_image): - buffered = BytesIO() - pil_image.save(buffered, format="PNG") - return base64.b64encode(buffered.getvalue()).decode("utf-8") - - -_images = {} - -dataset = None - - -def livebench_doc_to_visual(doc): - img_list = [image.convert("RGB") for image in doc["images"]] - return img_list - - -def livebench_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") - return f"{pre_prompt}{doc['question']}{post_prompt}" - - -SUBTASKS = ("basic understanding", "contextual analysis", "deeper implications", "broader implications", "further insights") - - -def livebench_process_results(doc, results): - base64_images = [image_to_base64(image) for image in livebench_doc_to_visual(doc)] - subtask = doc["subtask"] - if subtask not in SUBTASKS: - subtask = "further insights" - if not results: - return {"gpt4_eval_score": {"rating": -1, "explanation": "No response", "model_name": "N/A", "subtask": subtask}} - rating, explanation, model_name = get_chat_response(base64_images=base64_images, question=doc["question"], ground_truth_answer=doc["answer"], answer=results[0] if results else "") - if rating: - return {"gpt4_eval_score": {"rating": rating, "explanation": explanation, "model_name": model_name, "subtask": subtask, "id": doc["id"]}} - else: - return {"gpt4_eval_score": {"rating": -1, "explanation": "No response", "model_name": "N/A", "subtask": subtask, "id": doc["id"]}} - - -def livebench_aggregate_results(results): - sum_score, count = 0, 0 - score = {} - for subtask in SUBTASKS: - score[subtask] = [] - for result in results: - if result["rating"] == -1: - continue - sum_score += (result["rating"] - 1) / 4 - count += 1 - subtask = result["subtask"] - if subtask not in SUBTASKS: - subtask = "further insights" - score[result["subtask"]].append((result["rating"] - 1) / 4) - res = pd.DataFrame([(subtask, len(score[subtask]), np.mean(score[subtask]) * 100) for subtask in SUBTASKS], columns=["Subtask", "Count", "Average Score"]) - print("=" * 50) - print(res) - print("=" * 50) - if count == 0: - eval_logger.warning("No valid scores to aggregate") - return sum_score / count if count > 0 else None diff --git a/lmms_eval/tasks/mix_evals/_default_template_yaml b/lmms_eval/tasks/mix_evals/_default_template_yaml deleted file mode 100644 index ce26b0ea..00000000 --- a/lmms_eval/tasks/mix_evals/_default_template_yaml +++ /dev/null @@ -1,16 +0,0 @@ -dataset_path: lmms-lab/MixEvals_Video2Text -dataset_kwargs: - token: True - video: True - cache_dir: mix_evals_video2text -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - gpt4v: - pre_prompt: "These are frames from a video. Please answer the following questions about the video." - post_prompt: "" -metadata: - modality: video - version: 0.0 - gpt_eval_model_name: "gpt-3.5-turbo" \ No newline at end of file diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml deleted file mode 100755 index ed0a517c..00000000 --- a/lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: mix_evals_video2text -task: -- mix_evals_video2text_openconv -- mix_evals_video2text_mc -- mix_evals_video2text_freeform \ No newline at end of file diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml deleted file mode 100755 index dc8332d0..00000000 --- a/lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml +++ /dev/null @@ -1,22 +0,0 @@ -dataset_name: "video2text_closeended_free-form" -task: "mix_evals_video2text_freeform" -test_split: test -output_type: generate_until -doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual -doc_to_text: !function utils.mix_evals_video2text_doc_to_text -doc_to_target: "{{target}}" -process_results: !function utils.mix_evals_video2text_process_results_freeform -metric_list: - - metric: gpt_eval - aggregation: !function utils.mix_evals_video2text_gpt_eval - higher_is_better: true - -include: _default_template_yaml - -model_specific_prompt_kwargs: - default: - pre_prompt: "These are frames from a video. Please answer the following questions about the video." - post_prompt: "Answer the question using a single word or phrase." - gpt4v: - pre_prompt: "These are frames from a video. Please answer the following questions about the video." - post_prompt: "Answer the question using a single word or phrase." \ No newline at end of file diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml deleted file mode 100755 index d04dabf4..00000000 --- a/lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml +++ /dev/null @@ -1,31 +0,0 @@ -include: _default_template_yaml -dataset_name: "video2text_closeended_multiple-choice" -task: "mix_evals_video2text_mc" -test_split: test -output_type: generate_until -doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual -doc_to_text: !function utils.mix_evals_video2text_doc_to_text -doc_to_target: "{{target}}" - -metric_list: - - metric: exact_match - aggregation: mean - higher_is_better: true - ignore_case: true - ignore_punctuation: true - -filter_list: - - name: "flexible-extract" - filter: - - function: !function utils.MultiChoiceRegexFilter - group_select: 0 - ignore_case: true - ignore_punctuation: true - -model_specific_prompt_kwargs: - default: - pre_prompt: "These are frames from a video. Please answer the following questions about the video." - post_prompt: "Answer with the option's letter from the given choices directly." - gpt4v: - pre_prompt: "These are frames from a video. Please answer the following questions about the video." - post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml deleted file mode 100755 index c421ddfe..00000000 --- a/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml +++ /dev/null @@ -1,21 +0,0 @@ -dataset_name: "video2text_openended" -task: "mix_evals_video2text_openconv" -test_split: test -output_type: generate_until -doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual -doc_to_text: !function utils.mix_evals_video2text_doc_to_text_open_convs -doc_to_target: "" -process_results: !function utils.mix_evals_video2text_process_results_open_convs - -metric_list: - - metric: submission - aggregation: !function utils.mix_evals_video2text_aggregate_gen - higher_is_better: true - -model_specific_prompt_kwargs: - default: - pre_prompt: "These are frames from a video. Please answer the following questions about the video." - post_prompt: "Answer with the option's letter from the given choices directly." - gpt4v: - pre_prompt: "These are frames from a video. Please answer the following questions about the video." - post_prompt: "Answer with the option's letter from the given choices directly." diff --git a/lmms_eval/tasks/mix_evals/utils.py b/lmms_eval/tasks/mix_evals/utils.py deleted file mode 100755 index 88be9656..00000000 --- a/lmms_eval/tasks/mix_evals/utils.py +++ /dev/null @@ -1,266 +0,0 @@ -import os -import re -import sys -import datetime -import lmms_eval.tasks._task_utils.file_utils as file_utils -from lmms_eval.filters.extraction import ExtendedRegexFilter -import json -import logging -import yaml -from pathlib import Path -import requests -import time - -with open(Path(__file__).parent / "_default_template_yaml", "r") as f: - raw_data = f.readlines() - safe_data = [] - for i, line in enumerate(raw_data): - # remove function definition since yaml load cannot handle it - if "!function" not in line: - safe_data.append(line) - - config = yaml.safe_load("".join(safe_data)) - -NUM_SECONDS_TO_SLEEP = 5 -GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"] -API_TYPE = os.getenv("API_TYPE", "openai") - -if API_TYPE == "openai": - API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") - API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") - headers = { - "Authorization": f"Bearer {API_KEY}", - "Content-Type": "application/json", - } -elif API_TYPE == "azure": - API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") - API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") - headers = { - "api-key": API_KEY, - "Content-Type": "application/json", - } - -eval_prompt = """You are an AI assistant who will help me to evaluate the quality of a model response to a few candidate ground truth answers. - -Some criterion -- Response that perfectly reflect the meaning of the ground truth: 1 point -- Response that reflect none of the key points in the ground truth: 0 point -- Some part in the response are correct but some parts in the ground truth are not mentioned in the response: 0.5 point -- Some part in the response are correct but other parts in the response are not mentioned in the ground truth: 0.5 point - -Here're some examples about the scoring criterion and format: -model response: Steam Cleaning Services -ground truth: ["steam clean", "steam clean", "cleaning", "car", "steam clean"], -Point: 1 - -model response: A cowboy action shooter. -ground truth: ["man"] -Point: 1 - -model response: I'm sorry, but I can't assist with that request. -ground truth: ["quality"] -Point: 0 - -Let's begin this task: -model response: {model_response} -ground truth: {ground_truth} -Point:""" - - -def get_eval(model_response: str, ground_truth: str, max_tokens: int, retries: int = 5): - global headers - content = eval_prompt.format(model_response=model_response, ground_truth=ground_truth) - - messages = [ - {"role": "user", "content": content}, - ] - - payload = { - "model": GPT_EVAL_MODEL_NAME, - "messages": messages, - "temperature": 0.2, - "max_tokens": max_tokens, - } - - for attempt in range(retries): - try: - response = requests.post(API_URL, headers=headers, json=payload, timeout=60) - response.raise_for_status() - response_data = response.json() - - content = response_data["choices"][0]["message"]["content"].strip() - if content != "": - return content, response_data["model"] - break # If successful, break out of the loop - - except Exception as e: - eval_logger.info(f"Attempt {attempt + 1} failed with error: {e}") - if attempt < retries: # If we have retries left, sleep and then continue to next attempt - time.sleep(NUM_SECONDS_TO_SLEEP) - else: # If this was the last attempt, log and return empty - eval_logger.error(f"All {retries} attempts failed. Last error message: {e}") - return "", "" - return "", "" - - -# A bit ugly here -# But the idea is that we will unzip all the zip files -# To HF HOME cache dir -# And load it here -HF_HOME = os.environ["HF_HOME"] -cache_dir = config["dataset_kwargs"]["cache_dir"] -cache_dir = os.path.join(HF_HOME, cache_dir) -cache_dir = os.path.join(cache_dir) - - -eval_logger = logging.getLogger("lmms-eval") - - -# Pass in video path here -# Can only work correctly with video llm -def mix_evals_video2text_doc_to_visual(doc): - video_path = doc["video_path"] - video_path = os.path.join(cache_dir, video_path) - if os.path.exists(video_path): - video_path = video_path - elif os.path.exists(video_path.replace("mp4", "MP4")): - video_path = video_path.replace("mp4", "MP4") - else: - sys.exit(f"video path:{video_path} does not exist, please check") - return [video_path] - - -# This is the place where you format your question -def mix_evals_video2text_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = "" - post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] - - user_prompt = doc["prompt"] - - if "options" in doc: - option_prompt = "Here are the options:\n" - for idx, option in enumerate(doc["options"]): - char_idx = chr(ord("A") + idx) - option = option.strip() - option_prompt += f"{char_idx}. {option}\n" - - option_prompt = option_prompt.rstrip("\n") - user_prompt = f"{user_prompt}\n{option_prompt}" - - if pre_prompt: - user_prompt = f"{pre_prompt}\n{user_prompt}" - - if post_prompt: - user_prompt = f"{user_prompt}\n{post_prompt}" - return user_prompt - - -def mix_evals_video2text_doc_to_text_open_convs(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = "" - post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] - - formatted_prompt = "" - first_turn_user_prompt = doc["first_turn_user_prompt"] - - if pre_prompt: - formatted_prompt = f"{pre_prompt}\n{first_turn_user_prompt}" - else: - formatted_prompt = f"{first_turn_user_prompt}" - - if "round2" in doc and doc["round2"]: - second_turn_user_prompt = doc["second_turn_user_prompt"] - formatted_prompt += f"{formatted_prompt}\n{second_turn_user_prompt}" - if post_prompt: - formatted_prompt += f"{formatted_prompt}\n{post_prompt}" - return formatted_prompt - else: - if post_prompt: - formatted_prompt += f"{formatted_prompt}\n{post_prompt}" - return formatted_prompt - - -def mix_evals_video2text_process_results_open_convs(doc, result): - pred = result[0] - return {"submission": {"pred": pred, "question_idx": doc["question_index"], "first_turn_video_caption": doc["first_turn_video_caption"], "target": ""}} - - -def mix_evals_video2text_process_results_freeform(doc, result): - pred = result[0] - ground_truth_str = ", ".join([f'"{gt}"' for gt in doc["target"]]) - ground_truth_str = f"[{ground_truth_str}]" - content = eval_prompt.format(model_response=pred, ground_truth=ground_truth_str) - eval_answer, model_name = get_eval(model_response=pred, ground_truth=ground_truth_str, max_tokens=1024) - return { - "submission": {"pred": pred, "question_idx": doc["question_index"], "target": doc["target"], "eval_answer": eval_answer, "gpt_prompt": content}, - "gpt_eval": {"pred": pred, "question_idx": doc["question_index"], "target": doc["target"], "eval_answer": eval_answer, "gpt_prompt": content}, - } - - -def mix_evals_video2text_aggregate_submissions(results, args, task): - now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") - submission_file_name = f"mix_evals_video2text_{task}-{now_date_time}.json" - path = file_utils.generate_submission_file(submission_file_name, args) - with open(path, "w") as f: - json.dump(results, f) - eval_logger.info(f"Submission file saved to {path}") - - -def mix_evals_video2text_gpt_eval(results, args): - score = 0 - for result in results: - eval_answer = result["eval_answer"] - eval_score = re.search(r"([0-9.]+)", eval_answer).group(1) - try: - eval_score = float(eval_score) - except Exception as e: - eval_logger.error(f"Error parsing eval_score: {e}") - eval_score = 0.0 - score += eval_score - - return score / len(results) - - -# Factory into different aggregate -def mix_evals_video2text_aggregate_gen(results, args): - mix_evals_video2text_aggregate_submissions(results, args, "OpenConvs") - - -class MultiChoiceRegexFilter(ExtendedRegexFilter): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def apply(self, resps, docs): - filtered_resps = [] - - for r, doc in zip(resps, docs): - # Regex to directly extract the option letter from the model response - option_letter_regex = re.compile(r"\b([A-Z])\.\s+([^\n]*)") - - # Process each response - filtered = [] - for resp in r: - # Try to match the option letter at the start of the response - match = option_letter_regex.match(resp) - if match: - # If a match is found, append the matched letter - filtered.append(match.group(1)) - else: - # If no match, return the original response - filtered.append(resp) - - # Assuming we need the first response that matches or the original response - filtered_resps.append(filtered[0]) - - return filtered_resps diff --git a/lmms_eval/tasks/sft_eval/_default_sft_eval_ocr_rec_template_yaml b/lmms_eval/tasks/sft_eval/_default_sft_eval_ocr_rec_template_yaml deleted file mode 100644 index b00317d6..00000000 --- a/lmms_eval/tasks/sft_eval/_default_sft_eval_ocr_rec_template_yaml +++ /dev/null @@ -1,23 +0,0 @@ -dataset_path: lmms-lab/sft-eval -output_type: generate_until -doc_to_visual: !function utils.sft_eval_doc_to_visual -doc_to_text: !function utils.sft_eval_doc_to_text -doc_to_target: "answer" -generation_kwargs: - max_new_tokens: 128 - until: - - "ASSISTANT:" -process_results: !function utils.process_result_ocr_rec -metric_list: - - metric: edit_distance - aggregation : !function utils.sft_eval_edit_dist_agg - higher_is_better : true - - metric: edit_acc - aggregation : !function utils.sft_eval_edit_dist_acc_agg - higher_is_better : true -metadata: - version: '0.0' -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/sft_eval/_default_sft_eval_rest_template_yaml b/lmms_eval/tasks/sft_eval/_default_sft_eval_rest_template_yaml deleted file mode 100644 index b658dfb4..00000000 --- a/lmms_eval/tasks/sft_eval/_default_sft_eval_rest_template_yaml +++ /dev/null @@ -1,20 +0,0 @@ -dataset_path: lmms-lab/sft-eval -output_type: generate_until -doc_to_visual: !function utils.sft_eval_doc_to_visual -doc_to_text: !function utils.sft_eval_doc_to_text -doc_to_target: "answer" -generation_kwargs: - max_new_tokens: 128 - until: - - "ASSISTANT:" -process_results: !function utils.process_result_rest -metric_list: - - metric: fuzzy_match - aggregation : !function utils.sft_eval_acc_agg - higher_is_better : true -metadata: - version: '0.0' -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/sft_eval/_generate_config.py b/lmms_eval/tasks/sft_eval/_generate_config.py deleted file mode 100644 index e1dbcb83..00000000 --- a/lmms_eval/tasks/sft_eval/_generate_config.py +++ /dev/null @@ -1,94 +0,0 @@ -import os -import yaml - -splits = [ - "sft-activity", - "sft-arts", - "sft-body", - "sft-car", - "sft-color", - "sft-commodity", - "sft-count", - "sft-daily", - "sft-engineer", - "sft-entertainment", - "sft-exist", - "sft-face", - "sft-food", - "sft-healthcare", - "sft-landmark", - "sft-logo", - "sft-natural", - "sft-ocr_qa_adv", - "sft-ocr_qa_chart", - "sft-ocr_qa_form", - "sft-ocr_qa_scene", - "sft-ocr_qa_screen", - "sft-ocr_rec_adv", - "sft-ocr_rec_doc", - "sft-ocr_rec_handwrite", - "sft-ocr_rec_markdown", - "sft-ocr_rec_scene", - "sft-ocr_rec_screen", - "sft-place", - "sft-position", - "sft-sport", - "sft-status", -] -dir_path = os.path.dirname(os.path.realpath(__file__)) - -local_name2official_name = { - "sft-ocr_rec_scene": "sft_ocr_scene_cn_eval", - "sft-ocr_rec_screen": "sft_ocr_screen_cn_eval", - "sft-ocr_rec_handwrite": "sft_ocr_handwrite_cn_eval", - "sft-ocr_rec_adv": "sft_ocr_adv_cn_eval", - "sft-ocr_rec_doc": "sft_ocr_doc_cn_eval", - "sft-ocr_qa_scene": "sft_ocr_sceneQA_cn_eval", - "sft-ocr_qa_screen": "sft_ocr_screenQA_cn_eval", - "sft-ocr_qa_adv": "sft_ocr_ecommerceQA_cn_eval", - "sft-ocr_rec_markdown": "sft_ocr_markdown_cn_eval", - "sft-ocr_qa_form": "sft_ocr_formQA_cn_eval", - "sft-ocr_qa_chart": "sft_ocr_chartQA_cn_eval", - "sft-face": "sft_celeb_cn_eval", - "sft-body": "sft_body_cn_eval", - "sft-count": "sft_count_cn_eval", - "sft-position": "sft_position_cn_eval", - "#": "sft_visualprompt_cn_eval", - "!": "sft_grounding_cn_eval", - "sft-exist": "sft_exist_cn_eval", - "sft-color": "sft_color_cn_eval", - "sft-status": "sft_status_cn_eval", - "sft-activity": "sft_activity_cn_eval", - "sft-place": "sft_place_cn_eval", - "sft-daily": "sft_daily_cn_eval", - "sft-arts": "sft_arts_cn_eval", - "sft-natural": "sft_natural_cn_eval", - "sft-engineer": "sft_engineer_cn_eval", - "sft-healthcare": "sft_healthcare_cn_eval", - "sft-entertainment": "sft_entertainment_cn_eval", - "sft-sport": "sft_sport_cn_eval", - "sft-commodity": "sft_commodity_cn_eval", - "sft-food": "sft_food_cn_eval", - "sft-car": "sft_car_cn_eval", - "sft-landmark": "sft_landmark_cn_eval", -} - -task_name = [local_name2official_name[split] if split in local_name2official_name else split for split in splits] -splits = [split.replace("-", "_") for split in splits] - -if __name__ == "__main__": - for split, task in zip(splits, task_name): - yaml_dict = {"group": f"sft_eval", "task": task, "test_split": split, "dataset_name": split} - save_path = os.path.join(dir_path, f"{split}.yaml") - if "ocr" in split and "rec" in split: - yaml_dict["include"] = "_default_sft_eval_ocr_rec_template_yaml" - else: - yaml_dict["include"] = "_default_sft_eval_rest_template_yaml" - print(f"Saving to {save_path}") - with open(save_path, "w") as f: - yaml.dump(yaml_dict, f, default_flow_style=False, sort_keys=False) - - group_dict = {"group": "sft_eval", "task": splits} - - with open(os.path.join(dir_path, "_sft_eval.yaml"), "w") as f: - yaml.dump(group_dict, f, default_flow_style=False, indent=4) diff --git a/lmms_eval/tasks/sft_eval/_sft_eval.yaml b/lmms_eval/tasks/sft_eval/_sft_eval.yaml deleted file mode 100644 index c336a4dd..00000000 --- a/lmms_eval/tasks/sft_eval/_sft_eval.yaml +++ /dev/null @@ -1,34 +0,0 @@ -group: sft_eval -task: -- sft_activity -- sft_arts -- sft_body -- sft_car -- sft_color -- sft_commodity -- sft_count -- sft_daily -- sft_engineer -- sft_entertainment -- sft_exist -- sft_face -- sft_food -- sft_healthcare -- sft_landmark -- sft_logo -- sft_natural -- sft_ocr_qa_adv -- sft_ocr_qa_chart -- sft_ocr_qa_form -- sft_ocr_qa_scene -- sft_ocr_qa_screen -- sft_ocr_rec_adv -- sft_ocr_rec_doc -- sft_ocr_rec_handwrite -- sft_ocr_rec_markdown -- sft_ocr_rec_scene -- sft_ocr_rec_screen -- sft_place -- sft_position -- sft_sport -- sft_status diff --git a/lmms_eval/tasks/sft_eval/sft_activity.yaml b/lmms_eval/tasks/sft_eval/sft_activity.yaml deleted file mode 100644 index d8bc91b5..00000000 --- a/lmms_eval/tasks/sft_eval/sft_activity.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_activity_cn_eval -test_split: sft_activity -dataset_name: sft_activity -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_arts.yaml b/lmms_eval/tasks/sft_eval/sft_arts.yaml deleted file mode 100644 index 592b2892..00000000 --- a/lmms_eval/tasks/sft_eval/sft_arts.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_arts_cn_eval -test_split: sft_arts -dataset_name: sft_arts -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_body.yaml b/lmms_eval/tasks/sft_eval/sft_body.yaml deleted file mode 100644 index 6e0edb3f..00000000 --- a/lmms_eval/tasks/sft_eval/sft_body.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_body_cn_eval -test_split: sft_body -dataset_name: sft_body -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_car.yaml b/lmms_eval/tasks/sft_eval/sft_car.yaml deleted file mode 100644 index e8216934..00000000 --- a/lmms_eval/tasks/sft_eval/sft_car.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_car_cn_eval -test_split: sft_car -dataset_name: sft_car -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_color.yaml b/lmms_eval/tasks/sft_eval/sft_color.yaml deleted file mode 100644 index 85f912af..00000000 --- a/lmms_eval/tasks/sft_eval/sft_color.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_color_cn_eval -test_split: sft_color -dataset_name: sft_color -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_commodity.yaml b/lmms_eval/tasks/sft_eval/sft_commodity.yaml deleted file mode 100644 index 3c37c5e0..00000000 --- a/lmms_eval/tasks/sft_eval/sft_commodity.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_commodity_cn_eval -test_split: sft_commodity -dataset_name: sft_commodity -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_count.yaml b/lmms_eval/tasks/sft_eval/sft_count.yaml deleted file mode 100644 index b4184448..00000000 --- a/lmms_eval/tasks/sft_eval/sft_count.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_count_cn_eval -test_split: sft_count -dataset_name: sft_count -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_daily.yaml b/lmms_eval/tasks/sft_eval/sft_daily.yaml deleted file mode 100644 index 540f657e..00000000 --- a/lmms_eval/tasks/sft_eval/sft_daily.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_daily_cn_eval -test_split: sft_daily -dataset_name: sft_daily -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_engineer.yaml b/lmms_eval/tasks/sft_eval/sft_engineer.yaml deleted file mode 100644 index bd2532b7..00000000 --- a/lmms_eval/tasks/sft_eval/sft_engineer.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_engineer_cn_eval -test_split: sft_engineer -dataset_name: sft_engineer -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_entertainment.yaml b/lmms_eval/tasks/sft_eval/sft_entertainment.yaml deleted file mode 100644 index 04f84127..00000000 --- a/lmms_eval/tasks/sft_eval/sft_entertainment.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_entertainment_cn_eval -test_split: sft_entertainment -dataset_name: sft_entertainment -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_exist.yaml b/lmms_eval/tasks/sft_eval/sft_exist.yaml deleted file mode 100644 index b3ec83f7..00000000 --- a/lmms_eval/tasks/sft_eval/sft_exist.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_exist_cn_eval -test_split: sft_exist -dataset_name: sft_exist -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_face.yaml b/lmms_eval/tasks/sft_eval/sft_face.yaml deleted file mode 100644 index 5dd6962f..00000000 --- a/lmms_eval/tasks/sft_eval/sft_face.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_celeb_cn_eval -test_split: sft_face -dataset_name: sft_face -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_food.yaml b/lmms_eval/tasks/sft_eval/sft_food.yaml deleted file mode 100644 index a6a3dad4..00000000 --- a/lmms_eval/tasks/sft_eval/sft_food.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_food_cn_eval -test_split: sft_food -dataset_name: sft_food -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_healthcare.yaml b/lmms_eval/tasks/sft_eval/sft_healthcare.yaml deleted file mode 100644 index c63d093a..00000000 --- a/lmms_eval/tasks/sft_eval/sft_healthcare.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_healthcare_cn_eval -test_split: sft_healthcare -dataset_name: sft_healthcare -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_landmark.yaml b/lmms_eval/tasks/sft_eval/sft_landmark.yaml deleted file mode 100644 index 4c4c1209..00000000 --- a/lmms_eval/tasks/sft_eval/sft_landmark.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_landmark_cn_eval -test_split: sft_landmark -dataset_name: sft_landmark -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_logo.yaml b/lmms_eval/tasks/sft_eval/sft_logo.yaml deleted file mode 100644 index 35116a2d..00000000 --- a/lmms_eval/tasks/sft_eval/sft_logo.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft-logo -test_split: sft_logo -dataset_name: sft_logo -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_natural.yaml b/lmms_eval/tasks/sft_eval/sft_natural.yaml deleted file mode 100644 index 78668e9f..00000000 --- a/lmms_eval/tasks/sft_eval/sft_natural.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_natural_cn_eval -test_split: sft_natural -dataset_name: sft_natural -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_adv.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_adv.yaml deleted file mode 100644 index 0e7243ff..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_adv.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_ecommerceQA_cn_eval -test_split: sft_ocr_qa_adv -dataset_name: sft_ocr_qa_adv -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_chart.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_chart.yaml deleted file mode 100644 index 031eaa72..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_chart.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_chartQA_cn_eval -test_split: sft_ocr_qa_chart -dataset_name: sft_ocr_qa_chart -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_form.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_form.yaml deleted file mode 100644 index 18e059e8..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_form.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_formQA_cn_eval -test_split: sft_ocr_qa_form -dataset_name: sft_ocr_qa_form -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_scene.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_scene.yaml deleted file mode 100644 index 0af342d9..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_scene.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_sceneQA_cn_eval -test_split: sft_ocr_qa_scene -dataset_name: sft_ocr_qa_scene -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_screen.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_screen.yaml deleted file mode 100644 index 586194a9..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_screen.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_screenQA_cn_eval -test_split: sft_ocr_qa_screen -dataset_name: sft_ocr_qa_screen -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_adv.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_adv.yaml deleted file mode 100644 index 69e116f8..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_adv.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_adv_cn_eval -test_split: sft_ocr_rec_adv -dataset_name: sft_ocr_rec_adv -include: _default_sft_eval_ocr_rec_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_doc.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_doc.yaml deleted file mode 100644 index 0d498c46..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_doc.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_doc_cn_eval -test_split: sft_ocr_rec_doc -dataset_name: sft_ocr_rec_doc -include: _default_sft_eval_ocr_rec_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_handwrite.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_handwrite.yaml deleted file mode 100644 index 5eb39256..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_handwrite.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_handwrite_cn_eval -test_split: sft_ocr_rec_handwrite -dataset_name: sft_ocr_rec_handwrite -include: _default_sft_eval_ocr_rec_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_markdown.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_markdown.yaml deleted file mode 100644 index 7fb34090..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_markdown.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_markdown_cn_eval -test_split: sft_ocr_rec_markdown -dataset_name: sft_ocr_rec_markdown -include: _default_sft_eval_ocr_rec_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_scene.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_scene.yaml deleted file mode 100644 index 8ef84382..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_scene.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_scene_cn_eval -test_split: sft_ocr_rec_scene -dataset_name: sft_ocr_rec_scene -include: _default_sft_eval_ocr_rec_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_screen.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_screen.yaml deleted file mode 100644 index eba50bdb..00000000 --- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_screen.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_ocr_screen_cn_eval -test_split: sft_ocr_rec_screen -dataset_name: sft_ocr_rec_screen -include: _default_sft_eval_ocr_rec_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_place.yaml b/lmms_eval/tasks/sft_eval/sft_place.yaml deleted file mode 100644 index 1bc615b4..00000000 --- a/lmms_eval/tasks/sft_eval/sft_place.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_place_cn_eval -test_split: sft_place -dataset_name: sft_place -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_position.yaml b/lmms_eval/tasks/sft_eval/sft_position.yaml deleted file mode 100644 index ef489818..00000000 --- a/lmms_eval/tasks/sft_eval/sft_position.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_position_cn_eval -test_split: sft_position -dataset_name: sft_position -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_sport.yaml b/lmms_eval/tasks/sft_eval/sft_sport.yaml deleted file mode 100644 index 94a6140b..00000000 --- a/lmms_eval/tasks/sft_eval/sft_sport.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_sport_cn_eval -test_split: sft_sport -dataset_name: sft_sport -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/sft_status.yaml b/lmms_eval/tasks/sft_eval/sft_status.yaml deleted file mode 100644 index 9f28ba51..00000000 --- a/lmms_eval/tasks/sft_eval/sft_status.yaml +++ /dev/null @@ -1,5 +0,0 @@ -group: sft_eval -task: sft_status_cn_eval -test_split: sft_status -dataset_name: sft_status -include: _default_sft_eval_rest_template_yaml diff --git a/lmms_eval/tasks/sft_eval/utils.py b/lmms_eval/tasks/sft_eval/utils.py deleted file mode 100644 index 8ed2ef0c..00000000 --- a/lmms_eval/tasks/sft_eval/utils.py +++ /dev/null @@ -1,109 +0,0 @@ -from rapidfuzz.distance import Levenshtein - - -def _normalize_text(text): - import string - - text = "".join(filter(lambda x: x in (string.digits + string.ascii_letters), text)) - - -def process_result_ocr_rec(doc, result): - return {"edit_distance": {"gts": [gt.replace("\n", "") for gt in doc["answer"]], "predictions": result}, "edit_acc": {"gts": [gt.replace("\n", "") for gt in doc["answer"]], "predictions": result}} - - -def process_result_rest(doc, result): - return {"fuzzy_match": {"gts": [gt.replace("\n", "") for gt in doc["answer"]], "predictions": result, "question_id": doc["question_id"]}} - - -def run_editdistance(gts, predictions, ignore_space=True, is_filter=False): - eps = 1e-6 - correct_num = 0 - all_num = 0 - norm_edit_dis = 0.0 - edit_norm_score_list = list() - for idx in range(len(predictions)): - target, pred = gts[idx][0], predictions[idx] - if ignore_space: - pred = pred.replace(" ", "") - target = target.replace(" ", "") - if is_filter: - pred = _normalize_text(pred) - target = _normalize_text(target) - - ned = Levenshtein.normalized_distance(pred, target) - norm_edit_dis += ned - edit_norm_score_list.append(1 - ned) - if pred == target: - correct_num += 1 - all_num += 1 - # import pdb; pdb.set_trace() - metric = {"acc": correct_num / (all_num + eps), "norm_edit_dis": 1 - norm_edit_dis / (all_num + eps)} - return metric, edit_norm_score_list - - -def fuzzy_match_multi_answers(results, gt_dict): - acc = [] - for result in results: - question_id = result["question_id"] - try: - gt_ans = gt_dict[question_id] - except: - import pdb - - pdb.set_trace() - pred = result["text"] - for gt in gt_ans: - vqa_acc = 1 - if not ( - (gt == "是" and gt in pred and "不是" not in pred) - or (gt == "对" and gt in pred and "不对" not in pred) - or (gt == "相同" and gt in pred and "不相同" not in pred) - or (gt == "有" and gt in pred and "没有" not in pred) - or (gt == "在" and gt in pred and "不在" not in pred) - or (gt == "一样" and gt in pred and "不一样" not in pred) - or (gt not in ["是", "在", "对", "有", "一样", "相同"] and gt.lower() in pred.lower()) - ): - vqa_acc = 0 - if vqa_acc == 1: - break - acc.append(vqa_acc) - accuracy = sum(acc) / len(acc) * 100 - return {"Acc": accuracy} - - -def sft_eval_doc_to_visual(doc): - return [doc["image"].convert("RGB")] - - -def sft_eval_doc_to_text(doc): - return doc["question"] - - -def sft_eval_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") - question = f"{pre_prompt}{doc['question']}{post_prompt}" - return question - - -def sft_eval_edit_dist_acc_agg(results): - predictions = [result["predictions"][0] for result in results] - gts = [result["gts"] for result in results] - acc, _ = run_editdistance(gts, predictions) - return acc["acc"] - - -def sft_eval_edit_dist_agg(results): - predictions = [result["predictions"][0] for result in results] - gts = [result["gts"] for result in results] - acc, _ = run_editdistance(gts, predictions) - return acc["norm_edit_dis"] - - -def sft_eval_acc_agg(results): - gts_dict = {result["question_id"]: result["gts"] for result in results} - predictions = [{"question_id": result["question_id"], "text": result["predictions"][0]} for result in results] - acc = fuzzy_match_multi_answers(predictions, gts_dict) - return acc["Acc"] diff --git a/example_eval.yaml b/miscs/example_eval.yaml similarity index 100% rename from example_eval.yaml rename to miscs/example_eval.yaml diff --git a/llava_repr_requirements.txt b/miscs/llava_repr_requirements.txt similarity index 100% rename from llava_repr_requirements.txt rename to miscs/llava_repr_requirements.txt diff --git a/tools/live_bench/__init__.py b/tools/live_bench/__init__.py deleted file mode 100644 index 6ff6a72c..00000000 --- a/tools/live_bench/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from lmms_eval.live_bench.data_generator import LiveBench diff --git a/tools/live_bench/api/live_bench.py b/tools/live_bench/api/live_bench.py deleted file mode 100644 index 047a66ef..00000000 --- a/tools/live_bench/api/live_bench.py +++ /dev/null @@ -1,20 +0,0 @@ -from lmms_eval.live_bench.websites import load_websites, load_websites_from_file -from lmms_eval.live_bench import LiveBench - - -def generate_live_bench(*, force_clear=False, screen_shoter="single_screen", qa_generator="gpt4v", scorer="gpt4v", checker="gemini", driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}): - website = load_websites() - dataset = LiveBench(force_clear=force_clear) - dataset.capture(websites=website, screen_shoter=screen_shoter, qa_generator=qa_generator, scorer=scorer, checker=checker, driver_kwargs=driver_kwargs, shoter_kwargs=shoter_kwargs, generator_kwargs=generator_kwargs) - dataset.upload() - - -def generate_live_bench_from_path(path, *, force_clear=False, qa_generator="gpt4v", scorer="gpt4v", checker="gemini", driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}): - website = load_websites_from_file(path) - dataset = LiveBench(force_clear=force_clear) - dataset.capture(websites=website, screen_shoter="human", qa_generator=qa_generator, scorer=scorer, checker=checker, driver_kwargs=driver_kwargs, shoter_kwargs=shoter_kwargs, generator_kwargs=generator_kwargs) - dataset.upload() - - -if __name__ == "__main__": - generate_live_bench() diff --git a/tools/live_bench/data_generator/__init__.py b/tools/live_bench/data_generator/__init__.py deleted file mode 100644 index e8b8850b..00000000 --- a/tools/live_bench/data_generator/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from lmms_eval.live_bench.data_generator.qa_generator import get_generator, get_random_generator -from lmms_eval.live_bench.data_generator.live_bench_data import LiveBenchData -from lmms_eval.live_bench.data_generator.live_bench import LiveBench -from lmms_eval.live_bench.data_generator.response import Response diff --git a/tools/live_bench/data_generator/check_prompt.md b/tools/live_bench/data_generator/check_prompt.md deleted file mode 100644 index 08c73476..00000000 --- a/tools/live_bench/data_generator/check_prompt.md +++ /dev/null @@ -1,11 +0,0 @@ -I would like you to act as a quizmaster who designs questions based on a provided image that would challenge adults to think critically. The image in question is a screenshot from the homepage or section of news website. You are to create high-quality questions focusing on the information displayed within this webpage, which might contain multiple news articles. Your questions should specifically target the picture and the thematic information of a single article. Your question should be answerable, and checkable. Please disregard redundant elements of the website such as headers, and focus on the events depicted in the images themselves. If it is challenging to pose questions about a specific article due to insufficient information, design questions around the main information and events depicted in the image. - -A well-crafted question about an event should allow respondents to gain deeper insights by observing and analyzing the event, paying attention to the following aspects: - -- Basic Understanding -- Contextual Analysis -- Deeper Implications -- Broader Implications -- Further Insights - -Now, you are given a screenshot of the homepage of a news website, with a already generated question and answer. Your task is to refine the question and answer, and refractor them to make the question more answerable, checkable, and challenging. If you don't think the question is good, please provide a new question and answer. diff --git a/tools/live_bench/data_generator/example/example_output.json b/tools/live_bench/data_generator/example/example_output.json deleted file mode 100644 index 3cedea00..00000000 --- a/tools/live_bench/data_generator/example/example_output.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "Basic Understanding": { - "Question": "What is the central issue discussed in the article titled \"Kidnapped by One Side, Maimed by the Other: A Teenager’s Ordeal in Congo’s 30-Year War\" and who is responsible for the information presented?", - "Answer": "The central issue discussed is the impact of a new offensive by a Rwanda-backed militia in Congo, leading to significant civilian distress. The article is authored by Gabriele Steinhauser, with photographs by Jonathan Torgovnik for The Wall Street Journal." - }, - "Contextual Analysis": { - "Question": "Analyze how the photo accompanying the article about the Congo war might influence the reader's perception of the conflict.", - "Answer": "The photograph shows a young individual, possibly the teenager mentioned in the title, in a vulnerable state which personalizes the conflict and evokes empathy. This visual representation could lead readers to feel a deeper connection to the issue, emphasizing the human cost of the conflict over political or strategic aspects." - }, - "Deeper Implications": { - "Question": "Considering the ongoing violence described in the \"New Darfur Attacks Feed Concerns Over More Large-Scale Violence\" article, what could be the broader implications for regional stability in Africa?", - "Answer": "The surge in violence in North Darfur, as noted, raises concerns about escalating conflicts that could destabilize neighboring regions and lead to wider security challenges across Africa. It might strain international relations and humanitarian resources, potentially leading to international interventions or increased refugee flows." - }, - "Comparative Analysis": { - "Question": "Compare and contrast the international reactions to the events in Congo and Darfur, as suggested by the articles. What might explain any differences in these reactions?", - "Answer": "The international reaction to Darfur’s violence involves direct warnings from U.S. officials and the U.N., suggesting a proactive stance. In contrast, the Congo situation appears to be treated as part of a longer, ongoing conflict with possibly less immediate international urgency. Differences in reactions could be influenced by geopolitical interests, media attention, and the current strategic priorities of international players." - } -} diff --git a/tools/live_bench/data_generator/example/example_website.png b/tools/live_bench/data_generator/example/example_website.png deleted file mode 100644 index 3942a7a44e6e0c2dd4093f9aa5f914b60083e944..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2228301 zcmeFZbySpbw>AtYASebB0um}i3`%zk2r_gGNP~2DcPOHiz|h?cFd#X!gml-?A>G~e z-kfipbKdWH)>-HAxYoD6Kh7Vp1{m(SXYc*n``XvO_5{hxO5ov=;-aCU;Yq$1Q$$01 zu!x3+Q*aLp_|4%ADi#e5x4=|XR9;e4luF*t+Q`(x5Do2p5G)2;S!oU5=f>Td7w1hH z1gB2Y8a*?BLU?UN{5=K^s6|-*$E$A)sdO)2rXp6;ef;tjTRwj%F248SXEoI~ziLJz ziw`rH5A~d9U3ZeWPP*fEWS3LoZjx_=j4{wYwGss>>bar~2E_el7Ls$5ws=l1gz-4_ zVXiO^cQFsiOQZMi9}63{UTe+FV)kd&84AX2%zEDxrT<(qv_bO`k(Zq_vKf>Y3|fCC zLEU>7jpxhxQMB<_nlo@GJIe%B=ObDk)A%+T9=)$Dv3apyn3d*^UA*Qlw|TcCg{r4!&df`C)tvo!Qkm 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4cd9da42..00000000 --- a/tools/live_bench/data_generator/live_bench.py +++ /dev/null @@ -1,102 +0,0 @@ -import os -from typing import List -from tqdm import tqdm -from lmms_eval.live_bench.data_generator.live_bench_data import LiveBenchData -from datasets import Dataset, load_dataset -from lmms_eval.live_bench.websites import Website -from lmms_eval.live_bench.driver import load_driver -from lmms_eval.live_bench.data_generator import get_generator, get_random_generator -from lmms_eval.live_bench.screen_shoter import get_shoter -from lmms_eval.live_bench.data_generator.qa_generator import QAGenerator, QAData -from lmms_eval.live_bench.screen_shoter import ScreenImage, ScreenShoter -from lmms_eval.live_bench.data_generator.score_getter import get_score_getter, get_random_score_getter -import datasets - -from typing import List -import logging - -logger = logging.getLogger("lmms-eval") - - -def get_qa_data(images: ScreenImage, qa_generator: QAGenerator, test=False) -> List[QAData]: - response = qa_generator.generate(images, test=test) - qa_data = qa_generator.format_response(response) - return qa_data - - -def get_live_bench_data(driver, website: Website, screen_shoter: ScreenShoter, qa_generator: QAGenerator, checker: QAGenerator, test=False, scorer=None) -> List[LiveBenchData]: - images = screen_shoter.capture(driver, website) - qa_data = get_qa_data(images, qa_generator, test=test) - data = [] - for qa in qa_data: - data.append(LiveBenchData(screen=images, question=qa.question, answer=qa.answer, subtask=qa.subtask, data_generator=qa_generator.get_name(), checker=checker, scorer=scorer)) - return data - - -class LiveBench(object): - def __init__(self, path: str = "lmms-lab/LiveBench", *, name="default", split="test", cache_dir=None, remote_path=None, trust_remote_code=True, force_clear=False, **kwargs): - self.path = path - self.name = name - self.split = split - self.cache_dir = cache_dir - self.dataset_kwargs = kwargs - if remote_path is None: - self.remote_path = path - if force_clear: - self.clear() - else: - try: - self.hf_data = load_dataset(self.path, name=name, split=split, cache_dir=cache_dir, trust_remote_code=trust_remote_code, **kwargs) - except Exception as e: - logger.error(f"Error loading dataset: {e}") - self.clear() - - def clear(self): - self.hf_data = Dataset.from_dict( - {"id": [], "images": [], "website": [], "question": [], "answer": [], "subtask": [], "data_generator": [], "checker": [], "date_time": [], "screen_shoter": [], "screen_size": [], "score": [], "reason": [], "scorer_name": []}, - features=LiveBenchData.features, - ) - - def add(self, data: LiveBenchData, id: int = None): - if id is None: - id = len(self.hf_data) - organized_data = data.to_hf_dict() - organized_data["id"] = id - self.hf_data = self.hf_data.add_item(organized_data) - - def capture(self, websites: List[Website] = None, *, screen_shoter="single_screen", qa_generator=None, checker=None, driver=None, scorer=None, test=False, driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}): - can_quit_driver = False - if driver is None and screen_shoter != "human": - driver = load_driver(**driver_kwargs) - can_quit_driver = True - screen_shoter = get_shoter(screen_shoter, **shoter_kwargs) - if qa_generator is not None: - qa_generator = get_generator(qa_generator, **generator_kwargs) - else: - qa_generator = get_random_generator(**generator_kwargs) - if checker is None: - checker = get_random_generator(**generator_kwargs) - else: - checker = get_generator(checker, **generator_kwargs) - if scorer is not None and isinstance(scorer, str): - scorer = get_score_getter(scorer) - elif scorer is None: - scorer = get_random_score_getter() - for website in tqdm(websites, desc="Capturing websites"): - try: - data = get_live_bench_data(driver, website, screen_shoter, qa_generator, checker, test=test, scorer=scorer) - for d in data: - self.add(d) - except Exception as e: - logger.error(f"Error capturing website: {e}") - logger.error(f"Website: {website.get_info()}") - continue - if can_quit_driver: - driver.quit() - - def upload(self, **kwargs): - self.hf_data.push_to_hub(self.remote_path, config_name=self.name, split=self.split, **kwargs) - - def save(self, path: str): - self.hf_data.save_to_disk(path) - logger.info(f"Data saved to {os.path.abspath(path)}") diff --git a/tools/live_bench/data_generator/live_bench_data.py b/tools/live_bench/data_generator/live_bench_data.py deleted file mode 100644 index f2e378c0..00000000 --- a/tools/live_bench/data_generator/live_bench_data.py +++ /dev/null @@ -1,94 +0,0 @@ -from lmms_eval.live_bench.screen_shoter.screen import ScreenImage -import datasets - - -class LiveBenchData(object): - features = datasets.Features( - { - "id": datasets.Value("int32"), - "images": datasets.Sequence(datasets.Image()), - "website": datasets.Value("string"), - "question": datasets.Value("string"), - "answer": datasets.Value("string"), - "subtask": datasets.Value("string"), - "data_generator": datasets.Value("string"), - "checker": datasets.Value("string"), - "date_time": datasets.Value("string"), - "screen_shoter": datasets.Value("string"), - "screen_size": datasets.Value("string"), - "score": datasets.Value("int32"), - "reason": datasets.Value("string"), - "scorer_name": datasets.Value("string"), - } - ) - - def __init__(self, *, screen: ScreenImage, question: str, answer: str, subtask: str, data_generator: str, score: int = None, reason: str = None, checker=None, scorer_name=None, scorer=None): - self.screen = screen - self.question = question - self.answer = answer - self.subtask = subtask - self.data_generator = data_generator - self.checker = None - if checker is not None: - response = checker.check(screen, question, answer, subtask) - if response.success: - formatted_response = checker.format_checked_response(response) - if formatted_response.question and formatted_response.answer: - self.question = formatted_response.question - self.answer = formatted_response.answer - if formatted_response.subtask: - self.subtask = formatted_response.subtask - else: - self.subtask = subtask - self.checker = checker.get_name() - if score is not None: - self.score = score - self.reason = reason - self.scorer_name = scorer_name - else: - score = scorer.get_score(question, answer, screen.images) - self.score = score.score - self.reason = score.reason - self.scorer_name = scorer.get_name() - - def to_dict(self): - images = self.screen.images - website = self.screen.website.get_info() - question = self.question - answer = self.answer - subtask = self.subtask - data_generator = self.data_generator - date_time = self.screen.capture_datetime - screen_shoter = self.screen.shoter - screen_size = self.screen.screen_size - return { - "images": images, - "website": website, - "question": question, - "answer": answer, - "subtask": subtask, - "data_generator": data_generator, - "checker": self.checker, - "date_time": date_time, - "screen_shoter": screen_shoter, - "screen_size": screen_size, - "score": self.score, - "reason": self.reason, - "scorer_name": self.scorer_name, - } - - def to_hf_dict(self): - return self.features.encode_example(self.to_dict()) - - def to_output_dict(self): - return { - "screen": self.screen.to_output_dict(), - "question": self.question, - "answer": self.answer, - "subtask": self.subtask, - "data_generator": self.data_generator, - "checker": self.checker, - "score": self.score, - "reason": self.reason, - "scorer_name": self.scorer_name, - } diff --git a/tools/live_bench/data_generator/prompt.md b/tools/live_bench/data_generator/prompt.md deleted file mode 100644 index 0b6c5c3f..00000000 --- a/tools/live_bench/data_generator/prompt.md +++ /dev/null @@ -1,13 +0,0 @@ -I would like you to act as a quizmaster who designs questions based on a provided image that would challenge adults to think critically. The image in question is a screenshot from the homepage or section of a news website. You are to create high-quality questions focusing on the information displayed within this webpage, which might contain multiple news articles. Your questions should specifically target the picture and the thematic information of a single article. Your question should be answerable, and checkable. Please disregard redundant elements of the website such as headers, and focus on the events depicted in the images themselves. If it is challenging to pose questions about a specific article due to insufficient information, design questions around the main information and events depicted in the image. - -A well-crafted question about an event should allow respondents to gain deeper insights by observing and analyzing the event, paying attention to the following aspects: - -- Basic Understanding -- Contextual Analysis -- Deeper Implications -- Broader Implications -- Further Insights - -Consider designing a multi-round Q&A process, progressively deepening the understanding of the event’s essence. Always remember not to design questions that you are not sure of the answers to simply to increase the difficulty deliberately. - -Here are few examples about the task and the expected output format. diff --git a/tools/live_bench/data_generator/qa_generator.py b/tools/live_bench/data_generator/qa_generator.py deleted file mode 100644 index 107172f6..00000000 --- a/tools/live_bench/data_generator/qa_generator.py +++ /dev/null @@ -1,337 +0,0 @@ -import io -import re -import os -import openai -import base64 -import json -import random -import logging -import pathlib -import textwrap -import google.generativeai as genai -from time import sleep -from PIL import Image -from typing import List -from abc import ABC, abstractmethod -from lmms_eval.live_bench.data_generator.response import Response -from lmms_eval.live_bench.screen_shoter import ScreenImage -from lmms_eval.live_bench.data_generator.utils.gpt4v import format_gpt4v_images, gpt4v_generate_response -from lmms_eval.live_bench.data_generator.utils.gemini import gemini_generate_response - -logger = logging.getLogger("lmms-eval") - - -class QAData(object): - def __init__(self, question: str = None, answer: str = None, subtask: str = None): - self.question = question - self.answer = answer - self.subtask = subtask - - def to_dict(self): - return {"question": self.question, "answer": self.answer} - - -class QAGenerator(ABC): - def __init__(self, prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md")): - self.prompt_file = prompt_file - self.prompt = self._load_prompt() - - def _load_prompt(self): - with open(self.prompt_file, "r") as f: - return f.read() - - def __call__(self, images: ScreenImage, *args, **kwargs): - return self.generate(images, *args, **kwargs) - - def generate(self, images: ScreenImage, *, test=False, **kwargs) -> Response: - if test: - return Response(success=True, content="This is a test response.", full_log={}) - return self._generate(images, **kwargs) - - def check(self, images: ScreenImage, question, answer, subtask, *, test=False, **kwargs) -> Response: - if test: - return Response(success=True, content="This is a test response.", full_log={}) - return self._check(images, question, answer, subtask, **kwargs) - - @abstractmethod - def _generate(self, images: ScreenImage, **kwargs) -> Response: - raise NotImplementedError("_generate not implemented") - - @abstractmethod - def _check(self, images: ScreenImage, question, answer, subtask, **kwargs) -> Response: - raise NotImplementedError("_check not implemented") - - def format_response(self, response: Response) -> QAData: - if response.success: - qa_data = self._format_response(response) - if qa_data is None: - return [] - else: - return qa_data - else: - return [] - - @abstractmethod - def _format_response(self, response: Response) -> str: - raise NotImplementedError("format_response not implemented") - - @abstractmethod - def format_checked_response(self, response: Response) -> QAData: - raise NotImplementedError("format_checked_response not implemented") - - def get_name(self) -> str: - raise NotImplementedError("get_name not implemented") - - -class GeneratorRegistry: - def __init__(self): - self.generators = {} - - def register_generator(self, name): - def decorator(cls): - self.generators[name] = cls - cls.get_name = lambda self: name - return cls - - return decorator - - def get_generator(self, name) -> QAGenerator: - return self.generators[name] - - def get_random_generator(self) -> QAGenerator: - return random.choice(list(self.generators.values())) - - -generator_registry = GeneratorRegistry() - - -def register_generator(name): - return generator_registry.register_generator(name) - - -def get_generator(name, *args, **kwargs) -> QAGenerator: - return generator_registry.get_generator(name)(*args, **kwargs) - - -def get_random_generator(*args, **kwargs) -> QAGenerator: - return generator_registry.get_random_generator()(*args, **kwargs) - - -@register_generator("gpt4v") -class GPT4Generator(QAGenerator): - def __init__( - self, - prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md"), - model="gpt-4-turbo", - example_path=os.path.join(os.path.dirname(__file__), "example"), - check_prompt=os.path.join(os.path.dirname(__file__), "check_prompt.md"), - ): - super().__init__(prompt_file) - API_KEY = os.getenv("OPENAI_API_KEY") - if not API_KEY: - raise ValueError("OPENAI_API_KEY environment variable not set.") - self.api_key = API_KEY - self.client = openai.OpenAI(api_key=self.api_key) - self.model = model - if os.path.exists(example_path): - self.example_path = example_path - else: - self.example_path = None - if os.path.exists(check_prompt): - with open(check_prompt, "r") as f: - self.check_prompt = f.read() - else: - self.check_prompt = check_prompt - - def format_messages(self, images: List[Image.Image], example_image: Image.Image, example_output: str): - example = [ - format_gpt4v_images(example_image), - { - "type": "text", - "text": example_output, - }, - ] - content = example + [format_gpt4v_images(image) for image in images] - content.append( - { - "type": "text", - "text": "Please generate high-quality questions focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.", - }, - ) - messages = [ - { - "role": "system", - "content": self.prompt, - }, - { - "role": "user", - "content": content, - }, - ] - return messages - - def _generate(self, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs): - if self.example_path: - example_image_path = os.path.join(self.example_path, "example_website.png") - example_output_path = os.path.join(self.example_path, "example_output.json") - example_image = Image.open(example_image_path) - with open(example_output_path, "r") as f: - example_output = f.read() - - messages = self.format_messages(images.images, example_image, example_output) - - return gpt4v_generate_response(self.client, self.model, messages, max_tokens, max_try_times, **kwargs) - - def get_check_prompt(self, question: str, answer: str, subtask, images: List[Image.Image]): - messages = [ - { - "role": "system", - "content": self.check_prompt, - } - ] - content = [] - for img in images: - content.append(format_gpt4v_images(img)) - content.append( - { - "type": "text", - "text": f"Question: {question}\nQuestioner's Answer: {answer}\nSubtask: {subtask}", - }, - ) - content.append( - { - "type": "text", - "text": "Please rephrase or rewrite the high-quality question focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.", - }, - ) - messages.append( - { - "role": "user", - "content": content, - } - ) - return messages - - def _check(self, images: ScreenImage, question, answer, subtask, *, max_tokens=4096, max_try_times=5, **kwargs): - messages = self.get_check_prompt(question, answer, subtask, images.images) - return gpt4v_generate_response(self.client, self.model, messages, max_tokens, max_try_times, **kwargs) - - def format_checked_response(self, response: Response): - data = json.loads(response.content) - question = data.get("question", None) - answer = data.get("answer", None) - subtask = data.get("subtask", None) - return QAData(question=question, answer=answer, subtask=subtask) - - def _format_response(self, response: Response) -> List[QAData]: - try: - qa_data = [] - content = json.loads(response.content) - for subtask, message in content.items(): - subtask = subtask.lower() - message_lower = {k.lower(): v for k, v in message.items()} - try: - question = message_lower["question"] - answer = message_lower["answer"] - qa_data.append(QAData(question=question, answer=answer, subtask=subtask)) - except KeyError as e: - logger.error(f"Failed to parse response: {message}") - logger.error(f"Error: {e}") - return qa_data - except Exception as e: - logger.error(f"Failed to format response: {e}") - return [] - - -@register_generator("gemini") -class GeminiGenerator(QAGenerator): - def __init__( - self, - prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md"), - model="gemini-pro-vision", - example_path=os.path.join(os.path.dirname(__file__), "example"), - check_prompt=os.path.join(os.path.dirname(__file__), "check_prompt.md"), - ): - super().__init__(prompt_file) - GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") - if not GOOGLE_API_KEY: - raise ValueError("GOOGLE_API_KEY environment variable not set.") - genai.configure(api_key=GOOGLE_API_KEY) - - self.api_key = GOOGLE_API_KEY - self.model = model - self.client = genai.GenerativeModel(model) - if os.path.exists(example_path): - self.example_path = example_path - else: - self.example_path = None - if os.path.exists(check_prompt): - with open(check_prompt, "r") as f: - self.check_prompt = f.read() - else: - self.check_prompt = check_prompt - - def format_messages(self, images: List[Image.Image], example_image: Image.Image, example_output: str): - content = [self.prompt, "\n", "Example Image:", example_image, "\n", "Example Output:", example_output] - content.extend(images) - content.append("Please generate high-quality questions focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.") - return content - - def _generate(self, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs): - if self.example_path: - example_image_path = os.path.join(self.example_path, "example_website.png") - example_output_path = os.path.join(self.example_path, "example_output.json") - example_image = Image.open(example_image_path) - with open(example_output_path, "r") as f: - example_output = f.read() - - messages = self.format_messages(images.images, example_image, example_output) - - return gemini_generate_response(self.client, messages, max_tokens, max_try_times, **kwargs) - - def get_check_prompt(self, question: str, answer: str, subtask, images: List[Image.Image]): - content = [self.check_prompt] + images - content.append(f"Question: {question}\nQuestioner's Answer: {answer}\nSubtask: {subtask}") - content.append("Your response should be strictly in the below format:\n\nQuestion: \nAnswer: \nSubtask: ") - return content - - def _check(self, images: ScreenImage, question, answer, subtask, *, max_tokens=4096, max_try_times=5, **kwargs): - messages = self.get_check_prompt(question, answer, subtask, images.images) - return gemini_generate_response(self.client, messages, max_tokens, max_try_times, **kwargs) - - def format_checked_response(self, response: Response): - # Extract the question, answer, and subtask from the normalized content - question_match = re.search(r"question:\s*(.*?)\nAnswer:", response.content, re.IGNORECASE | re.DOTALL) - answer_match = re.search(r"answer:\s*(.*?)\n(Subtask:|$)", response.content, re.IGNORECASE | re.DOTALL) - subtask_match = re.search(r"subtask:\s*(.*)", response.content, re.IGNORECASE) - - question = answer = subtask = None - - if question_match: - # Extract the matched groups - question = question_match.group(1).strip() - if answer_match: - answer = answer_match.group(1).strip() - if subtask_match: - subtask = subtask_match.group(1).strip() - - return QAData(question=question, answer=answer, subtask=subtask) - - def _format_response(self, response: Response) -> List[QAData]: - try: - qa_data = [] - content = json.loads(response.content) - for subtask, message in content.items(): - subtask = subtask.lower() - message_lower = {k.lower(): v for k, v in message.items()} - try: - question = message_lower["question"] - answer = message_lower["answer"] - qa_data.append(QAData(question=question, answer=answer, subtask=subtask)) - except KeyError as e: - logger.error(f"Failed to parse response: {message}") - logger.error(f"Error: {e}") - return qa_data - except Exception as e: - logger.error(f"Failed to format response: {e}") - return [] diff --git a/tools/live_bench/data_generator/response.py b/tools/live_bench/data_generator/response.py deleted file mode 100644 index 5106b70a..00000000 --- a/tools/live_bench/data_generator/response.py +++ /dev/null @@ -1,5 +0,0 @@ -class Response(object): - def __init__(self, success: bool, content: str, full_log: dict): - self.success = success - self.content = content - self.full_log = full_log diff --git a/tools/live_bench/data_generator/score_getter.py b/tools/live_bench/data_generator/score_getter.py deleted file mode 100644 index 990fc306..00000000 --- a/tools/live_bench/data_generator/score_getter.py +++ /dev/null @@ -1,157 +0,0 @@ -import os -import json -import random -import openai -import anthropic -from abc import ABC, abstractmethod -from typing import List -from PIL import Image -from lmms_eval.live_bench.screen_shoter import ScreenImage -from lmms_eval.live_bench.data_generator.qa_generator import Response -from lmms_eval.live_bench.data_generator.utils.gpt4v import format_gpt4v_images, gpt4v_generate_response -from lmms_eval.live_bench.data_generator.utils.claude import format_claude_images, claude_generate_response - - -class Score(object): - def __init__(self, score: int, reason: str): - self.score = score - self.reason = reason - - -class ScoreGetter(ABC): - def get_name(self): - return self.name - - @abstractmethod - def get_score(self, question: str, answer: str, images: ScreenImage): - raise NotImplementedError("get_score not implemented") - - def __call__(self, question: str, answer: str, images: ScreenImage, **kwargs): - return self.get_score(question, answer, images, **kwargs) - - -class ScoreGetterRegistry: - def __init__(self): - self.score_getters = {} - - def register_score_getter(self, name): - def decorator(cls): - self.score_getters[name] = cls - cls.name = name - return cls - - return decorator - - def get_score_getter(self, name) -> ScoreGetter: - return self.score_getters[name] - - def get_random_score_getter(self) -> ScoreGetter: - return random.choice(list(self.score_getters.values())) - - -generator_registry = ScoreGetterRegistry() - - -def register_score_getter(name): - return generator_registry.register_score_getter(name) - - -def get_score_getter(name, *args, **kwargs) -> ScoreGetter: - return generator_registry.get_score_getter(name)(*args, **kwargs) - - -def get_random_score_getter(*args, **kwargs) -> ScoreGetter: - return generator_registry.get_random_score_getter()(*args, **kwargs) - - -@register_score_getter("gpt4v") -class GPT4VScoreGetter(ScoreGetter): - def __init__(self, prompt: str = os.path.join(os.path.dirname(__file__), "score_prompt.md"), model="gpt-4-turbo", example_path=os.path.join(os.path.dirname(__file__), "example")): - super().__init__() - if os.path.exists(prompt): - with open(prompt, "r") as f: - self.prompt = f.read() - else: - self.prompt = prompt - API_KEY = os.getenv("OPENAI_API_KEY") - if not API_KEY: - raise ValueError("OPENAI_API_KEY environment variable not set.") - self.api_key = API_KEY - self.client = openai.OpenAI(api_key=self.api_key) - self.model = model - if os.path.exists(example_path) and os.path.isfile(os.path.join(example_path, "example_score_input.md")): - with open(example_path, "r") as f: - self.example = f.read() - else: - self.example = None - - def _format_prompt(self, question: str, answer: str, images: List[Image.Image]): - prompt = [{"role": "system", "content": self.prompt}] - messages = [] - for image in images: - messages.append(format_gpt4v_images(image)) - messages.append({"type": "text", "text": f"Question: {question}\nQuestioner's Answer: {answer}"}) - messages.append({"type": "text", "text": 'You should format you answer into json format like this: {"score": 10, "reason": "some reason"}'}) - prompt.append({"role": "user", "content": messages}) - return prompt - - def get_score(self, question: str, answer: str, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs) -> Score: - prompt = self._format_prompt(question, answer, images) - try: - response = gpt4v_generate_response(self.client, self.model, prompt, max_tokens, max_try_times, **kwargs) - if response.success: - content = json.loads(response.content) - score = content.get("score", None) - reason = content.get("reason", None) - return Score(score=score, reason=reason) - else: - return Score(score=None, reason=response.content) - except Exception as e: - return Score(score=None, reason=str(e)) - - -@register_score_getter("claude") -class ClaudeScoreGetter(ScoreGetter): - def __init__(self, prompt: str = os.path.join(os.path.dirname(__file__), "score_prompt.md"), model="claude-3-opus-20240229", example_path=os.path.join(os.path.dirname(__file__), "example")): - super().__init__() - if os.path.exists(prompt): - with open(prompt, "r") as f: - self.prompt = f.read() - else: - self.prompt = prompt - API_KEY = os.getenv("ANTHROPIC_API_KEY") - if not API_KEY: - raise ValueError("ANTHROPIC_API_KEY environment variable not set.") - self.api_key = API_KEY - self.client = anthropic.Anthropic(api_key=self.api_key) - self.model = model - if os.path.exists(example_path) and os.path.isfile(os.path.join(example_path, "example_score_input.md")): - with open(example_path, "r") as f: - self.example = f.read() - else: - self.example = None - - def _format_prompt(self, question: str, answer: str, images: List[Image.Image]): - # prompt = [{"role": "system", "content": self.prompt}] - prompt = [] - messages = [] - for image in images: - messages.append(format_claude_images(image)) - messages.append({"type": "text", "text": f"Question: {question}\nQuestioner's Answer: {answer}"}) - messages.append({"type": "text", "text": 'You should format you answer into json format like this: {"score": 10, "reason": "some reason"}'}) - prompt.append({"role": "user", "content": messages}) - return prompt - - def get_score(self, question: str, answer: str, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs) -> Score: - prompt = self._format_prompt(question, answer, images) - try: - response = claude_generate_response(self.client, self.model, prompt, self.prompt, max_tokens, max_try_times, **kwargs) - if response.success: - content = json.loads(response.content) - score = content.get("score", None) - reason = content.get("reason", None) - return Score(score=score, reason=reason) - else: - return Score(score=None, reason=response.content) - except Exception as e: - return Score(score=None, reason=str(e)) diff --git a/tools/live_bench/data_generator/score_prompt.md b/tools/live_bench/data_generator/score_prompt.md deleted file mode 100644 index 3940863e..00000000 --- a/tools/live_bench/data_generator/score_prompt.md +++ /dev/null @@ -1 +0,0 @@ -Based on the multi-round Q&A regarding to the image, please evaluate each question and answer from the multi-round Q&A based on the image for their authenticity (whether the information can be directly obtained from the image or reasonably inferred) and logical coherence. For each Q&A pair, provide a rating from 1 to 10, where 1 indicates very poor and 10 indicates excellent. Additionally, please provide a brief explanation for each rating. diff --git a/tools/live_bench/data_generator/utils/__init__.py b/tools/live_bench/data_generator/utils/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tools/live_bench/data_generator/utils/claude.py b/tools/live_bench/data_generator/utils/claude.py deleted file mode 100644 index c33f81db..00000000 --- a/tools/live_bench/data_generator/utils/claude.py +++ /dev/null @@ -1,42 +0,0 @@ -from PIL import Image -import io -import base64 -from lmms_eval.live_bench.data_generator.response import Response -import logging -from time import sleep - -logger = logging.getLogger("lmms-eval") - - -def format_claude_images(image: Image.Image): - buffered = io.BytesIO() - image.save(buffered, format="PNG") - img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") - return { - "type": "image", - "source": { - "type": "base64", - "media_type": "image/png", - "data": img_str, - }, - } - - -def claude_generate_response(client, model, messages, system, max_tokens: int, max_try_times, **kwargs): - messages.append({"role": "assistant", "content": "{"}) - - def _generate(): - return client.messages.create(model=model, messages=messages, max_tokens=max_tokens, system=system, **kwargs) - - for times in range(max_try_times): - try: - response = _generate() - return Response(success=True, content="{" + response.content[0].text, full_log={"input": messages, "output": response}) - except Exception as e: - logger.error(f"Failed to generate response: {e}") - if times < max_try_times - 1: - logger.info(f"Retrying... ({times+1}/{max_try_times})") - sleep(3) - else: - logger.error("Failed to generate response after retrying.") - return Response(success=False, content=str(e), full_log={"input": messages, "output": None}) diff --git a/tools/live_bench/data_generator/utils/gemini.py b/tools/live_bench/data_generator/utils/gemini.py deleted file mode 100644 index 2e425826..00000000 --- a/tools/live_bench/data_generator/utils/gemini.py +++ /dev/null @@ -1,26 +0,0 @@ -import google.generativeai as genai -from time import sleep -from lmms_eval.live_bench.data_generator.response import Response -import logging - -logger = logging.getLogger("lmms-eval") - - -def gemini_generate_response(client: genai.GenerativeModel, messages, max_tokens: int, max_try_times, **kwargs): - generation_config = genai.GenerationConfig(max_output_tokens=max_tokens) - - def _generate(): - return client.generate_content(messages, generation_config=generation_config, **kwargs) - - for times in range(max_try_times): - try: - response = _generate() - return Response(success=True, content=response.text, full_log={"input": messages, "output": response}) - except Exception as e: - logger.error(f"Failed to generate response: {e}") - if times < max_try_times - 1: - logger.info(f"Retrying... ({times+1}/{max_try_times})") - sleep(3) - else: - logger.error("Failed to generate response after retrying.") - return Response(success=False, content=str(e), full_log={"input": messages, "output": None}) diff --git a/tools/live_bench/data_generator/utils/gpt4v.py b/tools/live_bench/data_generator/utils/gpt4v.py deleted file mode 100644 index 4b4c1cd8..00000000 --- a/tools/live_bench/data_generator/utils/gpt4v.py +++ /dev/null @@ -1,38 +0,0 @@ -from PIL import Image -import io -import base64 -from lmms_eval.live_bench.data_generator.response import Response -import logging -from time import sleep - -logger = logging.getLogger("lmms-eval") - - -def format_gpt4v_images(image: Image.Image): - buffered = io.BytesIO() - image.save(buffered, format="PNG") - img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") - return { - "type": "image_url", - "image_url": { - "url": f"data:image/png;base64,{img_str}", - }, - } - - -def gpt4v_generate_response(client, model, messages, max_tokens: int, max_try_times, **kwargs): - def _generate(): - return client.chat.completions.create(model=model, messages=messages, max_tokens=max_tokens, response_format={"type": "json_object"}, **kwargs) - - for times in range(max_try_times): - try: - response = _generate() - return Response(success=True, content=response.choices[0].message.content, full_log={"input": messages, "output": response}) - except Exception as e: - logger.error(f"Failed to generate response: {e}") - if times < max_try_times - 1: - logger.info(f"Retrying... ({times+1}/{max_try_times})") - sleep(3) - else: - logger.error("Failed to generate response after retrying.") - return Response(success=False, content=str(e), full_log={"input": messages, "output": None}) diff --git a/tools/live_bench/driver/.gitignore b/tools/live_bench/driver/.gitignore deleted file mode 100644 index 0ef18421..00000000 --- a/tools/live_bench/driver/.gitignore +++ /dev/null @@ -1 +0,0 @@ -extensions/ diff --git a/tools/live_bench/driver/__init__.py b/tools/live_bench/driver/__init__.py deleted file mode 100644 index a1565d0b..00000000 --- a/tools/live_bench/driver/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from lmms_eval.live_bench.driver.load_driver import load_driver diff --git a/tools/live_bench/driver/load_driver.py b/tools/live_bench/driver/load_driver.py deleted file mode 100644 index e8b1702b..00000000 --- a/tools/live_bench/driver/load_driver.py +++ /dev/null @@ -1,51 +0,0 @@ -import os -import zipfile -import requests -from selenium import webdriver -from webdriver_manager.chrome import ChromeDriverManager -from selenium.webdriver.chrome.options import Options - -import undetected_chromedriver as uc - - -def load_driver( - window_size="auto", headless=True, driver="undetected_chromedriver", adblock=True, adblock_version="6.0.2-mv3", extension_cache_dir=os.path.join(os.path.dirname(__file__), "extensions"), *, service=None, additional_options=None -): - options = Options() - if headless: - options.add_argument("--headless") - if adblock: - try: - adblock_url = f"https://code.getadblock.com/releases/adblockchrome-{adblock_version}.zip" - adblock_path = os.path.join(extension_cache_dir, f"adblockchrome-{adblock_version}") - if not os.path.isdir(adblock_path): - os.makedirs(os.path.join(adblock_path, ".."), exist_ok=True) - # Download the adblock zip file - response = requests.get(adblock_url) - with open(f"{adblock_path}.zip", "wb") as file: - file.write(response.content) - # Unzip the downloaded file - with zipfile.ZipFile(f"{adblock_path}.zip", "r") as zip_ref: - zip_ref.extractall(adblock_path) - # Remove the zip file after extraction - os.remove(f"{adblock_path}.zip") - options.add_argument(f"--load-extension={os.path.abspath(adblock_path)}") - except Exception as e: - print(f"Error loading adblock extension: {e}") - if driver == "undetected_chromedriver": - driver = uc.Chrome(headless=headless, options=options) - if window_size != "auto": - driver.set_window_size(*window_size) - return driver - elif driver == "chrome": - options = Options() - if additional_options is not None: - for option in additional_options: - options.add_argument(option) - if service is None: - service = ChromeDriverManager().install() - service = webdriver.chrome.service.Service(service) - driver = webdriver.Chrome(service=service, options=options) - if window_size != "auto": - driver.set_window_size(*window_size) - return driver diff --git a/tools/live_bench/example.ipynb b/tools/live_bench/example.ipynb deleted file mode 100644 index 01f64890..00000000 --- a/tools/live_bench/example.ipynb +++ /dev/null @@ -1,103 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from random import sample\n", - "\n", - "from lmms_eval.live_bench.websites.website import DefaultWebsite\n", - "from lmms_eval.live_bench.websites import load_websites\n", - "\n", - "# website = load_websites()\n", - "# website = sample(website, 1)\n", - "# website[0].url\n", - "website = [DefaultWebsite(url=\"https://www.bbc.com/\")] # , DefaultWebsite(url=\"https://www.bbc.com/sport\"), DefaultWebsite(url=\"https://www.bbc.com/business\")]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from lmms_eval.live_bench import LiveBench" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = LiveBench(force_clear=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset.capture(websites=website, driver_kwargs={\"headless\": True}, screen_shoter=\"single_screen\", shoter_kwargs={\"screen_size\": (1024, 1024)}, qa_generator=\"gpt4v\", scorer=\"gpt4v\", checker=\"gemini\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset.hf_data[0][\"images\"][0].save(\"img.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset.upload()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "lmms-eval", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tools/live_bench/screen_shoter/__init__.py b/tools/live_bench/screen_shoter/__init__.py deleted file mode 100644 index 6e596c7d..00000000 --- a/tools/live_bench/screen_shoter/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from lmms_eval.live_bench.screen_shoter.screen_shoter import ScreenShoter, register_shoter, get_shoter -from lmms_eval.live_bench.screen_shoter.screen import ScreenImage diff --git a/tools/live_bench/screen_shoter/screen.py b/tools/live_bench/screen_shoter/screen.py deleted file mode 100644 index e8610ce4..00000000 --- a/tools/live_bench/screen_shoter/screen.py +++ /dev/null @@ -1,30 +0,0 @@ -import io -import base64 - -from PIL import Image -from typing import List, Tuple - -from lmms_eval.live_bench.websites import Website - - -def image_to_base64(image: Image.Image) -> str: - buffered = io.BytesIO() - image.save(buffered, format="PNG") - return base64.b64encode(buffered.getvalue()).decode("utf-8") - - -class ScreenImage(object): - def __init__(self, images: List[Image.Image], website: Website, shoter: str, screen_size: Tuple[int, int], capture_datetime: str): - self.images = images - self.website = website - self.shoter = shoter - self.screen_size = screen_size - self.capture_datetime = capture_datetime - - def to_dict(self): - return {"images": self.images, "website": self.website.get_info(), "shoter": self.shoter, "screen_size": self.screen_size, "capture_datetime": self.capture_datetime} - - def to_output_dict(self): - output = self.to_dict() - output["images"] = [image_to_base64(image) for image in self.images] - return output diff --git a/tools/live_bench/screen_shoter/screen_shoter.py b/tools/live_bench/screen_shoter/screen_shoter.py deleted file mode 100644 index 45a4c9be..00000000 --- a/tools/live_bench/screen_shoter/screen_shoter.py +++ /dev/null @@ -1,141 +0,0 @@ -from selenium import webdriver -from PIL import Image -from lmms_eval.live_bench.websites import Website -from lmms_eval.live_bench.screen_shoter.screen import ScreenImage -from typing import List -from abc import ABC, abstractmethod -from datetime import datetime -from PIL import Image -import os -import io -import logging - -logger = logging.getLogger("lmms-eval") - - -class ScreenShoter(ABC): - def __init__(self, screen_size=(1024, 1024)): - self.screen_size = screen_size - - def capture(self, driver: webdriver.Chrome, website: Website) -> ScreenImage: - if driver is not None: - website.visit(driver) - if self.screen_size != "auto": - driver.set_window_size(self.screen_size[0], self.screen_size[1]) - else: - driver.set_window_size(1024, 1024) - page_width = driver.execute_script("return document.body.scrollWidth") - driver.set_window_size(page_width, 1024) - # print("Screen size:", driver.get_window_size()) - images = self.get_screenshot(driver) - return ScreenImage(images, website, self.get_name(), self.screen_size, datetime.now().strftime("%Y-%m-%d %H:%M:%S")) - - def __call__(self, driver: webdriver.Chrome, website: Website) -> List[Image.Image]: - return self.capture(driver, website) - - def get_name(self) -> str: - raise NotImplementedError("get_name not implemented") - - @abstractmethod - def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]: - pass - - -class ScreenShoterRegistry: - def __init__(self): - self.shoters = {} - - def register_shoter(self, name): - def decorator(cls): - self.shoters[name] = cls - cls.get_name = lambda self: name - return cls - - return decorator - - def get_shoter(self, name) -> ScreenShoter: - return self.shoters[name] - - -shoter_registry = ScreenShoterRegistry() - - -def register_shoter(name): - return shoter_registry.register_shoter(name) - - -def get_shoter(name, *args, **kwargs) -> ScreenShoter: - return shoter_registry.get_shoter(name)(*args, **kwargs) - - -@register_shoter("human") -class HumanScreenShoter(ScreenShoter): - def __init__(self, screen_size=None): - super().__init__(screen_size) - - def capture(self, driver: webdriver.Chrome, website: Website) -> ScreenImage: - path = website.get_path() - images = [] - - def get_image(path): - try: - with open(path, "rb") as f: - image_data = f.read() - image = Image.open(io.BytesIO(image_data)) - images.append(image) - except Exception as e: - logger.error(f"Error loading image {path}: {e}") - - if os.path.isdir(path): - for root, dirs, files in os.walk(path): - for file_name in files: - get_image(os.path.join(root, file_name)) - else: - try: - get_image(path) - except Exception as e: - logger.error(f"Error loading image {path}: {e}") - if not images: - raise ValueError(f"No images found in {path}") - return ScreenImage(images, website, self.get_name(), self.screen_size, datetime.now().strftime("%Y-%m-%d %H:%M:%S")) - - def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]: - return [] - - -@register_shoter("single_screen") -class SingleScreenShoter(ScreenShoter): - def __init__(self, screen_size=(1024, 1024)): - super().__init__(screen_size) - - def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]: - screenshot = driver.get_screenshot_as_png() - return [Image.open(io.BytesIO(screenshot))] - - -@register_shoter("rolling_screen") -class RollingScreenShoter(ScreenShoter): - def __init__(self, screen_size=(1024, 1024)): - super().__init__(screen_size) - - def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]: - screenshots = [] - # Scroll to the top of the page before taking the first screenshot - driver.execute_script("window.scrollTo(0, 0)") - # Get the total height of the web page - total_height = driver.execute_script("return document.body.parentNode.scrollHeight") - # Get the viewport height - viewport_height = driver.execute_script("return window.innerHeight") - # Initialize the current scroll position - current_scroll_position = 0 - - # Scroll through the page and take screenshots - while current_scroll_position < total_height: - # Take screenshot and append to the list - screenshot = driver.get_screenshot_as_png() - screenshots.append(Image.open(io.BytesIO(screenshot))) - # Scroll down by the viewport height - current_scroll_position += viewport_height - driver.execute_script(f"window.scrollTo(0, {current_scroll_position})") - - return screenshots diff --git a/tools/live_bench/websites/__init__.py b/tools/live_bench/websites/__init__.py deleted file mode 100644 index 721a374f..00000000 --- a/tools/live_bench/websites/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from lmms_eval.live_bench.websites.load_website import load_websites, load_websites_from_file -from lmms_eval.live_bench.websites.website import Website diff --git a/tools/live_bench/websites/load_website.py b/tools/live_bench/websites/load_website.py deleted file mode 100644 index 56b96357..00000000 --- a/tools/live_bench/websites/load_website.py +++ /dev/null @@ -1,34 +0,0 @@ -import yaml -import os -from random import sample -from lmms_eval.live_bench.websites.website import Website, DefaultWebsite, HumanScreenShotWebsite - - -def get_website(website_dict): - if "website_class" not in website_dict: - website_class = DefaultWebsite - else: - website_class = website_dict["website_class"] - url = website_dict["url"] - if "args" in website_dict: - return website_class(url, **website_dict["args"]) - else: - return website_class(url) - - -def load_websites(num_sample: int = -1): - website_list_path = os.path.join(os.path.dirname(__file__), "website_list.yaml") - with open(website_list_path, "r") as f: - website_list = yaml.full_load(f)["websites"] - if num_sample > 0: - website_list = sample(website_list, num_sample) - return [get_website(website_dict) for website_dict in website_list] - - -def load_websites_from_file(file_path): - names = os.listdir(file_path) - websites = [] - for name in names: - path = os.path.join(file_path, name) - websites.append(HumanScreenShotWebsite(path=path, name=name)) - return websites diff --git a/tools/live_bench/websites/website.py b/tools/live_bench/websites/website.py deleted file mode 100644 index 327bad37..00000000 --- a/tools/live_bench/websites/website.py +++ /dev/null @@ -1,62 +0,0 @@ -import time -import os - -from webdriver_manager.core.driver import Driver -from abc import ABC, abstractmethod - - -class Website(ABC): - def __init__(self, url=None, name=None, path=None): - self.url = url - self.name = name - self.path = path - assert self.url is not None or self.path is not None, "Either url or path must be provided" - - def get_path(self): - if self.url: - return self.url - else: - return self.path - - def visit(self, driver: Driver): - self.pre_visit(driver) - driver.get(self.url) - self.post_visit(driver) - - def get_info(self): - info = {} - if self.url: - info["url"] = self.url - if self.name: - info["name"] = self.name - return info - - @abstractmethod - def pre_visit(self, driver: Driver): - raise NotImplementedError("pre_action not implemented") - - @abstractmethod - def post_visit(self, driver: Driver): - raise NotImplementedError("post_action not implemented") - - -class DefaultWebsite(Website): - def __init__(self, url, name=None): - super().__init__(url, name) - - def pre_visit(self, driver: Driver): - pass - - def post_visit(self, driver: Driver): - time.sleep(5) # Wait for 5 seconds to allow adblock to finish - - -class HumanScreenShotWebsite(Website): - def __init__(self, name=None, path=None): - super().__init__(name=name, path=path) - - def pre_visit(self, driver: Driver): - pass - - def post_visit(self, driver: Driver): - pass diff --git a/tools/live_bench/websites/website_list.yaml b/tools/live_bench/websites/website_list.yaml deleted file mode 100644 index 4c7b348a..00000000 --- a/tools/live_bench/websites/website_list.yaml +++ /dev/null @@ -1,77 +0,0 @@ -websites: -- url: https://www.bbc.com/ - # can add below line to specify the class to use for this website - # website_class: !constructor website.DefaultWebsite - # can add args tag to specify the arguments to pass to the class constructor - # args: - # arg1: value1 - # arg2: value2 -- url: https://www.bbc.com/news -- url: https://www.bbc.com/sport -- url: https://www.bbc.com/business -- url: https://www.bbc.com/innovation -- url: https://www.bbc.com/culture -- url: https://www.bbc.com/travel -- url: https://www.bbc.com/future-planet -# - url: https://edition.cnn.com/ -# - url: https://edition.cnn.com/politics -# - url: https://edition.cnn.com/entertainment -# - url: https://edition.cnn.com/style -# - url: https://www.bloomberg.com/economics -# - url: https://www.bloomberg.com/industries -# - url: https://www.bloomberg.com/technology -# - url: https://www.bloomberg.com/politics -# - url: https://www.bloomberg.com/opinion -# - url: https://www.wsj.com/ -# - url: https://www.wsj.com/world/africa?mod=nav_top_subsection -# - url: https://www.wsj.com/world/americas?mod=nav_top_subsection -# - url: https://www.wsj.com/world/asia?mod=nav_top_subsection -# - url: https://www.wsj.com/world/china?mod=nav_top_subsection -# - url: https://www.wsj.com/world/europe?mod=nav_top_subsection -# - url: https://www.wsj.com/world/middle-east?mod=nav_top_subsection -# - url: https://www.wsj.com/world/india?mod=nav_top_subsection -# - url: https://www.wsj.com/world/oceania?mod=nav_top_subsection -# - url: https://www.wsj.com/world/russia?mod=nav_top_subsection -# - url: https://www.wsj.com/world/uk?mod=nav_top_subsection -# - url: https://www.wsj.com/science?mod=nav_top_subsection -# - url: https://www.wsj.com/science/archaeology?mod=nav_top_subsection -# - url: https://www.wsj.com/science/biology?mod=nav_top_subsection -# - url: https://www.wsj.com/science/environment?mod=nav_top_subsection -# - url: https://www.wsj.com/science/physics?mod=nav_top_subsection -# - url: https://www.wsj.com/science/space-astronomy?mod=nav_top_subsection -# - url: https://www.wsj.com/economy/central-banking?mod=nav_top_subsection -# - url: https://www.wsj.com/economy/consumers?mod=nav_top_subsection -# - url: https://www.wsj.com/economy/housing?mod=nav_top_subsection -# - url: https://www.wsj.com/economy/jobs?mod=nav_top_subsection -# - url: https://www.wsj.com/economy/trade?mod=nav_top_subsection -# - url: https://www.wsj.com/economy/global -# - url: https://www.wsj.com/tech/ai?mod=nav_top_subsection -# - url: https://www.wsj.com/tech/biotech -# - url: https://www.wsj.com/tech/cybersecurity?mod=nav_top_subsection -# - url: https://www.wsj.com/tech/personal-tech?mod=nav_top_subsection -# - url: https://www.reuters.com/ -# - url: https://www.reuters.com/business/aerospace-defense/ -# - url: https://www.reuters.com/business/autos-transportation/ -# - url: https://www.reuters.com/business/davos/ -# - url: https://www.reuters.com/business/energy/ -# - url: https://www.reuters.com/business/environment/ -# - url: https://www.reuters.com/business/finance/ -# - url: https://www.reuters.com/business/healthcare-pharmaceuticals/ -# - url: https://www.reuters.com/business/media-telecom/ -# - url: https://www.reuters.com/business/retail-consumer/ -# - url: https://www.reuters.com/business/future-of-health/ -# - url: https://www.reuters.com/business/future-of-money/ -# - url: https://www.reuters.com/business/take-five/ -# - url: https://www.reuters.com/business/world-at-work/ -# - url: https://www.reuters.com/breakingviews/ -# - url: https://www.reuters.com/technology/ -# - url: https://www.reuters.com/technology/cybersecurity/ -# - url: https://www.reuters.com/technology/space/ -# - url: https://www.reuters.com/technology/disrupted/ -# - url: https://www.reuters.com/technology/reuters-momentum/ -# - url: https://www.reuters.com/investigations/ -# - url: https://a16z.com/news-content/#latest -# - url: https://news.ycombinator.com/ -# - url: https://www.reddit.com/?rdt=48006 -# - url: https://news.crunchbase.com/ -- url: https://www.cctv.com/ From c9b2252fb8a15dd04252af5e6b4613855afd6ada Mon Sep 17 00:00:00 2001 From: Bo Li Date: Wed, 12 Jun 2024 15:33:48 +0000 Subject: [PATCH 070/196] Bump version to 0.2.0.dev0 --- README.md | 317 ++++++++++++++---------------------------- docs/current_tasks.md | 122 ++++++++++++++++ pyproject.toml | 2 +- 3 files changed, 228 insertions(+), 213 deletions(-) create mode 100644 docs/current_tasks.md diff --git a/README.md b/README.md index 415ea5c7..a4cc11e2 100755 --- a/README.md +++ b/README.md @@ -9,76 +9,23 @@ 🏠 [LMMs-Lab Homepage](https://lmms-lab.github.io/) | 🎉 [Blog](https://lmms-lab.github.io/lmms-eval-blog/lmms-eval-0.1/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | Discord_Thread [discord/lmms-eval](https://discord.gg/zdkwKUqrPy) -In today's world, we're on an exciting journey toward creating Artificial General Intelligence (AGI), much like the enthusiasm of the 1960s moon landing. This journey is powered by advanced large language models (LLMs) and large multimodal models (LMMs), which are complex systems capable of understanding, learning, and performing a wide variety of human tasks. - -To gauge how advanced these models are, we use a variety of evaluation benchmarks. These benchmarks are tools that help us understand the capabilities of these models, showing us how close we are to achieving AGI. However, finding and using these benchmarks is a big challenge. The necessary benchmarks and datasets are spread out and hidden in various places like Google Drive, Dropbox, and different school and research lab websites. It feels like we're on a treasure hunt, but the maps are scattered everywhere. - -In the field of language models, there has been a valuable precedent set by the work of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). They offer integrated data and model interfaces, enabling rapid evaluation of language models and serving as the backend support framework for the [open-llm-leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and has gradually become the underlying ecosystem of the era of foundation models. - -However, though there are many new evaluation datasets are recently proposed, the efficient evaluation pipeline of LMM is still in its infancy, and there is no unified evaluation framework that can be used to evaluate LMM across a wide range of datasets. To address this challenge, we introduce **lmms-eval**, an evaluation framework meticulously crafted for consistent and efficient evaluation of LMM. - -We humbly obsorbed the exquisite and efficient design of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). Building upon its foundation, we implemented our `lmms-eval` framework with performance optimizations specifically for LMMs. - -## Necessity of lmms-eval - -We believe our effort could provide an efficient interface for the detailed comparison of publicly available models to discern their strengths and weaknesses. It's also useful for research institutions and production-oriented companies to accelerate the development of large multimodal models. With the `lmms-eval`, we have significantly accelerated the lifecycle of model iteration. Inside the LLaVA team, the utilization of `lmms-eval` largely improves the efficiency of the model development cycle, as we are able to evaluate weekly trained hundreds of checkpoints on 20-30 datasets, identifying the strengths and weaknesses, and then make targeted improvements. - # Annoucement -## Contribution Guidance - -We've added guidance on contributing new datasets and models. Please refer to our [documentation](docs/README.md). If you need assistance, you can contact us via [discord/lmms-eval](https://discord.gg/ebAMGSsS). - -## v0.1.0 Released - -The first version of the `lmms-eval` is released. We are working on providing an one-command evaluation suite for accelerating the development of LMMs. - -> In [LLaVA Next](https://llava-vl.github.io/blog/2024-01-30-llava-next/) development, we internally utilize this suite to evaluate the multiple different model versions on various datasets. It significantly accelerates the model development cycle for it's easy integration and fast evaluation speed. - -The main feature includes: - -

    - -

    - -### One-command evaluation, with detailed logs and samples. -You can evaluate the models on multiple datasets with a single command. No model/data preparation is needed, just one command line, few minutes, and get the results. Not just a result number, but also the detailed logs and samples, including the model args, input question, model response, and ground truth answer. - -```python -# Evaluating LLaVA on multiple datasets -accelerate launch --num_processes=8 -m lmms_eval --model llava --model_args pretrained="liuhaotian/llava-v1.5-7b" --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ # -``` - -### Accelerator support and Tasks grouping. -We support the usage of `accelerate` to wrap the model for distributed evaluation, supporting multi-gpu and tensor parallelism. With **Task Grouping**, all instances from all tasks are grouped and evaluated in parallel, which significantly improves the throughput of the evaluation. After evaluation, all instances are sent to postprocessing module for metric calcuations and potential GPT4-eval queries. - -Below are the total runtime on different datasets using 4 x A100 40G. +- [2024-06] The `lmms-eval/v0.2` has been upgraded to support video evaluations, and other feature updates. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details -| Dataset (#num) | LLaVA-v1.5-7b | LLaVA-v1.5-13b | -| :---------------------- | :----------------- | :----------------- | -| mme (2374) | 2 mins 43 seconds | 3 mins 27 seconds | -| gqa (12578) | 10 mins 43 seconds | 14 mins 23 seconds | -| scienceqa_img (2017) | 1 mins 58 seconds | 2 mins 52 seconds | -| ai2d (3088) | 3 mins 17 seconds | 4 mins 12 seconds | -| coco2017_cap_val (5000) | 14 mins 13 seconds | 19 mins 58 seconds | +- [2024-03] We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details -### All-In-One HF dataset hubs. +# Why `lmms-eval`? -We are hosting more than 40 (and increasing) datasets on [huggingface/lmms-lab](https://huggingface.co/lmms-lab), we carefully converted these datasets from original sources and included all variants, versions and splits. Now they can be directly accessed without any burden of data preprocessing. They also serve for the purpose of visualizing the data and grasping the sense of evaluation tasks distribution. - -

    - -

    +In today's world, we're on an exciting journey toward creating Artificial General Intelligence (AGI), much like the enthusiasm of the 1960s moon landing. This journey is powered by advanced large language models (LLMs) and large multimodal models (LMMs), which are complex systems capable of understanding, learning, and performing a wide variety of human tasks. -### Detailed Logging Utilites +To gauge how advanced these models are, we use a variety of evaluation benchmarks. These benchmarks are tools that help us understand the capabilities of these models, showing us how close we are to achieving AGI. -We provide detailed logging utilities to help you understand the evaluation process and results. The logs include the model args, generation parameters, input question, model response, and ground truth answer. You can also record every details and visualize them inside runs on Weights & Biases. +However, finding and using these benchmarks is a big challenge. The necessary benchmarks and datasets are spread out and hidden in various places like Google Drive, Dropbox, and different school and research lab websites. It feels like we're on a treasure hunt, but the maps are scattered everywhere. -{% include figure.liquid loading="eager" path="assets/img/wandb_table.png" class="img-fluid rounded z-depth-1" zoomable=true %} +In the field of language models, there has been a valuable precedent set by the work of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). They offer integrated data and model interfaces, enabling rapid evaluation of language models and serving as the backend support framework for the [open-llm-leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and has gradually become the underlying ecosystem of the era of foundation models. -

    - -

    +We humbly obsorbed the exquisite and efficient design of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and introduce **lmms-eval**, an evaluation framework meticulously crafted for consistent and efficient evaluation of LMM. # Installation @@ -95,37 +42,110 @@ pip install -e . ``` If you wanted to test llava, you will have to clone their repo from [LLaVA](https://github.com/haotian-liu/LLaVA) and -``` -git clone https://github.com/haotian-liu/LLaVA -cd LLaVA +```bash +# for llava 1.5 +# git clone https://github.com/haotian-liu/LLaVA +# cd LLaVA +# pip install -e . + +# for llava-next (1.6) +git clone https://github.com/LLaVA-VL/LLaVA-NeXT +cd LLaVA-NeXT pip install -e . ``` +
    +Reproduction of LLaVA-1.5's paper results + You can check the [environment install script](miscs/repr_scripts.sh) and [torch environment info](miscs/repr_torch_envs.txt) to **reproduce LLaVA-1.5's paper results**. We found torch/cuda versions difference would cause small variations in the results, we provide the [results check](miscs/llava_result_check.md) with different environments. +
    + If you want to test on caption dataset such as `coco`, `refcoco`, and `nocaps`, you will need to have `java==1.8.0 ` to let pycocoeval api to work. If you don't have it, you can install by using conda ``` conda install openjdk=8 ``` you can then check your java version by `java -version` -# Usage +# Multiple Usages ```bash -# Evaluating LLaVA on MME -accelerate launch --num_processes=8 -m lmms_eval --model llava --model_args pretrained="liuhaotian/llava-v1.5-7b" --tasks mme --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme --output_path ./logs/ - -# Evaluating LLaVA on multiple datasets -accelerate launch --num_processes=8 -m lmms_eval --model llava --model_args pretrained="liuhaotian/llava-v1.5-7b" --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ # +# Evaluation of LLaVA on MME +python3 -m accelerate.commands.launch \ + --num_processes=8 \ + -m lmms_eval \ + --model llava \ + --model_args pretrained="liuhaotian/llava-v1.5-7b" \ + --tasks mme \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix llava_v1.5_mme \ + --output_path ./logs/ + +# Evaluation of LLaVA on multiple datasets +python3 -m accelerate.commands.launch \ + --num_processes=8 \ + -m lmms_eval \ + --model llava \ + --model_args pretrained="liuhaotian/llava-v1.5-7b" \ + --tasks mme,mmbench_en \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix llava_v1.5_mme_mmbenchen \ + --output_path ./logs/ # For other variants llava. Note that `conv_template` is an arg of the init function of llava in `lmms_eval/models/llava.py` -accelerate launch --num_processes=8 -m lmms_eval --model llava --model_args pretrained="liuhaotian/llava-v1.6-mistral-7b,conv_template=mistral_instruct" --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ # -accelerate launch --num_processes=8 -m lmms_eval --model llava --model_args pretrained="liuhaotian/llava-v1.6-34b,conv_template=mistral_direct" --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ # - -# From a predefined configuration, supporting evaluation of multiple models and datasets -accelerate launch --num_processes=8 -m lmms_eval --config ./miscs/example_eval.yaml +python3 -m accelerate.commands.launch \ + --num_processes=8 \ + -m lmms_eval \ + --model llava \ + --model_args pretrained="liuhaotian/llava-v1.6-mistral-7b,conv_template=mistral_instruct" \ + --tasks mme,mmbench_en \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix llava_v1.5_mme_mmbenchen \ + --output_path ./logs/ + +# Evaluation of larger lmms (llava-v1.6-34b) +python3 -m accelerate.commands.launch \ + --num_processes=8 \ + -m lmms_eval \ + --model llava \ + --model_args pretrained="liuhaotian/llava-v1.6-34b,conv_template=mistral_direct" \ + --tasks mme,mmbench_en \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix llava_v1.5_mme_mmbenchen \ + --output_path ./logs/ + +# Evaluation with a set of configurations, supporting evaluation of multiple models and datasets +python3 -m accelerate.commands.launch --num_processes=8 -m lmms_eval --config ./miscs/example_eval.yaml + +# Evaluation with naive model sharding for bigger model (llava-next-72b) +python3 -m lmms_eval \ + --model=llava \ + --model_args=pretrained=lmms-lab/llava-next-72b,conv_template=qwen_1_5,device_map=auto,model_name=llava_qwen \ + --tasks=pope,vizwiz_vqa_val,scienceqa_img \ + --batch_size=1 \ + --log_samples \ + --log_samples_suffix=llava_qwen \ + --output_path="./logs/" \ + --wandb_args=project=lmms-eval,job_type=eval,entity=llava-vl + +# Evaluation with SGLang for bigger model (llava-next-72b) +python3 -m lmms_eval \ + --model=llava_sglang \ + --model_args=pretrained=lmms-lab/llava-next-72b,tokenizer=lmms-lab/llavanext-qwen-tokenizer,conv_template=chatml-llava,tp_size=8,parallel=8 \ + --tasks=mme \ + --batch_size=1 \ + --log_samples \ + --log_samples_suffix=llava_qwen \ + --output_path=./logs/ \ + --verbosity=INFO ``` -# Model Results +
    +Comprehensive Evaluation Results of LLaVA Family Models +
    As demonstrated by the extensive table below, we aim to provide detailed information for readers to understand the datasets included in lmms-eval and some specific details about these datasets (we remain grateful for any corrections readers may have during our evaluation process). @@ -137,146 +157,19 @@ We provide a Google Sheet for the detailed results of the LLaVA series models on We also provide the raw data exported from Weights & Biases for the detailed results of the LLaVA series models on different datasets. You can access the raw data [here](https://docs.google.com/spreadsheets/d/1AvaEmuG4csSmXaHjgu4ei1KBMmNNW8wflOD_kkTDdv8/edit?usp=sharing). -> Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub. +
    +
    + +Our Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub. ## Supported models -- GPT4V (API, only generation-based evaluation) -- LLaVA-v1.5/v1.6-7B/13B/34B (ppl-based, generation-based) -- Qwen-VL series (ppl-based, generation-based) -- Fuyu series (ppl-based, generation-based) -- InstructBLIP series (generation-based) - -## Supported datasets -> () indicates the task name in the lmms_eval. The task name is also used to specify the dataset in the configuration file. - -- AI2D (ai2d) -- ChartQA (chartqa) -- CMMMU (cmmmu) - - CMMMU Validation (cmmmu_val) - - CMMMU Test (cmmmu_test) -- COCO Caption (coco_cap) - - COCO 2014 Caption (coco2014_cap) - - COCO 2014 Caption Validation (coco2014_cap_val) - - COCO 2014 Caption Test (coco2014_cap_test) - - COCO 2017 Caption (coco2017_cap) - - COCO 2017 Caption MiniVal (coco2017_cap_val) - - COCO 2017 Caption MiniTest (coco2017_cap_test) -- [ConBench](https://github.com/foundation-multimodal-models/ConBench) (conbench) -- DOCVQA (docvqa) - - DOCVQA Validation (docvqa_val) - - DOCVQA Test (docvqa_test) -- Ferret (ferret) -- Flickr30K (flickr30k) - - Ferret Test (ferret_test) -- GQA (gqa) -- HallusionBenchmark (hallusion_bench_image) -- Infographic VQA (info_vqa) - - Infographic VQA Validation (info_vqa_val) - - Infographic VQA Test (info_vqa_test) -- LLaVA-Bench (llava_in_the_wild) -- LLaVA-Bench-COCO (llava_bench_coco) -- MathVerse (mathverse) - - MathVerse Text Dominant (mathverse_testmini_text_dominant) - - MathVerse Text Only (mathverse_testmini_text_only) - - MathVerse Text Lite (mathverse_testmini_text_lite) - - MathVerse Vision Dominant (mathverse_testmini_vision_dominant) - - MathVerse Vision Intensive (mathverse_testmini_vision_intensive) - - MathVerse Vision Only (mathverse_testmini_vision_only) -- MathVista (mathvista) - - MathVista Validation (mathvista_testmini) - - MathVista Test (mathvista_test) -- MMBench (mmbench) - - MMBench English (mmbench_en) - - MMBench English Dev (mmbench_en_dev) - - MMBench English Test (mmbench_en_test) - - MMBench Chinese (mmbench_cn) - - MMBench Chinese Dev (mmbench_cn_dev) - - MMBench Chinese Test (mmbench_cn_test) -- MME (mme) -- MMMU (mmmu) - - MMMU Validation (mmmu_val) - - MMMU Test (mmmu_test) -- MMUPD (mmupd) - - MMUPD Base (mmupd_base) - - MMAAD Base (mmaad_base) - - MMIASD Base (mmiasd_base) - - MMIVQD Base (mmivqd_base) - - MMUPD Option (mmupd_option) - - MMAAD Option (mmaad_option) - - MMIASD Option (mmiasd_option) - - MMIVQD Option (mmivqd_option) - - MMUPD Instruction (mmupd_instruction) - - MMAAD Instruction (mmaad_instruction) - - MMIASD Instruction (mmiasd_instruction) - - MMIVQD Instruction (mmivqd_instruction) -- MMVet (mmvet) -- Multi-DocVQA (multidocvqa) - - Multi-DocVQA Validation (multidocvqa_val) - - Multi-DocVQA Test (multidocvqa_test) -- NoCaps (nocaps) - - NoCaps Validation (nocaps_val) - - NoCaps Test (nocaps_test) -- OKVQA (ok_vqa) - - OKVQA Validation 2014 (ok_vqa_val2014) -- POPE (pope) -- RefCOCO (refcoco) - - refcoco_seg_test - - refcoco_seg_val - - refcoco_seg_testA - - refcoco_seg_testB - - refcoco_bbox_test - - refcoco_bbox_val - - refcoco_bbox_testA - - refcoco_bbox_testB -- RefCOCO+ (refcoco+) - - refcoco+_seg - - refcoco+_seg_val - - refcoco+_seg_testA - - refcoco+_seg_testB - - refcoco+_bbox - - refcoco+_bbox_val - - refcoco+_bbox_testA - - refcoco+_bbox_testB -- RefCOCOg (refcocog) - - refcocog_seg_test - - refcocog_seg_val - - refcocog_bbox_test - - refcocog_bbox_val -- ScienceQA (scienceqa_full) - - ScienceQA Full (scienceqa) - - ScienceQA IMG (scienceqa_img) -- ScreenSpot (screenspot) - - ScreenSpot REC / Grounding (screenspot_rec) - - ScreenSpot REG / Instruction Generation (screenspot_reg) -- SeedBench (seedbench) -- SeedBench 2 (seedbench_2) -- ST-VQA (stvqa) -- TextCaps (textcaps) - - TextCaps Validation (textcaps_val) - - TextCaps Test (textcaps_test) -- TextVQA (textvqa) - - TextVQA Validation (textvqa_val) - - TextVQA Test (textvqa_test) -- VizWizVQA (vizwiz_vqa) - - VizWizVQA Validation (vizwiz_vqa_val) - - VizWizVQA Test (vizwiz_vqa_test) -- VQAv2 (vqav2) - - VQAv2 Validation (vqav2_val) - - VQAv2 Test (vqav2_test) -- WebSRC (websrc) - - WebSRC Validation (websrc_val) - - WebSRC Test (websrc_test) - -## Datasets to be added and tested -- TallyQA (tallyqa) -- VSR (vsr) -- Winoground (winoground) -- NLVR2 (nlvr2) -- RavenIQ-Test (raveniq) -- IconQA (iconqa) -- VistBench (vistbench) +Please check [supported models](lmms_eval/models/__init__.py) for more details. + +## Supported tasks + +Please check [supported tasks](lmms_eval/docs/current_tasks.md) for more details. # Add Customized Model and Dataset diff --git a/docs/current_tasks.md b/docs/current_tasks.md new file mode 100644 index 00000000..1622e960 --- /dev/null +++ b/docs/current_tasks.md @@ -0,0 +1,122 @@ +# Current Tasks + +> () indicates the task name in the lmms_eval. The task name is also used to specify the dataset in the configuration file. +> The following is manually updated documentation. You could use `lmms_eval task --list` to list all supported tasks and their task names. + +- AI2D (ai2d) +- ChartQA (chartqa) +- CMMMU (cmmmu) + - CMMMU Validation (cmmmu_val) + - CMMMU Test (cmmmu_test) +- COCO Caption (coco_cap) + - COCO 2014 Caption (coco2014_cap) + - COCO 2014 Caption Validation (coco2014_cap_val) + - COCO 2014 Caption Test (coco2014_cap_test) + - COCO 2017 Caption (coco2017_cap) + - COCO 2017 Caption MiniVal (coco2017_cap_val) + - COCO 2017 Caption MiniTest (coco2017_cap_test) +- [ConBench](https://github.com/foundation-multimodal-models/ConBench) (conbench) +- DOCVQA (docvqa) + - DOCVQA Validation (docvqa_val) + - DOCVQA Test (docvqa_test) +- Ferret (ferret) +- Flickr30K (flickr30k) + - Ferret Test (ferret_test) +- GQA (gqa) +- HallusionBenchmark (hallusion_bench_image) +- Infographic VQA (info_vqa) + - Infographic VQA Validation (info_vqa_val) + - Infographic VQA Test (info_vqa_test) +- LLaVA-Bench (llava_in_the_wild) +- LLaVA-Bench-COCO (llava_bench_coco) +- MathVerse (mathverse) + - MathVerse Text Dominant (mathverse_testmini_text_dominant) + - MathVerse Text Only (mathverse_testmini_text_only) + - MathVerse Text Lite (mathverse_testmini_text_lite) + - MathVerse Vision Dominant (mathverse_testmini_vision_dominant) + - MathVerse Vision Intensive (mathverse_testmini_vision_intensive) + - MathVerse Vision Only (mathverse_testmini_vision_only) +- MathVista (mathvista) + - MathVista Validation (mathvista_testmini) + - MathVista Test (mathvista_test) +- MMBench (mmbench) + - MMBench English (mmbench_en) + - MMBench English Dev (mmbench_en_dev) + - MMBench English Test (mmbench_en_test) + - MMBench Chinese (mmbench_cn) + - MMBench Chinese Dev (mmbench_cn_dev) + - MMBench Chinese Test (mmbench_cn_test) +- MME (mme) +- MMMU (mmmu) + - MMMU Validation (mmmu_val) + - MMMU Test (mmmu_test) +- MMUPD (mmupd) + - MMUPD Base (mmupd_base) + - MMAAD Base (mmaad_base) + - MMIASD Base (mmiasd_base) + - MMIVQD Base (mmivqd_base) + - MMUPD Option (mmupd_option) + - MMAAD Option (mmaad_option) + - MMIASD Option (mmiasd_option) + - MMIVQD Option (mmivqd_option) + - MMUPD Instruction (mmupd_instruction) + - MMAAD Instruction (mmaad_instruction) + - MMIASD Instruction (mmiasd_instruction) + - MMIVQD Instruction (mmivqd_instruction) +- MMVet (mmvet) +- Multi-DocVQA (multidocvqa) + - Multi-DocVQA Validation (multidocvqa_val) + - Multi-DocVQA Test (multidocvqa_test) +- NoCaps (nocaps) + - NoCaps Validation (nocaps_val) + - NoCaps Test (nocaps_test) +- OKVQA (ok_vqa) + - OKVQA Validation 2014 (ok_vqa_val2014) +- POPE (pope) +- RefCOCO (refcoco) + - refcoco_seg_test + - refcoco_seg_val + - refcoco_seg_testA + - refcoco_seg_testB + - refcoco_bbox_test + - refcoco_bbox_val + - refcoco_bbox_testA + - refcoco_bbox_testB +- RefCOCO+ (refcoco+) + - refcoco+_seg + - refcoco+_seg_val + - refcoco+_seg_testA + - refcoco+_seg_testB + - refcoco+_bbox + - refcoco+_bbox_val + - refcoco+_bbox_testA + - refcoco+_bbox_testB +- RefCOCOg (refcocog) + - refcocog_seg_test + - refcocog_seg_val + - refcocog_bbox_test + - refcocog_bbox_val +- ScienceQA (scienceqa_full) + - ScienceQA Full (scienceqa) + - ScienceQA IMG (scienceqa_img) +- ScreenSpot (screenspot) + - ScreenSpot REC / Grounding (screenspot_rec) + - ScreenSpot REG / Instruction Generation (screenspot_reg) +- SeedBench (seedbench) +- SeedBench 2 (seedbench_2) +- ST-VQA (stvqa) +- TextCaps (textcaps) + - TextCaps Validation (textcaps_val) + - TextCaps Test (textcaps_test) +- TextVQA (textvqa) + - TextVQA Validation (textvqa_val) + - TextVQA Test (textvqa_test) +- VizWizVQA (vizwiz_vqa) + - VizWizVQA Validation (vizwiz_vqa_val) + - VizWizVQA Test (vizwiz_vqa_test) +- VQAv2 (vqav2) + - VQAv2 Validation (vqav2_val) + - VQAv2 Test (vqav2_test) +- WebSRC (websrc) + - WebSRC Validation (websrc_val) + - WebSRC Test (websrc_test) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 35089933..d11c1228 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta" [project] name = "lmms_eval" -version = "0.1.1" +version = "0.2.0.dev0" authors = [ { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" }, ] From 50575a950736bc8fc1e191310314cbb5fdff5720 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Wed, 12 Jun 2024 15:39:03 +0000 Subject: [PATCH 071/196] chore: Update lmms-eval to support video evaluations for LLaVA models --- README.md | 79 +++++++++++++++++++++++++++++++++++-------------------- 1 file changed, 50 insertions(+), 29 deletions(-) diff --git a/README.md b/README.md index a4cc11e2..41710790 100755 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -

    +

    @@ -11,7 +11,7 @@ # Annoucement -- [2024-06] The `lmms-eval/v0.2` has been upgraded to support video evaluations, and other feature updates. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details +- [2024-06] The `lmms-eval/v0.2` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details - [2024-03] We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details @@ -67,9 +67,32 @@ conda install openjdk=8 ``` you can then check your java version by `java -version` + +
    +Comprehensive Evaluation Results of LLaVA Family Models +
    + +As demonstrated by the extensive table below, we aim to provide detailed information for readers to understand the datasets included in lmms-eval and some specific details about these datasets (we remain grateful for any corrections readers may have during our evaluation process). + +We provide a Google Sheet for the detailed results of the LLaVA series models on different datasets. You can access the sheet [here](https://docs.google.com/spreadsheets/d/1a5ImfdKATDI8T7Cwh6eH-bEsnQFzanFraFUgcS9KHWc/edit?usp=sharing). It's a live sheet, and we are updating it with new results. + +

    + +

    + +We also provide the raw data exported from Weights & Biases for the detailed results of the LLaVA series models on different datasets. You can access the raw data [here](https://docs.google.com/spreadsheets/d/1AvaEmuG4csSmXaHjgu4ei1KBMmNNW8wflOD_kkTDdv8/edit?usp=sharing). + +
    +
    + + +Our Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub. + # Multiple Usages + +**Evaluation of LLaVA on MME** + ```bash -# Evaluation of LLaVA on MME python3 -m accelerate.commands.launch \ --num_processes=8 \ -m lmms_eval \ @@ -80,8 +103,11 @@ python3 -m accelerate.commands.launch \ --log_samples \ --log_samples_suffix llava_v1.5_mme \ --output_path ./logs/ +``` + +**Evaluation of LLaVA on multiple datasets** -# Evaluation of LLaVA on multiple datasets +```bash python3 -m accelerate.commands.launch \ --num_processes=8 \ -m lmms_eval \ @@ -92,8 +118,11 @@ python3 -m accelerate.commands.launch \ --log_samples \ --log_samples_suffix llava_v1.5_mme_mmbenchen \ --output_path ./logs/ +``` -# For other variants llava. Note that `conv_template` is an arg of the init function of llava in `lmms_eval/models/llava.py` +**For other variants llava. Note that `conv_template` is an arg of the init function of llava in `lmms_eval/models/llava.py`** + +```bash python3 -m accelerate.commands.launch \ --num_processes=8 \ -m lmms_eval \ @@ -104,8 +133,11 @@ python3 -m accelerate.commands.launch \ --log_samples \ --log_samples_suffix llava_v1.5_mme_mmbenchen \ --output_path ./logs/ +``` -# Evaluation of larger lmms (llava-v1.6-34b) +**Evaluation of larger lmms (llava-v1.6-34b)** + +```bash python3 -m accelerate.commands.launch \ --num_processes=8 \ -m lmms_eval \ @@ -116,11 +148,17 @@ python3 -m accelerate.commands.launch \ --log_samples \ --log_samples_suffix llava_v1.5_mme_mmbenchen \ --output_path ./logs/ +``` + +**Evaluation with a set of configurations, supporting evaluation of multiple models and datasets** -# Evaluation with a set of configurations, supporting evaluation of multiple models and datasets +```bash python3 -m accelerate.commands.launch --num_processes=8 -m lmms_eval --config ./miscs/example_eval.yaml +``` -# Evaluation with naive model sharding for bigger model (llava-next-72b) +**Evaluation with naive model sharding for bigger model (llava-next-72b)** + +```bash python3 -m lmms_eval \ --model=llava \ --model_args=pretrained=lmms-lab/llava-next-72b,conv_template=qwen_1_5,device_map=auto,model_name=llava_qwen \ @@ -130,8 +168,11 @@ python3 -m lmms_eval \ --log_samples_suffix=llava_qwen \ --output_path="./logs/" \ --wandb_args=project=lmms-eval,job_type=eval,entity=llava-vl +``` + +**Evaluation with SGLang for bigger model (llava-next-72b)** -# Evaluation with SGLang for bigger model (llava-next-72b) +```bash python3 -m lmms_eval \ --model=llava_sglang \ --model_args=pretrained=lmms-lab/llava-next-72b,tokenizer=lmms-lab/llavanext-qwen-tokenizer,conv_template=chatml-llava,tp_size=8,parallel=8 \ @@ -143,26 +184,6 @@ python3 -m lmms_eval \ --verbosity=INFO ``` -
    -Comprehensive Evaluation Results of LLaVA Family Models -
    - -As demonstrated by the extensive table below, we aim to provide detailed information for readers to understand the datasets included in lmms-eval and some specific details about these datasets (we remain grateful for any corrections readers may have during our evaluation process). - -We provide a Google Sheet for the detailed results of the LLaVA series models on different datasets. You can access the sheet [here](https://docs.google.com/spreadsheets/d/1a5ImfdKATDI8T7Cwh6eH-bEsnQFzanFraFUgcS9KHWc/edit?usp=sharing). It's a live sheet, and we are updating it with new results. - -

    - -

    - -We also provide the raw data exported from Weights & Biases for the detailed results of the LLaVA series models on different datasets. You can access the raw data [here](https://docs.google.com/spreadsheets/d/1AvaEmuG4csSmXaHjgu4ei1KBMmNNW8wflOD_kkTDdv8/edit?usp=sharing). - -
    -
    - - -Our Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub. - ## Supported models Please check [supported models](lmms_eval/models/__init__.py) for more details. From 34156335db74cef9e3f0915d7172fd6b22456c15 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Wed, 12 Jun 2024 15:43:16 +0000 Subject: [PATCH 072/196] Update llava conv_template in lmms_eval/models/llava.py --- README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 41710790..75ce3965 100755 --- a/README.md +++ b/README.md @@ -11,9 +11,9 @@ # Annoucement -- [2024-06] The `lmms-eval/v0.2` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details +- [2024-06] 🎬🎬 The `lmms-eval/v0.2` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details -- [2024-03] We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details +- [2024-03] 📝📝 We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details # Why `lmms-eval`? @@ -120,7 +120,9 @@ python3 -m accelerate.commands.launch \ --output_path ./logs/ ``` -**For other variants llava. Note that `conv_template` is an arg of the init function of llava in `lmms_eval/models/llava.py`** +**For other variants llava. Please change the `conv_template` in the `model_args`** + +> `conv_template` is an arg of the init function of llava in `lmms_eval/models/llava.py`, you could find the corresponding value at LLaVA's code, probably in a dict variable `conv_templates` in `llava/conversations.py` ```bash python3 -m accelerate.commands.launch \ From f00d5498b69dd4f7e54c907ac906abc7c128f000 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Wed, 12 Jun 2024 15:46:33 +0000 Subject: [PATCH 073/196] Update image alignment in README.md --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 75ce3965..acb9f3be 100755 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -

    +

    @@ -17,6 +17,10 @@ # Why `lmms-eval`? +

    + +

    + In today's world, we're on an exciting journey toward creating Artificial General Intelligence (AGI), much like the enthusiasm of the 1960s moon landing. This journey is powered by advanced large language models (LLMs) and large multimodal models (LMMs), which are complex systems capable of understanding, learning, and performing a wide variety of human tasks. To gauge how advanced these models are, we use a variety of evaluation benchmarks. These benchmarks are tools that help us understand the capabilities of these models, showing us how close we are to achieving AGI. From cbeee20bc4ffb510a2b23d96cdaf4077be7c2a9e Mon Sep 17 00:00:00 2001 From: Bo Li Date: Wed, 12 Jun 2024 15:50:30 +0000 Subject: [PATCH 074/196] chore: Update lmms-eval to support video evaluations for LLaVA models --- README.md | 30 +++++++++++++++++++++++++++++- 1 file changed, 29 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index acb9f3be..7dd775e6 100755 --- a/README.md +++ b/README.md @@ -8,6 +8,7 @@ 🏠 [LMMs-Lab Homepage](https://lmms-lab.github.io/) | 🎉 [Blog](https://lmms-lab.github.io/lmms-eval-blog/lmms-eval-0.1/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | Discord_Thread [discord/lmms-eval](https://discord.gg/zdkwKUqrPy) +--- # Annoucement @@ -206,14 +207,41 @@ Please refer to our [documentation](docs/README.md). lmms_eval is a fork of [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). We recommend you to read through the [docs of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs) for relevant information. +--- + Below are the changes we made to the original API: - Build context now only pass in idx and process image and doc during the model responding phase. This is due to the fact that dataset now contains lots of images and we can't store them in the doc like the original lm-eval-harness other wise the cpu memory would explode. - Instance.args (lmms_eval/api/instance.py) now contains a list of images to be inputted to lmms. - lm-eval-harness supports all HF language models as single model class. Currently this is not possible of lmms because the input/output format of lmms in HF are not yet unified. Thererfore, we have to create a new class for each lmms model. This is not ideal and we will try to unify them in the future. -We also thank: +--- + +During the initial stage of our project, we thank: - [Xiang Yue](https://xiangyue9607.github.io/), [Jingkang Yang](https://jingkang50.github.io/), [Dong Guo](https://www.linkedin.com/in/dongguoset/) and [Sheng Shen](https://sincerass.github.io/) for early discussion and testing. +--- + +During the `v0.1` to `v0.2`, we thank the community support from pull requests (PRs): + +**Datasets:** + +- VCR: Vision_Caption_Restoration (officially from the authors, MILA) +- ConBench (officially from the authors, PKU/Bytedance) +- MathVerse (officially from the authors, CUHK) +- MM-UPD (officially from the authors, University of Tokyo) +- Multi-lingual MMMU (officially from the authors, CUHK) +- WebSRC (from Hunter Heiden) +- ScreeSpot (from Hunter Heiden) +- RealworldQA (from Fanyi Pu, NTU) +- Multi-lingual LLaVA-W (from Gagan Bhatia, UBC) + +**Models:** + +- LLaVA-HF (officially from Huggingface) +- Idefics-2 (from the lmms-lab team) +- microsoft/Phi-3-Vision (officially from the authors, Microsoft) +- LLaVA-SGlang (from the lams-lab team) + ## Citations ```shell From 05dc8e853eab7c6bc782a1e2662d2efe7422f767 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Wed, 12 Jun 2024 15:56:04 +0000 Subject: [PATCH 075/196] chore: Update lmms-eval to support video evaluations for LLaVA models --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 7dd775e6..72b15fb1 100755 --- a/README.md +++ b/README.md @@ -223,6 +223,8 @@ During the initial stage of our project, we thank: During the `v0.1` to `v0.2`, we thank the community support from pull requests (PRs): +> Details are in [lmms-eval/v0.2.0 release notes](https://github.com/EvolvingLMMs-Lab/lmms-eval/releases/tag/untagged-9057ff0e9a72d5a5846f) + **Datasets:** - VCR: Vision_Caption_Restoration (officially from the authors, MILA) From ed8806839db5988ced672bd162b7b046edb4863a Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 13 Jun 2024 03:13:59 +0800 Subject: [PATCH 076/196] Update README.md --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 72b15fb1..f591fca1 100755 --- a/README.md +++ b/README.md @@ -10,13 +10,13 @@ --- -# Annoucement +## Annoucement - [2024-06] 🎬🎬 The `lmms-eval/v0.2` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details - [2024-03] 📝📝 We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details -# Why `lmms-eval`? +## Why `lmms-eval`?

    @@ -32,7 +32,7 @@ In the field of language models, there has been a valuable precedent set by the We humbly obsorbed the exquisite and efficient design of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and introduce **lmms-eval**, an evaluation framework meticulously crafted for consistent and efficient evaluation of LMM. -# Installation +## Installation For formal usage, you can install the package from PyPI by running the following command: ```bash @@ -93,7 +93,7 @@ We also provide the raw data exported from Weights & Biases for the detailed res Our Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub. -# Multiple Usages +## Multiple Usages **Evaluation of LLaVA on MME** @@ -191,19 +191,19 @@ python3 -m lmms_eval \ --verbosity=INFO ``` -## Supported models +### Supported models Please check [supported models](lmms_eval/models/__init__.py) for more details. -## Supported tasks +### Supported tasks Please check [supported tasks](lmms_eval/docs/current_tasks.md) for more details. -# Add Customized Model and Dataset +## Add Customized Model and Dataset Please refer to our [documentation](docs/README.md). -# Acknowledgement +## Acknowledgement lmms_eval is a fork of [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). We recommend you to read through the [docs of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs) for relevant information. From 5ed00356676cf5d0ff056cf27d1b519b8e303ff7 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 13 Jun 2024 03:21:42 +0800 Subject: [PATCH 077/196] Update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index f591fca1..3e2167a5 100755 --- a/README.md +++ b/README.md @@ -231,7 +231,6 @@ During the `v0.1` to `v0.2`, we thank the community support from pull requests ( - ConBench (officially from the authors, PKU/Bytedance) - MathVerse (officially from the authors, CUHK) - MM-UPD (officially from the authors, University of Tokyo) -- Multi-lingual MMMU (officially from the authors, CUHK) - WebSRC (from Hunter Heiden) - ScreeSpot (from Hunter Heiden) - RealworldQA (from Fanyi Pu, NTU) From 47b13b9b320d36ac53b3622557e31239f7c22621 Mon Sep 17 00:00:00 2001 From: Suyuchen Date: Wed, 12 Jun 2024 15:30:52 -0400 Subject: [PATCH 078/196] update aggregation function for vcr_wiki --- lmms_eval/tasks/vcr_wiki/utils.py | 15 ++++++++++++--- lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml | 4 ++-- .../tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml | 4 ++-- .../tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml | 4 ++-- lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml | 4 ++-- .../tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml | 4 ++-- .../tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml | 4 ++-- lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml | 4 ++-- .../tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml | 4 ++-- .../tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml | 4 ++-- lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml | 4 ++-- .../tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml | 4 ++-- .../tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml | 4 ++-- 13 files changed, 36 insertions(+), 27 deletions(-) diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index 5cd5ad9a..67e8b66d 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -6,10 +6,11 @@ from functools import partial import evaluate +import numpy as np import spacy from nltk.util import ngrams from spacy.cli import download -import numpy as np + from lmms_eval.tasks._task_utils.file_utils import generate_submission_file # Download the English and Chinese models @@ -262,7 +263,7 @@ def bootstrap_std(data, n_bootstrap=1000, ci=0.95): return std, lower_bound, upper_bound -def vcr_aggregate_results(results, args): +def vcr_aggregate_results(results, args, metric='exact_match'): """ Args: results: List[List[Dict]], list of results returned by process_results @@ -285,9 +286,17 @@ def vcr_aggregate_results(results, args): "detailed_results": output_dict_detail_result, } now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") - path = generate_submission_file(f"vcr_submission_{now_date_time}.json", args) + path = generate_submission_file(f"vcr_submission_{metric}_{now_date_time}.json", args) with open(path, "w", encoding="utf-8") as f: json.dump(output_dict, f, indent=4, ensure_ascii=False) # print(f"Submission file saved to {path}") eval_logger.info(f"Submission file saved to {path}") return mean_score + + +def vcr_aggregate_exact_match(results, args): + return vcr_aggregate_results(results, args, metric='exact_match') + + +def vcr_aggregate_jaccard(results, args): + return vcr_aggregate_results(results, args, metric='jaccard') diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml index cb9e2346..9ce88299 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml index 94d74448..37f0130b 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml index 5c70b133..0ccc1ed0 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml index 97c837f6..8c001e4e 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml index bf3dcc45..16c79b15 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml index 4b62e1f3..65616894 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_en_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml index 5ee284a9..a07cf230 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml index 9a478af9..fb41b668 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml index 393cfb08..e3654aed 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml index 3913d0b0..0401b3bd 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml index 7dd89841..f6512e29 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml index 3f7aa1a2..a36bb328 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml @@ -5,10 +5,10 @@ test_split: test process_results: !function utils.vcr_zh_process_results metric_list: - metric: jaccard - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_jaccard higher_is_better: true - metric: exact_match - aggregation: !function utils.vcr_aggregate_results + aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true model_specific_prompt_kwargs: default: From 0ce46d088e473d12d63de44f17c67dceab25658c Mon Sep 17 00:00:00 2001 From: Suyuchen Date: Wed, 12 Jun 2024 15:56:34 -0400 Subject: [PATCH 079/196] update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3e2167a5..93bbb781 100755 --- a/README.md +++ b/README.md @@ -227,7 +227,7 @@ During the `v0.1` to `v0.2`, we thank the community support from pull requests ( **Datasets:** -- VCR: Vision_Caption_Restoration (officially from the authors, MILA) +- VCR: Visual Caption Restoration (officially from the authors, MILA) - ConBench (officially from the authors, PKU/Bytedance) - MathVerse (officially from the authors, CUHK) - MM-UPD (officially from the authors, University of Tokyo) From 63d82f1ff11eb430d91a15d6788a1f0b4d596850 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 13 Jun 2024 11:04:32 +0800 Subject: [PATCH 080/196] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 93bbb781..2bf7a26e 100755 --- a/README.md +++ b/README.md @@ -241,7 +241,7 @@ During the `v0.1` to `v0.2`, we thank the community support from pull requests ( - LLaVA-HF (officially from Huggingface) - Idefics-2 (from the lmms-lab team) - microsoft/Phi-3-Vision (officially from the authors, Microsoft) -- LLaVA-SGlang (from the lams-lab team) +- LLaVA-SGlang (from the lmms-lab team) ## Citations From 66d4bb2d9c9afbbdea40196d4ad80e214d0b14b6 Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Thu, 13 Jun 2024 11:13:00 +0800 Subject: [PATCH 081/196] update version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index d11c1228..5f3b92fb 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta" [project] name = "lmms_eval" -version = "0.2.0.dev0" +version = "0.2.0" authors = [ { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" }, ] From 6248113f4e11a0ac396d31fa1b032a142fea8cb4 Mon Sep 17 00:00:00 2001 From: XinrunDu Date: Thu, 13 Jun 2024 09:32:06 +0000 Subject: [PATCH 082/196] add II-Bench --- lmms_eval/tasks/ii_bench/ii_bench.yaml | 20 ++++++++ lmms_eval/tasks/ii_bench/utils.py | 70 ++++++++++++++++++++++++++ 2 files changed, 90 insertions(+) create mode 100755 lmms_eval/tasks/ii_bench/ii_bench.yaml create mode 100755 lmms_eval/tasks/ii_bench/utils.py diff --git a/lmms_eval/tasks/ii_bench/ii_bench.yaml b/lmms_eval/tasks/ii_bench/ii_bench.yaml new file mode 100755 index 00000000..83eaca11 --- /dev/null +++ b/lmms_eval/tasks/ii_bench/ii_bench.yaml @@ -0,0 +1,20 @@ +dataset_path: m-a-p/II-Bench +task: "ii-bench" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.ii_bench_doc_to_visual +doc_to_text: !function utils.ii_bench_doc_to_text +doc_to_target: "answers" +generation_kwargs: + max_new_tokens: 32 + temperature: 0 + do_sample: False +process_results: !function utils.ii_bench_process_results +metric_list: + - metric: submission + aggregation: !function utils.ii_bench_aggregate_submissions +model_specific_prompt_kwargs: + default: + pre_prompt: "Instruction: Please try to answer the single-answer multiple choice question below based on the picture provided.\n" + post_prompt: "\nAnswer:" + \ No newline at end of file diff --git a/lmms_eval/tasks/ii_bench/utils.py b/lmms_eval/tasks/ii_bench/utils.py new file mode 100755 index 00000000..4a5f03c2 --- /dev/null +++ b/lmms_eval/tasks/ii_bench/utils.py @@ -0,0 +1,70 @@ +import json +import logging +import re +from collections import Counter +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file + +PROMPT = """Question: {} +(A) {} +(B) {} +(C) {} +(D) {} +(E) {} +(F) {}""" + +def ii_bench_doc_to_text(doc, model_specific_prompt_kwargs): + question = PROMPT.format(doc["question"], doc["option1"], doc["option2"], doc["option3"], doc["option4"], doc["option5"], doc["option6"]) + pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + post_prompt = model_specific_prompt_kwargs["post_prompt"] + return f"{pre_prompt}{question}{post_prompt}" + + +def ii_bench_doc_to_visual(doc): + return [doc["image"].convert("RGB")] + + +def extract_option_labels(text, options=None): + if isinstance(text, dict): + return 'error' + pattern = r"\(([A-F])\)" + matches = re.findall(pattern, text) + + if not matches: + pattern = r"\b([A-F])\b" + matches = re.findall(pattern, text) + + if matches: + counter = Counter(matches) + most_common = counter.most_common() + max_count = most_common[0][1] + candidates = [item for item in most_common if item[1] == max_count] + return candidates[-1][0] + else: + if options: + counter = Counter() + for i, option in enumerate(options, start=1): + label = chr(64 + i) + option_stripped = option.strip() + if option_stripped in text: + counter[label] += 1 + elif text in option: + counter[label] += 1 + if counter: + most_common = counter.most_common() + max_count = most_common[0][1] + candidates = [item for item in most_common if item[1] == max_count] + return candidates[-1][0] + return None + + +def ii_bench_process_results(doc, results): + response = results[0] + predict = extract_option_labels(response, [doc["option1"], doc["option2"], doc["option3"], doc["option4"], doc["option5"], doc["option6"]]) + return {"submission": {"id": doc["id"], "predict_answer": predict, "response": response}} + + +def ii_bench_aggregate_submissions(results, args): + file = generate_submission_file("ii_bench_test_for_submission.json", args) + with open(file, "w") as f: + json.dump(results, f, indent=4) + logging.getLogger("lmms-eval").info(f"Results saved to {file}") From bf14cb8527b2b7ac438a36567a875168bc02d294 Mon Sep 17 00:00:00 2001 From: XinrunDu Date: Thu, 13 Jun 2024 09:37:02 +0000 Subject: [PATCH 083/196] fix dataset_path --- lmms_eval/tasks/ii_bench/ii_bench.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/ii_bench/ii_bench.yaml b/lmms_eval/tasks/ii_bench/ii_bench.yaml index 83eaca11..482eb9cf 100755 --- a/lmms_eval/tasks/ii_bench/ii_bench.yaml +++ b/lmms_eval/tasks/ii_bench/ii_bench.yaml @@ -1,4 +1,4 @@ -dataset_path: m-a-p/II-Bench +dataset_path: lmms-lab/II-Bench task: "ii-bench" test_split: test output_type: generate_until From ea14cd4b361f4c95b3665cbdb95bc51754090eb5 Mon Sep 17 00:00:00 2001 From: teowu Date: Fri, 14 Jun 2024 15:01:52 +0000 Subject: [PATCH 084/196] Add qbench, qbench2, abench; fix phi3v as its current implementation does not support multi-image --- lmms_eval/models/phi3v.py | 16 +- lmms_eval/tasks/qbench/abench_dev.yaml | 22 +++ lmms_eval/tasks/qbench/qbench2_dev.yaml | 22 +++ lmms_eval/tasks/qbench/qbench_dev.yaml | 22 +++ lmms_eval/tasks/qbench/qbenchs_dev.yaml | 5 + lmms_eval/tasks/qbench/utils.py | 247 ++++++++++++++++++++++++ 6 files changed, 329 insertions(+), 5 deletions(-) create mode 100644 lmms_eval/tasks/qbench/abench_dev.yaml create mode 100644 lmms_eval/tasks/qbench/qbench2_dev.yaml create mode 100644 lmms_eval/tasks/qbench/qbench_dev.yaml create mode 100644 lmms_eval/tasks/qbench/qbenchs_dev.yaml create mode 100644 lmms_eval/tasks/qbench/utils.py diff --git a/lmms_eval/models/phi3v.py b/lmms_eval/models/phi3v.py index b34881ea..5e940169 100644 --- a/lmms_eval/models/phi3v.py +++ b/lmms_eval/models/phi3v.py @@ -185,9 +185,16 @@ def _collate(x): contexts = list(contexts) for i in range(len(contexts)): if "" in contexts[i]: - query = contexts[i].replace("", "<|image_1|>") + query = "" + contexts[i] + img_placeholder_count = 1 + while "" in query: + query = query.replace("", f"<|image_{img_placeholder_count}|>", 1) + img_placeholder_count += 1 else: - query = f"<|image_1|>\n{contexts[i]}" + query = "" + for placeholder_id in range(len(visuals)): + query += f"<|image_{placeholder_id+1}|>\n" + query += contexts[i] messages = [ {"role": "user", "content": query} ] @@ -196,12 +203,11 @@ def _collate(x): tokenize=False, add_generation_prompt=True) assert len(contexts) == 1 - # We always pass a single image given that the model only accepts one image (as of 5/21/24). + # context = contexts[0] - pil_image = visuals[0] input_ids = self._processor( text=context, - images=[pil_image], + images=visuals, return_tensors="pt").to(self._device, self.model.dtype) # Setting default parameters. if "max_new_tokens" not in gen_kwargs: diff --git a/lmms_eval/tasks/qbench/abench_dev.yaml b/lmms_eval/tasks/qbench/abench_dev.yaml new file mode 100644 index 00000000..8b44b5f7 --- /dev/null +++ b/lmms_eval/tasks/qbench/abench_dev.yaml @@ -0,0 +1,22 @@ +dataset_path: q-future/A-Bench-HF +task: "abench_dev" +test_split: dev +output_type: generate_until +doc_to_visual: !function utils.q_bench_doc_to_visual +doc_to_text: !function utils.q_bench_doc_to_text +doc_to_target: "correct_choice" +generation_kwargs: + max_new_tokens: 32 + temperature: 0 + do_sample: False +process_results: !function utils.a_bench_process_results +metric_list: + - metric: abench_acc + aggregation: !function utils.a_bench_aggregate_results + higher_is_better: true + +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "Answer with the option's letter from the given choices directly.\n" + \ No newline at end of file diff --git a/lmms_eval/tasks/qbench/qbench2_dev.yaml b/lmms_eval/tasks/qbench/qbench2_dev.yaml new file mode 100644 index 00000000..7412cb25 --- /dev/null +++ b/lmms_eval/tasks/qbench/qbench2_dev.yaml @@ -0,0 +1,22 @@ +dataset_path: q-future/Q-Bench2-HF +task: "qbench2_dev" +test_split: dev +output_type: generate_until +doc_to_visual: !function utils.q_bench_doc_to_visual +doc_to_text: !function utils.q_bench_doc_to_text +doc_to_target: "correct_choice" +generation_kwargs: + max_new_tokens: 32 + temperature: 0 + do_sample: False +process_results: !function utils.q_bench_process_results +metric_list: + - metric: qbench_acc + aggregation: !function utils.q_bench_aggregate_results + higher_is_better: true + +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "Answer with the option's letter from the given choices directly.\n" + \ No newline at end of file diff --git a/lmms_eval/tasks/qbench/qbench_dev.yaml b/lmms_eval/tasks/qbench/qbench_dev.yaml new file mode 100644 index 00000000..be901333 --- /dev/null +++ b/lmms_eval/tasks/qbench/qbench_dev.yaml @@ -0,0 +1,22 @@ +dataset_path: q-future/Q-Bench-HF +task: "qbench_dev" +test_split: dev +output_type: generate_until +doc_to_visual: !function utils.q_bench_doc_to_visual +doc_to_text: !function utils.q_bench_doc_to_text +doc_to_target: "correct_choice" +generation_kwargs: + max_new_tokens: 32 + temperature: 0 + do_sample: False +process_results: !function utils.q_bench_process_results +metric_list: + - metric: qbench_acc + aggregation: !function utils.q_bench_aggregate_results + higher_is_better: true + +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "Answer with the option's letter from the given choices directly.\n" + \ No newline at end of file diff --git a/lmms_eval/tasks/qbench/qbenchs_dev.yaml b/lmms_eval/tasks/qbench/qbenchs_dev.yaml new file mode 100644 index 00000000..3f7fc914 --- /dev/null +++ b/lmms_eval/tasks/qbench/qbenchs_dev.yaml @@ -0,0 +1,5 @@ +group: qbenchs_dev +task: +- qbench_dev +- qbench2_dev +- abench_dev diff --git a/lmms_eval/tasks/qbench/utils.py b/lmms_eval/tasks/qbench/utils.py new file mode 100644 index 00000000..8b69c9c4 --- /dev/null +++ b/lmms_eval/tasks/qbench/utils.py @@ -0,0 +1,247 @@ +import json +import logging +import re +from collections import Counter, defaultdict +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file + + +def q_bench_doc_to_text(doc, model_specific_prompt_kwargs): + candidates = [] + for i in range(4): + candidate = doc.get(f"option{i}") + if candidate != "N/A": + candidates.append(candidate) + + question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A")+i), candidate]) for i, candidate in enumerate(candidates)]) + pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + post_prompt = model_specific_prompt_kwargs["post_prompt"] + return f"{pre_prompt}{question}\n{post_prompt}" + + +def q_bench_doc_to_visual(doc): + if "image2" not in doc: + return [doc["image"].convert("RGB")] + else: + return [doc["image1"].convert("RGB"), doc["image2"].convert("RGB")] + + +def get_multi_choice_info(options): + """ + Given the list of options for multiple choice question + Return the index2ans and all_choices + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/data_utils.py#L54 + """ + + start_chr = "A" + all_choices = [] + index2ans = {} + for i, option in enumerate(options): + index2ans[chr(ord(start_chr) + i)] = option + all_choices.append(chr(ord(start_chr) + i)) + + return index2ans, all_choices + + +def parse_multi_choice_response(response, all_choices, index2ans): + """ + Parse the prediction from the generated response. + Return the predicted index e.g., A, B, C, D. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L10 + """ + for char in [",", ".", "!", "?", ";", ":", "'"]: + response = response.strip(char) + response = " " + response + " " # add space to avoid partial match + + index_ans = True + ans_with_brack = False + candidates = [] + for choice in all_choices: # e.g., (A) (B) (C) (D) + if f"({choice})" in response: + candidates.append(choice) + ans_with_brack = True + + if len(candidates) == 0: + for choice in all_choices: # e.g., A B C D + if f"{choice} " in response: + candidates.append(choice) + + if len(candidates) == 0: + for choice in all_choices: # e.g., A. B. C. D. + if f"{choice}." in response: + candidates.append(choice) + + # if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example + if len(candidates) == 0 and len(response.split()) > 5: + for index, ans in index2ans.items(): + if ans.lower() in response.lower(): + candidates.append(index) + index_ans = False # it's content ans. + + if len(candidates) == 0: # still not get answer, randomly choose one. + pred_index = random.choice(all_choices) + elif len(candidates) > 1: + start_indexes = [] + if index_ans: + if ans_with_brack: + for can in candidates: + index = response.rfind(f"({can})") + start_indexes.append(index) # -1 will be ignored anyway + # start_indexes = [generated_response.index(f'({can})') for can in candidates] + else: + for can in candidates: + index = response.rfind(f" {can} ") + start_indexes.append(index) + else: + for can in candidates: + index = response.lower().rfind(index2ans[can].lower()) + start_indexes.append(index) + # get the last one + pred_index = candidates[np.argmax(start_indexes)] + else: # if only one candidate, use it. + pred_index = candidates[0] + + return pred_index + + +def evaluate_q_bench(samples): + pred_correct = 0 + judge_dict = dict() + for sample in samples: + gold_i = sample["answer"] + pred_i = sample["parsed_pred"] + correct = eval_multi_choice(gold_i, pred_i) + + if correct: + judge_dict[sample["id"]] = "Correct" + pred_correct += 1 + else: + judge_dict[sample["id"]] = "Wrong" + + if len(samples) == 0: + return {"acc": 0} + return judge_dict, {"acc": pred_correct / len(samples)} + +def eval_multi_choice(gold_i, pred_i): + correct = False + # only they are exactly the same, we consider it as correct + if isinstance(gold_i, list): + for answer in gold_i: + if answer == pred_i: + correct = True + break + else: # gold_i is a string + if gold_i == pred_i: + correct = True + return correct + +def calculate_ins_level_acc(results): + """Calculate the instruction level accuracy for given Subject results + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L246 + """ + acc = 0 + ins_num = 0 + for cat_results in results.values(): + acc += cat_results["acc"] * cat_results["num_example"] + ins_num += cat_results["num_example"] + if ins_num == 0: + return 0 + return acc / ins_num + + +def q_bench_process_results(doc, results): + pred = results[0] + all_choices = [] + index2ans = {} + for i in range(4): + option = doc.get(f"option{i}") + if option == "N/A": + break + index2ans[chr(ord("A") + i)] = option + all_choices.append(chr(ord("A") + i)) + + parsed_pred = parse_multi_choice_response(pred, all_choices, index2ans) + id = doc["id"] + qbench_acc = {"id": id, "question_concern": doc["question_concern"], "question_type": doc["question_type"], "answer": doc["correct_choice"], "parsed_pred": parsed_pred} + return { + "qbench_acc": qbench_acc, + "submission": { + id: pred, + }, + } + + +concern_list = ["Global Distortion", "Global Others", "Local Distortion", "Local Others"] +question_list = ["Yes/No", "How", "What"] + +def q_bench_aggregate_results(results): + evaluation_result = {} + subset_to_eval_samples = defaultdict(list) + for result in results: + subset_to_eval_samples[concern_list[result["question_concern"]]].append(result) + subset_to_eval_samples[question_list[result["question_type"]]].append(result) + for subset, sub_eval_samples in subset_to_eval_samples.items(): + judge_dict, metric_dict = evaluate_q_bench(sub_eval_samples) + metric_dict.update({"num_example": len(sub_eval_samples)}) + evaluation_result[subset] = metric_dict + printable_results = {} + + for cat_name, cat_results in evaluation_result.items(): + printable_results[cat_name] = { + "num": int(cat_results["num_example"]), + "acc": round(cat_results["acc"], 5), + } + all_ins_acc = calculate_ins_level_acc(evaluation_result) + printable_results["Overall"] = { + "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]), + "acc": round(all_ins_acc, 5), + } + print(printable_results) + return printable_results["Overall"]["acc"] + +def a_bench_process_results(doc, results): + pred = results[0] + all_choices = [] + index2ans = {} + for i in range(4): + option = doc.get(f"option{i}") + if option == "N/A": + break + index2ans[chr(ord("A") + i)] = option + all_choices.append(chr(ord("A") + i)) + + parsed_pred = parse_multi_choice_response(pred, all_choices, index2ans) + id = doc["id"] + abench_acc = {"id": id, "category": doc["category"], "answer": doc["correct_choice"], "parsed_pred": parsed_pred} + return { + "abench_acc": abench_acc, + "submission": { + id: pred, + }, + } + + + +def a_bench_aggregate_results(results): + evaluation_result = {} + subset_to_eval_samples = defaultdict(list) + for result in results: + subset_to_eval_samples[result["category"]].append(result) + for subset, sub_eval_samples in subset_to_eval_samples.items(): + judge_dict, metric_dict = evaluate_q_bench(sub_eval_samples) + metric_dict.update({"num_example": len(sub_eval_samples)}) + evaluation_result[subset] = metric_dict + printable_results = {} + + for cat_name, cat_results in evaluation_result.items(): + printable_results[cat_name] = { + "num": int(cat_results["num_example"]), + "acc": round(cat_results["acc"], 5), + } + all_ins_acc = calculate_ins_level_acc(evaluation_result) + printable_results["Overall"] = { + "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]), + "acc": round(all_ins_acc, 5), + } + print(printable_results) + return printable_results["Overall"]["acc"] + From dbfb23873979f789477f4797ee2d6071e0fd921e Mon Sep 17 00:00:00 2001 From: Jingyang Date: Fri, 14 Jun 2024 16:20:42 -0400 Subject: [PATCH 085/196] add tinyllava --- lmms_eval/models/__init__.py | 1 + lmms_eval/models/tinyllava.py | 422 ++++++++++++++++++++++++++ miscs/tinyllava_repr_requirements.txt | 39 +++ miscs/tinyllava_repr_scripts.sh | 25 ++ 4 files changed, 487 insertions(+) create mode 100755 lmms_eval/models/tinyllava.py create mode 100644 miscs/tinyllava_repr_requirements.txt create mode 100755 miscs/tinyllava_repr_scripts.sh diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index 3fe74164..ec354cf3 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -28,6 +28,7 @@ "from_log": "FromLog", "mplug_owl_video": "mplug_Owl", "phi3v": "Phi3v", + "tinyllava": "TinyLlava", } for model_name, model_class in AVAILABLE_MODELS.items(): diff --git a/lmms_eval/models/tinyllava.py b/lmms_eval/models/tinyllava.py new file mode 100755 index 00000000..b26aed77 --- /dev/null +++ b/lmms_eval/models/tinyllava.py @@ -0,0 +1,422 @@ +import torch + +torch.backends.cuda.matmul.allow_tf32 = True + +import logging +import copy +from tqdm import tqdm +from datetime import timedelta + +from lmms_eval import utils +from lmms_eval.api.instance import Instance +from lmms_eval.api.model import lmms +from lmms_eval.api.registry import register_model +from lmms_eval.utils import stop_sequences_criteria + +from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs +from accelerate.state import AcceleratorState +from typing import List, Optional, Union, Tuple +from packaging import version +import warnings + +warnings.filterwarnings("ignore") + +eval_logger = logging.getLogger("lmms-eval") + +try: + from tinyllava.model import load_pretrained_model + from tinyllava.data import ImagePreprocess, TextPreprocess + from tinyllava.utils.constants import DEFAULT_IMAGE_TOKEN + from tinyllava.utils.message import Message +except Exception as e: + eval_logger.debug("TinyLLaVA_Factory is not installed. Please install TinyLLaVA_Factory to use this model.\nError: %s" % e) + +# inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5 +# if is_flash_attn_2_available: +# best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating + +if version.parse(torch.__version__) >= version.parse("2.1.2"): + best_fit_attn_implementation = "sdpa" +else: + best_fit_attn_implementation = "eager" + + +@register_model("tinyllava") +class TinyLlava(lmms): + """ + TinyLlava Model + """ + + def __init__( + self, + pretrained: str = "tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B", + device: Optional[str] = "cuda:0", + batch_size: Optional[Union[int, str]] = 1, + device_map="cuda:0", + conv_mode="phi", # TODO + use_cache=True, + **kwargs, + ) -> None: + super().__init__() + # Do not use kwargs for now + assert kwargs == {}, f"Unexpected kwargs: {kwargs}" + + accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) + accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + if accelerator.num_processes > 1: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + elif accelerator.num_processes == 1 and device_map == "auto": + self._device = torch.device(device) + self.device_map = device_map + else: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + + self._model, self._tokenizer, self._image_processor, self._max_length = load_pretrained_model( + pretrained, device_map=self.device_map + ) + data_args = self._model.config + self._image_processor = ImagePreprocess(self._image_processor, data_args) + assert self._tokenizer.padding_side == "right", "Not sure but seems like `right` is a natural choice for padding?" + self._text_processor = TextPreprocess(self._tokenizer, conv_mode) + + self._config = self._model.config + self.model.eval() + self.model.tie_weights() + # self.truncation = truncation + self.batch_size_per_gpu = int(batch_size) + # self.conv_template = conv_template + self.use_cache = use_cache + # self.truncate_context = truncate_context + + # assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue." + if accelerator.num_processes > 1: + assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." + # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model + # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works + # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. + if accelerator.distributed_type == DistributedType.DEEPSPEED: + kwargs = { + "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, + "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, + } + AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) + eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") + + if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: + self._model = accelerator.prepare(self.model) + else: + self._model = accelerator.prepare_model(self.model, evaluation_mode=True) + self.accelerator = accelerator + if self.accelerator.is_local_main_process: + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + elif accelerator.num_processes == 1 and device_map == "auto": + eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") + self._rank = 0 + self._word_size = 1 + else: + eval_logger.info(f"Using single device: {self._device}") + self.model.to(self._device) + self._rank = 0 + self._world_size = 1 + + @property + def config(self): + # return the associated transformers.AutoConfig for the given pretrained model. + return self._config + + @property + def tokenizer(self): + return self._tokenizer + + @property + def model(self): + # returns the model, unwrapping it if using Accelerate + if hasattr(self, "accelerator"): + return self.accelerator.unwrap_model(self._model) + else: + return self._model + + @property + def eot_token_id(self): + # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* + return self.tokenizer.eos_token_id + + @property + def max_length(self): + return self._max_length + + def pad_sequence(self, input_ids, batch_first, padding_value): + if self.tokenizer.padding_side == "left": + input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] + input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value) + if self.tokenizer.padding_side == "left": + input_ids = torch.flip(input_ids, [1]) + return input_ids + + @property + def batch_size(self): + return self.batch_size_per_gpu + + @property + def device(self): + return self._device + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + + def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]: + """ """ + add_special_tokens = False if add_special_tokens is None else add_special_tokens + encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens) + # left-truncate the encoded context to be at most `left_truncate_len` tokens long + if left_truncate_len: + encoding = encoding[-left_truncate_len:] + return encoding + + def tok_decode(self, tokens): + try: + return self.tokenizer.decode(tokens) + except: + return self.tokenizer.decode([tokens]) + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + # TODO + res = [] + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") + + for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: + # encode, pad, and truncate contexts for this batch + if type(doc_to_target) == str: + continuation = doc_to_target + else: + continuation = doc_to_target(self.task_dict[task][split][doc_id]) + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] + visuals = self.flatten(visuals) + image_sizes = [[visual.size[0], visual.size[1]] for visual in visuals] + if visuals: + # https://github.com/zjysteven/TinyLLaVA_Factory/blob/main/tinyllava/data/image_preprocess.py + # tinyllava's image processor seems to take each individual image as input + image = [self._image_processor(v) for v in visuals] + if type(image) is list: + image = [_image.to(dtype=torch.float16, device=self.device) for _image in image] + # as of 2024/06, tinyllava only accepts `images` input to be a tensor + image_tensor = torch.stack(image_tensor) + else: + image = image.to(dtype=torch.float16, device=self.device) + else: + image = None + + prompts_input = contexts[0] if isinstance(contexts, list) else contexts + + if image is not None and len(image) != 0 and DEFAULT_IMAGE_TOKEN not in prompts_input: + """ + Three senarios: + 1. No image, and there for, no image token should be added. + 2. image token is already specified in the context, so we don't need to add it. + 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. + """ + image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals) + image_tokens = " ".join(image_tokens) + prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts) + + msg = Message() + msg.add_message(prompts_input) + contxt_id = self._text_processor(msg.messages, mode='eval')["input_ids"] + # Add the answer of the second role + msg._messages[1]["value"] = continuation + input_ids = self._text_processor(msg.messages, mode='eval')["input_ids"] + + labels = input_ids.clone() + # Context part no need to calculate for loss + labels[0, : contxt_id.shape[1]] = -100 + + with torch.inference_mode(): + outputs = self.model( + input_ids=input_ids, + labels=labels, + images=image, + use_cache=True, + image_sizes=image_sizes + ) + loss = outputs["loss"] + # loss = torch.exp(loss) + logits = outputs["logits"] + greedy_tokens = logits.argmax(dim=-1) + cont_toks = input_ids[:, contxt_id.shape[1] :] # [1, seq] + greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]] # [1, seq] + max_equal = (greedy_tokens == cont_toks).all() + res.append((float(loss.item()), bool(max_equal))) + pbar.update(1) + pbar.close() + return res + + def generate_until(self, requests: List[Instance]) -> List[str]: + res = [] + + def _collate(x): + # the negative sign on len(toks) sorts descending - this has a few advantages: + # - time estimates will always be over not underestimates, which is more useful for planning + # - to know the size of a batch when going through the list, you know the first one is always the batch + # padded context length. this is useful to simplify the batching logic and more importantly to make + # automatic adaptive batches much much easier to implement + # - any OOMs will happen right away rather than near the end + toks = self.tok_encode(x[0]) + return -len(toks), x[0] + + # we group requests by their generation_kwargs, + # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling + # in the same batch. + re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) + chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) + num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1 + pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding") + for chunk in chunks: + contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk) + task = task[0] + split = split[0] + batched_visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] # [B, N] + flattened_visuals = self.flatten(batched_visuals) # [B*N] + # we assume all gen kwargs in the batch are the same + # this is safe to assume because the `grouper` object ensures it. + gen_kwargs = all_gen_kwargs[0] + + # Set default values for until and max_new_tokens + until = [self.tok_decode(self.eot_token_id)] + + # Update values from gen_kwargs if present + if "until" in gen_kwargs: + until = gen_kwargs.pop("until") + if isinstance(until, str): + until = [until] + elif not isinstance(until, list): + raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") + + if "image_aspect_ratio" in gen_kwargs.keys() and "image_aspect_ratio" not in self._config.__dict__: + # here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation + self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio") + eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") + # encode, pad, and truncate contexts for this batch + if flattened_visuals: + image_tensor = [self._image_processor(v) for v in flattened_visuals] + if type(image_tensor) is list: + image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] + # as of 2024/06, tinyllava only accepts `images` input to be a tensor + image_tensor = torch.stack(image_tensor) + else: + image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) + else: + image_tensor = None + + # prompts_input = contexts[0] + + question_input = [] + + for visual, context in zip(batched_visuals, contexts): + if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context: + """ + Three senarios: + 1. No image, and there for, no image token should be added. + 2. image token is already specified in the context, so we don't need to add it. + 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. + """ + image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visual) if isinstance(visual, list) else [DEFAULT_IMAGE_TOKEN] + image_tokens = " ".join(image_tokens) + question = image_tokens + "\n" + context + else: + question = context + + msg = Message() + msg.add_message(question) + prompt_question = self._text_processor(msg.messages, mode='eval')["prompt"] + question_input.append(prompt_question) + + # The above for loop has bugs. When there is no visuals, e.g. pure text, + # there will be no for loop execute resulting in an empty question_input (because no visuals) + # Scenario 1 won't even be execute + if len(flattened_visuals) == 0: + for context in contexts: + question = context + msg = Message() + msg.add_message(question) + prompt_question = self._text_processor(msg.messages, mode='eval')["prompt"] + question_input.append(prompt_question) + + # input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) + # preconfigure gen_kwargs with defaults + gen_kwargs["image_sizes"] = [flattened_visuals[idx].size for idx in range(len(flattened_visuals))] + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + if "top_p" not in gen_kwargs: + gen_kwargs["top_p"] = None + if "num_beams" not in gen_kwargs: + gen_kwargs["num_beams"] = 1 + + #input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] + input_ids_list = [ + self._text_processor.template.tokenizer_image_token(prompt, self.tokenizer, return_tensors='pt') + for prompt in question_input + ] + pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id + input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device) + attention_masks = input_ids.ne(pad_token_ids).to(self.device) + # These steps are not in LLaVA's original code, but are necessary for generation to work + # TODO: attention to this major generation step... + try: + cont = self.model.generate( + input_ids, + attention_mask=attention_masks, + pad_token_id=pad_token_ids, + images=image_tensor, + image_sizes=gen_kwargs["image_sizes"], + do_sample=True if gen_kwargs["temperature"] > 0 else False, + temperature=gen_kwargs["temperature"], + top_p=gen_kwargs["top_p"], + num_beams=gen_kwargs["num_beams"], + max_new_tokens=gen_kwargs["max_new_tokens"], + use_cache=self.use_cache, + ) + text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True) + except Exception as e: + raise e + eval_logger.error(f"Error {e} in generating") + cont = "" + text_outputs = [""] + + # cont_toks_list = cont.tolist() + # for cont_toks, context in zip(cont_toks_list, contexts): + # discard context + left-padding toks if using causal decoder-only LMM + # if self.truncate_context: + # cont_toks = cont_toks[input_ids.shape[1] :] + # use secondary stop seqs to cut off should-have-been-stopped content post-hoc + # if self.truncate_context: + # for term in until: + # if len(term) > 0: + # # ignore '' separator, + # # for seq2seq case where self.tok_decode(self.eot_token_id) = '' + # text_outputs = text_outputs.split(term)[0] + res.extend(text_outputs) + self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) + pbar.update(1) + # reorder this group of results back to original unsorted form + res = re_ords.get_original(res) + + pbar.close() + return res diff --git a/miscs/tinyllava_repr_requirements.txt b/miscs/tinyllava_repr_requirements.txt new file mode 100644 index 00000000..b6ab1cb1 --- /dev/null +++ b/miscs/tinyllava_repr_requirements.txt @@ -0,0 +1,39 @@ +accelerate==0.27.2 +datasets==2.16.1 +deepspeed==0.14.0 +einops==0.6.1 +einops-exts==0.0.4 +evaluate==0.4.1 +hf_transfer==0.1.6 +Jinja2==3.1.3 +numpy==1.26.4 +openai==1.13.3 +openpyxl +packaging==23.2 +pandas==2.2.1 +peft==0.10.0 +Pillow==10.2.0 +protobuf==4.25.3 +pycocoevalcap==1.2 +pycocotools==2.0.7 +pytablewriter==1.2.0 +pytest==8.0.2 +python_Levenshtein==0.25.0 +pytz==2024.1 +PyYAML==6.0.1 +Requests==2.31.0 +sacrebleu==2.4.0 +scikit_learn==1.2.2 +sentencepiece==0.1.99 +setuptools==68.2.2 +sglang==0.1.12 +shortuuid==1.0.12 +sqlitedict==2.1.0 +tenacity==8.2.3 +tiktoken +# torch==2.0.1 +# torchvision==0.15.2 +tokenizers==0.15.1 +timm==0.6.13 +tqdm==4.66.2 +transformers==4.39.3 diff --git a/miscs/tinyllava_repr_scripts.sh b/miscs/tinyllava_repr_scripts.sh new file mode 100755 index 00000000..902ff0d4 --- /dev/null +++ b/miscs/tinyllava_repr_scripts.sh @@ -0,0 +1,25 @@ +# install lmms_eval without building dependencies +cd lmms_eval; +pip install --no-deps -U -e . + +# install TinyLLaVA without building dependencies +cd .. +git clone https://github.com/TinyLLaVA/TinyLLaVA_Factory +cd TinyLLaVA_Factory +pip install --no-deps -U -e . + +# install all the requirements that require for reproduce llava results +pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118 +pip install -r tinyllava_repr_requirements.txt + +# Run and reproduce tinyllava best results! +accelerate launch \ + --num_processes=1 \ + -m lmms_eval \ + --model tinyllava \ + --model_args pretrained=tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B,conv_mode=phi \ + --tasks vqav2,gqa,scienceqa_img,textvqa,mmvet,pope,mme,mmmu_val \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix tinyllava-phi2-siglip-3.1b \ + --output_path ./logs/ \ No newline at end of file From 62ea8ceb223ef2b51ebab2bcd50d5cf339c35cfe Mon Sep 17 00:00:00 2001 From: teowu Date: Sat, 15 Jun 2024 08:30:11 +0000 Subject: [PATCH 086/196] LongVideoBench support: image LMMs (idefics2, phi3) and video LMMs (LLaVA-Next-Video-34B) --- lmms_eval/models/llava_vid.py | 3 +- .../longvideobench/longvideobench_val_i.yaml | 29 ++ .../longvideobench/longvideobench_val_v.yaml | 28 ++ lmms_eval/tasks/longvideobench/utils.py | 360 ++++++++++++++++++ 4 files changed, 419 insertions(+), 1 deletion(-) create mode 100644 lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml create mode 100644 lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml create mode 100644 lmms_eval/tasks/longvideobench/utils.py diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py index abd42c36..163a5e38 100755 --- a/lmms_eval/models/llava_vid.py +++ b/lmms_eval/models/llava_vid.py @@ -96,6 +96,7 @@ def __init__( self.mm_spatial_pool_out_channels = int(mm_spatial_pool_out_channels) self.mm_spatial_pool_mode = mm_spatial_pool_mode self.max_frames_num = int(max_frames_num) + print(self.max_frames_num) if self.overwrite == True: overwrite_config = {} overwrite_config["mm_resampler_type"] = self.mm_resampler_type @@ -404,7 +405,7 @@ def generate_until(self, requests) -> List[str]: attention_mask=attention_masks, modalities="video", use_cache=self.use_cache, - stopping_criteria=[stopping_criteria], + #stopping_criteria=[stopping_criteria], do_sample=True if gen_kwargs["temperature"] > 0 else False, temperature=gen_kwargs["temperature"], top_p=gen_kwargs["top_p"], diff --git a/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml b/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml new file mode 100644 index 00000000..decb12d6 --- /dev/null +++ b/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml @@ -0,0 +1,29 @@ +dataset_path: longvideobench/LongVideoBench +dataset_kwargs: + token: True + cache_dir: longvideobench + video: True + force_download: False + local_files_only: False + # From_YouTube: True +task: longvideobench_val_i +test_split: validation +doc_to_visual: !function utils.longvideobench_doc_to_visual_i +doc_to_text: !function utils.longvideobench_doc_to_text +doc_to_target: "correct_choice" +generation_kwargs: + max_new_tokens: 32 + temperature: 0 + do_sample: False +process_results: !function utils.longvideobench_process_results +metric_list: + - metric: lvb_acc + aggregation: !function utils.longvideobench_aggregate_results + higher_is_better: true + +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "Answer with the option's letter from the given choices directly.\n" + insert_interleave_subtitles: True + \ No newline at end of file diff --git a/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml b/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml new file mode 100644 index 00000000..e926ed45 --- /dev/null +++ b/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml @@ -0,0 +1,28 @@ +dataset_path: longvideobench/LongVideoBench +dataset_kwargs: + token: True + cache_dir: longvideobench + video: True + force_download: False + local_files_only: False + # From_YouTube: True +task: longvideobench_val_v +test_split: validation +doc_to_visual: !function utils.longvideobench_doc_to_visual_v +doc_to_text: !function utils.longvideobench_doc_to_text +doc_to_target: "correct_choice" +generation_kwargs: + max_new_tokens: 32 + temperature: 0 + do_sample: False +process_results: !function utils.longvideobench_process_results +metric_list: + - metric: lvb_acc + aggregation: !function utils.longvideobench_aggregate_results + higher_is_better: true + +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "Answer with the option's letter from the given choices directly.\n" + \ No newline at end of file diff --git a/lmms_eval/tasks/longvideobench/utils.py b/lmms_eval/tasks/longvideobench/utils.py new file mode 100644 index 00000000..5eb71498 --- /dev/null +++ b/lmms_eval/tasks/longvideobench/utils.py @@ -0,0 +1,360 @@ +import json +import logging +import re +from collections import Counter, defaultdict +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file +import random +import os + +import os +import decord +from decord import VideoReader, cpu +import numpy as np +from PIL import Image +import torch + +import logging +from pathlib import Path +import yaml +import sys +from typing import List, Dict, Optional, Union +import re + +import json + +def timestamp_to_seconds(timestamp): + # Split the timestamp into hours, minutes, and seconds + h, m, s = timestamp.split(':') + # Convert hours, minutes, and total seconds (including fractions) to float and compute total seconds + total_seconds = int(h) * 3600 + int(m) * 60 + float(s) + return total_seconds + +def load_video(video_file, duration, max_num_frames=16): + from decord import VideoReader + vr = VideoReader(video_file, ctx=cpu(0), num_threads=1) + fps = vr.get_avg_fps() + total_valid_frames = int(duration * fps) + num_frames = min(max_num_frames, int(duration)) + + frame_indices = [int(total_valid_frames / num_frames) * i for i in range(num_frames)] + + frames = vr.get_batch(frame_indices) + if isinstance(frames, torch.Tensor): + frames = frames.numpy() + else: + frames = frames.asnumpy() + frame_timestamps = [frame_index / fps for frame_index in frame_indices] + + return [Image.fromarray(fr).convert("RGB") for fr in frames] + +def compute_frame_timestamps(duration, max_num_frames=16): + if duration > max_num_frames: + return [duration / max_num_frames * i for i in range(max_num_frames)] + else: + return [i for i in range(int(duration))] + + +def insert_subtitles_into_frames(frame_timestamps, subtitles, + starting_timestamp_for_subtitles, duration): + interleaved_list = [] + cur_i = 0 + + for subtitle in subtitles: + if "timestamp" in subtitle: + start, end = subtitle["timestamp"] + + if not isinstance(end, float): + end = duration + + start -= starting_timestamp_for_subtitles + end -= starting_timestamp_for_subtitles + + + subtitle_timestamp = (start + end) / 2 + subtitle_text = subtitle["text"] + else: + start, end = subtitle["start"], subtitle["end"] + start = timestamp_to_seconds(start) + end = timestamp_to_seconds(end) + start -= starting_timestamp_for_subtitles + end -= starting_timestamp_for_subtitles + + subtitle_timestamp = (start + end) / 2 + subtitle_text = subtitle["line"] + + + for i, frame_timestamp in enumerate(frame_timestamps[cur_i:]): + if frame_timestamp <= subtitle_timestamp: + #print("frame:", frame_timestamp) + interleaved_list.append("") + cur_i += 1 + else: + break + + if end - start < 1: + end = subtitle_timestamp + 0.5 + start = subtitle_timestamp - 0.5 + + covering_frames = False + for frame_timestamp in frame_timestamps: + if frame_timestamp < end and frame_timestamp > start: + covering_frames = True + break + + if covering_frames: + #print("subtitle:", subtitle_timestamp, start, end) + interleaved_list.append(subtitle_text) + else: + pass + #print("leaving out subtitle:", start, end) + + for i, frame_timestamp in enumerate(frame_timestamps[cur_i:]): + #print(frame_timestamp) + interleaved_list.append("") + + return "\n".join(interleaved_list) + +def longvideobench_doc_to_text(doc, model_specific_prompt_kwargs): + + candidates = [] + + + for i in range(5): + candidate = doc.get(f"option{i}") + if candidate != "N/A": + candidates.append(candidate) + + question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A")+i), candidate]) for i, candidate in enumerate(candidates)]) + pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + post_prompt = model_specific_prompt_kwargs["post_prompt"] + + + if model_specific_prompt_kwargs.get("insert_interleave_subtitles", False): + with open(Path(__file__).parent / "longvideobench_val_i.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"] + subtitle_subdir_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("subtitle_subdir", "subtitles") + cache_dir = os.path.join(base_cache_dir, cache_name, subtitle_subdir_name) + with open(os.path.join(cache_dir, doc["subtitle_path"])) as f: + subtitles = json.load(f) + + max_num_frames = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("max_num_frames", 16) + + frame_timestamps = compute_frame_timestamps(doc["duration"], max_num_frames) + interleaved_prefix = insert_subtitles_into_frames(frame_timestamps, subtitles, doc["starting_timestamp_for_subtitles"], doc["duration"]) + return f"{pre_prompt}{interleaved_prefix}\n{question}\n{post_prompt}" + else: + return f"{pre_prompt}{question}\n{post_prompt}" + + +hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/") +base_cache_dir = os.path.expanduser(hf_home) + +def longvideobench_doc_to_visual_v(doc): + with open(Path(__file__).parent / "longvideobench_val_v.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"] + vid_subdir_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("video_subdir", "videos/") + cache_dir = os.path.join(base_cache_dir, cache_name, vid_subdir_name) + video_path = doc["video_path"] + video_path = os.path.join(cache_dir, video_path) + return [video_path] + +def longvideobench_doc_to_visual_i(doc): + with open(Path(__file__).parent / "longvideobench_val_i.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"] + vid_subdir_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("video_subdir", "videos/") + cache_dir = os.path.join(base_cache_dir, cache_name, vid_subdir_name) + video_path = doc["video_path"] + video_path = os.path.join(cache_dir, video_path) + max_num_frames = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("max_num_frames", 16) + return load_video(video_path, doc["duration"], max_num_frames) + + +def get_multi_choice_info(options): + """ + Given the list of options for multiple choice question + Return the index2ans and all_choices + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/data_utils.py#L54 + """ + + start_chr = "A" + all_choices = [] + index2ans = {} + for i, option in enumerate(options): + index2ans[chr(ord(start_chr) + i)] = option + all_choices.append(chr(ord(start_chr) + i)) + + return index2ans, all_choices + + +def parse_multi_choice_response(response, all_choices, index2ans): + """ + Parse the prediction from the generated response. + Return the predicted index e.g., A, B, C, D. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L10 + """ + for char in [",", ".", "!", "?", ";", ":", "'"]: + response = response.strip(char) + response = " " + response + " " # add space to avoid partial match + + index_ans = True + ans_with_brack = False + candidates = [] + for choice in all_choices: # e.g., (A) (B) (C) (D) + if f"({choice})" in response: + candidates.append(choice) + ans_with_brack = True + + if len(candidates) == 0: + for choice in all_choices: # e.g., A B C D + if f"{choice} " in response: + candidates.append(choice) + + if len(candidates) == 0: + for choice in all_choices: # e.g., A. B. C. D. + if f"{choice}." in response: + candidates.append(choice) + + # if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example + if len(candidates) == 0 and len(response.split()) > 5: + for index, ans in index2ans.items(): + if ans.lower() in response.lower(): + candidates.append(index) + index_ans = False # it's content ans. + + if len(candidates) == 0: # still not get answer, randomly choose one. + pred_index = random.choice(all_choices) + elif len(candidates) > 1: + start_indexes = [] + if index_ans: + if ans_with_brack: + for can in candidates: + index = response.rfind(f"({can})") + start_indexes.append(index) # -1 will be ignored anyway + # start_indexes = [generated_response.index(f'({can})') for can in candidates] + else: + for can in candidates: + index = response.rfind(f" {can} ") + start_indexes.append(index) + else: + for can in candidates: + index = response.lower().rfind(index2ans[can].lower()) + start_indexes.append(index) + # get the last one + pred_index = candidates[np.argmax(start_indexes)] + else: # if only one candidate, use it. + pred_index = candidates[0] + + return pred_index + + +def evaluate_longvideobench(samples): + pred_correct = 0 + judge_dict = dict() + for sample in samples: + gold_i = sample["answer"] + pred_i = sample["parsed_pred"] + correct = eval_multi_choice(gold_i, pred_i) + + if correct: + judge_dict[sample["id"]] = "Correct" + pred_correct += 1 + else: + judge_dict[sample["id"]] = "Wrong" + + if len(samples) == 0: + return {"acc": 0} + return judge_dict, {"acc": pred_correct / len(samples)} + +def eval_multi_choice(gold_i, pred_i): + correct = False + # only they are exactly the same, we consider it as correct + if isinstance(gold_i, list): + for answer in gold_i: + if answer == pred_i: + correct = True + break + else: # gold_i is a string + if gold_i == pred_i: + correct = True + return correct + +def calculate_ins_level_acc(results): + """Calculate the instruction level accuracy for given Subject results + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L246 + """ + acc = 0 + ins_num = 0 + for cat_results in results.values(): + acc += cat_results["acc"] * cat_results["num_example"] + ins_num += cat_results["num_example"] + if ins_num == 0: + return 0 + return acc / ins_num + + +def longvideobench_process_results(doc, results): + pred = results[0] + all_choices = [] + index2ans = {} + for i in range(5): + option = doc.get(f"option{i}") + if option == "N/A": + break + index2ans[chr(ord("A") + i)] = option + all_choices.append(chr(ord("A") + i)) + + parsed_pred = parse_multi_choice_response(pred, all_choices, index2ans) + id = doc["video_id"] + lvb_acc = {"id": id, "duration_group": doc["duration_group"], "question_category": doc["question_category"], "answer": chr(ord("A") + doc["correct_choice"]), "parsed_pred": parsed_pred} + return { + "lvb_acc": lvb_acc, + "submission": { + id: pred, + }, + } + + + +def longvideobench_aggregate_results(results): + evaluation_result = {} + subset_to_eval_samples = defaultdict(list) + for result in results: + subset_to_eval_samples[result["duration_group"]].append(result) + subset_to_eval_samples[result["question_category"]].append(result) + for subset, sub_eval_samples in subset_to_eval_samples.items(): + judge_dict, metric_dict = evaluate_longvideobench(sub_eval_samples) + metric_dict.update({"num_example": len(sub_eval_samples)}) + evaluation_result[subset] = metric_dict + printable_results = {} + + for cat_name, cat_results in evaluation_result.items(): + printable_results[cat_name] = { + "num": int(cat_results["num_example"]), + "acc": round(cat_results["acc"], 5), + } + all_ins_acc = calculate_ins_level_acc(evaluation_result) + printable_results["Overall"] = { + "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]), + "acc": round(all_ins_acc, 5), + } + print(printable_results) + return printable_results["Overall"]["acc"] + From a056f118704eccec86ce32ab86981ce4bc1e1deb Mon Sep 17 00:00:00 2001 From: teowu Date: Sun, 16 Jun 2024 07:23:54 +0000 Subject: [PATCH 087/196] fix #117, allow auto download with tar format videos --- lmms_eval/api/task.py | 49 +++++++++++++++++++++++++++++++++++ lmms_eval/models/llava_vid.py | 4 +-- 2 files changed, 51 insertions(+), 2 deletions(-) diff --git a/lmms_eval/api/task.py b/lmms_eval/api/task.py index c035a0a2..ed43d365 100755 --- a/lmms_eval/api/task.py +++ b/lmms_eval/api/task.py @@ -776,8 +776,10 @@ def _download_from_youtube(path): if accelerator.is_main_process: force_download = dataset_kwargs.get("force_download", False) force_unzip = dataset_kwargs.get("force_unzip", False) + print(force_download) cache_path = snapshot_download(repo_id=self.DATASET_PATH, repo_type="dataset", force_download=force_download, etag_timeout=60) zip_files = glob(os.path.join(cache_path, "**/*.zip"), recursive=True) + tar_files = glob(os.path.join(cache_path, "**/*.tar*"), recursive=True) def unzip_video_data(zip_file): import zipfile @@ -786,10 +788,57 @@ def unzip_video_data(zip_file): zip_ref.extractall(cache_dir) eval_logger.info(f"Extracted all files from {zip_file} to {cache_dir}") + def untar_video_data(tar_file): + import tarfile + with tarfile.open(tar_file, "r") as tar_ref: + tar_ref.extractall(cache_dir) + eval_logger.info(f"Extracted all files from {tar_file} to {cache_dir}") + + + + def concat_tar_parts(tar_parts, output_tar): + print("This is the output file:", output_tar, "from:", tar_parts) + try: + with open(output_tar, 'wb') as out_tar: + from tqdm import tqdm + for part in tqdm(sorted(tar_parts)): + with open(part, 'rb') as part_file: + out_tar.write(part_file.read()) + except Exception as ex: + print("Error!!!", ex) + eval_logger.info(f"Concatenated parts {tar_parts} into {output_tar}") + + # Unzip zip files if needed if force_unzip or (not os.path.exists(cache_dir) and len(zip_files) > 0): for zip_file in zip_files: unzip_video_data(zip_file) + # Concatenate and extract tar files if needed + if force_unzip or (not os.path.exists(cache_dir) and len(tar_files) > 0): + tar_parts_dict = {} + + # Group tar parts together + for tar_file in tar_files: + base_name = tar_file.split('.tar')[0] + if base_name not in tar_parts_dict: + tar_parts_dict[base_name] = [] + tar_parts_dict[base_name].append(tar_file) + + print(tar_parts_dict) + + # Concatenate and untar split parts + for base_name, parts in tar_parts_dict.items(): + eval_logger.info(f"Extracting following tar files: {parts}") + output_tar = base_name + ".tar" + if not os.path.exists(output_tar): + eval_logger.info(f"Start concatenating tar files") + + concat_tar_parts(parts, output_tar) + eval_logger.info(f"Finish concatenating tar files") + + if not os.path.exists(os.path.join(cache_dir, os.path.basename(base_name))): + untar_video_data(output_tar) + accelerator.wait_for_everyone() dataset_kwargs.pop("cache_dir") dataset_kwargs.pop("video") diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py index 163a5e38..6b57ccaa 100755 --- a/lmms_eval/models/llava_vid.py +++ b/lmms_eval/models/llava_vid.py @@ -405,7 +405,7 @@ def generate_until(self, requests) -> List[str]: attention_mask=attention_masks, modalities="video", use_cache=self.use_cache, - #stopping_criteria=[stopping_criteria], + stopping_criteria=[stopping_criteria], do_sample=True if gen_kwargs["temperature"] > 0 else False, temperature=gen_kwargs["temperature"], top_p=gen_kwargs["top_p"], @@ -417,4 +417,4 @@ def generate_until(self, requests) -> List[str]: outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() res.append(outputs) pbar.update(1) - return res + return res \ No newline at end of file From 5bf59ed250da98a408a94e214a73caa400cba842 Mon Sep 17 00:00:00 2001 From: teowu Date: Sun, 16 Jun 2024 07:27:28 +0000 Subject: [PATCH 088/196] fix #117, allow auto download with tar format videos --- lmms_eval/api/task.py | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/lmms_eval/api/task.py b/lmms_eval/api/task.py index ed43d365..a7cf5ae8 100755 --- a/lmms_eval/api/task.py +++ b/lmms_eval/api/task.py @@ -776,7 +776,6 @@ def _download_from_youtube(path): if accelerator.is_main_process: force_download = dataset_kwargs.get("force_download", False) force_unzip = dataset_kwargs.get("force_unzip", False) - print(force_download) cache_path = snapshot_download(repo_id=self.DATASET_PATH, repo_type="dataset", force_download=force_download, etag_timeout=60) zip_files = glob(os.path.join(cache_path, "**/*.zip"), recursive=True) tar_files = glob(os.path.join(cache_path, "**/*.tar*"), recursive=True) @@ -797,15 +796,11 @@ def untar_video_data(tar_file): def concat_tar_parts(tar_parts, output_tar): - print("This is the output file:", output_tar, "from:", tar_parts) - try: - with open(output_tar, 'wb') as out_tar: - from tqdm import tqdm - for part in tqdm(sorted(tar_parts)): - with open(part, 'rb') as part_file: - out_tar.write(part_file.read()) - except Exception as ex: - print("Error!!!", ex) + with open(output_tar, 'wb') as out_tar: + from tqdm import tqdm + for part in tqdm(sorted(tar_parts)): + with open(part, 'rb') as part_file: + out_tar.write(part_file.read()) eval_logger.info(f"Concatenated parts {tar_parts} into {output_tar}") # Unzip zip files if needed @@ -824,7 +819,6 @@ def concat_tar_parts(tar_parts, output_tar): tar_parts_dict[base_name] = [] tar_parts_dict[base_name].append(tar_file) - print(tar_parts_dict) # Concatenate and untar split parts for base_name, parts in tar_parts_dict.items(): From ef306512e5135f76dffa383f600b8733015836e8 Mon Sep 17 00:00:00 2001 From: Jingyang Zhang Date: Mon, 17 Jun 2024 17:57:02 -0400 Subject: [PATCH 089/196] fix typo --- lmms_eval/models/tinyllava.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/models/tinyllava.py b/lmms_eval/models/tinyllava.py index b26aed77..b9fdf743 100755 --- a/lmms_eval/models/tinyllava.py +++ b/lmms_eval/models/tinyllava.py @@ -216,7 +216,7 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: if type(image) is list: image = [_image.to(dtype=torch.float16, device=self.device) for _image in image] # as of 2024/06, tinyllava only accepts `images` input to be a tensor - image_tensor = torch.stack(image_tensor) + image = torch.stack(image) else: image = image.to(dtype=torch.float16, device=self.device) else: From 742651fc9daf97e2f57831ed6e6e7ee7ead7d555 Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Wed, 19 Jun 2024 07:44:26 +0800 Subject: [PATCH 090/196] feat: Add support for auto downloading tar format videos --- ...aset.ipynb => make_video_hf_dataset.ipynb} | 20 ------------------- 1 file changed, 20 deletions(-) rename tools/{make_hf_dataset.ipynb => make_video_hf_dataset.ipynb} (93%) diff --git a/tools/make_hf_dataset.ipynb b/tools/make_video_hf_dataset.ipynb similarity index 93% rename from tools/make_hf_dataset.ipynb rename to tools/make_video_hf_dataset.ipynb index 9336331a..dd801eb8 100755 --- a/tools/make_hf_dataset.ipynb +++ b/tools/make_video_hf_dataset.ipynb @@ -162,26 +162,6 @@ "dataset = Dataset.from_pandas(df_items, features=features)" ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Token is valid (permission: write).\n", - "Your token has been saved in your configured git credential helpers (store).\n", - "Your token has been saved to /home/tiger/.cache/huggingface/token\n", - "Login successful\n" - ] - } - ], - "source": [ - "!huggingface-cli login --token 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 --add-to-git-credential" - ] - }, { "cell_type": "code", "execution_count": 9, From efb529552c5e4ba039a4cba8e9aa5cb7ba65bf90 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Wed, 19 Jun 2024 10:25:58 +0800 Subject: [PATCH 091/196] Release llava-wilder --- .../tasks/llava_wilder/llava_wilder_full.yaml | 14 ----- .../llava_wilder/llava_wilder_medium.yaml | 14 ----- .../llava_wilder/llava_wilder_small.yaml | 5 +- lmms_eval/tasks/llava_wilder/utils.py | 57 ++----------------- 4 files changed, 6 insertions(+), 84 deletions(-) delete mode 100755 lmms_eval/tasks/llava_wilder/llava_wilder_full.yaml delete mode 100644 lmms_eval/tasks/llava_wilder/llava_wilder_medium.yaml diff --git a/lmms_eval/tasks/llava_wilder/llava_wilder_full.yaml b/lmms_eval/tasks/llava_wilder/llava_wilder_full.yaml deleted file mode 100755 index 65627d03..00000000 --- a/lmms_eval/tasks/llava_wilder/llava_wilder_full.yaml +++ /dev/null @@ -1,14 +0,0 @@ -dataset_path: lmms-lab/llava-wilder -dataset_name: Full -dataset_kwargs: - token: True -task: "llava_wilder_full" -test_split: test -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - xcomposer2_4khd: - pre_prompt: "[UNUSED_TOKEN_146]user\nQuestion: " - post_prompt: "[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n" -include: _default_template_wilder_yaml \ No newline at end of file diff --git a/lmms_eval/tasks/llava_wilder/llava_wilder_medium.yaml b/lmms_eval/tasks/llava_wilder/llava_wilder_medium.yaml deleted file mode 100644 index 90f7bcc2..00000000 --- a/lmms_eval/tasks/llava_wilder/llava_wilder_medium.yaml +++ /dev/null @@ -1,14 +0,0 @@ -dataset_path: lmms-lab/llava-wilder -dataset_name: Medium -dataset_kwargs: - token: True -task: "llava_wilder_medium" -test_split: test -model_specific_prompt_kwargs: - default: - pre_prompt: "" - post_prompt: "" - xcomposer2_4khd: - pre_prompt: "[UNUSED_TOKEN_146]user\nQuestion: " - post_prompt: "[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n" -include: _default_template_wilder_yaml \ No newline at end of file diff --git a/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml b/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml index 54b18897..1c493673 100644 --- a/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml +++ b/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml @@ -1,9 +1,8 @@ -dataset_path: lmms-lab/llava-wilder -dataset_name: Small +dataset_path: lmms-lab/llava-bench-wilder dataset_kwargs: token: True task: "llava_wilder_small" -test_split: train +test_split: small model_specific_prompt_kwargs: default: pre_prompt: "" diff --git a/lmms_eval/tasks/llava_wilder/utils.py b/lmms_eval/tasks/llava_wilder/utils.py index 15d44438..7d9a9133 100644 --- a/lmms_eval/tasks/llava_wilder/utils.py +++ b/lmms_eval/tasks/llava_wilder/utils.py @@ -13,17 +13,6 @@ # Set up a logger eval_logger = logging.getLogger("lmms-eval") -# Create a static variable to track if the message has been logged -if not hasattr(eval_logger, "dashcope_warning_logged"): - eval_logger.dashcope_warning_logged = False - -try: - import dashscope -except ImportError: - if not eval_logger.dashcope_warning_logged: - eval_logger.debug("Dashcope not found, make sure you install dashscope to use qwen vl") - eval_logger.dashcope_warning_logged = True - NUM_SECONDS_TO_SLEEP = 5 dir_path = os.path.dirname(os.path.realpath(__file__)) @@ -58,14 +47,6 @@ "Content-Type": "application/json", } -elif API_TYPE == "qwen_vl": - API_URL = os.getenv("QWEN_ENDPOINT", "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation") - API_KEY = os.getenv("DASHSCOPE_API_KEY", "YOUR_API_KEY") - headers = { - "Authorization": f"Bearer {API_KEY}", - "Content-Type": "application/json", - } - def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10): headers = { @@ -114,29 +95,6 @@ def image_to_base64(pil_image): return base64.b64encode(buffered.getvalue()).decode("utf-8") -def qwen_multimodal_conversation_call(text_content, image_content, retries=5): - """Simple single round multimodal conversation call.""" - messages = [{"role": "user", "content": [{"image": image_content}, {"text": text_content}]}] - for attempt in range(retries): - try: - response_data = dashscope.MultiModalConversation.call(model=GPT_EVAL_MODEL_NAME, messages=messages) - # The response status_code is HTTPStatus.OK indicate success, - # otherwise indicate request is failed, you can get error code - # and message from code and message. - content = response_data["output"]["choices"][0]["message"]["content"][0]["text"].strip() - if content != "": - return content, GPT_EVAL_MODEL_NAME - break # If successful, break out of the loop - except Exception as e: - eval_logger.info(f"Attempt {attempt + 1} failed with error: {e}") - if attempt < retries: # If we have retries left, sleep and then continue to next attempt - time.sleep(NUM_SECONDS_TO_SLEEP) - else: # If this was the last attempt, log and return empty - eval_logger.error(f"All {retries} attempts failed. Last error message: {e}") - return "", "" - return "", "" - - def parse_score(review): try: score_pair = review.split("\n")[0] @@ -162,20 +120,13 @@ def llava_process_results(doc, result): """ try: question = doc.get("question", "") - ans1 = doc.get("gpt4v_answer", "") + ans1 = doc.get("answer", "") ans2 = result[0] if result else "" content = f"[Question]\n{question}\n\n" + f"[Assistant 1]\n{ans1}\n\n[End of Assistant 1]\n\n" + f"[Assistant 2]\n{ans2}\n\n[End of Assistant 2]\n\n" f"[System]\n{judge_rules}\n\n" visuals = llava_doc_to_visual(doc) - if API_TYPE == "qwen_vl": - file_path = os.path.join(dir_path, f"tmp_{doc['question_id']}.jpg") - visuals[0].save(file_path) - image_content = "file://" + file_path - review, model_name = qwen_multimodal_conversation_call(content, image_content=image_content) - os.remove(file_path) - elif API_TYPE == "openai": - image_path = doc["image"] - base64_image = image_to_base64(image_path) - review, model_name = get_chat_response(base64_image, content) + image_path = doc["image"] + base64_image = image_to_base64(image_path) + review, model_name = get_chat_response(base64_image, content) scores = parse_score(review) except Exception as e: eval_logger.error(f"Error for Question ID: {doc.get('question_id', 'Unknown')}: {e}") From f513c520c2a3dad26d2b2ca5c4ed4db05a493c73 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Wed, 19 Jun 2024 06:51:19 +0000 Subject: [PATCH 092/196] chore: Update dependencies to fix potential risks and improve compatibility --- lmms_eval/models/llava_vid.py | 33 +++++++++------------------------ lmms_eval/tasks/mmmu/utils.py | 3 ++- pyproject.toml | 3 ++- 3 files changed, 13 insertions(+), 26 deletions(-) diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py index 6b57ccaa..1047e390 100755 --- a/lmms_eval/models/llava_vid.py +++ b/lmms_eval/models/llava_vid.py @@ -17,30 +17,21 @@ from lmms_eval.models.model_utils.load_video import read_video_pyav eval_logger = logging.getLogger("lmms-eval") -import sys -sys.path.append("llava-video") try: - from llavavid.model.language_model.llava_llama import LlavaConfig - - # from llavavid.model.language_model.llava_qwen import LlavaQwenConfig from llavavid.model.builder import load_pretrained_model from llavavid.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria - from llavavid.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN + from llavavid.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX from llavavid.conversation import conv_templates, SeparatorStyle - - # AutoConfig.register("llava_qwen", LlavaQwenConfig) - AutoConfig.register("llava_llama", LlavaConfig) - + from llavavid.mm_utils import tokenizer_image_token_qwen_merge, preprocess_qwen, preprocess_llama3 except ImportError: eval_logger.debug("LLaVA-Video is not installed. Please install LLaVA-Video to use this model.") -try: - from llavavid.model.language_model.llava_qwen import LlavaQwenConfig +from llavavid.model.language_model.llava_qwen import LlavaQwenConfig +from llavavid.model.language_model.llava_llama import LlavaConfig - AutoConfig.register("llava_qwen", LlavaQwenConfig) -except: - eval_logger.debug("") +AutoConfig.register("llava_qwen", LlavaQwenConfig) +AutoConfig.register("llava_llama", LlavaConfig) @register_model("llavavid") @@ -96,7 +87,6 @@ def __init__( self.mm_spatial_pool_out_channels = int(mm_spatial_pool_out_channels) self.mm_spatial_pool_mode = mm_spatial_pool_mode self.max_frames_num = int(max_frames_num) - print(self.max_frames_num) if self.overwrite == True: overwrite_config = {} overwrite_config["mm_resampler_type"] = self.mm_resampler_type @@ -361,7 +351,7 @@ def generate_until(self, requests) -> List[str]: if self.model.config.mm_use_im_start_end: qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs else: - qs = DEFAULT_IMAGE_TOKEN + "\n" + qs + qs = DEFAULT_IMAGE_TOKEN * len(videos) + "\n" + qs # This is much safer for llama3, as we now have some object type in it if "llama_3" in self.conv_template: @@ -379,11 +369,6 @@ def generate_until(self, requests) -> List[str]: pad_token_ids = 0 # lmms-lab/llama3-llava-8b is trained on this pad token id. You may need to customize this for other models. attention_masks = input_ids.ne(pad_token_ids).long().cuda() - # input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] - # pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id - # input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device) - # attention_masks = input_ids.ne(pad_token_ids).to(self.device) - stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids) @@ -393,7 +378,7 @@ def generate_until(self, requests) -> List[str]: if "max_new_tokens" not in gen_kwargs: gen_kwargs["max_new_tokens"] = 1024 if "temperature" not in gen_kwargs: - gen_kwargs["temperature"] = 0.2 + gen_kwargs["temperature"] = 0 if "top_p" not in gen_kwargs: gen_kwargs["top_p"] = None if "num_beams" not in gen_kwargs: @@ -417,4 +402,4 @@ def generate_until(self, requests) -> List[str]: outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() res.append(outputs) pbar.update(1) - return res \ No newline at end of file + return res diff --git a/lmms_eval/tasks/mmmu/utils.py b/lmms_eval/tasks/mmmu/utils.py index 5a1b91f6..ff4d6aeb 100755 --- a/lmms_eval/tasks/mmmu/utils.py +++ b/lmms_eval/tasks/mmmu/utils.py @@ -86,8 +86,9 @@ def extract_subset_name(input_string): def mmmu_test_aggregate_results_for_submission(results, args): path = generate_submission_file("mmmu_test_for_submission.json", args) + results_dict = {list(item.keys())[0]: list(item.values())[0] for item in results} with open(path, "w") as f: - json.dump(results, f) + json.dump(results_dict, f) lmms_logger.info(f"Results saved to {path}.") diff --git a/pyproject.toml b/pyproject.toml index 5f3b92fb..593ef151 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ dependencies = [ "httpx==0.23.3", "jsonlines", "numexpr", + "numpy==1.26.4", "peft>=0.2.0", "pybind11>=2.6.2", "pytablewriter", @@ -52,7 +53,7 @@ dependencies = [ "openpyxl", "Levenshtein", "hf_transfer", - "tenacity", + "tenacity==8.3.0", "wandb>=0.16.0", "tiktoken", "pre-commit", From ce6f889ba02d819979c7922f6336cf4f1f718f65 Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Wed, 19 Jun 2024 15:04:16 +0800 Subject: [PATCH 093/196] tutorial --- tools/make_image_hf_dataset.ipynb | 307 ++++++++++++++++++++++++++++++ 1 file changed, 307 insertions(+) create mode 100755 tools/make_image_hf_dataset.ipynb diff --git a/tools/make_image_hf_dataset.ipynb b/tools/make_image_hf_dataset.ipynb new file mode 100755 index 00000000..65ea5ca5 --- /dev/null +++ b/tools/make_image_hf_dataset.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Make Image dataset on Hugging Face Datasets\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/EvolvingLMMs-Lab/lmms-eval/blob/main/tools/make_image_hf_dataset.ipynb)\n", + "\n", + "This notebook will guide you to make correct format of Huggingface dataset, in proper parquet format and visualizable in Huggingface dataset hub.\n", + "\n", + "We will take the example of the dataset [`pufanyi/VQAv2_Example`](https://huggingface.co/datasets/lmms-lab/VQAv2) and convert it to the proper format." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Download Dataset\n", + "\n", + "We have uploaded the zip file of the dataset to [Hugging Face](https://huggingface.co/datasets/pufanyi/VQAv2_TOY/tree/main/source_data) for download. This dataset is a subset of the [VQAv2](https://visualqa.org/) dataset, with $10$ entries each from the `val`, `test`, and `test-dev` splits, for easier downloading." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "vscode": { + "languageId": "bat" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-06-19 14:09:51-- https://huggingface.co/datasets/pufanyi/VQAv2_TOY/resolve/main/source_data/sample_data.zip\n", + "Resolving huggingface.co (huggingface.co)... 13.33.30.114, 13.33.30.49, 13.33.30.76, ...\n", + "Connecting to huggingface.co (huggingface.co)|13.33.30.114|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://cdn-lfs-us-1.huggingface.co/repos/c9/82/c9827770a5c0b13c1b646a275968813f8705db30ac0de29f118bb316c2b2a4eb/8cc2e821b7c6e4b5726a6feeb6214cd2d4810d53f568a5f3565d78e6d1ee5403?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27sample_data.zip%3B+filename%3D%22sample_data.zip%22%3B&response-content-type=application%2Fzip&Expires=1719036591&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxOTAzNjU5MX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2M5LzgyL2M5ODI3NzcwYTVjMGIxM2MxYjY0NmEyNzU5Njg4MTNmODcwNWRiMzBhYzBkZTI5ZjExOGJiMzE2YzJiMmE0ZWIvOGNjMmU4MjFiN2M2ZTRiNTcyNmE2ZmVlYjYyMTRjZDJkNDgxMGQ1M2Y1NjhhNWYzNTY1ZDc4ZTZkMWVlNTQwMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=kppoby2Wg9BYA-L2HJ0uShfMSULqTXjtN3cbdBdZTvMf4NvNXBJxc0mcPSiz-sqV7d7hJn32IzHze2JnnTGxrVrozYdHeoTuG0EtF%7ERgQz17PbzbEps-MPzl-h4G9d5RImWDBNN3OYTWyvSxFzn12d-owQKrkdEXejUZEkGdzvHgECzLPpuMw%7EXIctwxBBbxrHRtBNU57K2KBwOqw5rujHtQevhMaCeRgxRFlpfc3FDxsl4rUVHrCM79UhPwutpEAtOh%7Ep6%7EdgLOXal6oZKCnejCQg3AjgvuMe4Eot3J37a7yUGToRtx6XX8Q9I1SC2nScXIWwZndOQY-1VNSL1s-A__&Key-Pair-Id=K2FPYV99P2N66Q [following]\n", + "--2024-06-19 14:09:51-- https://cdn-lfs-us-1.huggingface.co/repos/c9/82/c9827770a5c0b13c1b646a275968813f8705db30ac0de29f118bb316c2b2a4eb/8cc2e821b7c6e4b5726a6feeb6214cd2d4810d53f568a5f3565d78e6d1ee5403?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27sample_data.zip%3B+filename%3D%22sample_data.zip%22%3B&response-content-type=application%2Fzip&Expires=1719036591&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxOTAzNjU5MX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2M5LzgyL2M5ODI3NzcwYTVjMGIxM2MxYjY0NmEyNzU5Njg4MTNmODcwNWRiMzBhYzBkZTI5ZjExOGJiMzE2YzJiMmE0ZWIvOGNjMmU4MjFiN2M2ZTRiNTcyNmE2ZmVlYjYyMTRjZDJkNDgxMGQ1M2Y1NjhhNWYzNTY1ZDc4ZTZkMWVlNTQwMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=kppoby2Wg9BYA-L2HJ0uShfMSULqTXjtN3cbdBdZTvMf4NvNXBJxc0mcPSiz-sqV7d7hJn32IzHze2JnnTGxrVrozYdHeoTuG0EtF%7ERgQz17PbzbEps-MPzl-h4G9d5RImWDBNN3OYTWyvSxFzn12d-owQKrkdEXejUZEkGdzvHgECzLPpuMw%7EXIctwxBBbxrHRtBNU57K2KBwOqw5rujHtQevhMaCeRgxRFlpfc3FDxsl4rUVHrCM79UhPwutpEAtOh%7Ep6%7EdgLOXal6oZKCnejCQg3AjgvuMe4Eot3J37a7yUGToRtx6XX8Q9I1SC2nScXIWwZndOQY-1VNSL1s-A__&Key-Pair-Id=K2FPYV99P2N66Q\n", + "Resolving cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)... 3.165.102.80, 3.165.102.25, 3.165.102.95, ...\n", + "Connecting to cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)|3.165.102.80|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 2678607 (2.6M) [application/zip]\n", + "Saving to: ‘data/sample_data.zip’\n", + "\n", + "sample_data.zip 100%[===================>] 2.55M 7.46MB/s in 0.3s \n", + "\n", + "2024-06-19 14:09:52 (7.46 MB/s) - ‘data/sample_data.zip’ saved [2678607/2678607]\n", + "\n" + ] + } + ], + "source": [ + "!wget https://huggingface.co/datasets/pufanyi/VQAv2_TOY/resolve/main/source_data/sample_data.zip -P data\n", + "!unzip data/sample_data.zip -d data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can open `data/questions` to take a view of the dataset organization. We found that the toy-`VQAv2` dataset is organized as follows:\n", + "\n", + "```json\n", + "{\n", + " \"info\": { /* some infomation */ },\n", + " \"task_type\": \"TASK_TYPE\", \"data_type\": \"mscoco\",\n", + " \"license\": { /* some license */ },\n", + " \"questions\": [\n", + " {\n", + " \"image_id\": 262144, // integer id of the image\n", + " \"question\": \"Is the ball flying towards the batter?\",\n", + " \"question_id\": 262144000\n", + " },\n", + " /* ... */\n", + " ]\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Dataset Features _(Optional*)_\n", + "\n", + "You can define the features of the dataset. For more details, please refer to the [official documentation](https://huggingface.co/docs/datasets/en/about_dataset_features).\n", + "\n", + "* _Note that if the dataset features are consistent and all entries in your dataset table are non-null **for all splits of data**, you can skip this step._" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datasets\n", + "\n", + "features = datasets.Features(\n", + " {\n", + " \"question\": datasets.Value(\"string\"),\n", + " \"question_id\": datasets.Value(\"int64\"),\n", + " \"image_id\": datasets.Value(\"string\"),\n", + " \"image\": datasets.Image(),\n", + " \"answers\": datasets.Sequence(datasets.Sequence(feature={\"answer\": datasets.Value(\"string\"), \"answer_confidence\": datasets.Value(\"string\"), \"answer_id\": datasets.Value(\"int64\")})),\n", + " \"answer_type\": datasets.Value(\"string\"),\n", + " \"multiple_choice_answer\": datasets.Value(\"string\"),\n", + " \"question_type\": datasets.Value(\"string\"),\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Data Generator\n", + "\n", + "We use [`datasets.Dataset.from_generator`](https://huggingface.co/docs/datasets/v2.20.0/en/package_reference/main_classes#datasets.Dataset.from_generator) to create the dataset.\n", + "\n", + "The generator function should `yield` dictionaries with the keys corresponding to the dataset features. This can save memory when loading large datasets.\n", + "\n", + "For the image data, we can convert the image to [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html) object.\n", + "\n", + "Note that if some columns are missing in some splits of the dataset (for example, the `answer` column is usually missing in the `test` split), we need to set these columns to null to ensure that all splits have the same features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "from PIL import Image\n", + "\n", + "KEYS = [\"question\", \"question_id\", \"image_id\", \"answers\", \"answer_type\", \"multiple_choice_answer\", \"question_type\"]\n", + "\n", + "def generator(qa_file, image_folder, image_prefix):\n", + " # Open and load the question-answer file\n", + " with open(qa_file, \"r\") as f:\n", + " data = json.load(f)\n", + " qa = data[\"questions\"]\n", + "\n", + " for q in qa:\n", + " # Get the image id\n", + " image_id = q[\"image_id\"]\n", + " # Construct the image path\n", + " image_path = os.path.join(image_folder, f\"{image_prefix}_{image_id:012}.jpg\")\n", + " # Open the image and add it to the question-answer dictionary\n", + " q[\"image\"] = Image.open(image_path)\n", + " # Check if all keys are present in the question-answer dictionary, if not add them with None value\n", + " for key in KEYS:\n", + " if key not in q:\n", + " q[key] = None\n", + " # Yield the question-answer dictionary\n", + " yield q" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Dataset\n", + "\n", + "We generate the dataset using the generator function.\n", + "\n", + "Note that if you skip the step of defining dataset features, there is no need to pass the `features` argument. The dataset infer the features from the dataset automatically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NUM_PROC = 32 # number of processes to use for multiprocessing, set to 1 for no multiprocessing\n", + "\n", + "data_val = datasets.Dataset.from_generator(\n", + " generator,\n", + " gen_kwargs={\n", + " \"qa_file\": \"data/questions/v2_OpenEnded_mscoco_val2014_questions.json\",\n", + " \"image_folder\": \"data/images/val2014\",\n", + " \"image_prefix\": \"COCO_val2014\",\n", + " },\n", + " features=features,\n", + " num_proc=NUM_PROC,\n", + ")\n", + "\n", + "data_test = datasets.Dataset.from_generator(\n", + " generator,\n", + " gen_kwargs={\n", + " \"qa_file\": \"data/questions/v2_OpenEnded_mscoco_test2015_questions.json\",\n", + " \"image_folder\": \"data/images/test2015\",\n", + " \"image_prefix\": \"COCO_test2015\",\n", + " },\n", + " features=features,\n", + " num_proc=NUM_PROC,\n", + ")\n", + "\n", + "data_test_dev = datasets.Dataset.from_generator(\n", + " generator,\n", + " gen_kwargs={\n", + " \"qa_file\": \"data/questions/v2_OpenEnded_mscoco_test-dev2015_questions.json\",\n", + " \"image_folder\": \"data/images/test2015\",\n", + " \"image_prefix\": \"COCO_test2015\",\n", + " },\n", + " features=features,\n", + " num_proc=NUM_PROC,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset Upload\n", + "\n", + "Finally, we group the dataset with different splits and upload it to the Huggingface dataset hub." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = datasets.DatasetDict({\"val\": data_val, \"test\": data_test, \"test_dev\": data_test_dev})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.push_to_hub(\"pufanyi/VQAv2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CommitInfo(commit_url='https://huggingface.co/datasets/pufanyi/VQAv2_TOY/commit/b057eff450520a6e3fc7e6be88c3a172c4b5d99b', commit_message='Upload source_data/sample_data.zip with huggingface_hub', commit_description='', oid='b057eff450520a6e3fc7e6be88c3a172c4b5d99b', pr_url=None, pr_revision=None, pr_num=None)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from huggingface_hub import HfApi\n", + "\n", + "api = HfApi()\n", + "api.upload_file(\n", + " path_or_fileobj=\"/data/pufanyi/project/lmms-eval-public/tools/data/sample_data.zip\",\n", + " path_in_repo=\"source_data/sample_data.zip\",\n", + " repo_id=\"pufanyi/VQAv2_TOY\",\n", + " repo_type=\"dataset\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lmms-eval", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 19f9bd621c76a483ff98f8c7eb78f64753da683a Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Wed, 19 Jun 2024 15:23:24 +0800 Subject: [PATCH 094/196] docs --- tools/make_image_hf_dataset.ipynb | 92 ++++++------------------------- 1 file changed, 17 insertions(+), 75 deletions(-) diff --git a/tools/make_image_hf_dataset.ipynb b/tools/make_image_hf_dataset.ipynb index 65ea5ca5..e0791f78 100755 --- a/tools/make_image_hf_dataset.ipynb +++ b/tools/make_image_hf_dataset.ipynb @@ -28,38 +28,15 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": { "vscode": { "languageId": "bat" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-06-19 14:09:51-- https://huggingface.co/datasets/pufanyi/VQAv2_TOY/resolve/main/source_data/sample_data.zip\n", - "Resolving huggingface.co (huggingface.co)... 13.33.30.114, 13.33.30.49, 13.33.30.76, ...\n", - "Connecting to huggingface.co (huggingface.co)|13.33.30.114|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://cdn-lfs-us-1.huggingface.co/repos/c9/82/c9827770a5c0b13c1b646a275968813f8705db30ac0de29f118bb316c2b2a4eb/8cc2e821b7c6e4b5726a6feeb6214cd2d4810d53f568a5f3565d78e6d1ee5403?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27sample_data.zip%3B+filename%3D%22sample_data.zip%22%3B&response-content-type=application%2Fzip&Expires=1719036591&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxOTAzNjU5MX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2M5LzgyL2M5ODI3NzcwYTVjMGIxM2MxYjY0NmEyNzU5Njg4MTNmODcwNWRiMzBhYzBkZTI5ZjExOGJiMzE2YzJiMmE0ZWIvOGNjMmU4MjFiN2M2ZTRiNTcyNmE2ZmVlYjYyMTRjZDJkNDgxMGQ1M2Y1NjhhNWYzNTY1ZDc4ZTZkMWVlNTQwMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=kppoby2Wg9BYA-L2HJ0uShfMSULqTXjtN3cbdBdZTvMf4NvNXBJxc0mcPSiz-sqV7d7hJn32IzHze2JnnTGxrVrozYdHeoTuG0EtF%7ERgQz17PbzbEps-MPzl-h4G9d5RImWDBNN3OYTWyvSxFzn12d-owQKrkdEXejUZEkGdzvHgECzLPpuMw%7EXIctwxBBbxrHRtBNU57K2KBwOqw5rujHtQevhMaCeRgxRFlpfc3FDxsl4rUVHrCM79UhPwutpEAtOh%7Ep6%7EdgLOXal6oZKCnejCQg3AjgvuMe4Eot3J37a7yUGToRtx6XX8Q9I1SC2nScXIWwZndOQY-1VNSL1s-A__&Key-Pair-Id=K2FPYV99P2N66Q [following]\n", - "--2024-06-19 14:09:51-- https://cdn-lfs-us-1.huggingface.co/repos/c9/82/c9827770a5c0b13c1b646a275968813f8705db30ac0de29f118bb316c2b2a4eb/8cc2e821b7c6e4b5726a6feeb6214cd2d4810d53f568a5f3565d78e6d1ee5403?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27sample_data.zip%3B+filename%3D%22sample_data.zip%22%3B&response-content-type=application%2Fzip&Expires=1719036591&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxOTAzNjU5MX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2M5LzgyL2M5ODI3NzcwYTVjMGIxM2MxYjY0NmEyNzU5Njg4MTNmODcwNWRiMzBhYzBkZTI5ZjExOGJiMzE2YzJiMmE0ZWIvOGNjMmU4MjFiN2M2ZTRiNTcyNmE2ZmVlYjYyMTRjZDJkNDgxMGQ1M2Y1NjhhNWYzNTY1ZDc4ZTZkMWVlNTQwMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=kppoby2Wg9BYA-L2HJ0uShfMSULqTXjtN3cbdBdZTvMf4NvNXBJxc0mcPSiz-sqV7d7hJn32IzHze2JnnTGxrVrozYdHeoTuG0EtF%7ERgQz17PbzbEps-MPzl-h4G9d5RImWDBNN3OYTWyvSxFzn12d-owQKrkdEXejUZEkGdzvHgECzLPpuMw%7EXIctwxBBbxrHRtBNU57K2KBwOqw5rujHtQevhMaCeRgxRFlpfc3FDxsl4rUVHrCM79UhPwutpEAtOh%7Ep6%7EdgLOXal6oZKCnejCQg3AjgvuMe4Eot3J37a7yUGToRtx6XX8Q9I1SC2nScXIWwZndOQY-1VNSL1s-A__&Key-Pair-Id=K2FPYV99P2N66Q\n", - "Resolving cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)... 3.165.102.80, 3.165.102.25, 3.165.102.95, ...\n", - "Connecting to cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)|3.165.102.80|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 2678607 (2.6M) [application/zip]\n", - "Saving to: ‘data/sample_data.zip’\n", - "\n", - "sample_data.zip 100%[===================>] 2.55M 7.46MB/s in 0.3s \n", - "\n", - "2024-06-19 14:09:52 (7.46 MB/s) - ‘data/sample_data.zip’ saved [2678607/2678607]\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "!wget https://huggingface.co/datasets/pufanyi/VQAv2_TOY/resolve/main/source_data/sample_data.zip -P data\n", + "!wget https://huggingface.co/datasets/lmms-lab/VQAv2_TOY/resolve/main/source_data/sample_data.zip -P data\n", "!unzip data/sample_data.zip -d data" ] }, @@ -107,14 +84,10 @@ "\n", "features = datasets.Features(\n", " {\n", - " \"question\": datasets.Value(\"string\"),\n", " \"question_id\": datasets.Value(\"int64\"),\n", + " \"question\": datasets.Value(\"string\"),\n", " \"image_id\": datasets.Value(\"string\"),\n", " \"image\": datasets.Image(),\n", - " \"answers\": datasets.Sequence(datasets.Sequence(feature={\"answer\": datasets.Value(\"string\"), \"answer_confidence\": datasets.Value(\"string\"), \"answer_id\": datasets.Value(\"int64\")})),\n", - " \"answer_type\": datasets.Value(\"string\"),\n", - " \"multiple_choice_answer\": datasets.Value(\"string\"),\n", - " \"question_type\": datasets.Value(\"string\"),\n", " }\n", ")" ] @@ -144,26 +117,15 @@ "import json\n", "from PIL import Image\n", "\n", - "KEYS = [\"question\", \"question_id\", \"image_id\", \"answers\", \"answer_type\", \"multiple_choice_answer\", \"question_type\"]\n", - "\n", "def generator(qa_file, image_folder, image_prefix):\n", - " # Open and load the question-answer file\n", " with open(qa_file, \"r\") as f:\n", " data = json.load(f)\n", " qa = data[\"questions\"]\n", "\n", " for q in qa:\n", - " # Get the image id\n", " image_id = q[\"image_id\"]\n", - " # Construct the image path\n", " image_path = os.path.join(image_folder, f\"{image_prefix}_{image_id:012}.jpg\")\n", - " # Open the image and add it to the question-answer dictionary\n", " q[\"image\"] = Image.open(image_path)\n", - " # Check if all keys are present in the question-answer dictionary, if not add them with None value\n", - " for key in KEYS:\n", - " if key not in q:\n", - " q[key] = None\n", - " # Yield the question-answer dictionary\n", " yield q" ] }, @@ -189,33 +151,34 @@ "data_val = datasets.Dataset.from_generator(\n", " generator,\n", " gen_kwargs={\n", - " \"qa_file\": \"data/questions/v2_OpenEnded_mscoco_val2014_questions.json\",\n", - " \"image_folder\": \"data/images/val2014\",\n", + " \"qa_file\": \"data/questions/vqav2_toy_questions_val2014.json\",\n", + " \"image_folder\": \"data/images\",\n", " \"image_prefix\": \"COCO_val2014\",\n", " },\n", - " features=features,\n", + " # For this dataset, there is no need to specify the features, as all cells are non-null and all splits have the same schema\n", + " # features=features,\n", " num_proc=NUM_PROC,\n", ")\n", "\n", "data_test = datasets.Dataset.from_generator(\n", " generator,\n", " gen_kwargs={\n", - " \"qa_file\": \"data/questions/v2_OpenEnded_mscoco_test2015_questions.json\",\n", - " \"image_folder\": \"data/images/test2015\",\n", + " \"qa_file\": \"data/questions/vqav2_toy_questions_test2015.json\",\n", + " \"image_folder\": \"data/images\",\n", " \"image_prefix\": \"COCO_test2015\",\n", " },\n", - " features=features,\n", + " # features=features,\n", " num_proc=NUM_PROC,\n", ")\n", "\n", "data_test_dev = datasets.Dataset.from_generator(\n", " generator,\n", " gen_kwargs={\n", - " \"qa_file\": \"data/questions/v2_OpenEnded_mscoco_test-dev2015_questions.json\",\n", - " \"image_folder\": \"data/images/test2015\",\n", + " \"qa_file\": \"data/questions/vqav2_toy_questions_test-dev2015.json\",\n", + " \"image_folder\": \"data/images\",\n", " \"image_prefix\": \"COCO_test2015\",\n", " },\n", - " features=features,\n", + " # features=features,\n", " num_proc=NUM_PROC,\n", ")" ] @@ -244,35 +207,14 @@ "metadata": {}, "outputs": [], "source": [ - "data.push_to_hub(\"pufanyi/VQAv2\")" + "data.push_to_hub(\"lmms-lab/VQAv2_TOY\") # replace lmms-lab to your username" ] }, { - "cell_type": "code", - "execution_count": 44, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CommitInfo(commit_url='https://huggingface.co/datasets/pufanyi/VQAv2_TOY/commit/b057eff450520a6e3fc7e6be88c3a172c4b5d99b', commit_message='Upload source_data/sample_data.zip with huggingface_hub', commit_description='', oid='b057eff450520a6e3fc7e6be88c3a172c4b5d99b', pr_url=None, pr_revision=None, pr_num=None)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "from huggingface_hub import HfApi\n", - "\n", - "api = HfApi()\n", - "api.upload_file(\n", - " path_or_fileobj=\"/data/pufanyi/project/lmms-eval-public/tools/data/sample_data.zip\",\n", - " path_in_repo=\"source_data/sample_data.zip\",\n", - " repo_id=\"pufanyi/VQAv2_TOY\",\n", - " repo_type=\"dataset\",\n", - ")" + "Now, you can check the dataset on the [Hugging Face dataset hub](https://huggingface.co/datasets/lmms-lab/VQAv2_TOY)." ] }, { From 7f1159a1fe04cfb783dc31d4fbdef3bda0ce19e4 Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Wed, 19 Jun 2024 15:35:05 +0800 Subject: [PATCH 095/196] update preparation --- tools/make_image_hf_dataset.ipynb | 44 ++++++++++++++++++++++++++++++- 1 file changed, 43 insertions(+), 1 deletion(-) diff --git a/tools/make_image_hf_dataset.ipynb b/tools/make_image_hf_dataset.ipynb index e0791f78..b8b9957b 100755 --- a/tools/make_image_hf_dataset.ipynb +++ b/tools/make_image_hf_dataset.ipynb @@ -6,13 +6,55 @@ "source": [ "# Make Image dataset on Hugging Face Datasets\n", "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/EvolvingLMMs-Lab/lmms-eval/blob/main/tools/make_image_hf_dataset.ipynb)\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/EvolvingLMMs-Lab/lmms-eval/blob/pufanyi/hf_dataset_docs/tools/make_image_hf_dataset.ipynb)\n", "\n", "This notebook will guide you to make correct format of Huggingface dataset, in proper parquet format and visualizable in Huggingface dataset hub.\n", "\n", "We will take the example of the dataset [`pufanyi/VQAv2_Example`](https://huggingface.co/datasets/lmms-lab/VQAv2) and convert it to the proper format." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparation\n", + "\n", + "We need to install `datasets` library to create the dataset and `Pillow` to handle images." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "bat" + } + }, + "outputs": [], + "source": [ + "!pip install datasets Pillow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we need to login into Hugging Face to upload the dataset. You should goto the [Hugging Face website](https://huggingface.co/settings/tokens) to get your API token." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "bat" + } + }, + "outputs": [], + "source": [ + "!huggingface-cli login --token hf_YOUR_HF_TOKEN # replace hf_YOUR_HF_TOKEN to your own Hugging Face token." + ] + }, { "cell_type": "markdown", "metadata": { From 63748e9718f287ad433afc90e340b5e17a89c1ed Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Wed, 19 Jun 2024 15:36:43 +0800 Subject: [PATCH 096/196] small fix --- tools/make_image_hf_dataset.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/make_image_hf_dataset.ipynb b/tools/make_image_hf_dataset.ipynb index b8b9957b..bb6b47db 100755 --- a/tools/make_image_hf_dataset.ipynb +++ b/tools/make_image_hf_dataset.ipynb @@ -6,11 +6,11 @@ "source": [ "# Make Image dataset on Hugging Face Datasets\n", "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/EvolvingLMMs-Lab/lmms-eval/blob/pufanyi/hf_dataset_docs/tools/make_image_hf_dataset.ipynb)\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/EvolvingLMMs-Lab/lmms-eval/blob/main/tools/make_image_hf_dataset.ipynb)\n", "\n", "This notebook will guide you to make correct format of Huggingface dataset, in proper parquet format and visualizable in Huggingface dataset hub.\n", "\n", - "We will take the example of the dataset [`pufanyi/VQAv2_Example`](https://huggingface.co/datasets/lmms-lab/VQAv2) and convert it to the proper format." + "We will take the example of the dataset [`lmms-lab/VQAv2_TOY`](https://huggingface.co/datasets/lmms-lab/VQAv2_TOY) and convert it to the proper format." ] }, { From d0f8851d42ba31f5da2a7a65e91499db45174dbc Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Wed, 19 Jun 2024 15:36:48 +0800 Subject: [PATCH 097/196] small fix --- tools/make_image_hf_dataset.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/make_image_hf_dataset.ipynb b/tools/make_image_hf_dataset.ipynb index bb6b47db..6346b4e5 100755 --- a/tools/make_image_hf_dataset.ipynb +++ b/tools/make_image_hf_dataset.ipynb @@ -65,7 +65,7 @@ "source": [ "## Download Dataset\n", "\n", - "We have uploaded the zip file of the dataset to [Hugging Face](https://huggingface.co/datasets/pufanyi/VQAv2_TOY/tree/main/source_data) for download. This dataset is a subset of the [VQAv2](https://visualqa.org/) dataset, with $10$ entries each from the `val`, `test`, and `test-dev` splits, for easier downloading." + "We have uploaded the zip file of the dataset to [Hugging Face](https://huggingface.co/datasets/pufanyi/VQAv2_TOY/tree/main/source_data) for download. This dataset is a subset of the [VQAv2](https://visualqa.org/) dataset, with $20$ entries each from the `val`, `test`, and `test-dev` splits, for easier downloading." ] }, { From a14684b8557d5894976448a5c559ed7a66a6cf16 Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Wed, 19 Jun 2024 15:37:04 +0800 Subject: [PATCH 098/196] lint --- tools/make_image_hf_dataset.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tools/make_image_hf_dataset.ipynb b/tools/make_image_hf_dataset.ipynb index 6346b4e5..fde707ef 100755 --- a/tools/make_image_hf_dataset.ipynb +++ b/tools/make_image_hf_dataset.ipynb @@ -159,6 +159,7 @@ "import json\n", "from PIL import Image\n", "\n", + "\n", "def generator(qa_file, image_folder, image_prefix):\n", " with open(qa_file, \"r\") as f:\n", " data = json.load(f)\n", @@ -188,7 +189,7 @@ "metadata": {}, "outputs": [], "source": [ - "NUM_PROC = 32 # number of processes to use for multiprocessing, set to 1 for no multiprocessing\n", + "NUM_PROC = 32 # number of processes to use for multiprocessing, set to 1 for no multiprocessing\n", "\n", "data_val = datasets.Dataset.from_generator(\n", " generator,\n", @@ -249,7 +250,7 @@ "metadata": {}, "outputs": [], "source": [ - "data.push_to_hub(\"lmms-lab/VQAv2_TOY\") # replace lmms-lab to your username" + "data.push_to_hub(\"lmms-lab/VQAv2_TOY\") # replace lmms-lab to your username" ] }, { From 813877bfe5ac590cdbe92dd74d18f83a2091f748 Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Wed, 19 Jun 2024 15:37:52 +0800 Subject: [PATCH 099/196] to sh script --- tools/make_image_hf_dataset.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tools/make_image_hf_dataset.ipynb b/tools/make_image_hf_dataset.ipynb index fde707ef..3f24c363 100755 --- a/tools/make_image_hf_dataset.ipynb +++ b/tools/make_image_hf_dataset.ipynb @@ -27,7 +27,7 @@ "execution_count": null, "metadata": { "vscode": { - "languageId": "bat" + "languageId": "shellscript" } }, "outputs": [], @@ -47,7 +47,7 @@ "execution_count": null, "metadata": { "vscode": { - "languageId": "bat" + "languageId": "shellscript" } }, "outputs": [], @@ -73,7 +73,7 @@ "execution_count": null, "metadata": { "vscode": { - "languageId": "bat" + "languageId": "shellscript" } }, "outputs": [], From cb0d3f49b72790b081f981e0e6147131542f7f68 Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Thu, 20 Jun 2024 02:11:18 +0800 Subject: [PATCH 100/196] update readme --- README.md | 2 +- tools/make_image_hf_dataset.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 2bf7a26e..1d6f7db3 100755 --- a/README.md +++ b/README.md @@ -201,7 +201,7 @@ Please check [supported tasks](lmms_eval/docs/current_tasks.md) for more details ## Add Customized Model and Dataset -Please refer to our [documentation](docs/README.md). +Please refer to our [documentation](tools/make_image_hf_dataset.ipynb). ## Acknowledgement diff --git a/tools/make_image_hf_dataset.ipynb b/tools/make_image_hf_dataset.ipynb index 3f24c363..89fa0a71 100755 --- a/tools/make_image_hf_dataset.ipynb +++ b/tools/make_image_hf_dataset.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Make Image dataset on Hugging Face Datasets\n", + "# Contribute Your Models and Datasets\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/EvolvingLMMs-Lab/lmms-eval/blob/main/tools/make_image_hf_dataset.ipynb)\n", "\n", From 3463651b8c54d36cd94169e3d376f5ed225a195a Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 20 Jun 2024 03:54:33 +0000 Subject: [PATCH 101/196] Remove handling non-visual loop in llava --- lmms_eval/models/llava.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py index b49cf55b..1dc4bb76 100755 --- a/lmms_eval/models/llava.py +++ b/lmms_eval/models/llava.py @@ -358,17 +358,6 @@ def _collate(x): prompt_question = conv.get_prompt() question_input.append(prompt_question) - # The above for loop has bugs. When there is no visuals, e.g. pure text, - # there will be no for loop execute resulting in an empty question_input (because no visuals) - # Scenario 1 won't even be execute - if len(flattened_visuals) == 0: - for context in contexts: - question = context - conv = conv_templates[self.conv_template].copy() - conv.append_message(conv.roles[0], question) - conv.append_message(conv.roles[1], None) - prompt_question = conv.get_prompt() - question_input.append(prompt_question) # input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) # preconfigure gen_kwargs with defaults From 45876652a877a8006b828f32f5cc4660629f9190 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 20 Jun 2024 03:55:30 +0000 Subject: [PATCH 102/196] Add llava_hf back to registry --- lmms_eval/models/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index ec354cf3..ed1b9ff5 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -29,6 +29,7 @@ "mplug_owl_video": "mplug_Owl", "phi3v": "Phi3v", "tinyllava": "TinyLlava", + "llava_hf" : "LlavaHf", } for model_name, model_class in AVAILABLE_MODELS.items(): From cab8159ff35db330536c0b6dfb4b0a3b24142209 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 20 Jun 2024 13:30:29 +0800 Subject: [PATCH 103/196] Update README.md --- docs/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index 020e351b..2c912363 100755 --- a/docs/README.md +++ b/docs/README.md @@ -8,4 +8,5 @@ Majority of this documentation is adapted from [lm-eval-harness](https://github. * To learn about the command line flags, see the [commands](commands.md) * To learn how to add a new moddel, see the [Model Guide](model_guide.md). -* For a crash course on adding new tasks to the library, see our [Task Guide](task_guide.md). \ No newline at end of file +* For a crash course on adding new tasks to the library, see our [Task Guide](task_guide.md). +* If you need to upload your datasets into correct HF format with viewer supported, please refer to [tools](https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/pufanyi/hf_dataset_docs/tools) From d94f83cb3f08b61a2c75cc4326e58792100605b3 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 20 Jun 2024 13:30:59 +0800 Subject: [PATCH 104/196] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1d6f7db3..2bf7a26e 100755 --- a/README.md +++ b/README.md @@ -201,7 +201,7 @@ Please check [supported tasks](lmms_eval/docs/current_tasks.md) for more details ## Add Customized Model and Dataset -Please refer to our [documentation](tools/make_image_hf_dataset.ipynb). +Please refer to our [documentation](docs/README.md). ## Acknowledgement From dbe63293245a5141fdfd80bda7657c304f6bd32f Mon Sep 17 00:00:00 2001 From: choiszt Date: Thu, 20 Jun 2024 15:14:21 +0800 Subject: [PATCH 105/196] update ablation for videomme datasets --- lmms_eval/tasks/videomme/utils.py | 123 ++++++++++++++++-- lmms_eval/tasks/videomme/videomme.yaml | 0 .../tasks/videomme/videomme_w_subtitle.yaml | 44 +++++++ 3 files changed, 158 insertions(+), 9 deletions(-) mode change 100755 => 100644 lmms_eval/tasks/videomme/utils.py mode change 100755 => 100644 lmms_eval/tasks/videomme/videomme.yaml create mode 100644 lmms_eval/tasks/videomme/videomme_w_subtitle.yaml diff --git a/lmms_eval/tasks/videomme/utils.py b/lmms_eval/tasks/videomme/utils.py old mode 100755 new mode 100644 index 3631f87c..a7fa9192 --- a/lmms_eval/tasks/videomme/utils.py +++ b/lmms_eval/tasks/videomme/utils.py @@ -10,6 +10,8 @@ import sys from typing import List, Dict, Optional, Union import re +import cv2 +import numpy as np eval_logger = logging.getLogger("lmms-eval") @@ -80,17 +82,55 @@ # cache_dir = os.path.join(hf_home, cache_dir) # base_cache_dir = config["dataset_kwargs"]["cache_dir"] base_cache_dir = os.path.expanduser(hf_home) - +with open(Path(__file__).parent / "videomme.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) +cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"] + + +def parse_subtitle_time(time_str): + h, m, s_ms = time_str.split(':') + s, ms = s_ms.split(',') + return int(h) * 3600 + int(m) * 60 + int(s) + int(ms) / 1000 + +def load_subtitles(subtitle_path): + subtitles = {} + with open(subtitle_path, 'r', encoding='utf-8') as file: + content = file.read().split('\n\n') + for section in content: + if section.strip(): + lines = section.split('\n') + if len(lines) >= 3: + time_range = lines[1].split(' --> ') + start_time = parse_subtitle_time(time_range[0]) + end_time = parse_subtitle_time(time_range[1]) + text = ' '.join(line for line in lines[2:]) + subtitles[(start_time, end_time)] = text + return subtitles + +def convert_time_to_frame(time_in_seconds, fps): + return int(time_in_seconds * fps) + +def extract_subtitles(video_path, subtitle_path): + video = cv2.VideoCapture(video_path) + fps = video.get(cv2.CAP_PROP_FPS) + total_frame=int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + subtitles = load_subtitles(subtitle_path) + + subtitle_frames = [] + for (start_time, end_time), text in subtitles.items(): + start_frame = convert_time_to_frame(start_time, fps) + end_frame = convert_time_to_frame(end_time, fps) + subtitle_frames.append((start_frame, end_frame, text)) + + return subtitle_frames,total_frame def videomme_doc_to_visual(doc): - with open(Path(__file__).parent / "videomme.yaml", "r") as f: - raw_data = f.readlines() - safe_data = [] - for i, line in enumerate(raw_data): - # remove function definition since yaml load cannot handle it - if "!function" not in line: - safe_data.append(line) - cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"] + cache_dir = os.path.join(base_cache_dir, cache_name) video_path = doc["videoID"] + ".mp4" video_path = os.path.join(cache_dir, video_path) @@ -106,6 +146,71 @@ def videomme_doc_to_visual(doc): def videomme_doc_to_text(doc, model_specific_prompt_kwargs=None): + option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option." + question = doc["question"] + option = str(doc["options"]) + question = question + "\n" + option + full_prompt=option_prompt+"\n"+question+"\n"+"The best answer is:" + return full_prompt +# Frames + Subs +# This video's subtitles are listed below: +# 【subtitles】 + +# Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option. +# 【question】 +# The best answer is: +# Frames / Frames + Audio +# Select the best answer to the following multiple-choice question based on the video. Respond with only the letter (A, B, C, or D) of the correct option. +# 【question】 +# The best answer is: + +def videomme_doc_to_text_subtitle(doc, model_specific_prompt_kwargs=None): + cache_dir = os.path.join(base_cache_dir, cache_name) + video_path = doc["videoID"] + ".mp4" + subtitle_path=os.path.join(cache_dir,"subtitle",doc["videoID"]+".srt") + video_path = os.path.join(cache_dir, video_path) + if os.path.exists(subtitle_path): #Denote have subtitle + subtitle=open(subtitle_path).readlines() + else: + subtitle="" + subtitles_prompt="This video's subtitles are listed below: \n" + if subtitle=="": + subtitle="No subtitles available" + else: + if "gemini_api_flag" in model_specific_prompt_kwargs: #specific for gemini_api + if model_specific_prompt_kwargs['gemini_api_flag']=="full subtitle": + textlist=[] + for ele in subtitle: + pattern = r'(.*?)' + matches = re.findall(pattern, ele) + if matches: + textlist.append(matches[0]) + subtitle_text="\n".join(textlist) + else: + if "frame_num" in model_specific_prompt_kwargs: + frame_num=model_specific_prompt_kwargs['frame_num'] + subtitle_by_frame,total_frame=extract_subtitles(video_path,subtitle_path) + uniform_sampled_frames = np.linspace(0, total_frame - 1, frame_num, dtype=int).tolist() + + subtitle_by_frame_idx=[] + for frame_idx in uniform_sampled_frames: + for idx,title in enumerate(subtitle_by_frame): + if frame_idx=title[0]: + subtitle_by_frame_idx.append(idx) + subtitle_by_frame_idx=list(set(subtitle_by_frame_idx)) + + textlist=[] + for idx in subtitle_by_frame_idx: + pattern = r'(.*?)' + raw_text=re.findall(pattern, subtitle_by_frame[idx][2]) + try: + textlist.append(raw_text[0]) + except: + continue + subtitle_text="\n".join(textlist) + subtitle=subtitle_text + + option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option." question = doc["question"] option = str(doc["options"]) question = question + "\n" + option + model_specific_prompt_kwargs["post_prompt"] diff --git a/lmms_eval/tasks/videomme/videomme.yaml b/lmms_eval/tasks/videomme/videomme.yaml old mode 100755 new mode 100644 diff --git a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml new file mode 100644 index 00000000..ac34cdf4 --- /dev/null +++ b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml @@ -0,0 +1,44 @@ +dataset_path: lmms-lab/Video-MME +dataset_kwargs: + token: True + cache_dir: videomme + video: True + # From_YouTube: True +task: videomme_w_subtitle +test_split: test +output_type: generate_until +doc_to_visual: !function utils.videomme_doc_to_visual +doc_to_text: !function utils.videomme_doc_to_text_subtitle +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 16 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +# The return value of process_results will be used by metrics +process_results: !function utils.videomme_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: videomme_percetion_score + aggregation: !function utils.videomme_aggregate_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + frame_num: 32 + gemini_api: + gemini_api_flag: "full subtitle" + # gpt4v: + # pre_prompt: "" + # post_prompt: + # # qwen_vl: + # # pre_prompt: "" + # # post_prompt: " Answer:" + # # otterhd: + # # pre_prompt: "" + # # post_prompt: " Answer:" + # xcomposer2_4khd: + # pre_prompt: "[UNUSED_TOKEN_146]user\n" + # post_prompt: " Answer this question with A, B, C, or D.[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n" +metadata: + - version: 0.0 From af38885fc2e066f5ea44388f33e07176f836fe28 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Thu, 20 Jun 2024 12:07:09 +0000 Subject: [PATCH 106/196] chore: Handle ImportError when importing models Handle the ImportError exception when importing models in the lmms_eval package. This change adds a try-except block to catch the ImportError and print an error message indicating the failed import. This will help with troubleshooting and identifying any issues with the model imports. --- lmms_eval/models/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index ec354cf3..ee237fff 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -34,5 +34,6 @@ for model_name, model_class in AVAILABLE_MODELS.items(): try: exec(f"from .{model_name} import {model_class}") - except ImportError: + except ImportError as e: + print(f"Failed to import {model_class} from {model_name}: {e}") pass From 8ef24740dd48a11c97eb627f2fff4aca107fef0d Mon Sep 17 00:00:00 2001 From: Bo Li Date: Thu, 20 Jun 2024 12:11:03 +0000 Subject: [PATCH 107/196] chore: Remove unused models from lmms_eval package --- lmms_eval/models/__init__.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index ee237fff..60204095 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -22,9 +22,7 @@ "idefics2": "Idefics2", "internvl": "InternVLChat", "gemini_api": "GeminiAPI", - "gemini_model": "GeminiModel", "reka": "Reka", - "llava_onevision": "Llava_OneVision", "from_log": "FromLog", "mplug_owl_video": "mplug_Owl", "phi3v": "Phi3v", From b80a91f73e15ddd0b0ce1322d7d121fa14030eed Mon Sep 17 00:00:00 2001 From: Lorenzo Mammana Date: Wed, 5 Jun 2024 13:04:55 +0000 Subject: [PATCH 108/196] feat: Allow loading model configurations from other packages --- lmms_eval/models/__init__.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index ed1b9ff5..89f06a56 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -1,3 +1,4 @@ +import importlib import os import hf_transfer @@ -37,3 +38,17 @@ exec(f"from .{model_name} import {model_class}") except ImportError: pass + +if os.environ.get("LMMS_EVAL_PLUGINS", None): + # Allow specifying other packages to import models from + for plugin in os.environ["LMMS_EVAL_PLUGINS"].split(","): + m = importlib.import_module(f"{plugin}.models") + for model_name, model_class in getattr(m, "AVAILABLE_MODELS").items(): + try: + exec(f"from {plugin}.models.{model_name} import {model_class}") + except ImportError: + pass + +import hf_transfer + +os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" From 03947e14a46fd25b412931f7c9c25f4a2971d0b4 Mon Sep 17 00:00:00 2001 From: Lorenzo Mammana Date: Wed, 5 Jun 2024 13:40:41 +0000 Subject: [PATCH 109/196] feat: Allow including external tasks from plugins --- lmms_eval/__main__.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/lmms_eval/__main__.py b/lmms_eval/__main__.py index 2949705f..80beb6b9 100755 --- a/lmms_eval/__main__.py +++ b/lmms_eval/__main__.py @@ -1,3 +1,4 @@ +import importlib import os import yaml import sys @@ -245,6 +246,12 @@ def cli_evaluate_single(args: Union[argparse.Namespace, None] = None) -> None: eval_logger.info(f"Including path: {args.include_path}") include_path(args.include_path) + if os.environ.get("LMMS_EVAL_PLUGINS", None): + for plugin in os.environ["LMMS_EVAL_PLUGINS"].split(","): + package_tasks_location = importlib.util.find_spec(f"{plugin}.tasks").submodule_search_locations[0] + eval_logger.info(f"Including path: {args.include_path}") + include_path(package_tasks_location) + if args.tasks is None: task_names = ALL_TASKS elif args.tasks == "list": From 12cea76f1f0f14b1fd1007c9d39a9b0557368637 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Thu, 20 Jun 2024 18:15:32 +0000 Subject: [PATCH 110/196] chore: Add loguru for logging in lmms_eval package --- lmms_eval/__main__.py | 29 +- lmms_eval/api/metrics.py | 5 +- lmms_eval/api/model.py | 4 +- lmms_eval/api/registry.py | 3 +- lmms_eval/api/task.py | 17 +- lmms_eval/evaluator.py | 6 +- lmms_eval/logging_utils.py | 7 +- lmms_eval/models/__init__.py | 9 +- lmms_eval/models/batch_gpt4.py | 7 +- lmms_eval/models/claude.py | 10 +- lmms_eval/models/from_log.py | 3 +- lmms_eval/models/fuyu.py | 5 +- lmms_eval/models/gemini_api.py | 4 +- lmms_eval/models/gpt4v.py | 4 +- lmms_eval/models/idefics2.py | 6 +- lmms_eval/models/instructblip.py | 5 +- lmms_eval/models/internvl.py | 3 +- lmms_eval/models/llama_vid.py | 3 +- lmms_eval/models/llava.py | 4 +- lmms_eval/models/llava_hf.py | 6 +- lmms_eval/models/llava_sglang.py | 4 +- lmms_eval/models/llava_vid.py | 3 +- lmms_eval/models/minicpm_v.py | 4 +- .../model_utils/qwen/qwen_generate_utils.py | 3 +- lmms_eval/models/mplug_owl_video.py | 7 +- .../configuration_mplug_owl.py | 4 +- .../mplug_owl_video/modeling_mplug_owl.py | 9 +- .../mplug_owl_video/tokenization_mplug_owl.py | 4 +- lmms_eval/models/phi3v.py | 79 ++--- lmms_eval/models/qwen_vl.py | 4 +- lmms_eval/models/qwen_vl_api.py | 4 +- lmms_eval/models/reka.py | 9 +- lmms_eval/models/tinyllava.py | 47 +-- lmms_eval/models/video_chatgpt.py | 11 +- lmms_eval/models/video_chatgpt/utils.py | 117 ------- lmms_eval/models/video_llava.py | 25 +- lmms_eval/models/xcomposer2_4KHD.py | 6 +- lmms_eval/models/xcomposer2_4khd.py | 4 +- lmms_eval/tasks/__init__.py | 10 +- lmms_eval/tasks/activitynetqa/utils.py | 5 +- lmms_eval/tasks/cmmmu/utils.py | 4 +- lmms_eval/tasks/coco_cap/utils.py | 3 +- lmms_eval/tasks/conbench/utils.py | 21 +- lmms_eval/tasks/cvrr/utils.py | 5 +- lmms_eval/tasks/docvqa/utils.py | 4 +- lmms_eval/tasks/egoschema/utils.py | 4 +- lmms_eval/tasks/ferret/utils.py | 5 +- lmms_eval/tasks/flickr30k/utils.py | 3 +- .../tasks/hallusion_bench/evaluate_hb.py | 4 +- lmms_eval/tasks/hallusion_bench/utils.py | 4 +- lmms_eval/tasks/ii_bench/utils.py | 13 +- lmms_eval/tasks/infovqa/utils.py | 5 +- .../tasks/internal_eval/d170_cn_utils.py | 4 +- .../tasks/internal_eval/d170_en_utils.py | 4 +- .../tasks/internal_eval/dc100_en_utils.py | 4 +- .../tasks/internal_eval/dc200_cn_utils.py | 4 +- lmms_eval/tasks/llava-bench-coco/utils.py | 5 +- lmms_eval/tasks/llava-in-the-wild/utils.py | 5 +- lmms_eval/tasks/llava_wilder/utils.py | 4 +- lmms_eval/tasks/longvideobench/utils.py | 65 ++-- lmms_eval/tasks/mathverse/mathverse_evals.py | 41 ++- lmms_eval/tasks/mathverse/utils.py | 11 +- lmms_eval/tasks/mathvista/mathvista_evals.py | 4 +- lmms_eval/tasks/mathvista/utils.py | 3 +- lmms_eval/tasks/mmbench/cc_utils.py | 3 +- lmms_eval/tasks/mmbench/cn_utils.py | 3 +- lmms_eval/tasks/mmbench/en_utils.py | 3 +- lmms_eval/tasks/mmbench/mmbench_evals.py | 3 +- lmms_eval/tasks/mme/utils.py | 3 +- lmms_eval/tasks/mmmu/utils.py | 6 +- lmms_eval/tasks/mmmu/utils_group_img.py | 6 +- lmms_eval/tasks/mmupd/mmupd_evals.py | 321 +++++++++--------- lmms_eval/tasks/mmupd/utils.py | 8 +- lmms_eval/tasks/mmvet/utils.py | 4 +- lmms_eval/tasks/multidocvqa/utils.py | 6 +- .../utils.py | 5 +- lmms_eval/tasks/nextqa/utils.py | 4 +- lmms_eval/tasks/nocaps/utils.py | 3 +- lmms_eval/tasks/ocrbench/utils.py | 4 +- lmms_eval/tasks/ok_vqa/utils.py | 12 +- lmms_eval/tasks/olympiadbench/cn_utils.py | 6 +- lmms_eval/tasks/olympiadbench/en_utils.py | 6 +- .../olympiadbench/olympiadbench_evals.py | 5 +- lmms_eval/tasks/perceptiontest/test/utils.py | 4 +- lmms_eval/tasks/perceptiontest/val/utils.py | 4 +- lmms_eval/tasks/qbench/utils.py | 14 +- lmms_eval/tasks/refcoco+/utils.py | 3 +- lmms_eval/tasks/refcoco/utils.py | 3 +- lmms_eval/tasks/refcocog/utils.py | 3 +- lmms_eval/tasks/screenspot/utils.py | 7 +- lmms_eval/tasks/screenspot/utils_rec.py | 77 +++-- lmms_eval/tasks/stvqa/utils.py | 5 +- lmms_eval/tasks/synthdog/donut_evaluator.py | 4 +- lmms_eval/tasks/synthdog/utils.py | 3 +- lmms_eval/tasks/tempcompass/utils.py | 6 +- lmms_eval/tasks/textcaps/utils.py | 3 +- lmms_eval/tasks/textvqa/utils.py | 17 +- lmms_eval/tasks/vatex/utils.py | 4 +- lmms_eval/tasks/vcr_wiki/utils.py | 15 +- .../tasks/video_detail_description/utils.py | 4 +- lmms_eval/tasks/videochatgpt/utils.py | 4 +- lmms_eval/tasks/videomme/utils.py | 4 +- lmms_eval/tasks/vizwiz_vqa/utils.py | 12 +- lmms_eval/tasks/vqav2/utils.py | 16 +- lmms_eval/tasks/websrc/utils.py | 51 ++- lmms_eval/tasks/worldqa/utils.py | 4 +- .../tasks/worldqa/worldqa_mc_evaluator.py | 3 +- lmms_eval/tasks/youcook2/utils.py | 4 +- lmms_eval/utils.py | 30 +- pyproject.toml | 1 + 110 files changed, 586 insertions(+), 858 deletions(-) diff --git a/lmms_eval/__main__.py b/lmms_eval/__main__.py index 2949705f..96be8f06 100755 --- a/lmms_eval/__main__.py +++ b/lmms_eval/__main__.py @@ -1,12 +1,10 @@ import os import yaml import sys -import copy import json -import logging + import traceback import argparse -import torch import numpy as np import datetime @@ -25,10 +23,7 @@ from lmms_eval.tasks import initialize_tasks, include_path, get_task_dict from lmms_eval.api.registry import ALL_TASKS from lmms_eval.logging_utils import WandbLogger -from lmms_eval.utils import PathFormatter - - -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def _handle_non_serializable(o): @@ -166,9 +161,10 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None: print("└───────────────────────────────────────────────────────────────────────────────┘") sys.exit(1) - set_loggers(args) - eval_logger = logging.getLogger("lmms-eval") - eval_logger.setLevel(getattr(logging, f"{args.verbosity}")) + # reset logger + eval_logger.remove() + eval_logger.add(sys.stdout, colorize=True, level=args.verbosity) + eval_logger.add(sys.stderr, level=args.verbosity) eval_logger.info(f"Verbosity set to {args.verbosity}") os.environ["TOKENIZERS_PARALLELISM"] = "false" @@ -228,11 +224,6 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None: def cli_evaluate_single(args: Union[argparse.Namespace, None] = None) -> None: - eval_logger = logging.getLogger("lmms-eval") - eval_logger.setLevel(getattr(logging, f"{args.verbosity}")) - eval_logger.info(f"Verbosity set to {args.verbosity}") - os.environ["TOKENIZERS_PARALLELISM"] = "false" - initialize_tasks(args.verbosity) if args.predict_only: @@ -350,13 +341,5 @@ def print_results(args, results): print(evaluator.make_table(results, "groups")) -def set_loggers(args): - eval_logger = logging.getLogger("lmms-eval") - ch = logging.StreamHandler() - formatter = PathFormatter("%(asctime)s [%(pathname)s:%(lineno)d] %(levelname)s %(message)s", "%m-%d %H:%M:%S", timezone=args.timezone) - ch.setFormatter(formatter) - eval_logger.addHandler(ch) - - if __name__ == "__main__": cli_evaluate() diff --git a/lmms_eval/api/metrics.py b/lmms_eval/api/metrics.py index c0e5c505..157ed2df 100755 --- a/lmms_eval/api/metrics.py +++ b/lmms_eval/api/metrics.py @@ -9,10 +9,7 @@ import torch from lmms_eval.api.registry import register_metric, register_aggregation - -import logging - -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Register Aggregations First diff --git a/lmms_eval/api/model.py b/lmms_eval/api/model.py index 3a2e2d97..e6eeaba6 100755 --- a/lmms_eval/api/model.py +++ b/lmms_eval/api/model.py @@ -8,9 +8,9 @@ from lmms_eval.api.instance import Instance from tqdm import tqdm from lmms_eval import utils -import logging -eval_logger = logging.getLogger("lmms-eval") + +from loguru import logger as eval_logger T = TypeVar("T", bound="lmms") diff --git a/lmms_eval/api/registry.py b/lmms_eval/api/registry.py index 253341db..6b911997 100755 --- a/lmms_eval/api/registry.py +++ b/lmms_eval/api/registry.py @@ -1,10 +1,9 @@ from lmms_eval.api.model import lmms from typing import Callable, Dict -import logging import evaluate as hf_evaluate -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger MODEL_REGISTRY = {} diff --git a/lmms_eval/api/task.py b/lmms_eval/api/task.py index a7cf5ae8..5560745c 100755 --- a/lmms_eval/api/task.py +++ b/lmms_eval/api/task.py @@ -2,7 +2,7 @@ import ast import itertools import json -import logging + import os import random import re @@ -37,7 +37,7 @@ ) from lmms_eval.filters import build_filter_ensemble -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # HuggingfaceM4/NoCaps contains truncated image in test split # Include this inside code block to avoid error @@ -789,17 +789,17 @@ def unzip_video_data(zip_file): def untar_video_data(tar_file): import tarfile + with tarfile.open(tar_file, "r") as tar_ref: tar_ref.extractall(cache_dir) eval_logger.info(f"Extracted all files from {tar_file} to {cache_dir}") - - def concat_tar_parts(tar_parts, output_tar): - with open(output_tar, 'wb') as out_tar: + with open(output_tar, "wb") as out_tar: from tqdm import tqdm + for part in tqdm(sorted(tar_parts)): - with open(part, 'rb') as part_file: + with open(part, "rb") as part_file: out_tar.write(part_file.read()) eval_logger.info(f"Concatenated parts {tar_parts} into {output_tar}") @@ -811,15 +811,14 @@ def concat_tar_parts(tar_parts, output_tar): # Concatenate and extract tar files if needed if force_unzip or (not os.path.exists(cache_dir) and len(tar_files) > 0): tar_parts_dict = {} - + # Group tar parts together for tar_file in tar_files: - base_name = tar_file.split('.tar')[0] + base_name = tar_file.split(".tar")[0] if base_name not in tar_parts_dict: tar_parts_dict[base_name] = [] tar_parts_dict[base_name].append(tar_file) - # Concatenate and untar split parts for base_name, parts in tar_parts_dict.items(): eval_logger.info(f"Extracting following tar files: {parts}") diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py index 8a4c49d8..0104b01a 100755 --- a/lmms_eval/evaluator.py +++ b/lmms_eval/evaluator.py @@ -7,7 +7,7 @@ from tqdm import tqdm import torch -import logging + import numpy as np from datasets import Image, Sequence @@ -17,8 +17,6 @@ import lmms_eval.api.metrics import lmms_eval.api.registry -import re - from lmms_eval.utils import ( positional_deprecated, run_task_tests, @@ -28,7 +26,7 @@ simple_parse_args_string, ) -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger @positional_deprecated diff --git a/lmms_eval/logging_utils.py b/lmms_eval/logging_utils.py index 6107d21b..4d7e0910 100755 --- a/lmms_eval/logging_utils.py +++ b/lmms_eval/logging_utils.py @@ -1,6 +1,6 @@ # Code mostly from: https://github.com/EleutherAI/lm-evaluation-harness/pull/1339, credit to: https://github.com/ayulockin import copy -import logging + import re import os import json @@ -9,13 +9,10 @@ import numpy as np from datetime import datetime from typing import Any, Dict, List, Literal, Tuple, Union - from packaging.version import Version - from lmms_eval import utils import tenacity - -logger = logging.getLogger(__name__) +from loguru import logger try: import wandb diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index 60204095..b999f99d 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -1,7 +1,8 @@ -import os -import hf_transfer +from loguru import logger +import sys -os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" +logger.remove() +logger.add(sys.stdout, level="WARNING") AVAILABLE_MODELS = { "llava": "Llava", @@ -33,5 +34,5 @@ try: exec(f"from .{model_name} import {model_class}") except ImportError as e: - print(f"Failed to import {model_class} from {model_name}: {e}") + # logger.warning(f"Failed to import {model_class} from {model_name}: {e}") pass diff --git a/lmms_eval/models/batch_gpt4.py b/lmms_eval/models/batch_gpt4.py index 54bfa149..8f4c2220 100755 --- a/lmms_eval/models/batch_gpt4.py +++ b/lmms_eval/models/batch_gpt4.py @@ -2,7 +2,7 @@ from copy import deepcopy from io import BytesIO import base64 -import logging + import os import time import json @@ -20,14 +20,13 @@ from lmms_eval.api.instance import Instance from lmms_eval.api.model import lmms from lmms_eval.api.registry import register_model -from lmms_eval import utils +from loguru import logger as eval_logger # Conditional imports try: from decord import VideoReader, cpu except ImportError: - eval_logger = logging.getLogger("lmms-eval") - eval_logger.info("Decord is not installed. Video input will not be supported.") + eval_logger.warning("Decord is not installed. Video input will not be supported.") # Constants and global configurations API_TYPE = os.getenv("API_TYPE", "openai") diff --git a/lmms_eval/models/claude.py b/lmms_eval/models/claude.py index c629ca06..4c967e88 100644 --- a/lmms_eval/models/claude.py +++ b/lmms_eval/models/claude.py @@ -5,28 +5,28 @@ import json from typing import List, Tuple, Union from tqdm import tqdm -import requests as url_requests import time -import logging from lmms_eval.api.instance import Instance from lmms_eval.api.model import lmms from lmms_eval.api.registry import register_model -from lmms_eval import utils from accelerate import Accelerator, DistributedType from PIL import Image NUM_SECONDS_TO_SLEEP = 5 -eval_logger = logging.getLogger("lmms-eval") + +from loguru import logger + +eval_logger = logger try: import anthropic from decord import VideoReader, cpu import numpy as np except Exception as e: - eval_logger.error(f"Error importing claude: {e}") + eval_logger.warning(f"Error importing claude: {e}") API_URL = os.getenv("ANTHROPIC_API_URL", "https://api.anthropic.com/v1/complete") API_KEY = os.getenv("ANTHROPIC_API_KEY", "YOUR_API_KEY") diff --git a/lmms_eval/models/from_log.py b/lmms_eval/models/from_log.py index 4c573e0f..c774a086 100644 --- a/lmms_eval/models/from_log.py +++ b/lmms_eval/models/from_log.py @@ -1,4 +1,3 @@ -import logging import json import os import re @@ -11,7 +10,7 @@ from lmms_eval.api.instance import Instance from accelerate import Accelerator, DistributedType -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger @register_model("from_log") diff --git a/lmms_eval/models/fuyu.py b/lmms_eval/models/fuyu.py index 5960d69e..fa108358 100755 --- a/lmms_eval/models/fuyu.py +++ b/lmms_eval/models/fuyu.py @@ -15,11 +15,8 @@ from tqdm import tqdm from accelerate import Accelerator, DistributedType from accelerate.state import AcceleratorState -import logging -import logging - -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger @register_model("fuyu") diff --git a/lmms_eval/models/gemini_api.py b/lmms_eval/models/gemini_api.py index 0b2be05e..4a43c9af 100644 --- a/lmms_eval/models/gemini_api.py +++ b/lmms_eval/models/gemini_api.py @@ -1,7 +1,7 @@ import io import os import time -import logging + import json from PIL import Image @@ -12,7 +12,7 @@ from lmms_eval.api.instance import Instance from accelerate import Accelerator, DistributedType -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: import google.generativeai as genai diff --git a/lmms_eval/models/gpt4v.py b/lmms_eval/models/gpt4v.py index d28c1b00..729e73f7 100755 --- a/lmms_eval/models/gpt4v.py +++ b/lmms_eval/models/gpt4v.py @@ -7,7 +7,7 @@ from tqdm import tqdm import requests as url_requests import time -import logging + from lmms_eval.api.instance import Instance from lmms_eval.api.model import lmms @@ -26,7 +26,7 @@ API_TYPE = os.getenv("API_TYPE", "openai") NUM_SECONDS_TO_SLEEP = 30 -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger if API_TYPE == "openai": API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") diff --git a/lmms_eval/models/idefics2.py b/lmms_eval/models/idefics2.py index 9e274c8a..090cdee5 100644 --- a/lmms_eval/models/idefics2.py +++ b/lmms_eval/models/idefics2.py @@ -1,5 +1,5 @@ import torch -import logging + from tqdm import tqdm from lmms_eval import utils from lmms_eval.api.instance import Instance @@ -14,7 +14,7 @@ warnings.filterwarnings("ignore") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger DEFAULT_IMAGE_TOKEN = "" try: @@ -203,7 +203,7 @@ def _collate(x): gen_kwargs["max_new_tokens"] = 1024 if "temperature" not in gen_kwargs: gen_kwargs["temperature"] = 0 - + prompts = [] for context, visual in zip(contexts, visuals): content = [] diff --git a/lmms_eval/models/instructblip.py b/lmms_eval/models/instructblip.py index 3ca068ed..c33ab122 100755 --- a/lmms_eval/models/instructblip.py +++ b/lmms_eval/models/instructblip.py @@ -1,5 +1,5 @@ import torch -import logging + import copy from tqdm import tqdm from lmms_eval import utils @@ -20,8 +20,7 @@ warnings.filterwarnings("ignore") -eval_logger = logging.getLogger("lmms-eval") -transformers.logging.set_verbosity_error() +from loguru import logger as eval_logger @register_model("instructblip") diff --git a/lmms_eval/models/internvl.py b/lmms_eval/models/internvl.py index d808081a..6238d7fc 100644 --- a/lmms_eval/models/internvl.py +++ b/lmms_eval/models/internvl.py @@ -1,4 +1,3 @@ -import logging import os from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs from accelerate.state import AcceleratorState @@ -26,7 +25,7 @@ import sys sys.path.append(os.path.join(str(wd), "InternVL", "internvl_chat")) -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger if not hasattr(eval_logger, "internvl_warning_logged"): eval_logger.internvl_warning_logged = False diff --git a/lmms_eval/models/llama_vid.py b/lmms_eval/models/llama_vid.py index 69627fe8..e62b4ef7 100644 --- a/lmms_eval/models/llama_vid.py +++ b/lmms_eval/models/llama_vid.py @@ -1,4 +1,3 @@ -import logging import os from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs from accelerate.state import AcceleratorState @@ -22,7 +21,7 @@ import subprocess -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: from llamavid.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py index b49cf55b..a1976715 100755 --- a/lmms_eval/models/llava.py +++ b/lmms_eval/models/llava.py @@ -2,7 +2,7 @@ torch.backends.cuda.matmul.allow_tf32 = True -import logging + import copy from tqdm import tqdm from datetime import timedelta @@ -21,7 +21,7 @@ warnings.filterwarnings("ignore") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: from llava.model.builder import load_pretrained_model diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py index 342824f1..547daa29 100644 --- a/lmms_eval/models/llava_hf.py +++ b/lmms_eval/models/llava_hf.py @@ -1,5 +1,5 @@ import torch -import logging + from tqdm import tqdm from lmms_eval import utils from lmms_eval.api.instance import Instance @@ -14,7 +14,7 @@ warnings.filterwarnings("ignore") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger DEFAULT_IMAGE_TOKEN = "" @@ -324,7 +324,7 @@ def _collate(x): if "1.5" in self.pretrained: text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() elif "mistral" in self.pretrained: - text_outputs = text_outputs.split("[/INST]")[-1].strip() + text_outputs = text_outputs.split("[/INST]")[-1].strip() else: text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() diff --git a/lmms_eval/models/llava_sglang.py b/lmms_eval/models/llava_sglang.py index 47c67288..d473e4c1 100644 --- a/lmms_eval/models/llava_sglang.py +++ b/lmms_eval/models/llava_sglang.py @@ -3,7 +3,7 @@ torch.backends.cuda.matmul.allow_tf32 = True -import logging + from tqdm import tqdm from datetime import timedelta @@ -19,7 +19,7 @@ from concurrent.futures import ThreadPoolExecutor, as_completed import tempfile -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: import sglang as sgl diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py index 1047e390..6188dd95 100755 --- a/lmms_eval/models/llava_vid.py +++ b/lmms_eval/models/llava_vid.py @@ -1,4 +1,3 @@ -import logging from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs from accelerate.state import AcceleratorState from typing import List, Optional, Union, Tuple @@ -16,7 +15,7 @@ from lmms_eval.api.registry import register_model from lmms_eval.models.model_utils.load_video import read_video_pyav -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: from llavavid.model.builder import load_pretrained_model diff --git a/lmms_eval/models/minicpm_v.py b/lmms_eval/models/minicpm_v.py index e951a3df..9a6e0215 100755 --- a/lmms_eval/models/minicpm_v.py +++ b/lmms_eval/models/minicpm_v.py @@ -1,5 +1,5 @@ import torch -import logging + from tqdm import tqdm from lmms_eval import utils from lmms_eval.api.instance import Instance @@ -15,7 +15,7 @@ warnings.filterwarnings("ignore") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger @register_model("minicpm_v") diff --git a/lmms_eval/models/model_utils/qwen/qwen_generate_utils.py b/lmms_eval/models/model_utils/qwen/qwen_generate_utils.py index 7e7dd97c..3717665a 100755 --- a/lmms_eval/models/model_utils/qwen/qwen_generate_utils.py +++ b/lmms_eval/models/model_utils/qwen/qwen_generate_utils.py @@ -15,10 +15,9 @@ import torch import torch.nn.functional as F from transformers import PreTrainedTokenizer -from transformers import logging from transformers.generation import LogitsProcessor -logger = logging.get_logger(__name__) +from loguru import logger # Types. HistoryType = List[Tuple[str, str]] diff --git a/lmms_eval/models/mplug_owl_video.py b/lmms_eval/models/mplug_owl_video.py index bfc52d23..1055f4dd 100644 --- a/lmms_eval/models/mplug_owl_video.py +++ b/lmms_eval/models/mplug_owl_video.py @@ -1,4 +1,3 @@ -import logging from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs from accelerate.state import AcceleratorState from typing import List, Optional, Union, Tuple @@ -7,17 +6,17 @@ from tqdm import tqdm from datetime import timedelta -from lmms_eval import utils from lmms_eval.api.instance import Instance from lmms_eval.api.model import lmms from lmms_eval.api.registry import register_model -from lmms_eval.utils import stop_sequences_criteria from lmms_eval.models.mplug_owl_video.modeling_mplug_owl import MplugOwlForConditionalGeneration from lmms_eval.models.mplug_owl_video.processing_mplug_owl import MplugOwlImageProcessor, MplugOwlProcessor -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger + +eval_logger = logger @register_model("mplug_owl_video") diff --git a/lmms_eval/models/mplug_owl_video/configuration_mplug_owl.py b/lmms_eval/models/mplug_owl_video/configuration_mplug_owl.py index 6b5d458d..ce5b777b 100644 --- a/lmms_eval/models/mplug_owl_video/configuration_mplug_owl.py +++ b/lmms_eval/models/mplug_owl_video/configuration_mplug_owl.py @@ -19,11 +19,9 @@ from transformers.configuration_utils import PretrainedConfig from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES -from transformers.utils import logging from transformers.models.auto import CONFIG_MAPPING - -logger = logging.get_logger(__name__) +from loguru import logger MPLUG_OWL_PRETRAINED_CONFIG_ARCHIVE_MAP = { "MAGAer13/mplug-owl-llama-7b": "https://huggingface.co/MAGAer13/mplug-owl-llama-7b/resolve/main/config.json", diff --git a/lmms_eval/models/mplug_owl_video/modeling_mplug_owl.py b/lmms_eval/models/mplug_owl_video/modeling_mplug_owl.py index 6c5b7592..07bf9f0c 100644 --- a/lmms_eval/models/mplug_owl_video/modeling_mplug_owl.py +++ b/lmms_eval/models/mplug_owl_video/modeling_mplug_owl.py @@ -14,9 +14,9 @@ # limitations under the License. """ PyTorch MplugOwl model. """ -import logging import math from typing import Any, Optional, Tuple, Union +from loguru import logger try: from flash_attn.flash_attn_interface import flash_attn_unpadded_func @@ -24,7 +24,8 @@ flash_attn_func = flash_attn_unpadded_func except: flash_attn_func = None - print("Error importing flash_attn in mplug_owl. Please install flash-attn first.") + logger.warning("Error importing flash_attn in mplug_owl. Please install flash-attn first.") + import math from dataclasses import dataclass from typing import Any, Optional, Tuple, Union @@ -41,15 +42,11 @@ ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, - logging, replace_return_docstrings, ) from transformers.models.auto import AutoModelForCausalLM from .configuration_mplug_owl import MplugOwlConfig, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig - -logger = logging.get_logger(__name__) - _CHECKPOINT_FOR_DOC = "MAGAer13/mplug-owl-llama-7b" _CONFIG_FOR_DOC = "MplugOwlConfig" diff --git a/lmms_eval/models/mplug_owl_video/tokenization_mplug_owl.py b/lmms_eval/models/mplug_owl_video/tokenization_mplug_owl.py index 22384b44..bd7f42f1 100644 --- a/lmms_eval/models/mplug_owl_video/tokenization_mplug_owl.py +++ b/lmms_eval/models/mplug_owl_video/tokenization_mplug_owl.py @@ -14,11 +14,9 @@ # limitations under the License. """Tokenization classes for MplugOwl.""" -from transformers.utils import logging from transformers.models.llama.tokenization_llama import LlamaTokenizer - -logger = logging.get_logger(__name__) +from loguru import logger VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} diff --git a/lmms_eval/models/phi3v.py b/lmms_eval/models/phi3v.py index 5e940169..c30a7081 100644 --- a/lmms_eval/models/phi3v.py +++ b/lmms_eval/models/phi3v.py @@ -1,5 +1,5 @@ import torch -import logging + from accelerate import Accelerator, DistributedType from lmms_eval import utils @@ -11,7 +11,7 @@ from transformers import AutoProcessor from typing import List, Optional, Tuple, Union -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger @register_model("phi3v") @@ -30,15 +30,16 @@ class Phi3v(lmms): accelerate launch --num_processes=4 -m lmms_eval --model phi3v --tasks mmmu_val \ --batch_size 1 --log_samples --log_samples_suffix phi3v_mmmu --output_path ./logs/ """ + def __init__( - self, - model_id_name: str = "microsoft/Phi-3-vision-128k-instruct", - device: str = "cuda", - dtype: Optional[Union[str, torch.dtype]] = "auto", - batch_size: int = 1, - trust_remote_code: Optional[bool] = True, - use_cache: bool = True, - **kwargs, + self, + model_id_name: str = "microsoft/Phi-3-vision-128k-instruct", + device: str = "cuda", + dtype: Optional[Union[str, torch.dtype]] = "auto", + batch_size: int = 1, + trust_remote_code: Optional[bool] = True, + use_cache: bool = True, + **kwargs, ) -> None: super().__init__() # Do not use kwargs for now @@ -46,40 +47,25 @@ def __init__( # Setup accelerator. accelerator = Accelerator() if accelerator.num_processes > 1: - self._device = torch.device( - f"cuda:{accelerator.local_process_index}") + self._device = torch.device(f"cuda:{accelerator.local_process_index}") else: self._device = device # Load model. - self._model = AutoModelForCausalLM.from_pretrained( - model_id_name, - device_map=device, - trust_remote_code=trust_remote_code, - torch_dtype=dtype) - self._processor = AutoProcessor.from_pretrained( - model_id_name, - trust_remote_code=trust_remote_code) + self._model = AutoModelForCausalLM.from_pretrained(model_id_name, device_map=device, trust_remote_code=trust_remote_code, torch_dtype=dtype) + self._processor = AutoProcessor.from_pretrained(model_id_name, trust_remote_code=trust_remote_code) self._processor.tokenizer.padding_side = "left" self._tokenizer = self._processor.tokenizer self._config = self._model.config self.batch_size_per_gpu = int(batch_size) - assert self.batch_size_per_gpu == 1, \ - "batch_size_per_gpu > 1 is not supported for now." + assert self.batch_size_per_gpu == 1, "batch_size_per_gpu > 1 is not supported for now." self.use_cache = use_cache if accelerator.num_processes > 1: - distributed_type_list = [ - DistributedType.FSDP, - DistributedType.MULTI_GPU, - DistributedType.DEEPSPEED - ] - assert accelerator.distributed_type in distributed_type_list, \ - "Unsupported distributed type provided. Only DDP and FSDP are supported." + distributed_type_list = [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED] + assert accelerator.distributed_type in distributed_type_list, "Unsupported distributed type provided. Only DDP and FSDP are supported." if accelerator.distributed_type == DistributedType.FSDP: self._model = accelerator.prepare(self.model) else: - self._model = accelerator.prepare_model( - self.model, - evaluation_mode=True) + self._model = accelerator.prepare_model(self.model, evaluation_mode=True) self.accelerator = accelerator if self.accelerator.is_local_main_process: eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") @@ -179,8 +165,7 @@ def _collate(x): if isinstance(until, str): until = [until] elif not isinstance(until, list): - raise ValueError( - f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") + raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") if isinstance(contexts, tuple): contexts = list(contexts) for i in range(len(contexts)): @@ -195,20 +180,12 @@ def _collate(x): for placeholder_id in range(len(visuals)): query += f"<|image_{placeholder_id+1}|>\n" query += contexts[i] - messages = [ - {"role": "user", "content": query} - ] - contexts[i] = self._tokenizer.apply_chat_template( - messages, - tokenize=False, - add_generation_prompt=True) + messages = [{"role": "user", "content": query}] + contexts[i] = self._tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) assert len(contexts) == 1 - # + # context = contexts[0] - input_ids = self._processor( - text=context, - images=visuals, - return_tensors="pt").to(self._device, self.model.dtype) + input_ids = self._processor(text=context, images=visuals, return_tensors="pt").to(self._device, self.model.dtype) # Setting default parameters. if "max_new_tokens" not in gen_kwargs: gen_kwargs["max_new_tokens"] = 1024 @@ -219,8 +196,7 @@ def _collate(x): if "num_beams" not in gen_kwargs: gen_kwargs["num_beams"] = 1 # Generate answer. - pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None \ - else self.tokenizer.eod_id + pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eod_id generate_ids = self.model.generate( **input_ids, eos_token_id=self.tokenizer.eos_token_id, @@ -232,11 +208,8 @@ def _collate(x): max_new_tokens=gen_kwargs["max_new_tokens"], use_cache=self.use_cache, ) - generate_ids = generate_ids[:, input_ids['input_ids'].shape[1]:] - response = self._processor.batch_decode( - generate_ids, - skip_special_tokens=True, - clean_up_tokenization_spaces=False)[0] + generate_ids = generate_ids[:, input_ids["input_ids"].shape[1] :] + response = self._processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] res.append(response) self.cache_hook.add_partial("generate_until", (context, gen_kwargs), response) pbar.update(1) diff --git a/lmms_eval/models/qwen_vl.py b/lmms_eval/models/qwen_vl.py index e55ad7c9..dff301d3 100755 --- a/lmms_eval/models/qwen_vl.py +++ b/lmms_eval/models/qwen_vl.py @@ -1,5 +1,5 @@ import torch -import logging + from tqdm import tqdm from lmms_eval import utils from lmms_eval.api.instance import Instance @@ -16,7 +16,7 @@ warnings.simplefilter("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger from transformers import AutoModelForCausalLM, AutoTokenizer diff --git a/lmms_eval/models/qwen_vl_api.py b/lmms_eval/models/qwen_vl_api.py index af7f2713..8a0ccc65 100644 --- a/lmms_eval/models/qwen_vl_api.py +++ b/lmms_eval/models/qwen_vl_api.py @@ -6,7 +6,7 @@ from tqdm import tqdm import requests as url_requests import time -import logging + from lmms_eval.api.instance import Instance from lmms_eval.api.model import lmms @@ -16,7 +16,7 @@ from PIL import Image NUM_SECONDS_TO_SLEEP = 5 -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: import dashscope diff --git a/lmms_eval/models/reka.py b/lmms_eval/models/reka.py index d5e85d5d..bc461cad 100644 --- a/lmms_eval/models/reka.py +++ b/lmms_eval/models/reka.py @@ -8,7 +8,7 @@ from tqdm import tqdm import requests as url_requests import time -import logging + import json from lmms_eval.api.instance import Instance @@ -17,14 +17,17 @@ from accelerate import Accelerator, DistributedType NUM_SECONDS_TO_SLEEP = 30 -eval_logger = logging.getLogger("lmms-eval") + +from loguru import logger + +eval_logger = logger try: from reka.client import Reka as RekaClient from reka import ChatMessage from decord import VideoReader, cpu except Exception as e: - eval_logger.error(f"Error importing reka: {e}") + eval_logger.warning(f"Error importing reka: {e}") @register_model("reka") diff --git a/lmms_eval/models/tinyllava.py b/lmms_eval/models/tinyllava.py index b9fdf743..e07c47b8 100755 --- a/lmms_eval/models/tinyllava.py +++ b/lmms_eval/models/tinyllava.py @@ -2,7 +2,7 @@ torch.backends.cuda.matmul.allow_tf32 = True -import logging + import copy from tqdm import tqdm from datetime import timedelta @@ -21,7 +21,7 @@ warnings.filterwarnings("ignore") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: from tinyllava.model import load_pretrained_model @@ -53,7 +53,7 @@ def __init__( device: Optional[str] = "cuda:0", batch_size: Optional[Union[int, str]] = 1, device_map="cuda:0", - conv_mode="phi", # TODO + conv_mode="phi", # TODO use_cache=True, **kwargs, ) -> None: @@ -73,9 +73,7 @@ def __init__( self._device = torch.device(f"cuda:{accelerator.local_process_index}") self.device_map = f"cuda:{accelerator.local_process_index}" - self._model, self._tokenizer, self._image_processor, self._max_length = load_pretrained_model( - pretrained, device_map=self.device_map - ) + self._model, self._tokenizer, self._image_processor, self._max_length = load_pretrained_model(pretrained, device_map=self.device_map) data_args = self._model.config self._image_processor = ImagePreprocess(self._image_processor, data_args) assert self._tokenizer.padding_side == "right", "Not sure but seems like `right` is a natural choice for padding?" @@ -89,7 +87,7 @@ def __init__( # self.conv_template = conv_template self.use_cache = use_cache # self.truncate_context = truncate_context - + # assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue." if accelerator.num_processes > 1: assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." @@ -187,7 +185,7 @@ def tok_decode(self, tokens): return self.tokenizer.decode(tokens) except: return self.tokenizer.decode([tokens]) - + def flatten(self, input): new_list = [] for i in input: @@ -216,7 +214,7 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: if type(image) is list: image = [_image.to(dtype=torch.float16, device=self.device) for _image in image] # as of 2024/06, tinyllava only accepts `images` input to be a tensor - image = torch.stack(image) + image = torch.stack(image) else: image = image.to(dtype=torch.float16, device=self.device) else: @@ -237,23 +235,17 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: msg = Message() msg.add_message(prompts_input) - contxt_id = self._text_processor(msg.messages, mode='eval')["input_ids"] + contxt_id = self._text_processor(msg.messages, mode="eval")["input_ids"] # Add the answer of the second role msg._messages[1]["value"] = continuation - input_ids = self._text_processor(msg.messages, mode='eval')["input_ids"] - + input_ids = self._text_processor(msg.messages, mode="eval")["input_ids"] + labels = input_ids.clone() # Context part no need to calculate for loss labels[0, : contxt_id.shape[1]] = -100 - + with torch.inference_mode(): - outputs = self.model( - input_ids=input_ids, - labels=labels, - images=image, - use_cache=True, - image_sizes=image_sizes - ) + outputs = self.model(input_ids=input_ids, labels=labels, images=image, use_cache=True, image_sizes=image_sizes) loss = outputs["loss"] # loss = torch.exp(loss) logits = outputs["logits"] @@ -317,7 +309,7 @@ def _collate(x): if type(image_tensor) is list: image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] # as of 2024/06, tinyllava only accepts `images` input to be a tensor - image_tensor = torch.stack(image_tensor) + image_tensor = torch.stack(image_tensor) else: image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) else: @@ -340,10 +332,10 @@ def _collate(x): question = image_tokens + "\n" + context else: question = context - + msg = Message() msg.add_message(question) - prompt_question = self._text_processor(msg.messages, mode='eval')["prompt"] + prompt_question = self._text_processor(msg.messages, mode="eval")["prompt"] question_input.append(prompt_question) # The above for loop has bugs. When there is no visuals, e.g. pure text, @@ -354,7 +346,7 @@ def _collate(x): question = context msg = Message() msg.add_message(question) - prompt_question = self._text_processor(msg.messages, mode='eval')["prompt"] + prompt_question = self._text_processor(msg.messages, mode="eval")["prompt"] question_input.append(prompt_question) # input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) @@ -369,11 +361,8 @@ def _collate(x): if "num_beams" not in gen_kwargs: gen_kwargs["num_beams"] = 1 - #input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] - input_ids_list = [ - self._text_processor.template.tokenizer_image_token(prompt, self.tokenizer, return_tensors='pt') - for prompt in question_input - ] + # input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] + input_ids_list = [self._text_processor.template.tokenizer_image_token(prompt, self.tokenizer, return_tensors="pt") for prompt in question_input] pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device) attention_masks = input_ids.ne(pad_token_ids).to(self.device) diff --git a/lmms_eval/models/video_chatgpt.py b/lmms_eval/models/video_chatgpt.py index a724cd98..089a2c5d 100644 --- a/lmms_eval/models/video_chatgpt.py +++ b/lmms_eval/models/video_chatgpt.py @@ -1,5 +1,4 @@ import os -from lmms_eval import utils from lmms_eval.api.instance import Instance from lmms_eval.api.model import lmms from lmms_eval.api.registry import register_model @@ -11,21 +10,21 @@ from PIL import Image from datetime import timedelta -import logging from typing import List, Tuple, Optional, Union from tqdm import tqdm +from loguru import logger + +eval_logger = logger + try: from lmms_eval.models.video_chatgpt.eval.model_utils import load_video, initialize_model from lmms_eval.models.video_chatgpt.inference import video_chatgpt_infer, video_chatgpt_infer_ppl, get_spatio_temporal_features_torch except ImportError: - eval_logger = logging.getLogger("lmms-eval") - eval_logger.info("Failed to import video_chatgpt modules") + eval_logger.warning("Failed to import video_chatgpt modules") from lmms_eval.models.model_utils.load_video import read_video_pyav -eval_logger = logging.getLogger("lmms-eval") - @register_model("video_chatgpt") class VideoChatGPT(lmms): diff --git a/lmms_eval/models/video_chatgpt/utils.py b/lmms_eval/models/video_chatgpt/utils.py index eb6fa78e..1c8b4c3f 100644 --- a/lmms_eval/models/video_chatgpt/utils.py +++ b/lmms_eval/models/video_chatgpt/utils.py @@ -1,94 +1,3 @@ -import logging -import logging.handlers -import os -import sys - -import requests - -from lmms_eval.models.video_chatgpt.constants import LOGDIR - -server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**" -moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN." - -handler = None - - -def build_logger(logger_name, logger_filename): - global handler - - formatter = logging.Formatter( - fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - ) - - # Set the format of root handlers - if not logging.getLogger().handlers: - logging.basicConfig(level=logging.INFO) - logging.getLogger().handlers[0].setFormatter(formatter) - - # Redirect stdout and stderr to loggers - stdout_logger = logging.getLogger("stdout") - stdout_logger.setLevel(logging.INFO) - sl = StreamToLogger(stdout_logger, logging.INFO) - sys.stdout = sl - - stderr_logger = logging.getLogger("stderr") - stderr_logger.setLevel(logging.ERROR) - sl = StreamToLogger(stderr_logger, logging.ERROR) - sys.stderr = sl - - # Get logger - logger = logging.getLogger(logger_name) - logger.setLevel(logging.INFO) - - # Add a file handler for all loggers - if handler is None: - os.makedirs(LOGDIR, exist_ok=True) - filename = os.path.join(LOGDIR, logger_filename) - handler = logging.handlers.TimedRotatingFileHandler(filename, when="D", utc=True) - handler.setFormatter(formatter) - - for name, item in logging.root.manager.loggerDict.items(): - if isinstance(item, logging.Logger): - item.addHandler(handler) - - return logger - - -class StreamToLogger(object): - """ - Fake file-like stream object that redirects writes to a logger instance. - """ - - def __init__(self, logger, log_level=logging.INFO): - self.terminal = sys.stdout - self.logger = logger - self.log_level = log_level - self.linebuf = "" - - def __getattr__(self, attr): - return getattr(self.terminal, attr) - - def write(self, buf): - temp_linebuf = self.linebuf + buf - self.linebuf = "" - for line in temp_linebuf.splitlines(True): - # From the io.TextIOWrapper docs: - # On output, if newline is None, any '\n' characters written - # are translated to the system default line separator. - # By default sys.stdout.write() expects '\n' newlines and then - # translates them so this is still cross platform. - if line[-1] == "\n": - self.logger.log(self.log_level, line.rstrip()) - else: - self.linebuf += line - - def flush(self): - if self.linebuf != "": - self.logger.log(self.log_level, self.linebuf.rstrip()) - self.linebuf = "" - - def disable_torch_init(): """ Disable the redundant torch default initialization to accelerate model creation. @@ -97,29 +6,3 @@ def disable_torch_init(): setattr(torch.nn.Linear, "reset_parameters", lambda self: None) setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) - - -def violates_moderation(text): - """ - Check whether the text violates OpenAI moderation API. - """ - url = "https://api.openai.com/v1/moderations" - headers = {"Content-Type": "application/json", "Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]} - text = text.replace("\n", "") - data = "{" + '"input": ' + f'"{text}"' + "}" - data = data.encode("utf-8") - try: - ret = requests.post(url, headers=headers, data=data, timeout=5) - flagged = ret.json()["results"][0]["flagged"] - except requests.exceptions.RequestException as e: - flagged = False - except KeyError as e: - flagged = False - - return flagged - - -def pretty_print_semaphore(semaphore): - if semaphore is None: - return "None" - return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})" diff --git a/lmms_eval/models/video_llava.py b/lmms_eval/models/video_llava.py index 98acebfe..0acc6356 100644 --- a/lmms_eval/models/video_llava.py +++ b/lmms_eval/models/video_llava.py @@ -1,4 +1,3 @@ -import logging from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs from accelerate.state import AcceleratorState from typing import List, Optional, Union, Tuple @@ -15,17 +14,19 @@ from lmms_eval.api.registry import register_model from lmms_eval.utils import stop_sequences_criteria -eval_logger = logging.getLogger("lmms-eval") - -# try: -# import torch -# from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN -# from videollava.conversation import conv_templates, SeparatorStyle -# from videollava.model.builder import load_pretrained_model -# from videollava.utils import disable_torch_init -# from videollava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria -# except ImportError: -# eval_logger.debug("Video-LLaVA is not installed. Please install Video-LLaVA to use this model.") +from loguru import logger + +eval_logger = logger + +try: + import torch + from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN + from videollava.conversation import conv_templates, SeparatorStyle + from videollava.model.builder import load_pretrained_model + from videollava.utils import disable_torch_init + from videollava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria +except ImportError as e: + eval_logger.warning(f"Error importing Video-LLaVA: {e}") from transformers import VideoLlavaProcessor, VideoLlavaForConditionalGeneration from lmms_eval.models.model_utils.load_video import read_video_pyav diff --git a/lmms_eval/models/xcomposer2_4KHD.py b/lmms_eval/models/xcomposer2_4KHD.py index 2c49e43b..6c4f81a7 100644 --- a/lmms_eval/models/xcomposer2_4KHD.py +++ b/lmms_eval/models/xcomposer2_4KHD.py @@ -5,7 +5,7 @@ import numpy as np import torchvision.transforms as transforms from datetime import timedelta -import logging + from lmms_eval import utils from lmms_eval.api.instance import Instance @@ -22,7 +22,7 @@ pattern = re.compile(r"[A-Z]") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔). - InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed\ @@ -146,7 +146,7 @@ def generate_until(self, requests) -> List[str]: for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: # encode, pad, and truncate contexts for this batch if "[UNUSED_TOKEN_146]" not in contexts: - contexts = f"[UNUSED_TOKEN_146]user\n{contexts}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n" + contexts = f"[UNUSED_TOKEN_146]user\n{contexts}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n" visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] visuals = self.flatten(visuals) diff --git a/lmms_eval/models/xcomposer2_4khd.py b/lmms_eval/models/xcomposer2_4khd.py index b43f12e4..6c4f81a7 100644 --- a/lmms_eval/models/xcomposer2_4khd.py +++ b/lmms_eval/models/xcomposer2_4khd.py @@ -5,7 +5,7 @@ import numpy as np import torchvision.transforms as transforms from datetime import timedelta -import logging + from lmms_eval import utils from lmms_eval.api.instance import Instance @@ -22,7 +22,7 @@ pattern = re.compile(r"[A-Z]") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔). - InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed\ diff --git a/lmms_eval/tasks/__init__.py b/lmms_eval/tasks/__init__.py index 0679eae3..19f7fea2 100755 --- a/lmms_eval/tasks/__init__.py +++ b/lmms_eval/tasks/__init__.py @@ -1,4 +1,4 @@ -import os +import os, sys from typing import List, Union, Dict from lmms_eval import utils @@ -13,9 +13,9 @@ ALL_TASKS, ) -import logging +from loguru import logger -eval_logger = logging.getLogger("lmms-eval") +eval_logger = logger def register_configurable_task(config: Dict[str, str]) -> int: @@ -109,7 +109,9 @@ def include_path(task_dir): def initialize_tasks(verbosity="INFO"): - eval_logger.setLevel(getattr(logging, f"{verbosity}")) + logger.remove() + eval_logger.add(sys.stdout, colorize=True, level=verbosity) + eval_logger.add(sys.stderr, level=verbosity) task_dir = os.path.dirname(os.path.abspath(__file__)) + "/" include_path(task_dir) diff --git a/lmms_eval/tasks/activitynetqa/utils.py b/lmms_eval/tasks/activitynetqa/utils.py index 7c236e92..0c77ff79 100755 --- a/lmms_eval/tasks/activitynetqa/utils.py +++ b/lmms_eval/tasks/activitynetqa/utils.py @@ -5,7 +5,7 @@ import datetime import lmms_eval.tasks._task_utils.file_utils as file_utils import json -import logging + import yaml from pathlib import Path @@ -15,7 +15,7 @@ import time import ast -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger with open(Path(__file__).parent / "_default_template_yaml", "r") as f: raw_data = f.readlines() @@ -239,7 +239,6 @@ def activitynetqa_gpt_eval(results, args): # Factory into different aggregate def activitynetqa_aggregate_score(results, args): - yes_count = 0 no_count = 0 total_score = 0 diff --git a/lmms_eval/tasks/cmmmu/utils.py b/lmms_eval/tasks/cmmmu/utils.py index 7f50a80b..a20eaa60 100755 --- a/lmms_eval/tasks/cmmmu/utils.py +++ b/lmms_eval/tasks/cmmmu/utils.py @@ -3,11 +3,11 @@ import random import os import json -import logging + from collections import Counter from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger PROMPT = { "task_instructions": [ diff --git a/lmms_eval/tasks/coco_cap/utils.py b/lmms_eval/tasks/coco_cap/utils.py index ab3a736a..edb0e31e 100755 --- a/lmms_eval/tasks/coco_cap/utils.py +++ b/lmms_eval/tasks/coco_cap/utils.py @@ -6,9 +6,8 @@ from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger dir_name = os.path.dirname(os.path.abspath(__file__)) diff --git a/lmms_eval/tasks/conbench/utils.py b/lmms_eval/tasks/conbench/utils.py index f80f855d..6e7803b1 100644 --- a/lmms_eval/tasks/conbench/utils.py +++ b/lmms_eval/tasks/conbench/utils.py @@ -2,17 +2,16 @@ import os from anls import anls_score -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger dir_name = os.path.dirname(os.path.abspath(__file__)) # 19 classes eval_type_dict = { - "Sensation": ["count","color", "scene", "poster", "attribute_recognition", "ocr", "position"], + "Sensation": ["count", "color", "scene", "poster", "attribute_recognition", "ocr", "position"], "Cognition": ["calculation", "code", "translation", "math", "cross_instance_reason", "attribute_reason"], - "Knowledge": ["celebrity", "chemistry", "physics", "biology", "landmark", "artwork"] + "Knowledge": ["celebrity", "chemistry", "physics", "biology", "landmark", "artwork"], } @@ -54,7 +53,7 @@ def conbench_process_results(doc, results): a dictionary with key: metric name (in this case mme score), value: metric value """ pred = results[0] - pred = pred.replace('\n', "").lower() + pred = pred.replace("\n", "").lower() # parser if doc["question_field"] == "N/Y": pred_ans = parse_pred_ans_NY(pred) @@ -62,16 +61,14 @@ def conbench_process_results(doc, results): pred_ans = parse_pred_ans_choice(pred) else: pred_ans = pred - + gt_ans = doc["answer"].lower() # score - score = 1 if (doc["question_field"] == "Q/A" and anls_score(prediction=pred_ans, gold_labels=[gt_ans], threshold=0.95) >= 0.4) \ - or (gt_ans == pred_ans) \ - else 0 + score = 1 if (doc["question_field"] == "Q/A" and anls_score(prediction=pred_ans, gold_labels=[gt_ans], threshold=0.95) >= 0.4) or (gt_ans == pred_ans) else 0 # Note: the key name here is very important. It decides which aggregation function will receive the results # We note down the question id/category to help us aggregate the results later - return {"ConScore_D":{"image_id": doc["image_id"], "question_field": doc["question_field"], "score": score}} + return {"ConScore_D": {"image_id": doc["image_id"], "question_field": doc["question_field"], "score": score}} def conbench_aggregate_results(results): @@ -93,8 +90,8 @@ def conbench_aggregate_results(results): for image_id, score in summary.items(): if score == 3: cnt_con += 1 - + print("Consistency Cases are ", cnt_con) cnt_con = cnt_con / (len(results) / 3) eval_logger.info(f"ConScore_D: {cnt_con:.2f}") - return cnt_con \ No newline at end of file + return cnt_con diff --git a/lmms_eval/tasks/cvrr/utils.py b/lmms_eval/tasks/cvrr/utils.py index eef51ddc..b2eb25c4 100755 --- a/lmms_eval/tasks/cvrr/utils.py +++ b/lmms_eval/tasks/cvrr/utils.py @@ -4,7 +4,7 @@ import datetime import lmms_eval.tasks._task_utils.file_utils as file_utils import json -import logging + import yaml from pathlib import Path @@ -15,7 +15,7 @@ import ast from tqdm import tqdm -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger with open(Path(__file__).parent / "_default_template_yaml", "r") as f: raw_data = f.readlines() @@ -45,7 +45,6 @@ # Pass in video path here # Can only work correctly with video llm def cvrr_doc_to_visual(doc): - # Unzip all the zip files to HF HOME cache dir HF_HOME = os.environ["HF_HOME"] cache_dir = config["dataset_kwargs"]["cache_dir"] diff --git a/lmms_eval/tasks/docvqa/utils.py b/lmms_eval/tasks/docvqa/utils.py index 0a785d23..bb17aa03 100755 --- a/lmms_eval/tasks/docvqa/utils.py +++ b/lmms_eval/tasks/docvqa/utils.py @@ -1,10 +1,10 @@ import json import os -import logging + from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -logger = logging.getLogger("lmms-eval") +from loguru import logger def docvqa_doc_to_visual(doc): diff --git a/lmms_eval/tasks/egoschema/utils.py b/lmms_eval/tasks/egoschema/utils.py index 7278db34..34d2ab58 100755 --- a/lmms_eval/tasks/egoschema/utils.py +++ b/lmms_eval/tasks/egoschema/utils.py @@ -5,7 +5,7 @@ import datetime import lmms_eval.tasks._task_utils.file_utils as file_utils import json -import logging + import yaml from pathlib import Path @@ -27,7 +27,7 @@ cache_dir = os.path.join(HF_HOME, cache_dir) cache_dir = os.path.join(cache_dir, "videos") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Pass in video path here diff --git a/lmms_eval/tasks/ferret/utils.py b/lmms_eval/tasks/ferret/utils.py index 7e86ea47..bb96b4a1 100755 --- a/lmms_eval/tasks/ferret/utils.py +++ b/lmms_eval/tasks/ferret/utils.py @@ -1,5 +1,5 @@ import json -import logging + import os import requests import numpy as np @@ -10,7 +10,8 @@ from pathlib import Path from copy import deepcopy -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger + NUM_SECONDS_TO_SLEEP = 0.5 FERRET_W_METRICS = ["gpt_eval_ferret_refer_desc", "gpt_eval_ferret_refer_reason", "gpt_eval_ferret_ground_conv"] diff --git a/lmms_eval/tasks/flickr30k/utils.py b/lmms_eval/tasks/flickr30k/utils.py index 8fa1069a..bc470f4d 100755 --- a/lmms_eval/tasks/flickr30k/utils.py +++ b/lmms_eval/tasks/flickr30k/utils.py @@ -6,9 +6,8 @@ from lmms_eval.tasks._task_utils.file_utils import generate_submission_file import datetime -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger dir_name = os.path.dirname(os.path.abspath(__file__)) diff --git a/lmms_eval/tasks/hallusion_bench/evaluate_hb.py b/lmms_eval/tasks/hallusion_bench/evaluate_hb.py index 87c65519..f35c67a2 100755 --- a/lmms_eval/tasks/hallusion_bench/evaluate_hb.py +++ b/lmms_eval/tasks/hallusion_bench/evaluate_hb.py @@ -1,6 +1,6 @@ import os import json -import logging + from tqdm import tqdm from lmms_eval.tasks.hallusion_bench.utils import evaluate_by_chatgpt, check_same_by_chatgpt, assign_correctness, get_eval_all, get_eval_fig, get_eval_pair_all @@ -11,7 +11,7 @@ metric = ["aAcc", "fAcc", "qAcc"] -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def hb_doc_to_text(doc, model_specific_prompt_kwargs=None): diff --git a/lmms_eval/tasks/hallusion_bench/utils.py b/lmms_eval/tasks/hallusion_bench/utils.py index 1d8dfdaf..f0ea9c86 100755 --- a/lmms_eval/tasks/hallusion_bench/utils.py +++ b/lmms_eval/tasks/hallusion_bench/utils.py @@ -7,7 +7,7 @@ import openai import threading import requests -import logging + API_TYPE = os.getenv("API_TYPE", "openai") @@ -26,7 +26,7 @@ "Content-Type": "application/json", } -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def evaluate_by_chatgpt(data, output_entry, correctness_entry, gpt_model="gpt-4", load_json=False, save_json_path="./hallusion_output.json", retries=3): diff --git a/lmms_eval/tasks/ii_bench/utils.py b/lmms_eval/tasks/ii_bench/utils.py index 4a5f03c2..f298703a 100755 --- a/lmms_eval/tasks/ii_bench/utils.py +++ b/lmms_eval/tasks/ii_bench/utils.py @@ -1,9 +1,11 @@ import json -import logging + import re from collections import Counter from lmms_eval.tasks._task_utils.file_utils import generate_submission_file +from loguru import logger + PROMPT = """Question: {} (A) {} (B) {} @@ -12,6 +14,7 @@ (E) {} (F) {}""" + def ii_bench_doc_to_text(doc, model_specific_prompt_kwargs): question = PROMPT.format(doc["question"], doc["option1"], doc["option2"], doc["option3"], doc["option4"], doc["option5"], doc["option6"]) pre_prompt = model_specific_prompt_kwargs["pre_prompt"] @@ -25,14 +28,14 @@ def ii_bench_doc_to_visual(doc): def extract_option_labels(text, options=None): if isinstance(text, dict): - return 'error' + return "error" pattern = r"\(([A-F])\)" matches = re.findall(pattern, text) - + if not matches: pattern = r"\b([A-F])\b" matches = re.findall(pattern, text) - + if matches: counter = Counter(matches) most_common = counter.most_common() @@ -67,4 +70,4 @@ def ii_bench_aggregate_submissions(results, args): file = generate_submission_file("ii_bench_test_for_submission.json", args) with open(file, "w") as f: json.dump(results, f, indent=4) - logging.getLogger("lmms-eval").info(f"Results saved to {file}") + logger.info(f"Results saved to {file}") diff --git a/lmms_eval/tasks/infovqa/utils.py b/lmms_eval/tasks/infovqa/utils.py index a4b1ab5c..9907a6cb 100755 --- a/lmms_eval/tasks/infovqa/utils.py +++ b/lmms_eval/tasks/infovqa/utils.py @@ -1,11 +1,10 @@ import json import os -import logging from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -lmms_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def infovqa_doc_to_visual(doc): @@ -30,4 +29,4 @@ def infovqa_test_aggregate_results(results, args): file = generate_submission_file("infovqa_test_for_submission.json", args) with open(file, "w") as f: json.dump(results, f) - lmms_logger.info(f"Results saved to {file}") + eval_logger.info(f"Results saved to {file}") diff --git a/lmms_eval/tasks/internal_eval/d170_cn_utils.py b/lmms_eval/tasks/internal_eval/d170_cn_utils.py index 060229d7..aeed548c 100755 --- a/lmms_eval/tasks/internal_eval/d170_cn_utils.py +++ b/lmms_eval/tasks/internal_eval/d170_cn_utils.py @@ -1,7 +1,7 @@ import os import requests import time -import logging + import yaml from pathlib import Path import re @@ -9,7 +9,7 @@ from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger with open(Path(__file__).parent / "d170_cn.yaml", "r") as f: raw_data = f.readlines() diff --git a/lmms_eval/tasks/internal_eval/d170_en_utils.py b/lmms_eval/tasks/internal_eval/d170_en_utils.py index b96017b4..5bf2b9d9 100755 --- a/lmms_eval/tasks/internal_eval/d170_en_utils.py +++ b/lmms_eval/tasks/internal_eval/d170_en_utils.py @@ -1,7 +1,7 @@ import os import requests import time -import logging + import yaml from pathlib import Path import re @@ -9,7 +9,7 @@ from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger with open(Path(__file__).parent / "d170_en.yaml", "r") as f: raw_data = f.readlines() diff --git a/lmms_eval/tasks/internal_eval/dc100_en_utils.py b/lmms_eval/tasks/internal_eval/dc100_en_utils.py index 5e2b2a33..4fd4d79b 100755 --- a/lmms_eval/tasks/internal_eval/dc100_en_utils.py +++ b/lmms_eval/tasks/internal_eval/dc100_en_utils.py @@ -1,7 +1,7 @@ import base64 import requests import re -import logging + import time import os import yaml @@ -18,7 +18,7 @@ def doc_to_visual(doc): return [doc["image"].convert("RGB")] -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Assuming the config is loaded similarly as in d170_en/utils.py with open(Path(__file__).parent / "dc100_en.yaml", "r") as f: diff --git a/lmms_eval/tasks/internal_eval/dc200_cn_utils.py b/lmms_eval/tasks/internal_eval/dc200_cn_utils.py index f5d7e87d..3549b22a 100755 --- a/lmms_eval/tasks/internal_eval/dc200_cn_utils.py +++ b/lmms_eval/tasks/internal_eval/dc200_cn_utils.py @@ -1,7 +1,7 @@ import base64 import requests import re -import logging + import os import yaml import json @@ -18,7 +18,7 @@ def doc_to_visual(doc): return [doc["image"].convert("RGB")] -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Assuming the config is loaded similarly as in d170_en/utils.py with open(Path(__file__).parent / "dc200_cn.yaml", "r") as f: diff --git a/lmms_eval/tasks/llava-bench-coco/utils.py b/lmms_eval/tasks/llava-bench-coco/utils.py index 8858637f..08bf2515 100755 --- a/lmms_eval/tasks/llava-bench-coco/utils.py +++ b/lmms_eval/tasks/llava-bench-coco/utils.py @@ -1,5 +1,5 @@ import json -import logging + import os import requests import numpy as np @@ -10,7 +10,8 @@ from pathlib import Path from copy import deepcopy -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger + NUM_SECONDS_TO_SLEEP = 0.5 LLAVA_W_METRICS = ["gpt_eval_llava_conv", "gpt_eval_llava_detail", "gpt_eval_llava_complex"] diff --git a/lmms_eval/tasks/llava-in-the-wild/utils.py b/lmms_eval/tasks/llava-in-the-wild/utils.py index ac86ee99..25bf37a3 100755 --- a/lmms_eval/tasks/llava-in-the-wild/utils.py +++ b/lmms_eval/tasks/llava-in-the-wild/utils.py @@ -1,5 +1,5 @@ import json -import logging + import os import requests import numpy as np @@ -10,7 +10,8 @@ from pathlib import Path from copy import deepcopy -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger + NUM_SECONDS_TO_SLEEP = 5 LLAVA_W_METRICS = ["gpt_eval_llava_conv", "gpt_eval_llava_detail", "gpt_eval_llava_complex"] diff --git a/lmms_eval/tasks/llava_wilder/utils.py b/lmms_eval/tasks/llava_wilder/utils.py index 15d44438..047e2366 100644 --- a/lmms_eval/tasks/llava_wilder/utils.py +++ b/lmms_eval/tasks/llava_wilder/utils.py @@ -3,7 +3,7 @@ import os from pathlib import Path import requests -import logging + import time from copy import deepcopy import numpy as np @@ -11,7 +11,7 @@ from io import BytesIO # Set up a logger -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Create a static variable to track if the message has been logged if not hasattr(eval_logger, "dashcope_warning_logged"): diff --git a/lmms_eval/tasks/longvideobench/utils.py b/lmms_eval/tasks/longvideobench/utils.py index 5eb71498..4b5c745e 100644 --- a/lmms_eval/tasks/longvideobench/utils.py +++ b/lmms_eval/tasks/longvideobench/utils.py @@ -1,5 +1,5 @@ import json -import logging + import re from collections import Counter, defaultdict from lmms_eval.tasks._task_utils.file_utils import generate_submission_file @@ -13,7 +13,7 @@ from PIL import Image import torch -import logging + from pathlib import Path import yaml import sys @@ -22,54 +22,56 @@ import json + def timestamp_to_seconds(timestamp): # Split the timestamp into hours, minutes, and seconds - h, m, s = timestamp.split(':') + h, m, s = timestamp.split(":") # Convert hours, minutes, and total seconds (including fractions) to float and compute total seconds total_seconds = int(h) * 3600 + int(m) * 60 + float(s) return total_seconds + def load_video(video_file, duration, max_num_frames=16): from decord import VideoReader + vr = VideoReader(video_file, ctx=cpu(0), num_threads=1) fps = vr.get_avg_fps() total_valid_frames = int(duration * fps) num_frames = min(max_num_frames, int(duration)) frame_indices = [int(total_valid_frames / num_frames) * i for i in range(num_frames)] - + frames = vr.get_batch(frame_indices) if isinstance(frames, torch.Tensor): frames = frames.numpy() else: frames = frames.asnumpy() frame_timestamps = [frame_index / fps for frame_index in frame_indices] - + return [Image.fromarray(fr).convert("RGB") for fr in frames] + def compute_frame_timestamps(duration, max_num_frames=16): if duration > max_num_frames: return [duration / max_num_frames * i for i in range(max_num_frames)] else: return [i for i in range(int(duration))] - -def insert_subtitles_into_frames(frame_timestamps, subtitles, - starting_timestamp_for_subtitles, duration): + +def insert_subtitles_into_frames(frame_timestamps, subtitles, starting_timestamp_for_subtitles, duration): interleaved_list = [] cur_i = 0 - + for subtitle in subtitles: if "timestamp" in subtitle: start, end = subtitle["timestamp"] if not isinstance(end, float): end = duration - + start -= starting_timestamp_for_subtitles end -= starting_timestamp_for_subtitles - - + subtitle_timestamp = (start + end) / 2 subtitle_text = subtitle["text"] else: @@ -78,18 +80,17 @@ def insert_subtitles_into_frames(frame_timestamps, subtitles, end = timestamp_to_seconds(end) start -= starting_timestamp_for_subtitles end -= starting_timestamp_for_subtitles - + subtitle_timestamp = (start + end) / 2 subtitle_text = subtitle["line"] - for i, frame_timestamp in enumerate(frame_timestamps[cur_i:]): - if frame_timestamp <= subtitle_timestamp: - #print("frame:", frame_timestamp) - interleaved_list.append("") - cur_i += 1 - else: - break + if frame_timestamp <= subtitle_timestamp: + # print("frame:", frame_timestamp) + interleaved_list.append("") + cur_i += 1 + else: + break if end - start < 1: end = subtitle_timestamp + 0.5 @@ -102,33 +103,31 @@ def insert_subtitles_into_frames(frame_timestamps, subtitles, break if covering_frames: - #print("subtitle:", subtitle_timestamp, start, end) + # print("subtitle:", subtitle_timestamp, start, end) interleaved_list.append(subtitle_text) else: pass - #print("leaving out subtitle:", start, end) - + # print("leaving out subtitle:", start, end) + for i, frame_timestamp in enumerate(frame_timestamps[cur_i:]): - #print(frame_timestamp) + # print(frame_timestamp) interleaved_list.append("") - + return "\n".join(interleaved_list) -def longvideobench_doc_to_text(doc, model_specific_prompt_kwargs): +def longvideobench_doc_to_text(doc, model_specific_prompt_kwargs): candidates = [] - for i in range(5): candidate = doc.get(f"option{i}") if candidate != "N/A": candidates.append(candidate) - question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A")+i), candidate]) for i, candidate in enumerate(candidates)]) + question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A") + i), candidate]) for i, candidate in enumerate(candidates)]) pre_prompt = model_specific_prompt_kwargs["pre_prompt"] post_prompt = model_specific_prompt_kwargs["post_prompt"] - if model_specific_prompt_kwargs.get("insert_interleave_subtitles", False): with open(Path(__file__).parent / "longvideobench_val_i.yaml", "r") as f: raw_data = f.readlines() @@ -155,6 +154,7 @@ def longvideobench_doc_to_text(doc, model_specific_prompt_kwargs): hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/") base_cache_dir = os.path.expanduser(hf_home) + def longvideobench_doc_to_visual_v(doc): with open(Path(__file__).parent / "longvideobench_val_v.yaml", "r") as f: raw_data = f.readlines() @@ -170,6 +170,7 @@ def longvideobench_doc_to_visual_v(doc): video_path = os.path.join(cache_dir, video_path) return [video_path] + def longvideobench_doc_to_visual_i(doc): with open(Path(__file__).parent / "longvideobench_val_i.yaml", "r") as f: raw_data = f.readlines() @@ -283,6 +284,7 @@ def evaluate_longvideobench(samples): return {"acc": 0} return judge_dict, {"acc": pred_correct / len(samples)} + def eval_multi_choice(gold_i, pred_i): correct = False # only they are exactly the same, we consider it as correct @@ -296,6 +298,7 @@ def eval_multi_choice(gold_i, pred_i): correct = True return correct + def calculate_ins_level_acc(results): """Calculate the instruction level accuracy for given Subject results https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L246 @@ -332,7 +335,6 @@ def longvideobench_process_results(doc, results): } - def longvideobench_aggregate_results(results): evaluation_result = {} subset_to_eval_samples = defaultdict(list) @@ -344,7 +346,7 @@ def longvideobench_aggregate_results(results): metric_dict.update({"num_example": len(sub_eval_samples)}) evaluation_result[subset] = metric_dict printable_results = {} - + for cat_name, cat_results in evaluation_result.items(): printable_results[cat_name] = { "num": int(cat_results["num_example"]), @@ -357,4 +359,3 @@ def longvideobench_aggregate_results(results): } print(printable_results) return printable_results["Overall"]["acc"] - diff --git a/lmms_eval/tasks/mathverse/mathverse_evals.py b/lmms_eval/tasks/mathverse/mathverse_evals.py index 5b0b6f50..5894f6f7 100644 --- a/lmms_eval/tasks/mathverse/mathverse_evals.py +++ b/lmms_eval/tasks/mathverse/mathverse_evals.py @@ -1,10 +1,10 @@ import time import requests -import logging + from tqdm import tqdm import pandas as pd -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger DEMO_PROMPT_EXTRACT = """ I am providing you a response from a model to a math problem, termed 'Model Response'. You should extract the answer from the response as 'Extracted Answer'. Directly output the extracted answer with no explanation. @@ -71,6 +71,7 @@ [Model_answer] : {extraction} Judgement: """ + class MathVerseEvaluator: API_URL = "https://api.openai.com/v1/chat/completions" @@ -108,15 +109,15 @@ def get_chat_response(self, prompt, temperature=0, max_tokens=256, n=1, patience except Exception as e: # some model may output repetitive answer, which ChatGPT will throw an error. - if 'repetitive patterns' in str(e): + if "repetitive patterns" in str(e): print(str(e)) print("Continue with empty answer") return "" # some answer may contain some sensitive words, like 'test' - if 'sensitive' in str(e) or '400' in str(e): + if "sensitive" in str(e) or "400" in str(e): print(str(e)) print("Continue with empty answer") - return "0" + return "0" if "Rate limit" not in str(e): eval_logger.error(e) @@ -153,7 +154,6 @@ def create_match_prompt(self, demo_prompt, question, answer, extraction): return full_prompt def extract_answer(self, response): - if not response: return "" @@ -177,7 +177,7 @@ def score_answer(self, question, answer, extraction, quick_match=False): while True: extraction = self.get_chat_response(full_prompt, temperature=0, max_tokens=8, n=1) judgement = extraction.replace("Judgement:", "").strip() - if judgement.strip() in ['0', '1']: + if judgement.strip() in ["0", "1"]: return int(judgement) == 1 except Exception as e: @@ -186,7 +186,6 @@ def score_answer(self, question, answer, extraction, quick_match=False): return False - def get_acc_with_contion(self, res_pd, key, value): """ Calculate the accuracy of predictions with a specific condition @@ -240,11 +239,11 @@ def create_one_query(self, problem, shot_type, hint, query_type, examples=None, hint_text = "" # custom-prompt elif shot_type == "custom-prompt": - if question_type == 'multi-choice': - hint_text = hint['multi-choice'] - else: # free-form - hint_text = hint['free-form'] - + if question_type == "multi-choice": + hint_text = hint["multi-choice"] + else: # free-form + hint_text = hint["free-form"] + # question if shot_type == "format-prompt": question_text = f"{problem[query_type]}" @@ -262,23 +261,23 @@ def create_one_query(self, problem, shot_type, hint, query_type, examples=None, def eval_results(self, results, config): # extract and score for each question for inst in tqdm(results): - full_prediction = inst['prediction'].strip() + full_prediction = inst["prediction"].strip() problem = { "question_type": inst["question_type"], "answer": inst["answer"] if "answer" in inst else None, "question_for_eval": inst["question_for_eval"], } - if config['metadata'].get('trunk_response', -1) > 0: - prediction = ' '.join(full_prediction.split(' ')[-config['metadata']['trunk_response']:]) + if config["metadata"].get("trunk_response", -1) > 0: + prediction = " ".join(full_prediction.split(" ")[-config["metadata"]["trunk_response"] :]) else: prediction = full_prediction extraction = self.extract_answer(prediction) # set test set answer to None - true_false = self.score_answer(problem['question_for_eval'], problem["answer"], extraction, config['metadata']['quick_match']) if problem["answer"] is not None else False + true_false = self.score_answer(problem["question_for_eval"], problem["answer"], extraction, config["metadata"]["quick_match"]) if problem["answer"] is not None else False - inst['extraction'] = extraction - inst['prediction'] = prediction - inst['true_false'] = true_false + inst["extraction"] = extraction + inst["prediction"] = prediction + inst["true_false"] = true_false # calculate total scores sample_index = [result["sample_index"] for result in results] @@ -303,4 +302,4 @@ def eval_results(self, results, config): scores[key][value] = {"accuracy": acc, "correct": correct, "total": total} scores[key] = dict(sorted(scores[key].items(), key=lambda item: float(item[1]["accuracy"]), reverse=True)) - return results_dict, scores \ No newline at end of file + return results_dict, scores diff --git a/lmms_eval/tasks/mathverse/utils.py b/lmms_eval/tasks/mathverse/utils.py index 1b6d8db3..de22d93d 100644 --- a/lmms_eval/tasks/mathverse/utils.py +++ b/lmms_eval/tasks/mathverse/utils.py @@ -1,11 +1,10 @@ -import logging import yaml import os from pathlib import Path import pandas as pd import json -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger from lmms_eval.tasks.mathverse.mathverse_evals import MathVerseEvaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file @@ -23,7 +22,7 @@ def mathverse_doc_to_visual(doc): - if str(doc["image"]).strip() == '': + if str(doc["image"]).strip() == "": return [] return [doc["image"].convert("RGB")] @@ -37,11 +36,12 @@ def mathverse_doc_to_text(doc, model_specific_prompt_kwargs=None): "question_type": doc["question_type"], "problem_version": doc["problem_version"], } - query_prompt = mathverse_evaluator.create_one_query(problem, examples=None, shot_num=0, shot_type=model_specific_prompt_kwargs["shot_type"], hint=model_specific_prompt_kwargs.get("hint", None),query_type=model_specific_prompt_kwargs["query_type"]) + query_prompt = mathverse_evaluator.create_one_query( + problem, examples=None, shot_num=0, shot_type=model_specific_prompt_kwargs["shot_type"], hint=model_specific_prompt_kwargs.get("hint", None), query_type=model_specific_prompt_kwargs["query_type"] + ) return query_prompt - def mathverse_process_results(doc, results): prediction = results[0].strip() @@ -72,6 +72,7 @@ def mathverse_aggregate_results_submission(results, args, *, calculate_gain=Fals eval_logger.info(f"Saved results to {path}") + def mathverse_aggregate_results_eval(results, args, *, calculate_gain=False, random_scores=None): split_flag = results[0]["metadata"]["split"] # save the result first, in case the gpt evaluation fails diff --git a/lmms_eval/tasks/mathvista/mathvista_evals.py b/lmms_eval/tasks/mathvista/mathvista_evals.py index 90d367ec..afc5b1b5 100755 --- a/lmms_eval/tasks/mathvista/mathvista_evals.py +++ b/lmms_eval/tasks/mathvista/mathvista_evals.py @@ -2,9 +2,9 @@ import requests import re from Levenshtein import distance -import logging -eval_logger = logging.getLogger("lmms-eval") + +from loguru import logger as eval_logger # pids: 799, 681, 615 shot_examples = [ diff --git a/lmms_eval/tasks/mathvista/utils.py b/lmms_eval/tasks/mathvista/utils.py index 3bbe96a9..4f8ad21b 100755 --- a/lmms_eval/tasks/mathvista/utils.py +++ b/lmms_eval/tasks/mathvista/utils.py @@ -1,11 +1,10 @@ -import logging import yaml import os from pathlib import Path import pandas as pd import json -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger from lmms_eval.tasks.mathvista.mathvista_evals import MathVistaEvaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file diff --git a/lmms_eval/tasks/mmbench/cc_utils.py b/lmms_eval/tasks/mmbench/cc_utils.py index a5b9326c..8cd6665d 100755 --- a/lmms_eval/tasks/mmbench/cc_utils.py +++ b/lmms_eval/tasks/mmbench/cc_utils.py @@ -1,11 +1,10 @@ -import logging import yaml import os from pathlib import Path import pandas as pd import json -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file diff --git a/lmms_eval/tasks/mmbench/cn_utils.py b/lmms_eval/tasks/mmbench/cn_utils.py index ba076b6f..2a767d7a 100755 --- a/lmms_eval/tasks/mmbench/cn_utils.py +++ b/lmms_eval/tasks/mmbench/cn_utils.py @@ -1,4 +1,3 @@ -import logging import yaml import os from pathlib import Path @@ -6,7 +5,7 @@ import json from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file diff --git a/lmms_eval/tasks/mmbench/en_utils.py b/lmms_eval/tasks/mmbench/en_utils.py index 60121ec5..f0b5e9f8 100755 --- a/lmms_eval/tasks/mmbench/en_utils.py +++ b/lmms_eval/tasks/mmbench/en_utils.py @@ -1,11 +1,10 @@ -import logging import yaml import os from pathlib import Path import pandas as pd import json -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file diff --git a/lmms_eval/tasks/mmbench/mmbench_evals.py b/lmms_eval/tasks/mmbench/mmbench_evals.py index 7868157e..7f2c246f 100755 --- a/lmms_eval/tasks/mmbench/mmbench_evals.py +++ b/lmms_eval/tasks/mmbench/mmbench_evals.py @@ -10,9 +10,8 @@ import pandas as pd from tqdm import tqdm -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger class MMBench_Evaluator: diff --git a/lmms_eval/tasks/mme/utils.py b/lmms_eval/tasks/mme/utils.py index 26d5942c..5aa4ae18 100755 --- a/lmms_eval/tasks/mme/utils.py +++ b/lmms_eval/tasks/mme/utils.py @@ -4,9 +4,8 @@ import json from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger dir_name = os.path.dirname(os.path.abspath(__file__)) diff --git a/lmms_eval/tasks/mmmu/utils.py b/lmms_eval/tasks/mmmu/utils.py index ff4d6aeb..83aeed20 100755 --- a/lmms_eval/tasks/mmmu/utils.py +++ b/lmms_eval/tasks/mmmu/utils.py @@ -5,11 +5,11 @@ import numpy as np import os import json -import logging + from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -lmms_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger MULTI_CHOICE_PROMPT = "Answer with the option's letter from the given choices directly." OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase." @@ -89,7 +89,7 @@ def mmmu_test_aggregate_results_for_submission(results, args): results_dict = {list(item.keys())[0]: list(item.values())[0] for item in results} with open(path, "w") as f: json.dump(results_dict, f) - lmms_logger.info(f"Results saved to {path}.") + eval_logger.info(f"Results saved to {path}.") def mmmu_aggregate_results(results): diff --git a/lmms_eval/tasks/mmmu/utils_group_img.py b/lmms_eval/tasks/mmmu/utils_group_img.py index 2470d7c7..5de728b6 100644 --- a/lmms_eval/tasks/mmmu/utils_group_img.py +++ b/lmms_eval/tasks/mmmu/utils_group_img.py @@ -5,7 +5,7 @@ import numpy as np import os import json -import logging + import matplotlib.font_manager as fm from lmms_eval.tasks._task_utils.file_utils import generate_submission_file @@ -129,7 +129,7 @@ def process_images(images, size=1008): return concat_horizontal -lmms_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger MULTI_CHOICE_PROMPT = "Answer with the option's letter from the given choices directly." OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase." @@ -209,7 +209,7 @@ def mmmu_test_aggregate_results_for_submission(results, args): path = generate_submission_file("mmmu_test_for_submission.json", args) with open(path, "w") as f: json.dump(results, f) - lmms_logger.info(f"Results saved to {path}.") + eval_logger.info(f"Results saved to {path}.") def mmmu_aggregate_results(results): diff --git a/lmms_eval/tasks/mmupd/mmupd_evals.py b/lmms_eval/tasks/mmupd/mmupd_evals.py index 4878763d..00dde912 100644 --- a/lmms_eval/tasks/mmupd/mmupd_evals.py +++ b/lmms_eval/tasks/mmupd/mmupd_evals.py @@ -7,24 +7,23 @@ import numpy as np import pandas as pd import pickle -import logging + import json -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def dump(data, f): - def dump_pkl(data, pth): - pickle.dump(data, open(pth, 'wb')) + pickle.dump(data, open(pth, "wb")) def dump_json(data, pth): - json.dump(data, open(pth, 'w')) + json.dump(data, open(pth, "w")) def dump_jsonl(data, f): lines = [json.dumps(x, ensure_ascii=False) for x in data] - with open(f, 'w', encoding='utf8') as fout: - fout.write('\n'.join(lines)) + with open(f, "w", encoding="utf8") as fout: + fout.write("\n".join(lines)) def dump_xlsx(data, f): data.to_excel(f, index=False) @@ -33,45 +32,41 @@ def dump_csv(data, f): data.to_csv(f, index=False) def dump_tsv(data, f): - data.to_csv(f, sep='\t', index=False) - - handlers = dict(pkl=dump_pkl, - json=dump_json, - jsonl=dump_jsonl, - xlsx=dump_xlsx, - csv=dump_csv, - tsv=dump_tsv) - suffix = f.split('.')[-1] + data.to_csv(f, sep="\t", index=False) + + handlers = dict(pkl=dump_pkl, json=dump_json, jsonl=dump_jsonl, xlsx=dump_xlsx, csv=dump_csv, tsv=dump_tsv) + suffix = f.split(".")[-1] return handlers[suffix](data, f) def load(f): """ - Loads data from various file formats. + Loads data from various file formats. - Parameters: - - file_path: Path to the file to be loaded. + Parameters: + - file_path: Path to the file to be loaded. - Returns: - - Loaded data. + Returns: + - Loaded data. """ + def load_pkl(pth): - return pickle.load(open(pth, 'rb')) + return pickle.load(open(pth, "rb")) def load_json(pth): - return json.load(open(pth, 'r', encoding='utf-8')) + return json.load(open(pth, "r", encoding="utf-8")) def load_jsonl(f): - lines = open(f, encoding='utf-8').readlines() + lines = open(f, encoding="utf-8").readlines() lines = [x.strip() for x in lines] - if lines[-1] == '': + if lines[-1] == "": lines = lines[:-1] data = [json.loads(x) for x in lines] return data def load_xlsx(f): df = pd.read_excel(f) - df = df.dropna(subset=['prediction']) + df = df.dropna(subset=["prediction"]) return df @@ -79,15 +74,10 @@ def load_csv(f): return pd.read_csv(f) def load_tsv(f): - return pd.read_csv(f, sep='\t') - - handlers = dict(pkl=load_pkl, - json=load_json, - jsonl=load_jsonl, - xlsx=load_xlsx, - csv=load_csv, - tsv=load_tsv) - suffix = f.split('.')[-1] + return pd.read_csv(f, sep="\t") + + handlers = dict(pkl=load_pkl, json=load_json, jsonl=load_jsonl, xlsx=load_xlsx, csv=load_csv, tsv=load_tsv) + suffix = f.split(".")[-1] return handlers[suffix](f) @@ -115,9 +105,9 @@ def is_none(self, value): return True if type(value) is float and math.isnan(value): return True - if type(value) is str and value.lower() == 'nan': + if type(value) is str and value.lower() == "nan": return True - if type(value) is str and value.lower() == 'none': + if type(value) is str and value.lower() == "none": return True return False @@ -134,7 +124,7 @@ def build_option_str(self, option_list): def extract_options(self, item): options = [] - for c in 'ABCDE': + for c in "ABCDE": try: if self.is_none(item[c]) is False: options.append(item[c]) @@ -144,7 +134,7 @@ def extract_options(self, item): def build_choices(self, item): ret = {} - for ch in 'ABCDE': + for ch in "ABCDE": try: if self.is_none(item[ch]) is False: ret[ch] = item[ch] @@ -154,193 +144,196 @@ def build_choices(self, item): def build_option_str_w_gt(self, option_list, gt_text, eval_type, question_type, upd_type): chars = string.ascii_uppercase - s = 'There are several options: \n' + s = "There are several options: \n" valid_option = [] answer_option = [] for c, opt in zip(chars, option_list): if self.is_none(opt) is False: - s += f'{c}. {opt}\n' + s += f"{c}. {opt}\n" valid_option.append(c) if upd_type == "aad": if eval_type == "aad": gt_option = "" - for option in ['C', 'D', 'E', 'F']: + for option in ["C", "D", "E", "F"]: if option not in valid_option: gt_option = option break - none_option_mapping = {'C': 'D', 'D': 'E', 'E': 'F', 'F': 'G'} - s += f'{gt_option}. {gt_text}\n' + none_option_mapping = {"C": "D", "D": "E", "E": "F", "F": "G"} + s += f"{gt_option}. {gt_text}\n" none_option = none_option_mapping[gt_option] - s += f'{none_option}. The correct answer is No answer, None of the above, \ - all provided options are incorrect, or I cannot answer.\n' + s += f"{none_option}. The correct answer is No answer, None of the above, \ + all provided options are incorrect, or I cannot answer.\n" valid_option.append(gt_option) valid_option.append(none_option) answer_option = [gt_option, none_option] - if question_type == 'inst': - s += f'F. None of the above\n' + if question_type == "inst": + s += f"F. None of the above\n" valid_option.append("F") answer_option = [gt_option, none_option, "F"] if eval_type == "standard": none_option = "" - for option in ['C', 'D', 'E', 'F']: + for option in ["C", "D", "E", "F"]: if option not in valid_option: none_option = option break - s += f'{none_option}. The correct answer is No answer, None of the above, \ - all provided options are incorrect, or I cannot answer.\n' + s += f"{none_option}. The correct answer is No answer, None of the above, \ + all provided options are incorrect, or I cannot answer.\n" valid_option.append(none_option) - if question_type == 'inst': - s += 'F. None of the above\n' + if question_type == "inst": + s += "F. None of the above\n" valid_option.append("F") elif upd_type == "iasd": if eval_type == "iasd": gt_option = "" - for option in ['C', 'D', 'E', 'F']: + for option in ["C", "D", "E", "F"]: if option not in valid_option: gt_option = option break - s += f'{gt_option}. {gt_text}\n' + s += f"{gt_option}. {gt_text}\n" valid_option.append(gt_option) - if question_type == 'inst': - if gt_option == 'E': - s += f'F. None of the above\n' - valid_option.append('F') - s += 'G. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n' - valid_option.append('G') - answer_option = [gt_option, 'F', 'G'] + if question_type == "inst": + if gt_option == "E": + s += f"F. None of the above\n" + valid_option.append("F") + s += "G. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n" + valid_option.append("G") + answer_option = [gt_option, "F", "G"] else: - none_option_mapping = {'C': 'D', 'D': 'E'} + none_option_mapping = {"C": "D", "D": "E"} none_option = none_option_mapping[gt_option] - s += f'{none_option}. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n' + s += f"{none_option}. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n" valid_option.append(none_option) - s += f'F. None of the above\n' - valid_option.append('F') - answer_option = [gt_option, none_option, 'F'] + s += f"F. None of the above\n" + valid_option.append("F") + answer_option = [gt_option, none_option, "F"] else: - none_option_mapping = {'C': 'D', 'D': 'E', 'E': 'F', 'F': 'G'} + none_option_mapping = {"C": "D", "D": "E", "E": "F", "F": "G"} none_option = none_option_mapping[gt_option] - s += f'{none_option}. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n' + s += f"{none_option}. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n" valid_option.append(none_option) answer_option = [gt_option, none_option] if eval_type == "standard": none_option = "" - for option in ['C', 'D', 'E', 'F']: + for option in ["C", "D", "E", "F"]: if option not in valid_option: none_option = option break - s += f'{none_option}. The correct answer is No answer, None of the above, \ - all provided options are irrelevant or incorrect, or I cannot answer.\n' + s += f"{none_option}. The correct answer is No answer, None of the above, \ + all provided options are irrelevant or incorrect, or I cannot answer.\n" valid_option.append(none_option) - if question_type == 'inst': - s += f'F. None of the above\n' + if question_type == "inst": + s += f"F. None of the above\n" valid_option.append("F") elif upd_type == "ivqd": if eval_type == "ivqd": none_option = "" - for option in ['C', 'D', 'E', 'F']: + for option in ["C", "D", "E", "F"]: if option not in valid_option: none_option = option break - s += f'{none_option}. The correct answer is that The image is incompatible with the question, or I cannot answer.\n' + s += f"{none_option}. The correct answer is that The image is incompatible with the question, or I cannot answer.\n" valid_option.append(none_option) answer_option = [none_option] - if question_type == 'inst': - s += f'F. The image and question are irrelevant.\n' + if question_type == "inst": + s += f"F. The image and question are irrelevant.\n" valid_option.append("F") answer_option = [none_option, "F"] if eval_type == "standard": none_option = "" - for option in ['C', 'D', 'E', 'F']: + for option in ["C", "D", "E", "F"]: if option not in valid_option: none_option = option break - s += f'{none_option}. The correct answer is that The image is incompatible with the question, or I cannot answer.\n' + s += f"{none_option}. The correct answer is that The image is incompatible with the question, or I cannot answer.\n" valid_option.append(none_option) - if question_type == 'inst': - s += f'F. The image and question are irrelevant.\n' + if question_type == "inst": + s += f"F. The image and question are irrelevant.\n" valid_option.append("F") return s, valid_option, answer_option def build_prompt(self, question, options, prediction, upd_type): if upd_type == "aad": tmpl = ( - 'You are an AI assistant who will help me to match an answer ' - 'with several options of a single-choice question. ' - 'You are provided with a question, several options, and an answer, ' - 'and you need to find which option is most similar to the answer. ' - 'If the meaning of all options are significantly different ' - 'from the answer, output H. ' - 'Your should output a single uppercase character in A, B, C, D, E, F, G ' - '(if they are valid options), and H. \n' - 'Example 1: \n' - 'Question: What is the main object in image?\nOptions: A. teddy bear ' - 'B. rabbit C. cat D. dog E. no answer \nAnswer: a cute teddy bear\nYour output: A\n' - 'Example 2: \n' - 'Question: What is the main object in image?\nOptions: A. teddy bear ' - 'B. rabbit C. cat D. dog E. None of the above \nAnswer: no answer \nYour output: E\n' - 'Example 3: \n' - 'Question: What is the main object in image?\nOptions: A. teddy bear ' - 'B. rabbit C. cat D. dog E. None of the above \nAnswer: fish \nYour output: H\n' - 'Example 4: \n' - 'Question: {}?\nOptions: {}\nAnswer: {}\nYour output: ') + "You are an AI assistant who will help me to match an answer " + "with several options of a single-choice question. " + "You are provided with a question, several options, and an answer, " + "and you need to find which option is most similar to the answer. " + "If the meaning of all options are significantly different " + "from the answer, output H. " + "Your should output a single uppercase character in A, B, C, D, E, F, G " + "(if they are valid options), and H. \n" + "Example 1: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog E. no answer \nAnswer: a cute teddy bear\nYour output: A\n" + "Example 2: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog E. None of the above \nAnswer: no answer \nYour output: E\n" + "Example 3: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog E. None of the above \nAnswer: fish \nYour output: H\n" + "Example 4: \n" + "Question: {}?\nOptions: {}\nAnswer: {}\nYour output: " + ) elif upd_type == "iasd": tmpl = ( - 'You are an AI assistant who will help me to match an answer ' - 'with several options of a single-choice question. ' - 'You are provided with a question, several options, and an answer, ' - 'and you need to find which option is most similar to the answer. ' - 'If the meaning of all options are significantly different ' - 'from the answer, output H. ' - 'Your should output a single uppercase character in A, B, C, D, E, F, G ' - '(if they are valid options), and H. \n' - 'Example 1: \n' - 'Question: What is the main object in image?\nOptions: A. teddy bear ' - 'B. rabbit C. cat D. dog E. no answer \nAnswer: a cute teddy bear\nYour output: A\n' - 'Example 2: \n' - 'Question: What is the main object in image?\nOptions: A. teddy bear ' - 'B. rabbit C. cat D. dog E. None of the above \nAnswer: no answer \nYour output: E\n' - 'Example 3: \n' - 'Question: What is the main object in image?\nOptions: A. teddy bear ' - 'B. rabbit C. cat D. dog E. None of the above \nAnswer: fish \nYour output: H\n' - 'Example 4: \n' - 'Question: {}?\nOptions: {}\nAnswer: {}\nYour output: ') + "You are an AI assistant who will help me to match an answer " + "with several options of a single-choice question. " + "You are provided with a question, several options, and an answer, " + "and you need to find which option is most similar to the answer. " + "If the meaning of all options are significantly different " + "from the answer, output H. " + "Your should output a single uppercase character in A, B, C, D, E, F, G " + "(if they are valid options), and H. \n" + "Example 1: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog E. no answer \nAnswer: a cute teddy bear\nYour output: A\n" + "Example 2: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog E. None of the above \nAnswer: no answer \nYour output: E\n" + "Example 3: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog E. None of the above \nAnswer: fish \nYour output: H\n" + "Example 4: \n" + "Question: {}?\nOptions: {}\nAnswer: {}\nYour output: " + ) elif upd_type == "ivqd": tmpl = ( - 'You are an AI assistant who will help me to match an answer ' - 'with several options of a single-choice question. ' - 'You are provided with a question, several options, and an answer, ' - 'and you need to find which option is most similar to the answer. ' - 'If the meaning of all options are significantly different ' - 'from the answer, output H. ' - 'Your should output a single uppercase character in A, B, C, D, E, F, G ' - '(if they are valid options), and H. \n' - 'Example 1: \n' - 'Question: What is the main object in image?\nOptions: A. teddy bear ' - 'B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: a cute teddy bear\nYour output: A\n' - 'Example 2: \n' - 'Question: What is the main object in image?\nOptions: A. teddy bear ' - 'B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: The updloaded image and question are incompatible. \nYour output: E\n' - 'Example 3: \n' - 'Question: What is the main object in image?\nOptions: A. teddy bear ' - 'B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: fish \nYour output: H\n' - 'Example 4: \n' - 'Question: {}?\nOptions: {}\nAnswer: {}\nYour output: ') + "You are an AI assistant who will help me to match an answer " + "with several options of a single-choice question. " + "You are provided with a question, several options, and an answer, " + "and you need to find which option is most similar to the answer. " + "If the meaning of all options are significantly different " + "from the answer, output H. " + "Your should output a single uppercase character in A, B, C, D, E, F, G " + "(if they are valid options), and H. \n" + "Example 1: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: a cute teddy bear\nYour output: A\n" + "Example 2: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: The updloaded image and question are incompatible. \nYour output: E\n" + "Example 3: \n" + "Question: What is the main object in image?\nOptions: A. teddy bear " + "B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: fish \nYour output: H\n" + "Example 4: \n" + "Question: {}?\nOptions: {}\nAnswer: {}\nYour output: " + ) return tmpl.format(question, options, prediction) # Prefetch Answers def can_infer_option(self, answer, option_dict, question_type=None, valid_option=None): if valid_option is None: valid_option = list(option_dict.keys()) - if question_type == 'inst': + if question_type == "inst": valid_option.append("F") - if 'Failed to obtain answer via API' in answer: + if "Failed to obtain answer via API" in answer: return False answer = answer.strip() @@ -444,22 +437,22 @@ def extract_answer_from_item(self, item, gt_text, eval_type, question_type, upd_ options = self.extract_options(item) option_str, valid_option, answer_option = self.build_option_str_w_gt(options, gt_text, eval_type, question_type=question_type, upd_type=upd_type) - prompt = self.build_prompt(item['question'], option_str, item['prediction'], upd_type=upd_type) + prompt = self.build_prompt(item["question"], option_str, item["prediction"], upd_type=upd_type) retry = 3 choices = self.build_choices(item) - ret = self.can_infer(item['prediction'], choices, valid_option=valid_option) + ret = self.can_infer(item["prediction"], choices, valid_option=valid_option) if ret: - return ret, item['prediction'], answer_option + return ret, item["prediction"], answer_option while retry: ans = self.get_chat_response(prompt, temperature=0.7) - if 'Failed to obtain answer via API' in ans: - msg = 'GPT API failed to answer. ' + if "Failed to obtain answer via API" in ans: + msg = "GPT API failed to answer. " eval_logger.info(msg) retry -= 1 else: - ret = self.can_infer(ans, choices, valid_option=valid_option) + ret = self.can_infer(ans, choices, valid_option=valid_option) if ret: return ret, ans, answer_option else: @@ -467,7 +460,7 @@ def extract_answer_from_item(self, item, gt_text, eval_type, question_type, upd_ retry -= 1 if retry == 0: - return 'H', 'Failed to predict. ', answer_option + return "H", "Failed to predict. ", answer_option def eval_sub_data(self, sub_data, answer_map, gt_text_map, question_type, eval_type, upd_type): lt = len(sub_data) @@ -475,7 +468,7 @@ def eval_sub_data(self, sub_data, answer_map, gt_text_map, question_type, eval_t for i in range(lt): item = sub_data.iloc[i] - idx = item['index'] + idx = item["index"] GT.append(answer_map[idx]) PRED.append(self.prefetch_answer(item, question_type)) if PRED[-1] and (GT[-1] != PRED[-1]): @@ -486,7 +479,7 @@ def eval_sub_data(self, sub_data, answer_map, gt_text_map, question_type, eval_t continue else: item = sub_data.iloc[i] - idx = item['index'] + idx = item["index"] gt_text = gt_text_map[idx] if gt_text_map is not None else None ret, _, answer_option = self.extract_answer_from_item(sub_data.iloc[i], gt_text, eval_type, question_type=question_type, upd_type=upd_type) PRED[i] = ret @@ -549,7 +542,7 @@ def eval_result(self, results, eval_method, upd_type, question_type, eval_type): cate_map = {i: c for i, c in zip(data["index"], data["category"])} answer_map = {i: c for i, c in zip(data["index"], data["answer"])} - gt_text_map = {i: c for i, c in zip(data['index'], data['masked_answer'])} + gt_text_map = {i: c for i, c in zip(data["index"], data["masked_answer"])} lt = len(data_main) hit, tot = 0, 0 @@ -568,8 +561,7 @@ def eval_result(self, results, eval_method, upd_type, question_type, eval_type): sub_data = data[data["index"] % int(1e6) == idx] - ret = self.eval_sub_data(sub_data, answer_map, gt_text_map,\ - question_type=question_type, eval_type=eval_type, upd_type=upd_type) + ret = self.eval_sub_data(sub_data, answer_map, gt_text_map, question_type=question_type, eval_type=eval_type, upd_type=upd_type) result[idx] = ret hit += ret tot += 1 @@ -584,15 +576,15 @@ def eval_result(self, results, eval_method, upd_type, question_type, eval_type): return overall_hit_rate, category_hit_rate, data_main - def report_acc(self, df, groupd='category'): - assert 'split' in df - assert groupd in [None, 'category', 'l2-category'] + def report_acc(self, df, groupd="category"): + assert "split" in df + assert groupd in [None, "category", "l2-category"] res = defaultdict(list) - res['split'] = ['test'] + res["split"] = ["test"] if groupd is None: - res['overall'] = [ - np.mean(df['hit']), + res["overall"] = [ + np.mean(df["hit"]), ] return pd.DataFrame(res) @@ -602,17 +594,16 @@ def report_acc(self, df, groupd='category'): for ab in abilities: sub_df = df[df[groupd] == ab] res[ab] = [ - np.mean(sub_df['hit']), + np.mean(sub_df["hit"]), ] return pd.DataFrame(res) def calculate_dual_acc(self, standard_df, upd_df): # dual results - dual_df = pd.merge(standard_df, upd_df, on='index', - suffixes=('_standard', '_upd')) - dual_df['hit'] = dual_df.apply(lambda row: 1 if row['hit_standard'] == 1 and row['hit_upd'] == 1 else 0, axis=1) - dual_df['split'] = dual_df['split_standard'] - dual_df['category'] = dual_df['category_standard'] + dual_df = pd.merge(standard_df, upd_df, on="index", suffixes=("_standard", "_upd")) + dual_df["hit"] = dual_df.apply(lambda row: 1 if row["hit_standard"] == 1 and row["hit_upd"] == 1 else 0, axis=1) + dual_df["split"] = dual_df["split_standard"] + dual_df["category"] = dual_df["category_standard"] # Evaluate dual results overall_hit_rate, category_hit_rate = self.calculate_hit_rates(dual_df) diff --git a/lmms_eval/tasks/mmupd/utils.py b/lmms_eval/tasks/mmupd/utils.py index a2f237d3..bfc724a5 100644 --- a/lmms_eval/tasks/mmupd/utils.py +++ b/lmms_eval/tasks/mmupd/utils.py @@ -1,4 +1,3 @@ -import logging import yaml import os from pathlib import Path @@ -10,7 +9,7 @@ from lmms_eval.tasks.mmupd.mmupd_evals import MMUPD_Evaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger with open(Path(__file__).parent / "mmupd.yaml", "r") as f: raw_data = f.readlines() @@ -78,7 +77,7 @@ def mmupd_process_results(doc, results): "split": doc["split"], "category": doc["category"], "type": doc["type"], - "masked_answer": doc["masked_answer"] + "masked_answer": doc["masked_answer"], }, "submission": { "index": doc["index"], @@ -90,7 +89,7 @@ def mmupd_process_results(doc, results): "split": doc["split"], "category": doc["category"], "type": doc["type"], - "masked_answer": doc["masked_answer"] + "masked_answer": doc["masked_answer"], }, } option_candidate = ["A", "B", "C", "D", "E"] @@ -137,7 +136,6 @@ def mmivqd_instruction(results, args): def mmupd_results_eval(results, args, upd_type, question_type): - print("============= MMUPD Bench Detailed Results =============") overall_acc_standard, category_acc_standard, standard_results_df = mmupd_evaluator.eval_result(results, eval_method="openai", upd_type=upd_type, question_type=question_type, eval_type="standard") diff --git a/lmms_eval/tasks/mmvet/utils.py b/lmms_eval/tasks/mmvet/utils.py index 5caaba46..37b15ad7 100755 --- a/lmms_eval/tasks/mmvet/utils.py +++ b/lmms_eval/tasks/mmvet/utils.py @@ -1,12 +1,12 @@ import os import requests import time -import logging + import pandas as pd import yaml from pathlib import Path -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger with open(Path(__file__).parent / "mmvet.yaml", "r") as f: raw_data = f.readlines() diff --git a/lmms_eval/tasks/multidocvqa/utils.py b/lmms_eval/tasks/multidocvqa/utils.py index 10fd85e6..f53da020 100755 --- a/lmms_eval/tasks/multidocvqa/utils.py +++ b/lmms_eval/tasks/multidocvqa/utils.py @@ -2,11 +2,11 @@ import re import ast import json -import logging + from lmms_eval.api.metrics import levenshtein_distance from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -lmms_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def multidocvqa_doc_to_text(doc, model_specific_prompt_kwargs): @@ -55,7 +55,7 @@ def multidocvqa_test_aggregate_results_for_submission(results, args): path = generate_submission_file("multidocvqa_test_for_submission.json", args) with open(path, "w") as f: json.dump(results, f) - lmms_logger.info(f"Results saved to {path}.") + eval_logger.info(f"Results saved to {path}.") ################## diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py index 6666de45..a74a02d1 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py @@ -1,5 +1,5 @@ import json -import logging + import os import requests import numpy as np @@ -10,7 +10,8 @@ from pathlib import Path from copy import deepcopy -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger + NUM_SECONDS_TO_SLEEP = 5 LLAVA_W_METRICS = ["gpt_eval_llava_conv", "gpt_eval_llava_detail", "gpt_eval_llava_complex"] diff --git a/lmms_eval/tasks/nextqa/utils.py b/lmms_eval/tasks/nextqa/utils.py index ca5d8bac..4fa46523 100644 --- a/lmms_eval/tasks/nextqa/utils.py +++ b/lmms_eval/tasks/nextqa/utils.py @@ -1,12 +1,12 @@ import os import yaml -import logging + import random import pandas as pd from pathlib import Path -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: from pywsd.utils import lemmatize_sentence diff --git a/lmms_eval/tasks/nocaps/utils.py b/lmms_eval/tasks/nocaps/utils.py index 9b1d4df6..ce6e1d89 100755 --- a/lmms_eval/tasks/nocaps/utils.py +++ b/lmms_eval/tasks/nocaps/utils.py @@ -6,9 +6,8 @@ from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger dir_name = os.path.dirname(os.path.abspath(__file__)) diff --git a/lmms_eval/tasks/ocrbench/utils.py b/lmms_eval/tasks/ocrbench/utils.py index fe1c1fcb..b590cbda 100644 --- a/lmms_eval/tasks/ocrbench/utils.py +++ b/lmms_eval/tasks/ocrbench/utils.py @@ -1,8 +1,6 @@ -import logging - from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -logger = logging.getLogger("lmms-eval") +from loguru import logger # Add the following functions to your existing utils.py file OCRBench_score = { diff --git a/lmms_eval/tasks/ok_vqa/utils.py b/lmms_eval/tasks/ok_vqa/utils.py index 087d51fa..249e1a0c 100755 --- a/lmms_eval/tasks/ok_vqa/utils.py +++ b/lmms_eval/tasks/ok_vqa/utils.py @@ -3,14 +3,14 @@ import json import yaml import pathlib -import logging + import datetime import statistics from lmms_eval.tasks._task_utils.file_utils import generate_submission_file from lmms_eval.tasks._task_utils.vqa_eval_metric import EvalAIAnswerProcessor -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def ok_vqa_doc_to_visual(doc): @@ -19,9 +19,7 @@ def ok_vqa_doc_to_visual(doc): def ok_vqa_process_results(doc, result): eval_ai_processor = EvalAIAnswerProcessor() - assert ( - len(result) == 1 - ), f"The result should be a list of length 1, but got {len(result)}." + assert len(result) == 1, f"The result should be a list of length 1, but got {len(result)}." resAns = eval_ai_processor(result[0]) accuracy = 0 @@ -32,9 +30,7 @@ def ok_vqa_process_results(doc, result): doc["answers"][i] = eval_ai_processor(doc["answers"][i]) for i in range(len(doc["answers"])): - otherGTAns = [ - doc["answers"][j] for j in range(len(doc["answers"])) if i != j - ] + otherGTAns = [doc["answers"][j] for j in range(len(doc["answers"])) if i != j] matchingAns = [item for item in otherGTAns if item == resAns] acc = min(1, float(len(matchingAns)) / 3) gtAcc.append(acc) diff --git a/lmms_eval/tasks/olympiadbench/cn_utils.py b/lmms_eval/tasks/olympiadbench/cn_utils.py index 51511862..64578e8a 100644 --- a/lmms_eval/tasks/olympiadbench/cn_utils.py +++ b/lmms_eval/tasks/olympiadbench/cn_utils.py @@ -4,9 +4,9 @@ from lmms_eval.tasks.olympiadbench.olympiadbench_evals import OlympiadBenchEvaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger + dir_name = os.path.dirname(os.path.abspath(__file__)) try: @@ -69,4 +69,4 @@ def olympiadbench_aggregate_results(results, args): path = generate_submission_file(submission_file_name, args) with open(path, "w") as f: json.dump(results, f, ensure_ascii=False) - print(f"Submission file saved to {path}") \ No newline at end of file + print(f"Submission file saved to {path}") diff --git a/lmms_eval/tasks/olympiadbench/en_utils.py b/lmms_eval/tasks/olympiadbench/en_utils.py index e98c0294..b3186c98 100644 --- a/lmms_eval/tasks/olympiadbench/en_utils.py +++ b/lmms_eval/tasks/olympiadbench/en_utils.py @@ -4,9 +4,9 @@ from lmms_eval.tasks.olympiadbench.olympiadbench_evals import OlympiadBenchEvaluator from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger + dir_name = os.path.dirname(os.path.abspath(__file__)) try: @@ -71,4 +71,4 @@ def olympiadbench_aggregate_results(results, args): path = generate_submission_file(submission_file_name, args) with open(path, "w") as f: json.dump(results, f, ensure_ascii=False) - print(f"Submission file saved to {path}") \ No newline at end of file + print(f"Submission file saved to {path}") diff --git a/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py b/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py index 2e2de5ff..9897447f 100644 --- a/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py +++ b/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py @@ -1,9 +1,8 @@ import re import sympy as sp -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: from sympy import simplify, Eq, sympify, Pow @@ -352,4 +351,4 @@ def can_compute_power(self, expr): else: return False else: - return True # Not a power expression, can compute \ No newline at end of file + return True # Not a power expression, can compute diff --git a/lmms_eval/tasks/perceptiontest/test/utils.py b/lmms_eval/tasks/perceptiontest/test/utils.py index 35307179..522a6a04 100755 --- a/lmms_eval/tasks/perceptiontest/test/utils.py +++ b/lmms_eval/tasks/perceptiontest/test/utils.py @@ -5,7 +5,7 @@ import datetime import lmms_eval.tasks._task_utils.file_utils as file_utils import json -import logging + import yaml from pathlib import Path @@ -27,7 +27,7 @@ cache_dir = os.path.join(HF_HOME, cache_dir) cache_dir = os.path.join(cache_dir, "videos") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Pass in video path here diff --git a/lmms_eval/tasks/perceptiontest/val/utils.py b/lmms_eval/tasks/perceptiontest/val/utils.py index ceb05784..562cc0fe 100755 --- a/lmms_eval/tasks/perceptiontest/val/utils.py +++ b/lmms_eval/tasks/perceptiontest/val/utils.py @@ -5,7 +5,7 @@ import datetime import lmms_eval.tasks._task_utils.file_utils as file_utils import json -import logging + import yaml from pathlib import Path @@ -27,7 +27,7 @@ cache_dir = os.path.join(HF_HOME, cache_dir) cache_dir = os.path.join(cache_dir, "videos") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Pass in video path here diff --git a/lmms_eval/tasks/qbench/utils.py b/lmms_eval/tasks/qbench/utils.py index 8b69c9c4..5648b57b 100644 --- a/lmms_eval/tasks/qbench/utils.py +++ b/lmms_eval/tasks/qbench/utils.py @@ -1,5 +1,5 @@ import json -import logging + import re from collections import Counter, defaultdict from lmms_eval.tasks._task_utils.file_utils import generate_submission_file @@ -12,7 +12,7 @@ def q_bench_doc_to_text(doc, model_specific_prompt_kwargs): if candidate != "N/A": candidates.append(candidate) - question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A")+i), candidate]) for i, candidate in enumerate(candidates)]) + question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A") + i), candidate]) for i, candidate in enumerate(candidates)]) pre_prompt = model_specific_prompt_kwargs["pre_prompt"] post_prompt = model_specific_prompt_kwargs["post_prompt"] return f"{pre_prompt}{question}\n{post_prompt}" @@ -121,6 +121,7 @@ def evaluate_q_bench(samples): return {"acc": 0} return judge_dict, {"acc": pred_correct / len(samples)} + def eval_multi_choice(gold_i, pred_i): correct = False # only they are exactly the same, we consider it as correct @@ -134,6 +135,7 @@ def eval_multi_choice(gold_i, pred_i): correct = True return correct + def calculate_ins_level_acc(results): """Calculate the instruction level accuracy for given Subject results https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L246 @@ -173,6 +175,7 @@ def q_bench_process_results(doc, results): concern_list = ["Global Distortion", "Global Others", "Local Distortion", "Local Others"] question_list = ["Yes/No", "How", "What"] + def q_bench_aggregate_results(results): evaluation_result = {} subset_to_eval_samples = defaultdict(list) @@ -184,7 +187,7 @@ def q_bench_aggregate_results(results): metric_dict.update({"num_example": len(sub_eval_samples)}) evaluation_result[subset] = metric_dict printable_results = {} - + for cat_name, cat_results in evaluation_result.items(): printable_results[cat_name] = { "num": int(cat_results["num_example"]), @@ -198,6 +201,7 @@ def q_bench_aggregate_results(results): print(printable_results) return printable_results["Overall"]["acc"] + def a_bench_process_results(doc, results): pred = results[0] all_choices = [] @@ -220,7 +224,6 @@ def a_bench_process_results(doc, results): } - def a_bench_aggregate_results(results): evaluation_result = {} subset_to_eval_samples = defaultdict(list) @@ -231,7 +234,7 @@ def a_bench_aggregate_results(results): metric_dict.update({"num_example": len(sub_eval_samples)}) evaluation_result[subset] = metric_dict printable_results = {} - + for cat_name, cat_results in evaluation_result.items(): printable_results[cat_name] = { "num": int(cat_results["num_example"]), @@ -244,4 +247,3 @@ def a_bench_aggregate_results(results): } print(printable_results) return printable_results["Overall"]["acc"] - diff --git a/lmms_eval/tasks/refcoco+/utils.py b/lmms_eval/tasks/refcoco+/utils.py index f1d43606..c71ddf86 100755 --- a/lmms_eval/tasks/refcoco+/utils.py +++ b/lmms_eval/tasks/refcoco+/utils.py @@ -5,9 +5,8 @@ COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"] # , "SPICE"] -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def refcoco_bbox_doc_to_visual(doc): diff --git a/lmms_eval/tasks/refcoco/utils.py b/lmms_eval/tasks/refcoco/utils.py index f1d43606..c71ddf86 100755 --- a/lmms_eval/tasks/refcoco/utils.py +++ b/lmms_eval/tasks/refcoco/utils.py @@ -5,9 +5,8 @@ COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"] # , "SPICE"] -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def refcoco_bbox_doc_to_visual(doc): diff --git a/lmms_eval/tasks/refcocog/utils.py b/lmms_eval/tasks/refcocog/utils.py index f1d43606..c71ddf86 100755 --- a/lmms_eval/tasks/refcocog/utils.py +++ b/lmms_eval/tasks/refcocog/utils.py @@ -5,9 +5,8 @@ COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"] # , "SPICE"] -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def refcoco_bbox_doc_to_visual(doc): diff --git a/lmms_eval/tasks/screenspot/utils.py b/lmms_eval/tasks/screenspot/utils.py index cb6ceda9..68a81f95 100644 --- a/lmms_eval/tasks/screenspot/utils.py +++ b/lmms_eval/tasks/screenspot/utils.py @@ -6,9 +6,8 @@ # COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"] # , "SPICE"] COCO_METRICS = ["CIDEr"] -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def screenspot_bbox_doc_to_visual(doc): @@ -30,7 +29,7 @@ def screenspot_process_result(doc, result): """ pred = result[0] if len(result) > 0 else "" ann_id = doc["file_name"] - data_dict = {"instruction": doc["instruction"], "pred": pred, "ann_id": ann_id, 'data_type': doc['data_type'], 'data_source': doc['data_source']} + data_dict = {"instruction": doc["instruction"], "pred": pred, "ann_id": ann_id, "data_type": doc["data_type"], "data_source": doc["data_source"]} return {f"screenspot_{metric}": data_dict for metric in COCO_METRICS} @@ -55,7 +54,7 @@ def screenspot_aggregation_result(results, metric): for result in results: stored_results.append({"image_id": idx, "caption": result["pred"]}) # for s in result["answer"]: - dataset["annotations"].append({"image_id": idx, "caption": result['instruction'], "id": ann_id}) + dataset["annotations"].append({"image_id": idx, "caption": result["instruction"], "id": ann_id}) ann_id += 1 dataset["images"].append({"id": idx}) diff --git a/lmms_eval/tasks/screenspot/utils_rec.py b/lmms_eval/tasks/screenspot/utils_rec.py index 5cdf5b60..1b65ea62 100644 --- a/lmms_eval/tasks/screenspot/utils_rec.py +++ b/lmms_eval/tasks/screenspot/utils_rec.py @@ -1,13 +1,12 @@ -import re -import logging +import re + from datasets import Dataset -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger REC_METRICS = ["IoU", "ACC@0.1", "ACC@0.3", "ACC@0.5", "ACC@0.7", "ACC@0.9", "Center_ACC"] - def screenspot_rec_doc_to_visual(doc): # Image is presented as is image = doc["image"].convert("RGB") @@ -15,7 +14,10 @@ def screenspot_rec_doc_to_visual(doc): def screenspot_rec_doc_to_text(doc): - return "Bounding box coordinates are specified in the format (top-left x, top-left y, bottom-right x, bottom-right y). All values are floating point numbers bounded between 0 and 1 with two decimal places of precision (e.g., 0.15). Please provide the bounding box coordinates of the region that corresponds to the command: " + doc["instruction"] + return ( + "Bounding box coordinates are specified in the format (top-left x, top-left y, bottom-right x, bottom-right y). All values are floating point numbers bounded between 0 and 1 with two decimal places of precision (e.g., 0.15). Please provide the bounding box coordinates of the region that corresponds to the command: " + + doc["instruction"] + ) def parse_float_sequence_within(input_str): @@ -29,15 +31,15 @@ def parse_float_sequence_within(input_str): list: A list of four floats if the pattern is found, or a list of four zeros if the pattern is not found. """ # Define the regex pattern to find the first instance of four floats within square brackets - pattern = r'\[\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?)\s*\]' - + pattern = r"\[\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?)\s*\]" + # Use re.search to find the first match of the pattern in the input string match = re.search(pattern, input_str) - + # If a match is found, convert the captured groups into a list of floats if match: return [float(match.group(i)) for i in range(1, 5)] - + # If the input does not contain the pattern, return the null float sequence return [0, 0, 0, 0] @@ -53,7 +55,7 @@ def screenspot_rec_process_result(doc, result): pred = result[0] if len(result) > 0 else "" pred = parse_float_sequence_within(pred) ann_id = doc["file_name"] - data_dict = {"instruction": doc["instruction"], "pred": pred, "ann_id": ann_id, 'bbox': doc['bbox'], 'data_type': doc['data_type'], 'data_source': doc['data_source']} + data_dict = {"instruction": doc["instruction"], "pred": pred, "ann_id": ann_id, "bbox": doc["bbox"], "data_type": doc["data_type"], "data_source": doc["data_source"]} return {f"screenspot_{metric}": data_dict for metric in REC_METRICS} @@ -137,46 +139,46 @@ def screenspot_rec_aggregation_result(results, metric): - dict: Dictionary containing the aggregated results for the specified metric. """ scorers = { - 'IoU': compute_iou, - 'ACC@0.1': lambda x, y: compute_accuracy(x, y, 0.1), - 'ACC@0.3': lambda x, y: compute_accuracy(x, y, 0.3), - 'ACC@0.5': lambda x, y: compute_accuracy(x, y, 0.5), - 'ACC@0.7': lambda x, y: compute_accuracy(x, y, 0.7), - 'ACC@0.9': lambda x, y: compute_accuracy(x, y, 0.9), - 'Center_ACC': compute_center_accuracy + "IoU": compute_iou, + "ACC@0.1": lambda x, y: compute_accuracy(x, y, 0.1), + "ACC@0.3": lambda x, y: compute_accuracy(x, y, 0.3), + "ACC@0.5": lambda x, y: compute_accuracy(x, y, 0.5), + "ACC@0.7": lambda x, y: compute_accuracy(x, y, 0.7), + "ACC@0.9": lambda x, y: compute_accuracy(x, y, 0.9), + "Center_ACC": compute_center_accuracy, } results_dict = { - metric: [], - metric + '-mobile_text': [], - metric + '-mobile_icon': [], - metric + '-web_text': [], - metric + '-web_icon': [], - metric + '-desktop_text': [], - metric + '-desktop_icon': [], + metric: [], + metric + "-mobile_text": [], + metric + "-mobile_icon": [], + metric + "-web_text": [], + metric + "-web_icon": [], + metric + "-desktop_text": [], + metric + "-desktop_icon": [], } for result in results: # Extract the ground truth and predicted bounding boxes - gt = result['bbox'] - pred = result['pred'] + gt = result["bbox"] + pred = result["pred"] # Compute the specified metric between the ground truth and predicted bounding boxes score = scorers[metric](gt, pred) results_dict[metric].append(score) - if result['data_type'] == 'text': - if 'ios' in result['data_source'] or 'android' in result['data_source']: - results_dict[metric + '-mobile_text'].append(score) - elif 'macos' in result['data_source'] or 'windows' in result['data_source']: - results_dict[metric + '-desktop_text'].append(score) + if result["data_type"] == "text": + if "ios" in result["data_source"] or "android" in result["data_source"]: + results_dict[metric + "-mobile_text"].append(score) + elif "macos" in result["data_source"] or "windows" in result["data_source"]: + results_dict[metric + "-desktop_text"].append(score) else: - results_dict[metric + '-web_text'].append(score) + results_dict[metric + "-web_text"].append(score) else: - if 'ios' in result['data_source'] or 'android' in result['data_source']: - results_dict[metric + '-mobile_icon'].append(score) - elif 'macos' in result['data_source'] or 'windows' in result['data_source']: - results_dict[metric + '-desktop_icon'].append(score) + if "ios" in result["data_source"] or "android" in result["data_source"]: + results_dict[metric + "-mobile_icon"].append(score) + elif "macos" in result["data_source"] or "windows" in result["data_source"]: + results_dict[metric + "-desktop_icon"].append(score) else: - results_dict[metric + '-web_icon'].append(score) + results_dict[metric + "-web_icon"].append(score) for key in results_dict: if len(results_dict[key]) == 0: @@ -195,6 +197,7 @@ def screenspot_rec_iou(results): def screenspot_rec_acc01(results): return screenspot_rec_aggregation_result(results, "ACC@0.1") + def screenspot_rec_acc03(results): return screenspot_rec_aggregation_result(results, "ACC@0.3") diff --git a/lmms_eval/tasks/stvqa/utils.py b/lmms_eval/tasks/stvqa/utils.py index 81fca5b4..ff033f1f 100755 --- a/lmms_eval/tasks/stvqa/utils.py +++ b/lmms_eval/tasks/stvqa/utils.py @@ -1,6 +1,5 @@ -import os import json -import logging +from loguru import logger from lmms_eval.tasks._task_utils.file_utils import generate_submission_file @@ -25,4 +24,4 @@ def stvqa_aggregate_submissions(results, args): file = generate_submission_file("stvqa_test_for_submission.json", args) with open(file, "w") as f: json.dump(results, f) - logging.getLogger("lmms-eval").info(f"Results saved to {file}") + logger.info(f"Results saved to {file}") diff --git a/lmms_eval/tasks/synthdog/donut_evaluator.py b/lmms_eval/tasks/synthdog/donut_evaluator.py index dd9b022f..4059cab9 100644 --- a/lmms_eval/tasks/synthdog/donut_evaluator.py +++ b/lmms_eval/tasks/synthdog/donut_evaluator.py @@ -11,9 +11,7 @@ from transformers.modeling_utils import PreTrainedModel -import logging - -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger try: import zss diff --git a/lmms_eval/tasks/synthdog/utils.py b/lmms_eval/tasks/synthdog/utils.py index 42e7c62d..274daa42 100644 --- a/lmms_eval/tasks/synthdog/utils.py +++ b/lmms_eval/tasks/synthdog/utils.py @@ -1,9 +1,8 @@ -import logging import json from lmms_eval.tasks._task_utils.file_utils import generate_submission_file from lmms_eval.tasks.synthdog.donut_evaluator import JSONParseEvaluator -logger = logging.getLogger("lmms-eval") +from loguru import logger evaluator = JSONParseEvaluator() diff --git a/lmms_eval/tasks/tempcompass/utils.py b/lmms_eval/tasks/tempcompass/utils.py index 83574347..dbe4f831 100644 --- a/lmms_eval/tasks/tempcompass/utils.py +++ b/lmms_eval/tasks/tempcompass/utils.py @@ -5,7 +5,7 @@ import datetime import lmms_eval.tasks._task_utils.file_utils as file_utils import json -import logging + import yaml from pathlib import Path @@ -48,7 +48,7 @@ cache_dir = os.path.join(HF_HOME, cache_dir) cache_dir = os.path.join(cache_dir, "videos") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Pass in video path here @@ -365,7 +365,6 @@ def tempcompass_process_results_captioning(doc, result): # utils functions for captioning: parse gpt outputs def parse_llm_output_for_captioning(llm_output, gt_answer): - if llm_output == "invalid_request_error" or not llm_output: eval_result = {"rating": -1, "chatgpt-answer": None, "chatgpt-reasoning": None} return eval_result @@ -461,7 +460,6 @@ def eval_rule(video_llm_output, question, answer): # utils function for yes_no def extract_pred(video_llm_output): - # Extract the yes/no predction from the original video llm output video_llm_output = video_llm_output.lower() if video_llm_output.startswith("yes"): diff --git a/lmms_eval/tasks/textcaps/utils.py b/lmms_eval/tasks/textcaps/utils.py index 12dc277f..d21a92fd 100755 --- a/lmms_eval/tasks/textcaps/utils.py +++ b/lmms_eval/tasks/textcaps/utils.py @@ -5,9 +5,8 @@ from pycocotools.coco import COCO from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger dir_name = os.path.dirname(os.path.abspath(__file__)) diff --git a/lmms_eval/tasks/textvqa/utils.py b/lmms_eval/tasks/textvqa/utils.py index 7ffb6570..0c8de47d 100755 --- a/lmms_eval/tasks/textvqa/utils.py +++ b/lmms_eval/tasks/textvqa/utils.py @@ -3,14 +3,14 @@ import json import yaml import pathlib -import logging + import datetime import statistics from lmms_eval.tasks._task_utils.vqa_eval_metric import EvalAIAnswerProcessor from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def textvqa_doc_to_visual(doc): @@ -19,9 +19,7 @@ def textvqa_doc_to_visual(doc): def textvqa_process_results(doc, result): eval_ai_processor = EvalAIAnswerProcessor() - assert ( - len(result) == 1 - ), f"The result should be a list of length 1, but got {len(result)}." + assert len(result) == 1, f"The result should be a list of length 1, but got {len(result)}." resAns = eval_ai_processor(result[0]) accuracy = 0 @@ -32,9 +30,7 @@ def textvqa_process_results(doc, result): doc["answers"][i] = eval_ai_processor(doc["answers"][i]) for i in range(len(doc["answers"])): - otherGTAns = [ - doc["answers"][j] for j in range(len(doc["answers"])) if i != j - ] + otherGTAns = [doc["answers"][j] for j in range(len(doc["answers"])) if i != j] matchingAns = [item for item in otherGTAns if item == resAns] acc = min(1, float(len(matchingAns)) / 3) gtAcc.append(acc) @@ -58,10 +54,7 @@ def textvqa_doc_to_text(doc, model_specific_prompt_kwargs=None): pre_prompt = model_specific_prompt_kwargs["pre_prompt"] if "post_prompt" in model_specific_prompt_kwargs: post_prompt = model_specific_prompt_kwargs["post_prompt"] - if ( - "ocr" in model_specific_prompt_kwargs - and model_specific_prompt_kwargs["ocr"] - ): + if "ocr" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["ocr"]: ocr_ref = f"\nReference OCR token: {', '.join(doc['ocr_tokens'])}" return f"{pre_prompt}{doc['question'].capitalize()}{ocr_ref}{post_prompt}" diff --git a/lmms_eval/tasks/vatex/utils.py b/lmms_eval/tasks/vatex/utils.py index 55ffa9c5..cdb62806 100755 --- a/lmms_eval/tasks/vatex/utils.py +++ b/lmms_eval/tasks/vatex/utils.py @@ -5,11 +5,11 @@ from pycocotools.coco import COCO from lmms_eval.tasks._task_utils.file_utils import generate_submission_file from pathlib import Path -import logging + import yaml import sys -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger dir_name = os.path.dirname(os.path.abspath(__file__)) diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index 67e8b66d..82b769f7 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -1,6 +1,6 @@ import datetime import json -import logging + import os from difflib import SequenceMatcher as SM from functools import partial @@ -29,7 +29,8 @@ nlp = {"en": nlp_en, "zh": nlp_zh} rouge = evaluate.load("rouge") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger + dir_name = os.path.dirname(os.path.abspath(__file__)) aggregate_results_template = { @@ -118,9 +119,7 @@ def vcr_process_results_single(crossed_text, result, language): max_sim_string = "" max_sim_ngram = [] tokens_crossed_text_set = set(tokens_crossed_text) - ngrams_hasjoint = [ - ngram for ngram in ngrams_ if not set(ngram).isdisjoint(tokens_crossed_text_set) - ] + ngrams_hasjoint = [ngram for ngram in ngrams_ if not set(ngram).isdisjoint(tokens_crossed_text_set)] for ngram in ngrams_hasjoint: result_ngram = splitter.join(ngram) @@ -263,7 +262,7 @@ def bootstrap_std(data, n_bootstrap=1000, ci=0.95): return std, lower_bound, upper_bound -def vcr_aggregate_results(results, args, metric='exact_match'): +def vcr_aggregate_results(results, args, metric="exact_match"): """ Args: results: List[List[Dict]], list of results returned by process_results @@ -295,8 +294,8 @@ def vcr_aggregate_results(results, args, metric='exact_match'): def vcr_aggregate_exact_match(results, args): - return vcr_aggregate_results(results, args, metric='exact_match') + return vcr_aggregate_results(results, args, metric="exact_match") def vcr_aggregate_jaccard(results, args): - return vcr_aggregate_results(results, args, metric='jaccard') + return vcr_aggregate_results(results, args, metric="jaccard") diff --git a/lmms_eval/tasks/video_detail_description/utils.py b/lmms_eval/tasks/video_detail_description/utils.py index 382175cd..536776fb 100755 --- a/lmms_eval/tasks/video_detail_description/utils.py +++ b/lmms_eval/tasks/video_detail_description/utils.py @@ -6,7 +6,7 @@ import datetime import lmms_eval.tasks._task_utils.file_utils as file_utils import json -import logging + import yaml from pathlib import Path @@ -44,7 +44,7 @@ cache_dir = os.path.join(HF_HOME, cache_dir) cache_dir = os.path.join(cache_dir, "Test_Videos") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Pass in video path here diff --git a/lmms_eval/tasks/videochatgpt/utils.py b/lmms_eval/tasks/videochatgpt/utils.py index ba6cf07d..e59f2dbf 100755 --- a/lmms_eval/tasks/videochatgpt/utils.py +++ b/lmms_eval/tasks/videochatgpt/utils.py @@ -5,7 +5,7 @@ import datetime import lmms_eval.tasks._task_utils.file_utils as file_utils import json -import logging + import yaml from pathlib import Path @@ -16,7 +16,7 @@ import ast from tqdm import tqdm -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger with open(Path(__file__).parent / "_default_template_yaml", "r") as f: raw_data = f.readlines() diff --git a/lmms_eval/tasks/videomme/utils.py b/lmms_eval/tasks/videomme/utils.py index 3631f87c..f329810d 100755 --- a/lmms_eval/tasks/videomme/utils.py +++ b/lmms_eval/tasks/videomme/utils.py @@ -4,14 +4,14 @@ import json from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -import logging + from pathlib import Path import yaml import sys from typing import List, Dict, Optional, Union import re -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger VIDEO_TYPE = ["short", "medium", "long"] CATEGORIES = ["Knowledge", "Film & Television", "Sports Competition", "Artistic Performance", "Life Record", "Multilingual"] diff --git a/lmms_eval/tasks/vizwiz_vqa/utils.py b/lmms_eval/tasks/vizwiz_vqa/utils.py index 4e84303e..9be1f574 100755 --- a/lmms_eval/tasks/vizwiz_vqa/utils.py +++ b/lmms_eval/tasks/vizwiz_vqa/utils.py @@ -3,14 +3,14 @@ import json import yaml import pathlib -import logging + import datetime import statistics from lmms_eval.tasks._task_utils.file_utils import generate_submission_file from lmms_eval.tasks._task_utils.vqa_eval_metric import EvalAIAnswerProcessor -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def vizwiz_vqa_doc_to_visual(doc): @@ -19,9 +19,7 @@ def vizwiz_vqa_doc_to_visual(doc): def vizwiz_vqa_process_results(doc, result): eval_ai_processor = EvalAIAnswerProcessor() - assert ( - len(result) == 1 - ), f"The result should be a list of length 1, but got {len(result)}." + assert len(result) == 1, f"The result should be a list of length 1, but got {len(result)}." resAns = eval_ai_processor(result[0]) accuracy = 0 @@ -32,9 +30,7 @@ def vizwiz_vqa_process_results(doc, result): doc["answers"][i] = eval_ai_processor(doc["answers"][i]) for i in range(len(doc["answers"])): - otherGTAns = [ - doc["answers"][j] for j in range(len(doc["answers"])) if i != j - ] + otherGTAns = [doc["answers"][j] for j in range(len(doc["answers"])) if i != j] matchingAns = [item for item in otherGTAns if item == resAns] acc = min(1, float(len(matchingAns)) / 3) gtAcc.append(acc) diff --git a/lmms_eval/tasks/vqav2/utils.py b/lmms_eval/tasks/vqav2/utils.py index 3f254ce9..f70a3965 100755 --- a/lmms_eval/tasks/vqav2/utils.py +++ b/lmms_eval/tasks/vqav2/utils.py @@ -1,7 +1,7 @@ import re import os import json -import logging + import datetime import statistics @@ -10,7 +10,7 @@ from lmms_eval.tasks._task_utils.vqa_eval_metric import EvalAIAnswerProcessor -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def vqav2_doc_to_visual(doc): @@ -19,9 +19,7 @@ def vqav2_doc_to_visual(doc): def vqav2_process_results(doc, result): eval_ai_processor = EvalAIAnswerProcessor() - assert ( - len(result) == 1 - ), f"The result should be a list of length 1, but got {len(result)}." + assert len(result) == 1, f"The result should be a list of length 1, but got {len(result)}." resAns = eval_ai_processor(result[0]) accuracy = 0 @@ -35,12 +33,8 @@ def vqav2_process_results(doc, result): if len(set(gtAnswers)) > 1: for ansDic in doc["answers"]: - ansDic["answer"] = eval_ai_processor.process_punctuation( - ansDic["answer"] - ) - ansDic["answer"] = eval_ai_processor.process_digit_article( - ansDic["answer"] - ) + ansDic["answer"] = eval_ai_processor.process_punctuation(ansDic["answer"]) + ansDic["answer"] = eval_ai_processor.process_digit_article(ansDic["answer"]) resAns = eval_ai_processor.process_punctuation(resAns) resAns = eval_ai_processor.process_digit_article(resAns) diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py index e76037c2..ffe53323 100644 --- a/lmms_eval/tasks/websrc/utils.py +++ b/lmms_eval/tasks/websrc/utils.py @@ -7,12 +7,12 @@ import numpy as np import os import json -import logging + from PIL import Image from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -lmms_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase." @@ -31,7 +31,7 @@ def websrc_doc_to_text(doc): def websrc_doc_to_visual(doc): img_bs64 = doc["image"] img = Image.open(io.BytesIO(base64.b64decode(img_bs64))) - del doc['image'] + del doc["image"] return [img] @@ -39,18 +39,20 @@ def websrc_process_results(doc, results): pred = results[0] parsed_pred = pred id = doc["page_id"] - websrc_ans = {"id": id, "domain": doc['domain'], "parsed_pred": parsed_pred} + websrc_ans = {"id": id, "domain": doc["domain"], "parsed_pred": parsed_pred} if "answer" in doc: websrc_ans["answer"] = doc["answer"] - if 'id' in doc: - websrc_ans['question_id'] = doc['id'] + if "id" in doc: + websrc_ans["question_id"] = doc["id"] return { "websrc_squad_f1": websrc_ans, "submission": { - websrc_ans['question_id']: pred, - } if 'question_id' in websrc_ans else None + websrc_ans["question_id"]: pred, + } + if "question_id" in websrc_ans + else None, } @@ -61,7 +63,7 @@ def websrc_test_aggregate_results_for_submission(results, args): for result in results: out.update(result) json.dump(out, f, indent=4) - lmms_logger.info(f"Results saved to {path}.") + eval_logger.info(f"Results saved to {path}.") def websrc_aggregate_results(results): @@ -87,9 +89,7 @@ def websrc_aggregate_results(results): "num": int(evaluation_result[domain]["num_example"]), "f1": round(evaluation_result[domain]["f1"], 3), } - all_ins_f1 = np.sum([cat_results["f1"] * cat_results["num_example"] for cat_results in evaluation_result.values()]) / sum( - [cat_results["num_example"] for cat_results in evaluation_result.values()] - ) + all_ins_f1 = np.sum([cat_results["f1"] * cat_results["num_example"] for cat_results in evaluation_result.values()]) / sum([cat_results["num_example"] for cat_results in evaluation_result.values()]) printable_results["Overall"] = { "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]), "f1": round(all_ins_f1, 3), @@ -102,34 +102,33 @@ def websrc_aggregate_results(results): # Helper functions written by official MMMU repo. ################## DOMAINS = [ - 'auto', - 'book', - 'camera', - 'game', - 'jobs', - 'movie', - 'phone', - 'restaurant', - 'sports', - 'university', - 'hotel', + "auto", + "book", + "camera", + "game", + "jobs", + "movie", + "phone", + "restaurant", + "sports", + "university", + "hotel", ] def evaluate_websrc(samples): - def _normalize_str(string): # lower it string = string.lower() # strip leading and trailing whitespaces string = string.strip() - + return string def _tokenize(text): # Regex pattern to match words and isolate punctuation - pattern = r'\w+|[^\w\s]' + pattern = r"\w+|[^\w\s]" tokens = re.findall(pattern, text) return tokens diff --git a/lmms_eval/tasks/worldqa/utils.py b/lmms_eval/tasks/worldqa/utils.py index aa528432..bd9b89ef 100755 --- a/lmms_eval/tasks/worldqa/utils.py +++ b/lmms_eval/tasks/worldqa/utils.py @@ -6,7 +6,7 @@ from lmms_eval.filters.extraction import ExtendedRegexFilter from lmms_eval.tasks.worldqa.worldqa_mc_evaluator import WorldQA_MC_Evaluator import json -import logging + import yaml from pathlib import Path import requests @@ -114,7 +114,7 @@ def get_eval(question: str, ground_truth: str, candidate: str, max_tokens: int, cache_dir = os.path.join(cache_dir, "videos") -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger # Pass in video path here diff --git a/lmms_eval/tasks/worldqa/worldqa_mc_evaluator.py b/lmms_eval/tasks/worldqa/worldqa_mc_evaluator.py index e1e2ae76..85e617c5 100644 --- a/lmms_eval/tasks/worldqa/worldqa_mc_evaluator.py +++ b/lmms_eval/tasks/worldqa/worldqa_mc_evaluator.py @@ -10,9 +10,8 @@ import pandas as pd from tqdm import tqdm -import logging -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger class WorldQA_MC_Evaluator: diff --git a/lmms_eval/tasks/youcook2/utils.py b/lmms_eval/tasks/youcook2/utils.py index 1761a8b1..f4e9af91 100644 --- a/lmms_eval/tasks/youcook2/utils.py +++ b/lmms_eval/tasks/youcook2/utils.py @@ -1,6 +1,6 @@ import os import yaml -import logging + import string import random import numpy as np @@ -15,7 +15,7 @@ COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"] # , "SPICE"] -eval_logger = logging.getLogger("lmms-eval") +from loguru import logger as eval_logger def remove_nonascii(text): diff --git a/lmms_eval/utils.py b/lmms_eval/utils.py index 2e315fbe..80231c9c 100755 --- a/lmms_eval/utils.py +++ b/lmms_eval/utils.py @@ -35,36 +35,8 @@ from jinja2 import BaseLoader, Environment, StrictUndefined from itertools import islice import pytz -import logging - - -class PathFormatter(logging.Formatter): - def __init__(self, fmt=None, datefmt=None, timezone="UTC"): - super().__init__(fmt, datefmt) - self.timezone = timezone - - def formatTime(self, record, datefmt=None): - # Convert to Asia/Singapore timezone - ct = datetime.datetime.fromtimestamp(record.created, pytz.timezone(self.timezone)) - if datefmt: - s = ct.strftime(datefmt) - else: - try: - s = ct.isoformat(timespec="milliseconds") - except TypeError: - s = ct.isoformat() - return s - - def format(self, record): - # Extract the pathname from the record - pathname = record.pathname - # Split the pathname into folders - folders = pathname.split(os.sep) - # Get the last two folders and the filename - if len(folders) > 2: - record.pathname = os.sep.join(folders[-3:]) - return super(PathFormatter, self).format(record) +from loguru import logger as eval_logger SPACING = " " * 47 diff --git a/pyproject.toml b/pyproject.toml index 593ef151..818c3f4b 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,7 @@ dependencies = [ "mpmath", "Jinja2", "openpyxl", + "loguru", "Levenshtein", "hf_transfer", "tenacity==8.3.0", From 12f480699c71a12a24d4349d9b0681933201a3a6 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Fri, 21 Jun 2024 08:35:39 +0000 Subject: [PATCH 111/196] Remove unnecessary lines since use batched visuals now in llava --- lmms_eval/models/llava.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py index a1976715..6de4c8f8 100755 --- a/lmms_eval/models/llava.py +++ b/lmms_eval/models/llava.py @@ -358,18 +358,6 @@ def _collate(x): prompt_question = conv.get_prompt() question_input.append(prompt_question) - # The above for loop has bugs. When there is no visuals, e.g. pure text, - # there will be no for loop execute resulting in an empty question_input (because no visuals) - # Scenario 1 won't even be execute - if len(flattened_visuals) == 0: - for context in contexts: - question = context - conv = conv_templates[self.conv_template].copy() - conv.append_message(conv.roles[0], question) - conv.append_message(conv.roles[1], None) - prompt_question = conv.get_prompt() - question_input.append(prompt_question) - # input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) # preconfigure gen_kwargs with defaults gen_kwargs["image_sizes"] = [flattened_visuals[idx].size for idx in range(len(flattened_visuals))] From 7d6201f921088afd3f52a35076e3c6fcc9aa518c Mon Sep 17 00:00:00 2001 From: kcz358 Date: Fri, 21 Jun 2024 08:38:24 +0000 Subject: [PATCH 112/196] Add longva --- lmms_eval/models/longva.py | 474 +++++++++++++++++++++++++++++++++++++ 1 file changed, 474 insertions(+) create mode 100644 lmms_eval/models/longva.py diff --git a/lmms_eval/models/longva.py b/lmms_eval/models/longva.py new file mode 100644 index 00000000..1888c9a0 --- /dev/null +++ b/lmms_eval/models/longva.py @@ -0,0 +1,474 @@ +from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs +from accelerate.state import AcceleratorState +from transformers import AutoConfig + +import math +import torch + +torch.backends.cuda.matmul.allow_tf32 = True + +from tqdm import tqdm +from datetime import timedelta +from decord import VideoReader, cpu +import numpy as np + +import copy +import PIL +from typing import List, Optional, Union, Tuple +from packaging import version +import warnings +import logging + +warnings.filterwarnings("ignore") + +eval_logger = logging.getLogger("lmms-eval") + +from lmms_eval import utils +from lmms_eval.api.instance import Instance +from lmms_eval.api.model import lmms +from lmms_eval.api.registry import register_model +from lmms_eval.models.model_utils.load_video import read_video_pyav + +try: + from longva.model.builder import load_pretrained_model + from longva.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token, KeywordsStoppingCriteria + from longva.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX + from longva.conversation import conv_templates, SeparatorStyle + +except Exception as e: + eval_logger.debug("longva is not installed. Please install longva to use this model.\nError: %s" % e) + +# inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5 +# if is_flash_attn_2_available: +# best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating + +if version.parse(torch.__version__) >= version.parse("2.1.2"): + best_fit_attn_implementation = "sdpa" +else: + best_fit_attn_implementation = "eager" + + +@register_model("longva") +class LongVA(lmms): + + def __init__( + self, + pretrained: str = "lmms-lab/LongVA-7B", + truncation: Optional[bool] = True, + device: Optional[str] = "cuda:0", + batch_size: Optional[Union[int, str]] = 1, + model_name: Optional[str] = None, + attn_implementation: Optional[str] = best_fit_attn_implementation, + device_map: Optional[str] = "cuda:0", + conv_template: Optional[str] = "vicuna_v1", + use_cache: Optional[bool] = True, + truncate_context: Optional[bool] = False, # whether to truncate the context in generation, set it False for LLaVA-1.6 + customized_config: Optional[str] = None, # ends in json + max_frames_num: Optional[int] = 32, + mm_spatial_pool_stride: Optional[int] = 2, + mm_spatial_pool_mode: Optional[str] = "average", + token_strategy: Optional[str] = "single", # could be "single" or "multiple", "multiple" denotes adding multiple tokens for each frame + video_decode_backend: str = "pyav", + **kwargs, + ) -> None: + super().__init__() + # Do not use kwargs for now + assert kwargs == {}, f"Unexpected kwargs: {kwargs}" + + accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) + accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + if accelerator.num_processes > 1: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + elif accelerator.num_processes == 1 and device_map == "auto": + self._device = torch.device(device) + self.device_map = device_map + else: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + + llava_model_args = { + "multimodal": True, + } + if customized_config is not None: + llava_model_args["customized_config"] = customized_config + if attn_implementation is not None: + llava_model_args["attn_implementation"] = attn_implementation + if "use_flash_attention_2" in kwargs: + llava_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"] + model_name = model_name if model_name is not None else get_model_name_from_path(pretrained) + + self.pretrained = pretrained + self.token_strategy = token_strategy + self.max_frames_num = max_frames_num + self.mm_spatial_pool_stride = mm_spatial_pool_stride + self.mm_spatial_pool_mode = mm_spatial_pool_mode + self.video_decode_backend = video_decode_backend + + overwrite_config = {} + overwrite_config["mm_spatial_pool_stride"] = self.mm_spatial_pool_stride + overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode + cfg_pretrained = AutoConfig.from_pretrained(self.pretrained) + + if cfg_pretrained.architectures[0] == "LlavaLlamaForCausalLM": # Ugly code, only used in vicuna that needs ROPE + if "224" in cfg_pretrained.mm_vision_tower: + least_token_number = self.max_frames_num * (16 // self.mm_spatial_pool_stride) ** 2 + 1000 + else: + least_token_number = self.max_frames_num * (24 // self.mm_spatial_pool_stride) ** 2 + 1000 + + scaling_factor = math.ceil(least_token_number / 4096) + if scaling_factor >= 2: + overwrite_config["rope_scaling"] = {"factor": float(scaling_factor), "type": "linear"} + overwrite_config["max_sequence_length"] = 4096 * scaling_factor + overwrite_config["tokenizer_model_max_length"] = 4096 * scaling_factor + + llava_model_args["overwrite_config"] = overwrite_config + try: + # Try to load the model with the multimodal argument + self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) + except TypeError: + # for older versions of LLaVA that don't have multimodal argument + llava_model_args.pop("multimodal", None) + self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) + + self._config = self._model.config + self.model.eval() + self.model.tie_weights() + self.truncation = truncation + self.batch_size_per_gpu = int(batch_size) + self.conv_template = conv_template + self.use_cache = use_cache + self.truncate_context = truncate_context + assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue." + + if accelerator.num_processes > 1: + assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." + # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model + # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works + # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. + if accelerator.distributed_type == DistributedType.DEEPSPEED: + kwargs = { + "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, + "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, + } + AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) + eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") + + if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: + self._model = accelerator.prepare(self.model) + else: + self._model = accelerator.prepare_model(self.model, evaluation_mode=True) + self.accelerator = accelerator + if self.accelerator.is_local_main_process: + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + + elif accelerator.num_processes == 1 and device_map == "auto": + eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") + self._rank = 0 + self._word_size = 1 + + else: + eval_logger.info(f"Using single device: {self._device}") + self.model.to(self._device) + self._rank = 0 + self._world_size = 1 + + @property + def config(self): + # return the associated transformers.AutoConfig for the given pretrained model. + return self._config + + @property + def tokenizer(self): + return self._tokenizer + + @property + def model(self): + # returns the model, unwrapping it if using Accelerate + if hasattr(self, "accelerator"): + return self.accelerator.unwrap_model(self._model) + else: + return self._model + + @property + def eot_token_id(self): + # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* + return self.tokenizer.eos_token_id + + @property + def max_length(self): + return self._max_length + + def pad_sequence(self, input_ids, batch_first, padding_value): + if self.tokenizer.padding_side == "left": + input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] + input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value) + if self.tokenizer.padding_side == "left": + input_ids = torch.flip(input_ids, [1]) + return input_ids + + @property + def batch_size(self): + return self.batch_size_per_gpu + + @property + def device(self): + return self._device + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + + def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]: + """ """ + add_special_tokens = False if add_special_tokens is None else add_special_tokens + encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens) + # left-truncate the encoded context to be at most `left_truncate_len` tokens long + if left_truncate_len: + encoding = encoding[-left_truncate_len:] + return encoding + + def tok_decode(self, tokens): + try: + return self.tokenizer.decode(tokens) + except: + return self.tokenizer.decode([tokens]) + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + # TODO + res = [] + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") + + for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: + # encode, pad, and truncate contexts for this batch + if type(doc_to_target) == str: + continuation = doc_to_target + else: + continuation = doc_to_target(self.task_dict[task][split][doc_id]) + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] + visuals = self.flatten(visuals) + image_sizes = [[visual.size[0], visual.size[1]] for visual in visuals] + if visuals: + image = process_images(visuals, self._image_processor, self._config) + if type(image) is list: + image = [_image.to(dtype=torch.float16, device=self.device) for _image in image] + else: + image = image.to(dtype=torch.float16, device=self.device) + else: + image = None + + prompts_input = contexts[0] if isinstance(contexts, list) else contexts + + if image is not None and len(image) != 0 and DEFAULT_IMAGE_TOKEN not in prompts_input: + """ + Three senarios: + 1. No image, and there for, no image token should be added. + 2. image token is already specified in the context, so we don't need to add it. + 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. + """ + image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals) + image_tokens = " ".join(image_tokens) + prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts) + + # This is much safer for llama3, as we now have some object type in it + if "llama_3" in self.conv_template: + conv = copy.deepcopy(conv_templates[self.conv_template]) + else: + conv = conv_templates[self.conv_template].copy() + + conv.append_message(conv.roles[0], prompts_input) + conv.append_message(conv.roles[1], None) + prompt = conv.get_prompt() + pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id + contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) + # Add the answer of the second role + conv.messages[1][1] = continuation + + prompt = conv.get_prompt() + input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) + labels = input_ids.clone() + # Context part no need to calculate for loss + labels[0, : contxt_id.shape[1]] = -100 + with torch.inference_mode(): + outputs = self.model(input_ids=input_ids, labels=labels, images=image, use_cache=True, image_sizes=image_sizes) + loss = outputs["loss"] + # loss = torch.exp(loss) + logits = outputs["logits"] + greedy_tokens = logits.argmax(dim=-1) + cont_toks = input_ids[:, contxt_id.shape[1] :] # [1, seq] + greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]] # [1, seq] + max_equal = (greedy_tokens == cont_toks).all() + res.append((float(loss.item()), bool(max_equal))) + pbar.update(1) + + pbar.close() + return res + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + def load_video(self, video_path, max_frames_num): + if type(video_path) == str: + vr = VideoReader(video_path, ctx=cpu(0)) + else: + vr = VideoReader(video_path[0], ctx=cpu(0)) + total_frame_num = len(vr) + uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int) + frame_idx = uniform_sampled_frames.tolist() + spare_frames = vr.get_batch(frame_idx).asnumpy() + return spare_frames # (frames, height, width, channels) + + def generate_until(self, requests: List[Instance]) -> List[str]: + res = [] + + def _collate(x): + # the negative sign on len(toks) sorts descending - this has a few advantages: + # - time estimates will always be over not underestimates, which is more useful for planning + # - to know the size of a batch when going through the list, you know the first one is always the batch + # padded context length. this is useful to simplify the batching logic and more importantly to make + # automatic adaptive batches much much easier to implement + # - any OOMs will happen right away rather than near the end + toks = self.tok_encode(x[0]) + return -len(toks), x[0] + + # we group requests by their generation_kwargs, + # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling + # in the same batch. + re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) + chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) + num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1 + pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding") + for chunk in chunks: + batched_contexts, all_gen_kwargs, batched_doc_to_visual, batched_doc_id, batched_task, batched_split = zip(*chunk) + task = batched_task[0] + split = batched_split[0] + batched_visuals = [batched_doc_to_visual[0](self.task_dict[task][split][ids]) for ids in batched_doc_id] # [B, N] + flattened_visuals = self.flatten(batched_visuals) # [B*N] + assert len(batched_visuals) == 1 + + # we assume all gen kwargs in the batch are the same + # this is safe to assume because the `grouper` object ensures it. + gen_kwargs = all_gen_kwargs[0] + if "until" in gen_kwargs: + gen_kwargs.pop("until") + + question_input = [] + + for visual, context in zip(batched_visuals, batched_contexts): + if "image_aspect_ratio" in gen_kwargs.keys() and "image_aspect_ratio" not in self._config.__dict__: + # here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation + self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio") + eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") + + # encode, pad, and truncate contexts for this batch + if type(visual[0]) == PIL.Image.Image: # For image task + image_tensor = process_images(visual, self._image_processor, self._config) + if type(image_tensor) is list: + image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] + else: + image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) + + task_type = "image" + + elif type(visual[0]) == str: # For video task + image_tensor = [] + try: + if self.video_decode_backend == "decord": + frames = self.load_video(visual, self.max_frames_num) + elif self.video_decode_backend == "pyav": + frames = read_video_pyav(visual[0], num_frm=self.max_frames_num) + frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda() + image_tensor.append(frames) + except Exception as e: + eval_logger.error(f"Error {e} in loading video") + image_tensor = None + + task_type = "video" + + if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context: + """ + Three senarios: + 1. No image, and there for, no image token should be added. + 2. image token is already specified in the context, so we don't need to add it. + 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. + 4. For video tasks, we could add a token or multiple tokens for each frame in the context. This depends on the training strategy and should balance in test to decide which is better + """ + if task_type == "image": + image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visual) if isinstance(visual, list) else [DEFAULT_IMAGE_TOKEN] + elif task_type == "video": + image_tokens = [DEFAULT_IMAGE_TOKEN] * len(frames) if self.token_strategy == "multiple" else [DEFAULT_IMAGE_TOKEN] + + image_tokens = " ".join(image_tokens) + question = image_tokens + "\n" + context + else: + question = context + + # This is much safer for llama3, as we now have some object type in it + if "llama_3" in self.conv_template: + conv = copy.deepcopy(conv_templates[self.conv_template]) + else: + conv = conv_templates[self.conv_template].copy() + conv.append_message(conv.roles[0], question) + conv.append_message(conv.roles[1], None) + prompt_question = conv.get_prompt() + question_input.append(prompt_question) + + # preconfigure gen_kwargs with defaults + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + if "do_sample" not in gen_kwargs: + gen_kwargs["do_sample"] = False + if "top_p" not in gen_kwargs: + gen_kwargs["top_p"] = None + if "num_beams" not in gen_kwargs: + gen_kwargs["num_beams"] = 1 + + input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] + pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id + input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device) + attention_masks = input_ids.ne(pad_token_ids).to(self.device) + + if task_type == "image": + gen_kwargs["image_sizes"] = [flattened_visuals[idx].size for idx in range(len(flattened_visuals))] + elif task_type == "video": + stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 + keywords = [stop_str] + stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids) + gen_kwargs["modalities"] = ["video"] + gen_kwargs["stopping_criteria"] = [stopping_criteria] + self._config.mm_spatial_pool_stride = self.mm_spatial_pool_stride + self._config.mm_spatial_pool_mode = self.mm_spatial_pool_mode + + # These steps are not in LLaVA's original code, but are necessary for generation to work + # TODO: attention to this major generation step... + if "image_aspect_ratio" in gen_kwargs.keys(): + gen_kwargs.pop("image_aspect_ratio") + try: + with torch.inference_mode(): + cont = self.model.generate(input_ids, attention_mask=attention_masks, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs) + + text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True) + except Exception as e: + raise e + + text_outputs = [response.strip() for response in text_outputs] + res.extend(text_outputs) + self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) + pbar.update(1) + # reorder this group of results back to original unsorted form + res = re_ords.get_original(res) + + pbar.close() + return res \ No newline at end of file From 120c474b056f9177c74e1fd9691d59e2f234b785 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Fri, 21 Jun 2024 08:38:41 +0000 Subject: [PATCH 113/196] Revise model registry for llava_hf and longva --- lmms_eval/models/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index b999f99d..2c59ea8d 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -28,6 +28,8 @@ "mplug_owl_video": "mplug_Owl", "phi3v": "Phi3v", "tinyllava": "TinyLlava", + "llava_hf": "LlavaHf", + "longva": "LongVA", } for model_name, model_class in AVAILABLE_MODELS.items(): From ebe7217a486c1e754e42c2cbdb834e09fbbcc9b0 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Sat, 22 Jun 2024 02:57:08 +0000 Subject: [PATCH 114/196] Delete unnecessary lines --- lmms_eval/models/longva.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/lmms_eval/models/longva.py b/lmms_eval/models/longva.py index 1888c9a0..7202c49c 100644 --- a/lmms_eval/models/longva.py +++ b/lmms_eval/models/longva.py @@ -110,18 +110,6 @@ def __init__( overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode cfg_pretrained = AutoConfig.from_pretrained(self.pretrained) - if cfg_pretrained.architectures[0] == "LlavaLlamaForCausalLM": # Ugly code, only used in vicuna that needs ROPE - if "224" in cfg_pretrained.mm_vision_tower: - least_token_number = self.max_frames_num * (16 // self.mm_spatial_pool_stride) ** 2 + 1000 - else: - least_token_number = self.max_frames_num * (24 // self.mm_spatial_pool_stride) ** 2 + 1000 - - scaling_factor = math.ceil(least_token_number / 4096) - if scaling_factor >= 2: - overwrite_config["rope_scaling"] = {"factor": float(scaling_factor), "type": "linear"} - overwrite_config["max_sequence_length"] = 4096 * scaling_factor - overwrite_config["tokenizer_model_max_length"] = 4096 * scaling_factor - llava_model_args["overwrite_config"] = overwrite_config try: # Try to load the model with the multimodal argument From 903b042be016016d4ebeecb07701f3076a2d323c Mon Sep 17 00:00:00 2001 From: kcz358 Date: Sat, 22 Jun 2024 12:07:13 +0000 Subject: [PATCH 115/196] Remove unnecessary lines for video llava --- lmms_eval/models/video_llava.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/lmms_eval/models/video_llava.py b/lmms_eval/models/video_llava.py index 0acc6356..4c8d7058 100644 --- a/lmms_eval/models/video_llava.py +++ b/lmms_eval/models/video_llava.py @@ -18,16 +18,6 @@ eval_logger = logger -try: - import torch - from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN - from videollava.conversation import conv_templates, SeparatorStyle - from videollava.model.builder import load_pretrained_model - from videollava.utils import disable_torch_init - from videollava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria -except ImportError as e: - eval_logger.warning(f"Error importing Video-LLaVA: {e}") - from transformers import VideoLlavaProcessor, VideoLlavaForConditionalGeneration from lmms_eval.models.model_utils.load_video import read_video_pyav From 8f9d620fcd9d0a0742ee6bcf51ea63bd6b088a36 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Sun, 23 Jun 2024 14:02:25 +0800 Subject: [PATCH 116/196] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 818c3f4b..c65d3b8c 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta" [project] name = "lmms_eval" -version = "0.2.0" +version = "0.2.0.post1" authors = [ { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" }, ] From b388d79e0df6f60068196cb7047453ebd22d6ef1 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Sun, 23 Jun 2024 22:31:16 +0800 Subject: [PATCH 117/196] Update activitynetqa_generation.yaml --- lmms_eval/tasks/activitynetqa/activitynetqa_generation.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/lmms_eval/tasks/activitynetqa/activitynetqa_generation.yaml b/lmms_eval/tasks/activitynetqa/activitynetqa_generation.yaml index fe0c18e4..fd7e069a 100755 --- a/lmms_eval/tasks/activitynetqa/activitynetqa_generation.yaml +++ b/lmms_eval/tasks/activitynetqa/activitynetqa_generation.yaml @@ -1,4 +1,3 @@ -dataset_name: "Generation" task: "activitynetqa" test_split: test output_type: generate_until From 06fa000f60d3e4d160fac8ceb9959ae92a98f752 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 25 Jun 2024 06:41:13 +0000 Subject: [PATCH 118/196] Fix vid mme post prompt issue --- lmms_eval/tasks/videomme/videomme_w_subtitle.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml index ac34cdf4..cfa5f070 100644 --- a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml +++ b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml @@ -26,6 +26,8 @@ metric_list: model_specific_prompt_kwargs: default: frame_num: 32 + pre_prompt: "" + post_prompt: "\nAnswer the question using a single word or phrase." gemini_api: gemini_api_flag: "full subtitle" # gpt4v: From a0de89708d5e6f259bb17f0eaace3c5b901b275c Mon Sep 17 00:00:00 2001 From: Dannoopsy Date: Tue, 25 Jun 2024 11:11:37 +0000 Subject: [PATCH 119/196] new task gqa-ru --- lmms_eval/tasks/gqa_ru/gqa_ru.yaml | 29 +++++++++++++++++++++++++++++ lmms_eval/tasks/gqa_ru/utils.py | 23 +++++++++++++++++++++++ 2 files changed, 52 insertions(+) create mode 100644 lmms_eval/tasks/gqa_ru/gqa_ru.yaml create mode 100644 lmms_eval/tasks/gqa_ru/utils.py diff --git a/lmms_eval/tasks/gqa_ru/gqa_ru.yaml b/lmms_eval/tasks/gqa_ru/gqa_ru.yaml new file mode 100644 index 00000000..2a3d1097 --- /dev/null +++ b/lmms_eval/tasks/gqa_ru/gqa_ru.yaml @@ -0,0 +1,29 @@ +dataset_path: deepvk/GQA-ru +dataset_name: testdev_balanced_instructions +dataset_kwargs: + token: True +task: "gqa-ru" +test_split: testdev +output_type: generate_until +doc_to_visual: !function utils.gqa_doc_to_visual +doc_to_text: !function utils.gqa_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 16 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +metric_list: + - metric: exact_match + aggregation: mean + higher_is_better: true + ignore_case: true + ignore_punctuation: true +metadata: + - version: 0.0 + +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nОтветь одним словом." \ No newline at end of file diff --git a/lmms_eval/tasks/gqa_ru/utils.py b/lmms_eval/tasks/gqa_ru/utils.py new file mode 100644 index 00000000..9c1acb9c --- /dev/null +++ b/lmms_eval/tasks/gqa_ru/utils.py @@ -0,0 +1,23 @@ +from datasets import load_dataset + +GQA_RAW_IMAGE_DATASET = None +GQA_ID2IMAGE = None + + +def gqa_doc_to_visual(doc): + global GQA_RAW_IMAGE_DATASET + global GQA_ID2IMAGE + if GQA_RAW_IMAGE_DATASET is None: + GQA_RAW_IMAGE_DATASET = load_dataset("deepvk/GQA-ru", "testdev_balanced_images", split="testdev", token=True) + GQA_ID2IMAGE = {} + for row in GQA_RAW_IMAGE_DATASET: + GQA_ID2IMAGE[row["id"]] = row["image"].convert("RGB") + image = GQA_ID2IMAGE[doc["imageId"]] + return [image] + + +def gqa_doc_to_text(doc, model_specific_prompt_kwargs): + question = doc["question"] + pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + post_prompt = model_specific_prompt_kwargs["post_prompt"] + return f"{pre_prompt}{question}{post_prompt}" From 27ea9c0055a8abf3a8198829b8617018479918e2 Mon Sep 17 00:00:00 2001 From: Dannoopsy Date: Thu, 27 Jun 2024 17:17:29 +0000 Subject: [PATCH 120/196] add mmbench_ru_dev --- .../mmbench/_default_template_mmbench_ru_yaml | 24 ++++ lmms_eval/tasks/mmbench/mmbench.yaml | 1 + lmms_eval/tasks/mmbench/mmbench_ru_dev.yaml | 10 ++ lmms_eval/tasks/mmbench/ru_utils.py | 128 ++++++++++++++++++ 4 files changed, 163 insertions(+) create mode 100644 lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml create mode 100644 lmms_eval/tasks/mmbench/mmbench_ru_dev.yaml create mode 100644 lmms_eval/tasks/mmbench/ru_utils.py diff --git a/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml b/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml new file mode 100644 index 00000000..fad7f927 --- /dev/null +++ b/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml @@ -0,0 +1,24 @@ +dataset_path: deepvk/MMBench-ru +dataset_kwargs: + token: True +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." +doc_to_visual: !function ru_utils.mmbench_doc_to_visual +doc_to_text: !function ru_utils.mmbench_doc_to_text +doc_to_target: "answer" +process_results: !function ru_utils.mmbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/mmbench/mmbench.yaml b/lmms_eval/tasks/mmbench/mmbench.yaml index 821065ee..f2546aa5 100755 --- a/lmms_eval/tasks/mmbench/mmbench.yaml +++ b/lmms_eval/tasks/mmbench/mmbench.yaml @@ -5,6 +5,7 @@ task: - mmbench_cn_dev - mmbench_cn_test - mmbench_cn_cc + - mmbench_ru_dev metadata: version: 0.0 sys_prompt: "There are several options:" diff --git a/lmms_eval/tasks/mmbench/mmbench_ru_dev.yaml b/lmms_eval/tasks/mmbench/mmbench_ru_dev.yaml new file mode 100644 index 00000000..46407ae4 --- /dev/null +++ b/lmms_eval/tasks/mmbench/mmbench_ru_dev.yaml @@ -0,0 +1,10 @@ +task: "mmbench_ru_dev" +test_split: dev +include: _default_template_mmbench_ru_yaml +metric_list: + - metric: gpt_eval_score + aggregation: !function ru_utils.mmbench_aggregate_dev_results_eval + higher_is_better: true + - metric: submission + aggregation: !function ru_utils.mmbench_aggregate_dev_results_submission + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mmbench/ru_utils.py b/lmms_eval/tasks/mmbench/ru_utils.py new file mode 100644 index 00000000..3ff515c7 --- /dev/null +++ b/lmms_eval/tasks/mmbench/ru_utils.py @@ -0,0 +1,128 @@ +import yaml +import os +from pathlib import Path +import pandas as pd +import json + +from loguru import logger as eval_logger +from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file + +with open(Path(__file__).parent / "mmbench.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + + config = yaml.safe_load("".join(safe_data)) + +GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"] +API_TYPE = os.getenv("API_TYPE", "openai") + +if API_TYPE == "openai": + API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") + API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") +elif API_TYPE == "azure": + API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") + API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") +else: + API_URL = "YOUR_API_URL" + API_KEY = "YOUR_API_KEY" + + +mmbench_evaluator = MMBench_Evaluator(sys_prompt=config["metadata"]["sys_prompt"], API_KEY=API_KEY, API_URL=API_URL, model_version=GPT_EVAL_MODEL_NAME) + + +def mmbench_doc_to_visual(doc): + return [doc["image"].convert("RGB")] + + +def mmbench_doc_to_text(doc, model_specific_prompt_kwargs=None): + option_candidate = ["A", "B", "C", "D", "E"] + options_prompt, options_dict = mmbench_evaluator.create_options_prompt(doc, option_candidate) + + data = { + # "img": doc["image"], + "question": doc["question"], + "answer": doc.get("answer", None), + "options": options_prompt, + "category": doc["category"], + "L2-category": doc["l2-category"], + "options_dict": options_dict, + "index": doc["index"], + "hint": doc["hint"], + "source": doc["source"], + "split": doc["split"], + } + + query_prompt = f"{data['hint']} {data['question']} {data['options']}" if pd.notna(data["hint"]) and data["hint"] != "nan" else f"{data['question']} {data['options']}" + + if model_specific_prompt_kwargs: + query_prompt = f"{query_prompt}\n{model_specific_prompt_kwargs['post_prompt']}" + + return query_prompt + + +def mmbench_process_results(doc, results): + model_response = results[0].strip() + data = { + "gpt_eval_score": { + "index": doc["index"], + "question": doc["question"], + "answer": doc["answer"], + "prediction": model_response, + "hint": doc["hint"], + "source": doc["source"], + "split": doc["split"], + "category": doc["category"], + "L2-category": doc["l2-category"], + }, + "submission": { + "index": doc["index"], + "question": doc["question"], + "answer": doc["answer"], + "prediction": model_response, + "hint": doc["hint"], + "source": doc["source"], + "split": doc["split"], + "category": doc["category"], + "L2-category": doc["l2-category"], + }, + } + option_candidate = ["A", "B", "C", "D", "E"] + for c in option_candidate: + data["submission"][c] = doc.get(c, "nan") + data["gpt_eval_score"][c] = doc.get(c, "nan") + return data + + +def mmbench_aggregate_dev_results_eval(results, args): + print(f"============= MMBench-RU(Dev) Detailed Results =============") + overall_acc, category_acc, l2_category_acc = mmbench_evaluator.eval_result(results, eval_method="openai") + file = generate_submission_file("mmbench_ru_dev_results.json", args) + details_info = { + "overall_acc": overall_acc, + "category_acc": category_acc, + "l2_category_acc": l2_category_acc, + } + with open(file, "w") as f: + json.dump(details_info, f) + return overall_acc * 100 + + +def mmbench_aggregate_dev_results_submission(results, args): + df = pd.DataFrame(results) + excel_write_path = generate_submission_file("mmbench_ru_dev_results.xlsx", args) + with pd.ExcelWriter(excel_write_path) as writer: + df.to_excel(writer, index=False) + eval_logger.info(f"Saved results to {excel_write_path}") + + +def mmbench_aggregate_test_results(results, args): + df = pd.DataFrame(results) + excel_write_path = generate_submission_file("mmbench_ru_test_results.xlsx", args) + with pd.ExcelWriter(excel_write_path) as writer: + df.to_excel(writer, index=False) + eval_logger.info(f"Saved results to {excel_write_path}") From ba7081c0abac840002d320e30733e891298dfa11 Mon Sep 17 00:00:00 2001 From: Dannoopsy <63581325+Dannoopsy@users.noreply.github.com> Date: Fri, 28 Jun 2024 12:21:05 +0300 Subject: [PATCH 121/196] change prompt to ru --- lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml b/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml index fad7f927..993cd52a 100644 --- a/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml +++ b/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml @@ -5,7 +5,7 @@ doc_to_target: "answer" model_specific_prompt_kwargs: default: pre_prompt: "" - post_prompt: "\nAnswer with the option's letter from the given choices directly." + post_prompt: "\nВыбери правильный вариант ответа буквой." doc_to_visual: !function ru_utils.mmbench_doc_to_visual doc_to_text: !function ru_utils.mmbench_doc_to_text doc_to_target: "answer" From a75225926e5954f85466d257f99acf0163fde596 Mon Sep 17 00:00:00 2001 From: lscpku Date: Fri, 28 Jun 2024 20:37:06 +0800 Subject: [PATCH 122/196] create new task vitatecs --- .../tasks/vitatecs/_default_template_yaml | 9 + lmms_eval/tasks/vitatecs/_vitatecs.yaml | 8 + lmms_eval/tasks/vitatecs/utils.py | 225 ++++++++++++++++++ .../vitatecs/vitatecs_compositionality.yaml | 13 + .../tasks/vitatecs/vitatecs_direction.yaml | 13 + .../tasks/vitatecs/vitatecs_intensity.yaml | 13 + .../tasks/vitatecs/vitatecs_localization.yaml | 13 + .../tasks/vitatecs/vitatecs_sequence.yaml | 13 + lmms_eval/tasks/vitatecs/vitatecs_type.yaml | 13 + 9 files changed, 320 insertions(+) create mode 100644 lmms_eval/tasks/vitatecs/_default_template_yaml create mode 100755 lmms_eval/tasks/vitatecs/_vitatecs.yaml create mode 100644 lmms_eval/tasks/vitatecs/utils.py create mode 100644 lmms_eval/tasks/vitatecs/vitatecs_compositionality.yaml create mode 100644 lmms_eval/tasks/vitatecs/vitatecs_direction.yaml create mode 100644 lmms_eval/tasks/vitatecs/vitatecs_intensity.yaml create mode 100644 lmms_eval/tasks/vitatecs/vitatecs_localization.yaml create mode 100644 lmms_eval/tasks/vitatecs/vitatecs_sequence.yaml create mode 100644 lmms_eval/tasks/vitatecs/vitatecs_type.yaml diff --git a/lmms_eval/tasks/vitatecs/_default_template_yaml b/lmms_eval/tasks/vitatecs/_default_template_yaml new file mode 100644 index 00000000..e93ce7d5 --- /dev/null +++ b/lmms_eval/tasks/vitatecs/_default_template_yaml @@ -0,0 +1,9 @@ +dataset_path: lscpku/VITATECS +dataset_kwargs: + token: True + video: True + cache_dir: vitatecs +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nPlease response with a single letter (A or B):" \ No newline at end of file diff --git a/lmms_eval/tasks/vitatecs/_vitatecs.yaml b/lmms_eval/tasks/vitatecs/_vitatecs.yaml new file mode 100755 index 00000000..07677c28 --- /dev/null +++ b/lmms_eval/tasks/vitatecs/_vitatecs.yaml @@ -0,0 +1,8 @@ +group: vitatecs +task: +- vitatecs_direction +- vitatecs_intensity +- vitatecs_sequence +- vitatecs_compositionality +- vitatecs_localization +- vitatecs_type diff --git a/lmms_eval/tasks/vitatecs/utils.py b/lmms_eval/tasks/vitatecs/utils.py new file mode 100644 index 00000000..9d16c593 --- /dev/null +++ b/lmms_eval/tasks/vitatecs/utils.py @@ -0,0 +1,225 @@ +from decord import VideoReader, cpu +import numpy as np +import os +import sys +import datetime +import lmms_eval.tasks._task_utils.file_utils as file_utils +import json +import logging +import yaml +from pathlib import Path + +import requests +import openai +from openai import OpenAI +import time +import ast +from tqdm import tqdm +import random + +import re + +with open(Path(__file__).parent / "_default_template_yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + + config = yaml.safe_load("".join(safe_data)) + + +API_TYPE = os.getenv("API_TYPE", "openai") + +if API_TYPE == "openai": + API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") + API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") + headers = { + "Authorization": f"Bearer {API_KEY}", + "Content-Type": "application/json", + } + +# We will unzip all the zip files +# To HF HOME cache dir +# And load it here +HF_HOME = os.environ["HF_HOME"] +cache_dir = config["dataset_kwargs"]["cache_dir"] +cache_dir = os.path.join(HF_HOME, cache_dir) + +eval_logger = logging.getLogger("lmms-eval") + + +# Pass in video path here +# Can only work correctly with video llm +def vitatecs_doc_to_visual(doc): + video_path = os.path.join(cache_dir, doc["src_dataset"], doc["video_name"]) + if os.path.exists(video_path): + video_path = video_path + else: + sys.exit(f"video path:{video_path} does not exist, please check") + return [video_path] + + +# This is the place where you format your question +def vitatecs_doc_to_text(doc, model_specific_prompt_kwargs=None): + if model_specific_prompt_kwargs is None: + model_specific_prompt_kwargs = {} + pre_prompt = "" + post_prompt = "" + if "pre_prompt" in model_specific_prompt_kwargs: + pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + if "post_prompt" in model_specific_prompt_kwargs: + post_prompt = model_specific_prompt_kwargs["post_prompt"] + + question, _, _ = format_question_and_answer(doc) + return f"{pre_prompt}{question}{post_prompt}" + + +def process_option_for_question(sent): + if not sent.endswith("."): + sent += "." + return sent.capitalize() + + +def process_option_for_matching(sent): + if sent.endswith("."): + sent = sent[:-1] + return sent.lower() + + +def format_question_and_answer(doc): + seed = sum(ord(c) for c in doc['caption'] + doc['counterfactual']) % 100 + random.seed(seed) + if random.random() > 0.5: + option_a = process_option_for_question(doc['caption']) + option_b = process_option_for_question(doc['counterfactual']) + answer = "(A) " + option_a + else: + option_a = process_option_for_question(doc['counterfactual']) + option_b = process_option_for_question(doc['caption']) + answer = "(B) " + option_b + options = [process_option_for_matching(doc['caption']), process_option_for_matching(doc['counterfactual'])] + + question = f"Which of the following best describes the content of the video: \n(A) {option_a} \n(B) {option_b}" + return question, answer, options + + +def vitatecs_doc_to_answer(doc): + _, answer, _ = format_question_and_answer(doc) + return answer + + +# Process result +def vitatecs_process_results(doc, result): + pred = result[0] + rating = 0 + match_success = True + chatgpt_response = None + question, answer, options = format_question_and_answer(doc) + + # Some hand-crafted matching rules + if options[0] in pred.lower() and options[1] not in pred.lower(): + rating = 1 + elif options[1] in pred.lower() and options[0] not in pred.lower(): + rating = 0 + elif pred in ["A", "B"]: + rating = 1 if pred == answer[1] else 0 + elif any(pred.startswith(prefix) for prefix in ["A.", "B."]): + rating = 1 if pred.split(".")[0] == answer[1] else 0 + elif any(pred.startswith(prefix) for prefix in ["A)", "B)"]): + rating = 1 if pred.split(")")[0] == answer[1] else 0 + elif any(pred.startswith(prefix) for prefix in ["(A)", "(B)"]): + rating = 1 if pred.split(")")[1] == answer[1] else 0 + else: + # Fail to match answer in the video-llm response. Use ChatGPT to evaluate. + match_success = False + + base_prompt = """You will receive a caption matching question, the ground-truth answer and the prediction from a question answering (QA) model. Your task is to determine whether QA model prediction is correct, based on the question and ground-truth answer. If the prediction is correct, respond "Correct". If the prediction is incorrect, respond "Incorrect". """ + prompt = f"""{base_prompt}\n\nCaption Matching Question: {question}\n\nGround-Truth Answer: {answer}\n\nModel Prediction: {pred}""" + chatgpt_response, rating = get_eval_result(prompt) + + if not match_success: + return { + "accuracy": { + "src_dataset": doc["src_dataset"], + "video_id": doc["video_name"], + "question": question, + "gt-answer": answer, + "video-llm-prediction": pred, + "match_success": match_success, + "rating": rating, + # "chatgpt_prompt": prompt, + "chatgpt_response": chatgpt_response, + "aspect": doc["aspect"], + }, + } + else: + return { + "accuracy": { + "src_dataset": doc["src_dataset"], + "video_id": doc["video_name"], + "question": question, + "gt-answer": answer, + "video-llm-prediction": pred, + "match_success": match_success, + "rating": rating, + "aspect": doc["aspect"], + }, + } + + +# utils function for gpt_evaluation when rule-based matching is unsuccessful +def get_eval_result(prompt, maxtry=10, sys_prompt=None): + llm_output = None + while True: + try: + llm_output = get_llm_output(prompt, sys_prompt) + rating = llm_output_to_rating(llm_output) + return llm_output, rating + except: + if maxtry <= 0: + return llm_output, 0 + maxtry -= 1 + print(f"Not success! {maxtry} retries remaining...") + time.sleep(random.uniform(1, 2)) + + +# utils function for gpt evaluation +def get_llm_output(prompt, sys_prompt, max_tokens=128): + if sys_prompt is None: + sys_prompt = "You are an AI assistant for question answering." + data = {"max_tokens": max_tokens, "model": "gpt-3.5-turbo-1106", "temperature": 1.0, "top_p": 1, "presence_penalty": 1, "messages": [{"role": "system", "content": sys_prompt}, {"role": "user", "content": prompt}]} + response = requests.post(API_URL, headers=headers, data=json.dumps(data).encode("utf-8")) + result = response.content.decode("utf-8") + dict_result = json.loads(result) + llm_output = dict_result["choices"][0]["message"]["content"].strip() + return llm_output + + +# utils function that converts gpt evaluation into rating +def llm_output_to_rating(llm_output): + assert "Correct" in llm_output or "Incorrect" in llm_output + if llm_output.startswith("Correct"): + rating = 1 + elif llm_output.startswith("Incorrect"): + rating = 0 + elif ("Correct" in llm_output) and ("Incorrect" not in llm_output): + rating = 1 + elif "Incorrect" in llm_output: + rating = 0 + return rating + + +# Factory into different aggregate +def vitatecs_aggregate_rating(results, args): + yes_count = 0 + + # results is a list of dict + for answer_dict in results: + if answer_dict["rating"] == 1: + yes_count += 1 + + accuracy = yes_count / len(results) + + return accuracy * 100 diff --git a/lmms_eval/tasks/vitatecs/vitatecs_compositionality.yaml b/lmms_eval/tasks/vitatecs/vitatecs_compositionality.yaml new file mode 100644 index 00000000..eb73ef00 --- /dev/null +++ b/lmms_eval/tasks/vitatecs/vitatecs_compositionality.yaml @@ -0,0 +1,13 @@ +dataset_name: "Compositionality" +task: "vitatecs_compositionality" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vitatecs_doc_to_visual +doc_to_text: !function utils.vitatecs_doc_to_text +doc_to_target: !function utils.vitatecs_doc_to_answer +process_results: !function utils.vitatecs_process_results +metric_list: + - metric: accuracy + aggregation: !function utils.vitatecs_aggregate_rating + higher_is_better: true +include: _default_template_yaml diff --git a/lmms_eval/tasks/vitatecs/vitatecs_direction.yaml b/lmms_eval/tasks/vitatecs/vitatecs_direction.yaml new file mode 100644 index 00000000..8c4b5b60 --- /dev/null +++ b/lmms_eval/tasks/vitatecs/vitatecs_direction.yaml @@ -0,0 +1,13 @@ +dataset_name: "Direction" +task: "vitatecs_direction" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vitatecs_doc_to_visual +doc_to_text: !function utils.vitatecs_doc_to_text +doc_to_target: !function utils.vitatecs_doc_to_answer +process_results: !function utils.vitatecs_process_results +metric_list: + - metric: accuracy + aggregation: !function utils.vitatecs_aggregate_rating + higher_is_better: true +include: _default_template_yaml diff --git a/lmms_eval/tasks/vitatecs/vitatecs_intensity.yaml b/lmms_eval/tasks/vitatecs/vitatecs_intensity.yaml new file mode 100644 index 00000000..a12a4dea --- /dev/null +++ b/lmms_eval/tasks/vitatecs/vitatecs_intensity.yaml @@ -0,0 +1,13 @@ +dataset_name: "Intensity" +task: "vitatecs_intensity" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vitatecs_doc_to_visual +doc_to_text: !function utils.vitatecs_doc_to_text +doc_to_target: !function utils.vitatecs_doc_to_answer +process_results: !function utils.vitatecs_process_results +metric_list: + - metric: accuracy + aggregation: !function utils.vitatecs_aggregate_rating + higher_is_better: true +include: _default_template_yaml diff --git a/lmms_eval/tasks/vitatecs/vitatecs_localization.yaml b/lmms_eval/tasks/vitatecs/vitatecs_localization.yaml new file mode 100644 index 00000000..633fae76 --- /dev/null +++ b/lmms_eval/tasks/vitatecs/vitatecs_localization.yaml @@ -0,0 +1,13 @@ +dataset_name: "Localization" +task: "vitatecs_localization" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vitatecs_doc_to_visual +doc_to_text: !function utils.vitatecs_doc_to_text +doc_to_target: !function utils.vitatecs_doc_to_answer +process_results: !function utils.vitatecs_process_results +metric_list: + - metric: accuracy + aggregation: !function utils.vitatecs_aggregate_rating + higher_is_better: true +include: _default_template_yaml diff --git a/lmms_eval/tasks/vitatecs/vitatecs_sequence.yaml b/lmms_eval/tasks/vitatecs/vitatecs_sequence.yaml new file mode 100644 index 00000000..6ad43491 --- /dev/null +++ b/lmms_eval/tasks/vitatecs/vitatecs_sequence.yaml @@ -0,0 +1,13 @@ +dataset_name: "Sequence" +task: "vitatecs_sequence" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vitatecs_doc_to_visual +doc_to_text: !function utils.vitatecs_doc_to_text +doc_to_target: !function utils.vitatecs_doc_to_answer +process_results: !function utils.vitatecs_process_results +metric_list: + - metric: accuracy + aggregation: !function utils.vitatecs_aggregate_rating + higher_is_better: true +include: _default_template_yaml diff --git a/lmms_eval/tasks/vitatecs/vitatecs_type.yaml b/lmms_eval/tasks/vitatecs/vitatecs_type.yaml new file mode 100644 index 00000000..7034a45c --- /dev/null +++ b/lmms_eval/tasks/vitatecs/vitatecs_type.yaml @@ -0,0 +1,13 @@ +dataset_name: "Type" +task: "vitatecs_type" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.vitatecs_doc_to_visual +doc_to_text: !function utils.vitatecs_doc_to_text +doc_to_target: !function utils.vitatecs_doc_to_answer +process_results: !function utils.vitatecs_process_results +metric_list: + - metric: accuracy + aggregation: !function utils.vitatecs_aggregate_rating + higher_is_better: true +include: _default_template_yaml From 7c208b76640c986cfe94233dce735c3ca4ad4319 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Mon, 1 Jul 2024 11:53:31 +0800 Subject: [PATCH 123/196] Update README.md From ed381736730d8fb785b4ee919fdb751734ecef25 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Mon, 1 Jul 2024 06:06:38 +0000 Subject: [PATCH 124/196] Add wild vision 0617 --- .../wild_vision_bench/_default_template_yaml | 23 +++ lmms_eval/tasks/wild_vision_bench/utils.py | 168 ++++++++++++++++++ .../wild_vision_bench0617.yaml | 9 + 3 files changed, 200 insertions(+) create mode 100644 lmms_eval/tasks/wild_vision_bench/_default_template_yaml create mode 100644 lmms_eval/tasks/wild_vision_bench/utils.py create mode 100644 lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml diff --git a/lmms_eval/tasks/wild_vision_bench/_default_template_yaml b/lmms_eval/tasks/wild_vision_bench/_default_template_yaml new file mode 100644 index 00000000..7ce709dc --- /dev/null +++ b/lmms_eval/tasks/wild_vision_bench/_default_template_yaml @@ -0,0 +1,23 @@ +dataset_path: WildVision/wildvision-arena-data +dataset_kwargs: + token: True +output_type: generate_until +doc_to_visual: !function utils.wild_vision_doc_to_visual +doc_to_text: !function utils.wild_vision_doc_to_text +doc_to_target: !function utils.wild_vision_doc_to_target +generation_kwargs: + max_new_tokens: 4096 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +# The return value of process_results will be used by metrics +process_results: !function utils.wild_vision_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.wild_vision_aggregation + higher_is_better: true +metadata: + judge_model: gpt-4o + baseline_model: claude-3-sonnet-20240229 diff --git a/lmms_eval/tasks/wild_vision_bench/utils.py b/lmms_eval/tasks/wild_vision_bench/utils.py new file mode 100644 index 00000000..747a4bb6 --- /dev/null +++ b/lmms_eval/tasks/wild_vision_bench/utils.py @@ -0,0 +1,168 @@ +import json + +import os +import requests +import numpy as np +import time +import yaml +from pathlib import Path +from copy import deepcopy +from io import BytesIO +import base64 + +from loguru import logger as eval_logger + +NUM_SECONDS_TO_SLEEP = 5 + + +with open(Path(__file__).parent / "_default_template_yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + + config = yaml.safe_load("".join(safe_data)) + +GPT_EVAL_MODEL_NAME = config["metadata"]["judge_model"] +BASELINE_MODEL_NAME = config["metadata"]["baseline_model"] + +API_TYPE = os.getenv("API_TYPE", "openai") + +if API_TYPE == "openai": + API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") + API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") + headers = { + "Authorization": f"Bearer {API_KEY}", + "Content-Type": "application/json", + } +elif API_TYPE == "azure": + API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") + API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") + headers = { + "api-key": API_KEY, + "Content-Type": "application/json", + } + +system_prompt = """\ +Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user prompt displayed below. You will be given assistant A's answer and assistant B's answer. Your job is to evaluate which assistant's answer is better. + +Begin your evaluation by generating your own answer to the prompt. You must provide your answers before judging any answers. + +When evaluating the assistants' answers, compare both assistants' answers with your answer. You must identify and correct any mistakes or inaccurate information. + +Then consider if the assistant's answers are helpful, relevant, and concise. Helpful means the answer correctly responds to the prompt or follows the instructions. Note when user prompt has any ambiguity or more than one interpretation, it is more helpful and appropriate to ask for clarifications or more information from the user than providing an answer based on assumptions. Relevant means all parts of the response closely connect or are appropriate to what is being asked. Concise means the response is clear and not verbose or excessive. + +Then consider the creativity and novelty of the assistant's answers when needed. Finally, identify any missing important information in the assistants' answers that would be beneficial to include when responding to the user prompt. + +After providing your explanation, you must output only one of the following choices as your final verdict with a label: + +1. Assistant A is significantly better: [[A>>B]] +2. Assistant A is slightly better: [[A>B]] +3. Tie, relatively the same: [[A=B]] +4. Assistant B is slightly better: [[B>A]] +5. Assistant B is significantly better: [[B>>A]] + +Example output: "My final verdict is tie: [[A=B]]".\ +""" + +prompt_template = "<|User Prompt|>\n{question_1}\n\n<|The Start of Assistant A's Answer|>\n{answer_1}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{answer_2}\n<|The End of Assistant B's Answer|>" + +def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10): + headers = { + "Authorization": f"Bearer {API_KEY}", + "Content-Type": "application/json", + } + + payload = { + "model": GPT_EVAL_MODEL_NAME, + "messages": [ + {"role": "system", "content": system_prompt}, + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": f"data:image/jpeg;base64,{base64_image}", + }, + ], + } + ], + "max_tokens": 1024, + "temperature": 0.0, + } + + for attempt in range(max_retries): + try: + response = requests.post(API_URL, headers=headers, json=payload, timeout=60) + response.raise_for_status() + response_data = response.json() + return response_data["choices"][0]["message"]["content"], GPT_EVAL_MODEL_NAME + except requests.exceptions.RequestException as e: + eval_logger.warning(f"Request failed on attempt {attempt+1}: {e}") + time.sleep(wait_time) + if attempt == max_retries - 1: + eval_logger.error(f"Failed to get response after {max_retries} attempts") + return "", GPT_EVAL_MODEL_NAME + except Exception as e: + eval_logger.error(f"Error on attempt {attempt+1}: {e}") + return "", GPT_EVAL_MODEL_NAME + + +def image_to_base64(pil_image): + buffered = BytesIO() + pil_image.save(buffered, format="PNG") + return base64.b64encode(buffered.getvalue()).decode("utf-8") + +def get_score(judgement, pattern, pairwise=True): + matches = pattern.findall(judgement) + matches = [m for m in matches if m != ""] + if len(set(matches)) == 0: + return None, True + elif len(set(matches)) == 1: + if pairwise: + return matches[0].strip("\n"), False + return int(matches[0]) + else: + return None, False + +def wild_vision_doc_to_visual(doc): + return [doc["image"].convert('RGB')] + + +def wild_vision_doc_to_text(doc, model_specific_prompt_kwargs=None): + question = doc["instruction"].strip() + if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "": + question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}" + if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "": + question = f"{question}{model_specific_prompt_kwargs['post_prompt']}" + return question + +def wild_vision_doc_to_target(doc): + return doc[BASELINE_MODEL_NAME] + + +def wild_vision_process_results(doc, results): + pred = results[0] + user_prompt = prompt_template.format(question_1=doc["instruction"], answer_1=doc[BASELINE_MODEL_NAME], answer_2=pred) + base64_image = image_to_base64(doc["image"]) + resps, gpt_name = get_chat_response(base64_image, user_prompt) + score, _ = get_score(resps, pattern="\[\[([AB<>=]+)\]\]") + try: + score = int(score) + except: + score = 0 + + return {"gpt_eval_score" : {"question" : doc["instruction"], "score" : score, "gpt_resps" : resps, "ans_1" : doc[BASELINE_MODEL_NAME], "ans_2" : pred}} + + +def wild_vision_aggregation(results): + score = 0 + for res in results: + score += res["score"] + + return score / len(results) + + diff --git a/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml b/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml new file mode 100644 index 00000000..67ce4c78 --- /dev/null +++ b/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml @@ -0,0 +1,9 @@ +task: wildvision_0617 +dataset_name: release_bench_0617_with_modeljudgement +test_split: test500 +output_type: generate_until +include: _default_template_yaml +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" \ No newline at end of file From e2990d0a69e876721256fdf946c68ba7ae0cbdc1 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Mon, 1 Jul 2024 06:06:57 +0000 Subject: [PATCH 125/196] Hardcode to keep image for wild vision --- lmms_eval/evaluator.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py index 0104b01a..6788467e 100755 --- a/lmms_eval/evaluator.py +++ b/lmms_eval/evaluator.py @@ -325,7 +325,12 @@ def evaluate( # hack: remove image columns to speed avoid loading images and speed up postprocessing # reason: doc_iterator will actually load image if it's in the doc. docs = task.test_docs() if task.has_test_docs() else task.validation_docs() - if "d170" not in task_name and "dc100" not in task_name and "dc200" not in task_name and "llava_wilder" not in task_name and "livebench" not in task_name: + if "d170" not in task_name \ + and "dc100" not in task_name \ + and "dc200" not in task_name \ + and "llava_wilder" not in task_name \ + and "livebench" not in task_name \ + and "wildvision" not in task_name: remove_cols = [] features = docs.features # If it is an Image instance or a Sequence of Image instance. Remove it From 8d963e132ac03fc0d835d480cfcfcabe72af143c Mon Sep 17 00:00:00 2001 From: kcz358 Date: Mon, 1 Jul 2024 08:24:51 +0000 Subject: [PATCH 126/196] Fixing scoring logic --- lmms_eval/tasks/wild_vision_bench/utils.py | 47 +++++++++++++++------- 1 file changed, 32 insertions(+), 15 deletions(-) diff --git a/lmms_eval/tasks/wild_vision_bench/utils.py b/lmms_eval/tasks/wild_vision_bench/utils.py index 747a4bb6..6841d793 100644 --- a/lmms_eval/tasks/wild_vision_bench/utils.py +++ b/lmms_eval/tasks/wild_vision_bench/utils.py @@ -1,5 +1,5 @@ import json - +import re import os import requests import numpy as np @@ -78,14 +78,15 @@ def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10): payload = { "model": GPT_EVAL_MODEL_NAME, "messages": [ - {"role": "system", "content": system_prompt}, + {"role": "system", "content": [{"type": "text", "text": system_prompt}]}, { "role": "user", "content": [ {"type": "text", "text": prompt}, - { - "type": "image_url", - "image_url": f"data:image/jpeg;base64,{base64_image}", + {"type": "image_url", + "image_url" : { + "url" : f"data:image/jpeg;base64, {base64_image}" + } }, ], } @@ -101,16 +102,16 @@ def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10): response_data = response.json() return response_data["choices"][0]["message"]["content"], GPT_EVAL_MODEL_NAME except requests.exceptions.RequestException as e: - eval_logger.warning(f"Request failed on attempt {attempt+1}: {e}") - time.sleep(wait_time) + print(f"Request failed on attempt {attempt+1}: {e}") if attempt == max_retries - 1: - eval_logger.error(f"Failed to get response after {max_retries} attempts") + print(f"Failed to get response after {max_retries} attempts") return "", GPT_EVAL_MODEL_NAME except Exception as e: - eval_logger.error(f"Error on attempt {attempt+1}: {e}") + print(f"Error on attempt {attempt+1}: {e}") return "", GPT_EVAL_MODEL_NAME + def image_to_base64(pil_image): buffered = BytesIO() pil_image.save(buffered, format="PNG") @@ -149,13 +150,29 @@ def wild_vision_process_results(doc, results): user_prompt = prompt_template.format(question_1=doc["instruction"], answer_1=doc[BASELINE_MODEL_NAME], answer_2=pred) base64_image = image_to_base64(doc["image"]) resps, gpt_name = get_chat_response(base64_image, user_prompt) - score, _ = get_score(resps, pattern="\[\[([AB<>=]+)\]\]") - try: - score = int(score) - except: - score = 0 + score, _ = get_score(resps, pattern=re.compile("\[\[([AB<>=]+)\]\]")) + + if "A>B" in score: + final_score = -1 + judgement = "Worse" #Baseline better + elif "A>>B" in score: + final_score = -2 + judgement = "Worse++" + elif "A=B" in score: + final_score = 0 + judgement = "Tie" + elif "B>A" in score: + final_score = 1 + judgement = "Better" + elif "B>>A" in score: + final_score = 2 + judgement = "Better++" + else: + final_score = 0 + judgement = "Unclear" + - return {"gpt_eval_score" : {"question" : doc["instruction"], "score" : score, "gpt_resps" : resps, "ans_1" : doc[BASELINE_MODEL_NAME], "ans_2" : pred}} + return {"gpt_eval_score" : {"question" : doc["instruction"], "score" : final_score, "gpt_resps" : resps, "ans_1" : doc[BASELINE_MODEL_NAME], "ans_2" : pred, "filtered_resps" : score, "judgement" : judgement}} def wild_vision_aggregation(results): From 725fac2781446958b905e1e6c6eb3c0a8e582e49 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Mon, 1 Jul 2024 08:25:42 +0000 Subject: [PATCH 127/196] Fixing dataset name --- lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml b/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml index 67ce4c78..93576768 100644 --- a/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml +++ b/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml @@ -1,9 +1,9 @@ task: wildvision_0617 -dataset_name: release_bench_0617_with_modeljudgement +dataset_name: release_bench_0617_with_modelresponse test_split: test500 output_type: generate_until include: _default_template_yaml model_specific_prompt_kwargs: default: pre_prompt: "" - post_prompt: "" \ No newline at end of file + post_prompt: "" From 79514eeebcfd6f655be2a10c776037d12a7b7214 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Mon, 1 Jul 2024 15:10:02 +0000 Subject: [PATCH 128/196] Fixing handling None filtered score --- lmms_eval/tasks/wild_vision_bench/utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/lmms_eval/tasks/wild_vision_bench/utils.py b/lmms_eval/tasks/wild_vision_bench/utils.py index 6841d793..bb426557 100644 --- a/lmms_eval/tasks/wild_vision_bench/utils.py +++ b/lmms_eval/tasks/wild_vision_bench/utils.py @@ -152,6 +152,9 @@ def wild_vision_process_results(doc, results): resps, gpt_name = get_chat_response(base64_image, user_prompt) score, _ = get_score(resps, pattern=re.compile("\[\[([AB<>=]+)\]\]")) + if score is None: + score = resps + if "A>B" in score: final_score = -1 judgement = "Worse" #Baseline better From c6e211d5f9dbb7572d3a141b6504cb1ca2007c33 Mon Sep 17 00:00:00 2001 From: ByteDance Date: Sun, 7 Jul 2024 23:04:13 +0800 Subject: [PATCH 129/196] Add detailcaps --- .../_default_template_detailcaps_yaml | 3 + lmms_eval/tasks/detailcaps/detailcaps.yaml | 46 ++++ lmms_eval/tasks/detailcaps/utils.py | 207 ++++++++++++++++++ 3 files changed, 256 insertions(+) create mode 100644 lmms_eval/tasks/detailcaps/_default_template_detailcaps_yaml create mode 100644 lmms_eval/tasks/detailcaps/detailcaps.yaml create mode 100644 lmms_eval/tasks/detailcaps/utils.py diff --git a/lmms_eval/tasks/detailcaps/_default_template_detailcaps_yaml b/lmms_eval/tasks/detailcaps/_default_template_detailcaps_yaml new file mode 100644 index 00000000..f673aeb7 --- /dev/null +++ b/lmms_eval/tasks/detailcaps/_default_template_detailcaps_yaml @@ -0,0 +1,3 @@ +model_specific_prompt_kwargs: + default: + prompt: "Describe this image in detail." \ No newline at end of file diff --git a/lmms_eval/tasks/detailcaps/detailcaps.yaml b/lmms_eval/tasks/detailcaps/detailcaps.yaml new file mode 100644 index 00000000..c509f965 --- /dev/null +++ b/lmms_eval/tasks/detailcaps/detailcaps.yaml @@ -0,0 +1,46 @@ +dataset_path: foundation-multimodal-models/DetailCaps-4870 +dataset_kwargs: + token: True +task: "detailcaps" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.detailcaps_doc_to_visual +doc_to_text: !function utils.detailcaps_doc_to_text +doc_to_target: !function utils.detailcaps_doc_to_target +generation_kwargs: + max_new_tokens: 512 + temperature: 0 + top_p: 0 + num_beams: 1 + do_sample: false +process_results: !function utils.detailcaps_process_result +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: detailcaps_CAPTURE + aggregation : !function utils.detailcaps_capture + higher_is_better : true + - metric: detailcaps_Bleu_4 + aggregation : !function utils.detailcaps_bleu4 + higher_is_better : true + - metric: detailcaps_Bleu_3 + aggregation : !function utils.detailcaps_bleu3 + higher_is_better : true + - metric: detailcaps_Bleu_2 + aggregation : !function utils.detailcaps_bleu2 + higher_is_better : true + - metric: detailcaps_Bleu_1 + aggregation : !function utils.detailcaps_bleu1 + higher_is_better : true + - metric: detailcaps_METEOR + aggregation : !function utils.detailcaps_meteor + higher_is_better : true + - metric: detailcaps_ROUGE_L + aggregation : !function utils.detailcaps_rougel + higher_is_better : true + - metric: detailcaps_CIDEr + aggregation : !function utils.detailcaps_cider + higher_is_better : true + +metadata: + - version: 0.0 +include: _default_template_detailcaps_yaml \ No newline at end of file diff --git a/lmms_eval/tasks/detailcaps/utils.py b/lmms_eval/tasks/detailcaps/utils.py new file mode 100644 index 00000000..a9f6a925 --- /dev/null +++ b/lmms_eval/tasks/detailcaps/utils.py @@ -0,0 +1,207 @@ +import collections +import os +import json +from capture_metric.capture import CAPTURE +from pycocoevalcap.eval import COCOEvalCap, Bleu, Meteor, Rouge, Cider, Spice +from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer +from pycocotools.coco import COCO +import io +from PIL import Image + +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file + +import logging + +eval_logger = logging.getLogger("lmms-eval") + +dir_name = os.path.dirname(os.path.abspath(__file__)) + +detailcaps_METRICS = ["CAPTURE", "Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"] # , "SPICE"] + + +def detailcaps_doc_to_visual(doc): + return [Image.open(io.BytesIO(doc["binary"])).convert("RGB")] + + +def detailcaps_doc_to_text(doc, model_specific_prompt_kwargs=None): + # question = "Please carefully observe the image and come up with a caption for the image" + return model_specific_prompt_kwargs["prompt"] + +def detailcaps_doc_to_target(doc): + references = [ + doc['GT_Caption_GPT4O'], + doc['GT_Caption_GPT4V'], + doc['GT_Caption_Gemini15Pro'], + ] + return references + + +def detailcaps_process_result(doc, result): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name, value: metric value + """ + + pred = result[0] + # The question id in our dataset is the image file itself + image_id = doc["image"] + + data_dict = {"answer": detailcaps_doc_to_target(doc), "pred": pred, "image_id": image_id} + + return {f"detailcaps_{metric}": data_dict for metric in detailcaps_METRICS} + + +def check_if_context_is_set(expected_context='spawn'): + # 获取默认上下文的名称 + default_context_name = mp.get_context().get_start_method() + + # 检查当前上下文是否与预期的上下文相匹配 + is_set_to_expected = default_context_name == expected_context + + return is_set_to_expected + + +def detailcaps_aggregation_result(results, metric, args=None): + + scorers = [ + (Bleu(4), "Bleu_1"), + (Bleu(4), "Bleu_2"), + (Bleu(4), "Bleu_3"), + (Bleu(4), "Bleu_4"), + (Meteor(), "METEOR"), + (Rouge(), "ROUGE_L"), + (Cider(), "CIDEr"), + (CAPTURE(), "CAPTURE") + ] + scorers_dict = {s[1]: s for s in scorers} + + stored_results = [] + # In order to make the coco eval tools to successfully create index + # We need at least two dict in the dataset + # 'annotation' and 'images' + # 'annotation' exactly reproduce the original annotation + # 'images' however only need the image id which is contained in the file name + dataset = {"annotations": [], "images": []} + idx = 0 + + for result in results: + stored_results.append({"image_id": result["image_id"], "caption": result["pred"]}) + for a in result["answer"]: + dataset["annotations"].append({"image_id": result["image_id"], "caption": a, "id": idx}) + idx += 1 + dataset["images"].append({"id": result["image_id"]}) + + coco = COCO() + # Manually create index here + coco.dataset = dataset + coco.createIndex() + + detailcaps_result = coco.loadRes(stored_results) + detailcaps_eval = COCOEvalCap(coco, detailcaps_result) + + imgIds = detailcaps_eval.params["image_id"] + gts = {} + res = {} + for imgId in imgIds: + gts[imgId] = detailcaps_eval.coco.imgToAnns[imgId] + res[imgId] = detailcaps_eval.cocoRes.imgToAnns[imgId] + + eval_logger.info("tokenization...") + tokenizer = PTBTokenizer() + + if metric == 'CAPTURE': + reorg_gts, reorg_res = collections.defaultdict(list), collections.defaultdict(list) + for _, samples in gts.items(): + for sample in samples: + reorg_gts[sample['image_id']].append(sample['caption']) + for _, samples in res.items(): + for sample in samples: + reorg_res[sample['image_id']].append(sample['caption']) + gts, res = reorg_gts, reorg_res + else: + gts = tokenizer.tokenize(gts) + res = tokenizer.tokenize(res) + + eval_logger.info(f"Computing {metric} scores...") + + # if int(os.environ.get("RANK", 0)) == 0: + # from IPython import embed; embed() + # else: + # import time; time.sleep(1200) + + score, scores = scorers_dict[metric][0].compute_score(gts, res) + # When metric is one of the Bleu, score will be a list + if type(score) == list: + n = int(metric.split("_")[-1]) + score = score[n - 1] + + path = generate_submission_file(f"detailcaps_val_{metric}_scores.json", args) + eval_logger.info("Storing prediction that can be submitted to the server ...") + with open(path, "w") as f: + json.dump(stored_results, f, indent=4) + eval_logger.info(f"Your result has been saved to {path}.") + + return score + + +def detailcaps_bleu4(results, args=None): + return detailcaps_aggregation_result(results, "Bleu_4", args) + + +def detailcaps_bleu3(results, args=None): + return detailcaps_aggregation_result(results, "Bleu_3", args) + + +def detailcaps_bleu2(results, args=None): + return detailcaps_aggregation_result(results, "Bleu_2", args) + + +def detailcaps_bleu1(results, args=None): + return detailcaps_aggregation_result(results, "Bleu_1", args) + + +def detailcaps_meteor(results, args=None): + return detailcaps_aggregation_result(results, "METEOR", args) + + +def detailcaps_rougel(results, args=None): + return detailcaps_aggregation_result(results, "ROUGE_L", args) + + +def detailcaps_cider(results, args=None): + return detailcaps_aggregation_result(results, "CIDEr", args) + + +def detailcaps_spice(results, args=None): + return detailcaps_aggregation_result(results, "SPICE", args) + + +def detailcaps_capture(results, args=None): + return detailcaps_aggregation_result(results, "CAPTURE", args) + + +def detailcaps_test_process_result(doc, result): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name (in this case detailcaps_passthrough), value: metric value + """ + return {"detailcaps_passthrough": {"pred": result[0], "image_id": doc["image_id"]}} + + +def detailcaps_test_aggregation_result(results, args=None): + stored_results = [] + for result in results: + stored_results.append({"image_id": int(result["image_id"]), "caption": result["pred"]}) + + path = generate_submission_file("detailcaps_captions_detailcaps_test_alg_results.json", args) + eval_logger.info("Storing prediction that can be submitted to the server ...") + with open(path, "w") as f: + json.dump(stored_results, f, indent=4) + + eval_logger.info(f"Your test result has been stored in {path}. Make sure you also have the val result stored to submit to the server on https://codalab.lisn.upsaclay.fr/competitions/7404#participate.") From b23d349e46d60dc149ffaa54d6e019f4996ed92d Mon Sep 17 00:00:00 2001 From: ByteDance Date: Sun, 7 Jul 2024 23:24:19 +0800 Subject: [PATCH 130/196] Add install capture_metric in env --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index c65d3b8c..c9ea022a 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -63,6 +63,7 @@ dependencies = [ "decord", "zss", "pywsd", + "capture_metric" ] [tool.setuptools.packages.find] From 557083a156c3dd67ac79e22b4202e9b69b6b00f4 Mon Sep 17 00:00:00 2001 From: Yan Shu <570533048@qq.com> Date: Mon, 8 Jul 2024 16:56:50 +0800 Subject: [PATCH 131/196] Add files via upload --- .../mlvu/__pycache__/utils.cpython-310.pyc | Bin 0 -> 3207 bytes lmms_eval/tasks/mlvu/mlvu.yaml | 21 +++ lmms_eval/tasks/mlvu/utils.py | 124 ++++++++++++++++++ 3 files changed, 145 insertions(+) create mode 100644 lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc create mode 100644 lmms_eval/tasks/mlvu/mlvu.yaml create mode 100644 lmms_eval/tasks/mlvu/utils.py diff --git a/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc b/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1a9fda0f2b70ce7af4bbcc1868a64d7c8e67571d GIT binary patch literal 3207 zcmZ`*-E$ka5eIO`*_PaPf+llwI)Kzk z_d(;1w8c4g+Hv~ShxR=mL-noyME?-H=E?tpXENz7j*{&pJ@NqT-eLjl{&pAH>}-?3 z6MyrM?k8x_Dx5T_S^9AMC#DX{roaw@i&&bb$8?GDv5LUw~eT!<(vwt!yl$yBPKkbXYcO`<%HvefTJv4n5-!=Tvf*xLSm zlo#6m2&QQFLB9ZzAl9`%q%Z(6C(e33sX7hL9&c;5xy{)hceu6nXj8L$+uVBiXp>tz zJ9jzTd;srVUBCaafB(TJ_eP6r>wj5a3xjY^uI~+cy(sNnpc!Fw0pd>fjA?9&M=tinEGV_E{j zR>+uD+>(|IxD{1{F;y2zpi9=HJEU+Xbphiq0dZej=(CF7vA1}07zvsAX^_a#j2U}< zu-V4wf+sSWdudj9@)=m~n%9pd?AwE*h6f#2vn?Kmd7<*9zo?Y4iQfEs+ZXnuyGLSj?dvw0} zAdL^bUNn?mu_wJ<;Cq=t;967D=BWMKSDX!R)ttUlQ!A+Ee3A(n`*~l6Q8xv^7rDn=iZ^H~e(;dX|F$WE4^r`cxZSHlW6!KYd8@M^8)9kOrezTQ6zn1!B9ybS*Db)Yr8r z74jK?fadB7d;ugWDXPtyZJQl0!zNV_7Jdj=fL^NHSF$Ic{Tim^EofZo(k7jQz1vKo ztKM*=1B{ax`Tz*n;WTPW_MCzvjZ?vS;Mf&X+9h&qaC7Y|dt4JvS&JZkI`z?in;txy z5U(3%gcQJQ!wbA9&5IxnrI&S$hY^(CG{#S4mCPY!`HL0Q`IU9WsV*;wz>~0a;&+o>oS)uQkSW9Am6}e1HZ#- z@cA*EG9qJI5U5v;F%#fz!M?~5mUw?}b1~+grQYeMzs3&OR8zWg(8ehf;Mjm0;vxWFYo@fW`6g{t zwH5DGz8NYDC)crN$I-5`k+Si^qn2>!K$?NekiztoT8fL55Q)h|%g>B&N-LtWqN`g>U-)jV>W@>xGf#UPhTEg-|r`ZCq7Z|;4* zubn>J9nwP6u5Kt9h&W1Ru3Z&8^)U(YMjmvf8R~|FO_=+mrjfQ$5)xw{^a)*w#aSTK z8QjuYZ1B2Z3Z=<>CM6jozvjnR70kT@C43Oe4>1AfA41bIHLG$LR 0 else 0 : .1f}%") + + total_correct = 0 + total_answered = 0 + for k, v in category2score.items(): + total_correct += v["correct"] + total_answered += v["answered"] + eval_logger.info(f"Overall Performance: {100 * total_correct / total_answered if total_answered > 0 else 0 : .1f}%") + + return 100 * total_correct / total_answered if total_answered > 0 else 0 From 672d7e5bb49dcb34e1b2fdeb09f3f4588dc583a6 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Tue, 9 Jul 2024 11:43:41 +1000 Subject: [PATCH 132/196] feat: Add tie_weights parameter to Llava model initialization --- lmms_eval/models/llava.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py index 7528e356..7d6420ba 100755 --- a/lmms_eval/models/llava.py +++ b/lmms_eval/models/llava.py @@ -58,6 +58,7 @@ def __init__( device_map="cuda:0", conv_template="vicuna_v1", use_cache=True, + tie_weights: bool = True, truncate_context=False, # whether to truncate the context in generation, set it False for LLaVA-1.6 customized_config=None, # ends in json **kwargs, @@ -97,6 +98,8 @@ def __init__( self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) self._config = self._model.config self.model.eval() + if tie_weights: + self.model.tie_weights() self.truncation = truncation self.batch_size_per_gpu = int(batch_size) From cd1858523fcd8630082cbefba8710e0de3ee8805 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Tue, 9 Jul 2024 11:52:23 +1000 Subject: [PATCH 133/196] Upgrade lmms-eval to support more models and evaluation tasks --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 2bf7a26e..b9fb9474 100755 --- a/README.md +++ b/README.md @@ -12,6 +12,8 @@ ## Annoucement +- [2024-07] 👨‍💻👨‍💻 The `lmms-eval/v0.2.1` has been upgraded to support more models, including [LongVA](https://github.com/EvolvingLMMs-Lab/LongVA), [InterVL-2](https://github.com/OpenGVLab/InternVL), [VILA](https://github.com/NVlabs/VILA), and many more evaluation tasks, e.g. [Details Captions](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/136), [MLVU](https://arxiv.org/abs/2406.04264), [WildVision-Bench](https://huggingface.co/datasets/WildVision/wildvision-arena-data), [VITATECS](https://github.com/lscpku/VITATECS) and [LLaVA-Interleave-Bench](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/). + - [2024-06] 🎬🎬 The `lmms-eval/v0.2` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details - [2024-03] 📝📝 We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details From 6da76f36ecf5f9aa73057e767a4fcb60c99ff896 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Tue, 9 Jul 2024 11:55:56 +1000 Subject: [PATCH 134/196] Upgrade lmms-eval to version 0.2.1 --- README.md | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b9fb9474..7be1c6c7 100755 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ - [2024-07] 👨‍💻👨‍💻 The `lmms-eval/v0.2.1` has been upgraded to support more models, including [LongVA](https://github.com/EvolvingLMMs-Lab/LongVA), [InterVL-2](https://github.com/OpenGVLab/InternVL), [VILA](https://github.com/NVlabs/VILA), and many more evaluation tasks, e.g. [Details Captions](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/136), [MLVU](https://arxiv.org/abs/2406.04264), [WildVision-Bench](https://huggingface.co/datasets/WildVision/wildvision-arena-data), [VITATECS](https://github.com/lscpku/VITATECS) and [LLaVA-Interleave-Bench](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/). -- [2024-06] 🎬🎬 The `lmms-eval/v0.2` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details +- [2024-06] 🎬🎬 The `lmms-eval/v0.2.0` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details - [2024-03] 📝📝 We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details diff --git a/pyproject.toml b/pyproject.toml index aa0afd04..da2f2120 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta" [project] name = "lmms_eval" -version = "0.2.0.post1" +version = "0.2.1" authors = [ { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" }, ] From 1d8c980d1089f9d7702c3b92d5c85039f2809c6d Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 9 Jul 2024 02:08:52 +0000 Subject: [PATCH 135/196] Rename xcomposer 4KHD --- lmms_eval/models/{xcomposer2_4khd.py => xcomposer2_4KHD.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename lmms_eval/models/{xcomposer2_4khd.py => xcomposer2_4KHD.py} (100%) diff --git a/lmms_eval/models/xcomposer2_4khd.py b/lmms_eval/models/xcomposer2_4KHD.py similarity index 100% rename from lmms_eval/models/xcomposer2_4khd.py rename to lmms_eval/models/xcomposer2_4KHD.py From e31cd7883d4555c7530795c7f102b8d78cbd372f Mon Sep 17 00:00:00 2001 From: Bo Li Date: Wed, 10 Jul 2024 12:08:08 +1000 Subject: [PATCH 136/196] chore: Update lmms_eval/models/vila.py and lmms_eval/tasks/__init__.py --- lmms_eval/models/vila.py | 18 +++++++++--------- lmms_eval/tasks/__init__.py | 2 +- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/lmms_eval/models/vila.py b/lmms_eval/models/vila.py index 295bf123..2adff620 100755 --- a/lmms_eval/models/vila.py +++ b/lmms_eval/models/vila.py @@ -34,8 +34,8 @@ from llava.mm_utils import process_images except ImportError as e: print(e) - # import pdb;pdb.set_trace() - eval_logger.debug("VILA is not installed. Please install VILA to use this model.") + + eval_logger.debug("VILA is not installed. Please install VILA to use this model. Error: {e}") @register_model("vila") @@ -202,7 +202,7 @@ def load_video(self, video_path, max_frames_num): return [Image.fromarray(img) for img in spare_frames] except Exception as e: eval_logger.error(f"Failed to load video {video_path} with error: {e}") - # import pdb;pdb.set_trace() + return [Image.new("RGB", (448, 448), (0, 0, 0))] * max_frames_num def tok_decode(self, tokens): @@ -279,7 +279,7 @@ def generate_until(self, requests) -> List[str]: for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: # if self.task_dict[task][split][doc_id]["duration"] != "short": - # # import pdb;pdb.set_trace() + # # res.append("A") # pbar.update(1) # continue @@ -289,7 +289,7 @@ def generate_until(self, requests) -> List[str]: num_video_frames = self.model.config.num_video_frames videos = [] - # import pdb;pdb.set_trace() + if self.max_frames_num == 0: images = [Image.new("RGB", (448, 448), (0, 0, 0))] * num_video_frames video = process_images(images, self.model.image_processor, self.model.config).half().cuda() @@ -297,12 +297,12 @@ def generate_until(self, requests) -> List[str]: else: for visual in visuals: # images, video_loading_succeed = LazySupervisedDataset._load_video(visual, num_video_frames, self.model) - # import pdb;pdb.set_trace() + if self.video_decode_backend == "decord": images = self.load_video(visual, num_video_frames) elif self.video_decode_backend == "pyav": images = read_video_pyav(visual, num_frm=num_video_frames) - # import pdb;pdb.set_trace() + video = process_images(images, self.model.image_processor, self.model.config).half().cuda() videos.append(video) @@ -350,7 +350,7 @@ def generate_until(self, requests) -> List[str]: if "num_beams" not in gen_kwargs: gen_kwargs["num_beams"] = 1 - # import pdb;pdb.set_trace() + with torch.inference_mode(): output_ids = self.model.generate( input_ids=input_ids, @@ -370,7 +370,7 @@ def generate_until(self, requests) -> List[str]: outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() print("Question: ", cur_prompt) print("Answer: ", outputs) - # import pdb;pdb.set_trace() + res.append(outputs) pbar.update(1) return res diff --git a/lmms_eval/tasks/__init__.py b/lmms_eval/tasks/__init__.py index 22912ec9..4bb2e01d 100755 --- a/lmms_eval/tasks/__init__.py +++ b/lmms_eval/tasks/__init__.py @@ -72,7 +72,7 @@ def include_task_folder(task_dir: str, register_task: bool = True) -> None: # if (subdirs == [] or subdirs == ["__pycache__"]) and (len(file_list) > 0): for f in file_list: # if "detail" in f: - # import pdb;pdb.set_trace() + # # if "vatex" in f: # print("a") if f.endswith(".yaml"): From 78e0c3b73f39c8aa5d2343ec93ec2ca28cd00a8b Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 11 Jul 2024 12:21:47 +0800 Subject: [PATCH 137/196] Update utils.py --- lmms_eval/tasks/vcr_wiki/utils.py | 49 ++++++++++++++++++------------- 1 file changed, 29 insertions(+), 20 deletions(-) diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index 82b769f7..635d3ba6 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -1,9 +1,6 @@ import datetime import json - import os -from difflib import SequenceMatcher as SM -from functools import partial import evaluate import numpy as np @@ -11,27 +8,39 @@ from nltk.util import ngrams from spacy.cli import download -from lmms_eval.tasks._task_utils.file_utils import generate_submission_file - -# Download the English and Chinese models -try: - nlp_en = spacy.load("en_core_web_sm") -except Exception as e: - download("en_core_web_sm") - nlp_en = spacy.load("en_core_web_sm") +from difflib import SequenceMatcher as SM +from functools import partial -try: - nlp_zh = spacy.load("zh_core_web_sm") -except Exception as e: - download("zh_core_web_sm") - nlp_zh = spacy.load("zh_core_web_sm") +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file +from loguru import logger as eval_logger -nlp = {"en": nlp_en, "zh": nlp_zh} -rouge = evaluate.load("rouge") +with open(Path(__file__).parent / "_default_template_vcr_yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) -from loguru import logger as eval_logger + config = yaml.safe_load("".join(safe_data)) -dir_name = os.path.dirname(os.path.abspath(__file__)) +# Download the English and Chinese models +if config["load_package"]: + try: + nlp_en = spacy.load("en_core_web_sm") + nlp_zh = spacy.load("zh_core_web_sm") + nlp = {"en": nlp_en, "zh": nlp_zh} + rouge = evaluate.load("rouge") + except Exception as e: + eval_logger.debug(f"Failed to load spacy models: {e}") + download("en_core_web_sm") + nlp_en = spacy.load("en_core_web_sm") + download("zh_core_web_sm") + nlp_zh = spacy.load("zh_core_web_sm") +else: + nlp = {"en": None, "zh": None} + rouge = None + eval_logger.debug("Spacy models not loaded due to load_package is False. Please set load_package to True in the config file to load them.") aggregate_results_template = { "max_sim_val": 0, From f628f0863b7cfc3a3566322c2b25dd1465a53e0c Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 11 Jul 2024 12:22:06 +0800 Subject: [PATCH 138/196] Update _default_template_vcr_yaml --- lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml b/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml index 37ab5e74..a61c5f62 100644 --- a/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml +++ b/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml @@ -14,4 +14,5 @@ generation_kwargs: # The return value of process_results will be used by metrics # Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results metadata: - - version: 0.0.1 \ No newline at end of file + - version: 0.0.1 + - load_package: False From e0e05a74083a159f7836c89ca69ce2ec053aad60 Mon Sep 17 00:00:00 2001 From: ByteDance Date: Thu, 11 Jul 2024 13:07:40 +0800 Subject: [PATCH 139/196] add process sync via temp file in lmms_eval/evaluator.py --- lmms_eval/evaluator.py | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py index 6788467e..041eec31 100755 --- a/lmms_eval/evaluator.py +++ b/lmms_eval/evaluator.py @@ -1,3 +1,5 @@ +import os +import time import random import itertools import json @@ -428,6 +430,12 @@ def evaluate( # Ensure all ranks wait for rank 0 to finish aggregation torch.distributed.barrier() + # Synchronize processes with a temp file in case the evluation metric requires gpus + # TODO: fix barriers' taking up gpu computation + os.makedirs(cli_args.output_path, exist_ok=True) + if os.path.exists(f"{cli_args.output_path}/rank{int(os.environ.get('RANK', 0))}_metric_eval_done.txt"): + os.remove(f"{cli_args.output_path}/rank{int(os.environ.get('RANK', 0))}_metric_eval_done.txt") + if lm.rank == 0: ### Get task ordering for correct sample-wide aggregation group_to_task = {} @@ -628,8 +636,12 @@ def print_tasks(task_hierarchy, task_order, task_version, task_group_alias): } if log_samples: results_dict["samples"] = dict(samples) - - return results_dict - else: - return None + results_dict = None + + with open(f"{cli_args.output_path}/rank{int(os.environ.get('RANK', 0))}_metric_eval_done.txt", 'w') as f: + f.write(f"rank {int(os.environ.get('RANK', 0))} eval done") + while len([file for file in os.listdir(cli_args.output_path) if file.endswith('metric_eval_done.txt')]) < lm.accelerator.num_processes: + time.sleep(1) + + return results_dict From 4912aeddf0e12bbffd46a7d1684c4ec602384a24 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Sun, 14 Jul 2024 23:09:47 +0800 Subject: [PATCH 140/196] Update utils.py --- lmms_eval/tasks/vcr_wiki/utils.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index 635d3ba6..5519c078 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -1,4 +1,5 @@ import datetime +import yaml import json import os @@ -8,6 +9,7 @@ from nltk.util import ngrams from spacy.cli import download +from pathlib import Path from difflib import SequenceMatcher as SM from functools import partial @@ -25,7 +27,7 @@ config = yaml.safe_load("".join(safe_data)) # Download the English and Chinese models -if config["load_package"]: +if config["metadata"]["load_package"]: try: nlp_en = spacy.load("en_core_web_sm") nlp_zh = spacy.load("zh_core_web_sm") @@ -37,10 +39,10 @@ nlp_en = spacy.load("en_core_web_sm") download("zh_core_web_sm") nlp_zh = spacy.load("zh_core_web_sm") + eval_logger.debug("Spacy models not loaded due to load_package is False. Please set load_package to True in the config file to load them.") else: nlp = {"en": None, "zh": None} rouge = None - eval_logger.debug("Spacy models not loaded due to load_package is False. Please set load_package to True in the config file to load them.") aggregate_results_template = { "max_sim_val": 0, From c49579b306f19c5524d34f8b5fd3204047037cce Mon Sep 17 00:00:00 2001 From: Li Bo Date: Sun, 14 Jul 2024 23:10:05 +0800 Subject: [PATCH 141/196] Update _default_template_vcr_yaml --- lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml b/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml index a61c5f62..3394fbec 100644 --- a/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml +++ b/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml @@ -14,5 +14,5 @@ generation_kwargs: # The return value of process_results will be used by metrics # Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results metadata: - - version: 0.0.1 - - load_package: False + version: 0.0.1 + load_package: False From b186acd3b9a90dbf491106fec0553eb53350701e Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 16 Jul 2024 02:08:24 +0000 Subject: [PATCH 142/196] Add muirbench --- lmms_eval/tasks/muirbench/muirbench.yaml | 41 ++++++++ lmms_eval/tasks/muirbench/utils.py | 117 +++++++++++++++++++++++ 2 files changed, 158 insertions(+) create mode 100644 lmms_eval/tasks/muirbench/muirbench.yaml create mode 100644 lmms_eval/tasks/muirbench/utils.py diff --git a/lmms_eval/tasks/muirbench/muirbench.yaml b/lmms_eval/tasks/muirbench/muirbench.yaml new file mode 100644 index 00000000..43b8ab7c --- /dev/null +++ b/lmms_eval/tasks/muirbench/muirbench.yaml @@ -0,0 +1,41 @@ + +dataset_path: MUIRBENCH/MUIRBENCH +task: "muirbench" +dataset_kwargs: + token: True +test_split: test +output_type: generate_until +doc_to_visual: !function utils.muir_doc_to_visual +doc_to_text: !function utils.muir_doc_to_text +doc_to_target: !function utils.muir_doc_to_target +process_results: !function utils.muir_process_results + +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." + + +generation_kwargs: + max_new_tokens: 16 + temperature: 0 + do_sample: False + +filter_list: + - name: "flexible-extract" + filter: + - function: !function utils.MultiChoiceRegexFilter + group_select: 0 + ignore_case: true + ignore_punctuation: true + regex_pattern: "([A-Z])\\." + +metric_list: + - metric: muirbench_score_overall + aggregation: !function utils.muir_aggregation + higher_is_better: true + ignore_case: true + ignore_punctuation: true + +metadata: + - version: 0.0 diff --git a/lmms_eval/tasks/muirbench/utils.py b/lmms_eval/tasks/muirbench/utils.py new file mode 100644 index 00000000..2924edac --- /dev/null +++ b/lmms_eval/tasks/muirbench/utils.py @@ -0,0 +1,117 @@ + +from lmms_eval.filters.extraction import ExtendedRegexFilter +from lmms_eval.filters.transformation import MapFilter +import re +import pandas as pd + + +def muir_doc_to_text(doc, model_specific_prompt_kwargs=None): + question, choices = doc["question"], doc["options"] + len_choices = len(choices) + post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + options = [chr(ord("A") + i) for i in range(len_choices)] + choices_str = "\n".join([f"{option}. {choice}" for option, choice in zip(options, choices)]) + return f"{pre_prompt}{question}\n{choices_str}{post_prompt}" + + +def muir_doc_to_visual(doc): + image_list = [image.convert("RGB") for image in doc["image_list"]] + return image_list + + +def muir_doc_to_target(doc): + return doc["answer"] + + +def muir_process_results(doc, result): + pred = result[0] + task = doc["task"] + idx = doc["idx"] + image_relation = doc["image_relation"] + answer = doc["answer"] + image_type = doc["image_type"] + + data_dict = { + "pred" : pred, + "task" : task, + "idx" : idx, + "image_relation" : image_relation, + "answer" : answer, + "image_type" : image_type, + } + + return {"muirbench_score_overall" : data_dict} + + +def muir_aggregation(results): + task_num = {} + score = 0 + task_score = {} + for result in results: + if result["task"] not in task_score: + task_score[result["task"]] = 0 + + if result["task"] not in task_num: + task_num[result["task"]] = 0 + + if result["pred"].lower().strip() == result["answer"].lower().strip(): + task_score[result["task"]] += 1 + score += 1 + task_num[result["task"]] += 1 + + score = score / len(results) + + task_score = {k : v / task_num[k] for k,v in task_score.items()} + + print("=" * 50) + for k, v in task_score.items(): + print(f"{k} : {v:.2f}") + print("=" * 50) + + return score + + + + +class MultiChoiceRegexFilter(ExtendedRegexFilter): + def __init__(self, *args, **kwargs): + """ + regex_pattern: The basic regex pattern to use. If fails to match, we will use the customized match procedure + - step 1 : We parse the choices between ([A-Z])s then try to find these choices in the response. + - step 2 : We parse the choice with regex :[\s]*([A-?]), where ? varies by number of choices. + group_select: Selects the (group_select)th match from the findall result. + ignore_case: Ignores the case during step 1 matching + ignore_punctuation: Remove the punctuation during step 1 matching + regexes_to_ignore: Remove these regexes during step 1 matching + """ + super().__init__(*args, **kwargs) + + def apply(self, resps, docs): + # here, we assume we have a list, in which each element is + # a list of model responses for some particular input/target pair. + # so we process each of these (same input/target response sets) + # independently (and keep them a list.) + + filtered_resps = [] + + for r, doc in zip(resps, docs): + # Regex to directly extract the option letter from the model response + option_letter_regex = re.compile(r"^\s*([A-Z])\.") + + # Process each response + filtered = [] + for resp in r: + # Try to match the option letter at the start of the response + match = option_letter_regex.match(resp) + if match: + # If a match is found, append the matched letter + filtered.append(match.group(1)) + else: + # If no match, return the original response + filtered.append(resp) + + # Assuming we need the first response that matches or the original response + filtered_resps.append(filtered[0]) + + return filtered_resps From bb85c2724ba0e9aa924e6cecf227561ba84fe327 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 16 Jul 2024 06:56:20 +0000 Subject: [PATCH 143/196] Add lmms lite tools --- tools/lite/embed.py | 57 ++++++++++++ tools/lite/embedder/BaseEmbedder.py | 62 +++++++++++++ tools/lite/embedder/ClipBgeEmbedder.py | 92 +++++++++++++++++++ tools/lite/embedder/__init__.py | 2 + tools/lite/shrink.py | 53 +++++++++++ tools/lite/shrinker/BaseShrinker.py | 35 +++++++ tools/lite/shrinker/EmbedShrinker.py | 53 +++++++++++ tools/lite/shrinker/__init__.py | 2 + .../shrinker/sampling_methods/__init__.py | 24 +++++ .../sampling_methods/kcenter_greedy.py | 80 ++++++++++++++++ .../shrinker/sampling_methods/sampling_def.py | 45 +++++++++ 11 files changed, 505 insertions(+) create mode 100644 tools/lite/embed.py create mode 100644 tools/lite/embedder/BaseEmbedder.py create mode 100644 tools/lite/embedder/ClipBgeEmbedder.py create mode 100644 tools/lite/embedder/__init__.py create mode 100644 tools/lite/shrink.py create mode 100644 tools/lite/shrinker/BaseShrinker.py create mode 100644 tools/lite/shrinker/EmbedShrinker.py create mode 100644 tools/lite/shrinker/__init__.py create mode 100644 tools/lite/shrinker/sampling_methods/__init__.py create mode 100644 tools/lite/shrinker/sampling_methods/kcenter_greedy.py create mode 100644 tools/lite/shrinker/sampling_methods/sampling_def.py diff --git a/tools/lite/embed.py b/tools/lite/embed.py new file mode 100644 index 00000000..e8a0c30e --- /dev/null +++ b/tools/lite/embed.py @@ -0,0 +1,57 @@ +import embedder +from lmms_eval.utils import simple_parse_args_string +from lmms_eval.tasks import initialize_tasks, include_path, get_task_dict, ConfigurableTask +from lmms_eval.api.registry import ALL_TASKS, GROUP_REGISTRY + +import argparse +import os + +import torch.distributed as dist + + +def rank0_print(*args): + if dist.is_initialized(): + if dist.get_rank() == 0: + print(f"Rank {dist.get_rank()}: ", *args) + else: + print(*args) + + +def parse_arguments(): + parser = argparse.ArgumentParser() + parser.add_argument("--name", type=str) + parser.add_argument("--output_path", type=str) + parser.add_argument("--tasks", type=str, required=False, default="") + parser.add_argument("--data_path", type=str, required=False, default="") + parser.add_argument("--image_folder", type=str, required=False, default="") + parser.add_argument("--embedder_kwargs", type=str, default="") + + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_arguments() + embedder_name = args.name + output_path = args.output_path + if args.tasks.lower().strip() == "all": + initialize_tasks() + for task in list(ALL_TASKS): + if task in GROUP_REGISTRY: + ALL_TASKS.remove(task) + tasks = list(ALL_TASKS) + else: + tasks = args.tasks.split(",") + + cached_idx = [] + for idx in range(len(tasks)): + if os.path.exists(os.path.join(output_path, f"{tasks[idx]}_embed.npy")): + rank0_print(f"Task {tasks[idx]} exists in cache folder, load from cache") + cached_idx.append(idx) + tasks = [tasks[idx] for idx in range(len(tasks)) if idx not in cached_idx] + rank0_print(f"Tasks : {tasks}") + embedder_kwargs = simple_parse_args_string(args.embedder_kwargs) + + embedder_cls = getattr(embedder, embedder_name) + embedder_obj = embedder_cls(name=embedder_name, output_path=output_path, **embedder_kwargs) + for task in tasks: + embedder_obj.embed_task(task) diff --git a/tools/lite/embedder/BaseEmbedder.py b/tools/lite/embedder/BaseEmbedder.py new file mode 100644 index 00000000..88031fa4 --- /dev/null +++ b/tools/lite/embedder/BaseEmbedder.py @@ -0,0 +1,62 @@ +from abc import ABC, abstractmethod +from typing import List, Optional, Union, Dict +import os + +from lmms_eval.tasks import initialize_tasks, include_path, get_task_dict, ConfigurableTask +from lmms_eval.api.registry import ALL_TASKS + +import torch.distributed as dist + + +def rank0_print(*args): + if dist.is_initialized(): + if dist.get_rank() == 0: + print(f"Rank {dist.get_rank()}: ", *args) + else: + print(*args) + + +class BaseEmbedder(ABC): + def __init__(self, name: str, output_path: str) -> None: + super().__init__() + self.name = name + self.output_path = output_path + os.makedirs(self.output_path, exist_ok=True) + initialize_tasks() + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + # A static method to build requests for lmms_eval tasks + # Pass in task name and return a list of Requests + @staticmethod + def init_task(task: str, ignored_ids: Union[set, List] = None): + task_dict = get_task_dict([task], model_name="llava") + task_obj = task_dict[task] + if type(task_obj) == tuple: + group, task_obj = task_obj + DATASET_PATH = task_obj.DATASET_PATH + DATASET_NAME = None + if task_obj.DATASET_NAME is not None: + DATASET_NAME = task_obj.DATASET_NAME + + docs = task_obj.test_docs() if task_obj.has_test_docs() else task_obj.validation_docs() + split = task_obj.config.test_split if task_obj.has_test_docs() else task_obj.config.validation_split + rank0_print(f"\nTask : {task_obj.config.task}\n - #num : {len(task_obj.test_docs()) if task_obj.has_test_docs() else task_obj.validation_docs()}") + task_obj.build_all_requests() + requests = [] + for instance in task_obj.instances: + reqtype = instance.request_type + contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = instance.args + if ignored_ids is not None and doc_id in ignored_ids: + continue + requests.append(instance) + return DATASET_PATH, DATASET_NAME, split, requests, task_obj, docs + + @abstractmethod + def embed_task(self, task: str): + return diff --git a/tools/lite/embedder/ClipBgeEmbedder.py b/tools/lite/embedder/ClipBgeEmbedder.py new file mode 100644 index 00000000..11973df4 --- /dev/null +++ b/tools/lite/embedder/ClipBgeEmbedder.py @@ -0,0 +1,92 @@ +from copy import deepcopy +from datetime import timedelta +from typing import Optional, Union, List, Sequence, Dict +import os +import json + +from .BaseEmbedder import BaseEmbedder + +from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs +from accelerate.state import AcceleratorState +from accelerate.utils import gather_object + +import numpy as np +from transformers import CLIPModel, CLIPProcessor +from sentence_transformers import SentenceTransformer +from tqdm import tqdm +import torch +from PIL import Image + + +class ClipBgeEmbedder(BaseEmbedder): + def __init__( + self, + name: str, + output_path: str, + mm_pretrained: str = "openai/clip-vit-large-patch14", + txt_pretrained: str = "BAAI/bge-m3", + device: str = "cuda", + device_map: str = "", + ) -> None: + super().__init__(name, output_path) + self.model = CLIPModel.from_pretrained(mm_pretrained) + self.processor = CLIPProcessor.from_pretrained(mm_pretrained) + self.text_model = SentenceTransformer(txt_pretrained) + accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) + self.accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + if self.accelerator.num_processes > 1 and device_map == "": + self.device = torch.device(f"cuda:{self.accelerator.local_process_index}") + self.device_map = f"cuda:{self.accelerator.local_process_index}" + else: + self.device = torch.device(device) + self.device_map = device_map + + self.model.to(self.device) + self.text_model.to(self.device) + + def embed_task(self, task: str, ignored_ids: Union[set, List] = None): + DATASET_PATH, DATASET_NAME, split, requests, task_obj, self.docs = BaseEmbedder.init_task(task, ignored_ids) + self.accelerator.wait_for_everyone() + with self.accelerator.split_between_processes(requests, apply_padding=False) as requests_split: + results = {"outputs": []} + for req in tqdm(requests_split, disable=not self.accelerator.is_main_process): + contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = req.args + visuals = [doc_to_visual(self.docs[doc_id])] + visuals = self.flatten(visuals) + + text_embedding = self.text_model.encode([contexts]) + text_embedding = torch.from_numpy(text_embedding).flatten() + + if len(visuals) > 0: + img_inputs = self.processor(images=visuals, return_tensors="pt") + img_inputs = {k: v.to(self.device) for k, v in img_inputs.items()} + + # For multiple images, we take the mean of it + image_embedding = self.model.get_image_features(**img_inputs).mean(dim=0).detach().cpu() + else: + image_embedding = torch.zeros(self.model.config.projection_dim) + + embedding = torch.concat([image_embedding, text_embedding]) + + results["outputs"].append(embedding) + results = [results] + + self.accelerator.wait_for_everyone() + results_gathered = gather_object(results) + if self.accelerator.is_main_process: + outputs = [] + for r in results_gathered: + outputs += r["outputs"] + results_gathered = torch.stack(outputs) + np.save(open(os.path.join(self.output_path, f"{task}_embed.npy"), "wb"), results_gathered) + return results_gathered + + +if __name__ == "__main__": + model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14").to("cuda") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") + text = ["a photo of a cat", "a photo of a dog"] + inputs = processor(text=text, return_tensors="pt") + inputs = {k: v.to("cuda") for k, v in inputs.items()} + outputs = model.get_text_features(**inputs) + print(outputs.mean(dim=0).shape) diff --git a/tools/lite/embedder/__init__.py b/tools/lite/embedder/__init__.py new file mode 100644 index 00000000..c86c10ff --- /dev/null +++ b/tools/lite/embedder/__init__.py @@ -0,0 +1,2 @@ +from .BaseEmbedder import BaseEmbedder +from .ClipBgeEmbedder import ClipBgeEmbedder diff --git a/tools/lite/shrink.py b/tools/lite/shrink.py new file mode 100644 index 00000000..23cba7a4 --- /dev/null +++ b/tools/lite/shrink.py @@ -0,0 +1,53 @@ +import argparse +import importlib +import os +import yaml +import json +from pathlib import Path +import hashlib + +from accelerate import Accelerator +from accelerate.utils import InitProcessGroupKwargs +import numpy as np +import datetime + +from lmms_eval.utils import simple_parse_args_string +import shrinker as shrinker_module + + +AVAILABEL_SHRINKER = {"embed": "Embed_Shrinker"} + + +def parse_arguments(): + parser = argparse.ArgumentParser() + parser.add_argument("--shrinker", type=str, help="The type of shrinker you want to use") + parser.add_argument("--num_items", type=str, help="The number of items you want in your shrinked dataset") + parser.add_argument("--tasks", type=str, help="The task you want to shrink. Separate each task with comma, will be parsed in to list") + parser.add_argument("--push_to_hub", action="store_true", default=False, help="Whether to push the shrinked dataset to hub") + parser.add_argument("--shrinker_kwargs", type=str, help="In args=xxx,args2=xxx format. Will be parsed into dict") + + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_arguments() + shrinker_kwargs = simple_parse_args_string(args.shrinker_kwargs) + shrinker_name = args.shrinker + tasks = args.tasks.split(",") + num_items = args.num_items.split(",") + assert len(num_items) == 1 or len(num_items) == len(tasks), "Either provide one num items for all task or one num item for each task" + if len(num_items) == 1: + num_items = [float(num_items[0])] * len(tasks) + else: + num_items = [float(n) for n in num_items] + push_to_hub = args.push_to_hub + assert len(num_items) == len(tasks) or len(num_items) == 1, "Either pass in one num_items for whole tasks, or pass in num items for each task" + assert shrinker_name in AVAILABEL_SHRINKER, f"Unavailable shrinker {shrinker_name}. You can choose from {AVAILABEL_SHRINKER.keys()}" + + kwargs_handler = InitProcessGroupKwargs(timeout=datetime.timedelta(seconds=60000)) + accelerator = Accelerator(kwargs_handlers=[kwargs_handler]) + for idx, task in enumerate(tasks): + shrinker = getattr(shrinker_module, f"{AVAILABEL_SHRINKER[shrinker_name]}") + shrinker = shrinker(task=task, num_items=num_items[idx], push_to_hub=push_to_hub, name=shrinker_name, **shrinker_kwargs) + shrinker.shrink() + accelerator.wait_for_everyone() diff --git a/tools/lite/shrinker/BaseShrinker.py b/tools/lite/shrinker/BaseShrinker.py new file mode 100644 index 00000000..742650d3 --- /dev/null +++ b/tools/lite/shrinker/BaseShrinker.py @@ -0,0 +1,35 @@ +from abc import ABC, abstractmethod +from glob import glob +import os +import json +import numpy as np +from numpy.polynomial.polynomial import polyfit +import matplotlib.pyplot as plt +from sklearn.metrics import mean_squared_error +from typing import Dict, List, Union +from distutils.dir_util import copy_tree +import random +import torch +import lmms_eval +from lmms_eval.evaluator import evaluate +from lmms_eval.tasks import initialize_tasks, include_path, get_task_dict, ConfigurableTask +from lmms_eval.api.registry import ALL_TASKS +import logging +from lmms_eval.utils import simple_parse_args_string + + +eval_logger = logging.getLogger("lmms-eval") + + +class BaseShrinker(ABC): + def __init__(self, task: str, num_items: Union[int, float], name: str, push_to_hub: bool = True) -> None: + + super().__init__() + self.name = name + self.task = task + self.num_items = float(num_items) + self.push_to_hub = push_to_hub + + @abstractmethod + def shrink(self): + return diff --git a/tools/lite/shrinker/EmbedShrinker.py b/tools/lite/shrinker/EmbedShrinker.py new file mode 100644 index 00000000..0d4f892a --- /dev/null +++ b/tools/lite/shrinker/EmbedShrinker.py @@ -0,0 +1,53 @@ +from .BaseShrinker import BaseShrinker +import sys +from .sampling_methods import AVAILABEL_METHODS +from lmms_eval.tasks import initialize_tasks + +import torch +from typing import List, Dict, Union +import numpy as np +import os + +sys.path.append("..") +from embedder import BaseEmbedder +from shrinker import sampling_methods as sampling_methods_module + + +class Embed_Shrinker(BaseShrinker): + def __init__( + self, + task: str, + num_items: Union[int, float], + name: str, + embed_cache_path: str, + sampling_methods: str, + push_to_hub: bool, + ) -> None: + super().__init__(task, num_items, name, push_to_hub) + self.embed_cache_path = embed_cache_path + initialize_tasks() + self.DATASET_PATH, self.DATASET_NAME, self.split, _, self.task_obj, docs = BaseEmbedder.init_task(task) + assert sampling_methods in AVAILABEL_METHODS, f"Not available sampling methods, Choose from {AVAILABEL_METHODS.keys()}" + self.sampling_methods = getattr(sampling_methods_module, AVAILABEL_METHODS[sampling_methods]) + + def shrink(self): + task_embedding = np.load(open(os.path.join(self.embed_cache_path, f"{self.task}_embed.npy"), "rb")) + task_embedding = torch.from_numpy(task_embedding) + # I know torch.squeeze is safe but numpy reshape sometimes may not + # so I just do it here by converting to torch + if len(task_embedding.shape) == 3: + task_embedding = task_embedding.squeeze(1) + task_embedding = task_embedding.numpy() + self.sampling_methods = self.sampling_methods(X=task_embedding) + # centroids = self.cluster(task_embedding) + if self.num_items < 1.0: + self.num_items = int(task_embedding.shape[0] * self.num_items) + else: + self.num_items = int(self.num_items) + anchor_points = self.sampling_methods.select_batch(N=self.num_items) + dataset = self.task_obj.dataset[self.split] + tiny_dataset = dataset.select(anchor_points) + + if self.push_to_hub: + tiny_dataset.push_to_hub(repo_id=f"lmms-lab/LMMs-Eval-Lite", config_name=self.task, split="lite") + return diff --git a/tools/lite/shrinker/__init__.py b/tools/lite/shrinker/__init__.py new file mode 100644 index 00000000..737538ba --- /dev/null +++ b/tools/lite/shrinker/__init__.py @@ -0,0 +1,2 @@ +from .BaseShrinker import BaseShrinker +from .EmbedShrinker import Embed_Shrinker diff --git a/tools/lite/shrinker/sampling_methods/__init__.py b/tools/lite/shrinker/sampling_methods/__init__.py new file mode 100644 index 00000000..6dad5f78 --- /dev/null +++ b/tools/lite/shrinker/sampling_methods/__init__.py @@ -0,0 +1,24 @@ +# Copyright 2017 Google Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +AVAILABEL_METHODS = { + "kcenter_greedy": "kCenterGreedy", +} + +for name, class_name in AVAILABEL_METHODS.items(): + try: + exec(f"from .{name} import {class_name}") + except Exception as e: + print(f"Error : \n\n{e}\n\n When trying to import {class_name}") diff --git a/tools/lite/shrinker/sampling_methods/kcenter_greedy.py b/tools/lite/shrinker/sampling_methods/kcenter_greedy.py new file mode 100644 index 00000000..69d993fa --- /dev/null +++ b/tools/lite/shrinker/sampling_methods/kcenter_greedy.py @@ -0,0 +1,80 @@ +import numpy as np +from sklearn.metrics import pairwise_distances +from tqdm import tqdm + + +from .sampling_def import SamplingMethod + + +class kCenterGreedy(SamplingMethod): + def __init__(self, X: np.array): + self.X = X + self.flat_X = self.flatten_X() + self.name = "kcenter" + self.features = self.flat_X + self.min_distances = None + self.n_obs = self.X.shape[0] + self.already_selected = None + + def update_distances(self, cluster_centers, only_new=True, reset_dist=False): + """Update min distances given cluster centers. + + Args: + cluster_centers: indices of cluster centers + only_new: only calculate distance for newly selected points and update + min_distances. + rest_dist: whether to reset min_distances. + """ + + if reset_dist: + self.min_distances = None + if only_new: + cluster_centers = [d for d in cluster_centers if d not in self.already_selected] + if cluster_centers: + # Update min_distances for all examples given new cluster center. + x = self.features[cluster_centers] + dist = pairwise_distances(self.features, x, metric="euclidean") + + if self.min_distances is None: + self.min_distances = np.min(dist, axis=1).reshape(-1, 1) + else: + self.min_distances = np.minimum(self.min_distances, dist) + + def select_batch(self, N): + """ + Diversity promoting active learning method that greedily forms a batch + to minimize the maximum distance to a cluster center among all unlabeled + datapoints. + + Args: + model: model with scikit-like API with decision_function implemented + already_selected: index of datapoints already selected + N: batch size + + Returns: + indices of points selected to minimize distance to cluster centers + """ + + print("Using flat_X as features.") + + new_batch = [] + + for _ in tqdm(range(N), desc="K-Center Greedy"): + if self.already_selected is None: + # Initialize centers with a randomly selected datapoint + # ind = np.random.choice(np.arange(self.n_obs)) + ind = 0 # To avoid randomness + self.already_selected = [] + else: + ind = np.argmax(self.min_distances) + # New examples should not be in already selected since those points + # should have min_distance of zero to a cluster center. + assert ind not in self.already_selected + + self.update_distances([ind], only_new=True, reset_dist=False) + new_batch.append(ind) + self.already_selected.append(ind) + print("Maximum distance from cluster centers is %0.2f" % max(self.min_distances)) + + new_batch = np.array(new_batch) + return new_batch diff --git a/tools/lite/shrinker/sampling_methods/sampling_def.py b/tools/lite/shrinker/sampling_methods/sampling_def.py new file mode 100644 index 00000000..b7d8a40c --- /dev/null +++ b/tools/lite/shrinker/sampling_methods/sampling_def.py @@ -0,0 +1,45 @@ +# Copyright 2017 Google Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Abstract class for sampling methods. + +Provides interface to sampling methods that allow same signature +for select_batch. Each subclass implements select_batch_ with the desired +signature for readability. +""" + +import abc +import numpy as np + + +class SamplingMethod(object): + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def __init__(self, X, **kwargs): + self.X = X + + def flatten_X(self): + shape = self.X.shape + flat_X = self.X + if len(shape) > 2: + flat_X = np.reshape(self.X, (shape[0], np.product(shape[1:]))) + return flat_X + + @abc.abstractmethod + def select_batch(self): + return + + def to_dict(self): + return None From b0894c59cf1729ce1d10207e919d056c2bb988f7 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Fri, 28 Jun 2024 08:55:12 +0000 Subject: [PATCH 144/196] Add lmms-eval lite --- lmms_eval/tasks/ai2d/ai2d_lite.yaml | 56 +++++++++++++++++++ lmms_eval/tasks/chartqa/chartqa_lite.yaml | 35 ++++++++++++ .../tasks/coco_cap/coco2017_cap_val_lite.yaml | 45 +++++++++++++++ lmms_eval/tasks/docvqa/docvqa_val_lite.yaml | 26 +++++++++ .../tasks/flickr30k/flickr30k_test_lite.yaml | 45 +++++++++++++++ lmms_eval/tasks/gqa/gqa_lite.yaml | 32 +++++++++++ lmms_eval/tasks/infovqa/infovqa_val_lite.yaml | 22 ++++++++ lmms_eval/tasks/lmms_eval_lite/lite.yaml | 25 +++++++++ .../tasks/mmbench/mmbench_cn_dev_lite.yaml | 33 +++++++++++ .../tasks/mmbench/mmbench_en_dev_lite.yaml | 36 ++++++++++++ .../tasks/ok_vqa/ok_vqa_val2014_lite.yaml | 28 ++++++++++ .../tasks/refcoco/refcoco_bbox_val_lite.yaml | 39 +++++++++++++ lmms_eval/tasks/seedbench/seedbench_lite.yaml | 29 ++++++++++ .../tasks/textcaps/textcaps_val_lite.yaml | 48 ++++++++++++++++ lmms_eval/tasks/textvqa/textvqa_val_lite.yaml | 30 ++++++++++ .../tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml | 28 ++++++++++ lmms_eval/tasks/vqav2/vqav2_val_lite.yaml | 25 +++++++++ 17 files changed, 582 insertions(+) create mode 100644 lmms_eval/tasks/ai2d/ai2d_lite.yaml create mode 100644 lmms_eval/tasks/chartqa/chartqa_lite.yaml create mode 100644 lmms_eval/tasks/coco_cap/coco2017_cap_val_lite.yaml create mode 100644 lmms_eval/tasks/docvqa/docvqa_val_lite.yaml create mode 100644 lmms_eval/tasks/flickr30k/flickr30k_test_lite.yaml create mode 100644 lmms_eval/tasks/gqa/gqa_lite.yaml create mode 100644 lmms_eval/tasks/infovqa/infovqa_val_lite.yaml create mode 100644 lmms_eval/tasks/lmms_eval_lite/lite.yaml create mode 100644 lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml create mode 100644 lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml create mode 100644 lmms_eval/tasks/ok_vqa/ok_vqa_val2014_lite.yaml create mode 100644 lmms_eval/tasks/refcoco/refcoco_bbox_val_lite.yaml create mode 100644 lmms_eval/tasks/seedbench/seedbench_lite.yaml create mode 100644 lmms_eval/tasks/textcaps/textcaps_val_lite.yaml create mode 100644 lmms_eval/tasks/textvqa/textvqa_val_lite.yaml create mode 100644 lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml create mode 100644 lmms_eval/tasks/vqav2/vqav2_val_lite.yaml diff --git a/lmms_eval/tasks/ai2d/ai2d_lite.yaml b/lmms_eval/tasks/ai2d/ai2d_lite.yaml new file mode 100644 index 00000000..bdeb9724 --- /dev/null +++ b/lmms_eval/tasks/ai2d/ai2d_lite.yaml @@ -0,0 +1,56 @@ +dataset_path: lmms-lab/LMMs-Eval-Lite +task: "ai2d_lite" +dataset_kwargs: + token: True +dataset_name: ai2d +test_split: lite +output_type: generate_until +doc_to_visual: !function utils.ai2d_doc_to_visual +doc_to_text: !function utils.ai2d_doc_to_text +doc_to_target: !function utils.ai2d_doc_to_target + +model_specific_prompt_kwargs: + default: + prompt_format: mcq + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." + gpt4v: + prompt_format: mcq + pre_prompt: "" + post_prompt: "\nAbove choices are given in {option}. {content} format.\nPlease answer with the option letter from the given choices directly." + qwen_vl: + prompt_format: qa + pre_prompt: "" + post_prompt: " Answer:" + xcomposer2_4khd: + prompt_format: mcq_xcomposer + pre_prompt: "[UNUSED_TOKEN_146]user\nQuestion: " + post_prompt: "[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\nThe answer is" + +model_specific_target_kwargs: + default: "mcq" + qwen_vl: "qa" + +generation_kwargs: + max_new_tokens: 16 + temperature: 0 + do_sample: False + +filter_list: + - name: "flexible-extract" + filter: + - function: !function utils.MultiChoiceRegexFilter + group_select: 0 + ignore_case: true + ignore_punctuation: true + regex_pattern: "([A-Z])\\." + +metric_list: + - metric: exact_match + aggregation: mean + higher_is_better: true + ignore_case: true + ignore_punctuation: true + +metadata: + - version: 0.0 \ No newline at end of file diff --git a/lmms_eval/tasks/chartqa/chartqa_lite.yaml b/lmms_eval/tasks/chartqa/chartqa_lite.yaml new file mode 100644 index 00000000..4fbce497 --- /dev/null +++ b/lmms_eval/tasks/chartqa/chartqa_lite.yaml @@ -0,0 +1,35 @@ +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_kwargs: + token: True +task: "chartqa_lite" +dataset_name: chartqa +test_split: lite +output_type: generate_until +doc_to_visual: !function utils.chartqa_doc_to_visual +doc_to_text: !function utils.chartqa_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 16 + temperature: 0 + do_sample: False +process_results: !function utils.chartqa_process_results +metric_list: + - metric: relaxed_overall + aggregation: mean + higher_is_better: true + - metric: relaxed_human_split + aggregation: mean + higher_is_better: true + - metric: relaxed_augmented_split + aggregation: mean + higher_is_better: true +metadata: + - version: 0.0 +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer the question with a single word." + qwen_vl: + pre_prompt: "" + post_prompt: " Answer:" + diff --git a/lmms_eval/tasks/coco_cap/coco2017_cap_val_lite.yaml b/lmms_eval/tasks/coco_cap/coco2017_cap_val_lite.yaml new file mode 100644 index 00000000..f04fd514 --- /dev/null +++ b/lmms_eval/tasks/coco_cap/coco2017_cap_val_lite.yaml @@ -0,0 +1,45 @@ +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_kwargs: + token: True +task: "coco2017_cap_val_lite" +dataset_name: coco2017_cap_val +test_split: lite +output_type: generate_until +doc_to_visual: !function utils.coco_doc_to_visual +doc_to_text: !function utils.coco_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 64 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +process_results: !function utils.coco_process_result +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: coco_Bleu_4 + aggregation : !function utils.coco_bleu4 + higher_is_better : true + - metric: coco_Bleu_3 + aggregation : !function utils.coco_bleu3 + higher_is_better : true + - metric: coco_Bleu_2 + aggregation : !function utils.coco_bleu2 + higher_is_better : true + - metric: coco_Bleu_1 + aggregation : !function utils.coco_bleu1 + higher_is_better : true + - metric: coco_METEOR + aggregation : !function utils.coco_meteor + higher_is_better : true + - metric: coco_ROUGE_L + aggregation : !function utils.coco_rougel + higher_is_better : true + - metric: coco_CIDEr + aggregation : !function utils.coco_cider + higher_is_better : true + #- metric: coco_SPICE + # aggregation : !function utils.coco_spice + # higher_is_better : true +metadata: + - version: 0.0 \ No newline at end of file diff --git a/lmms_eval/tasks/docvqa/docvqa_val_lite.yaml b/lmms_eval/tasks/docvqa/docvqa_val_lite.yaml new file mode 100644 index 00000000..afda5eb5 --- /dev/null +++ b/lmms_eval/tasks/docvqa/docvqa_val_lite.yaml @@ -0,0 +1,26 @@ +task: "docvqa_val_lite" +test_split: lite +metric_list: + - metric: anls + aggregation: mean + higher_is_better: true +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: docvqa_val +dataset_kwargs: + token: True +output_type: generate_until +doc_to_visual: !function utils.docvqa_doc_to_visual +doc_to_text: !function utils.docvqa_doc_to_text +doc_to_target: "answers" +generation_kwargs: + max_new_tokens: 32 + temperature: 0 + do_sample: False +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer the question using a single word or phrase." + qwen_vl: + pre_prompt: "" + post_prompt: " Answer:" + diff --git a/lmms_eval/tasks/flickr30k/flickr30k_test_lite.yaml b/lmms_eval/tasks/flickr30k/flickr30k_test_lite.yaml new file mode 100644 index 00000000..782e72ee --- /dev/null +++ b/lmms_eval/tasks/flickr30k/flickr30k_test_lite.yaml @@ -0,0 +1,45 @@ +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_kwargs: + token: True +task : "flickr30k_test_test" +dataset_name: flickr30k_test +test_split: lite +output_type: generate_until +doc_to_visual: !function utils.flickr_doc_to_visual +doc_to_text: !function utils.flickr_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 64 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +process_results: !function utils.flickr_process_result +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: flickr_Bleu_4 + aggregation : !function utils.flickr_bleu4 + higher_is_better : true + - metric: flickr_Bleu_3 + aggregation : !function utils.flickr_bleu3 + higher_is_better : true + - metric: flickr_Bleu_2 + aggregation : !function utils.flickr_bleu2 + higher_is_better : true + - metric: flickr_Bleu_1 + aggregation : !function utils.flickr_bleu1 + higher_is_better : true + - metric: flickr_METEOR + aggregation : !function utils.flickr_meteor + higher_is_better : true + - metric: flickr_ROUGE_L + aggregation : !function utils.flickr_rougel + higher_is_better : true + - metric: flickr_CIDEr + aggregation : !function utils.flickr_cider + higher_is_better : true + #- metric: flickr_SPICE + # aggregation : !function utils.flickr_spice + # higher_is_better : true +metadata: + - version: 0.0 \ No newline at end of file diff --git a/lmms_eval/tasks/gqa/gqa_lite.yaml b/lmms_eval/tasks/gqa/gqa_lite.yaml new file mode 100644 index 00000000..81042041 --- /dev/null +++ b/lmms_eval/tasks/gqa/gqa_lite.yaml @@ -0,0 +1,32 @@ +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: gqa +dataset_kwargs: + token: True +task: "gqa_lite" +test_split: lite +output_type: generate_until +doc_to_visual: !function utils.gqa_doc_to_visual +doc_to_text: !function utils.gqa_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 16 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +metric_list: + - metric: exact_match + aggregation: mean + higher_is_better: true + ignore_case: true + ignore_punctuation: true +metadata: + - version: 0.0 + +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer the question using a single word or phrase." + qwen_vl: + pre_prompt: "" + post_prompt: " Answer:" \ No newline at end of file diff --git a/lmms_eval/tasks/infovqa/infovqa_val_lite.yaml b/lmms_eval/tasks/infovqa/infovqa_val_lite.yaml new file mode 100644 index 00000000..eab75120 --- /dev/null +++ b/lmms_eval/tasks/infovqa/infovqa_val_lite.yaml @@ -0,0 +1,22 @@ +task: "infovqa_val_lite" +test_split: lite +output_type: generate_until +metric_list: + - metric: anls + aggregation: mean + higher_is_better: true +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: infovqa_val +dataset_kwargs: + token: True +doc_to_target: "answers" +doc_to_visual: !function utils.infovqa_doc_to_visual +doc_to_text: !function utils.infovqa_doc_to_text +generation_kwargs: + max_new_tokens: 32 + temperature: 0 + do_sample: False +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer the question using a single word or phrase." \ No newline at end of file diff --git a/lmms_eval/tasks/lmms_eval_lite/lite.yaml b/lmms_eval/tasks/lmms_eval_lite/lite.yaml new file mode 100644 index 00000000..8a1de7e4 --- /dev/null +++ b/lmms_eval/tasks/lmms_eval_lite/lite.yaml @@ -0,0 +1,25 @@ +group: lite +task: +- ai2d_lite +- chartqa_lite +- coco2017_cap_val_lite +- docvqa_val_lite +- flickr30k_test_test +- gqa_lite +- infovqa_val_lite +- mmbench_cn_dev_lite +- mmbench_en_dev_lite +- ok_vqa_val2014_lite +- refcoco_bbox_val_lite +- seedbench_lite +- textcaps_val_lite +- textvqa_val_lite +- vizwiz_vqa_val_lite +- vqav2_val_lite +- mmmu_val +- cmmmu_val +- mmvet +- mathvista_testmini +- realworldqa +- llava_in_the_wild +- llava_wilder_small \ No newline at end of file diff --git a/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml b/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml new file mode 100644 index 00000000..8efc1195 --- /dev/null +++ b/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml @@ -0,0 +1,33 @@ +task: "mmbench_cn_dev_lite" +test_split: "lite" +metric_list: + - metric: gpt_eval_score + aggregation: !function cn_utils.mmbench_aggregate_dev_results_eval + higher_is_better: true + - metric: submission + higher_is_better: true + aggregation: !function cn_utils.mmbench_aggregate_dev_results +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: mmbench_cn_dev +dataset_kwargs: + token: True +doc_to_target: "answer" +dataset_name: "cn" +output_type: generate_until +doc_to_visual: !function cn_utils.mmbench_doc_to_visual +doc_to_text: !function cn_utils.mmbench_doc_to_text +generation_kwargs: + max_new_tokens: 256 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +process_results: !function cn_utils.mmbench_process_results +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\n请直接使用所提供的选项字母作为答案回答。" +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original + diff --git a/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml b/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml new file mode 100644 index 00000000..0bab059d --- /dev/null +++ b/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml @@ -0,0 +1,36 @@ +task: "mmbench_en_dev_lite" +test_split: lite +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: mmbench_en_dev +dataset_kwargs: + token: True +doc_to_target: "answer" +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly." +doc_to_visual: !function en_utils.mmbench_doc_to_visual +doc_to_text: !function en_utils.mmbench_doc_to_text +doc_to_target: "answer" +process_results: !function en_utils.mmbench_process_results +model_specific_generation_kwargs: + llava: + image_aspect_ratio: original +output_type: generate_until +dataset_name: "en" +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 1024 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false + +metric_list: + - metric: gpt_eval_score + aggregation: !function en_utils.mmbench_aggregate_dev_results_eval + higher_is_better: true + - metric: submission + aggregation: !function en_utils.mmbench_aggregate_dev_results_submission + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/ok_vqa/ok_vqa_val2014_lite.yaml b/lmms_eval/tasks/ok_vqa/ok_vqa_val2014_lite.yaml new file mode 100644 index 00000000..60b76f73 --- /dev/null +++ b/lmms_eval/tasks/ok_vqa/ok_vqa_val2014_lite.yaml @@ -0,0 +1,28 @@ +group: ok_vqa +task: ok_vqa_val2014_lite +test_split: lite +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: ok_vqa_val2014 +output_type: generate_until +doc_to_visual: !function utils.ok_vqa_doc_to_visual +doc_to_text: !function utils.ok_vqa_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" +metric_list: + - metric: exact_match + aggregation: mean + higher_is_better: true + ignore_case: true + ignore_punctuation: true + - metric: submission + aggregation: !function utils.ok_vqa_aggregate_submissions + higher_is_better: true +process_results: !function utils.ok_vqa_process_results +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase." +metadata: + - version: 0.0 \ No newline at end of file diff --git a/lmms_eval/tasks/refcoco/refcoco_bbox_val_lite.yaml b/lmms_eval/tasks/refcoco/refcoco_bbox_val_lite.yaml new file mode 100644 index 00000000..aebd9565 --- /dev/null +++ b/lmms_eval/tasks/refcoco/refcoco_bbox_val_lite.yaml @@ -0,0 +1,39 @@ +task: refcoco_bbox_val_lite +test_split: lite +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: refcoco_bbox_val +output_type: generate_until +doc_to_visual: !function utils.refcoco_bbox_doc_to_visual +doc_to_text: !function utils.refcoco_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" +process_results: !function utils.refcoco_process_result +metric_list: + - metric: refcoco_Bleu_4 + aggregation : !function utils.refcoco_bleu4 + higher_is_better : true + - metric: refcoco_Bleu_3 + aggregation : !function utils.refcoco_bleu3 + higher_is_better : true + - metric: refcoco_Bleu_2 + aggregation : !function utils.refcoco_bleu2 + higher_is_better : true + - metric: refcoco_Bleu_1 + aggregation : !function utils.refcoco_bleu1 + higher_is_better : true + - metric: refcoco_METEOR + aggregation : !function utils.refcoco_meteor + higher_is_better : true + - metric: refcoco_ROUGE_L + aggregation : !function utils.refcoco_rougel + higher_is_better : true + - metric: refcoco_CIDEr + aggregation : !function utils.refcoco_cider + higher_is_better : true + #- metric: refcoco_SPICE + # aggregation : !function utils.refcoco_spice + # higher_is_better : true +metadata: + version: '0.0' diff --git a/lmms_eval/tasks/seedbench/seedbench_lite.yaml b/lmms_eval/tasks/seedbench/seedbench_lite.yaml new file mode 100644 index 00000000..b8b59851 --- /dev/null +++ b/lmms_eval/tasks/seedbench/seedbench_lite.yaml @@ -0,0 +1,29 @@ +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: seedbench +dataset_kwargs: + token: True +task: "seedbench_lite" +test_split: lite +output_type: generate_until +doc_to_visual: !function utils.seed_doc_to_visual +doc_to_text: !function utils.seed_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" + image_aspect_ratio: original +# The return value of process_results will be used by metrics +process_results: !function utils.seed_process_result +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: seed_image + aggregation: !function utils.seed_aggregation_result + higher_is_better: true + - metric: seed_video + aggregation: !function utils.seed_aggregation_result + higher_is_better: true + - metric: seed_all + aggregation: !function utils.seed_aggregation_result + higher_is_better: true +metadata: + - version: 0.0 \ No newline at end of file diff --git a/lmms_eval/tasks/textcaps/textcaps_val_lite.yaml b/lmms_eval/tasks/textcaps/textcaps_val_lite.yaml new file mode 100644 index 00000000..95525e07 --- /dev/null +++ b/lmms_eval/tasks/textcaps/textcaps_val_lite.yaml @@ -0,0 +1,48 @@ +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: textcaps_val +dataset_kwargs: + token: True +task: "textcaps_val_lite" +test_split: lite +output_type: generate_until +doc_to_visual: !function utils.textcaps_doc_to_visual +doc_to_text: !function utils.textcaps_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 64 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +process_results: !function utils.textcaps_process_result +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: textcaps_Bleu_4 + aggregation : !function utils.textcaps_bleu4 + higher_is_better : true + - metric: textcaps_Bleu_3 + aggregation : !function utils.textcaps_bleu3 + higher_is_better : true + - metric: textcaps_Bleu_2 + aggregation : !function utils.textcaps_bleu2 + higher_is_better : true + - metric: textcaps_Bleu_1 + aggregation : !function utils.textcaps_bleu1 + higher_is_better : true + - metric: textcaps_METEOR + aggregation : !function utils.textcaps_meteor + higher_is_better : true + - metric: textcaps_ROUGE_L + aggregation : !function utils.textcaps_rougel + higher_is_better : true + - metric: textcaps_CIDEr + aggregation : !function utils.textcaps_cider + higher_is_better : true + #- metric: textcaps_SPICE + # aggregation : !function utils.textcaps_spice + # higher_is_better : true +metadata: + - version: 0.0 +model_specific_prompt_kwargs: + default: + prompt: Provide a one-sentence caption for the provided image. \ No newline at end of file diff --git a/lmms_eval/tasks/textvqa/textvqa_val_lite.yaml b/lmms_eval/tasks/textvqa/textvqa_val_lite.yaml new file mode 100644 index 00000000..5438228a --- /dev/null +++ b/lmms_eval/tasks/textvqa/textvqa_val_lite.yaml @@ -0,0 +1,30 @@ +task: textvqa_val_lite +test_split: lite +metric_list: + - metric: exact_match + aggregation: mean + higher_is_better: true + ignore_case: true + ignore_punctuation: true + - metric: submission + aggregation: !function utils.textvqa_aggregate_submissions + higher_is_better: true +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: textvqa_val +output_type: generate_until +doc_to_visual: !function utils.textvqa_doc_to_visual +doc_to_text: !function utils.textvqa_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" +process_results: !function utils.textvqa_process_results +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer the question using a single word or phrase." + ocr: false + qwen_vl: + pre_prompt: "" + post_prompt: " Answer:" + diff --git a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml new file mode 100644 index 00000000..d686d3ff --- /dev/null +++ b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml @@ -0,0 +1,28 @@ +task: vizwiz_vqa_val_lite +test_split: lite +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: vizwiz_vqa_val +output_type: generate_until +doc_to_visual: !function utils.vizwiz_vqa_doc_to_visual +doc_to_text: !function utils.vizwiz_vqa_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" +metadata: + - version: 0.0 +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase." +process_results: !function utils.vizwiz_vqa_process_results + +metric_list: + - metric: exact_match + aggregation: mean + higher_is_better: true + ignore_case: true + ignore_punctuation: true + # - metric: submission + # aggregation: !function utils.vizwiz_vqa_aggregate_submissions + # higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/vqav2/vqav2_val_lite.yaml b/lmms_eval/tasks/vqav2/vqav2_val_lite.yaml new file mode 100644 index 00000000..1446c8df --- /dev/null +++ b/lmms_eval/tasks/vqav2/vqav2_val_lite.yaml @@ -0,0 +1,25 @@ +task: "vqav2_val_lite" +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: vqav2_val +dataset_kwargs: + token: True +output_type: generate_until +doc_to_visual: !function utils.vqav2_doc_to_visual +doc_to_text: !function utils.vqav2_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 16 +metadata: + - version: 0.0 +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer the question using a single word or phrase." +test_split: lite +metric_list: + - metric: exact_match + aggregation: mean + higher_is_better: true + ignore_case: true + ignore_punctuation: true +process_results: !function utils.vqav2_process_results_val From 9a4bc1091464db26591d7b4aa835fce3f0d2e76c Mon Sep 17 00:00:00 2001 From: kcz358 Date: Fri, 28 Jun 2024 14:04:03 +0000 Subject: [PATCH 145/196] Better seedbench aggregation --- lmms_eval/tasks/seedbench/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/seedbench/utils.py b/lmms_eval/tasks/seedbench/utils.py index c2938f13..7a8683ac 100755 --- a/lmms_eval/tasks/seedbench/utils.py +++ b/lmms_eval/tasks/seedbench/utils.py @@ -28,7 +28,7 @@ def seed_aggregation_result(results): total_count = 0 total_correct = 0 for result in results: - if result["pred"] == result["answer"]: + if result["pred"].lower().strip() == result["answer"].lower.strip(): total_correct += 1 total_count += 1 return total_correct / total_count From 6dded72193e4ac53402f83810667952af4e2c4d1 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 4 Jul 2024 04:14:00 +0000 Subject: [PATCH 146/196] Fix config yaml file name --- lmms_eval/tasks/flickr30k/flickr30k_test_lite.yaml | 4 ++-- lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml | 1 - lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml | 3 +-- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/lmms_eval/tasks/flickr30k/flickr30k_test_lite.yaml b/lmms_eval/tasks/flickr30k/flickr30k_test_lite.yaml index 782e72ee..8d5e85c7 100644 --- a/lmms_eval/tasks/flickr30k/flickr30k_test_lite.yaml +++ b/lmms_eval/tasks/flickr30k/flickr30k_test_lite.yaml @@ -1,7 +1,7 @@ dataset_path: lmms-lab/LMMs-Eval-Lite dataset_kwargs: token: True -task : "flickr30k_test_test" +task : "flickr30k_test_lite" dataset_name: flickr30k_test test_split: lite output_type: generate_until @@ -42,4 +42,4 @@ metric_list: # aggregation : !function utils.flickr_spice # higher_is_better : true metadata: - - version: 0.0 \ No newline at end of file + - version: 0.0 diff --git a/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml b/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml index 8efc1195..4fc38f96 100644 --- a/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml @@ -12,7 +12,6 @@ dataset_name: mmbench_cn_dev dataset_kwargs: token: True doc_to_target: "answer" -dataset_name: "cn" output_type: generate_until doc_to_visual: !function cn_utils.mmbench_doc_to_visual doc_to_text: !function cn_utils.mmbench_doc_to_text diff --git a/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml b/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml index 0bab059d..60b9574e 100644 --- a/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml @@ -17,7 +17,6 @@ model_specific_generation_kwargs: llava: image_aspect_ratio: original output_type: generate_until -dataset_name: "en" generation_kwargs: until: - "ASSISTANT:" @@ -33,4 +32,4 @@ metric_list: higher_is_better: true - metric: submission aggregation: !function en_utils.mmbench_aggregate_dev_results_submission - higher_is_better: true \ No newline at end of file + higher_is_better: true From 2f2862fba92057bbdffdb13fe594414a2f307135 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 4 Jul 2024 04:56:13 +0000 Subject: [PATCH 147/196] Remove --- lmms_eval/tasks/lmms_eval_lite/lite.yaml | 25 ------------------------ 1 file changed, 25 deletions(-) delete mode 100644 lmms_eval/tasks/lmms_eval_lite/lite.yaml diff --git a/lmms_eval/tasks/lmms_eval_lite/lite.yaml b/lmms_eval/tasks/lmms_eval_lite/lite.yaml deleted file mode 100644 index 8a1de7e4..00000000 --- a/lmms_eval/tasks/lmms_eval_lite/lite.yaml +++ /dev/null @@ -1,25 +0,0 @@ -group: lite -task: -- ai2d_lite -- chartqa_lite -- coco2017_cap_val_lite -- docvqa_val_lite -- flickr30k_test_test -- gqa_lite -- infovqa_val_lite -- mmbench_cn_dev_lite -- mmbench_en_dev_lite -- ok_vqa_val2014_lite -- refcoco_bbox_val_lite -- seedbench_lite -- textcaps_val_lite -- textvqa_val_lite -- vizwiz_vqa_val_lite -- vqav2_val_lite -- mmmu_val -- cmmmu_val -- mmvet -- mathvista_testmini -- realworldqa -- llava_in_the_wild -- llava_wilder_small \ No newline at end of file From 048ed0f6d3b9920cb69601d5ada3310ea5a28aec Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 4 Jul 2024 08:23:12 +0000 Subject: [PATCH 148/196] Add nocaps lite config --- lmms_eval/tasks/nocaps/nocaps_val_lite.yaml | 46 +++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 lmms_eval/tasks/nocaps/nocaps_val_lite.yaml diff --git a/lmms_eval/tasks/nocaps/nocaps_val_lite.yaml b/lmms_eval/tasks/nocaps/nocaps_val_lite.yaml new file mode 100644 index 00000000..a297842f --- /dev/null +++ b/lmms_eval/tasks/nocaps/nocaps_val_lite.yaml @@ -0,0 +1,46 @@ +dataset_path: lmms-lab/LMMs-Eval-Lite +dataset_name: nocaps_val +dataset_kwargs: + token: True +task: "nocaps_val_lite" +test_split: lite +output_type: generate_until +doc_to_visual: !function utils.nocaps_doc_to_visual +doc_to_text: !function utils.nocaps_doc_to_text +doc_to_target: "annotations_captions" +generation_kwargs: + max_new_tokens: 64 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +process_results: !function utils.nocaps_process_result +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: nocaps_Bleu_4 + aggregation : !function utils.nocaps_bleu4 + higher_is_better : true + - metric: nocaps_Bleu_3 + aggregation : !function utils.nocaps_bleu3 + higher_is_better : true + - metric: nocaps_Bleu_2 + aggregation : !function utils.nocaps_bleu2 + higher_is_better : true + - metric: nocaps_Bleu_1 + aggregation : !function utils.nocaps_bleu1 + higher_is_better : true + - metric: nocaps_METEOR + aggregation : !function utils.nocaps_meteor + higher_is_better : true + - metric: nocaps_ROUGE_L + aggregation : !function utils.nocaps_rougel + higher_is_better : true + - metric: nocaps_CIDEr + aggregation : !function utils.nocaps_cider + higher_is_better : true + #- metric: nocaps_SPICE + # aggregation : !function utils.nocaps_spice + # higher_is_better : true +metadata: + - version: 0.0 +include: _default_template_nocaps_yaml \ No newline at end of file From c19936422ecabad3854163faea208de74d88bb43 Mon Sep 17 00:00:00 2001 From: Hongseok Oh <97136787+abzb1@users.noreply.github.com> Date: Wed, 17 Jul 2024 15:53:06 +0900 Subject: [PATCH 149/196] Fix broken hyperlink in README.md Correct hyperlink to supported task documentation --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7be1c6c7..041e215a 100755 --- a/README.md +++ b/README.md @@ -199,7 +199,7 @@ Please check [supported models](lmms_eval/models/__init__.py) for more details. ### Supported tasks -Please check [supported tasks](lmms_eval/docs/current_tasks.md) for more details. +Please check [supported tasks](docs/current_tasks.md) for more details. ## Add Customized Model and Dataset From 2f65711832361a738d09920efd1d5d4a2adb6e9b Mon Sep 17 00:00:00 2001 From: ys-zong Date: Wed, 17 Jul 2024 14:56:58 +0000 Subject: [PATCH 150/196] add MIRB --- lmms_eval/tasks/mirb/mirb.yaml | 30 ++++ lmms_eval/tasks/mirb/utils.py | 300 +++++++++++++++++++++++++++++++++ 2 files changed, 330 insertions(+) create mode 100644 lmms_eval/tasks/mirb/mirb.yaml create mode 100644 lmms_eval/tasks/mirb/utils.py diff --git a/lmms_eval/tasks/mirb/mirb.yaml b/lmms_eval/tasks/mirb/mirb.yaml new file mode 100644 index 00000000..b42e3c84 --- /dev/null +++ b/lmms_eval/tasks/mirb/mirb.yaml @@ -0,0 +1,30 @@ + +dataset_path: VLLMs/MIRB-hf +task: mirb +dataset_kwargs: + token: True +test_split: test +output_type: generate_until +doc_to_visual: !function utils.mirb_doc_to_visual +doc_to_text: !function utils.mirb_doc_to_text +doc_to_target: !function utils.mirb_doc_to_target +process_results: !function utils.mirb_process_results + +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "" + + +generation_kwargs: + max_new_tokens: 64 + temperature: 0 + do_sample: False + +metric_list: + - metric: mirb_score + aggregation: !function utils.mirb_aggregation + higher_is_better: true + +metadata: + - version: 0.0 diff --git a/lmms_eval/tasks/mirb/utils.py b/lmms_eval/tasks/mirb/utils.py new file mode 100644 index 00000000..2ed447d0 --- /dev/null +++ b/lmms_eval/tasks/mirb/utils.py @@ -0,0 +1,300 @@ + +from lmms_eval.filters.extraction import ExtendedRegexFilter +from lmms_eval.filters.transformation import MapFilter +import re +import numpy as np + + +import logging +eval_logger = logging.getLogger("lmms-eval") + +def get_task_instruction(dataset): + if dataset in ['analogy', 'attribute', 'plot_code', 'visual_chain', 'sightseeing']: + instr = 'Answer with a single word.' + elif dataset in ['codeu', 'food', 'image_jigsaw']: + instr = 'Answer with the option symbol.' + elif dataset in ['arxiv']: + instr = 'Answer with the paper title.' + elif dataset in ['count']: + instr = 'Answer with a single number.' + elif dataset in ['3d_scene']: + instr = 'The following images are different views of the same 3D scene. Answer with a single number.' + + return instr + +def mirb_doc_to_text(doc, model_specific_prompt_kwargs=None): + subset, question = doc['subset'], doc["questions"] + task_instruction = get_task_instruction(subset) + post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + return f"{pre_prompt}{task_instruction}{question}{post_prompt}" + +def mirb_doc_to_visual(doc): + image_list = [image.convert("RGB") for image in doc["image_list"]] + return image_list + +def mirb_doc_to_target(doc): + return doc["answers"] + + +def extract_numbers(string): + """ + Exact all forms of numbers from a string with regex. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L100 + """ + # Pattern for numbers with commas + pattern_commas = r"-?\b\d{1,3}(?:,\d{3})+\b" + # Pattern for scientific notation + pattern_scientific = r"-?\d+(?:\.\d+)?[eE][+-]?\d+" + # Pattern for simple numbers without commas + pattern_simple = r"-?(?:\d+\.\d+|\.\d+|\d+\b)(?![eE][+-]?\d+)(?![,\d])" + + # Extract numbers with commas + numbers_with_commas = re.findall(pattern_commas, string) + # Extract numbers in scientific notation + numbers_scientific = re.findall(pattern_scientific, string) + # Extract simple numbers without commas + numbers_simple = re.findall(pattern_simple, string) + + # Combine all extracted numbersz + all_numbers = numbers_with_commas + numbers_scientific + numbers_simple + return all_numbers + +def check_is_number(string): + """ + Check if the given string a number. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L65 + """ + try: + float(string.replace(",", "")) + return True + except ValueError: + # check if there's comma inside + return False + +def normalize_str(string): + """ + Normalize the str to lower case and make them float numbers if possible. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L76 + """ + # check if characters in the string + + # if number, numerize it. + string = string.strip() + + is_number = check_is_number(string) + + if is_number: + string = string.replace(",", "") + string = float(string) + # leave 2 decimal + string = round(string, 2) + return [string] + else: # it's likely to be a string + # lower it + string = string.lower() + if len(string) == 1: + return [" " + string, string + " "] # avoid trivial matches + return [string] + +def parse_multi_choice_response(response): + # here, we assume we have a list, in which each element is + # a list of model responses for some particular input/target pair. + # so we process each of these (same input/target response sets) + # independently (and keep them a list.) + filtered_resps = [] + option_letter_regex = re.compile(r"^\s*([A-Z])\.") + match = option_letter_regex.match(response) + if match: + # If a match is found, append the matched letter + filtered_resps = match.group(1) + else: + # If no match, return the original response + filtered_resps = response + return filtered_resps + + +def parse_open_response(response): + """ + Parse the prediction from the generated response. + Return a list of predicted strings or numbers. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L122 + """ + + # content = content.strip("\n").strip(".").strip(" ") + def get_key_subresponses(response): + key_responses = [] + response = response.strip().strip(".").lower() + sub_responses = re.split(r"\.\s(?=[A-Z])|\n", response) + indicators_of_keys = [ + "could be ", + "so ", + "is ", + "thus ", + "therefore ", + "final ", + "answer ", + "result ", + ] + key_responses = [] + for index, resp in enumerate(sub_responses): + # if last one, accept it's an equation (the entire response can be just one sentence with equation) + if index == len(sub_responses) - 1: + indicators_of_keys.extend(["="]) + shortest_key_response = None # the shortest response that may contain the answer (tail part of the response) + for indicator in indicators_of_keys: + if indicator in resp: + if not shortest_key_response: + shortest_key_response = resp.split(indicator)[-1].strip() + else: + if len(resp.split(indicator)[-1].strip()) < len(shortest_key_response): + shortest_key_response = resp.split(indicator)[-1].strip() + # key_responses.append(resp.split(indicator)[1].strip()) + + if shortest_key_response: + # and it's not trivial + if shortest_key_response.strip() not in [ + ":", + ",", + ".", + "!", + "?", + ";", + ":", + "'", + ]: + key_responses.append(shortest_key_response) + if len(key_responses) == 0: # did not found any + return [response] + return key_responses + + # pdb.set_trace() + key_responses = get_key_subresponses(response) + + pred_list = key_responses.copy() # keep the original string response + for resp in key_responses: + pred_list.extend(extract_numbers(resp)) + + tmp_pred_list = [] + for i in range(len(pred_list)): + tmp_pred_list.extend(normalize_str(pred_list[i])) + pred_list = tmp_pred_list + + # remove duplicates + pred_list = list(set(pred_list)) + + return pred_list + +def mirb_process_results(doc, results): + pred = results[0] + subset, answer = doc["subset"], doc["answers"] + if answer in ['A', 'B', 'C', 'D', 'E']: # MCQ tasks + parsed_pred = parse_multi_choice_response(pred) + else: + parsed_pred = parse_open_response(pred) + task_type = doc["subset"] + data_dict = {"question_id": doc["question_id"], "subset": task_type, "pred_answer": parsed_pred, "answers": doc["answers"]} + return {f"mirb_score": data_dict} + +def eval_multi_choice(gold_i, pred_i): + """ + Evaluate a multiple choice instance. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L175 + """ + correct = False + # only they are exactly the same, we consider it as correct + if isinstance(gold_i, list): + for answer in gold_i: + if answer == pred_i: + correct = True + break + else: # gold_i is a string + if gold_i == pred_i: + correct = True + return correct + + +def eval_open(gold_i, pred_i): + """ + Evaluate an open question instance + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L191 + """ + correct = False + if isinstance(gold_i, list): + # use float to avoid trivial matches + norm_answers = [] + for answer in gold_i: + norm_answers.extend(normalize_str(answer)) + else: + norm_answers = normalize_str(gold_i) + for pred in pred_i: # pred is already normalized in parse response phase + if isinstance(pred, str): # if it's a string, then find if ans in the pred_i + for norm_ans in norm_answers: + # only see if the string answer in the string pred + if isinstance(norm_ans, str) and norm_ans in pred: + if not correct: + correct = True + break + else: # it's a float number + if pred in norm_answers: + if not correct: + correct = True + break + return correct + +def mirb_aggregation(results): + task_num = {} + score = 0 + task_score = {} + for result in results: + if result["subset"] not in task_score: + task_score[result["subset"]] = 0 + task_num[result["subset"]] = 0 + + if result['answers'] in ['A', 'B', 'C', 'D', 'E']: # MCQ tasks + correct = eval_multi_choice(result["answers"], result["pred_answer"]) + task_score[result["subset"]] += correct + score += correct + else: + correct = eval_open(result["answers"], result["pred_answer"]) + task_score[result["subset"]] += correct + score += correct + task_num[result["subset"]] += 1 + avg_score = score / len(results) + + task_score = {k : v / task_num[k] for k,v in task_score.items()} + + print("Performances for different subsets:") + print("=" * 50) + for k, v in task_score.items(): + print(f"{k} : {v:.2f}") + print("=" * 50) + + # print across evaluation dimension + groups = { + "Knowledge": ["food", "sightseeing"], + "Reasoning": ["codeu", "plot_code", "analogy", "3d_scene"], + "Perception": ["image_jigsaw", "count", "attribute"], + "Multi-Hop": ["visual_chain", "arxiv"] + } + + # Compute the averages for each group + averages_dict = compute_averages_from_task_scores(task_score, groups) + + # Print group performances + print("Performances for different dimensions:") + print("=" * 50) + for group, score in averages_dict.items(): + print(f"{group} : {score:.2f}") + print("=" * 50) + + return avg_score + + +# Compute averages for each group based on task scores +def compute_averages_from_task_scores(task_score, groups): + averages = {} + for group, features in groups.items(): + values = [task_score[feature] for feature in features] + averages[group] = np.mean(values) + return averages \ No newline at end of file From 1703b02ed66e06636929f213156c82e392ea3fd6 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Fri, 19 Jul 2024 08:19:06 +0000 Subject: [PATCH 151/196] Add self.accelerator to multiple models --- lmms_eval/models/internvl2.py | 1 + lmms_eval/models/llava.py | 1 + lmms_eval/models/longva.py | 1 + 3 files changed, 3 insertions(+) diff --git a/lmms_eval/models/internvl2.py b/lmms_eval/models/internvl2.py index 5f4365d0..509df583 100644 --- a/lmms_eval/models/internvl2.py +++ b/lmms_eval/models/internvl2.py @@ -145,6 +145,7 @@ def __init__( accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + self.accelerator = accelerator if accelerator.num_processes > 1: self._device = torch.device(f"cuda:{accelerator.local_process_index}") self.device_map = f"cuda:{accelerator.local_process_index}" diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py index 7d6420ba..8537f999 100755 --- a/lmms_eval/models/llava.py +++ b/lmms_eval/models/llava.py @@ -69,6 +69,7 @@ def __init__( accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + self.accelerator = accelerator if accelerator.num_processes > 1: self._device = torch.device(f"cuda:{accelerator.local_process_index}") self.device_map = f"cuda:{accelerator.local_process_index}" diff --git a/lmms_eval/models/longva.py b/lmms_eval/models/longva.py index c5bf6861..040c551d 100644 --- a/lmms_eval/models/longva.py +++ b/lmms_eval/models/longva.py @@ -76,6 +76,7 @@ def __init__( accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + self.accelerator = accelerator if accelerator.num_processes > 1: self._device = torch.device(f"cuda:{accelerator.local_process_index}") self.device_map = f"cuda:{accelerator.local_process_index}" From 43335b80ffe00eb3276bc627ea1b2ae2fb0c5679 Mon Sep 17 00:00:00 2001 From: choiszt Date: Fri, 19 Jul 2024 22:27:51 +0800 Subject: [PATCH 152/196] update arguments in llavavid model --- lmms_eval/models/llava_vid.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py index 14cd7e61..24651d59 100755 --- a/lmms_eval/models/llava_vid.py +++ b/lmms_eval/models/llava_vid.py @@ -57,6 +57,8 @@ def __init__( mm_spatial_pool_stride: int = 2, mm_spatial_pool_out_channels: int = 1024, mm_spatial_pool_mode: str = "average", + mm_resampler_location:str = "before", + mm_newline_position: str = "grid", overwrite: bool = True, video_decode_backend: str = "pyav", delay_load: bool = False, @@ -89,6 +91,7 @@ def __init__( self.mm_spatial_pool_mode = mm_spatial_pool_mode self.max_frames_num = int(max_frames_num) self.mm_resampler_location = mm_resampler_location + self.mm_newline_position=mm_newline_position self.delay_load = delay_load if self.overwrite == True: overwrite_config = {} @@ -97,7 +100,7 @@ def __init__( overwrite_config["mm_spatial_pool_out_channels"] = self.mm_spatial_pool_out_channels overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode overwrite_config["mm_pooling_position"] = self.mm_resampler_location - overwrite_config["mm_newline_position"] = mm_newline_position + overwrite_config["mm_newline_position"] = self.mm_newline_position overwrite_config["add_faster_video"] = False overwrite_config["delay_load"] = self.delay_load # overwrite_config["attn_implementation"] = attn_implementation From 44f30154590a55da749f6bccef592813fa4baa88 Mon Sep 17 00:00:00 2001 From: Dannoopsy <63581325+Dannoopsy@users.noreply.github.com> Date: Sat, 20 Jul 2024 10:43:34 +0300 Subject: [PATCH 153/196] Changes in llava_hf.py. Corrected the response split by role and added the ability to specify an EOS token (#153) * get model ans w/o split by ASSISTANT * specified_eot_token_id for gen problems --- lmms_eval/models/llava_hf.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py index 547daa29..af0b554e 100644 --- a/lmms_eval/models/llava_hf.py +++ b/lmms_eval/models/llava_hf.py @@ -52,6 +52,7 @@ def __init__( device_map: str = "", chat_template: Optional[str] = None, use_cache: bool = True, + specified_eot_token_id: Optional[int] = None, **kwargs, ) -> None: super().__init__() @@ -85,6 +86,7 @@ def __init__( self.batch_size_per_gpu = int(batch_size) self.chat_template = chat_template self.use_cache = use_cache + self.specified_eot_token_id = specified_eot_token_id if accelerator.num_processes > 1 and device_map == "": assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model @@ -316,18 +318,13 @@ def _collate(x): max_new_tokens=gen_kwargs["max_new_tokens"], use_cache=self.use_cache, pad_token_id=self.tokenizer.eos_token_id, + eos_token_id=self.specified_eot_token_id, ) + cont = cont[:, inputs["input_ids"].shape[-1]:] except Exception as e: eval_logger.error(f"Error {e} in generating") cont = "" text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)[0] - if "1.5" in self.pretrained: - text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() - elif "mistral" in self.pretrained: - text_outputs = text_outputs.split("[/INST]")[-1].strip() - else: - text_outputs = text_outputs.split("ASSISTANT:")[-1].strip() - if self.accelerator.is_main_process and doc_id[0] % 100 == 0: eval_logger.debug(f"Generated text for doc ID {doc_id[0]}:\n\n{text_outputs}\n") From f3b3c89b184a1ca8846d10b46a947fce9aa7d689 Mon Sep 17 00:00:00 2001 From: skyil7 Date: Mon, 22 Jul 2024 23:59:48 +0900 Subject: [PATCH 154/196] Add MMStar --- docs/current_tasks.md | 1 + lmms_eval/tasks/mmstar/mmstar.yaml | 37 +++++++++ lmms_eval/tasks/mmstar/utils.py | 120 +++++++++++++++++++++++++++++ 3 files changed, 158 insertions(+) create mode 100644 lmms_eval/tasks/mmstar/mmstar.yaml create mode 100644 lmms_eval/tasks/mmstar/utils.py diff --git a/docs/current_tasks.md b/docs/current_tasks.md index 1622e960..6e0e5238 100644 --- a/docs/current_tasks.md +++ b/docs/current_tasks.md @@ -50,6 +50,7 @@ - MMMU (mmmu) - MMMU Validation (mmmu_val) - MMMU Test (mmmu_test) +- MMStar (mmstar) - MMUPD (mmupd) - MMUPD Base (mmupd_base) - MMAAD Base (mmaad_base) diff --git a/lmms_eval/tasks/mmstar/mmstar.yaml b/lmms_eval/tasks/mmstar/mmstar.yaml new file mode 100644 index 00000000..157d7fc1 --- /dev/null +++ b/lmms_eval/tasks/mmstar/mmstar.yaml @@ -0,0 +1,37 @@ +dataset_path: Lin-Chen/MMStar +dataset_kwargs: + token: True +task: "mmstar" +test_split: val +output_type: generate_until +doc_to_visual: !function utils.mmstar_doc_to_visual +doc_to_text: !function utils.mmstar_doc_to_text +doc_to_target: "answer" +# The return value of process_results will be used by metrics +process_results: !function utils.mmstar_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: coarse perception + aggregation: !function utils.mmstar_aggregate_results + higher_is_better: true + - metric: fine-grained perception + aggregation: !function utils.mmstar_aggregate_results + higher_is_better: true + - metric: instance reasoning + aggregation: !function utils.mmstar_aggregate_results + higher_is_better: true + - metric: logical reasoning + aggregation: !function utils.mmstar_aggregate_results + higher_is_better: true + - metric: science & technology + aggregation: !function utils.mmstar_aggregate_results + higher_is_better: true + - metric: mathematics + aggregation: !function utils.mmstar_aggregate_results + higher_is_better: true +model_specific_prompt_kwargs: + default: + pre_prompt: "" + post_prompt: "\nAnswer with the option's letter from the given choices directly" +metadata: + - version: 0.0 diff --git a/lmms_eval/tasks/mmstar/utils.py b/lmms_eval/tasks/mmstar/utils.py new file mode 100644 index 00000000..b292afe2 --- /dev/null +++ b/lmms_eval/tasks/mmstar/utils.py @@ -0,0 +1,120 @@ +from collections import defaultdict +import os +import datetime +import json +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file + + +from loguru import logger as eval_logger + +dir_name = os.path.dirname(os.path.abspath(__file__)) + +eval_type_dict = { + "coarse perception" : [ + "image scene and topic", + "image style & quality", + "image emotion" + ], + "fine-grained perception" : [ + "object counting", + "recognition", + "localization" + ], + "instance reasoning" : [ + "single-instance reasoning", + "cross-instance attribute reasoning", + "cross-instance relation reasoning" + ], + "logical reasoning" : [ + "code & sequence reasoning", + "diagram reasoning", + "common reasoning" + ], + "science & technology" : [ + "biology & chemistry & physics", + "electronics & energy & mechanical eng.", + "geography & earth science & agriculture" + ], + "mathematics" : [ + "geometry", + "numeric commonsense and calculation", + "statistical reasoning" + ] +} + + +replace_prompt = " Please answer yes or no." + + +def mmstar_doc_to_visual(doc): + return [doc["image"].convert("RGB")] + + +def mmstar_doc_to_text(doc, model_specific_prompt_kwargs=None): + question = doc["question"].strip() + if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "": + question = question.replace(replace_prompt, "") + question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}" + if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "": + question = question.replace(replace_prompt, "") + question = f"{question}{model_specific_prompt_kwargs['post_prompt']}" + return question + + +def exact_match(pred, gt): + """Brought from MMStar""" + answer = gt.lower().strip().replace('\n', ' ') + predict = pred.lower().strip().replace('\n', ' ') + try: + if answer == predict[0]: + return 1.0 + elif predict[0] == '(' and answer == predict[1]: + return 1.0 + elif predict[0:7] == 'option ' and answer == predict[7]: + return 1.0 + elif predict[0:14] == 'the answer is ' and answer == predict[14]: + return 1.0 + except Exception as e: + return 0.0 + return 0.0 + + +def mmstar_process_results(doc, results): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name, value: metric value + """ + pred = results[0] + gt = doc["answer"] + + score = exact_match(pred, gt) + category = doc["category"] + l2_category = doc["l2_category"] + return {category: {"question_id": doc["index"], "l2_category": l2_category, "score": score}} + + +def mmstar_aggregate_results(results): + """ + Args: + results: a list of values returned by process_results + Returns: + A score + """ + l2_category_scores = defaultdict(list) + for result in results: + score = result["score"] + l2_category = result["l2_category"] + l2_category_scores[l2_category].append(score) + + l2_category_avg_score = {} + for l2_category, scores in l2_category_scores.items(): + avg_score = sum(scores) / len(scores) + l2_category_avg_score[l2_category] = avg_score + eval_logger.info(f"{l2_category}: {avg_score:.2f}") + + avg_score = sum(l2_category_avg_score.values()) / len(l2_category_avg_score) + return avg_score + \ No newline at end of file From 5bf531b3758f027692a42106ab12d486f26416a8 Mon Sep 17 00:00:00 2001 From: GIO PAIK Date: Tue, 23 Jul 2024 11:16:04 +0900 Subject: [PATCH 155/196] Fix typo in mmstar.yaml --- lmms_eval/tasks/mmstar/mmstar.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/mmstar/mmstar.yaml b/lmms_eval/tasks/mmstar/mmstar.yaml index 157d7fc1..84597846 100644 --- a/lmms_eval/tasks/mmstar/mmstar.yaml +++ b/lmms_eval/tasks/mmstar/mmstar.yaml @@ -26,7 +26,7 @@ metric_list: - metric: science & technology aggregation: !function utils.mmstar_aggregate_results higher_is_better: true - - metric: mathematics + - metric: math aggregation: !function utils.mmstar_aggregate_results higher_is_better: true model_specific_prompt_kwargs: From f969d9fab25ce6f58c1036ce694d349a43a389bd Mon Sep 17 00:00:00 2001 From: GIO PAIK Date: Tue, 23 Jul 2024 11:16:28 +0900 Subject: [PATCH 156/196] Fix typo in utils.py --- lmms_eval/tasks/mmstar/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/tasks/mmstar/utils.py b/lmms_eval/tasks/mmstar/utils.py index b292afe2..66933f29 100644 --- a/lmms_eval/tasks/mmstar/utils.py +++ b/lmms_eval/tasks/mmstar/utils.py @@ -35,7 +35,7 @@ "electronics & energy & mechanical eng.", "geography & earth science & agriculture" ], - "mathematics" : [ + "math" : [ "geometry", "numeric commonsense and calculation", "statistical reasoning" From ee2d73c1c721c6145b8762274e82a6453303d477 Mon Sep 17 00:00:00 2001 From: Kaichen Zhang - NTU Date: Tue, 23 Jul 2024 11:51:42 +0800 Subject: [PATCH 157/196] Update README.md --- README.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 041e215a..4cd3e89c 100755 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ --- ## Annoucement - +- [2024-07] 🎉🎉 We have release the [technical report](https://arxiv.org/abs/2407.12772) and [LiveBench](https://huggingface.co/spaces/lmms-lab/LiveBench)! - [2024-07] 👨‍💻👨‍💻 The `lmms-eval/v0.2.1` has been upgraded to support more models, including [LongVA](https://github.com/EvolvingLMMs-Lab/LongVA), [InterVL-2](https://github.com/OpenGVLab/InternVL), [VILA](https://github.com/NVlabs/VILA), and many more evaluation tasks, e.g. [Details Captions](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/136), [MLVU](https://arxiv.org/abs/2406.04264), [WildVision-Bench](https://huggingface.co/datasets/WildVision/wildvision-arena-data), [VITATECS](https://github.com/lscpku/VITATECS) and [LLaVA-Interleave-Bench](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/). - [2024-06] 🎬🎬 The `lmms-eval/v0.2.0` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details @@ -165,6 +165,18 @@ python3 -m accelerate.commands.launch \ python3 -m accelerate.commands.launch --num_processes=8 -m lmms_eval --config ./miscs/example_eval.yaml ``` +**Evaluation of video model (llava-next-video-32B)** +```bash +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model llavavid \ + --model_args pretrained=lmms-lab/LLaVA-NeXT-Video-32B-Qwen,conv_template=qwen_1_5,video_decode_backend=decord,max_frames_num=32 \ + --tasks videomme \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix llava_vid_32B \ + --output_path ./logs/ +``` + **Evaluation with naive model sharding for bigger model (llava-next-72b)** ```bash From aad243d0502a8fcba76fba35628c972829d3c003 Mon Sep 17 00:00:00 2001 From: Kaichen Zhang - NTU Date: Tue, 23 Jul 2024 11:53:48 +0800 Subject: [PATCH 158/196] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4cd3e89c..775a1625 100755 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ --- ## Annoucement -- [2024-07] 🎉🎉 We have release the [technical report](https://arxiv.org/abs/2407.12772) and [LiveBench](https://huggingface.co/spaces/lmms-lab/LiveBench)! +- [2024-07] 🎉🎉 We have released the [technical report](https://arxiv.org/abs/2407.12772) and [LiveBench](https://huggingface.co/spaces/lmms-lab/LiveBench)! - [2024-07] 👨‍💻👨‍💻 The `lmms-eval/v0.2.1` has been upgraded to support more models, including [LongVA](https://github.com/EvolvingLMMs-Lab/LongVA), [InterVL-2](https://github.com/OpenGVLab/InternVL), [VILA](https://github.com/NVlabs/VILA), and many more evaluation tasks, e.g. [Details Captions](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/136), [MLVU](https://arxiv.org/abs/2406.04264), [WildVision-Bench](https://huggingface.co/datasets/WildVision/wildvision-arena-data), [VITATECS](https://github.com/lscpku/VITATECS) and [LLaVA-Interleave-Bench](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/). - [2024-06] 🎬🎬 The `lmms-eval/v0.2.0` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details From 9025a64a18ddf40ec2f45e80a62678e24443d565 Mon Sep 17 00:00:00 2001 From: Kaichen Zhang - NTU Date: Tue, 23 Jul 2024 21:30:32 +0800 Subject: [PATCH 159/196] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 775a1625..0e5569f5 100755 --- a/README.md +++ b/README.md @@ -169,7 +169,7 @@ python3 -m accelerate.commands.launch --num_processes=8 -m lmms_eval --config ./ ```bash accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ --model llavavid \ - --model_args pretrained=lmms-lab/LLaVA-NeXT-Video-32B-Qwen,conv_template=qwen_1_5,video_decode_backend=decord,max_frames_num=32 \ + --model_args pretrained=lmms-lab/LLaVA-NeXT-Video-32B-Qwen,conv_template=qwen_1_5,video_decode_backend=decord,max_frames_num=32，mm_spatial_pool_mode=average,mm_newline_position=grid,mm_resampler_location=after \ --tasks videomme \ --batch_size 1 \ --log_samples \ From 7ae6f71ef13e10f4a2066c061baa1844e1a4bde7 Mon Sep 17 00:00:00 2001 From: baichuanzhou Date: Tue, 23 Jul 2024 21:37:31 +0800 Subject: [PATCH 160/196] add mantis to lmms-eval --- lmms_eval/models/__init__.py | 1 + lmms_eval/models/mantis.py | 299 +++++++++++++++++++++++++++++++++++ 2 files changed, 300 insertions(+) create mode 100644 lmms_eval/models/mantis.py diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index 0ca7692c..7e28a9bf 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -41,6 +41,7 @@ "llava_hf": "LlavaHf", "longva": "LongVA", "vila": "VILA", + "mantis": "Mantis" } for model_name, model_class in AVAILABLE_MODELS.items(): diff --git a/lmms_eval/models/mantis.py b/lmms_eval/models/mantis.py new file mode 100644 index 00000000..edae238d --- /dev/null +++ b/lmms_eval/models/mantis.py @@ -0,0 +1,299 @@ +import torch + +torch.backends.cuda.matmul.allow_tf32 = True + + +import copy +from tqdm import tqdm +from datetime import timedelta + +from lmms_eval import utils +from lmms_eval.api.instance import Instance +from lmms_eval.api.model import lmms +from lmms_eval.api.registry import register_model +from lmms_eval.utils import stop_sequences_criteria + +from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs +from accelerate.state import AcceleratorState +from typing import List, Optional, Union, Tuple +from packaging import version +import warnings + +warnings.filterwarnings("ignore") + +from loguru import logger as eval_logger + +try: + from mantis.models.mllava import chat_mllava + from mantis.models.mllava import LlavaForConditionalGeneration, MLlavaProcessor + from mantis.models.conversation import conv_mllava_v1 as default_conv, conv_templates + +except Exception as e: + eval_logger.debug("Mantis is not installed. Please install Mantis to use this model.\nError: %s" % e) + +try: + from transformers import AutoModelForVision2Seq, AutoProcessor +except Exception as e: + eval_logger.debug("Upgrade transformers to use Mantis's idefics model.\nError: %s" % e) + +# inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5 +# if is_flash_attn_2_available: +# best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating + +if version.parse(torch.__version__) >= version.parse("2.1.2"): + best_fit_attn_implementation = "sdpa" +else: + best_fit_attn_implementation = "eager" + +DEFAULT_IMAGE_TOKEN = "" + +@register_model("mantis") +class Mantis(lmms): + """ + Mantis Model + This implementation is adpated from the Llava model from llava.py and the Idefics model from idefics.py + """ + + def __init__( + self, + pretrained: str = "TIGER-Lab/Mantis-8B-siglip-llama3", + truncation: Optional[bool] = True, + device: Optional[str] = "cuda:0", + batch_size: Optional[Union[int, str]] = 1, + attn_implementation=best_fit_attn_implementation, + device_map="cuda:0", + use_cache=True, + truncate_context=False, # whether to truncate the context in generation, set it False for LLaVA-1.6 + **kwargs, + ) -> None: + super().__init__() + # Do not use kwargs for now + assert kwargs == {}, f"Unexpected kwargs: {kwargs}" + + accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) + accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + if accelerator.num_processes > 1: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + elif accelerator.num_processes == 1 and device_map == "auto": + self._device = torch.device(device) + self.device_map = device_map + else: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + + self._is_idefics = "idefics" in pretrained.lower() + # Here we load the "non-idefics" Mantis model. + if not self._is_idefics: + self._processor = MLlavaProcessor.from_pretrained(pretrained) + self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation) + + else: + self._processor = AutoProcessor.from_pretrained(pretrained) + self._model = AutoModelForVision2Seq.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation) + eval_logger.info(f"Using {type(self._model)} to instantiate the Mantis model.") + + self._tokenizer = self._processor.tokenizer + + self._config = self._model.config + self.model.eval() + self.model.tie_weights() + self.truncation = truncation + self.batch_size_per_gpu = int(batch_size) + self.use_cache = use_cache + self.truncate_context = truncate_context + + if accelerator.num_processes > 1: + assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." + # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model + # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works + # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. + if accelerator.distributed_type == DistributedType.DEEPSPEED: + kwargs = { + "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, + "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, + } + AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) + eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") + + if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: + self._model = accelerator.prepare(self.model) + else: + self._model = accelerator.prepare_model(self.model, evaluation_mode=True) + self.accelerator = accelerator + if self.accelerator.is_local_main_process: + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + elif accelerator.num_processes == 1 and device_map == "auto": + eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") + self._rank = 0 + self._word_size = 1 + else: + eval_logger.info(f"Using single device: {self._device}") + self.model.to(self._device) + self._rank = 0 + self._world_size = 1 + + @property + def config(self): + # return the associated transformers.AutoConfig for the given pretrained model. + return self._config + + @property + def tokenizer(self): + return self._tokenizer + + @property + def model(self): + # returns the model, unwrapping it if using Accelerate + if hasattr(self, "accelerator"): + return self.accelerator.unwrap_model(self._model) + else: + return self._model + + @property + def eot_token_id(self): + # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* + return self.tokenizer.eos_token_id + + @property + def max_length(self): + return self._max_length + + def pad_sequence(self, input_ids, batch_first, padding_value): + if self.tokenizer.padding_side == "left": + input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] + input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value) + if self.tokenizer.padding_side == "left": + input_ids = torch.flip(input_ids, [1]) + return input_ids + + @property + def batch_size(self): + return self.batch_size_per_gpu + + @property + def device(self): + return self._device + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + + def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]: + """ """ + add_special_tokens = False if add_special_tokens is None else add_special_tokens + encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens) + # left-truncate the encoded context to be at most `left_truncate_len` tokens long + if left_truncate_len: + encoding = encoding[-left_truncate_len:] + return encoding + + def tok_decode(self, tokens): + try: + return self.tokenizer.decode(tokens) + except: + return self.tokenizer.decode([tokens]) + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + raise NotImplementedError + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + def generate_until(self, requests: List[Instance]) -> List[str]: + res = [] + + def _collate(x): + # the negative sign on len(toks) sorts descending - this has a few advantages: + # - time estimates will always be over not underestimates, which is more useful for planning + # - to know the size of a batch when going through the list, you know the first one is always the batch + # padded context length. this is useful to simplify the batching logic and more importantly to make + # automatic adaptive batches much much easier to implement + # - any OOMs will happen right away rather than near the end + toks = self.tok_encode(x[0]) + return -len(toks), x[0] + + # we group requests by their generation_kwargs, + # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling + # in the same batch. + re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) + chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) + num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1 + pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding") + for chunk in chunks: + contexts, all_gen_kwargs, doc_to_visuals, doc_id, tasks, splits = zip(*chunk) + visuals = [doc_to_visual(self.task_dict[task][split][ids]) for ids, task, split, doc_to_visual in zip(doc_id, tasks, splits, doc_to_visuals)] + + # we assume all gen kwargs in the batch are the same + # this is safe to assume because the `grouper` object ensures it. + gen_kwargs = all_gen_kwargs[0] + + until = gen_kwargs.pop("until", None) + image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio", None) + + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + + # prompts_input = contexts[0] + + prompts = [] + for visual, context in zip(visuals, contexts): + if self._is_idefics: + # Follow the idefics implementation: + content = [] + if DEFAULT_IMAGE_TOKEN not in context: + for _ in visual: + content.append({"type": "image"}) + content.append({"type": "text", "text": context}) + message = [{"role": "user", "content": content}] + prompt = self._processor.apply_chat_template(message, add_generation_prompt=True) + prompts.append(prompt) + else: + # We follow the Mantis code base: https://github.com/TIGER-AI-Lab/Mantis/blob/main/mantis/models/mllava/utils.py#L33 to make sure they are consistent + # Users don't need to define chat template as it is done here + if "llama-3" in self._model.language_model.name_or_path.lower(): + conv = conv_templates['llama_3'] + terminators = [ + self._processor.tokenizer.eos_token_id, + self._processor.tokenizer.convert_tokens_to_ids("<|eot_id|>") + ] + else: + conv = default_conv + terminators = None + + gen_kwargs["eos_token_id"] = terminators + + conv = conv.copy() + conv.append_message(conv.roles[0], context) + conv.append_message(conv.roles[1], "") + prompt = conv.get_prompt() + prompts.append(prompt) + inputs = self._processor(images=visuals, text=prompts, return_tensors="pt", truncation=True) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + output_ids = self.model.generate(**inputs, **gen_kwargs) + for output_id, input_id in zip(output_ids, inputs["input_ids"]): + generated_id = output_id[len(input_id) :] + generated_text = self.tokenizer.decode(generated_id, skip_special_tokens=True) + + res.append(generated_text) + + # self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) + pbar.update(1) + # reorder this group of results back to original unsorted form + res = re_ords.get_original(res) + + pbar.close() + return res From 328ed79698285a2d246b0d7a843ac0a78e76e999 Mon Sep 17 00:00:00 2001 From: baichuanzhou Date: Wed, 24 Jul 2024 22:43:58 +0800 Subject: [PATCH 161/196] update mantis with fuyu models supported --- lmms_eval/models/mantis.py | 30 ++++++++++++------ .../mlvu/__pycache__/utils.cpython-310.pyc | Bin 3207 -> 3222 bytes 2 files changed, 21 insertions(+), 9 deletions(-) diff --git a/lmms_eval/models/mantis.py b/lmms_eval/models/mantis.py index edae238d..fe1bb0b2 100644 --- a/lmms_eval/models/mantis.py +++ b/lmms_eval/models/mantis.py @@ -19,13 +19,13 @@ from packaging import version import warnings -warnings.filterwarnings("ignore") - from loguru import logger as eval_logger +warnings.filterwarnings("ignore") + try: - from mantis.models.mllava import chat_mllava from mantis.models.mllava import LlavaForConditionalGeneration, MLlavaProcessor + from mantis.models.mfuyu import MFuyuForCausalLM, MFuyuProcessor from mantis.models.conversation import conv_mllava_v1 as default_conv, conv_templates except Exception as e: @@ -40,9 +40,11 @@ # if is_flash_attn_2_available: # best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating -if version.parse(torch.__version__) >= version.parse("2.1.2"): - best_fit_attn_implementation = "sdpa" -else: +try: + import flash_attn + + best_fit_attn_implementation = "flash_attention_2" +except ImportError: best_fit_attn_implementation = "eager" DEFAULT_IMAGE_TOKEN = "" @@ -59,6 +61,7 @@ def __init__( pretrained: str = "TIGER-Lab/Mantis-8B-siglip-llama3", truncation: Optional[bool] = True, device: Optional[str] = "cuda:0", + dtype: Optional[Union[str, torch.dtype]] = "float16", batch_size: Optional[Union[int, str]] = 1, attn_implementation=best_fit_attn_implementation, device_map="cuda:0", @@ -83,14 +86,21 @@ def __init__( self.device_map = f"cuda:{accelerator.local_process_index}" self._is_idefics = "idefics" in pretrained.lower() + if isinstance(dtype, str) and dtype != "auto": + dtype = getattr(torch, dtype) + # Here we load the "non-idefics" Mantis model. if not self._is_idefics: - self._processor = MLlavaProcessor.from_pretrained(pretrained) - self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation) + if 'fuyu' in pretrained.lower(): + self._processor = MFuyuProcessor.from_pretrained(pretrained) + self._model = MFuyuForCausalLM.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation, torch_dtype=dtype) + else: + self._processor = MLlavaProcessor.from_pretrained(pretrained) + self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation, torch_dtype=dtype) else: self._processor = AutoProcessor.from_pretrained(pretrained) - self._model = AutoModelForVision2Seq.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation) + self._model = AutoModelForVision2Seq.from_pretrained(pretrained, device_map=self.device_map, torch_dtype=dtype) eval_logger.info(f"Using {type(self._model)} to instantiate the Mantis model.") self._tokenizer = self._processor.tokenizer @@ -281,6 +291,8 @@ def _collate(x): prompt = conv.get_prompt() prompts.append(prompt) inputs = self._processor(images=visuals, text=prompts, return_tensors="pt", truncation=True) + if "image_patches" in inputs.keys(): + inputs["image_patches"] = inputs["image_patches"][0] # FIXME: Fuyu model would return a list instead of a pytorch tensor. This weird behavior needs fixing. inputs = {k: v.to(self.device) for k, v in inputs.items()} output_ids = self.model.generate(**inputs, **gen_kwargs) diff --git a/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc b/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc index 1a9fda0f2b70ce7af4bbcc1868a64d7c8e67571d..fae54bfae0d4d33fd962b2688163c132b61018be 100644 GIT binary patch delta 811 zcmZ8fJ8Tm{5cQnT=k@uc$Vp6ogg9xED2Yjo_(%~bRSE=15&h{nuRn=>ciCM#fhK7X zB|f>QRrc!POQWNUYcyZ+injdMDO<@%KD8xKDvy)WC*sEgE1^2 z7WG$Kl~yoh>{#&HfIWvkKG{#5hYkHWH4c^27pY}{1^vO99=%BHEU`U5lm}dhb^X(s zIjUfM$q}=3ofcen+e(Tx^4O3p`foEeU-@3G6{x?<5pk^3?scjAfJ^E9$+3wylD5GZ zvzS!^W{E5427oX;nZ)U{^cR?-0^%A0i4|7~ss!u$N#^kA5}KwE_=-0oaeqSwk>G=- zoIy2{QNG;sh+*<7QhwV*j=(*c!oVG9<~0L*273Q1DD@TijfUV2%rVq(8|%uoL=i`y zW-x*h7%pHQzxu?z0NeVtJ3U^a<`@o#t5$~_dsjxG(cj!Hn9(cQt<~$KgQj7e23I`n znRK)6HKPs}=P2s(!`KgNU5P81)F;_0{+#c#8`TMVH$^Z{ut-45>Q7N@lQs7*lyx;% eEhlqO!anVOzb;tE*4fP2M4BbtEUv=M6@LTfsnU)B delta 828 zcmZ8fJ#Q015cRIl_WFEhnS3}HJ3$UYB!Lh}5C|nwWGb2nDS~F3)9G$wJF)Mq-L*-o zxTBz4M2RR#iNc?tpof!yA3#Be1X2Vub}SHAyxW<1Z)Rs_URIt}+}L&P6nvdm&-ZrT zx}Qj%)5p6BZR~RMos=`2HXd0Y?AGkIakoR`oiB70QZ~ks@)3MY8Fs-&rE(&A0RpDVqD%Hp3*#8CfwytNw($ zaZk77$m_|t+tYFZg1oTT?{p>}q>l>A8k~|+w=?K_VH|im_Ov+E_rR+dfJGfL10`kM z{IGQSDzdGf6yaDs%2PAL%>3LoN*n-27_mtSL>84xr|&W=ge;h^+3NH%stc(0{75|& zQmz;`S36z;|8iDVFr1S@^*dV0YhZCUAp6^^_Z_*yh$d9<#A zSc>tbvg2wzuYGmkA%nX*)_%tWjue6B#j!c=S%e8WNXX#-4$7kr{AN>%Cghm(z@e_X znVN!8eUF_oy zlCn*qMKnlqtv#<5cZGz+u#n=g=SN{*!ANGzNnr!F^>bl;V;Xg{2=fSa1RU1r73C(L m{%+FwEP3f?VOuS{FSQ9B(zDvx#T>J22S$;1rv3noIm&+k From 4ca1ce293b4fe292200f85200589f86acf7aed33 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 25 Jul 2024 08:16:48 +0800 Subject: [PATCH 162/196] revise llava_vid.py --- lmms_eval/models/llava_vid.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py index 163a5e38..e76840aa 100755 --- a/lmms_eval/models/llava_vid.py +++ b/lmms_eval/models/llava_vid.py @@ -96,7 +96,7 @@ def __init__( self.mm_spatial_pool_out_channels = int(mm_spatial_pool_out_channels) self.mm_spatial_pool_mode = mm_spatial_pool_mode self.max_frames_num = int(max_frames_num) - print(self.max_frames_num) + if self.overwrite == True: overwrite_config = {} overwrite_config["mm_resampler_type"] = self.mm_resampler_type @@ -128,7 +128,7 @@ def __init__( self._tokenizer = AutoTokenizer.from_pretrained(pretrained, use_fast=False) cfg_pretrained = LlavaConfig.from_pretrained(pretrained) if overwrite_config is not None: - print(f"Overwriting config with {overwrite_config}") + eval_logger.log(f"Overwriting config with {overwrite_config}") for k, v in overwrite_config.items(): setattr(cfg_pretrained, k, v) kwargs["torch_dtype"] = torch.float16 From ad8ba825bb4122fdd5063fd02936c30251a48201 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Thu, 25 Jul 2024 08:22:32 +0800 Subject: [PATCH 163/196] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 0e5569f5..ec054abd 100755 --- a/README.md +++ b/README.md @@ -11,6 +11,7 @@ --- ## Annoucement +- [2024-07] 🎉🎉 We welcome the new tasks [LongVideoBench](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/117), [MMStar](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/158), new models [Mantis](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/162). - [2024-07] 🎉🎉 We have released the [technical report](https://arxiv.org/abs/2407.12772) and [LiveBench](https://huggingface.co/spaces/lmms-lab/LiveBench)! - [2024-07] 👨‍💻👨‍💻 The `lmms-eval/v0.2.1` has been upgraded to support more models, including [LongVA](https://github.com/EvolvingLMMs-Lab/LongVA), [InterVL-2](https://github.com/OpenGVLab/InternVL), [VILA](https://github.com/NVlabs/VILA), and many more evaluation tasks, e.g. [Details Captions](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/136), [MLVU](https://arxiv.org/abs/2406.04264), [WildVision-Bench](https://huggingface.co/datasets/WildVision/wildvision-arena-data), [VITATECS](https://github.com/lscpku/VITATECS) and [LLaVA-Interleave-Bench](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/). From e3c9cd6a692bbee9617c53b640723f7c87684aad Mon Sep 17 00:00:00 2001 From: kcz358 Date: Thu, 25 Jul 2024 03:44:12 +0000 Subject: [PATCH 164/196] Fix seedbench aggregation --- .../tasks/mlvu/__pycache__/utils.cpython-310.pyc | Bin 3222 -> 0 bytes lmms_eval/tasks/seedbench/utils.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc diff --git a/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc b/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc deleted file mode 100644 index fae54bfae0d4d33fd962b2688163c132b61018be..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3222 zcmZ`*-E-T<5x)aK5TqztR%CtnBR<=WYo?B9I~}{#xS6Vs)6ArmKjgL(G#LzpJ5rGF zLEnLt#Q{6*IDKy4lT4_-^`Gcl|A~9;lm7+JWZK;WCEH0#33t1T#opcSZ+9=BpKlWQ z{rljX-t9Ia|3T*Up9jn@;Zct%A)IiUkd*qAB5fs>Z<*QlZ8JN*1GAmfQrCBlj+4|= z&-Z5f4ZmUZntl`JS~8cm{8l>e&!=s_ou2Yf&GZ)h1*3P`KW*kk|1Fr^WGOx4FH_Pd zy#5dBuLx_p%01pVvivn+?VX*T6KhA*KQAr_PrUt{?p@@~bA-=5xBkX_CfM}S5%Dkg ziGM}>NL+qSKO~>ee`fjb_*Z#LTpdw45?7`_7FUnpd{5&)zzH4VkmoifnRk<@zZ`%Z z|2~CBG^W@1{Inyk9ML1n+iQfMS|k3ue1V_-g7`n-XZa$3>kH~%=S%zyNZ#Yid0uE(WzC~g>z(0 zXbA{gBNMBVmbA1$T5&a?sk~4EUs`>-O$v8f=Rki6i2K^cm{szQv!$D(n2S8f!c>gs z49y1MvyJfu#&e-qmKRJs1MgjDgG9i-85}j*?|9nEm3B~9J9~MY>Glr14NeZDn-z(c zwQEc$ZL3502$-T{Yn$hhT!NDwZvu_|lSl@lm!EXgtmqCzAw|+t-SKWd+zI1ocNk{f zfz05O2PZh1uguJ(@(O-2*Bd6uv4nxd*prv=-NNfmDa5nlU63j)V~*9L zOW+WRhy5NX5tzmmw}2?glpK)>xTGW_B3FQ?9MT*CT2gMU!uVHk6hd(uw81I%)=S!X zfl%*Qx~2*l4|FZgxOfJbph{kcHvlXtc(qvue6{0M*rW`jB8YZFoP|_@6n*gwccNM_ zco57cU4Xqimh6JyjX=5}I7zA7z`zb?NmN?TDFo646@mwWT_dGaqQrqP*S>NlHSU(R z7}BXzAOEY#;I|3syJ0Bg0A3p`WO1g7FpC7sdnUpNN;XUR6J043R4}!{{)p%N+{hn` zVkooM42BF#9F{71$PVISm+gzg4VFSq#1X^1V%Opf7Ks&$Ff&d1rXW^PE=A`$gQbV! z03R|$}d6s7_Q_qrj zfPDj>4SWxe!RJSC%9u=OL7;s#CKiWibL%S$i{)MN6rv1r22r;$+JI1yL#&+|!HC}> zHn|H3F)|lrpjS7ew?_7@>iyFNWBvBTfuc}@$1UlU9--x>&#Xy(;sJ{4kQtB4TIq3j zjocxVTIm7}xB}XJ`isJv+PqOZP@bEm%jv!&clcacTOy@S|GH+{?fc(b zX<@d~|64(t71}e~fB!+-Ra>Z5KYFFH4VevyKQ-PnzsZsnd?nxTfCFY*FR z9G|OQvB#&=bYIiacave+=vL>3o{RD%0r!Hpl*IlfmtoKzq$OlDG;L>3!G+~2Hd)hJ z*G+3(4@}vpdMjSD0z*n{3%ZUiJc)OtgPeo+A9)^07s?FWjU<*|iFH`kall;FmK55K zMz?e=8>Y~$t!!X!WJ5WWn9%YA95Da2+ZTn%M!LS+3wHBV$Q6_}#j`<}@u3n@uA#um z2O`s6V6K5dpxr?zMOJ9q(+w#?p2V3@+LPfyfJI0*RM-)nMGK1{?1Sb!Ctt7Upt6)x;vtMK4o h3qsklTXY!$YXWLDA)MB?=f7R8dv Date: Thu, 25 Jul 2024 12:50:44 +0900 Subject: [PATCH 165/196] Fix Update.py (#165) Fix Update.py builtin method lower() From c28d63c3f1427900fa72c261b075ffba208bdf7b Mon Sep 17 00:00:00 2001 From: Bo Li Date: Thu, 25 Jul 2024 21:48:18 +1000 Subject: [PATCH 166/196] chore: Remove unused YAML file for mix_evals_video2text_openended_2nd task --- .../mix_evals_video2text_openended_2nd.yaml | 23 ------------------- 1 file changed, 23 deletions(-) delete mode 100644 lmms_eval/tasks/mix_evals/mix_evals_video2text_openended_2nd.yaml diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended_2nd.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended_2nd.yaml deleted file mode 100644 index e8c0f1fe..00000000 --- a/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended_2nd.yaml +++ /dev/null @@ -1,23 +0,0 @@ -include: _default_template_yaml -dataset_path: lmms-lab/MixEvals_Video2Text_OpenEnded_2nd -dataset_name: "video2text_openended" -task: "mix_evals_video2text_openconv_2nd" -test_split: test -output_type: generate_until -doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual -doc_to_text: !function utils.mix_evals_video2text_doc_to_text_open_convs -doc_to_target: "" -process_results: !function utils.mix_evals_video2text_process_results_open_convs - -metric_list: - - metric: submission - aggregation: !function utils.mix_evals_video2text_aggregate_gen - higher_is_better: true - -model_specific_prompt_kwargs: - default: - pre_prompt: "These are frames from a video. Please answer the following questions about the video." - post_prompt: "" - gpt4v: - pre_prompt: "These are frames from a video. Please answer the following questions about the video." - post_prompt: "" From 0f5185c3b18271d9fb5ba3af8dd3cca0c4bd3132 Mon Sep 17 00:00:00 2001 From: GIO PAIK Date: Fri, 26 Jul 2024 11:18:45 +0900 Subject: [PATCH 167/196] Add default prompt for seedbench_2.yaml --- lmms_eval/tasks/seedbench_2/seedbench_2.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/lmms_eval/tasks/seedbench_2/seedbench_2.yaml b/lmms_eval/tasks/seedbench_2/seedbench_2.yaml index c61e4104..5fb8cf78 100755 --- a/lmms_eval/tasks/seedbench_2/seedbench_2.yaml +++ b/lmms_eval/tasks/seedbench_2/seedbench_2.yaml @@ -41,6 +41,9 @@ metadata: - version: 0.0 model_specific_prompt_kwargs: + default : + img_token : + post_prompt : "Answer with the option's letter from the given choices directly." llava : img_token : post_prompt : "Answer with the option's letter from the given choices directly." From d0a42aeb1991a4bd4692279c5213179f313ec986 Mon Sep 17 00:00:00 2001 From: Pu Fanyi Date: Fri, 26 Jul 2024 17:52:40 -0700 Subject: [PATCH 168/196] Update evaluator.py --- lmms_eval/evaluator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py index aec3bcc2..132c7b8b 100755 --- a/lmms_eval/evaluator.py +++ b/lmms_eval/evaluator.py @@ -327,7 +327,7 @@ def evaluate( # hack: remove image columns to speed avoid loading images and speed up postprocessing # reason: doc_iterator will actually load image if it's in the doc. docs = task.test_docs() if task.has_test_docs() else task.validation_docs() - if "d170" not in task_name and "dc100" not in task_name and "dc200" not in task_name and "llava_wilder" not in task_name and "livebench" not in task_name and "wildvision" not in task_name: + if "d170" not in task_name and "dc100" not in task_name and "dc200" not in task_name and "llava_wilder" not in task_name and "live_bench" not in task_name and "wildvision" not in task_name: remove_cols = [] features = docs.features # If it is an Image instance or a Sequence of Image instance. Remove it From 10f3de521f482629090fdbdd5f6c8394d1c3cb7f Mon Sep 17 00:00:00 2001 From: Jean de Dieu Nyandwi Date: Sat, 27 Jul 2024 07:25:46 -0400 Subject: [PATCH 169/196] Moved changes from main to cambrian branch --- lmms_eval/models/__init__.py | 1 + lmms_eval/models/cambrian1.py | 420 ++++++++++++++++++++++++++++++++++ 2 files changed, 421 insertions(+) create mode 100644 lmms_eval/models/cambrian1.py diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index 0ca7692c..486ea911 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -12,6 +12,7 @@ logger.add(sys.stdout, level="WARNING") AVAILABLE_MODELS = { + "cambrian1": "Cambrian1", "llava": "Llava", "qwen_vl": "Qwen_VL", "fuyu": "Fuyu", diff --git a/lmms_eval/models/cambrian1.py b/lmms_eval/models/cambrian1.py new file mode 100644 index 00000000..06648482 --- /dev/null +++ b/lmms_eval/models/cambrian1.py @@ -0,0 +1,420 @@ +import torch + +torch.backends.cuda.matmul.allow_tf32 = True + + +import copy +from tqdm import tqdm +from datetime import timedelta + +from lmms_eval import utils +from lmms_eval.api.instance import Instance +from lmms_eval.api.model import lmms +from lmms_eval.api.registry import register_model +from lmms_eval.utils import stop_sequences_criteria + +from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs +from accelerate.state import AcceleratorState +from typing import List, Optional, Union, Tuple +from packaging import version +import warnings + +warnings.filterwarnings("ignore") + +from loguru import logger as eval_logger + +try: + from cambrian.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN + from cambrian.conversation import conv_templates + from cambrian.model.builder import load_pretrained_model + from cambrian.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path +except Exception as e: + eval_logger.debug("Cambrian is not installed. Please head over https://github.com/cambrian-mllm/cambrian/ and install Cambrian to use this model.\nError: %s" % e) + +if version.parse(torch.__version__) >= version.parse("2.1.2"): + best_fit_attn_implementation = "sdpa" +else: + best_fit_attn_implementation = "eager" + +# Cambrian model names and their corresponding conversation templates +# So that we can use the correct conversation template for the model +conv_modes = { + "cambrian-phi3-3b": "phi3", + "cambrian-8b": "llama_3", + "cambrian-13b": "vicuna_v1", + "cambrian-34b": "chatml_direct", +} + +@register_model("cambrian1") +class Cambrian1(lmms): + """ + Cambrian1 Model + """ + + def __init__( + self, + pretrained: str = "nyu-visionx/cambrian-8b", + truncation: Optional[bool] = True, + device: Optional[str] = "cuda:0", + batch_size: Optional[Union[int, str]] = 1, + model_name=None, + attn_implementation=best_fit_attn_implementation, + device_map="cuda:0", + conv_template="llama_3", + use_cache=True, + tie_weights: bool = True, + truncate_context=False, # whether to truncate the context in generation, set it False + customized_config=None, # ends in json + **kwargs, + ) -> None: + super().__init__() + # Do not use kwargs for now + assert kwargs == {}, f"Unexpected kwargs: {kwargs}" + + accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) + accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + self.accelerator = accelerator + if accelerator.num_processes > 1: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + elif accelerator.num_processes == 1 and device_map == "auto": + self._device = torch.device(device) + self.device_map = device_map + else: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + + cambrian_model_args = {} + if customized_config is not None: + cambrian_model_args["customized_config"] = customized_config + if attn_implementation is not None: + cambrian_model_args["attn_implementation"] = attn_implementation + if "use_flash_attention_2" in kwargs: + cambrian_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"] + model_name = model_name if model_name is not None else get_model_name_from_path(pretrained) + try: + # Try to load the model with the multimodal argument + self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **cambrian_model_args) + except TypeError: + # Load the model without cambrian_model_args + self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map) + + self._config = self._model.config + self.model.eval() + if tie_weights: + self.model.tie_weights() + + self.truncation = truncation + batch_size = int(batch_size) + assert batch_size == 1, f"Batch size should be 1 for Cambrian, but got {batch_size}." + self.batch_size_per_gpu = batch_size + self.conv_template = conv_modes[model_name] if model_name in conv_modes else conv_template + self.use_cache = use_cache + self.truncate_context = truncate_context + if accelerator.num_processes > 1: + assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." + # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model + # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works + # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. + if accelerator.distributed_type == DistributedType.DEEPSPEED: + kwargs = { + "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, + "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, + } + AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) + eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") + + if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: + self._model = accelerator.prepare(self.model) + else: + self._model = accelerator.prepare_model(self.model, evaluation_mode=True) + self.accelerator = accelerator + if self.accelerator.is_local_main_process: + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + elif accelerator.num_processes == 1 and device_map == "auto": + eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") + self._rank = 0 + self._word_size = 1 + else: + eval_logger.info(f"Using single device: {self._device}") + self.model.to(self._device) + self._rank = 0 + self._world_size = 1 + + @property + def config(self): + # return the associated transformers.AutoConfig for the given pretrained model. + return self._config + + @property + def tokenizer(self): + return self._tokenizer + + @property + def model(self): + # returns the model, unwrapping it if using Accelerate + if hasattr(self, "accelerator"): + return self.accelerator.unwrap_model(self._model) + else: + return self._model + + @property + def eot_token_id(self): + # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* + return self.tokenizer.eos_token_id + + @property + def max_length(self): + return self._max_length + + def pad_sequence(self, input_ids, batch_first, padding_value): + if self.tokenizer.padding_side == "left": + input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] + input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value) + if self.tokenizer.padding_side == "left": + input_ids = torch.flip(input_ids, [1]) + return input_ids + + @property + def batch_size(self): + return self.batch_size_per_gpu + + @property + def device(self): + return self._device + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + + def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]: + """ """ + add_special_tokens = False if add_special_tokens is None else add_special_tokens + encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens) + # left-truncate the encoded context to be at most `left_truncate_len` tokens long + if left_truncate_len: + encoding = encoding[-left_truncate_len:] + return encoding + + def tok_decode(self, tokens): + try: + return self.tokenizer.decode(tokens) + except: + return self.tokenizer.decode([tokens]) + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + # TODO + res = [] + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") + + for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: + # encode, pad, and truncate contexts for this batch + if type(doc_to_target) == str: + continuation = doc_to_target + else: + continuation = doc_to_target(self.task_dict[task][split][doc_id]) + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] + # If there are more than one image in the sample, take the first image + # Cambrian only supports one image at a time + for idx, visual in enumerate(visuals): + if len(visual) > 1: + visuals[idx] = [visual[0]] + + visuals = self.flatten(visuals) + image_sizes = [[visual.size[0], visual.size[1]] for visual in visuals] + if visuals: + image = process_images(visuals, self._image_processor, self._config) + if type(image) is list: + image = [_image.to(dtype=torch.float16, device=self.device) for _image in image] + else: + image = image.to(dtype=torch.float16, device=self.device) + else: + image = None + + prompts_input = contexts[0] if isinstance(contexts, list) else contexts + + if image is not None and len(image) != 0 and DEFAULT_IMAGE_TOKEN not in prompts_input: + """ + Three senarios: + 1. No image, and there for, no image token should be added. + 2. image token is already specified in the context, so we don't need to add it. + 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. + """ + if self.model.config.mm_use_im_start_end: + image_tokens = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + else: + image_tokens = DEFAULT_IMAGE_TOKEN + prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts) + + conv = conv_templates[self.conv_template].copy() + conv.append_message(conv.roles[0], prompts_input) + conv.append_message(conv.roles[1], None) + prompt = conv.get_prompt() + pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id + contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) + # Add the answer of the second role + conv.messages[1][1] = continuation + + prompt = conv.get_prompt() + input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) + labels = input_ids.clone() + # Context part no need to calculate for loss + labels[0, : contxt_id.shape[1]] = -100 + with torch.inference_mode(): + outputs = self.model(input_ids=input_ids, labels=labels, images=image, use_cache=True, image_sizes=image_sizes) + loss = outputs["loss"] + # loss = torch.exp(loss) + logits = outputs["logits"] + greedy_tokens = logits.argmax(dim=-1) + cont_toks = input_ids[:, contxt_id.shape[1] :] # [1, seq] + greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]] # [1, seq] + max_equal = (greedy_tokens == cont_toks).all() + res.append((float(loss.item()), bool(max_equal))) + pbar.update(1) + pbar.close() + return res + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + def generate_until(self, requests: List[Instance]) -> List[str]: + res = [] + + def _collate(x): + # the negative sign on len(toks) sorts descending - this has a few advantages: + # - time estimates will always be over not underestimates, which is more useful for planning + # - to know the size of a batch when going through the list, you know the first one is always the batch + # padded context length. this is useful to simplify the batching logic and more importantly to make + # automatic adaptive batches much much easier to implement + # - any OOMs will happen right away rather than near the end + toks = self.tok_encode(x[0]) + return -len(toks), x[0] + + # we group requests by their generation_kwargs, + # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling + # in the same batch. + re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) + chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) + num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1 + pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding") + for chunk in chunks: + contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk) + task = task[0] + split = split[0] + batched_visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] # [B, N] + # If there are more than one image in the sample, take the first image + # Cambrian only supports one image at a time + for idx, visual in enumerate(batched_visuals): + if len(visual) > 1: + batched_visuals[idx] = [visual[0]] + flattened_visuals = self.flatten(batched_visuals) # [B*N] + # we assume all gen kwargs in the batch are the same + # this is safe to assume because the `grouper` object ensures it. + gen_kwargs = all_gen_kwargs[0] + + # Set default values for until and max_new_tokens + until = [self.tok_decode(self.eot_token_id)] + + # Update values from gen_kwargs if present + if "until" in gen_kwargs: + until = gen_kwargs.pop("until") + if isinstance(until, str): + until = [until] + elif not isinstance(until, list): + raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") + + if "image_aspect_ratio" in gen_kwargs.keys() and "image_aspect_ratio" not in self._config.__dict__: + # here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation + self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio") + eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") + # encode, pad, and truncate contexts for this batch + if flattened_visuals: + image_tensor = process_images(flattened_visuals, self._image_processor, self._config) + if type(image_tensor) is list: + image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] + else: + image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) + else: + image_tensor = None + + # prompts_input = contexts[0] + + question_input = [] + + for visual, context in zip(batched_visuals, contexts): + if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context: + """ + Three senarios: + 1. No image, and there for, no image token should be added. + 2. image token is already specified in the context, so we don't need to add it. + 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. + """ + if self.model.config.mm_use_im_start_end: + image_tokens = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + else: + image_tokens = DEFAULT_IMAGE_TOKEN + question = image_tokens + "\n" + context + else: + question = context + + conv = conv_templates[self.conv_template].copy() + conv.append_message(conv.roles[0], question) + conv.append_message(conv.roles[1], None) + prompt_question = conv.get_prompt() + question_input.append(prompt_question) + + # preconfigure gen_kwargs with defaults + gen_kwargs["image_sizes"] = [flattened_visuals[idx].size for idx in range(len(flattened_visuals))] + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + if "top_p" not in gen_kwargs: + gen_kwargs["top_p"] = None + if "num_beams" not in gen_kwargs: + gen_kwargs["num_beams"] = 1 + + input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] + pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id + input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device) + attention_masks = input_ids.ne(pad_token_ids).to(self.device) + # These steps are not in Cambrian's original code, but are necessary for generation to work + try: + cont = self.model.generate( + input_ids, + attention_mask=attention_masks, + pad_token_id=pad_token_ids, + images=image_tensor, + image_sizes=gen_kwargs["image_sizes"], + do_sample=True if gen_kwargs["temperature"] > 0 else False, + temperature=gen_kwargs["temperature"], + top_p=gen_kwargs["top_p"], + num_beams=gen_kwargs["num_beams"], + max_new_tokens=gen_kwargs["max_new_tokens"], + use_cache=self.use_cache, + ) + text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True) + except Exception as e: + raise e + eval_logger.error(f"Error {e} in generating") + cont = "" + text_outputs = [""] + + res.extend(text_outputs) + self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) + pbar.update(1) + # reorder this group of results back to original unsorted form + res = re_ords.get_original(res) + + pbar.close() + return res \ No newline at end of file From 8e5758d999fa009141bc9c9d7cda3944d2748c70 Mon Sep 17 00:00:00 2001 From: GIO PAIK Date: Tue, 30 Jul 2024 09:53:19 +0900 Subject: [PATCH 170/196] Fixed `generate_until()` to handle an input without visuals --- lmms_eval/models/internvl2.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/lmms_eval/models/internvl2.py b/lmms_eval/models/internvl2.py index 509df583..478fd49c 100644 --- a/lmms_eval/models/internvl2.py +++ b/lmms_eval/models/internvl2.py @@ -213,13 +213,16 @@ def generate_until(self, requests) -> List[str]: visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] visuals = self.flatten(visuals) if self.modality == "image": - visuals = [load_image(visual).to(torch.bfloat16).cuda() for visual in visuals] - pixel_values = torch.cat(visuals, dim=0) - num_patches_list = [visual.size(0) for visual in visuals] if visuals: + visuals = [load_image(visual).to(torch.bfloat16).cuda() for visual in visuals] + pixel_values = torch.cat(visuals, dim=0) + num_patches_list = [visual.size(0) for visual in visuals] image_tokens = [""] * len(visuals) image_tokens = " ".join(image_tokens) contexts = image_tokens + "\n" + contexts + else: + pixel_values = None + num_patch_list = None response, history = self.model.chat(self.tokenizer, pixel_values, contexts, gen_kwargs, num_patches_list=num_patches_list, history=None, return_history=True) elif self.modality == "video": From 7445b548dd427c67a1ddfaa189061a528834f7e4 Mon Sep 17 00:00:00 2001 From: GIO PAIK Date: Tue, 30 Jul 2024 09:56:45 +0900 Subject: [PATCH 171/196] Fixed `generate_until()` to handle an input without visuals --- lmms_eval/models/internvl.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/lmms_eval/models/internvl.py b/lmms_eval/models/internvl.py index 6238d7fc..d5b668ce 100644 --- a/lmms_eval/models/internvl.py +++ b/lmms_eval/models/internvl.py @@ -449,7 +449,10 @@ def _collate(x): split = split[0] batched_visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] # [B, N] flattened_visuals = self.flatten(batched_visuals) - pixel_values = self.load_image(flattened_visuals, self.image_size).cuda().to(torch.bfloat16) + try: + pixel_values = self.load_image(flattened_visuals, self.image_size).cuda().to(torch.bfloat16) + except IndexError: + pixel_values = None gen_kwargs = all_gen_kwargs[0] if "max_new_tokens" not in gen_kwargs: From f06f8e679a3f69c361a700d157674280410bbf7c Mon Sep 17 00:00:00 2001 From: GIO PAIK Date: Tue, 30 Jul 2024 09:57:59 +0900 Subject: [PATCH 172/196] Fix result processing code for ScienceQA --- lmms_eval/tasks/scienceqa/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lmms_eval/tasks/scienceqa/utils.py b/lmms_eval/tasks/scienceqa/utils.py index eed6b26a..128ebf56 100755 --- a/lmms_eval/tasks/scienceqa/utils.py +++ b/lmms_eval/tasks/scienceqa/utils.py @@ -33,8 +33,8 @@ def sqa_doc_to_target(doc): def sqa_process_results(doc, results): # I know this is weird, but it's how llava parse it. - target = sqa_doc_to_target(doc) - pred = results[0] + target = sqa_doc_to_target(doc).strip().lower() + pred = results[0].strip().lower() if pred == target: return {"exact_match": 1.0} # pattern: ^[A-Z]\. .* From 3839da3b56dc5eec6c999cf05efbceb2da325b2f Mon Sep 17 00:00:00 2001 From: Li Bo Date: Wed, 31 Jul 2024 09:50:49 +1000 Subject: [PATCH 173/196] Revert "[New Model] Adding Cambrian Model" --- M-MMMU/README.md | 58 ----- lmms_eval/models/__init__.py | 1 - lmms_eval/models/cambrian1.py | 420 ---------------------------------- 3 files changed, 479 deletions(-) delete mode 100644 M-MMMU/README.md delete mode 100644 lmms_eval/models/cambrian1.py diff --git a/M-MMMU/README.md b/M-MMMU/README.md deleted file mode 100644 index f883830c..00000000 --- a/M-MMMU/README.md +++ /dev/null @@ -1,58 +0,0 @@ -# Multilingual MMMU -Multilingual MMMU: Multilingual Massive Multi-task Multimodality Understanding - -## Instruction -Multilingual MMMU is a benchmark that aims to evaluate the performance of multimodal foundation models in understanding multilingual multimodal data across multiple tasks. It provides a standardized evaluation framework and dataset for researchers to compare and analyze the general understandability of different multimodal models in handling various knowledge tasks in multilingual cases. - -We are looking for high school or college exam questions that have both image and text in the question: -- The question needs to contain both image and text. -- The question needs to be highly precise, and the answer needs to be easy to verify, e.g., a multiple-choice answer, a number, a list of number, or a boolean variable, etc. -- The topic covers Art, Business, Medical, Humanities, Science and Engineering. See the full list of the category in the following. -- You can either find questions from the Internet, or Textbook or come up with your own question (if you are an expert in that field). - -## Category -| Category | Translation Count | Annotation Count | -|--------------------------------------- |------------------- |------------------ | -| **Art and Design** | | | -| - Art | | | -| - Art_Theory | | | -| - Design | | | -| - Music | | | -| | | | -| **Business** | | | -| - Accounting | | | -| - Economics | | | -| - Finance | | | -| - Manage | | | -| - Marketing | | | -| | | | -| **Health and Medicine** | | | -| - Basic_Medical_Science | | | -| - Clinical_Medicine | | | -| - Diagnostics_and_Laboratory_Medicine | | | -| - Pharmacy | | | -| - Public_Health | | | -| | | | -| **Humanities and Social Science** | | | -| - History | | | -| - Literature | | | -| - Psychology | | | -| - Sociology | | | -| | | | -| **Science** | | | -| - Biology | | | -| - Chemistry | | | -| - Geography | | | -| - Math | | | -| - Physics | | | -| | | | -| **Tech and Engineering** | | | -| - Agriculture | | | -| - Architecture_and_Engineering | | | -| - Computer_Science | | | -| - Electronics | | | -| - Energy_and_Power | | | -| - Materials | | | -| - Mechanical_Engineering | | | - -## \ No newline at end of file diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index 86711a9c..7e28a9bf 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -12,7 +12,6 @@ logger.add(sys.stdout, level="WARNING") AVAILABLE_MODELS = { - "cambrian1": "Cambrian1", "llava": "Llava", "qwen_vl": "Qwen_VL", "fuyu": "Fuyu", diff --git a/lmms_eval/models/cambrian1.py b/lmms_eval/models/cambrian1.py deleted file mode 100644 index 06648482..00000000 --- a/lmms_eval/models/cambrian1.py +++ /dev/null @@ -1,420 +0,0 @@ -import torch - -torch.backends.cuda.matmul.allow_tf32 = True - - -import copy -from tqdm import tqdm -from datetime import timedelta - -from lmms_eval import utils -from lmms_eval.api.instance import Instance -from lmms_eval.api.model import lmms -from lmms_eval.api.registry import register_model -from lmms_eval.utils import stop_sequences_criteria - -from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs -from accelerate.state import AcceleratorState -from typing import List, Optional, Union, Tuple -from packaging import version -import warnings - -warnings.filterwarnings("ignore") - -from loguru import logger as eval_logger - -try: - from cambrian.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN - from cambrian.conversation import conv_templates - from cambrian.model.builder import load_pretrained_model - from cambrian.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path -except Exception as e: - eval_logger.debug("Cambrian is not installed. Please head over https://github.com/cambrian-mllm/cambrian/ and install Cambrian to use this model.\nError: %s" % e) - -if version.parse(torch.__version__) >= version.parse("2.1.2"): - best_fit_attn_implementation = "sdpa" -else: - best_fit_attn_implementation = "eager" - -# Cambrian model names and their corresponding conversation templates -# So that we can use the correct conversation template for the model -conv_modes = { - "cambrian-phi3-3b": "phi3", - "cambrian-8b": "llama_3", - "cambrian-13b": "vicuna_v1", - "cambrian-34b": "chatml_direct", -} - -@register_model("cambrian1") -class Cambrian1(lmms): - """ - Cambrian1 Model - """ - - def __init__( - self, - pretrained: str = "nyu-visionx/cambrian-8b", - truncation: Optional[bool] = True, - device: Optional[str] = "cuda:0", - batch_size: Optional[Union[int, str]] = 1, - model_name=None, - attn_implementation=best_fit_attn_implementation, - device_map="cuda:0", - conv_template="llama_3", - use_cache=True, - tie_weights: bool = True, - truncate_context=False, # whether to truncate the context in generation, set it False - customized_config=None, # ends in json - **kwargs, - ) -> None: - super().__init__() - # Do not use kwargs for now - assert kwargs == {}, f"Unexpected kwargs: {kwargs}" - - accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) - accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) - self.accelerator = accelerator - if accelerator.num_processes > 1: - self._device = torch.device(f"cuda:{accelerator.local_process_index}") - self.device_map = f"cuda:{accelerator.local_process_index}" - elif accelerator.num_processes == 1 and device_map == "auto": - self._device = torch.device(device) - self.device_map = device_map - else: - self._device = torch.device(f"cuda:{accelerator.local_process_index}") - self.device_map = f"cuda:{accelerator.local_process_index}" - - cambrian_model_args = {} - if customized_config is not None: - cambrian_model_args["customized_config"] = customized_config - if attn_implementation is not None: - cambrian_model_args["attn_implementation"] = attn_implementation - if "use_flash_attention_2" in kwargs: - cambrian_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"] - model_name = model_name if model_name is not None else get_model_name_from_path(pretrained) - try: - # Try to load the model with the multimodal argument - self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **cambrian_model_args) - except TypeError: - # Load the model without cambrian_model_args - self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map) - - self._config = self._model.config - self.model.eval() - if tie_weights: - self.model.tie_weights() - - self.truncation = truncation - batch_size = int(batch_size) - assert batch_size == 1, f"Batch size should be 1 for Cambrian, but got {batch_size}." - self.batch_size_per_gpu = batch_size - self.conv_template = conv_modes[model_name] if model_name in conv_modes else conv_template - self.use_cache = use_cache - self.truncate_context = truncate_context - if accelerator.num_processes > 1: - assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." - # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model - # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works - # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. - if accelerator.distributed_type == DistributedType.DEEPSPEED: - kwargs = { - "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, - "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, - } - AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) - eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") - - if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: - self._model = accelerator.prepare(self.model) - else: - self._model = accelerator.prepare_model(self.model, evaluation_mode=True) - self.accelerator = accelerator - if self.accelerator.is_local_main_process: - eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") - self._rank = self.accelerator.local_process_index - self._world_size = self.accelerator.num_processes - elif accelerator.num_processes == 1 and device_map == "auto": - eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") - self._rank = 0 - self._word_size = 1 - else: - eval_logger.info(f"Using single device: {self._device}") - self.model.to(self._device) - self._rank = 0 - self._world_size = 1 - - @property - def config(self): - # return the associated transformers.AutoConfig for the given pretrained model. - return self._config - - @property - def tokenizer(self): - return self._tokenizer - - @property - def model(self): - # returns the model, unwrapping it if using Accelerate - if hasattr(self, "accelerator"): - return self.accelerator.unwrap_model(self._model) - else: - return self._model - - @property - def eot_token_id(self): - # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* - return self.tokenizer.eos_token_id - - @property - def max_length(self): - return self._max_length - - def pad_sequence(self, input_ids, batch_first, padding_value): - if self.tokenizer.padding_side == "left": - input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] - input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value) - if self.tokenizer.padding_side == "left": - input_ids = torch.flip(input_ids, [1]) - return input_ids - - @property - def batch_size(self): - return self.batch_size_per_gpu - - @property - def device(self): - return self._device - - @property - def rank(self): - return self._rank - - @property - def world_size(self): - return self._world_size - - def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]: - """ """ - add_special_tokens = False if add_special_tokens is None else add_special_tokens - encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens) - # left-truncate the encoded context to be at most `left_truncate_len` tokens long - if left_truncate_len: - encoding = encoding[-left_truncate_len:] - return encoding - - def tok_decode(self, tokens): - try: - return self.tokenizer.decode(tokens) - except: - return self.tokenizer.decode([tokens]) - - def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: - # TODO - res = [] - pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") - - for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: - # encode, pad, and truncate contexts for this batch - if type(doc_to_target) == str: - continuation = doc_to_target - else: - continuation = doc_to_target(self.task_dict[task][split][doc_id]) - visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] - # If there are more than one image in the sample, take the first image - # Cambrian only supports one image at a time - for idx, visual in enumerate(visuals): - if len(visual) > 1: - visuals[idx] = [visual[0]] - - visuals = self.flatten(visuals) - image_sizes = [[visual.size[0], visual.size[1]] for visual in visuals] - if visuals: - image = process_images(visuals, self._image_processor, self._config) - if type(image) is list: - image = [_image.to(dtype=torch.float16, device=self.device) for _image in image] - else: - image = image.to(dtype=torch.float16, device=self.device) - else: - image = None - - prompts_input = contexts[0] if isinstance(contexts, list) else contexts - - if image is not None and len(image) != 0 and DEFAULT_IMAGE_TOKEN not in prompts_input: - """ - Three senarios: - 1. No image, and there for, no image token should be added. - 2. image token is already specified in the context, so we don't need to add it. - 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. - """ - if self.model.config.mm_use_im_start_end: - image_tokens = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN - else: - image_tokens = DEFAULT_IMAGE_TOKEN - prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts) - - conv = conv_templates[self.conv_template].copy() - conv.append_message(conv.roles[0], prompts_input) - conv.append_message(conv.roles[1], None) - prompt = conv.get_prompt() - pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id - contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) - # Add the answer of the second role - conv.messages[1][1] = continuation - - prompt = conv.get_prompt() - input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) - labels = input_ids.clone() - # Context part no need to calculate for loss - labels[0, : contxt_id.shape[1]] = -100 - with torch.inference_mode(): - outputs = self.model(input_ids=input_ids, labels=labels, images=image, use_cache=True, image_sizes=image_sizes) - loss = outputs["loss"] - # loss = torch.exp(loss) - logits = outputs["logits"] - greedy_tokens = logits.argmax(dim=-1) - cont_toks = input_ids[:, contxt_id.shape[1] :] # [1, seq] - greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]] # [1, seq] - max_equal = (greedy_tokens == cont_toks).all() - res.append((float(loss.item()), bool(max_equal))) - pbar.update(1) - pbar.close() - return res - - def flatten(self, input): - new_list = [] - for i in input: - for j in i: - new_list.append(j) - return new_list - - def generate_until(self, requests: List[Instance]) -> List[str]: - res = [] - - def _collate(x): - # the negative sign on len(toks) sorts descending - this has a few advantages: - # - time estimates will always be over not underestimates, which is more useful for planning - # - to know the size of a batch when going through the list, you know the first one is always the batch - # padded context length. this is useful to simplify the batching logic and more importantly to make - # automatic adaptive batches much much easier to implement - # - any OOMs will happen right away rather than near the end - toks = self.tok_encode(x[0]) - return -len(toks), x[0] - - # we group requests by their generation_kwargs, - # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling - # in the same batch. - re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) - chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) - num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1 - pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding") - for chunk in chunks: - contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk) - task = task[0] - split = split[0] - batched_visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] # [B, N] - # If there are more than one image in the sample, take the first image - # Cambrian only supports one image at a time - for idx, visual in enumerate(batched_visuals): - if len(visual) > 1: - batched_visuals[idx] = [visual[0]] - flattened_visuals = self.flatten(batched_visuals) # [B*N] - # we assume all gen kwargs in the batch are the same - # this is safe to assume because the `grouper` object ensures it. - gen_kwargs = all_gen_kwargs[0] - - # Set default values for until and max_new_tokens - until = [self.tok_decode(self.eot_token_id)] - - # Update values from gen_kwargs if present - if "until" in gen_kwargs: - until = gen_kwargs.pop("until") - if isinstance(until, str): - until = [until] - elif not isinstance(until, list): - raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") - - if "image_aspect_ratio" in gen_kwargs.keys() and "image_aspect_ratio" not in self._config.__dict__: - # here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation - self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio") - eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") - # encode, pad, and truncate contexts for this batch - if flattened_visuals: - image_tensor = process_images(flattened_visuals, self._image_processor, self._config) - if type(image_tensor) is list: - image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] - else: - image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) - else: - image_tensor = None - - # prompts_input = contexts[0] - - question_input = [] - - for visual, context in zip(batched_visuals, contexts): - if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context: - """ - Three senarios: - 1. No image, and there for, no image token should be added. - 2. image token is already specified in the context, so we don't need to add it. - 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. - """ - if self.model.config.mm_use_im_start_end: - image_tokens = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN - else: - image_tokens = DEFAULT_IMAGE_TOKEN - question = image_tokens + "\n" + context - else: - question = context - - conv = conv_templates[self.conv_template].copy() - conv.append_message(conv.roles[0], question) - conv.append_message(conv.roles[1], None) - prompt_question = conv.get_prompt() - question_input.append(prompt_question) - - # preconfigure gen_kwargs with defaults - gen_kwargs["image_sizes"] = [flattened_visuals[idx].size for idx in range(len(flattened_visuals))] - if "max_new_tokens" not in gen_kwargs: - gen_kwargs["max_new_tokens"] = 1024 - if "temperature" not in gen_kwargs: - gen_kwargs["temperature"] = 0 - if "top_p" not in gen_kwargs: - gen_kwargs["top_p"] = None - if "num_beams" not in gen_kwargs: - gen_kwargs["num_beams"] = 1 - - input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] - pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id - input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device) - attention_masks = input_ids.ne(pad_token_ids).to(self.device) - # These steps are not in Cambrian's original code, but are necessary for generation to work - try: - cont = self.model.generate( - input_ids, - attention_mask=attention_masks, - pad_token_id=pad_token_ids, - images=image_tensor, - image_sizes=gen_kwargs["image_sizes"], - do_sample=True if gen_kwargs["temperature"] > 0 else False, - temperature=gen_kwargs["temperature"], - top_p=gen_kwargs["top_p"], - num_beams=gen_kwargs["num_beams"], - max_new_tokens=gen_kwargs["max_new_tokens"], - use_cache=self.use_cache, - ) - text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True) - except Exception as e: - raise e - eval_logger.error(f"Error {e} in generating") - cont = "" - text_outputs = [""] - - res.extend(text_outputs) - self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) - pbar.update(1) - # reorder this group of results back to original unsorted form - res = re_ords.get_original(res) - - pbar.close() - return res \ No newline at end of file From ceb1371ab799e3efa1fae194db4c7121f3bc75c7 Mon Sep 17 00:00:00 2001 From: abzb1 Date: Fri, 2 Aug 2024 12:20:40 +0000 Subject: [PATCH 174/196] Add SEEDBench 2 Plus --- docs/current_tasks.md | 1 + lmms_eval/tasks/seedbench_2/seedbench_2.yaml | 2 +- .../seedbench_2_plus/seedbench_2_plus.yaml | 43 +++++++++++++++ lmms_eval/tasks/seedbench_2_plus/utils.py | 52 +++++++++++++++++++ 4 files changed, 97 insertions(+), 1 deletion(-) create mode 100755 lmms_eval/tasks/seedbench_2_plus/seedbench_2_plus.yaml create mode 100755 lmms_eval/tasks/seedbench_2_plus/utils.py diff --git a/docs/current_tasks.md b/docs/current_tasks.md index 6e0e5238..a9949cb6 100644 --- a/docs/current_tasks.md +++ b/docs/current_tasks.md @@ -105,6 +105,7 @@ - ScreenSpot REG / Instruction Generation (screenspot_reg) - SeedBench (seedbench) - SeedBench 2 (seedbench_2) +- SeedBench 2 Plus (seedbench_2_plus) - ST-VQA (stvqa) - TextCaps (textcaps) - TextCaps Validation (textcaps_val) diff --git a/lmms_eval/tasks/seedbench_2/seedbench_2.yaml b/lmms_eval/tasks/seedbench_2/seedbench_2.yaml index 5fb8cf78..596e2cc0 100755 --- a/lmms_eval/tasks/seedbench_2/seedbench_2.yaml +++ b/lmms_eval/tasks/seedbench_2/seedbench_2.yaml @@ -1,7 +1,7 @@ dataset_path: lmms-lab/SEED-Bench-2 dataset_kwargs: token: True -task: "seedbench-2" +task: "seedbench_2" test_split: test output_type: generate_until doc_to_visual: !function utils.seed_doc_to_visual diff --git a/lmms_eval/tasks/seedbench_2_plus/seedbench_2_plus.yaml b/lmms_eval/tasks/seedbench_2_plus/seedbench_2_plus.yaml new file mode 100755 index 00000000..c47a3dba --- /dev/null +++ b/lmms_eval/tasks/seedbench_2_plus/seedbench_2_plus.yaml @@ -0,0 +1,43 @@ +dataset_path: doolayer/SEED-Bench-2-Plus +dataset_kwargs: + token: True +task: "seedbench_2_plus" +test_split: test +output_type: generate_until +doc_to_visual: !function utils.seed_doc_to_visual +doc_to_text: !function utils.seed_doc_to_text +doc_to_target: "answer" +generation_kwargs: + until: + - "ASSISTANT:" + max_new_tokens: 16 + image_aspect_ratio: original +# The return value of process_results will be used by metrics +process_results: !function utils.seed_process_result +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: seedbench_2_plus_Chart + aggregation: !function utils.seed_aggregation_result + higher_is_better: true + - metric: seedbench_2_plus_Map + aggregation: !function utils.seed_aggregation_result + higher_is_better: true + - metric: seedbench_2_plus_Web + aggregation: !function utils.seed_aggregation_result + higher_is_better: true + - metric: seedbench_2_plus_all + aggregation: !function utils.seed_aggregation_result + higher_is_better: true +metadata: + - version: 0.0 + +model_specific_prompt_kwargs: + llava : + img_token : + post_prompt : "Answer with the option's letter from the given choices directly." + gpt4V : + img_token : + post_prompt : "Answer with the option's letter from the given choices directly." + default : + img_token : + post_prompt : "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/seedbench_2_plus/utils.py b/lmms_eval/tasks/seedbench_2_plus/utils.py new file mode 100755 index 00000000..3182b4bc --- /dev/null +++ b/lmms_eval/tasks/seedbench_2_plus/utils.py @@ -0,0 +1,52 @@ +import json + +def seed_doc_to_visual(doc): + return [doc["image"].convert("RGB")] + +def parse_choice_img(choice: str, img_token: str): + if "jpg" in choice or "png" in choice: + return img_token + return choice + + +def seed_doc_to_text(doc, model_specific_kwargs=None): + question = doc["question"] + question.replace("", model_specific_kwargs["img_token"]) + question += "\n" + f"A. {parse_choice_img(doc['choice_A'], model_specific_kwargs['img_token'])}\n" + question += f"B. {parse_choice_img(doc['choice_B'], model_specific_kwargs['img_token'])}\n" + question += f"C. {parse_choice_img(doc['choice_C'], model_specific_kwargs['img_token'])}\n" + question += f"D. {parse_choice_img(doc['choice_D'], model_specific_kwargs['img_token'])}" + + return f"{question}\n{model_specific_kwargs['post_prompt']}" + + +def seed_process_result(doc, result): + pred = result[0].strip() + if len(pred) > 1: + pred = pred[0] + answer = doc["answer"] + data_type = doc["question_image_type"].capitalize() + + return {f"seedbench_2_plus_{data_type}": {"pred": pred, "answer": answer, "question_id": doc["question_id"]}, f"seedbench_2_plus_all": {"pred": pred, "answer": answer, "question_id": doc["question_id"]}} + + +def seed_aggregation_result(results): + total_count = 0 + total_correct = 0 + for result in results: + if result["pred"].lower().strip() == result["answer"].lower().strip(): + total_correct += 1 + total_count += 1 + return total_correct / total_count if total_count != 0 else 0 + + +def seed_aggregation_result_all(results): + score = seed_aggregation_result(results) + stored_results = [] + for result in results: + stored_results.append({"question_id": result["question_id"], "prediction": result["pred"]}) + with open("./seed_submission.json", "w") as f: + json.dump(stored_results, f, indent=4) + print("Storing files for seed_submission ...") + + return score From 1d3f34ab316d4a4ac1d7b2c656e2631a127104f9 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Wed, 7 Aug 2024 13:07:51 +1000 Subject: [PATCH 175/196] [New Updates] LLaVA OneVision Release; MVBench, InternVL2, IXC2.5 Interleave-Bench integration. (#182) * chore: Update .gitignore and add live_bench tasks * Update __init__.py --- .gitignore | 1 + docs/commands.md | 30 + docs/run_examples.md | 441 +++++++++++++ lmms_eval/api/instance.py | 2 +- lmms_eval/api/registry.py | 1 + lmms_eval/api/task.py | 54 +- lmms_eval/evaluator.py | 13 +- lmms_eval/models/__init__.py | 52 +- lmms_eval/models/internvl2.py | 48 +- lmms_eval/models/llava_onevision.py | 612 ++++++++++++++++++ lmms_eval/models/llava_vid.py | 15 +- lmms_eval/models/srt_api.py | 231 +++++++ lmms_eval/models/vila.py | 17 +- lmms_eval/models/xcomposer2d5.py | 183 ++++++ lmms_eval/tasks/__init__.py | 9 +- .../activitynetqa/_default_template_yaml | 2 +- lmms_eval/tasks/activitynetqa/utils.py | 14 +- lmms_eval/tasks/ai2d/ai2d.yaml | 2 +- lmms_eval/tasks/ai2d/utils.py | 14 +- lmms_eval/tasks/chartqa/chartqa.yaml | 2 +- lmms_eval/tasks/chartqa/utils.py | 6 +- lmms_eval/tasks/cvrr/_default_template_yaml | 2 +- lmms_eval/tasks/cvrr/utils.py | 14 +- 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.../tasks/live_bench/live_bench_2406.yaml | 3 + .../tasks/live_bench/live_bench_2407.yaml | 3 + .../tasks/live_bench/live_bench_template_yaml | 29 + lmms_eval/tasks/live_bench/utils.py | 214 ++++++ .../llava-bench-coco/llava-bench-coco.yaml | 2 +- lmms_eval/tasks/llava-bench-coco/utils.py | 10 +- .../llava-in-the-wild/llava-in-the-wild.yaml | 4 +- lmms_eval/tasks/llava-in-the-wild/utils.py | 10 +- .../_default_template_interleave_yaml | 2 +- .../llava_interleave_bench/in_domain.yaml | 2 +- .../multi_view_in_domain.yaml | 2 +- .../llava_interleave_bench/out_of_domain.yaml | 2 +- .../tasks/llava_interleave_bench/utils.py | 50 +- .../_default_template_wilder_yaml | 3 +- .../llava_wilder/llava_wilder_small.yaml | 2 +- lmms_eval/tasks/llava_wilder/utils.py | 10 +- .../longvideobench/longvideobench_val_i.yaml | 2 +- .../longvideobench/longvideobench_val_v.yaml | 2 +- lmms_eval/tasks/longvideobench/utils.py | 8 +- lmms_eval/tasks/mathverse/mathverse_evals.py | 2 +- .../tasks/mathverse/mathverse_testmini.yaml | 2 +- .../mathverse_testmini_text_dominant.yaml | 2 +- .../mathverse_testmini_text_lite.yaml | 2 +- .../mathverse_testmini_text_only.yaml | 2 +- .../mathverse/mathverse_testmini_vision.yaml | 10 + .../mathverse_testmini_vision_dominant.yaml | 2 +- .../mathverse_testmini_vision_intensive.yaml | 2 +- .../mathverse_testmini_vision_only.yaml | 2 +- lmms_eval/tasks/mathverse/utils.py | 17 +- lmms_eval/tasks/mathvista/mathvista.yaml | 2 +- lmms_eval/tasks/mathvista/mathvista_test.yaml | 9 +- .../tasks/mathvista/mathvista_testmini.yaml | 43 +- .../mathvista/mathvista_testmini_cot.yaml | 29 + .../mathvista/mathvista_testmini_format.yaml | 29 + .../mathvista_testmini_solution.yaml | 29 + lmms_eval/tasks/mathvista/utils.py | 10 +- lmms_eval/tasks/mlvu/mlvu.yaml | 6 + lmms_eval/tasks/mlvu/utils.py | 2 +- .../mmbench/_default_template_mmbench_cn_yaml | 2 +- .../mmbench/_default_template_mmbench_en_yaml | 2 +- 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.../mvbench/mvbench_fine_grained_action.yaml | 8 + .../mvbench/mvbench_fine_grained_pose.yaml | 8 + .../mvbench/mvbench_moving_attribute.yaml | 8 + .../tasks/mvbench/mvbench_moving_count.yaml | 8 + .../mvbench/mvbench_moving_direction.yaml | 8 + .../mvbench/mvbench_object_existence.yaml | 8 + .../mvbench/mvbench_object_interaction.yaml | 8 + .../tasks/mvbench/mvbench_object_shuffle.yaml | 8 + .../mvbench/mvbench_scene_transition.yaml | 8 + .../tasks/mvbench/mvbench_state_change.yaml | 8 + .../mvbench/mvbench_unexpected_action.yaml | 8 + lmms_eval/tasks/mvbench/utils.py | 194 ++++++ lmms_eval/tasks/nextqa/_default_template_yaml | 3 + lmms_eval/tasks/nextqa/nextqa_oe_test.yaml | 2 +- lmms_eval/tasks/nextqa/nextqa_oe_val.yaml | 2 +- lmms_eval/tasks/nextqa/utils.py | 66 +- .../nocaps/_default_template_nocaps_yaml | 2 +- lmms_eval/tasks/nocaps/utils.py | 4 +- .../tasks/ok_vqa/_default_template_vqa_yaml | 2 +- lmms_eval/tasks/ok_vqa/utils.py | 14 +- .../olympiadbench/olympiadbench_evals.py | 3 +- .../test/_default_template_yaml | 2 +- lmms_eval/tasks/perceptiontest/test/utils.py | 14 +- .../perceptiontest/val/_default_template_yaml | 2 +- lmms_eval/tasks/perceptiontest/val/utils.py | 42 +- lmms_eval/tasks/qbench/abench_dev.yaml | 2 +- lmms_eval/tasks/qbench/qbench2_dev.yaml | 2 +- lmms_eval/tasks/qbench/qbench_dev.yaml | 2 +- lmms_eval/tasks/qbench/utils.py | 6 +- lmms_eval/tasks/realworldqa/realworldqa.yaml | 2 +- lmms_eval/tasks/realworldqa/utils.py | 14 +- lmms_eval/tasks/scienceqa/scienceqa.yaml | 2 +- lmms_eval/tasks/scienceqa/scienceqa_img.yaml | 2 +- lmms_eval/tasks/scienceqa/utils.py | 12 +- lmms_eval/tasks/seedbench_2/seedbench_2.yaml | 8 +- lmms_eval/tasks/stvqa/stvqa.yaml | 2 +- lmms_eval/tasks/stvqa/utils.py | 6 +- .../tasks/tempcompass/_default_template_yaml | 2 +- lmms_eval/tasks/tempcompass/utils.py | 56 +- .../textcaps/_default_template_textcaps_yaml | 2 +- lmms_eval/tasks/textcaps/utils.py | 4 +- .../textvqa/_default_template_textvqa_yaml | 2 +- lmms_eval/tasks/textvqa/utils.py | 14 +- lmms_eval/tasks/vatex/utils.py | 8 +- lmms_eval/tasks/vatex/vatex_test.yaml | 2 +- lmms_eval/tasks/vatex/vatex_val_zh.yaml | 2 +- lmms_eval/tasks/vcr_wiki/utils.py | 10 +- .../tasks/vcr_wiki/vcr_wiki_en_easy.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_en_hard.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_zh_easy.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_zh_hard.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml | 2 +- .../tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml | 2 +- lmms_eval/tasks/vibe_eval/utils.py | 21 +- lmms_eval/tasks/vibe_eval/vibe_eval.yaml | 2 +- .../_default_template_yaml | 2 +- .../tasks/video_detail_description/utils.py | 14 +- 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lmms_eval/tasks/worldqa/worldqa_mcppl.yaml | 2 +- lmms_eval/tasks/youcook2/utils.py | 6 +- lmms_eval/tasks/youcook2/youcook2_val.yaml | 2 +- pyproject.toml | 8 +- tools/live_bench/create_dataset.py | 6 +- tools/live_bench/data_summary.ipynb | 112 ++-- tools/live_bench/example.ipynb | 481 ++++++++++++++ tools/live_bench/filter.ipynb | 18 +- tools/live_bench/live_bench/api/live_bench.py | 20 + .../live_bench/data_generator/live_bench.py | 28 +- .../data_generator/live_bench_data.py | 139 ++++ .../live_bench/data_generator/prompt.md | 4 +- .../live_bench/data_generator/qa_generator.py | 522 +++++++++++++++ .../data_generator/question_finalizer.py | 132 ++++ .../live_bench/data_generator/response.py | 12 + .../live_bench/data_generator/score_getter.py | 157 +++++ .../live_bench/data_generator/utils/claude.py | 68 ++ .../utils/extract_infomation.py | 44 +- .../live_bench/data_generator/utils/gemini.py | 37 ++ .../live_bench/data_generator/utils/gpt4v.py | 5 +- tools/live_bench/live_bench/driver/.gitignore | 1 + .../live_bench/driver/load_driver.py | 71 ++ .../live_bench/screen_shoter/screen.py | 30 + .../live_bench/screen_shoter/screen_shoter.py | 141 ++++ tools/live_bench/live_bench/view.ipynb | 393 ++++++----- .../live_bench/websites/load_website.py | 34 + .../live_bench/live_bench/websites/website.py | 62 ++ .../live_bench/websites/website_list.yaml | 78 +++ tools/live_bench/pyproject.toml | 0 tools/live_bench/refine_all_results.py | 36 ++ tools/live_bench/script/README.md | 15 + tools/live_bench/script/modify.ipynb | 461 +++++++++++++ tools/live_bench/script/upload_results.py | 178 +++++ tools/live_bench/setup.py | 0 248 files changed, 6172 insertions(+), 1000 deletions(-) create mode 100644 docs/run_examples.md create mode 100644 lmms_eval/models/llava_onevision.py create mode 100755 lmms_eval/models/srt_api.py create mode 100644 lmms_eval/models/xcomposer2d5.py create mode 100755 lmms_eval/tasks/live_bench/live_bench.yaml create mode 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lmms_eval/tasks/mvbench/mvbench_object_shuffle.yaml create mode 100644 lmms_eval/tasks/mvbench/mvbench_scene_transition.yaml create mode 100644 lmms_eval/tasks/mvbench/mvbench_state_change.yaml create mode 100644 lmms_eval/tasks/mvbench/mvbench_unexpected_action.yaml create mode 100644 lmms_eval/tasks/mvbench/utils.py create mode 100644 tools/live_bench/example.ipynb create mode 100644 tools/live_bench/live_bench/api/live_bench.py create mode 100644 tools/live_bench/live_bench/data_generator/live_bench_data.py create mode 100644 tools/live_bench/live_bench/data_generator/qa_generator.py create mode 100644 tools/live_bench/live_bench/data_generator/question_finalizer.py create mode 100644 tools/live_bench/live_bench/data_generator/response.py create mode 100644 tools/live_bench/live_bench/data_generator/score_getter.py create mode 100644 tools/live_bench/live_bench/data_generator/utils/claude.py create mode 100644 tools/live_bench/live_bench/data_generator/utils/gemini.py create mode 100644 tools/live_bench/live_bench/driver/.gitignore create mode 100644 tools/live_bench/live_bench/driver/load_driver.py create mode 100644 tools/live_bench/live_bench/screen_shoter/screen.py create mode 100644 tools/live_bench/live_bench/screen_shoter/screen_shoter.py create mode 100644 tools/live_bench/live_bench/websites/load_website.py create mode 100644 tools/live_bench/live_bench/websites/website.py create mode 100644 tools/live_bench/live_bench/websites/website_list.yaml mode change 100755 => 100644 tools/live_bench/pyproject.toml create mode 100644 tools/live_bench/refine_all_results.py create mode 100644 tools/live_bench/script/README.md create mode 100644 tools/live_bench/script/modify.ipynb create mode 100644 tools/live_bench/script/upload_results.py mode change 100755 => 100644 tools/live_bench/setup.py diff --git a/.gitignore b/.gitignore index 922b9f0b..edf2efef 100755 --- a/.gitignore +++ b/.gitignore @@ -38,3 +38,4 @@ llava-video/ Video-MME/ VATEX/ lmms_eval/tasks/vatex/__pycache__/utils.cpython-310.pyc +lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc \ No newline at end of file diff --git a/docs/commands.md b/docs/commands.md index 4f8c7a7d..e303fc64 100755 --- a/docs/commands.md +++ b/docs/commands.md @@ -22,3 +22,33 @@ This mode supports a number of command-line arguments, the details of which can * `--limit` : Accepts an integer, or a float between 0.0 and 1.0 . If passed, will limit the number of documents to evaluate to the first X documents (if an integer) per task or first X% of documents per task. Useful for debugging, especially on costly API models. +## Usage with SRT API + +> install sglang + +```bash +git clone https://github.com/EvolvingLMMs-Lab/sglang.git +cd sglang; +git checkout dev/onevision; +pip install -e "python[srt]" +``` + +> run sglang backend service with the following command + +```bash +# backend service +python -m sglang.launch_server --model-path "\path\to\onevision" --tokenizer-path lmms-lab/llavanext-qwen-siglip-tokenizer --port=30000 --host=127.0.0.1 --tp-size=8 --chat-template=chatml-llava + +# launch lmms-eval srt_api model +python -m accelerate.commands.launch --main_process_port=12580 --num_processes=1 lmms_eval --model=srt_api --model_args=modality=image,host=127.0.0.1,port=30000 --tasks=ai2d --batch_size=1 --log_samples --log_samples_suffix=debug --output_path=./logs/ --verbosity=DEBUG +``` + +You may need to install some dependencies for the above command to work (if you encounter some errors). + +```bash +pip install httpx==0.23.3 +pip install protobuf==3.20 +pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/ +``` + + diff --git a/docs/run_examples.md b/docs/run_examples.md new file mode 100644 index 00000000..99c2e716 --- /dev/null +++ b/docs/run_examples.md @@ -0,0 +1,441 @@ +# User Guide +This document details the running examples for different models in `lmms_eval`. We include commandas on how to prepare environments for different model and some commands to run these models + +## Environmental Variables + +Before running experiments and evaluations, we recommend you to export following environment variables to your environment. Some are necessary for certain tasks to run. + +```bash +export OPENAI_API_KEY="" +export HF_HOME="" +export HF_TOKEN="" +export HF_HUB_ENABLE_HF_TRANSFER="1" +export REKA_API_KEY="" +# Other possible environment variables include +# ANTHROPIC_API_KEY,DASHSCOPE_API_KEY etc. +``` + +## Some common environment issue +Sometimes you might encounter some common issues for example error related to `httpx` or `protobuf`. To solve these issues, you can first try + +```bash +python3 -m pip install httpx==0.23.3; +python3 -m pip install protobuf==3.20; +# If you are using numpy==2.x, sometimes may causing errors +python3 -m pip install numpy==1.26; +# Someties sentencepiece are required for tokenizer to work +python3 -m pip install sentencepiece; +``` + +# Image Model + +### LLaVA +First, you will need to clone repo of `lmms_eval` and repo of [`llava`](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/inference) + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +cd /path/to/LLaVA-NeXT; +python3 -m pip install -e ".[train]"; + + +TASK=$1 +CKPT_PATH=$2 +CONV_TEMPLATE=$3 +MODEL_NAME=$4 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +#mmbench_en_dev,mathvista_testmini,llava_in_the_wild,mmvet +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model llava \ + --model_args pretrained=$CKPT_PATH,conv_template=$CONV_TEMPLATE,model_name=$MODEL_NAME \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ +``` +If you are trying to use large LLaVA models such as LLaVA-NeXT-Qwen1.5-72B, you can try adding `device_map=auto` in model_args and change `num_processes` to 1. + +### IDEFICS2 + +You won't need to clone any other repos to run idefics. Making sure your transformers version supports idefics2 would be enough + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +python3 -m pip install transformers --upgrade; + +TASK=$1 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model idefics2 \ + --model_args pretrained=HuggingFaceM4/idefics2-8b \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ +``` + +### InternVL2 + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + + +python3 -m pip install flash-attn --no-build-isolation; +python3 -m pip install torchvision einops timm sentencepiece; + + +TASK=$1 +CKPT_PATH=$2 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12380 -m lmms_eval \ + --model internvl2 \ + --model_args pretrained=$CKPT_PATH \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ +``` + + +### InternVL-1.5 +First you need to fork [`InternVL`](https://github.com/OpenGVLab/InternVL) + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +cd /path/to/InternVL/internvl_chat +python3 -m pip install -e .; + +python3 -m pip install flash-attn==2.3.6 --no-build-isolation; + + +TASK=$1 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model internvl \ + --model_args pretrained="OpenGVLab/InternVL-Chat-V1-5"\ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ + +``` + +### Xcomposer-4KHD and Xcomposer-2d5 + +Both of these two models does not require external repo + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + + +python3 -m pip install flash-attn --no-build-isolation; +python3 -m pip install torchvision einops timm sentencepiece; + +TASK=$1 +MODALITY=$2 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +# For Xcomposer2d5 +accelerate launch --num_processes 8 --main_process_port 10000 -m lmms_eval \ + --model xcomposer2d5 \ + --model_args pretrained="internlm/internlm-xcomposer2d5-7b",device="cuda",modality=$MODALITY\ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ + +# For Xcomposer-4kHD +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model xcomposer2_4khd \ + --model_args pretrained="internlm/internlm-xcomposer2-4khd-7b" \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ + +``` + +### InstructBLIP + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +python3 -m pip install transformers --upgrade; + +CKPT_PATH=$1 +TASK=$2 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model instructblip \ + --model_args pretrained=$CKPT_PATH \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix instructblip \ + --output_path ./logs/ + +``` + +### SRT API MODEL +To enable faster testing speed for larger llava model, you can use this srt api model to enable testing through sglang. + +Here are the scripts if you want to test the result in one script. +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +cd /path/to/sglang; +python3 -m pip install -e "python[all]"; + + +python3 -m pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/ + + +CKPT_PATH=$1 +TOK_PATH=$2 +TASK=$3 +MODALITY=$4 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +# Serve sglang in backend +python3 -m sglang.launch_server --model-path ${CKPT_PATH} --tokenizer-path ${TOK_PATH} --port=30000 --host=127.0.0.1 --tp-size=8 --chat-template=chatml-llava & + +# Wait till the server is ready +sleep 360; + +python3 -m lmms_eval \ + --model srt_api \ + --model_args modality=$MODALITY,host=127.0.0.1,port=30000,timeout=600 \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ + +``` + +You can use the script in `sglang` under `test` folder to kill all sglang service + +# API Model + +### GPT + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +export OPENAI_API_KEY="" + +TASK=$1 +MODEL_VERSION=$2 +MODALITIES=$3 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 30000 -m lmms_eval \ + --model gpt4v \ + --model_args model_version=$MODEL_VERSION,modality=$MODALITIES\ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ + +``` + + +### Claude + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +export ANTHROPIC_API_KEY="" + +TASK=$1 +MODEL_VERSION=$2 +MODALITIES=$3 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model claude \ + --model_args model_version=$MODEL_VERSION\ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ +``` + + +# Video Model + +### LLaVA-VID + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +cd /path/to/LLaVA-NeXT; +python3 -m pip install -e ".[train]"; + +python3 -m pip install flash-attn --no-build-isolation; + +python3 -m pip install av; + + +TASK=$1 +CKPT_PATH=$2 +CONV_TEMPLATE=$3 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model llavavid \ + --model_args pretrained=$CKPT_PATH,conv_template=$CONV_TEMPLATE,video_decode_backend=decord,max_frames_num=32 \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ + +``` + + +### LLaMA-VID + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +# Notice that you should not leave the folder of LLaMA-VID when calling lmms-eval +# Because they left their processor's config inside the repo +cd /path/to/LLaMA-VID; +python3 -m pip install -e . + +python3 -m pip install av sentencepiece; + +TASK=$1 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model llama_vid \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ +``` + +### Video-LLaVA + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +python3 -m pip install transformers --upgrade; +python3 -m pip install av sentencepiece; + + +TASK=$1 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model video_llava \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ + +``` + + +### MPlug-Owl +Notice that this model will takes long time to load, please be patient :) + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +# It has to use an old transformers version to run +python3 -m pip install av sentencepiece protobuf==3.20 transformers==4.28.1 einops; + +TASK=$1 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model mplug_owl_video \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ + +``` + + +### Video-ChatGPT + +```bash +cd /path/to/lmms-eval +python3 -m pip install -e .; + +python3 -m pip install sentencepiece av; + +TASK=$1 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ + --model video_chatgpt \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ +``` \ No newline at end of file diff --git a/lmms_eval/api/instance.py b/lmms_eval/api/instance.py index 41875358..56285ec0 100755 --- a/lmms_eval/api/instance.py +++ b/lmms_eval/api/instance.py @@ -19,7 +19,7 @@ class Instance: def __post_init__(self) -> None: # unpack metadata field - self.task_name, self.doc_id, self.repeats = self.metadata + self.task_name, self.doc_id, self.repeats = self.metadata["task"], self.metadata["doc_id"], self.metadata["repeats"] @property def args(self): diff --git a/lmms_eval/api/registry.py b/lmms_eval/api/registry.py index 6b911997..556a9528 100755 --- a/lmms_eval/api/registry.py +++ b/lmms_eval/api/registry.py @@ -33,6 +33,7 @@ def get_model(model_name): TASK_REGISTRY = {} # Key: task name, Value: task ConfigurableTask class GROUP_REGISTRY = {} # Key: group name, Value: list of task names or group names +TASK_INITIALIZED = False ALL_TASKS = set() # Set of all task names and group names func2task_index = {} # Key: task ConfigurableTask class, Value: task name diff --git a/lmms_eval/api/task.py b/lmms_eval/api/task.py index 5560745c..310b4bfb 100755 --- a/lmms_eval/api/task.py +++ b/lmms_eval/api/task.py @@ -4,9 +4,10 @@ import json import os -import random import re +import random import shutil +import inspect import subprocess from collections.abc import Callable from dataclasses import dataclass, field, asdict @@ -67,8 +68,10 @@ class TaskConfig(dict): validation_split: str = None test_split: str = None fewshot_split: str = None # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?) + full_docs: bool = False # formatting / prompting options. # see docs/advanced_task_guide.md for more info + process_results_use_image: bool = False process_docs: Callable = None doc_to_visual: Union[Callable, str] = None doc_to_text: Union[Callable, str] = None @@ -93,7 +96,7 @@ class TaskConfig(dict): metadata: Union[str, list] = None # by default, not used in the code. allows for users to pass arbitrary info to tasks - model_specific_prompt_kwargs: dict = None + lmms_eval_specific_kwargs: dict = None model_specific_generation_kwargs: dict = None model_specific_target_kwargs: dict = None @@ -374,7 +377,12 @@ def build_all_requests(self, limit=None, rank=None, world_size=None) -> None: fewshot_ctx = self.fewshot_context(doc_id, 0 if self.config.num_fewshot is None else self.config.num_fewshot, self.config.training_split if self.has_training_docs() else split) # TODO: we should override self.config.repeats if doing greedy gen so users don't waste time+compute - inst = self.construct_requests(doc_id=doc_id, ctx=fewshot_ctx, metadata=(self.config["task"], doc_id, self.config.repeats), split=split) + per_task_metadata = {"task": self.config["task"], "doc_id": doc_id, "repeats": self.config.repeats} + + if self.config.metadata and type(self.config.metadata) == dict: # TODO: temporary fix for metadata loading, ignore the list of dict type. + per_task_metadata.update(self.config.metadata) + + inst = self.construct_requests(doc_id=doc_id, ctx=fewshot_ctx, metadata=per_task_metadata, split=split) if not isinstance(inst, list): inst = [inst] @@ -628,12 +636,14 @@ def __init__(self, model_name) -> None: # TODO no super() call here eval_logger.warning(f'Both target_delimiter "{self.config.target_delimiter}" and target choice: "{choice}" do not have whitespace, ignore if the language you are evaluating on does not require/use whitespace') def _prepare_model_specific_config(self): - self.model_specific_prompt_kwargs = self.config.model_specific_prompt_kwargs - if self.model_specific_prompt_kwargs is not None: - if self.model_name in self.model_specific_prompt_kwargs: - self.model_specific_prompt_kwargs = self.model_specific_prompt_kwargs[self.model_name] - else: - self.model_specific_prompt_kwargs = self.model_specific_prompt_kwargs.get("default", None) + self.lmms_eval_specific_kwargs = self.config.lmms_eval_specific_kwargs + if self.lmms_eval_specific_kwargs is not None: + if self.model_name in self.lmms_eval_specific_kwargs: + self.lmms_eval_specific_kwargs = self.lmms_eval_specific_kwargs[self.model_name] + if "default" in self.lmms_eval_specific_kwargs: + self.lmms_eval_specific_kwargs.update(self.lmms_eval_specific_kwargs.get("default", {})) + if "dataset" in self.lmms_eval_specific_kwargs: + self.lmms_eval_specific_kwargs.update(self.lmms_eval_specific_kwargs.get("dataset", {})) self.model_specific_target_kwargs = self.config.model_specific_target_kwargs if self.model_specific_target_kwargs is not None: @@ -782,10 +792,17 @@ def _download_from_youtube(path): def unzip_video_data(zip_file): import zipfile + import os with zipfile.ZipFile(zip_file, "r") as zip_ref: - zip_ref.extractall(cache_dir) - eval_logger.info(f"Extracted all files from {zip_file} to {cache_dir}") + for file_info in zip_ref.infolist(): + target_path = os.path.join(cache_dir, file_info.filename) + if not os.path.exists(target_path): + zip_ref.extract(file_info, cache_dir) + else: + eval_logger.info(f"Skipping existing file: {target_path}") + + eval_logger.info(f"Extracted all files from {zip_file} to {cache_dir}") def untar_video_data(tar_file): import tarfile @@ -1004,8 +1021,8 @@ def doc_to_text(self, doc): return text_string elif callable(doc_to_text): return ( - doc_to_text(doc, self.model_specific_prompt_kwargs) - if self.model_specific_prompt_kwargs is not None + doc_to_text(doc, self.lmms_eval_specific_kwargs) + if self.lmms_eval_specific_kwargs is not None else doc_to_text( doc, ) @@ -1065,9 +1082,16 @@ def doc_to_visual(self, doc: dict) -> Union[int, str, list]: assert self.config.doc_to_visual in self.features # Single image. Still return a list for consistency. return [doc[self.config.doc_to_visual]] + elif callable(self.config.doc_to_visual): + return ( + self.config.doc_to_visual(doc, self.lmms_eval_specific_kwargs) + if self.lmms_eval_specific_kwargs is not None and len(inspect.signature(self.config.doc_to_visual).parameters) == 2 + else self.config.doc_to_visual( + doc, + ) + ) else: - assert callable(self.config.doc_to_visual) - return self.config.doc_to_visual(doc) + raise TypeError def doc_to_choice(self, doc: Any) -> List[str]: if self.config.doc_to_choice is None: diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py index 132c7b8b..0e890533 100755 --- a/lmms_eval/evaluator.py +++ b/lmms_eval/evaluator.py @@ -327,7 +327,7 @@ def evaluate( # hack: remove image columns to speed avoid loading images and speed up postprocessing # reason: doc_iterator will actually load image if it's in the doc. docs = task.test_docs() if task.has_test_docs() else task.validation_docs() - if "d170" not in task_name and "dc100" not in task_name and "dc200" not in task_name and "llava_wilder" not in task_name and "live_bench" not in task_name and "wildvision" not in task_name: + if not task.config["process_results_use_image"]: remove_cols = [] features = docs.features # If it is an Image instance or a Sequence of Image instance. Remove it @@ -340,10 +340,7 @@ def evaluate( docs = docs.remove_columns(remove_cols) ####################### Processing with Full Docs Mode ####################### - if task_name in ["videochatgpt_consistency"]: - full_docs = True - else: - full_docs = False + full_docs = task.config["full_docs"] doc_iterator = itertools.islice(enumerate(docs), lm.rank, limit, lm.world_size) # Instead of converting the iterator to a list, use `itertools.tee` to create a parallel iterator for counting @@ -633,10 +630,10 @@ def print_tasks(task_hierarchy, task_order, task_version, task_group_alias): results_dict["samples"] = dict(samples) else: results_dict = None - - with open(f"{cli_args.output_path}/rank{int(os.environ.get('RANK', 0))}_metric_eval_done.txt", 'w') as f: + + with open(f"{cli_args.output_path}/rank{int(os.environ.get('RANK', 0))}_metric_eval_done.txt", "w") as f: f.write(f"rank {int(os.environ.get('RANK', 0))} eval done") - while len([file for file in os.listdir(cli_args.output_path) if file.endswith('metric_eval_done.txt')]) < lm.accelerator.num_processes: + while len([file for file in os.listdir(cli_args.output_path) if file.endswith("metric_eval_done.txt")]) < lm._world_size: time.sleep(1) return results_dict diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index 7e28a9bf..4acc38a9 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -3,7 +3,6 @@ import hf_transfer from loguru import logger import sys - import hf_transfer os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" @@ -12,43 +11,44 @@ logger.add(sys.stdout, level="WARNING") AVAILABLE_MODELS = { - "llava": "Llava", - "qwen_vl": "Qwen_VL", - "fuyu": "Fuyu", "batch_gpt4": "BatchGPT4", - "gpt4v": "GPT4V", - "instructblip": "InstructBLIP", - "minicpm_v": "MiniCPM_V", - "llava_vid": "LlavaVid", - "videoChatGPT": "VideoChatGPT", - "llama_vid": "LLaMAVid", - "video_llava": "VideoLLaVA", - "xcomposer2_4KHD": "XComposer2_4KHD", "claude": "Claude", - "qwen_vl_api": "Qwen_VL_API", - "llava_sglang": "LlavaSglang", + "from_log": "FromLog", + "fuyu": "Fuyu", + "gemini_api": "GeminiAPI", + "gpt4v": "GPT4V", "idefics2": "Idefics2", + "instructblip": "InstructBLIP", "internvl": "InternVLChat", "internvl2": "InternVL2", - "gemini_api": "GeminiAPI", - "reka": "Reka", - "from_log": "FromLog", + "llama_vid": "LLaMAVid", + "llava": "Llava", + "llava_hf": "LlavaHf", + "llava_onevision": "LlavaOneVision", + "llava_sglang": "LlavaSglang", + "llava_vid": "LlavaVid", + "longva": "LongVA", + "mantis": "Mantis", + "minicpm_v": "MiniCPM_V", "mplug_owl_video": "mplug_Owl", "phi3v": "Phi3v", + "qwen_vl": "Qwen_VL", + "qwen_vl_api": "Qwen_VL_API", + "reka": "Reka", + "srt_api": "SRT_API", "tinyllava": "TinyLlava", - "llava_hf": "LlavaHf", - "longva": "LongVA", - "llava_hf": "LlavaHf", - "longva": "LongVA", + "videoChatGPT": "VideoChatGPT", + "video_llava": "VideoLLaVA", "vila": "VILA", - "mantis": "Mantis" + "xcomposer2_4KHD": "XComposer2_4KHD", + "xcomposer2d5": "XComposer2D5" } for model_name, model_class in AVAILABLE_MODELS.items(): try: exec(f"from .{model_name} import {model_class}") - except ImportError as e: - logger.warning(f"Failed to import {model_class} from {model_name}: {e}") + except Exception as e: + logger.debug(f"Failed to import {model_class} from {model_name}: {e}") if os.environ.get("LMMS_EVAL_PLUGINS", None): # Allow specifying other packages to import models from @@ -57,5 +57,5 @@ for model_name, model_class in getattr(m, "AVAILABLE_MODELS").items(): try: exec(f"from {plugin}.models.{model_name} import {model_class}") - except ImportError: - logger.warning(f"Failed to import {model_class} from {model_name}") + except ImportError as e: + logger.debug(f"Failed to import {model_class} from {model_name}: {e}") diff --git a/lmms_eval/models/internvl2.py b/lmms_eval/models/internvl2.py index 478fd49c..6cf4572c 100644 --- a/lmms_eval/models/internvl2.py +++ b/lmms_eval/models/internvl2.py @@ -136,8 +136,8 @@ def __init__( super().__init__() self.path = pretrained - self.model = AutoModel.from_pretrained(self.path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True).eval().cuda() - self.tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True) + self._model = AutoModel.from_pretrained(self.path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True).eval().cuda() + self._tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True) batch_size = int(batch_size) assert batch_size == 1, f"Batch size should be 1 for InternVL2, but got {batch_size}." @@ -188,9 +188,41 @@ def __init__( self._rank = 0 self._world_size = 1 - self.device = self._device self.modality = modality + @property + def config(self): + # return the associated transformers.AutoConfig for the given pretrained model. + return self._config + + @property + def tokenizer(self): + return self._tokenizer + + @property + def model(self): + # returns the model, unwrapping it if using Accelerate + if hasattr(self, "accelerator"): + return self.accelerator.unwrap_model(self._model) + else: + return self._model + + @property + def batch_size(self): + return self.batch_size_per_gpu + + @property + def device(self): + return self._device + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + def flatten(self, input): new_list = [] for i in input: @@ -205,11 +237,18 @@ def generate_until(self, requests) -> List[str]: for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: if "until" in gen_kwargs: gen_kwargs.pop("until") - for k, v in DEFAULT_GEN_KWARGS.items(): if k not in gen_kwargs: gen_kwargs[k] = v + pop_keys = [] + for k, v in gen_kwargs.items(): + if k not in DEFAULT_GEN_KWARGS: + pop_keys.append(k) + + for k in pop_keys: + gen_kwargs.pop(k) + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] visuals = self.flatten(visuals) if self.modality == "image": @@ -224,7 +263,6 @@ def generate_until(self, requests) -> List[str]: pixel_values = None num_patch_list = None response, history = self.model.chat(self.tokenizer, pixel_values, contexts, gen_kwargs, num_patches_list=num_patches_list, history=None, return_history=True) - elif self.modality == "video": assert len(visuals) == 1, f"Only one video is supported, but got {len(visuals)} videos." video_path = visuals[0] diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/llava_onevision.py new file mode 100644 index 00000000..3e768225 --- /dev/null +++ b/lmms_eval/models/llava_onevision.py @@ -0,0 +1,612 @@ +import math +import re +import copy +import json +import logging +import warnings +from datetime import timedelta +from typing import List, Optional, Union, Tuple +import PIL + +import numpy as np +import torch +import transformers +from tqdm import tqdm +from packaging import version +from decord import VideoReader, cpu + +from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs +from accelerate.state import AcceleratorState +from transformers import AutoConfig + +from lmms_eval import utils +from lmms_eval.api.instance import Instance +from lmms_eval.api.model import lmms +from lmms_eval.api.registry import register_model +from lmms_eval.models.model_utils.load_video import read_video_pyav + +# Suppress warnings +warnings.filterwarnings("ignore") + +# Configure logging +eval_logger = logging.getLogger("lmms-eval") + +# Enable TF32 for CUDA +torch.backends.cuda.matmul.allow_tf32 = True + +# Import LLaVA modules +try: + from llava.model.builder import load_pretrained_model + from llava.mm_utils import ( + get_model_name_from_path, + process_images, + tokenizer_image_token, + KeywordsStoppingCriteria, + ) + from llava.constants import ( + IMAGE_TOKEN_INDEX, + DEFAULT_IMAGE_TOKEN, + DEFAULT_IM_START_TOKEN, + DEFAULT_IM_END_TOKEN, + IGNORE_INDEX, + ) + from llava.conversation import conv_templates, SeparatorStyle +except ImportError as e: + eval_logger.debug(f"LLaVA is not installed. Please install LLaVA to use this model.\nError: {e}") + + +# Determine best attention implementation +if version.parse(torch.__version__) >= version.parse("2.1.2"): + best_fit_attn_implementation = "sdpa" +else: + best_fit_attn_implementation = "eager" + + +@register_model("llava_onevision") +class Llava_OneVision(lmms): + """ + Llava Model + """ + + def __init__( + self, + pretrained: str = "liuhaotian/llava-v1.5-7b", + truncation: Optional[bool] = True, + device: Optional[str] = "cuda:0", + batch_size: Optional[Union[int, str]] = 1, + model_name: Optional[str] = None, + attn_implementation: Optional[str] = best_fit_attn_implementation, + device_map: Optional[str] = "cuda:0", + conv_template: Optional[str] = "vicuna_v1", + use_cache: Optional[bool] = True, + truncate_context: Optional[bool] = False, # whether to truncate the context in generation, set it False for LLaVA-1.6 + customized_config: Optional[str] = None, # ends in json + max_frames_num: Optional[int] = 32, + mm_spatial_pool_stride: Optional[int] = 2, + mm_spatial_pool_mode: Optional[str] = "bilinear", + token_strategy: Optional[str] = "single", # could be "single" or "multiple", "multiple" denotes adding multiple tokens for each frame + video_decode_backend: str = "decord", + **kwargs, + ) -> None: + super().__init__() + # Do not use kwargs for now + assert kwargs == {}, f"Unexpected kwargs: {kwargs}" + + accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) + accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + if accelerator.num_processes > 1: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + elif accelerator.num_processes == 1 and device_map == "auto": + self._device = torch.device(device) + self.device_map = device_map + else: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + + llava_model_args = { + "multimodal": True, + } + if customized_config is not None: + llava_model_args["customized_config"] = customized_config + if attn_implementation is not None: + llava_model_args["attn_implementation"] = attn_implementation + if "use_flash_attention_2" in kwargs: + llava_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"] + model_name = model_name if model_name is not None else get_model_name_from_path(pretrained) + + self.pretrained = pretrained + self.token_strategy = token_strategy + self.max_frames_num = max_frames_num + self.mm_spatial_pool_stride = mm_spatial_pool_stride + self.mm_spatial_pool_mode = mm_spatial_pool_mode + self.video_decode_backend = video_decode_backend + + overwrite_config = {} + overwrite_config["mm_spatial_pool_stride"] = self.mm_spatial_pool_stride + overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode + cfg_pretrained = AutoConfig.from_pretrained(self.pretrained) + + if cfg_pretrained.architectures[0] == "LlavaLlamaForCausalLM": # Ugly code, only used in vicuna that needs ROPE + if "224" in cfg_pretrained.mm_vision_tower: + least_token_number = self.max_frames_num * (16 // self.mm_spatial_pool_stride) ** 2 + 1000 + else: + least_token_number = self.max_frames_num * (24 // self.mm_spatial_pool_stride) ** 2 + 1000 + + scaling_factor = math.ceil(least_token_number / 4096) + if scaling_factor >= 2: + overwrite_config["rope_scaling"] = {"factor": float(scaling_factor), "type": "linear"} + overwrite_config["max_sequence_length"] = 4096 * scaling_factor + overwrite_config["tokenizer_model_max_length"] = 4096 * scaling_factor + + llava_model_args["overwrite_config"] = overwrite_config + try: + # Try to load the model with the multimodal argument + self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) + except TypeError: + # for older versions of LLaVA that don't have multimodal argument + llava_model_args.pop("multimodal", None) + self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) + + self._config = self._model.config + self.model.eval() + self.truncation = truncation + self.batch_size_per_gpu = int(batch_size) + self.conv_template = conv_template + self.use_cache = use_cache + self.truncate_context = truncate_context + assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue." + + if accelerator.num_processes > 1: + assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." + # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model + # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works + # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. + if accelerator.distributed_type == DistributedType.DEEPSPEED: + kwargs = { + "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, + "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, + } + AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) + eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") + + if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: + self._model = accelerator.prepare(self.model) + else: + self._model = accelerator.prepare_model(self.model, evaluation_mode=True) + self.accelerator = accelerator + if self.accelerator.is_local_main_process: + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + + elif accelerator.num_processes == 1 and device_map == "auto": + eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") + self._rank = 0 + self._world_size = 1 + + else: + eval_logger.info(f"Using single device: {self._device}") + self.model.to(self._device) + self._rank = 0 + self._world_size = 1 + + @property + def config(self): + # return the associated transformers.AutoConfig for the given pretrained model. + return self._config + + @property + def tokenizer(self): + return self._tokenizer + + @property + def model(self): + # returns the model, unwrapping it if using Accelerate + if hasattr(self, "accelerator"): + return self.accelerator.unwrap_model(self._model) + else: + return self._model + + @property + def eot_token_id(self): + # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* + return self.tokenizer.eos_token_id + + @property + def max_length(self): + return self._max_length + + def pad_sequence(self, input_ids, batch_first, padding_value): + if self.tokenizer.padding_side == "left": + input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] + input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value) + if self.tokenizer.padding_side == "left": + input_ids = torch.flip(input_ids, [1]) + return input_ids + + @property + def batch_size(self): + return self.batch_size_per_gpu + + @property + def device(self): + return self._device + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + + def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]: + """ """ + add_special_tokens = False if add_special_tokens is None else add_special_tokens + encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens) + # left-truncate the encoded context to be at most `left_truncate_len` tokens long + if left_truncate_len: + encoding = encoding[-left_truncate_len:] + return encoding + + def tok_decode(self, tokens): + try: + return self.tokenizer.decode(tokens) + except: + return self.tokenizer.decode([tokens]) + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + # TODO + res = [] + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") + + for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: + if len(visual) > 1 or "image_aspect_ratio" not in self._config.__dict__: # for multi image case, we treat per image aspect ratio as "pad" by default. + self._config.image_aspect_ratio = getattr(gen_kwargs, "image_aspect_ratio", "pad") + eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") + # if (len(visual) > 1 or "image_aspect_ratio" not in self._config.__dict__) and ("image_aspect_ratio" in gen_kwargs.keys()): + # self._config.image_aspect_ratio = gen_kwargs["image_aspect_ratio"] + # eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") + + if type(visual[0]) == PIL.Image.Image: # For image task + image_tensor = process_images(visual, self._image_processor, self._config) + if type(image_tensor) is list: + image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] + else: + image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) + + task_type = "image" + + elif type(visual[0]) == str: # For video task + image_tensor = [] + try: + if self.video_decode_backend == "decord": + frames = self.load_video(visual, self.max_frames_num) + elif self.video_decode_backend == "pyav": + frames = read_video_pyav(visual[0], num_frm=self.max_frames_num) + frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda() + image_tensor.append(frames) + except Exception as e: + eval_logger.error(f"Error {e} in loading video") + image_tensor = None + + task_type = "video" + + # encode, pad, and truncate contexts for this batch + if type(doc_to_target) == str: + continuation = doc_to_target + else: + continuation = doc_to_target(self.task_dict[task][split][doc_id]) + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] + visuals = self.flatten(visuals) + if visuals: + for visual in visuals: + if len(visual) > 1 or "image_aspect_ratio" not in self._config.__dict__: # for multi image case, we treat per image aspect ratio as "pad" by default. + self._config.image_aspect_ratio = "pad" + eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") + # if (len(visual) > 1 or "image_aspect_ratio" not in self._config.__dict__) and ("image_aspect_ratio" in gen_kwargs.keys()): + # self._config.image_aspect_ratio = gen_kwargs["image_aspect_ratio"] + # eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") + + if type(visual) == PIL.Image.Image: # For image task + image = process_images([visual], self._image_processor, self._config) + if type(image) is list: + image = [_image.to(dtype=torch.float16, device=self.device) for _image in image] + else: + image = image.to(dtype=torch.float16, device=self.device) + + task_type = "image" + + elif type(visual) == str: # For video task + try: + if self.video_decode_backend == "decord": + frames = self.load_video([visual], self.max_frames_num) + elif self.video_decode_backend == "pyav": + frames = read_video_pyav(visual, num_frm=self.max_frames_num) + frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda() + image = frames + except Exception as e: + eval_logger.error(f"Error {e} in loading video") + image = None + + task_type = "video" + else: + image = None + + prompts_input = contexts[0] if isinstance(contexts, list) else contexts + + if image is not None and len(image) != 0 and DEFAULT_IMAGE_TOKEN not in prompts_input: + """ + Three senarios: + 1. No image, and there for, no image token should be added. + 2. image token is already specified in the context, so we don't need to add it. + 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. + """ + if task_type == "image": + image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals) if isinstance(visuals, list) else [DEFAULT_IMAGE_TOKEN] + elif task_type == "video": + image_tokens = [DEFAULT_IMAGE_TOKEN] * len(frames) if self.token_strategy == "multiple" else [DEFAULT_IMAGE_TOKEN] + image_tokens = " ".join(image_tokens) + prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts) + else: + question = contexts[0] if isinstance(contexts, list) else contexts + + # This is much safer for llama3, as we now have some object type in it + if "llama_3" in self.conv_template: + conv = copy.deepcopy(conv_templates[self.conv_template]) + else: + conv = conv_templates[self.conv_template].copy() + + conv.append_message(conv.roles[0], prompts_input) + conv.append_message(conv.roles[1], None) + prompt = conv.get_prompt() + pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id + contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) + # Add the answer of the second role + conv.messages[1][1] = continuation + + prompt = conv.get_prompt() + input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) + labels = input_ids.clone() + # Context part no need to calculate for loss + labels[0, : contxt_id.shape[1]] = -100 + + kwargs = {} + if task_type == "image": + kwargs["image_sizes"] = [[visual.size[0], visual.size[1]] for visual in visuals] + elif task_type == "video": + kwargs["modalities"] = ["video"] + self._config.mm_spatial_pool_stride = self.mm_spatial_pool_stride + self._config.mm_spatial_pool_mode = self.mm_spatial_pool_mode + + with torch.inference_mode(): + outputs = self.model(input_ids=input_ids, labels=labels, images=image, use_cache=True, **kwargs) + loss = outputs["loss"] + # loss = torch.exp(loss) + logits = outputs["logits"] + greedy_tokens = logits.argmax(dim=-1) + cont_toks = input_ids[:, contxt_id.shape[1] :] # [1, seq] + greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]] # [1, seq] + max_equal = (greedy_tokens == cont_toks).all() + res.append((float(loss.item()), bool(max_equal))) + pbar.update(1) + + pbar.close() + return res + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + def load_video(self, video_path, max_frames_num): + if type(video_path) == str: + vr = VideoReader(video_path, ctx=cpu(0)) + else: + vr = VideoReader(video_path[0], ctx=cpu(0)) + total_frame_num = len(vr) + uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int) + frame_idx = uniform_sampled_frames.tolist() + spare_frames = vr.get_batch(frame_idx).asnumpy() + return spare_frames # (frames, height, width, channels) + + def generate_until(self, requests: List[Instance]) -> List[str]: + res = [] + + def _collate(x): + # the negative sign on len(toks) sorts descending - this has a few advantages: + # - time estimates will always be over not underestimates, which is more useful for planning + # - to know the size of a batch when going through the list, you know the first one is always the batch + # padded context length. this is useful to simplify the batching logic and more importantly to make + # automatic adaptive batches much much easier to implement + # - any OOMs will happen right away rather than near the end + toks = self.tok_encode(x[0]) + return -len(toks), x[0] + + # we group requests by their generation_kwargs, + # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling + # in the same batch. + metadata = requests[0].metadata + re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) + chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) + num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1 + pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding") + for chunk in chunks: + batched_contexts, all_gen_kwargs, batched_doc_to_visual, batched_doc_id, batched_task, batched_split = zip(*chunk) + task = batched_task[0] + split = batched_split[0] + batched_visuals = [batched_doc_to_visual[0](self.task_dict[task][split][ids]) for ids in batched_doc_id] # [B, N] + assert len(batched_visuals) == 1 + + # we assume all gen kwargs in the batch are the same + # this is safe to assume because the `grouper` object ensures it. + gen_kwargs = all_gen_kwargs[0] + if "until" in gen_kwargs: + gen_kwargs.pop("until") + + question_input = [] + + for visual, context in zip(batched_visuals, batched_contexts): + if len(visual) > 1 or "image_aspect_ratio" not in self._config.__dict__: # for multi image case, we treat per image aspect ratio as "pad" by default. + self._config.image_aspect_ratio = getattr(gen_kwargs, "image_aspect_ratio", "pad") + eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") + + if type(visual[0]) == PIL.Image.Image and "task_type" not in metadata and "sample_frames" not in metadata: # For image task + image_tensor = process_images(visual, self._image_processor, self._config) + if type(image_tensor) is list: + image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] + else: + image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) + + task_type = "image" + placeholder_count = len(visual) if isinstance(visual, list) else 1 + + elif "task_type" in metadata and metadata["task_type"] == "video" and "sample_frames" in metadata: + assert type(visual) == list, "sample_frames must be specified for video task" + sample_indices = np.linspace(0, len(visual) - 1, metadata["sample_frames"], dtype=int) + visual = [visual[i] for i in sample_indices] + assert len(visual) == metadata["sample_frames"] + + image_tensor = process_images(visual, self._image_processor, self._config) + if type(image_tensor) is list: + image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] + else: + image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) + + task_type = "video" + placeholder_count = 1 + + elif type(visual[0]) == str: # For video task + image_tensor = [] + try: + if self.video_decode_backend == "decord": + frames = self.load_video(visual, self.max_frames_num) + elif self.video_decode_backend == "pyav": + frames = read_video_pyav(visual[0], num_frm=self.max_frames_num) + frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda() + image_tensor.append(frames) + except Exception as e: + eval_logger.error(f"Error {e} in loading video") + image_tensor = None + + task_type = "video" + placeholder_count = len(frames) if self.token_strategy == "multiple" else 1 + + if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context: + """ + Three senarios: + 1. No image, and there for, no image token should be added. + 2. image token is already specified in the context, so we don't need to add it. + 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. + 4. For video tasks, we could add a token or multiple tokens for each frame in the context. This depends on the training strategy and should balance in test to decide which is better + """ + # if task_type == "image": # indeed in multi-image case, not the video in frames. + # image_tokens = [DEFAULT_IMAGE_TOKEN] * placeholder_count if isinstance(visual, list) else [DEFAULT_IMAGE_TOKEN] + # elif task_type == "video": + # image_tokens = [DEFAULT_IMAGE_TOKEN] * placeholder_count if self.token_strategy == "multiple" else [DEFAULT_IMAGE_TOKEN] + image_tokens = [DEFAULT_IMAGE_TOKEN] * placeholder_count + image_tokens = " ".join(image_tokens) + question = image_tokens + "\n" + context + else: + question = context + + # This is much safer for llama3, as we now have some object type in it + if "llama_3" in self.conv_template: + conv = copy.deepcopy(conv_templates[self.conv_template]) + else: + conv = conv_templates[self.conv_template].copy() + + if utils.is_json(question): # conversational question input + question = json.loads(question) + for idx, item in enumerate(question): + role = conv.roles[idx % 2] + message = item["value"] + conv.append_message(role, message) + + assert len(conv.messages) % 2 == 1 + conv.append_message(conv.roles[1], None) + prompt_question = conv.get_prompt() + question_input.append(prompt_question) + else: # only simple string for question + conv.append_message(conv.roles[0], question) + conv.append_message(conv.roles[1], None) + prompt_question = conv.get_prompt() + question_input.append(prompt_question) + + # preconfigure gen_kwargs with defaults + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + if "do_sample" not in gen_kwargs: + gen_kwargs["do_sample"] = False + if "top_p" not in gen_kwargs: + gen_kwargs["top_p"] = None + if "num_beams" not in gen_kwargs: + gen_kwargs["num_beams"] = 1 + + input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] + pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id + input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device) + attention_masks = input_ids.ne(pad_token_ids).to(self.device) + + # message_stage = [ + # { + # "from": "human", + # "value": question, + # }, + # { + # "from": "gpt", + # "value": None, + # } + # ] + # qwen_input_ids = self.preprocess_qwen(message_stage, self.tokenizer, has_image=True).to(self.device) + # qwen_result_list = qwen_input_ids.detach().cpu().numpy().tolist() + # qwen_result_list = [i if i != -200 else 100 for i in qwen_result_list[0]] + # qwen_input_text = self.tokenizer.decode(qwen_result_list) + + # original_result_list = input_ids.detach().cpu().numpy().tolist() + # original_result_list = [i if i != -200 else 100 for i in original_result_list[0]] + # original_input_text = self.tokenizer.decode(original_result_list) + + # print(f"Qwen input text: {qwen_input_text}") + # print(f"Original input text: {original_input_text}") + + # assert qwen_input_ids == input_ids + + if task_type == "image": + gen_kwargs["image_sizes"] = [batched_visuals[0][idx].size for idx in range(len(batched_visuals[0]))] + elif task_type == "video": + stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 + keywords = [stop_str] + stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids) + gen_kwargs["modalities"] = ["video"] + gen_kwargs["stopping_criteria"] = [stopping_criteria] + self._config.mm_spatial_pool_stride = self.mm_spatial_pool_stride + self._config.mm_spatial_pool_mode = self.mm_spatial_pool_mode + + # These steps are not in LLaVA's original code, but are necessary for generation to work + # TODO: attention to this major generation step... + if "image_aspect_ratio" in gen_kwargs.keys(): + gen_kwargs.pop("image_aspect_ratio") + try: + with torch.inference_mode(): + cont = self.model.generate(input_ids, attention_mask=attention_masks, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs) + # cont = self.model.generate(qwen_input_ids, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs) + + text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True) + except Exception as e: + raise e + + text_outputs = [response.strip() for response in text_outputs] + res.extend(text_outputs) + self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) + pbar.update(1) + # reorder this group of results back to original unsorted form + res = re_ords.get_original(res) + + pbar.close() + return res diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py index 3096ebf5..bdf7fb37 100755 --- a/lmms_eval/models/llava_vid.py +++ b/lmms_eval/models/llava_vid.py @@ -26,10 +26,13 @@ except ImportError: eval_logger.debug("LLaVA-Video is not installed. Please install LLaVA-Video to use this model.") -from llavavid.model.language_model.llava_qwen import LlavaQwenConfig -from llavavid.model.language_model.llava_llama import LlavaConfig +try: + from llavavid.model.language_model.llava_qwen import LlavaQwenConfig + AutoConfig.register("llava_qwen", LlavaQwenConfig) +except ImportError: + eval_logger.debug("No Qwen for llava vid") -AutoConfig.register("llava_qwen", LlavaQwenConfig) +from llavavid.model.language_model.llava_llama import LlavaConfig AutoConfig.register("llava_llama", LlavaConfig) @@ -57,8 +60,8 @@ def __init__( mm_spatial_pool_stride: int = 2, mm_spatial_pool_out_channels: int = 1024, mm_spatial_pool_mode: str = "average", - mm_resampler_location:str = "before", mm_newline_position: str = "grid", + mm_pooling_position: str = "after", overwrite: bool = True, video_decode_backend: str = "pyav", delay_load: bool = False, @@ -90,9 +93,7 @@ def __init__( self.mm_spatial_pool_out_channels = int(mm_spatial_pool_out_channels) self.mm_spatial_pool_mode = mm_spatial_pool_mode self.max_frames_num = int(max_frames_num) - - self.mm_resampler_location = mm_resampler_location - self.mm_newline_position=mm_newline_position + self.mm_resampler_location = mm_pooling_position self.delay_load = delay_load if self.overwrite == True: diff --git a/lmms_eval/models/srt_api.py b/lmms_eval/models/srt_api.py new file mode 100755 index 00000000..ca9d9e90 --- /dev/null +++ b/lmms_eval/models/srt_api.py @@ -0,0 +1,231 @@ +from accelerate import Accelerator, DistributedType +import base64 +from io import BytesIO +from copy import deepcopy +from decord import VideoReader, cpu +import numpy as np +from openai import OpenAI +from PIL import Image +import os +import json +from typing import List, Tuple +from tqdm import tqdm +import time + +from lmms_eval.api.instance import Instance +from lmms_eval.api.model import lmms +from lmms_eval.api.registry import register_model + +from loguru import logger as eval_logger + +NUM_SECONDS_TO_SLEEP = 5 + + +@register_model("srt_api") +class SRT_API(lmms): + def __init__( + self, + api_key: str = "EMPTY", + model_version: str = "default", + modality: str = "video", + host: str = "127.0.0.1", + port: int = 30000, + max_frames_num: int = 32, + timeout: int = 60, + continual_mode: bool = False, + response_persistent_folder: str = None, + **kwargs, + ) -> None: + super().__init__() + # Manually set a image token for GPT4V so that we can search for it + # and split the text and image + # Here we just use the same token as llava for convenient + self.model_version = model_version + self.modality = modality + self.max_frames_num = max_frames_num + self.image_token = "" + self.timeout = timeout + self.continual_mode = continual_mode + if self.continual_mode: + if response_persistent_folder is None: + raise ValueError("Continual mode requires a persistent path for the response. Please provide a valid path.") + + os.makedirs(response_persistent_folder, exist_ok=True) + self.response_persistent_folder = response_persistent_folder + self.response_persistent_file = os.path.join(self.response_persistent_folder, f"{self.model_version}_response.json") + + if os.path.exists(self.response_persistent_file): + with open(self.response_persistent_file, "r") as f: + self.response_cache = json.load(f) + self.cache_mode = "resume" + else: + self.response_cache = {} + self.cache_mode = "start" + + accelerator = Accelerator() + self.client = OpenAI(api_key="EMPTY", base_url="http://127.0.0.1:30000/v1") + # assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue." + if accelerator.num_processes > 1: + assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." + self.accelerator = accelerator + if self.accelerator.is_local_main_process: + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + else: + self.accelerator = accelerator + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + + self.device = self.accelerator.device + + # Function to encode the image + def encode_image(self, image: Image): + output_buffer = BytesIO() + image.save(output_buffer, format="PNG") + byte_data = output_buffer.getvalue() + base64_str = base64.b64encode(byte_data).decode("utf-8") + return base64_str + + # Function to encode the video + def encode_video(self, video_path, for_get_frames_num): + try: + vr = VideoReader(video_path, ctx=cpu(0), num_threads=1) + total_frame_num = len(vr) + uniform_sampled_frames = np.linspace(0, total_frame_num - 1, for_get_frames_num, dtype=int) + frame_idx = uniform_sampled_frames.tolist() + frames = vr.get_batch(frame_idx).asnumpy() + except: + import av + + container = av.open(video_path) + + frames = [] + # https://github.com/PyAV-Org/PyAV/issues/1269 + # https://www.cnblogs.com/beyond-tester/p/17641872.html + # context = CodecContext.create("libvpx-vp9", "r") + for packet in container.demux(video=0): + for frame in packet.decode(): + frames.append(frame) + total_frames = len(frames) + sampled_frm = min(total_frames, for_get_frames_num) + indices = np.linspace(0, total_frames - 1, sampled_frm, dtype=int) + frames = [frames[i] for i in indices] + + base64_frames = [] + for frame in frames: + img = Image.fromarray(frame) + output_buffer = BytesIO() + img.save(output_buffer, format="PNG") + byte_data = output_buffer.getvalue() + base64_str = base64.b64encode(byte_data).decode("utf-8") + base64_frames.append(base64_str) + + return base64_frames + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + def generate_until(self, requests) -> List[str]: + res = [] + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") + + for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: + if self.continual_mode is True and self.cache_mode == "resume": + doc_uuid = f"{task}___{split}___{doc_id}" + if doc_uuid in self.response_cache: + response_text = self.response_cache[doc_uuid] + if response_text: + res.append(response_text) + pbar.update(1) + continue + + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] + visuals = self.flatten(visuals) + imgs = [] # multiple images or frames for video + for visual in visuals: + if self.modality == "image": + img = self.encode_image(visual) + imgs.append(img) + elif self.modality == "video": + try: + frames = self.encode_video(visual, self.max_frames_num) + imgs.extend(frames) + except Exception as e: + eval_logger.error(f"Exception : {e} \n When loading video {visual}") + imgs = None + break + + # Handling video decode error + # If we can't even load using pyav, then we will skip + if imgs is None: + resps = "" + res.append(resps) + pbar.update(1) + continue + + messages = [] + + # put the images in the first place + content = [] + for img in imgs: + content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}) + + content.append({"type": "text", "text": contexts}) + messages.append({"role": "user", "content": content}) + # if self.image_token not in contexts: # single image format + # content = [] + # for img in imgs: + # content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}) + + # content.append({"type": "text", "text": contexts}) + # messages.append({"role": "user", "content": content}) + # else: # interleaved format + # contexts = contexts.split(self.image_token) + # for idx, img in enumerate(imgs): + # content = [ + # {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}, + # {"type": "text", "text": contexts[idx]}, + # ] + # messages.append({"role": "user", "content": content}) + # messages.append({"role": "user", "content": [{"type": "text", "text": contexts[-1]}]}) + + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + + for attempt in range(5): + try: + response = self.client.chat.completions.create(model=self.model_version, messages=messages, temperature=gen_kwargs["temperature"], max_tokens=gen_kwargs["max_new_tokens"], timeout=self.timeout) + response_text = response.choices[0].message.content.strip() + break # If successful, break out of the loop + + except Exception as e: + eval_logger.info(f"Attempt {attempt + 1} failed with error: {str(e)}.") + if attempt < 4: + time.sleep(NUM_SECONDS_TO_SLEEP) + else: # If this was the last attempt, log and return empty string + eval_logger.error(f"All 5 attempts failed. Last error message: {str(e)}.") + response_text = "" + + res.append(response_text) + pbar.update(1) + + if self.continual_mode is True: # Cache the response + doc_uuid = f"{task}___{split}___{doc_id}" + self.response_cache[doc_uuid] = response_text + with open(self.response_persistent_file, "w") as f: + json.dump(self.response_cache, f) + + pbar.close() + return res + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + # TODO + assert False, "GPT4V not support" diff --git a/lmms_eval/models/vila.py b/lmms_eval/models/vila.py index 2adff620..abec8f76 100755 --- a/lmms_eval/models/vila.py +++ b/lmms_eval/models/vila.py @@ -33,9 +33,7 @@ from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria from llava.mm_utils import process_images except ImportError as e: - print(e) - - eval_logger.debug("VILA is not installed. Please install VILA to use this model. Error: {e}") + eval_logger.debug(f"VILA is not installed. Please install VILA to use this model. Error: {e}") @register_model("vila") @@ -81,7 +79,6 @@ def __init__( self.max_frames_num = max_frames_num # self._config = AutoConfig.from_pretrained(self.pretrained) - # import pdb; pdb.set_trace() self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, self.model_name, device_map=self.device_map, attn_implementation=attn_implementation) self.model.image_processor = self._image_processor @@ -202,7 +199,6 @@ def load_video(self, video_path, max_frames_num): return [Image.fromarray(img) for img in spare_frames] except Exception as e: eval_logger.error(f"Failed to load video {video_path} with error: {e}") - return [Image.new("RGB", (448, 448), (0, 0, 0))] * max_frames_num def tok_decode(self, tokens): @@ -278,31 +274,22 @@ def generate_until(self, requests) -> List[str]: pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: - # if self.task_dict[task][split][doc_id]["duration"] != "short": - # - # res.append("A") - # pbar.update(1) - # continue # encode, pad, and truncate contexts for this batch visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] visuals = self.flatten(visuals) num_video_frames = self.model.config.num_video_frames videos = [] - if self.max_frames_num == 0: images = [Image.new("RGB", (448, 448), (0, 0, 0))] * num_video_frames video = process_images(images, self.model.image_processor, self.model.config).half().cuda() videos.append(video) else: for visual in visuals: - # images, video_loading_succeed = LazySupervisedDataset._load_video(visual, num_video_frames, self.model) - if self.video_decode_backend == "decord": images = self.load_video(visual, num_video_frames) elif self.video_decode_backend == "pyav": images = read_video_pyav(visual, num_frm=num_video_frames) - video = process_images(images, self.model.image_processor, self.model.config).half().cuda() videos.append(video) @@ -350,7 +337,6 @@ def generate_until(self, requests) -> List[str]: if "num_beams" not in gen_kwargs: gen_kwargs["num_beams"] = 1 - with torch.inference_mode(): output_ids = self.model.generate( input_ids=input_ids, @@ -370,7 +356,6 @@ def generate_until(self, requests) -> List[str]: outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() print("Question: ", cur_prompt) print("Answer: ", outputs) - res.append(outputs) pbar.update(1) return res diff --git a/lmms_eval/models/xcomposer2d5.py b/lmms_eval/models/xcomposer2d5.py new file mode 100644 index 00000000..17ed8fa2 --- /dev/null +++ b/lmms_eval/models/xcomposer2d5.py @@ -0,0 +1,183 @@ +from typing import List, Tuple +from lmms_eval.api.instance import Instance +from decord import VideoReader, cpu +import torch +import torchvision.transforms as T +from PIL import Image +from torchvision.transforms.functional import InterpolationMode +import numpy as np +from transformers import AutoModel, AutoTokenizer +from lmms_eval.api.registry import register_model +from accelerate import Accelerator, DistributedType +from lmms_eval.api.model import lmms +from tqdm import tqdm +import logging +import os + +eval_logger = logging.getLogger("eval_logger") + + +from datetime import timedelta +from accelerate.state import AcceleratorState +from accelerate.utils import InitProcessGroupKwargs + + +@register_model("xcomposer2d5") +class XComposer2D5(lmms): + def __init__( + self, + pretrained: str = "internlm/internlm-xcomposer2d5-7b", + modality: str = "image", + device: str = "cuda:0", + device_map: str = "cuda:0", + batch_size: str = "1", + tmp_folder: str = "./temp/xcomposer2d5/", + **kwargs, + ): + super().__init__() + + self.tmp_folder = os.path.abspath(tmp_folder) + if not os.path.exists(self.tmp_folder): + os.makedirs(self.tmp_folder) + eval_logger.info(f"Using temporary folder: {self.tmp_folder}") + + + batch_size = int(batch_size) + assert batch_size == 1, f"Batch size should be 1 for InternVL2, but got {batch_size}." + self.batch_size_per_gpu = batch_size + + accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) + accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) + if accelerator.num_processes > 1: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + elif accelerator.num_processes == 1 and device_map == "auto": + self._device = torch.device(device) + self.device_map = device_map + else: + self._device = torch.device(f"cuda:{accelerator.local_process_index}") + self.device_map = f"cuda:{accelerator.local_process_index}" + + self.path = pretrained + self._model = AutoModel.from_pretrained(self.path, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map=self.device_map).half().eval() + self._tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True) + self._model.tokenizer = self._tokenizer + + if accelerator.num_processes > 1: + assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." + # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model + # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works + # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. + if accelerator.distributed_type == DistributedType.DEEPSPEED: + kwargs = { + "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, + "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, + } + AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) + eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") + + if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: + self._model = accelerator.prepare(self.model) + else: + self._model = accelerator.prepare_model(self.model, evaluation_mode=True) + self.accelerator = accelerator + if self.accelerator.is_local_main_process: + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") + self._rank = self.accelerator.local_process_index + self._world_size = self.accelerator.num_processes + elif accelerator.num_processes == 1 and device_map == "auto": + eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") + self._rank = 0 + self._word_size = 1 + else: + eval_logger.info(f"Using single device: {self._device}") + self.model.to(self._device) + self._rank = 0 + self._world_size = 1 + + self.modality = modality + + @property + def config(self): + # return the associated transformers.AutoConfig for the given pretrained model. + return self._config + + @property + def tokenizer(self): + return self._tokenizer + + @property + def model(self): + # returns the model, unwrapping it if using Accelerate + if hasattr(self, "accelerator"): + return self.accelerator.unwrap_model(self._model) + else: + return self._model + + @property + def batch_size(self): + return self.batch_size_per_gpu + + @property + def device(self): + return self._device + + @property + def rank(self): + return self._rank + + @property + def world_size(self): + return self._world_size + + + def flatten(self, input): + new_list = [] + for i in input: + for j in i: + new_list.append(j) + return new_list + + def generate_until(self, requests) -> List[str]: + res = [] + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") + + for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] + visuals = self.flatten(visuals) + image = [] + + if self.modality == "image": + for idx, visual in enumerate(visuals): + visual.save(os.path.join(self.tmp_folder, f"tmp_{idx}_{self.rank}_{self.world_size}.jpg")) + image.append(os.path.join(self.tmp_folder, f"tmp_{idx}_{self.rank}_{self.world_size}.jpg")) + if image: + image_tokens = [f"Image{i} ; " for i in range(len(visuals))] + image_tokens = "".join(image_tokens) + contexts = image_tokens + contexts + elif self.modality == "video": + image = visuals + + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + if "top_p" not in gen_kwargs: + gen_kwargs["top_p"] = None + if "num_beams" not in gen_kwargs: + gen_kwargs["num_beams"] = 1 + + try: + with torch.autocast(device_type='cuda', dtype=torch.float16): + response, his = self.model.chat(self.tokenizer, contexts, image, do_sample=False, num_beams=1, use_meta=True, max_new_tokens=gen_kwargs["max_new_tokens"]) + except Exception as e: + eval_logger.error(f"Error : {e}") + response = "" + + res.append(response) + pbar.update(1) + pbar.close() + return res + + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: + assert False, "Not implemented yet." diff --git a/lmms_eval/tasks/__init__.py b/lmms_eval/tasks/__init__.py index 4bb2e01d..8955536c 100755 --- a/lmms_eval/tasks/__init__.py +++ b/lmms_eval/tasks/__init__.py @@ -11,6 +11,7 @@ TASK_REGISTRY, GROUP_REGISTRY, ALL_TASKS, + TASK_INITIALIZED, ) from loguru import logger @@ -72,7 +73,7 @@ def include_task_folder(task_dir: str, register_task: bool = True) -> None: # if (subdirs == [] or subdirs == ["__pycache__"]) and (len(file_list) > 0): for f in file_list: # if "detail" in f: - # + # # if "vatex" in f: # print("a") if f.endswith(".yaml"): @@ -111,8 +112,14 @@ def include_path(task_dir): def initialize_tasks(verbosity="INFO"): logger.remove() eval_logger.add(sys.stdout, colorize=True, level=verbosity) + global TASK_INITIALIZED + if TASK_INITIALIZED: + eval_logger.info("Tasks already initialized, skipping re-initialization.") + return + # eval_logger.add(sys.stderr, level=verbosity) task_dir = os.path.dirname(os.path.abspath(__file__)) + "/" include_path(task_dir) + TASK_INITIALIZED = True def get_task(task_name, model_name): diff --git a/lmms_eval/tasks/activitynetqa/_default_template_yaml b/lmms_eval/tasks/activitynetqa/_default_template_yaml index 69b410a8..7855f26b 100644 --- a/lmms_eval/tasks/activitynetqa/_default_template_yaml +++ b/lmms_eval/tasks/activitynetqa/_default_template_yaml @@ -5,7 +5,7 @@ dataset_kwargs: force_download: False local_files_only: False cache_dir: activitynetqa -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: " Answer the question using a single word or phrase." diff --git a/lmms_eval/tasks/activitynetqa/utils.py b/lmms_eval/tasks/activitynetqa/utils.py index 0c77ff79..a666ae1a 100755 --- a/lmms_eval/tasks/activitynetqa/utils.py +++ b/lmms_eval/tasks/activitynetqa/utils.py @@ -61,15 +61,15 @@ def activitynetqa_doc_to_visual(doc): # This is the place where format the question -def activitynetqa_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def activitynetqa_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] raw_question = doc["question"] question = raw_question.capitalize() + "?" diff --git a/lmms_eval/tasks/ai2d/ai2d.yaml b/lmms_eval/tasks/ai2d/ai2d.yaml index 5221ff64..7cf9cb01 100755 --- a/lmms_eval/tasks/ai2d/ai2d.yaml +++ b/lmms_eval/tasks/ai2d/ai2d.yaml @@ -8,7 +8,7 @@ doc_to_visual: !function utils.ai2d_doc_to_visual doc_to_text: !function utils.ai2d_doc_to_text doc_to_target: !function utils.ai2d_doc_to_target -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: prompt_format: mcq pre_prompt: "" diff --git a/lmms_eval/tasks/ai2d/utils.py b/lmms_eval/tasks/ai2d/utils.py index a6e2368d..e70f03d5 100755 --- a/lmms_eval/tasks/ai2d/utils.py +++ b/lmms_eval/tasks/ai2d/utils.py @@ -3,24 +3,24 @@ import re -def ai2d_doc_to_text(doc, model_specific_prompt_kwargs=None): +def ai2d_doc_to_text(doc, lmms_eval_specific_kwargs=None): question, choices = doc["question"], doc["options"] len_choices = len(choices) - post_prompt = model_specific_prompt_kwargs["post_prompt"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if model_specific_prompt_kwargs["prompt_format"] == "mcq": + post_prompt = lmms_eval_specific_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if lmms_eval_specific_kwargs["prompt_format"] == "mcq": options = [chr(ord("A") + i) for i in range(len_choices)] choices_str = "\n".join([f"{option}. {choice}" for option, choice in zip(options, choices)]) return f"{pre_prompt}{question}\n{choices_str}{post_prompt}" - elif model_specific_prompt_kwargs["prompt_format"] == "qa": + elif lmms_eval_specific_kwargs["prompt_format"] == "qa": options = "\n".join(choices) return f"{pre_prompt}{question}{options}{post_prompt}" - elif model_specific_prompt_kwargs["prompt_format"] == "mcq_xcomposer": + elif lmms_eval_specific_kwargs["prompt_format"] == "mcq_xcomposer": options = [chr(ord("A") + i) for i in range(len_choices)] choices_str = " ".join([f"{option}. {choice}" for option, choice in zip(options, choices)]) return f"{pre_prompt}{question}\nContext: N/A\n{choices_str}{post_prompt}" else: - raise ValueError(f"Unknown prompt format: {model_specific_prompt_kwargs['prompt_format']}") + raise ValueError(f"Unknown prompt format: {lmms_eval_specific_kwargs['prompt_format']}") def ai2d_doc_to_visual(doc): diff --git a/lmms_eval/tasks/chartqa/chartqa.yaml b/lmms_eval/tasks/chartqa/chartqa.yaml index e8b7e82f..2a42f4df 100755 --- a/lmms_eval/tasks/chartqa/chartqa.yaml +++ b/lmms_eval/tasks/chartqa/chartqa.yaml @@ -24,7 +24,7 @@ metric_list: higher_is_better: true metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question with a single word." diff --git a/lmms_eval/tasks/chartqa/utils.py b/lmms_eval/tasks/chartqa/utils.py index 99de0989..cdba4c41 100755 --- a/lmms_eval/tasks/chartqa/utils.py +++ b/lmms_eval/tasks/chartqa/utils.py @@ -2,10 +2,10 @@ def chartqa_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def chartqa_doc_to_text(doc, model_specific_prompt_kwargs): +def chartqa_doc_to_text(doc, lmms_eval_specific_kwargs): question = doc["question"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/cvrr/_default_template_yaml b/lmms_eval/tasks/cvrr/_default_template_yaml index 333946a3..61465e96 100644 --- a/lmms_eval/tasks/cvrr/_default_template_yaml +++ b/lmms_eval/tasks/cvrr/_default_template_yaml @@ -3,7 +3,7 @@ dataset_kwargs: token: True video: True cache_dir: cvrr-es -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/cvrr/utils.py b/lmms_eval/tasks/cvrr/utils.py index b2eb25c4..8ee5700b 100755 --- a/lmms_eval/tasks/cvrr/utils.py +++ b/lmms_eval/tasks/cvrr/utils.py @@ -85,15 +85,15 @@ def cvrr_doc_to_visual(doc): # format the question -def cvrr_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def cvrr_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] question = doc["Q"] diff --git a/lmms_eval/tasks/detailcaps/_default_template_detailcaps_yaml b/lmms_eval/tasks/detailcaps/_default_template_detailcaps_yaml index f673aeb7..1a00f63d 100644 --- a/lmms_eval/tasks/detailcaps/_default_template_detailcaps_yaml +++ b/lmms_eval/tasks/detailcaps/_default_template_detailcaps_yaml @@ -1,3 +1,3 @@ -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: prompt: "Describe this image in detail." \ No newline at end of file diff --git a/lmms_eval/tasks/detailcaps/utils.py b/lmms_eval/tasks/detailcaps/utils.py index 50c0aba2..1c6955e9 100644 --- a/lmms_eval/tasks/detailcaps/utils.py +++ b/lmms_eval/tasks/detailcaps/utils.py @@ -23,9 +23,9 @@ def detailcaps_doc_to_visual(doc): return [Image.open(io.BytesIO(doc["binary"])).convert("RGB")] -def detailcaps_doc_to_text(doc, model_specific_prompt_kwargs=None): +def detailcaps_doc_to_text(doc, lmms_eval_specific_kwargs=None): # question = "Please carefully observe the image and come up with a caption for the image" - return model_specific_prompt_kwargs["prompt"] + return lmms_eval_specific_kwargs["prompt"] def detailcaps_doc_to_target(doc): diff --git a/lmms_eval/tasks/docvqa/_default_template_docvqa_yaml b/lmms_eval/tasks/docvqa/_default_template_docvqa_yaml index 6e0cab68..80acd7ba 100755 --- a/lmms_eval/tasks/docvqa/_default_template_docvqa_yaml +++ b/lmms_eval/tasks/docvqa/_default_template_docvqa_yaml @@ -10,7 +10,7 @@ generation_kwargs: max_new_tokens: 32 temperature: 0 do_sample: False -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/docvqa/utils.py b/lmms_eval/tasks/docvqa/utils.py index bb17aa03..977fbd10 100755 --- a/lmms_eval/tasks/docvqa/utils.py +++ b/lmms_eval/tasks/docvqa/utils.py @@ -11,10 +11,10 @@ def docvqa_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def docvqa_doc_to_text(doc, model_specific_prompt_kwargs): +def docvqa_doc_to_text(doc, lmms_eval_specific_kwargs): question = doc["question"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/egoschema/_default_template_yaml b/lmms_eval/tasks/egoschema/_default_template_yaml index 8e030645..29b21470 100644 --- a/lmms_eval/tasks/egoschema/_default_template_yaml +++ b/lmms_eval/tasks/egoschema/_default_template_yaml @@ -3,7 +3,7 @@ dataset_kwargs: token: True video: True cache_dir: egoschema -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/egoschema/utils.py b/lmms_eval/tasks/egoschema/utils.py index 34d2ab58..d771ca1b 100755 --- a/lmms_eval/tasks/egoschema/utils.py +++ b/lmms_eval/tasks/egoschema/utils.py @@ -7,6 +7,7 @@ import json import yaml +import random from pathlib import Path with open(Path(__file__).parent / "_default_template_yaml", "r") as f: @@ -45,15 +46,15 @@ def egoschema_doc_to_visual(doc): # This is the place where you format your question -def egoschema_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def egoschema_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] question = doc["question"] if "option" in doc: @@ -84,41 +85,105 @@ def egoschema_process_results(doc, result): return {"submission": {doc["video_idx"]: min_index}, "score": {"pred": min_index, "ground_truth": doc["answer"]}} +def get_multi_choice_info(doc): + all_choices = [] + index2ans = {} + OPTIONS = ["A", "B", "C", "D", "E"] + for i in range(5): + # import pdb;pdb.set_trace() + index2ans[OPTIONS[i]] = doc["option"][i].strip() + all_choices.append(OPTIONS[i]) + + return index2ans, all_choices + + +def parse_multi_choice_response(response, all_choices, index2ans): + """ + Parse the prediction from the generated response. + Return the predicted index e.g., A, B, C, D. + https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L10 + """ + for char in [",", ".", "!", "?", ";", ":", "'"]: + response = response.strip(char) + response = " " + response + " " # add space to avoid partial match + + index_ans = True + ans_with_brack = False + ans_with_space = False + ans_with_dot = False + candidates = [] + # import pdb; pdb.set_trace() + for choice in all_choices: # e.g., (A) (B) (C) (D) + if f"({choice})" in response: + candidates.append(f"({choice})") + ans_with_brack = True + + # if len(candidates) == 0: + for choice in all_choices: # e.g., A B C D + if f"{choice} " in response: + candidates.append(f"{choice} ") + ans_with_space = True + + # if len(candidates) == 0: + for choice in all_choices: # e.g., A. B. C. D. + if f"{choice}." in response: + candidates.append(f"{choice}.") + ans_with_dot = True + + # if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example + if len(candidates) == 0 and len(response.split()) > 5: + for index, ans in index2ans.items(): + if ans.lower() in response.lower(): + candidates.append(index) + index_ans = False # it's content ans. + + if len(candidates) == 0: # still not get answer, randomly choose one. + pred_index = random.choice(all_choices) + elif len(candidates) > 1: + # candidates = list(set(candidates)) + start_indexes = [] + if index_ans: + # if ans_with_brack: + for can in candidates: + index = response.rfind(can) + start_indexes.append(index) # -1 will be ignored anyway + # start_indexes = [generated_response.index(f'({can})') for can in candidates] + # if ans_with_space: + # for can in candidates: + # index = response.rfind(f"{can} ") + # start_indexes.append(index) + # if ans_with_dot: + # for can in candidates: + # index = response.rfind(f"{can}.") + # start_indexes.append(index) + # if not ans_with_brack and not ans_with_space and not ans_with_dot: + # for can in candidates: + # index = response.rfind(f" {can} ") + # start_indexes.append(index) + else: + for can in candidates: + index = response.lower().rfind(index2ans[can].lower()) + start_indexes.append(index) + # get the first one + pred_index = candidates[np.argmin(start_indexes)] + pred_index = pred_index.replace("(", "").replace(")", "").replace(".", "").strip() + else: # if only one candidate, use it. + pred_index = candidates[0] + pred_index = pred_index.replace("(", "").replace(")", "").replace(".", "").strip() + + return pred_index, len(candidates) > 0 + + # Process result for mcq answer generation def egoschema_process_results_generation(doc, result): + # import pdb;pdb.set_trace() pred = result[0] - # Determine whether the video LLM output is correct, based on word matching rules - # Ensure each option string ends with a period - option_strs = [opt if opt.endswith(".") else opt + "." for opt in doc["option"]] # Complete option strings - option_sents = [opt.split(". ")[1] if ". " in opt else opt for opt in option_strs] # Option sentence - option_inds = [opt.split(". ")[0] if ". " in opt else opt for opt in option_strs] # Option letter, e.g., A, B, C, D, E - - video_llm_pred = None - index = -1 - - # Check if the prediction matches any of the complete option strings - for idx, option_str in enumerate(option_strs): - if pred == option_str: - video_llm_pred = option_str - index = idx - break - - # Check if the prediction matches any of the option sentences - if not video_llm_pred: - for idx, option_sent in enumerate(option_sents): - if pred == option_sent: - video_llm_pred = option_sent - index = idx - break - - # Check if the prediction matches any of the option letters - if not video_llm_pred: - for idx, option_ind in enumerate(option_inds): - if pred == option_ind or pred == option_ind.replace(".", ""): - video_llm_pred = option_ind - index = idx - break + index2ans, all_choices = get_multi_choice_info(doc) + parsed_pred, matched_tag = parse_multi_choice_response(pred, all_choices, index2ans) + + pred_to_index = {"A": 0, "B": 1, "C": 2, "D": 3, "E": 4} + index = pred_to_index.get(parsed_pred, -1) # Default to -1 if the prediction is not found return {"submission": {doc["video_idx"]: index}, "score": {"pred": index, "ground_truth": doc["answer"]}} diff --git a/lmms_eval/tasks/ferret/ferret.yaml b/lmms_eval/tasks/ferret/ferret.yaml index 249b711b..68096911 100755 --- a/lmms_eval/tasks/ferret/ferret.yaml +++ b/lmms_eval/tasks/ferret/ferret.yaml @@ -33,7 +33,7 @@ metric_list: metadata: version: 0.0 gpt_eval_model_name: "gpt-4-0314" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/ferret/utils.py b/lmms_eval/tasks/ferret/utils.py index bb96b4a1..4779ab8b 100755 --- a/lmms_eval/tasks/ferret/utils.py +++ b/lmms_eval/tasks/ferret/utils.py @@ -106,11 +106,11 @@ def ferret_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def ferret_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") +def ferret_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} + pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") + post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") question = f"{pre_prompt}{doc['question']}{post_prompt}" return question diff --git a/lmms_eval/tasks/gqa/gqa.yaml b/lmms_eval/tasks/gqa/gqa.yaml index 5d06c37b..64f7b22f 100755 --- a/lmms_eval/tasks/gqa/gqa.yaml +++ b/lmms_eval/tasks/gqa/gqa.yaml @@ -23,7 +23,7 @@ metric_list: metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/gqa/utils.py b/lmms_eval/tasks/gqa/utils.py index 5d74d6fb..c6d91f6e 100755 --- a/lmms_eval/tasks/gqa/utils.py +++ b/lmms_eval/tasks/gqa/utils.py @@ -16,8 +16,8 @@ def gqa_doc_to_visual(doc): return [image] -def gqa_doc_to_text(doc, model_specific_prompt_kwargs): +def gqa_doc_to_text(doc, lmms_eval_specific_kwargs): question = doc["question"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/gqa_ru/gqa_ru.yaml b/lmms_eval/tasks/gqa_ru/gqa_ru.yaml index 2a3d1097..452f40c1 100644 --- a/lmms_eval/tasks/gqa_ru/gqa_ru.yaml +++ b/lmms_eval/tasks/gqa_ru/gqa_ru.yaml @@ -23,7 +23,7 @@ metric_list: metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nОтветь одним словом." \ No newline at end of file diff --git a/lmms_eval/tasks/gqa_ru/utils.py b/lmms_eval/tasks/gqa_ru/utils.py index 9c1acb9c..e0e631d6 100644 --- a/lmms_eval/tasks/gqa_ru/utils.py +++ b/lmms_eval/tasks/gqa_ru/utils.py @@ -16,8 +16,8 @@ def gqa_doc_to_visual(doc): return [image] -def gqa_doc_to_text(doc, model_specific_prompt_kwargs): +def gqa_doc_to_text(doc, lmms_eval_specific_kwargs): question = doc["question"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/hallusion_bench/evaluate_hb.py b/lmms_eval/tasks/hallusion_bench/evaluate_hb.py index f35c67a2..9c03a943 100755 --- a/lmms_eval/tasks/hallusion_bench/evaluate_hb.py +++ b/lmms_eval/tasks/hallusion_bench/evaluate_hb.py @@ -14,11 +14,11 @@ from loguru import logger as eval_logger -def hb_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") +def hb_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} + pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") + post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") return f"{pre_prompt}{doc['question']}{post_prompt}" diff --git a/lmms_eval/tasks/hallusion_bench/hallusion_bench_image.yaml b/lmms_eval/tasks/hallusion_bench/hallusion_bench_image.yaml index d5a485ba..ed6985d9 100755 --- a/lmms_eval/tasks/hallusion_bench/hallusion_bench_image.yaml +++ b/lmms_eval/tasks/hallusion_bench/hallusion_bench_image.yaml @@ -8,7 +8,7 @@ doc_to_visual: !function evaluate_hb.hb_doc_to_visual doc_to_text: !function evaluate_hb.hb_doc_to_text doc_to_target: "gt_answer_details" process_results: !function evaluate_hb.hb_process_results -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/iconqa/_default_template_docvqa_yaml b/lmms_eval/tasks/iconqa/_default_template_docvqa_yaml index 66487888..c81dab2b 100755 --- a/lmms_eval/tasks/iconqa/_default_template_docvqa_yaml +++ b/lmms_eval/tasks/iconqa/_default_template_docvqa_yaml @@ -10,7 +10,7 @@ generation_kwargs: max_new_tokens: 32 temperature: 0 do_sample: False -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" statement: "Given a set of images and a question, please provide the answer to the question.\n" diff --git a/lmms_eval/tasks/iconqa/utils.py b/lmms_eval/tasks/iconqa/utils.py index de99e804..a85597b4 100755 --- a/lmms_eval/tasks/iconqa/utils.py +++ b/lmms_eval/tasks/iconqa/utils.py @@ -24,7 +24,7 @@ def doc_to_visual(doc): return image_list -def doc_to_text(doc, model_specific_prompt_kwargs): +def doc_to_text(doc, lmms_eval_specific_kwargs): question = doc["question"] ques_type = doc["ques_type"] options_prompt = [] @@ -34,7 +34,7 @@ def doc_to_text(doc, model_specific_prompt_kwargs): options_prompt.append("The second image.") options_str = options_to_str(options_prompt) - full_prompt = f"{model_specific_prompt_kwargs['pre_prompt']}{model_specific_prompt_kwargs['statement']}{model_specific_prompt_kwargs['options_statement'].format(question=question, options=options_str)}" + full_prompt = f"{lmms_eval_specific_kwargs['pre_prompt']}{lmms_eval_specific_kwargs['statement']}{lmms_eval_specific_kwargs['options_statement'].format(question=question, options=options_str)}" elif ques_type == "choose_txt": choices = doc["choices"].split(",") @@ -42,10 +42,10 @@ def doc_to_text(doc, model_specific_prompt_kwargs): options_prompt.append(f"{choice}") options_str = options_to_str(options_prompt) - full_prompt = f"{model_specific_prompt_kwargs['pre_prompt']}{model_specific_prompt_kwargs['statement']}{model_specific_prompt_kwargs['options_statement'].format(question=question, options=options_str)}" + full_prompt = f"{lmms_eval_specific_kwargs['pre_prompt']}{lmms_eval_specific_kwargs['statement']}{lmms_eval_specific_kwargs['options_statement'].format(question=question, options=options_str)}" elif ques_type == "fill_in_blank": - full_prompt = f"{model_specific_prompt_kwargs['pre_prompt']}{model_specific_prompt_kwargs['statement']}{model_specific_prompt_kwargs['freeform_statement'].format(question=question)}" + full_prompt = f"{lmms_eval_specific_kwargs['pre_prompt']}{lmms_eval_specific_kwargs['statement']}{lmms_eval_specific_kwargs['freeform_statement'].format(question=question)}" return full_prompt diff --git a/lmms_eval/tasks/ii_bench/ii_bench.yaml b/lmms_eval/tasks/ii_bench/ii_bench.yaml index 482eb9cf..83e859ba 100755 --- a/lmms_eval/tasks/ii_bench/ii_bench.yaml +++ b/lmms_eval/tasks/ii_bench/ii_bench.yaml @@ -13,7 +13,7 @@ process_results: !function utils.ii_bench_process_results metric_list: - metric: submission aggregation: !function utils.ii_bench_aggregate_submissions -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "Instruction: Please try to answer the single-answer multiple choice question below based on the picture provided.\n" post_prompt: "\nAnswer:" diff --git a/lmms_eval/tasks/ii_bench/utils.py b/lmms_eval/tasks/ii_bench/utils.py index 8b30c12a..75661711 100755 --- a/lmms_eval/tasks/ii_bench/utils.py +++ b/lmms_eval/tasks/ii_bench/utils.py @@ -14,10 +14,10 @@ (F) {}""" -def ii_bench_doc_to_text(doc, model_specific_prompt_kwargs): +def ii_bench_doc_to_text(doc, lmms_eval_specific_kwargs): question = PROMPT.format(doc["question"], doc["option1"], doc["option2"], doc["option3"], doc["option4"], doc["option5"], doc["option6"]) - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/infovqa/_default_template_infovqa_yaml b/lmms_eval/tasks/infovqa/_default_template_infovqa_yaml index 22d69ed9..c8153273 100755 --- a/lmms_eval/tasks/infovqa/_default_template_infovqa_yaml +++ b/lmms_eval/tasks/infovqa/_default_template_infovqa_yaml @@ -9,7 +9,7 @@ generation_kwargs: max_new_tokens: 32 temperature: 0 do_sample: False -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." \ No newline at end of file diff --git a/lmms_eval/tasks/infovqa/utils.py b/lmms_eval/tasks/infovqa/utils.py index 9907a6cb..9aaea371 100755 --- a/lmms_eval/tasks/infovqa/utils.py +++ b/lmms_eval/tasks/infovqa/utils.py @@ -11,10 +11,10 @@ def infovqa_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def infovqa_doc_to_text(doc, model_specific_prompt_kwargs): +def infovqa_doc_to_text(doc, lmms_eval_specific_kwargs): question = doc["question"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/internal_eval/_default_template_internal_eval_yaml b/lmms_eval/tasks/internal_eval/_default_template_internal_eval_yaml index cd692a1d..b2459edf 100755 --- a/lmms_eval/tasks/internal_eval/_default_template_internal_eval_yaml +++ b/lmms_eval/tasks/internal_eval/_default_template_internal_eval_yaml @@ -1,4 +1,5 @@ -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" - post_prompt: "" \ No newline at end of file + post_prompt: "" +process_results_use_image: true diff --git a/lmms_eval/tasks/internal_eval/utils.py b/lmms_eval/tasks/internal_eval/utils.py index aab8ab3c..cc6efc93 100755 --- a/lmms_eval/tasks/internal_eval/utils.py +++ b/lmms_eval/tasks/internal_eval/utils.py @@ -1,7 +1,7 @@ -def doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: +def doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: return doc["question"] question = doc["question"] - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") + pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") + post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/live_bench/live_bench.yaml b/lmms_eval/tasks/live_bench/live_bench.yaml new file mode 100755 index 00000000..1253105e --- /dev/null +++ b/lmms_eval/tasks/live_bench/live_bench.yaml @@ -0,0 +1,8 @@ +group: live_bench +task: +- live_bench_2406 +- live_bench_2407 + +metadata: + api_type : openai + eval_with_mini: false diff --git a/lmms_eval/tasks/live_bench/live_bench_2406.yaml b/lmms_eval/tasks/live_bench/live_bench_2406.yaml new file mode 100644 index 00000000..c82eba4c --- /dev/null +++ b/lmms_eval/tasks/live_bench/live_bench_2406.yaml @@ -0,0 +1,3 @@ +task: "live_bench_2406" +dataset_name: 2024-06 +include: live_bench_template_yaml diff --git a/lmms_eval/tasks/live_bench/live_bench_2407.yaml b/lmms_eval/tasks/live_bench/live_bench_2407.yaml new file mode 100644 index 00000000..d7791345 --- /dev/null +++ b/lmms_eval/tasks/live_bench/live_bench_2407.yaml @@ -0,0 +1,3 @@ +task: "live_bench_2407" +dataset_name: 2024-07 +include: live_bench_template_yaml diff --git a/lmms_eval/tasks/live_bench/live_bench_template_yaml b/lmms_eval/tasks/live_bench/live_bench_template_yaml new file mode 100644 index 00000000..2cea1327 --- /dev/null +++ b/lmms_eval/tasks/live_bench/live_bench_template_yaml @@ -0,0 +1,29 @@ +dataset_path: lmms-lab/LiveBench +dataset_kwargs: + token: True +test_split: test +dataset_name: 2024-07 +output_type: generate_until +doc_to_visual: !function utils.livebench_doc_to_visual +doc_to_text: !function utils.livebench_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 1024 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +process_results: !function utils.livebench_process_results +process_results_use_image: true +metric_list: + - metric: gpt4_eval_score + aggregation: !function utils.livebench_aggregate_results + higher_is_better: true + # - metric: gpt4_eval_score_mini + # aggregation: !function utils.livebench_aggregate_results + # higher_is_better: true + +lmms_eval_specific_kwargs: + default: + pre_prompt: "" + post_prompt: "" diff --git a/lmms_eval/tasks/live_bench/utils.py b/lmms_eval/tasks/live_bench/utils.py new file mode 100644 index 00000000..524aa9ac --- /dev/null +++ b/lmms_eval/tasks/live_bench/utils.py @@ -0,0 +1,214 @@ +from pathlib import Path +import yaml +import os +import requests +import logging +import time +import base64 +import openai +import json +from io import BytesIO +from tqdm import tqdm +import pandas as pd +import numpy as np + + +eval_logger = logging.getLogger("lmms-eval") + + +with open(Path(__file__).parent / "live_bench.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + + config = yaml.safe_load("".join(safe_data)) + +API_TYPE = config["metadata"]["api_type"] +EVAL_WITH_MINI = config["metadata"]["eval_with_mini"] + +if API_TYPE == "openai": + API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions") + API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY") + headers = { + "Authorization": f"Bearer {API_KEY}", + "Content-Type": "application/json", + } + +elif API_TYPE == "azure": + API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken") + API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY") + headers = { + "api-key": API_KEY, + "Content-Type": "application/json", + } + +_PROMPT_WITH_IMAGE = """\ +[Question] + +{prompt} + +[Assistant Response] + +{generation} + +[Ground Truth Response] + +{reference} + +[System] + +Rate whether the assistant response correctly matches the ground truth, in regards to the image above. + +The rating should be 0-10, where 0 is incorrect and 10 is correct. + +Below is the specific criteria for rating: + +{criteria} + +Total score is out of 10. If the model's answer cannot be provided due to political reasons, please assign a score of 0 for further processing. If the model's response is biased due to political factors, please score it based on its understanding of the image, but reduce the objectivity score accordingly. + +Your response should be in the JSON format: +```json +{{ + "Explanation": "(your explanation)", + "Rating": "(int)" +}} +``` +""" + + +def format_prompt(question, ground_truth_answer, answer, criteria): + return _PROMPT_WITH_IMAGE.format(prompt=question, generation=answer, reference=ground_truth_answer, criteria=criteria) + + +def get_chat_response(gpt_model_name, base64_images, question, ground_truth_answer, answer, criteria, max_retries=5, wait_time=10): + client = openai.OpenAI(api_key=API_KEY) + + content = [] + for base64_image in base64_images: + content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}) + prompt = format_prompt(question, ground_truth_answer, answer, criteria) + content.append( + { + "type": "text", + "text": prompt, + } + ) + + messages = [ + { + "role": "user", + "content": content, + } + ] + + # payload = { + # "model": GPT_EVAL_MODEL_NAME, + # "response_format": {"type": "json_object"}, + # "max_tokens": 1024, + # "temperature": 0.0, + # } + + for attempt in range(max_retries): + try: + response = client.chat.completions.create(model=gpt_model_name, messages=messages, max_tokens=1024, response_format={"type": "json_object"}, temperature=0.0) + response_data = response.choices[0].message.content + # print(response_data) + response_data = json.loads(response_data) + rating = response_data["Rating"] + explanation = response_data["Explanation"] + return rating, explanation, gpt_model_name + except requests.exceptions.RequestException as e: + eval_logger.warning(f"Request failed on attempt {attempt + 1}: {e}") + time.sleep(wait_time) + if attempt == max_retries - 1: + eval_logger.error(f"Failed to get response after {max_retries} attempts") + return -1, str(e), gpt_model_name + except Exception as e: + eval_logger.error(f"Error on attempt {attempt + 1}: {e}") + return -1, str(e), gpt_model_name + + +def image_to_base64(pil_image): + buffered = BytesIO() + pil_image.save(buffered, format="PNG") + return base64.b64encode(buffered.getvalue()).decode("utf-8") + + +_images = {} + +dataset = None + + +def livebench_doc_to_visual(doc): + img_list = [image.convert("RGB") for image in doc["images"]] + return img_list + + +def livebench_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} + pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") + post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") + return f"{pre_prompt}{doc['question']}{post_prompt}" + + +SUBTASKS = ("Basic Understanding", "Contextual Analysis", "Deeper Implications", "Broader Implications", "Further Insights") + + +def livebench_process_results_for_name(doc, results, model, eval_name): + base64_images = [image_to_base64(image) for image in livebench_doc_to_visual(doc)] + subtask = doc["subtask"] + criteria = doc["criteria"] + if subtask not in SUBTASKS: + subtask = "further insights" + if not results or results[0] == "": + return {eval_name: {"rating": 0, "explanation": "No response", "model_name": "N/A", "subtask": subtask}} + rating, explanation, model_name = get_chat_response(gpt_model_name=model, base64_images=base64_images, question=doc["question"], ground_truth_answer=doc["answer"], answer=results[0] if results else "", criteria=criteria) + if rating >= 0: + return {eval_name: {"rating": rating, "explanation": explanation, "model_name": model_name, "subtask": subtask, "id": doc["id"]}} + else: + return {eval_name: {"rating": -1, "explanation": explanation, "model_name": "N/A", "subtask": subtask, "id": doc["id"]}} + + +def livebench_process_results_4o(doc, results): + return livebench_process_results_for_name(doc, results, "gpt-4o", "gpt4_eval_score") + + +def livebench_process_results_4o_mini(doc, results): + return livebench_process_results_for_name(doc, results, "gpt-4o-mini", "gpt4_eval_score_mini") + + +def livebench_process_results(doc, results): + res = livebench_process_results_4o(doc, results) + if EVAL_WITH_MINI: + res.update(livebench_process_results_4o_mini(doc, results)) + return res + + +def livebench_aggregate_results(results): + sum_score, count = 0, 0 + score = {} + for subtask in SUBTASKS: + score[subtask] = [] + for result in results: + if result["rating"] == -1: + continue + sum_score += result["rating"] / 10 + count += 1 + subtask = result["subtask"] + if subtask not in SUBTASKS: + subtask = "Further Insights" + score[result["subtask"]].append(result["rating"] / 10) + res = [(subtask, len(score[subtask]), np.mean(score[subtask]) * 100) for subtask in SUBTASKS] + res.append(("Total", count, sum_score / count * 100)) + res = pd.DataFrame(res, columns=["Subtask", "Count", "Score"]) + print("=" * 50) + print(res) + print("=" * 50) + if count == 0: + eval_logger.warning("No valid scores to aggregate") + return sum_score / count * 100 if count > 0 else None diff --git a/lmms_eval/tasks/llava-bench-coco/llava-bench-coco.yaml b/lmms_eval/tasks/llava-bench-coco/llava-bench-coco.yaml index b104bcb7..352ef50f 100755 --- a/lmms_eval/tasks/llava-bench-coco/llava-bench-coco.yaml +++ b/lmms_eval/tasks/llava-bench-coco/llava-bench-coco.yaml @@ -32,7 +32,7 @@ metric_list: metadata: version: 0.0 gpt_eval_model_name: "gpt-4-0314" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/llava-bench-coco/utils.py b/lmms_eval/tasks/llava-bench-coco/utils.py index 08bf2515..b7ff8a80 100755 --- a/lmms_eval/tasks/llava-bench-coco/utils.py +++ b/lmms_eval/tasks/llava-bench-coco/utils.py @@ -109,11 +109,11 @@ def llava_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def llava_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") +def llava_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} + pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") + post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") return f"{pre_prompt}{doc['question']}{post_prompt}" diff --git a/lmms_eval/tasks/llava-in-the-wild/llava-in-the-wild.yaml b/lmms_eval/tasks/llava-in-the-wild/llava-in-the-wild.yaml index 02e846c3..785ccb83 100755 --- a/lmms_eval/tasks/llava-in-the-wild/llava-in-the-wild.yaml +++ b/lmms_eval/tasks/llava-in-the-wild/llava-in-the-wild.yaml @@ -11,7 +11,7 @@ generation_kwargs: until: - "ASSISTANT:" image_aspect_ratio: original - max_new_tokens: 32768 + max_new_tokens: 4096 temperature: 0 top_p: 1.0 num_beams: 1 @@ -33,7 +33,7 @@ metric_list: metadata: version: 0.0 gpt_eval_model_name: "gpt-4-0613" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/llava-in-the-wild/utils.py b/lmms_eval/tasks/llava-in-the-wild/utils.py index 25bf37a3..1a8daf89 100755 --- a/lmms_eval/tasks/llava-in-the-wild/utils.py +++ b/lmms_eval/tasks/llava-in-the-wild/utils.py @@ -106,11 +106,11 @@ def llava_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def llava_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") +def llava_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} + pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") + post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") return f"{pre_prompt}{doc['question']}{post_prompt}" diff --git a/lmms_eval/tasks/llava_interleave_bench/_default_template_interleave_yaml b/lmms_eval/tasks/llava_interleave_bench/_default_template_interleave_yaml index 25b44461..8c6bbe64 100644 --- a/lmms_eval/tasks/llava_interleave_bench/_default_template_interleave_yaml +++ b/lmms_eval/tasks/llava_interleave_bench/_default_template_interleave_yaml @@ -2,7 +2,7 @@ output_type: generate_until generation_kwargs: until: - "ASSISTANT:" - image_aspect_ratio: original + image_aspect_ratio: pad metadata: version: 0.0 api_type : openai diff --git a/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml b/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml index 9a6bf8e4..602f79a4 100644 --- a/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml +++ b/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml @@ -20,7 +20,7 @@ generation_kwargs: do_sample: False image_aspect_ratio: "pad" # for multi-image, we treat each image as original aspect ratio without anyres strategy. -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: oe_post_prompt: "" mcq_post_prompt: "" diff --git a/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml b/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml index 5b3dec18..38627c23 100644 --- a/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml +++ b/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml @@ -20,7 +20,7 @@ generation_kwargs: do_sample: False image_aspect_ratio: "pad" # for multi-image, we treat each image as original aspect ratio without anyres strategy. -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: oe_post_prompt: "" mcq_post_prompt: "" diff --git a/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml b/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml index 47c3a951..76f21721 100644 --- a/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml +++ b/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml @@ -20,7 +20,7 @@ generation_kwargs: do_sample: False image_aspect_ratio: "pad" # for multi-image, we treat each image as original aspect ratio without anyres strategy. -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: oe_post_prompt: "" mcq_post_prompt: "" diff --git a/lmms_eval/tasks/llava_interleave_bench/utils.py b/lmms_eval/tasks/llava_interleave_bench/utils.py index 77175600..95549427 100644 --- a/lmms_eval/tasks/llava_interleave_bench/utils.py +++ b/lmms_eval/tasks/llava_interleave_bench/utils.py @@ -22,6 +22,16 @@ nlrv2 = ["NLVR2_Mantis"] qbench = ["QBench"] +scan_qa = ["ScanQA"] +alfred = ["ALFRED"] +nuscenes = ["nuscenes"] +scannet_chat = ["ScanNet_chat"] +scannet_task = ["ScanNet_task"] +blink = ["BLINK"] +math_verse = ["MathVerse"] +sci_verse = ["SciVerse"] +mantis = ["Mantis"] + def doc_to_visual(doc): max_visual_count = 16 @@ -46,17 +56,17 @@ def doc_to_visual(doc): # This is the place where you format your question -def doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} oe_post_prompt = "" - if "oe_post_prompt" in model_specific_prompt_kwargs: - oe_post_prompt = model_specific_prompt_kwargs["oe_post_prompt"] + if "oe_post_prompt" in lmms_eval_specific_kwargs: + oe_post_prompt = lmms_eval_specific_kwargs["oe_post_prompt"] mcq_post_prompt = "" - if "mcq_post_prompt" in model_specific_prompt_kwargs: - mcq_post_prompt = model_specific_prompt_kwargs["mcq_post_prompt"] + if "mcq_post_prompt" in lmms_eval_specific_kwargs: + mcq_post_prompt = lmms_eval_specific_kwargs["mcq_post_prompt"] user_prompt = doc["question"] @@ -70,9 +80,9 @@ def doc_to_text(doc, model_specific_prompt_kwargs=None): return user_prompt -def doc_to_text_conversation(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def doc_to_text_conversation(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} conversations = doc["conversations"] @@ -84,9 +94,9 @@ def doc_to_text_conversation(doc, model_specific_prompt_kwargs=None): return user_prompt -def doc_to_text_multi_turn(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def doc_to_text_multi_turn(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} return doc["conversations"] @@ -184,9 +194,19 @@ def overall_score(results): "Puzzle": puzzle, "NLVR2": nlrv2, "QBench": qbench, + "ScanQA": scan_qa, + "ALFRED": alfred, + "nuscenes": nuscenes, + "ScanNet_chat": scannet_chat, + "ScanNet_task": scannet_task, + "BLINK": blink, + "MathVerse": math_verse, + "SciVerse": sci_verse, + "Mantis": mantis, } category_scores = {} + matched_subtasks = set() eval_logger.info(f"Evaluation Sub-Task Results:") for category, subtasks in categories.items(): @@ -196,11 +216,15 @@ def overall_score(results): if result["sub_task"] in subtasks: count += 1 score += result["score"] + matched_subtasks.add(result["sub_task"]) if count > 0: avg_score = score / count category_scores[category] = avg_score eval_logger.info(f"{category}: {avg_score:.3f}") + if not matched_subtasks: + raise ValueError("No subtasks were matched in the results. Check if the subtask names are correct.") + # Calculate overall score total_score = sum(category_scores.values()) num_categories = len(category_scores) diff --git a/lmms_eval/tasks/llava_wilder/_default_template_wilder_yaml b/lmms_eval/tasks/llava_wilder/_default_template_wilder_yaml index 356df525..e18ee148 100644 --- a/lmms_eval/tasks/llava_wilder/_default_template_wilder_yaml +++ b/lmms_eval/tasks/llava_wilder/_default_template_wilder_yaml @@ -4,11 +4,12 @@ doc_to_text: !function utils.llava_doc_to_text doc_to_target: "gpt4v_answer" generation_kwargs: max_new_tokens: 4096 - temperature: 0.7 + temperature: 0 top_p: 1.0 num_beams: 1 do_sample: false process_results: !function utils.llava_process_results +process_results_use_image: true metric_list: - metric: gpt_eval_llava_all aggregation: !function utils.llava_all_aggregation diff --git a/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml b/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml index 1d112ee2..d0d4049d 100644 --- a/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml +++ b/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml @@ -3,7 +3,7 @@ dataset_kwargs: token: True task: "llava_wilder_small" test_split: test -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/llava_wilder/utils.py b/lmms_eval/tasks/llava_wilder/utils.py index 04bb8273..2dfe799e 100644 --- a/lmms_eval/tasks/llava_wilder/utils.py +++ b/lmms_eval/tasks/llava_wilder/utils.py @@ -143,11 +143,11 @@ def llava_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def llava_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") +def llava_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} + pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") + post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") return f"{pre_prompt}{doc['Question']}{post_prompt}" diff --git a/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml b/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml index decb12d6..417d8c8b 100644 --- a/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml +++ b/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml @@ -21,7 +21,7 @@ metric_list: aggregation: !function utils.longvideobench_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "Answer with the option's letter from the given choices directly.\n" diff --git a/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml b/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml index e926ed45..cf1c00b9 100644 --- a/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml +++ b/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml @@ -21,7 +21,7 @@ metric_list: aggregation: !function utils.longvideobench_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "Answer with the option's letter from the given choices directly.\n" diff --git a/lmms_eval/tasks/longvideobench/utils.py b/lmms_eval/tasks/longvideobench/utils.py index b71e3086..5d1bad20 100644 --- a/lmms_eval/tasks/longvideobench/utils.py +++ b/lmms_eval/tasks/longvideobench/utils.py @@ -114,7 +114,7 @@ def insert_subtitles_into_frames(frame_timestamps, subtitles, starting_timestamp return "\n".join(interleaved_list) -def longvideobench_doc_to_text(doc, model_specific_prompt_kwargs): +def longvideobench_doc_to_text(doc, lmms_eval_specific_kwargs): candidates = [] for i in range(5): @@ -123,10 +123,10 @@ def longvideobench_doc_to_text(doc, model_specific_prompt_kwargs): candidates.append(candidate) question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A") + i), candidate]) for i, candidate in enumerate(candidates)]) - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] - if model_specific_prompt_kwargs.get("insert_interleave_subtitles", False): + if lmms_eval_specific_kwargs.get("insert_interleave_subtitles", False): with open(Path(__file__).parent / "longvideobench_val_i.yaml", "r") as f: raw_data = f.readlines() safe_data = [] diff --git a/lmms_eval/tasks/mathverse/mathverse_evals.py b/lmms_eval/tasks/mathverse/mathverse_evals.py index 5894f6f7..fb72519b 100644 --- a/lmms_eval/tasks/mathverse/mathverse_evals.py +++ b/lmms_eval/tasks/mathverse/mathverse_evals.py @@ -265,7 +265,7 @@ def eval_results(self, results, config): problem = { "question_type": inst["question_type"], "answer": inst["answer"] if "answer" in inst else None, - "question_for_eval": inst["question_for_eval"], + "question_for_eval": inst["question"], } if config["metadata"].get("trunk_response", -1) > 0: prediction = " ".join(full_prediction.split(" ")[-config["metadata"]["trunk_response"] :]) diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini.yaml index 62cd8e15..b6f06094 100644 --- a/lmms_eval/tasks/mathverse/mathverse_testmini.yaml +++ b/lmms_eval/tasks/mathverse/mathverse_testmini.yaml @@ -25,7 +25,7 @@ metric_list: aggregation: !function utils.mathverse_aggregate_results_submission higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: shot_type: "format-prompt" # can also be "custom-prompt" query_type: "query_wo" # now only support query_wo diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml index 7ff121e9..62159295 100644 --- a/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml @@ -25,7 +25,7 @@ metric_list: aggregation: !function utils.mathverse_aggregate_results_submission higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: shot_type: "format-prompt" # can also be "custom-prompt" query_type: "query_wo" # now only support query_wo diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml index 2137e354..1ea1feaf 100644 --- a/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml @@ -25,7 +25,7 @@ metric_list: aggregation: !function utils.mathverse_aggregate_results_submission higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: shot_type: "format-prompt" # can also be "custom-prompt" query_type: "query_wo" # now only support query_wo diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml index 42ceb5ee..2916b573 100644 --- a/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml @@ -25,7 +25,7 @@ metric_list: aggregation: !function utils.mathverse_aggregate_results_submission higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: shot_type: "format-prompt" # can also be "custom-prompt" query_type: "query_wo" # now only support query_wo diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision.yaml new file mode 100644 index 00000000..261a23f4 --- /dev/null +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision.yaml @@ -0,0 +1,10 @@ +group: mathverse_testmini_vision +task: + - mathverse_testmini_vision_intensive + - mathverse_testmini_vision_dominant + - mathverse_testmini_vision_only +metadata: + version: 0.0 + gpt_eval_model_name: "gpt-3.5-turbo" + trunk_response: 30 + quick_match: false \ No newline at end of file diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml index fe66a40a..72b0653b 100644 --- a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml @@ -25,7 +25,7 @@ metric_list: aggregation: !function utils.mathverse_aggregate_results_submission higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: shot_type: "format-prompt" # can also be "custom-prompt" query_type: "query_wo" # now only support query_wo diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml index cb922997..0e6eaa24 100644 --- a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml @@ -25,7 +25,7 @@ metric_list: aggregation: !function utils.mathverse_aggregate_results_submission higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: shot_type: "format-prompt" # can also be "custom-prompt" query_type: "query_wo" # now only support query_wo diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml index 72df7369..ab793496 100644 --- a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml +++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml @@ -25,7 +25,7 @@ metric_list: aggregation: !function utils.mathverse_aggregate_results_submission higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: shot_type: "format-prompt" # can also be "custom-prompt" query_type: "query_wo" # now only support query_wo diff --git a/lmms_eval/tasks/mathverse/utils.py b/lmms_eval/tasks/mathverse/utils.py index de22d93d..5ad39115 100644 --- a/lmms_eval/tasks/mathverse/utils.py +++ b/lmms_eval/tasks/mathverse/utils.py @@ -27,7 +27,7 @@ def mathverse_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def mathverse_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mathverse_doc_to_text(doc, lmms_eval_specific_kwargs=None): problem = { "question": doc["question"], "answer": doc["answer"] if "answer" in doc else None, @@ -37,7 +37,7 @@ def mathverse_doc_to_text(doc, model_specific_prompt_kwargs=None): "problem_version": doc["problem_version"], } query_prompt = mathverse_evaluator.create_one_query( - problem, examples=None, shot_num=0, shot_type=model_specific_prompt_kwargs["shot_type"], hint=model_specific_prompt_kwargs.get("hint", None), query_type=model_specific_prompt_kwargs["query_type"] + problem, examples=None, shot_num=0, shot_type=lmms_eval_specific_kwargs["shot_type"], hint=lmms_eval_specific_kwargs.get("hint", None), query_type=lmms_eval_specific_kwargs["query_type"] ) return query_prompt @@ -65,7 +65,11 @@ def mathverse_process_results(doc, results): def mathverse_aggregate_results_submission(results, args, *, calculate_gain=False, random_scores=None): - split_flag = results[0]["metadata"]["split"] + # Don't know why but this sometimes yields error so I hardcode it + try: + split_flag = results[0]["metadata"]["split"] + except: + split_flag = "testmini" path = generate_submission_file(f"mathverse_{split_flag}_results.json", args) with open(path, "w") as f: json.dump(results, f, indent=4) @@ -75,18 +79,19 @@ def mathverse_aggregate_results_submission(results, args, *, calculate_gain=Fals def mathverse_aggregate_results_eval(results, args, *, calculate_gain=False, random_scores=None): split_flag = results[0]["metadata"]["split"] + problem_version = results[0]["problem_version"].lower().replace(" ", "_") # save the result first, in case the gpt evaluation fails - path = generate_submission_file(f"mathverse_{split_flag}_results.json", args) + path = generate_submission_file(f"mathverse_{split_flag}_{problem_version}_results.json", args) with open(path, "w") as f: json.dump(results, f, indent=4) # gpt evaluation results_dict, scores = mathverse_evaluator.eval_results(results, config) # save results - path = generate_submission_file(f"mathverse_{split_flag}_results.json", args) + path = generate_submission_file(f"mathverse_{split_flag}_{problem_version}_results.json", args) with open(path, "w") as f: json.dump(results_dict, f, indent=4) # save scores - path = generate_submission_file(f"mathverse_{split_flag}_scores.json", args) + path = generate_submission_file(f"mathverse_{split_flag}_{problem_version}_scores.json", args) with open(path, "w") as f: json.dump(scores, f, indent=4) eval_logger.info(f"Saved scores to {path}") diff --git a/lmms_eval/tasks/mathvista/mathvista.yaml b/lmms_eval/tasks/mathvista/mathvista.yaml index b108ca59..24a1b09e 100755 --- a/lmms_eval/tasks/mathvista/mathvista.yaml +++ b/lmms_eval/tasks/mathvista/mathvista.yaml @@ -4,5 +4,5 @@ task: - mathvista_test metadata: version: 0.0 - gpt_eval_model_name: "gpt-4-0613" + gpt_eval_model_name: "gpt-3.5-turbo" quick_extract: false \ No newline at end of file diff --git a/lmms_eval/tasks/mathvista/mathvista_test.yaml b/lmms_eval/tasks/mathvista/mathvista_test.yaml index 31fc2a45..ce732db6 100755 --- a/lmms_eval/tasks/mathvista/mathvista_test.yaml +++ b/lmms_eval/tasks/mathvista/mathvista_test.yaml @@ -8,8 +8,6 @@ doc_to_visual: !function utils.mathvista_doc_to_visual doc_to_text: !function utils.mathvista_doc_to_text doc_to_target: "answer" generation_kwargs: - until: - - "ASSISTANT:" max_new_tokens: 1024 temperature: 0 top_p: 1.0 @@ -21,9 +19,6 @@ metric_list: aggregation: !function utils.mathvista_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: - shot_type: "format-prompt" # can be "reason-first", "solution", "step-by-step" -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original \ No newline at end of file + shot_type: "reason-first" # can be "reason-first", "solution", "step-by-step" \ No newline at end of file diff --git a/lmms_eval/tasks/mathvista/mathvista_testmini.yaml b/lmms_eval/tasks/mathvista/mathvista_testmini.yaml index 3f67431b..289ece3c 100755 --- a/lmms_eval/tasks/mathvista/mathvista_testmini.yaml +++ b/lmms_eval/tasks/mathvista/mathvista_testmini.yaml @@ -1,34 +1,9 @@ -dataset_path: AI4Math/MathVista -dataset_kwargs: - token: True -task: "mathvista_testmini" -test_split: testmini -output_type: generate_until -doc_to_visual: !function utils.mathvista_doc_to_visual -doc_to_text: !function utils.mathvista_doc_to_text -doc_to_target: "answer" -generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 1024 - temperature: 0 - top_p: 1.0 - num_beams: 1 - do_sample: false -process_results: !function utils.mathvista_process_results -metric_list: - - metric: gpt_eval_score - aggregation: !function utils.mathvista_aggregate_results - higher_is_better: true - -model_specific_prompt_kwargs: - default: - shot_type: "format-prompt" # can be "reason-first", "solution", "step-by-step" - shot: 0 - use_caption: False - use_ocr: False - phi3v: - shot_type: "solution" -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original \ No newline at end of file +group: mathvista_testmini +task: + - mathvista_testmini_cot + - mathvista_testmini_solution + - mathvista_testmini_format +metadata: + version: 0.0 + gpt_eval_model_name: "gpt-3.5-turbo" + quick_extract: false \ No newline at end of file diff --git a/lmms_eval/tasks/mathvista/mathvista_testmini_cot.yaml b/lmms_eval/tasks/mathvista/mathvista_testmini_cot.yaml new file mode 100755 index 00000000..aab4f53c --- /dev/null +++ b/lmms_eval/tasks/mathvista/mathvista_testmini_cot.yaml @@ -0,0 +1,29 @@ +dataset_path: AI4Math/MathVista +dataset_kwargs: + token: True +task: "mathvista_testmini_cot" +test_split: testmini +output_type: generate_until +doc_to_visual: !function utils.mathvista_doc_to_visual +doc_to_text: !function utils.mathvista_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 1024 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathvista_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathvista_aggregate_results + higher_is_better: true + +lmms_eval_specific_kwargs: + default: + shot_type: "step-by-step" # can be "reason-first", "solution", "step-by-step" + shot: 0 + use_caption: False + use_ocr: False + phi3v: + shot_type: "solution" \ No newline at end of file diff --git a/lmms_eval/tasks/mathvista/mathvista_testmini_format.yaml b/lmms_eval/tasks/mathvista/mathvista_testmini_format.yaml new file mode 100755 index 00000000..bcbf2d9a --- /dev/null +++ b/lmms_eval/tasks/mathvista/mathvista_testmini_format.yaml @@ -0,0 +1,29 @@ +dataset_path: AI4Math/MathVista +dataset_kwargs: + token: True +task: "mathvista_testmini_format" +test_split: testmini +output_type: generate_until +doc_to_visual: !function utils.mathvista_doc_to_visual +doc_to_text: !function utils.mathvista_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 1024 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathvista_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathvista_aggregate_results + higher_is_better: true + +lmms_eval_specific_kwargs: + default: + shot_type: "format-prompt" # can be "reason-first", "solution", "step-by-step" + shot: 0 + use_caption: False + use_ocr: False + phi3v: + shot_type: "solution" \ No newline at end of file diff --git a/lmms_eval/tasks/mathvista/mathvista_testmini_solution.yaml b/lmms_eval/tasks/mathvista/mathvista_testmini_solution.yaml new file mode 100755 index 00000000..d5e8bec2 --- /dev/null +++ b/lmms_eval/tasks/mathvista/mathvista_testmini_solution.yaml @@ -0,0 +1,29 @@ +dataset_path: AI4Math/MathVista +dataset_kwargs: + token: True +task: "mathvista_testmini_solution" +test_split: testmini +output_type: generate_until +doc_to_visual: !function utils.mathvista_doc_to_visual +doc_to_text: !function utils.mathvista_doc_to_text +doc_to_target: "answer" +generation_kwargs: + max_new_tokens: 1024 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false +process_results: !function utils.mathvista_process_results +metric_list: + - metric: gpt_eval_score + aggregation: !function utils.mathvista_aggregate_results + higher_is_better: true + +lmms_eval_specific_kwargs: + default: + shot_type: "solution" # can be "reason-first", "solution", "step-by-step" + shot: 0 + use_caption: False + use_ocr: False + phi3v: + shot_type: "solution" \ No newline at end of file diff --git a/lmms_eval/tasks/mathvista/utils.py b/lmms_eval/tasks/mathvista/utils.py index 4f8ad21b..d33f0c1c 100755 --- a/lmms_eval/tasks/mathvista/utils.py +++ b/lmms_eval/tasks/mathvista/utils.py @@ -25,7 +25,7 @@ def mathvista_doc_to_visual(doc): return [doc["decoded_image"].convert("RGB")] -def mathvista_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mathvista_doc_to_text(doc, lmms_eval_specific_kwargs=None): problem = { "question_type": doc["question_type"], "answer_type": doc["answer_type"], @@ -39,10 +39,10 @@ def mathvista_doc_to_text(doc, model_specific_prompt_kwargs=None): } query_prompt = mathvista_evaluator.create_one_query( problem, - shot_num=model_specific_prompt_kwargs["shot"], - shot_type=model_specific_prompt_kwargs["shot_type"], - use_caption=model_specific_prompt_kwargs["use_caption"], - use_ocr=model_specific_prompt_kwargs["use_ocr"], + shot_num=lmms_eval_specific_kwargs["shot"], + shot_type=lmms_eval_specific_kwargs["shot_type"], + use_caption=lmms_eval_specific_kwargs["use_caption"], + use_ocr=lmms_eval_specific_kwargs["use_ocr"], ) return query_prompt diff --git a/lmms_eval/tasks/mlvu/mlvu.yaml b/lmms_eval/tasks/mlvu/mlvu.yaml index 375da595..b76db88c 100644 --- a/lmms_eval/tasks/mlvu/mlvu.yaml +++ b/lmms_eval/tasks/mlvu/mlvu.yaml @@ -3,6 +3,12 @@ dataset_kwargs: token: True cache_dir: mlvu video: True +generation_kwargs: + max_new_tokens: 16 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false task: mlvu test_split: test output_type: generate_until diff --git a/lmms_eval/tasks/mlvu/utils.py b/lmms_eval/tasks/mlvu/utils.py index 9829ee8a..cb27aa3f 100644 --- a/lmms_eval/tasks/mlvu/utils.py +++ b/lmms_eval/tasks/mlvu/utils.py @@ -39,7 +39,7 @@ def mlvu_doc_to_visual(doc): return [video_path] -def mlvu_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mlvu_doc_to_text(doc, lmms_eval_specific_kwargs=None): # option_prompt="Carefully watch this video and pay attention to every detail. Based on your observations, select the best option that accurately addresses the question." option_prompt = "" question = doc["question"] + "\nOnly give the best option.\n" diff --git a/lmms_eval/tasks/mmbench/_default_template_mmbench_cn_yaml b/lmms_eval/tasks/mmbench/_default_template_mmbench_cn_yaml index 6a699725..7f2ecc42 100755 --- a/lmms_eval/tasks/mmbench/_default_template_mmbench_cn_yaml +++ b/lmms_eval/tasks/mmbench/_default_template_mmbench_cn_yaml @@ -13,7 +13,7 @@ generation_kwargs: num_beams: 1 do_sample: false process_results: !function cn_utils.mmbench_process_results -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\n请直接使用所提供的选项字母作为答案回答。" diff --git a/lmms_eval/tasks/mmbench/_default_template_mmbench_en_yaml b/lmms_eval/tasks/mmbench/_default_template_mmbench_en_yaml index 060bd5d1..71a6df9e 100755 --- a/lmms_eval/tasks/mmbench/_default_template_mmbench_en_yaml +++ b/lmms_eval/tasks/mmbench/_default_template_mmbench_en_yaml @@ -2,7 +2,7 @@ dataset_path: lmms-lab/MMBench dataset_kwargs: token: True doc_to_target: "answer" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." diff --git a/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml b/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml index 993cd52a..2caaeb02 100644 --- a/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml +++ b/lmms_eval/tasks/mmbench/_default_template_mmbench_ru_yaml @@ -2,7 +2,7 @@ dataset_path: deepvk/MMBench-ru dataset_kwargs: token: True doc_to_target: "answer" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nВыбери правильный вариант ответа буквой." diff --git a/lmms_eval/tasks/mmbench/cc_utils.py b/lmms_eval/tasks/mmbench/cc_utils.py index 8cd6665d..98748efa 100755 --- a/lmms_eval/tasks/mmbench/cc_utils.py +++ b/lmms_eval/tasks/mmbench/cc_utils.py @@ -38,7 +38,7 @@ def mmbench_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def mmbench_cn_cc_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mmbench_cn_cc_doc_to_text(doc, lmms_eval_specific_kwargs=None): option_candidate = ["A", "B", "C", "D", "E"] options_prompt, options_dict = mmbench_evaluator.create_options_prompt(doc, option_candidate) @@ -55,8 +55,8 @@ def mmbench_cn_cc_doc_to_text(doc, model_specific_prompt_kwargs=None): query_prompt = f"{data['question']} {data['options']}" - if model_specific_prompt_kwargs: - query_prompt = f"{query_prompt}\n{model_specific_prompt_kwargs['post_prompt']}" + if lmms_eval_specific_kwargs: + query_prompt = f"{query_prompt}\n{lmms_eval_specific_kwargs['post_prompt']}" return query_prompt diff --git a/lmms_eval/tasks/mmbench/cn_utils.py b/lmms_eval/tasks/mmbench/cn_utils.py index 2a767d7a..e36aa786 100755 --- a/lmms_eval/tasks/mmbench/cn_utils.py +++ b/lmms_eval/tasks/mmbench/cn_utils.py @@ -40,7 +40,7 @@ def mmbench_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def mmbench_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mmbench_doc_to_text(doc, lmms_eval_specific_kwargs=None): option_candidate = ["A", "B", "C", "D", "E"] options_prompt, options_dict = mmbench_evaluator.create_options_prompt(doc, option_candidate) @@ -60,8 +60,8 @@ def mmbench_doc_to_text(doc, model_specific_prompt_kwargs=None): query_prompt = f"{data['hint']} {data['question']} {data['options']}" if pd.notna(data["hint"]) else f"{data['question']} {data['options']}" - if model_specific_prompt_kwargs: - query_prompt = f"{query_prompt}\n{model_specific_prompt_kwargs['post_prompt']}" + if lmms_eval_specific_kwargs: + query_prompt = f"{query_prompt}\n{lmms_eval_specific_kwargs['post_prompt']}" return query_prompt diff --git a/lmms_eval/tasks/mmbench/en_utils.py b/lmms_eval/tasks/mmbench/en_utils.py index f0b5e9f8..4b138f13 100755 --- a/lmms_eval/tasks/mmbench/en_utils.py +++ b/lmms_eval/tasks/mmbench/en_utils.py @@ -39,7 +39,7 @@ def mmbench_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def mmbench_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mmbench_doc_to_text(doc, lmms_eval_specific_kwargs=None): option_candidate = ["A", "B", "C", "D", "E"] options_prompt, options_dict = mmbench_evaluator.create_options_prompt(doc, option_candidate) @@ -59,8 +59,8 @@ def mmbench_doc_to_text(doc, model_specific_prompt_kwargs=None): query_prompt = f"{data['hint']} {data['question']} {data['options']}" if pd.notna(data["hint"]) and data["hint"] != "nan" else f"{data['question']} {data['options']}" - if model_specific_prompt_kwargs: - query_prompt = f"{query_prompt}\n{model_specific_prompt_kwargs['post_prompt']}" + if lmms_eval_specific_kwargs: + query_prompt = f"{query_prompt}\n{lmms_eval_specific_kwargs['post_prompt']}" return query_prompt diff --git a/lmms_eval/tasks/mmbench/mmbench_cc.yaml b/lmms_eval/tasks/mmbench/mmbench_cc.yaml index 9b7b3cb1..2ce01ead 100755 --- a/lmms_eval/tasks/mmbench/mmbench_cc.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_cc.yaml @@ -25,7 +25,7 @@ metadata: version: 0.0 gpt_eval_model_name: "gpt-3.5-turbo-0613" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\n请直接使用所提供的选项字母作为答案回答。" diff --git a/lmms_eval/tasks/mmbench/ru_utils.py b/lmms_eval/tasks/mmbench/ru_utils.py index 3ff515c7..6904b06f 100644 --- a/lmms_eval/tasks/mmbench/ru_utils.py +++ b/lmms_eval/tasks/mmbench/ru_utils.py @@ -39,7 +39,7 @@ def mmbench_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def mmbench_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mmbench_doc_to_text(doc, lmms_eval_specific_kwargs=None): option_candidate = ["A", "B", "C", "D", "E"] options_prompt, options_dict = mmbench_evaluator.create_options_prompt(doc, option_candidate) @@ -59,8 +59,8 @@ def mmbench_doc_to_text(doc, model_specific_prompt_kwargs=None): query_prompt = f"{data['hint']} {data['question']} {data['options']}" if pd.notna(data["hint"]) and data["hint"] != "nan" else f"{data['question']} {data['options']}" - if model_specific_prompt_kwargs: - query_prompt = f"{query_prompt}\n{model_specific_prompt_kwargs['post_prompt']}" + if lmms_eval_specific_kwargs: + query_prompt = f"{query_prompt}\n{lmms_eval_specific_kwargs['post_prompt']}" return query_prompt diff --git a/lmms_eval/tasks/mme/mme.yaml b/lmms_eval/tasks/mme/mme.yaml index 489745aa..e2ffd29d 100755 --- a/lmms_eval/tasks/mme/mme.yaml +++ b/lmms_eval/tasks/mme/mme.yaml @@ -23,7 +23,7 @@ metric_list: - metric: mme_cognition_score aggregation: !function utils.mme_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/mme/utils.py b/lmms_eval/tasks/mme/utils.py index 5aa4ae18..ce4f9f1f 100755 --- a/lmms_eval/tasks/mme/utils.py +++ b/lmms_eval/tasks/mme/utils.py @@ -38,14 +38,14 @@ def mme_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def mme_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mme_doc_to_text(doc, lmms_eval_specific_kwargs=None): question = doc["question"].strip() - if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "": + if "pre_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["pre_prompt"] != "": question = question.replace(replace_prompt, "") - question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}" - if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "": + question = f"{lmms_eval_specific_kwargs['pre_prompt']}{question}" + if "post_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["post_prompt"] != "": question = question.replace(replace_prompt, "") - question = f"{question}{model_specific_prompt_kwargs['post_prompt']}" + question = f"{question}{lmms_eval_specific_kwargs['post_prompt']}" return question diff --git a/lmms_eval/tasks/mmmu/_default_template_yaml b/lmms_eval/tasks/mmmu/_default_template_yaml new file mode 100644 index 00000000..a5367534 --- /dev/null +++ b/lmms_eval/tasks/mmmu/_default_template_yaml @@ -0,0 +1,6 @@ +generation_kwargs: + max_new_tokens: 16 + +metadata: + version: 0.0 + interleaved_format: false \ No newline at end of file diff --git a/lmms_eval/tasks/mmmu/mmmu_test.yaml b/lmms_eval/tasks/mmmu/mmmu_test.yaml index 8f1a1f20..03564b6a 100755 --- a/lmms_eval/tasks/mmmu/mmmu_test.yaml +++ b/lmms_eval/tasks/mmmu/mmmu_test.yaml @@ -7,13 +7,10 @@ doc_to_text: !function utils.mmmu_doc_to_text doc_to_target: "answer" # The return value of process_results will be used by metrics process_results: !function utils.mmmu_process_results -# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results -generation_kwargs: - max_new_tokens: 16 - image_aspect_ratio: original + metric_list: - metric: submission aggregation: !function utils.mmmu_test_aggregate_results_for_submission higher_is_better: true -metadata: - - version: 0.0 \ No newline at end of file + +include: _default_template_yaml \ No newline at end of file diff --git a/lmms_eval/tasks/mmmu/mmmu_val.yaml b/lmms_eval/tasks/mmmu/mmmu_val.yaml index 9e1574c4..a301f7cb 100755 --- a/lmms_eval/tasks/mmmu/mmmu_val.yaml +++ b/lmms_eval/tasks/mmmu/mmmu_val.yaml @@ -7,15 +7,10 @@ doc_to_text: !function utils.mmmu_doc_to_text doc_to_target: "answer" # The return value of process_results will be used by metrics process_results: !function utils.mmmu_process_results -# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results -generation_kwargs: - max_new_tokens: 128 -model_specific_generation_kwargs: - llava: - image_aspect_ratio: original + metric_list: - metric: mmmu_acc aggregation: !function utils.mmmu_aggregate_results higher_is_better: true -metadata: - - version: 0.0 \ No newline at end of file + +include: _default_template_yaml \ No newline at end of file diff --git a/lmms_eval/tasks/mmmu/utils.py b/lmms_eval/tasks/mmmu/utils.py index 83aeed20..ffa89c3f 100755 --- a/lmms_eval/tasks/mmmu/utils.py +++ b/lmms_eval/tasks/mmmu/utils.py @@ -5,7 +5,8 @@ import numpy as np import os import json - +from pathlib import Path +import yaml from lmms_eval.tasks._task_utils.file_utils import generate_submission_file @@ -14,13 +15,23 @@ MULTI_CHOICE_PROMPT = "Answer with the option's letter from the given choices directly." OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase." +with open(Path(__file__).parent / "_default_template_yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + + config = yaml.safe_load("".join(safe_data)) + def replace_images_tokens(input_string): - # for i in range(1, 8): - # question_text = f"" - # query_text = "" - # if question_text in input_string: - # input_string = input_string.replace(question_text, query_text) + for i in range(1, 8): + question_text = f"" + query_text = "" + if question_text in input_string: + input_string = input_string.replace(question_text, query_text) return input_string @@ -44,7 +55,9 @@ def construct_prompt(doc): def mmmu_doc_to_text(doc): question = construct_prompt(doc) - return replace_images_tokens(question) + if config["metadata"]["interleaved_format"]: + question = replace_images_tokens(question) + return question def mmmu_doc_to_visual(doc): diff --git a/lmms_eval/tasks/mmstar/mmstar.yaml b/lmms_eval/tasks/mmstar/mmstar.yaml index 84597846..8d8715a1 100644 --- a/lmms_eval/tasks/mmstar/mmstar.yaml +++ b/lmms_eval/tasks/mmstar/mmstar.yaml @@ -29,7 +29,11 @@ metric_list: - metric: math aggregation: !function utils.mmstar_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: + - metric: average + aggregation: !function utils.mmstar_aggregate_results + higher_is_better: true + +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly" diff --git a/lmms_eval/tasks/mmstar/utils.py b/lmms_eval/tasks/mmstar/utils.py index 66933f29..aba6dd6a 100644 --- a/lmms_eval/tasks/mmstar/utils.py +++ b/lmms_eval/tasks/mmstar/utils.py @@ -10,36 +10,12 @@ dir_name = os.path.dirname(os.path.abspath(__file__)) eval_type_dict = { - "coarse perception" : [ - "image scene and topic", - "image style & quality", - "image emotion" - ], - "fine-grained perception" : [ - "object counting", - "recognition", - "localization" - ], - "instance reasoning" : [ - "single-instance reasoning", - "cross-instance attribute reasoning", - "cross-instance relation reasoning" - ], - "logical reasoning" : [ - "code & sequence reasoning", - "diagram reasoning", - "common reasoning" - ], - "science & technology" : [ - "biology & chemistry & physics", - "electronics & energy & mechanical eng.", - "geography & earth science & agriculture" - ], - "math" : [ - "geometry", - "numeric commonsense and calculation", - "statistical reasoning" - ] + "coarse perception": ["image scene and topic", "image style & quality", "image emotion"], + "fine-grained perception": ["object counting", "recognition", "localization"], + "instance reasoning": ["single-instance reasoning", "cross-instance attribute reasoning", "cross-instance relation reasoning"], + "logical reasoning": ["code & sequence reasoning", "diagram reasoning", "common reasoning"], + "science & technology": ["biology & chemistry & physics", "electronics & energy & mechanical eng.", "geography & earth science & agriculture"], + "math": ["geometry", "numeric commonsense and calculation", "statistical reasoning"], } @@ -50,29 +26,29 @@ def mmstar_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def mmstar_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mmstar_doc_to_text(doc, lmms_eval_specific_kwargs=None): question = doc["question"].strip() - if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "": + if "pre_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["pre_prompt"] != "": question = question.replace(replace_prompt, "") - question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}" - if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "": + question = f"{lmms_eval_specific_kwargs['pre_prompt']}{question}" + if "post_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["post_prompt"] != "": question = question.replace(replace_prompt, "") - question = f"{question}{model_specific_prompt_kwargs['post_prompt']}" + question = f"{question}{lmms_eval_specific_kwargs['post_prompt']}" return question def exact_match(pred, gt): """Brought from MMStar""" - answer = gt.lower().strip().replace('\n', ' ') - predict = pred.lower().strip().replace('\n', ' ') + answer = gt.lower().strip().replace("\n", " ") + predict = pred.lower().strip().replace("\n", " ") try: if answer == predict[0]: return 1.0 - elif predict[0] == '(' and answer == predict[1]: + elif predict[0] == "(" and answer == predict[1]: return 1.0 - elif predict[0:7] == 'option ' and answer == predict[7]: + elif predict[0:7] == "option " and answer == predict[7]: return 1.0 - elif predict[0:14] == 'the answer is ' and answer == predict[14]: + elif predict[0:14] == "the answer is " and answer == predict[14]: return 1.0 except Exception as e: return 0.0 @@ -93,7 +69,7 @@ def mmstar_process_results(doc, results): score = exact_match(pred, gt) category = doc["category"] l2_category = doc["l2_category"] - return {category: {"question_id": doc["index"], "l2_category": l2_category, "score": score}} + return {category: {"question_id": doc["index"], "l2_category": l2_category, "score": score}, "average": {"question_id": doc["index"], "l2_category": l2_category, "score": score}} def mmstar_aggregate_results(results): @@ -114,7 +90,6 @@ def mmstar_aggregate_results(results): avg_score = sum(scores) / len(scores) l2_category_avg_score[l2_category] = avg_score eval_logger.info(f"{l2_category}: {avg_score:.2f}") - + avg_score = sum(l2_category_avg_score.values()) / len(l2_category_avg_score) - return avg_score - \ No newline at end of file + return avg_score \ No newline at end of file diff --git a/lmms_eval/tasks/mmupd/mmaad_base.yaml b/lmms_eval/tasks/mmupd/mmaad_base.yaml index 9a66b3e8..bfa50a81 100644 --- a/lmms_eval/tasks/mmupd/mmaad_base.yaml +++ b/lmms_eval/tasks/mmupd/mmaad_base.yaml @@ -1,7 +1,7 @@ task: "mmaad_base" test_split: test dataset_name: mmaad_base -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\n" diff --git a/lmms_eval/tasks/mmupd/mmaad_instruction.yaml b/lmms_eval/tasks/mmupd/mmaad_instruction.yaml index 30a3bac1..20c83804 100644 --- a/lmms_eval/tasks/mmupd/mmaad_instruction.yaml +++ b/lmms_eval/tasks/mmupd/mmaad_instruction.yaml @@ -1,7 +1,7 @@ task: "mmaad_instruction" test_split: test dataset_name: mmaad_base -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." diff --git a/lmms_eval/tasks/mmupd/mmaad_option.yaml b/lmms_eval/tasks/mmupd/mmaad_option.yaml index f110b822..8a7c2830 100644 --- a/lmms_eval/tasks/mmupd/mmaad_option.yaml +++ b/lmms_eval/tasks/mmupd/mmaad_option.yaml @@ -1,7 +1,7 @@ task: "mmaad_option" test_split: test dataset_name: mmaad_option -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." diff --git a/lmms_eval/tasks/mmupd/mmiasd_base.yaml b/lmms_eval/tasks/mmupd/mmiasd_base.yaml index 6a9159fd..36e1b18a 100644 --- a/lmms_eval/tasks/mmupd/mmiasd_base.yaml +++ b/lmms_eval/tasks/mmupd/mmiasd_base.yaml @@ -1,7 +1,7 @@ task: "mmiasd_base" test_split: test dataset_name: mmiasd_base -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\n" diff --git a/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml b/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml index d0ac3a00..503de6d2 100644 --- a/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml +++ b/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml @@ -1,7 +1,7 @@ task: "mmiasd_instruction" test_split: test dataset_name: mmiasd_base -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"." diff --git a/lmms_eval/tasks/mmupd/mmiasd_option.yaml b/lmms_eval/tasks/mmupd/mmiasd_option.yaml index a1a1aead..16274c50 100644 --- a/lmms_eval/tasks/mmupd/mmiasd_option.yaml +++ b/lmms_eval/tasks/mmupd/mmiasd_option.yaml @@ -1,7 +1,7 @@ task: "mmiasd_option" test_split: test dataset_name: mmiasd_option -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." diff --git a/lmms_eval/tasks/mmupd/mmivqd_base.yaml b/lmms_eval/tasks/mmupd/mmivqd_base.yaml index 82853fde..1c8478e2 100644 --- a/lmms_eval/tasks/mmupd/mmivqd_base.yaml +++ b/lmms_eval/tasks/mmupd/mmivqd_base.yaml @@ -1,7 +1,7 @@ task: "mmivqd_base" test_split: test dataset_name: mmivqd_base -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\n" diff --git a/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml b/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml index a024022a..c6ee1998 100644 --- a/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml +++ b/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml @@ -1,7 +1,7 @@ task: "mmivqd_instruction" test_split: test dataset_name: mmivqd_base -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nIf the given image is irrelevant to the question, answer \"F. The image and question are irrelevant.\"." diff --git a/lmms_eval/tasks/mmupd/mmivqd_option.yaml b/lmms_eval/tasks/mmupd/mmivqd_option.yaml index 1363c345..d1da5e8c 100644 --- a/lmms_eval/tasks/mmupd/mmivqd_option.yaml +++ b/lmms_eval/tasks/mmupd/mmivqd_option.yaml @@ -1,7 +1,7 @@ task: "mmivqd_option" test_split: test dataset_name: mmivqd_option -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." diff --git a/lmms_eval/tasks/mmupd/utils.py b/lmms_eval/tasks/mmupd/utils.py index 818e8cb1..a6933835 100644 --- a/lmms_eval/tasks/mmupd/utils.py +++ b/lmms_eval/tasks/mmupd/utils.py @@ -42,7 +42,7 @@ def mmupd_doc_to_visual(doc): return [Image.open(BytesIO(base64.b64decode(doc["image"])))] -def mmupd_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mmupd_doc_to_text(doc, lmms_eval_specific_kwargs=None): option_candidate = ["A", "B", "C", "D", "E"] options_prompt, options_dict = mmupd_evaluator.create_options_prompt(doc, option_candidate) @@ -61,8 +61,8 @@ def mmupd_doc_to_text(doc, model_specific_prompt_kwargs=None): query_prompt = f"{data['hint']}\n{data['question']}{data['options']}" if pd.notna(data["hint"]) and data["hint"] != "nan" else f"{data['question']}{data['options']}" - if model_specific_prompt_kwargs: - query_prompt = f"{query_prompt}{model_specific_prompt_kwargs['post_prompt']}" + if lmms_eval_specific_kwargs: + query_prompt = f"{query_prompt}{lmms_eval_specific_kwargs['post_prompt']}" return query_prompt diff --git a/lmms_eval/tasks/mmvet/mmvet.yaml b/lmms_eval/tasks/mmvet/mmvet.yaml index 30c1907a..5567ebbc 100755 --- a/lmms_eval/tasks/mmvet/mmvet.yaml +++ b/lmms_eval/tasks/mmvet/mmvet.yaml @@ -8,10 +8,8 @@ doc_to_visual: !function utils.mmvet_doc_to_visual doc_to_text: !function utils.doc_to_text # Such that {{question}} will be replaced by doc["question"] doc_to_target: "{{answer}}" generation_kwargs: - until: - - "ASSISTANT:" - max_new_tokens: 32768 - temperature: 0 + max_new_tokens: 1024 + temperature: 0.2 top_p: 1.0 num_beams: 1 do_sample: false @@ -23,7 +21,7 @@ metric_list: metadata: version: 0.0 gpt_eval_model_name: "gpt-4-0613" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: - pre_prompt: "Please think step by step and try to provide best answer to the following question: \n\n" + pre_prompt: "First please perform reasoning, and think step by step to provide best answer to the following question: \n\n" post_prompt: "" diff --git a/lmms_eval/tasks/mmvet/utils.py b/lmms_eval/tasks/mmvet/utils.py index 37b15ad7..3b369b07 100755 --- a/lmms_eval/tasks/mmvet/utils.py +++ b/lmms_eval/tasks/mmvet/utils.py @@ -203,11 +203,11 @@ def mmvet_aggregate_results(results): return overall_score -def doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: +def doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: return doc["question"] question = doc["question"] - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") + pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") + post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/muirbench/muirbench.yaml b/lmms_eval/tasks/muirbench/muirbench.yaml index 43b8ab7c..ae09d2f2 100644 --- a/lmms_eval/tasks/muirbench/muirbench.yaml +++ b/lmms_eval/tasks/muirbench/muirbench.yaml @@ -1,4 +1,3 @@ - dataset_path: MUIRBENCH/MUIRBENCH task: "muirbench" dataset_kwargs: @@ -9,8 +8,8 @@ doc_to_visual: !function utils.muir_doc_to_visual doc_to_text: !function utils.muir_doc_to_text doc_to_target: !function utils.muir_doc_to_target process_results: !function utils.muir_process_results - -model_specific_prompt_kwargs: + +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." diff --git a/lmms_eval/tasks/muirbench/utils.py b/lmms_eval/tasks/muirbench/utils.py index 2924edac..719d911d 100644 --- a/lmms_eval/tasks/muirbench/utils.py +++ b/lmms_eval/tasks/muirbench/utils.py @@ -1,15 +1,15 @@ - from lmms_eval.filters.extraction import ExtendedRegexFilter from lmms_eval.filters.transformation import MapFilter import re import pandas as pd -def muir_doc_to_text(doc, model_specific_prompt_kwargs=None): +def muir_doc_to_text(doc, lmms_eval_specific_kwargs=None): question, choices = doc["question"], doc["options"] len_choices = len(choices) - post_prompt = model_specific_prompt_kwargs["post_prompt"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + options = [chr(ord("A") + i) for i in range(len_choices)] choices_str = "\n".join([f"{option}. {choice}" for option, choice in zip(options, choices)]) return f"{pre_prompt}{question}\n{choices_str}{post_prompt}" @@ -17,7 +17,7 @@ def muir_doc_to_text(doc, model_specific_prompt_kwargs=None): def muir_doc_to_visual(doc): image_list = [image.convert("RGB") for image in doc["image_list"]] - return image_list + return image_list def muir_doc_to_target(doc): @@ -33,15 +33,15 @@ def muir_process_results(doc, result): image_type = doc["image_type"] data_dict = { - "pred" : pred, - "task" : task, - "idx" : idx, - "image_relation" : image_relation, - "answer" : answer, - "image_type" : image_type, - } + "pred": pred, + "task": task, + "idx": idx, + "image_relation": image_relation, + "answer": answer, + "image_type": image_type, + } - return {"muirbench_score_overall" : data_dict} + return {"muirbench_score_overall": data_dict} def muir_aggregation(results): @@ -61,19 +61,15 @@ def muir_aggregation(results): task_num[result["task"]] += 1 score = score / len(results) - - task_score = {k : v / task_num[k] for k,v in task_score.items()} + task_score = {k: v / task_num[k] for k, v in task_score.items()} print("=" * 50) for k, v in task_score.items(): print(f"{k} : {v:.2f}") print("=" * 50) - return score - - class MultiChoiceRegexFilter(ExtendedRegexFilter): def __init__(self, *args, **kwargs): """ diff --git a/lmms_eval/tasks/multidocvqa/multidocvqa_test.yaml b/lmms_eval/tasks/multidocvqa/multidocvqa_test.yaml index d33c5a50..2b86155d 100755 --- a/lmms_eval/tasks/multidocvqa/multidocvqa_test.yaml +++ b/lmms_eval/tasks/multidocvqa/multidocvqa_test.yaml @@ -13,7 +13,7 @@ process_results: !function utils.multidocvqa_process_test_results_for_submission metric_list: - metric: submission aggregation: !function utils.multidocvqa_test_aggregate_results_for_submission -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/multidocvqa/multidocvqa_val.yaml b/lmms_eval/tasks/multidocvqa/multidocvqa_val.yaml index b4f5238b..6fe86fb0 100755 --- a/lmms_eval/tasks/multidocvqa/multidocvqa_val.yaml +++ b/lmms_eval/tasks/multidocvqa/multidocvqa_val.yaml @@ -17,7 +17,7 @@ metric_list: - metric: accuracy aggregation: !function utils.multidocvqa_aggregate_results_accuracy higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/multidocvqa/utils.py b/lmms_eval/tasks/multidocvqa/utils.py index f53da020..0996cdae 100755 --- a/lmms_eval/tasks/multidocvqa/utils.py +++ b/lmms_eval/tasks/multidocvqa/utils.py @@ -9,10 +9,10 @@ from loguru import logger as eval_logger -def multidocvqa_doc_to_text(doc, model_specific_prompt_kwargs): +def multidocvqa_doc_to_text(doc, lmms_eval_specific_kwargs): question = doc["question"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml index 3ae9260b..6be6bb22 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml @@ -29,7 +29,7 @@ metric_list: metadata: version: 0.0 gpt_eval_model_name: "gpt-4-0613" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py index b783cb40..bf29636a 100644 --- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py +++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py @@ -105,11 +105,11 @@ def llava_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def llava_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} - pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "") - post_prompt = model_specific_prompt_kwargs.get("post_prompt", "") +def llava_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} + pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "") + post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "") return f"{pre_prompt}{doc['question']}{post_prompt}" diff --git a/lmms_eval/tasks/mvbench/_default_template.yaml b/lmms_eval/tasks/mvbench/_default_template.yaml new file mode 100644 index 00000000..89d36dd7 --- /dev/null +++ b/lmms_eval/tasks/mvbench/_default_template.yaml @@ -0,0 +1,23 @@ +dataset_path: OpenGVLab/MVBench +dataset_kwargs: + token: True + cache_dir: mvbench + video: True +generation_kwargs: + max_new_tokens: 16 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false + +output_type: generate_until +doc_to_visual: !function utils.mvbench_doc_to_visual +doc_to_text: !function utils.mvbench_doc_to_text +doc_to_target: "answer" +# The return value of process_results will be used by metrics +process_results: !function utils.mvbench_process_results +# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results +metric_list: + - metric: mvbench_accuracy + aggregation: !function utils.mvbench_aggregate_results + higher_is_better: true \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench.yaml b/lmms_eval/tasks/mvbench/mvbench.yaml new file mode 100644 index 00000000..42b42c82 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench.yaml @@ -0,0 +1,24 @@ +group: mvbench +task: + - mvbench_action_sequence + - mvbench_moving_count + - mvbench_action_prediction + - mvbench_episodic_reasoning + - mvbench_action_antonym + - mvbench_action_count + - mvbench_scene_transition + - mvbench_object_shuffle + - mvbench_object_existence + - mvbench_fine_grained_pose + - mvbench_unexpected_action + - mvbench_moving_direction + - mvbench_state_change + - mvbench_object_interaction + - mvbench_character_order + - mvbench_action_localization + - mvbench_counterfactual_inference + - mvbench_fine_grained_action + - mvbench_moving_attribute + - mvbench_egocentric_navigation + +# ['action_sequence', 'moving_count', 'action_prediction', 'episodic_reasoning', 'action_antonym', 'action_count', 'scene_transition', 'object_shuffle', 'object_existence', 'fine_grained_pose', 'unexpected_action', 'moving_direction', 'state_change', 'object_interaction', 'character_order', 'action_localization', 'counterfactual_inference', 'fine_grained_action', 'moving_attribute', 'egocentric_navigation'] \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_action_antonym.yaml b/lmms_eval/tasks/mvbench/mvbench_action_antonym.yaml new file mode 100644 index 00000000..ca6dbf64 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_action_antonym.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_action_antonym +dataset_name: action_antonym +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: action_antonym + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_action_count.yaml b/lmms_eval/tasks/mvbench/mvbench_action_count.yaml new file mode 100644 index 00000000..65d41015 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_action_count.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_action_count +dataset_name: action_count +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: action_count + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_action_localization.yaml b/lmms_eval/tasks/mvbench/mvbench_action_localization.yaml new file mode 100644 index 00000000..c0596162 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_action_localization.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_action_localization +dataset_name: action_localization +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: action_localization + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_action_prediction.yaml b/lmms_eval/tasks/mvbench/mvbench_action_prediction.yaml new file mode 100644 index 00000000..d41c4f8e --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_action_prediction.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_action_prediction +dataset_name: action_prediction +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: action_prediction + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_action_sequence.yaml b/lmms_eval/tasks/mvbench/mvbench_action_sequence.yaml new file mode 100644 index 00000000..2d2c20ea --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_action_sequence.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_action_sequence +dataset_name: action_sequence +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: action_sequence + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_character_order.yaml b/lmms_eval/tasks/mvbench/mvbench_character_order.yaml new file mode 100644 index 00000000..d4972cea --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_character_order.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_character_order +dataset_name: character_order +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: character_order + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_counterfactual_inference.yaml b/lmms_eval/tasks/mvbench/mvbench_counterfactual_inference.yaml new file mode 100644 index 00000000..431c6a2d --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_counterfactual_inference.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_counterfactual_inference +dataset_name: counterfactual_inference +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: counterfactual_inference + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_egocentric_navigation.yaml b/lmms_eval/tasks/mvbench/mvbench_egocentric_navigation.yaml new file mode 100644 index 00000000..db0b16fb --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_egocentric_navigation.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_egocentric_navigation +dataset_name: egocentric_navigation +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: egocentric_navigation + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_episodic_reasoning.yaml b/lmms_eval/tasks/mvbench/mvbench_episodic_reasoning.yaml new file mode 100644 index 00000000..9500d28f --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_episodic_reasoning.yaml @@ -0,0 +1,20 @@ +include: _default_template.yaml +task: mvbench_episodic_reasoning +dataset_name: episodic_reasoning +test_split: train +doc_to_visual: !function utils.mvbench_frames_doc_to_visual +generation_kwargs: + max_new_tokens: 16 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false + +lmms_eval_specific_kwargs: + default: + sub_task: episodic_reasoning + post_prompt: "Answer with the option's letter from the given choices directly." + +metadata: + task_type: video + sample_frames: 32 # for episodic reasoning, it stores the frame images in folder so we need to set the number of frames to sample here. \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_fine_grained_action.yaml b/lmms_eval/tasks/mvbench/mvbench_fine_grained_action.yaml new file mode 100644 index 00000000..10146883 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_fine_grained_action.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_fine_grained_action +dataset_name: fine_grained_action +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: fine_grained_action + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_fine_grained_pose.yaml b/lmms_eval/tasks/mvbench/mvbench_fine_grained_pose.yaml new file mode 100644 index 00000000..5af1bfe6 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_fine_grained_pose.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_fine_grained_pose +dataset_name: fine_grained_pose +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: fine_grained_pose + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_moving_attribute.yaml b/lmms_eval/tasks/mvbench/mvbench_moving_attribute.yaml new file mode 100644 index 00000000..f5b20a13 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_moving_attribute.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_moving_attribute +dataset_name: moving_attribute +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: moving_attribute + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_moving_count.yaml b/lmms_eval/tasks/mvbench/mvbench_moving_count.yaml new file mode 100644 index 00000000..0a6b0709 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_moving_count.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_moving_count +dataset_name: moving_count +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: moving_count + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_moving_direction.yaml b/lmms_eval/tasks/mvbench/mvbench_moving_direction.yaml new file mode 100644 index 00000000..bff498f5 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_moving_direction.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_moving_direction +dataset_name: moving_direction +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: moving_direction + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_object_existence.yaml b/lmms_eval/tasks/mvbench/mvbench_object_existence.yaml new file mode 100644 index 00000000..342ff70f --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_object_existence.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_object_existence +dataset_name: object_existence +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: object_existence + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_object_interaction.yaml b/lmms_eval/tasks/mvbench/mvbench_object_interaction.yaml new file mode 100644 index 00000000..c884dec9 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_object_interaction.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_object_interaction +dataset_name: object_interaction +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: object_interaction + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_object_shuffle.yaml b/lmms_eval/tasks/mvbench/mvbench_object_shuffle.yaml new file mode 100644 index 00000000..f3119d84 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_object_shuffle.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_object_shuffle +dataset_name: object_shuffle +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: object_shuffle + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_scene_transition.yaml b/lmms_eval/tasks/mvbench/mvbench_scene_transition.yaml new file mode 100644 index 00000000..ec0b7277 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_scene_transition.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_scene_transition +dataset_name: scene_transition +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: scene_transition + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_state_change.yaml b/lmms_eval/tasks/mvbench/mvbench_state_change.yaml new file mode 100644 index 00000000..5d1195b2 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_state_change.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_state_change +dataset_name: state_change +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: state_change + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/mvbench_unexpected_action.yaml b/lmms_eval/tasks/mvbench/mvbench_unexpected_action.yaml new file mode 100644 index 00000000..e6239ea1 --- /dev/null +++ b/lmms_eval/tasks/mvbench/mvbench_unexpected_action.yaml @@ -0,0 +1,8 @@ +include: _default_template.yaml +task: mvbench_unexpected_action +dataset_name: unexpected_action +test_split: train +lmms_eval_specific_kwargs: + default: + sub_task: unexpected_action + post_prompt: "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/mvbench/utils.py b/lmms_eval/tasks/mvbench/utils.py new file mode 100644 index 00000000..5ac0d9a3 --- /dev/null +++ b/lmms_eval/tasks/mvbench/utils.py @@ -0,0 +1,194 @@ +from collections import defaultdict +import os +import datetime +import json +from lmms_eval.tasks._task_utils.file_utils import generate_submission_file +from pathlib import Path +import yaml +import sys, string +from typing import List, Dict, Optional, Union +import re +import PIL +import numpy as np +from loguru import logger as eval_logger + +DATA_LIST = { + "action_sequence": "star/Charades_v1_480/", + "action_prediction": "star/Charades_v1_480/", + "action_antonym": "ssv2_video/", + "fine_grained_action": "Moments_in_Time_Raw/videos/", + "unexpected_action": "FunQA_test/test/", + "object_existence": "clevrer/video_validation/", + "object_interaction": "star/Charades_v1_480/", + "object_shuffle": "perception/videos/", + "moving_direction": "clevrer/video_validation/", + "action_localization": "sta/sta_video/", + "scene_transition": "scene_qa/video/", + "action_count": "perception/videos/", + "moving_count": "clevrer/video_validation/", + "moving_attribute": "clevrer/video_validation/", + "state_change": "perception/videos/", + "fine_grained_pose": "nturgbd/", + "character_order": "perception/videos/", + "egocentric_navigation": "vlnqa/", + "episodic_reasoning": "tvqa/frames_fps3_hq/", + "counterfactual_inference": "clevrer/video_validation/", +} + +hf_home = os.getenv("HF_HOME", "./~/.cache/huggingface") +base_cache_dir = os.path.expanduser(hf_home) + +with open(Path(__file__).parent / "_default_template.yaml", "r") as f: + raw_data = f.readlines() + safe_data = [] + for i, line in enumerate(raw_data): + # remove function definition since yaml load cannot handle it + if "!function" not in line: + safe_data.append(line) + + +cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"] + + +def mvbench_doc_to_visual(doc, lmms_eval_specific_kwargs=None): + cache_dir = os.path.join(base_cache_dir, cache_name) + dataset_folder = DATA_LIST[lmms_eval_specific_kwargs["sub_task"]] + video_path = os.path.join(cache_dir, dataset_folder, doc["video"]) + if os.path.exists(video_path): + video_path = video_path + elif os.path.basename(dataset_folder) in ["clevrer", "star"]: + alternative_video_path = os.path.join(cache_dir, "data0613", dataset_folder, doc["video"]) + if os.path.exists(alternative_video_path): + video_path = alternative_video_path + else: + eval_logger.error(f"Video path: {video_path} does not exist, please check.") + else: + eval_logger.error(f"Video path: {video_path} does not exist, please check.") + return [video_path] + + +def mvbench_frames_doc_to_visual(doc, lmms_eval_specific_kwargs=None): + cache_dir = os.path.join(base_cache_dir, cache_name) + dataset_folder = DATA_LIST[lmms_eval_specific_kwargs["sub_task"]] + video_path = os.path.join(cache_dir, dataset_folder, doc["video"]) + if os.path.exists(video_path): + video_path = video_path + elif os.path.basename(dataset_folder) in ["clevrer", "star"]: + alternative_video_path = os.path.join(cache_dir, "data0613", dataset_folder, doc["video"]) + if os.path.exists(alternative_video_path): + video_path = alternative_video_path + else: + eval_logger.error(f"Video path: {video_path} does not exist, please check.") + else: + eval_logger.error(f"Video path: {video_path} does not exist, please check.") + + frame_path_list = [os.path.join(video_path, f) for f in os.listdir(video_path) if f.endswith(".jpg") or f.endswith(".png")] + frame_image_list = [PIL.Image.open(frame_path).convert("RGB") for frame_path in frame_path_list] + return frame_image_list + + +def mvbench_doc_to_text(doc, lmms_eval_specific_kwargs=None): + option_prompt = "" + option_list = doc["candidates"] + option_letters = string.ascii_uppercase + for char_index, option in enumerate(option_list): + option_letter = option_letters[char_index] + option_prompt += f"{option_letter}. {option}\n" + + full_text = doc["question"] + "\n" + option_prompt + lmms_eval_specific_kwargs["post_prompt"] + return full_text + + +def mcq_acc(answer, pred): + periodStrip = re.compile("(?!<=\d)(\.)(?!\d)") + commaStrip = re.compile("(\d)(\,)(\d)") + punct = [";", r"/", "[", "]", '"', "{", "}", "(", ")", "=", "+", "\\", "_", "-", ">", "<", "@", "`", ",", "?", "!"] + + def processPunctuation(inText): + outText = inText + for p in punct: + if (p + " " in inText or " " + p in inText) or (re.search(commaStrip, inText) != None): + outText = outText.replace(p, "") + else: + outText = outText.replace(p, " ") + outText = periodStrip.sub("", outText, re.UNICODE) + return outText + + def process(answer): + option_regex = re.compile(r"^([A-E])\.\s*(.+)$", re.IGNORECASE) + match = option_regex.match(answer.strip()) + + if match: + # If matched, return the option letter in uppercase + return match.group(1).upper() + else: + # If no match, process the answer as before + answer = answer.replace("\n", " ") + answer = answer.replace("\t", " ") + answer = answer.strip() + answer = processPunctuation(answer) + answer = answer.strip("'") + answer = answer.strip('"') + answer = answer.strip(")") + answer = answer.strip("(") + answer = answer.strip().lower() + + # Try to find any single letter (A-E) in the processed answer + letter_match = re.search(r"\b([A-E])\b", answer, re.IGNORECASE) + if letter_match: + return letter_match.group(1).upper() + + return answer + + pred = process(pred) + answer = process(answer) + + if pred == answer: + score = 1 + else: + score = 0 + + return score + + +def mvbench_process_results(doc, results): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name (in this case mvbench_perception_score), value: metric value + """ + pred = results[0] + + # Calculate the ground truth option letter + option_letters = string.ascii_uppercase + gt_option_letter = None + for i, candidate in enumerate(doc["candidates"]): + if candidate == doc["answer"]: + gt_option_letter = option_letters[i] + break + + # Calculate the score using mcq_acc function + score = mcq_acc(gt_option_letter, pred) + + data_dict = {"pred_answer": pred, "gt_answer": gt_option_letter, "score": score} + + return {"mvbench_accuracy": data_dict} + + +def mvbench_aggregate_results(results): + """ + Args: + results: a list of values returned by process_results + Returns: + A score + """ + total_answered = 0 + total_correct = 0 + for result in results: + if result["pred_answer"] != "": + total_answered += 1 + total_correct += result["score"] + + return 100 * total_correct / total_answered if total_answered > 0 else 0 diff --git a/lmms_eval/tasks/nextqa/_default_template_yaml b/lmms_eval/tasks/nextqa/_default_template_yaml index 65f3845a..e30dd25f 100644 --- a/lmms_eval/tasks/nextqa/_default_template_yaml +++ b/lmms_eval/tasks/nextqa/_default_template_yaml @@ -3,3 +3,6 @@ dataset_kwargs: token: True video: True cache_dir: nextqa +metadata: + version: 0.0.1 + load_package: False \ No newline at end of file diff --git a/lmms_eval/tasks/nextqa/nextqa_oe_test.yaml b/lmms_eval/tasks/nextqa/nextqa_oe_test.yaml index 7e5f2c70..17cae364 100644 --- a/lmms_eval/tasks/nextqa/nextqa_oe_test.yaml +++ b/lmms_eval/tasks/nextqa/nextqa_oe_test.yaml @@ -11,7 +11,7 @@ metric_list: aggregation: !function utils.nextqa_aggregate_results higher_is_better: true include: _default_template_yaml -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer a question using a short phrase or sentence." diff --git a/lmms_eval/tasks/nextqa/nextqa_oe_val.yaml b/lmms_eval/tasks/nextqa/nextqa_oe_val.yaml index 5511ea3a..0d9ced09 100644 --- a/lmms_eval/tasks/nextqa/nextqa_oe_val.yaml +++ b/lmms_eval/tasks/nextqa/nextqa_oe_val.yaml @@ -11,7 +11,7 @@ metric_list: aggregation: !function utils.nextqa_aggregate_results higher_is_better: true include: _default_template_yaml -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer a question using a short phrase or sentence." diff --git a/lmms_eval/tasks/nextqa/utils.py b/lmms_eval/tasks/nextqa/utils.py index 4fa46523..8162367f 100644 --- a/lmms_eval/tasks/nextqa/utils.py +++ b/lmms_eval/tasks/nextqa/utils.py @@ -1,40 +1,15 @@ import os import yaml - import random import pandas as pd - from pathlib import Path - from loguru import logger as eval_logger -try: - from pywsd.utils import lemmatize_sentence -except ImportError: - eval_logger.debug("pywsd not installed. Please install pywsd to use this module. You can install it by running 'pip install pywsd'") - -try: - from nltk.tokenize import word_tokenize - from nltk.corpus import wordnet - - try: - import nltk - - nltk.download("averaged_perceptron_tagger", quiet=True) - nltk.download("wordnet", quiet=True) - nltk.download("punkt", quiet=True) - except Exception as e: - eval_logger.debug(f"nltk download failed: {e}") -except ImportError: - eval_logger.debug("nltk not installed. Please install nltk to use this module. You can install it by running 'pip install nltk'") - from lmms_eval.tasks._task_utils.video_loader import get_cache_dir, get_video import numpy as np - OPTIONS = ["A", "B", "C", "D", "E"] - with open(Path(__file__).parent / "_default_template_yaml", "r") as f: raw_data = f.readlines() safe_data = [] @@ -45,6 +20,23 @@ config = yaml.safe_load("".join(safe_data)) +if config["metadata"]["load_package"]: + try: + from pywsd.utils import lemmatize_sentence + except ImportError: + eval_logger.debug("pywsd not installed. Please install pywsd to use this module. You can install it by running 'pip install pywsd'") + + try: + from nltk.tokenize import word_tokenize + from nltk.corpus import wordnet + import nltk + + nltk.download("averaged_perceptron_tagger", quiet=True) + nltk.download("wordnet", quiet=True) + nltk.download("punkt", quiet=True) + except ImportError: + eval_logger.debug("nltk not installed. Please install nltk to use this module. You can install it by running 'pip install nltk'") + stopwords = set(pd.read_csv(Path(__file__).parent / "stopwords.csv").squeeze()) cache_dir = get_cache_dir(config, "NExTVideo") @@ -54,26 +46,26 @@ def nextqa_doc_to_visual(doc): return [get_video(cache_dir, doc["video"])] -def nextqa_doc_to_text(doc, model_specific_prompt_kwargs=None): +def nextqa_doc_to_text(doc, lmms_eval_specific_kwargs=None): question = doc["question"].strip() - if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "": - question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}" - if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "": - question = f"{question}{model_specific_prompt_kwargs['post_prompt']}" + if "pre_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["pre_prompt"] != "": + question = f"{lmms_eval_specific_kwargs['pre_prompt']}{question}" + if "post_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["post_prompt"] != "": + question = f"{question}{lmms_eval_specific_kwargs['post_prompt']}" return question -def nextqa_doc_to_text_mc(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def nextqa_doc_to_text_mc(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} question = [doc["question"].strip()] for i in range(5): question.append(f"{OPTIONS[i]}. {doc[f'a{i}'].strip()}") question = "\n".join(question) - if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "": - question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}" - if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "": - question = f"{question}{model_specific_prompt_kwargs['post_prompt']}" + if "pre_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["pre_prompt"] != "": + question = f"{lmms_eval_specific_kwargs['pre_prompt']}{question}" + if "post_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["post_prompt"] != "": + question = f"{question}{lmms_eval_specific_kwargs['post_prompt']}" return question diff --git a/lmms_eval/tasks/nocaps/_default_template_nocaps_yaml b/lmms_eval/tasks/nocaps/_default_template_nocaps_yaml index 12fdbbc4..5953c1f2 100755 --- a/lmms_eval/tasks/nocaps/_default_template_nocaps_yaml +++ b/lmms_eval/tasks/nocaps/_default_template_nocaps_yaml @@ -1,3 +1,3 @@ -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: prompt: "Provide a one-sentence caption for the provided image." \ No newline at end of file diff --git a/lmms_eval/tasks/nocaps/utils.py b/lmms_eval/tasks/nocaps/utils.py index ce6e1d89..cdb85351 100755 --- a/lmms_eval/tasks/nocaps/utils.py +++ b/lmms_eval/tasks/nocaps/utils.py @@ -18,9 +18,9 @@ def nocaps_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def nocaps_doc_to_text(doc, model_specific_prompt_kwargs=None): +def nocaps_doc_to_text(doc, lmms_eval_specific_kwargs=None): # question = "Please carefully observe the image and come up with a caption for the image" - return model_specific_prompt_kwargs["prompt"] + return lmms_eval_specific_kwargs["prompt"] def nocaps_process_result(doc, result): diff --git a/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml b/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml index a6950129..70f59543 100755 --- a/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml +++ b/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml @@ -16,7 +16,7 @@ metric_list: aggregation: !function utils.ok_vqa_aggregate_submissions higher_is_better: true process_results: !function utils.ok_vqa_process_results -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/ok_vqa/utils.py b/lmms_eval/tasks/ok_vqa/utils.py index 249e1a0c..2d9a2139 100755 --- a/lmms_eval/tasks/ok_vqa/utils.py +++ b/lmms_eval/tasks/ok_vqa/utils.py @@ -48,16 +48,16 @@ def ok_vqa_process_results(doc, result): } -def ok_vqa_doc_to_text(doc, model_specific_prompt_kwargs=None): +def ok_vqa_doc_to_text(doc, lmms_eval_specific_kwargs=None): question = doc["question"] - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py b/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py index 9897447f..41ae7491 100644 --- a/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py +++ b/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py @@ -1,6 +1,6 @@ import re import sympy as sp - +import math from loguru import logger as eval_logger @@ -9,7 +9,6 @@ from sympy.parsing.latex import parse_latex except ImportError as e: eval_logger.debug("Please install sympy package by running 'pip install sympy' if you want to use OlympiadBenchEvaluator.") -import math # how to use # scorer = OlympiadBenchEvaluator() diff --git a/lmms_eval/tasks/perceptiontest/test/_default_template_yaml b/lmms_eval/tasks/perceptiontest/test/_default_template_yaml index 39ea0aa4..8a35beb5 100644 --- a/lmms_eval/tasks/perceptiontest/test/_default_template_yaml +++ b/lmms_eval/tasks/perceptiontest/test/_default_template_yaml @@ -3,7 +3,7 @@ dataset_kwargs: token: True video: True cache_dir: perceptiontest -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/perceptiontest/test/utils.py b/lmms_eval/tasks/perceptiontest/test/utils.py index 522a6a04..06294776 100755 --- a/lmms_eval/tasks/perceptiontest/test/utils.py +++ b/lmms_eval/tasks/perceptiontest/test/utils.py @@ -45,15 +45,15 @@ def perceptiontest_doc_to_visual(doc): # This is the place where you format your question -def perceptiontest_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def perceptiontest_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] question = doc["question"] if "options" in doc: diff --git a/lmms_eval/tasks/perceptiontest/val/_default_template_yaml b/lmms_eval/tasks/perceptiontest/val/_default_template_yaml index 82dd37ca..c586beb9 100644 --- a/lmms_eval/tasks/perceptiontest/val/_default_template_yaml +++ b/lmms_eval/tasks/perceptiontest/val/_default_template_yaml @@ -3,7 +3,7 @@ dataset_kwargs: token: True video: True cache_dir: perceptiontest_val -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/perceptiontest/val/utils.py b/lmms_eval/tasks/perceptiontest/val/utils.py index 562cc0fe..3caa73a8 100755 --- a/lmms_eval/tasks/perceptiontest/val/utils.py +++ b/lmms_eval/tasks/perceptiontest/val/utils.py @@ -45,15 +45,15 @@ def perceptiontest_val_doc_to_visual(doc): # This is the place where you format your question -def perceptiontest_val_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def perceptiontest_val_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] question = doc["question"] if "options" in doc: @@ -103,15 +103,35 @@ def perceptiontest_val_process_results_mc_ppl(doc, result): # Process result for generation +import re + + def perceptiontest_val_process_results_mc(doc, result): - pred = result[0] # string prediction "A", "B", "C" + pred = result[0].strip() # raw text prediction + + # Use regex to match A, B, C, or D + match = re.search(r"\b([A-D])\b", pred) + + if match: + pred = match.group(1) # Extract the matched letter + pred = pred.upper() + else: + pred = "" # Set to empty string if no match found # Map the prediction to an index - pred_to_index = {"A": 0, "B": 1, "C": 2} + pred_to_index = {"A": 0, "B": 1, "C": 2, "D": 3} index = pred_to_index.get(pred, -1) # Default to -1 if the prediction is not found + correct = 1 if index == int(doc["answer_id"]) else 0 + return { - "accuracy": {"video_name": doc["video_name"], "question": doc["question"], "question_id": doc["question_id"], "pred_id": index, "answer_id": doc["answer_id"], "area": doc["area"], "reasoning": doc["reasoning"], "tag": doc["tag"]} + "accuracy": { + "video_name": doc["video_name"], + "question": doc["question"], + "pred_id": index, + "answer_id": doc["answer_id"], + "correct": correct, + } } @@ -120,7 +140,7 @@ def perceptiontest_val_aggregate_accuracy(results, args): # results is a list of dict for answer_dict in results: - if str(answer_dict["answer_id"]) == str(answer_dict["pred_id"]): + if answer_dict["correct"] == 1: yes_count = yes_count + 1 accuracy = yes_count / len(results) diff --git a/lmms_eval/tasks/qbench/abench_dev.yaml b/lmms_eval/tasks/qbench/abench_dev.yaml index 8b44b5f7..fb8140e8 100644 --- a/lmms_eval/tasks/qbench/abench_dev.yaml +++ b/lmms_eval/tasks/qbench/abench_dev.yaml @@ -15,7 +15,7 @@ metric_list: aggregation: !function utils.a_bench_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "Answer with the option's letter from the given choices directly.\n" diff --git a/lmms_eval/tasks/qbench/qbench2_dev.yaml b/lmms_eval/tasks/qbench/qbench2_dev.yaml index 7412cb25..ae8dc5c7 100644 --- a/lmms_eval/tasks/qbench/qbench2_dev.yaml +++ b/lmms_eval/tasks/qbench/qbench2_dev.yaml @@ -15,7 +15,7 @@ metric_list: aggregation: !function utils.q_bench_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "Answer with the option's letter from the given choices directly.\n" diff --git a/lmms_eval/tasks/qbench/qbench_dev.yaml b/lmms_eval/tasks/qbench/qbench_dev.yaml index be901333..97d9df20 100644 --- a/lmms_eval/tasks/qbench/qbench_dev.yaml +++ b/lmms_eval/tasks/qbench/qbench_dev.yaml @@ -15,7 +15,7 @@ metric_list: aggregation: !function utils.q_bench_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "Answer with the option's letter from the given choices directly.\n" diff --git a/lmms_eval/tasks/qbench/utils.py b/lmms_eval/tasks/qbench/utils.py index a5641570..cc3ce120 100644 --- a/lmms_eval/tasks/qbench/utils.py +++ b/lmms_eval/tasks/qbench/utils.py @@ -4,7 +4,7 @@ from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -def q_bench_doc_to_text(doc, model_specific_prompt_kwargs): +def q_bench_doc_to_text(doc, lmms_eval_specific_kwargs): candidates = [] for i in range(4): candidate = doc.get(f"option{i}") @@ -12,8 +12,8 @@ def q_bench_doc_to_text(doc, model_specific_prompt_kwargs): candidates.append(candidate) question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A") + i), candidate]) for i, candidate in enumerate(candidates)]) - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}\n{post_prompt}" diff --git a/lmms_eval/tasks/realworldqa/realworldqa.yaml b/lmms_eval/tasks/realworldqa/realworldqa.yaml index 4b414967..cf407b2f 100644 --- a/lmms_eval/tasks/realworldqa/realworldqa.yaml +++ b/lmms_eval/tasks/realworldqa/realworldqa.yaml @@ -32,7 +32,7 @@ metric_list: ignore_case: true ignore_punctuation: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/realworldqa/utils.py b/lmms_eval/tasks/realworldqa/utils.py index 9ed645ed..ecde1f4b 100644 --- a/lmms_eval/tasks/realworldqa/utils.py +++ b/lmms_eval/tasks/realworldqa/utils.py @@ -9,17 +9,17 @@ def realworldqa_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def realworldqa_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def realworldqa_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" question = doc["question"].strip() - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"]: + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["post_prompt"]: question = question.replace(REPLACE_PROMPT, "") - post_prompt = model_specific_prompt_kwargs["post_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/scienceqa/scienceqa.yaml b/lmms_eval/tasks/scienceqa/scienceqa.yaml index 8190e3e9..f34b19fa 100755 --- a/lmms_eval/tasks/scienceqa/scienceqa.yaml +++ b/lmms_eval/tasks/scienceqa/scienceqa.yaml @@ -22,7 +22,7 @@ process_results: !function utils.sqa_process_results metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: format: default pre_prompt: "" diff --git a/lmms_eval/tasks/scienceqa/scienceqa_img.yaml b/lmms_eval/tasks/scienceqa/scienceqa_img.yaml index b6c7dcc6..7faa7728 100755 --- a/lmms_eval/tasks/scienceqa/scienceqa_img.yaml +++ b/lmms_eval/tasks/scienceqa/scienceqa_img.yaml @@ -22,7 +22,7 @@ process_results: !function utils.sqa_process_results metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: format: default pre_prompt: "" diff --git a/lmms_eval/tasks/scienceqa/utils.py b/lmms_eval/tasks/scienceqa/utils.py index 128ebf56..7cd42b71 100755 --- a/lmms_eval/tasks/scienceqa/utils.py +++ b/lmms_eval/tasks/scienceqa/utils.py @@ -1,22 +1,22 @@ -def sqa_doc_to_text(doc, model_specific_prompt_kwargs=None): +def sqa_doc_to_text(doc, lmms_eval_specific_kwargs=None): context, question, choices = doc["hint"], doc["question"], doc["choices"] len_choices = len(choices) options = [chr(ord("A") + i) for i in range(len_choices)] choices_str = "\n".join([f"{option}. {choice}" for option, choice in zip(options, choices)]) - if model_specific_prompt_kwargs["format"] == "default": + if lmms_eval_specific_kwargs["format"] == "default": if context: context = f"Context: {context}\n" - post_prompt = model_specific_prompt_kwargs["post_prompt"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] return f"{pre_prompt}{context}{question}\n{choices_str}{post_prompt}" - elif model_specific_prompt_kwargs["format"] == "qwen_vl": + elif lmms_eval_specific_kwargs["format"] == "qwen_vl": prompt = "Context: {}\nQuestion: {}\nOptions: {}\nAnswer:" context = context if context else "N/A" prompt = prompt.format(context, question, choices_str) return prompt else: - raise ValueError(f"Unknown prompt format: {model_specific_prompt_kwargs}") + raise ValueError(f"Unknown prompt format: {lmms_eval_specific_kwargs}") def sqa_doc_to_visual(doc): diff --git a/lmms_eval/tasks/seedbench_2/seedbench_2.yaml b/lmms_eval/tasks/seedbench_2/seedbench_2.yaml index 596e2cc0..3b83238c 100755 --- a/lmms_eval/tasks/seedbench_2/seedbench_2.yaml +++ b/lmms_eval/tasks/seedbench_2/seedbench_2.yaml @@ -40,13 +40,13 @@ metric_list: metadata: - version: 0.0 -model_specific_prompt_kwargs: - default : - img_token : - post_prompt : "Answer with the option's letter from the given choices directly." +lmms_eval_specific_kwargs: llava : img_token : post_prompt : "Answer with the option's letter from the given choices directly." + llava_onevision : + img_token : + post_prompt : "Answer with the option's letter from the given choices directly." gpt4V : img_token : post_prompt : "Answer with the option's letter from the given choices directly." \ No newline at end of file diff --git a/lmms_eval/tasks/stvqa/stvqa.yaml b/lmms_eval/tasks/stvqa/stvqa.yaml index 42ecaaca..9d049b4a 100755 --- a/lmms_eval/tasks/stvqa/stvqa.yaml +++ b/lmms_eval/tasks/stvqa/stvqa.yaml @@ -13,7 +13,7 @@ process_results: !function utils.stvqa_process_results metric_list: - metric: submission aggregation: !function utils.stvqa_aggregate_submissions -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/stvqa/utils.py b/lmms_eval/tasks/stvqa/utils.py index ff033f1f..0edd7d72 100755 --- a/lmms_eval/tasks/stvqa/utils.py +++ b/lmms_eval/tasks/stvqa/utils.py @@ -4,10 +4,10 @@ from lmms_eval.tasks._task_utils.file_utils import generate_submission_file -def stvqa_doc_to_text(doc, model_specific_prompt_kwargs): +def stvqa_doc_to_text(doc, lmms_eval_specific_kwargs): question = doc["question"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - post_prompt = model_specific_prompt_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/tempcompass/_default_template_yaml b/lmms_eval/tasks/tempcompass/_default_template_yaml index 404701d3..9b5087d4 100644 --- a/lmms_eval/tasks/tempcompass/_default_template_yaml +++ b/lmms_eval/tasks/tempcompass/_default_template_yaml @@ -3,7 +3,7 @@ dataset_kwargs: token: True video: True cache_dir: tempcompass -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: { diff --git a/lmms_eval/tasks/tempcompass/utils.py b/lmms_eval/tasks/tempcompass/utils.py index dbe4f831..cabc67da 100644 --- a/lmms_eval/tasks/tempcompass/utils.py +++ b/lmms_eval/tasks/tempcompass/utils.py @@ -64,57 +64,57 @@ def tempcompass_doc_to_visual(doc): # This is the place where you format your question -def tempcompass_doc_to_text_multi_choice(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def tempcompass_doc_to_text_multi_choice(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"]["multi-choice"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"]["multi-choice"] question = doc["question"] return f"{pre_prompt}{question}{post_prompt}" -def tempcompass_doc_to_text_yes_no(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def tempcompass_doc_to_text_yes_no(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"]["yes_no"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"]["yes_no"] question = doc["question"] return f"{pre_prompt}{question}{post_prompt}" -def tempcompass_doc_to_text_caption_matching(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def tempcompass_doc_to_text_caption_matching(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"]["caption_matching"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"]["caption_matching"] question = doc["question"] return f"{pre_prompt}{question}{post_prompt}" -def tempcompass_doc_to_text_captioning(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def tempcompass_doc_to_text_captioning(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"]["captioning"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"]["captioning"] question = doc["question"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/textcaps/_default_template_textcaps_yaml b/lmms_eval/tasks/textcaps/_default_template_textcaps_yaml index bb71de49..cfed932f 100755 --- a/lmms_eval/tasks/textcaps/_default_template_textcaps_yaml +++ b/lmms_eval/tasks/textcaps/_default_template_textcaps_yaml @@ -1,3 +1,3 @@ -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: prompt: Provide a one-sentence caption for the provided image. \ No newline at end of file diff --git a/lmms_eval/tasks/textcaps/utils.py b/lmms_eval/tasks/textcaps/utils.py index d21a92fd..bc8b266f 100755 --- a/lmms_eval/tasks/textcaps/utils.py +++ b/lmms_eval/tasks/textcaps/utils.py @@ -17,8 +17,8 @@ def textcaps_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def textcaps_doc_to_text(doc, model_specific_prompt_kwargs=None): - return model_specific_prompt_kwargs["prompt"] +def textcaps_doc_to_text(doc, lmms_eval_specific_kwargs=None): + return lmms_eval_specific_kwargs["prompt"] def textcaps_process_result(doc, result): diff --git a/lmms_eval/tasks/textvqa/_default_template_textvqa_yaml b/lmms_eval/tasks/textvqa/_default_template_textvqa_yaml index 282b401b..806edeca 100755 --- a/lmms_eval/tasks/textvqa/_default_template_textvqa_yaml +++ b/lmms_eval/tasks/textvqa/_default_template_textvqa_yaml @@ -7,7 +7,7 @@ generation_kwargs: until: - "ASSISTANT:" process_results: !function utils.textvqa_process_results -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/textvqa/utils.py b/lmms_eval/tasks/textvqa/utils.py index 0c8de47d..c9ea6d22 100755 --- a/lmms_eval/tasks/textvqa/utils.py +++ b/lmms_eval/tasks/textvqa/utils.py @@ -45,16 +45,16 @@ def textvqa_process_results(doc, result): } -def textvqa_doc_to_text(doc, model_specific_prompt_kwargs=None): +def textvqa_doc_to_text(doc, lmms_eval_specific_kwargs=None): pre_prompt = "" post_post = "" ocr_ref = "" - if model_specific_prompt_kwargs: - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] - if "ocr" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["ocr"]: + if lmms_eval_specific_kwargs: + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] + if "ocr" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["ocr"]: ocr_ref = f"\nReference OCR token: {', '.join(doc['ocr_tokens'])}" return f"{pre_prompt}{doc['question'].capitalize()}{ocr_ref}{post_prompt}" diff --git a/lmms_eval/tasks/vatex/utils.py b/lmms_eval/tasks/vatex/utils.py index cdb62806..ea3c93a0 100755 --- a/lmms_eval/tasks/vatex/utils.py +++ b/lmms_eval/tasks/vatex/utils.py @@ -77,14 +77,14 @@ def vatex_test_doc_to_visual(doc): return [video_path] -def vatex_ZH_doc_to_text(doc, model_specific_prompt_kwargs=None): +def vatex_ZH_doc_to_text(doc, lmms_eval_specific_kwargs=None): few_shot_prompt = """[视频1] 输出:一个穿黑运动服、戴红色头盔的男人正在攀登雪山。\n[视频2] 输出:一个戴着耳机男人在电脑面前模拟打架子鼓。\n[视频3] 输出:一个穿黑色短袖的男子的男子，双手十指交叉放在胸前，肘部放在面前的桌子上，桌子上有一台电脑，不一会儿，男子半个手臂都放在了桌子上。\n[视频4] 输出:一位女士在她的手上涂抹少量的面霜，并且在她的眼睛下涂抹。\n""" - return model_specific_prompt_kwargs["prompt"] + "\n" + few_shot_prompt + return lmms_eval_specific_kwargs["prompt"] + "\n" + few_shot_prompt -def vatex_test_doc_to_text(doc, model_specific_prompt_kwargs=None): +def vatex_test_doc_to_text(doc, lmms_eval_specific_kwargs=None): few_shot_prompt = """[video1] output: A man picks up a can of shoe paste, a towel, and brush from a table.\n[video2] output: A person places the frying pan on the stove and then another person flips over the food that is in it.\n[video3] output: A woman describes and demonstrates how to create a colorful cross stitch design.\n[video4] output: A little girl uses the grass in her yard as well as a blue mat to do flips.\n""" - return model_specific_prompt_kwargs["prompt"] + "\n" + few_shot_prompt + return lmms_eval_specific_kwargs["prompt"] + "\n" + few_shot_prompt def vatex_process_result(doc, result): diff --git a/lmms_eval/tasks/vatex/vatex_test.yaml b/lmms_eval/tasks/vatex/vatex_test.yaml index 0087e977..dd991945 100644 --- a/lmms_eval/tasks/vatex/vatex_test.yaml +++ b/lmms_eval/tasks/vatex/vatex_test.yaml @@ -32,7 +32,7 @@ dataset_kwargs: cache_dir: vatex_test # From_YouTube: True -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: prompt: Provide a brief single-sentence caption for the last video below. Do not give any reasoning, just the caption. You must follow the captioning style of the preceding videos. Do not start your response with "Output:", just provide the caption. gemini_api: diff --git a/lmms_eval/tasks/vatex/vatex_val_zh.yaml b/lmms_eval/tasks/vatex/vatex_val_zh.yaml index cc2a866b..598145a8 100755 --- a/lmms_eval/tasks/vatex/vatex_val_zh.yaml +++ b/lmms_eval/tasks/vatex/vatex_val_zh.yaml @@ -12,7 +12,7 @@ generation_kwargs: top_p: 1.0 num_beams: 1 do_sample: false -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: prompt: 请为提供的视频提供简短的描述。不要给出任何理由，只提供描述。您必须沿用前面视频的描述样式。不需要以 "输出"开头，只需提供描述即可. diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py index 5519c078..9ffde90c 100644 --- a/lmms_eval/tasks/vcr_wiki/utils.py +++ b/lmms_eval/tasks/vcr_wiki/utils.py @@ -73,11 +73,11 @@ def vcr_doc_to_visual(doc): return [doc["stacked_image"].convert("RGB")] -def vcr_doc_to_text(doc, model_specific_prompt_kwargs=None): - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] +def vcr_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{post_prompt}" diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml index 9ce88299..2fa1c602 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml index 37f0130b..3e99cec6 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml index 0ccc1ed0..c9ff384d 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml index 8c001e4e..a99eca67 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml index 16c79b15..4b00a2fc 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml index 65616894..f8788e66 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations." \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml index a07cf230..32cf502a 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml index fb41b668..0c317556 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml index e3654aed..0407f985 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml index 0401b3bd..38cc516e 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml index f6512e29..375dff20 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml index a36bb328..02b34f53 100644 --- a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml +++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml @@ -10,7 +10,7 @@ metric_list: - metric: exact_match aggregation: !function utils.vcr_aggregate_exact_match higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。" \ No newline at end of file diff --git a/lmms_eval/tasks/vibe_eval/utils.py b/lmms_eval/tasks/vibe_eval/utils.py index 9e3b3c56..09b0c58a 100644 --- a/lmms_eval/tasks/vibe_eval/utils.py +++ b/lmms_eval/tasks/vibe_eval/utils.py @@ -2,15 +2,18 @@ from dataclasses import dataclass from typing import Optional, List from pathlib import Path -import yaml - -from reka import ChatMessage -from reka.client import Reka +import yaml import re import os from copy import deepcopy +try: + from reka import ChatMessage + from reka.client import Reka +except ImportError: + eval_logger.warning("Reka is not installed, please install it by `pip install reka-api`") + REKA_API_KEY = os.getenv("REKA_API_KEY", "YOUR_API_KEY") with open(Path(__file__).parent / "vibe_eval.yaml", "r") as f: @@ -136,12 +139,12 @@ def vibe_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def vibe_doc_to_text(doc, model_specific_prompt_kwargs=None): +def vibe_doc_to_text(doc, lmms_eval_specific_kwargs=None): question = doc["prompt"].strip() - if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "": - question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}" - if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "": - question = f"{question}{model_specific_prompt_kwargs['post_prompt']}" + if "pre_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["pre_prompt"] != "": + question = f"{lmms_eval_specific_kwargs['pre_prompt']}{question}" + if "post_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["post_prompt"] != "": + question = f"{question}{lmms_eval_specific_kwargs['post_prompt']}" return question diff --git a/lmms_eval/tasks/vibe_eval/vibe_eval.yaml b/lmms_eval/tasks/vibe_eval/vibe_eval.yaml index ed7f31c6..a932b577 100644 --- a/lmms_eval/tasks/vibe_eval/vibe_eval.yaml +++ b/lmms_eval/tasks/vibe_eval/vibe_eval.yaml @@ -29,7 +29,7 @@ metric_list: metadata: evaluator: "reka-core-text" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" \ No newline at end of file diff --git a/lmms_eval/tasks/video_detail_description/_default_template_yaml b/lmms_eval/tasks/video_detail_description/_default_template_yaml index 914bf8ee..dd0948c1 100644 --- a/lmms_eval/tasks/video_detail_description/_default_template_yaml +++ b/lmms_eval/tasks/video_detail_description/_default_template_yaml @@ -3,7 +3,7 @@ dataset_kwargs: token: True video: True cache_dir: videochatgpt -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/video_detail_description/utils.py b/lmms_eval/tasks/video_detail_description/utils.py index 536776fb..f8dae7fb 100755 --- a/lmms_eval/tasks/video_detail_description/utils.py +++ b/lmms_eval/tasks/video_detail_description/utils.py @@ -64,15 +64,15 @@ def video_detail_description_doc_to_visual(doc): # format the question -def video_detail_description_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def video_detail_description_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] question = doc["question"] return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/videochatgpt/_default_template_yaml b/lmms_eval/tasks/videochatgpt/_default_template_yaml index 816297cc..6d3573af 100644 --- a/lmms_eval/tasks/videochatgpt/_default_template_yaml +++ b/lmms_eval/tasks/videochatgpt/_default_template_yaml @@ -3,7 +3,7 @@ dataset_kwargs: token: True video: True cache_dir: videochatgpt -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/videochatgpt/_videochatgpt.yaml b/lmms_eval/tasks/videochatgpt/_videochatgpt.yaml index b29f95e9..d8909232 100755 --- a/lmms_eval/tasks/videochatgpt/_videochatgpt.yaml +++ b/lmms_eval/tasks/videochatgpt/_videochatgpt.yaml @@ -1,5 +1,5 @@ group: videochatgpt task: -- videochatgpt_gen -- videochatgpt_temporal -- videochatgpt_consistency + - videochatgpt_gen + - videochatgpt_temporal + - videochatgpt_consistency diff --git a/lmms_eval/tasks/videochatgpt/utils.py b/lmms_eval/tasks/videochatgpt/utils.py index e59f2dbf..d700ee78 100755 --- a/lmms_eval/tasks/videochatgpt/utils.py +++ b/lmms_eval/tasks/videochatgpt/utils.py @@ -66,15 +66,15 @@ def videochatgpt_doc_to_visual(doc): # format the question -def videochatgpt_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def videochatgpt_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] question = doc["question"] @@ -82,15 +82,15 @@ def videochatgpt_doc_to_text(doc, model_specific_prompt_kwargs=None): # format the question for consistency -def videochatgpt_doc_to_text_consistency(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def videochatgpt_doc_to_text_consistency(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] if doc["question_1"] != "None": question = doc["question_1"] diff --git a/lmms_eval/tasks/videochatgpt/videochatgpt_consistency.yaml b/lmms_eval/tasks/videochatgpt/videochatgpt_consistency.yaml index 936878bf..f5562bf3 100755 --- a/lmms_eval/tasks/videochatgpt/videochatgpt_consistency.yaml +++ b/lmms_eval/tasks/videochatgpt/videochatgpt_consistency.yaml @@ -11,11 +11,9 @@ metric_list: aggregation: !function utils.videochatgpt_aggregate_consistency higher_is_better: true include: _default_template_yaml +full_docs: true generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original max_new_tokens: 1024 temperature: 0 top_p: 1.0 diff --git a/lmms_eval/tasks/videochatgpt/videochatgpt_generic.yaml b/lmms_eval/tasks/videochatgpt/videochatgpt_generic.yaml index 9affe534..4214eefd 100755 --- a/lmms_eval/tasks/videochatgpt/videochatgpt_generic.yaml +++ b/lmms_eval/tasks/videochatgpt/videochatgpt_generic.yaml @@ -17,3 +17,10 @@ metric_list: aggregation: !function utils.videochatgpt_aggregate_score higher_is_better: true include: _default_template_yaml + +generation_kwargs: + max_new_tokens: 1024 + temperature: 0 + top_p: 1.0 + num_beams: 1 + do_sample: false diff --git a/lmms_eval/tasks/videochatgpt/videochatgpt_temporal.yaml b/lmms_eval/tasks/videochatgpt/videochatgpt_temporal.yaml index 1e207c2a..54bb57a9 100755 --- a/lmms_eval/tasks/videochatgpt/videochatgpt_temporal.yaml +++ b/lmms_eval/tasks/videochatgpt/videochatgpt_temporal.yaml @@ -13,9 +13,6 @@ metric_list: include: _default_template_yaml generation_kwargs: - until: - - "ASSISTANT:" - image_aspect_ratio: original max_new_tokens: 1024 temperature: 0 top_p: 1.0 diff --git a/lmms_eval/tasks/videomme/utils.py b/lmms_eval/tasks/videomme/utils.py index c9ec7aa6..d0c7e93c 100644 --- a/lmms_eval/tasks/videomme/utils.py +++ b/lmms_eval/tasks/videomme/utils.py @@ -134,41 +134,45 @@ def extract_subtitles(video_path, subtitle_path): def parse_subtitle_time(time_str): - h, m, s_ms = time_str.split(':') - s, ms = s_ms.split(',') + h, m, s_ms = time_str.split(":") + s, ms = s_ms.split(",") return int(h) * 3600 + int(m) * 60 + int(s) + int(ms) / 1000 + def load_subtitles(subtitle_path): subtitles = {} - with open(subtitle_path, 'r', encoding='utf-8') as file: - content = file.read().split('\n\n') + with open(subtitle_path, "r", encoding="utf-8") as file: + content = file.read().split("\n\n") for section in content: if section.strip(): - lines = section.split('\n') + lines = section.split("\n") if len(lines) >= 3: - time_range = lines[1].split(' --> ') + time_range = lines[1].split(" --> ") start_time = parse_subtitle_time(time_range[0]) end_time = parse_subtitle_time(time_range[1]) - text = ' '.join(line for line in lines[2:]) + text = " ".join(line for line in lines[2:]) subtitles[(start_time, end_time)] = text return subtitles + def convert_time_to_frame(time_in_seconds, fps): return int(time_in_seconds * fps) + def extract_subtitles(video_path, subtitle_path): video = cv2.VideoCapture(video_path) fps = video.get(cv2.CAP_PROP_FPS) - total_frame=int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + total_frame = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) subtitles = load_subtitles(subtitle_path) - + subtitle_frames = [] for (start_time, end_time), text in subtitles.items(): start_frame = convert_time_to_frame(start_time, fps) end_frame = convert_time_to_frame(end_time, fps) subtitle_frames.append((start_frame, end_frame, text)) - return subtitle_frames,total_frame + return subtitle_frames, total_frame + def videomme_doc_to_visual(doc): cache_dir = os.path.join(base_cache_dir, cache_name) @@ -185,15 +189,18 @@ def videomme_doc_to_visual(doc): return [video_path] -def videomme_doc_to_text(doc, model_specific_prompt_kwargs=None): - option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option." +def videomme_doc_to_text(doc, lmms_eval_specific_kwargs=None): + option_prompt = "Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option." question = doc["question"] option = str(doc["options"]) question = question + "\n" + option - full_prompt=option_prompt+"\n"+question+"\n"+"The best answer is:" + post_prompt = lmms_eval_specific_kwargs["post_prompt"] if "post_prompt" in lmms_eval_specific_kwargs else "The best answer is:" + full_prompt = option_prompt + "\n" + question + "\n" + post_prompt return full_prompt + + # Frames + Subs -# This video's subtitles are listed below: +# This video's subtitles are listed below: # 【subtitles】 # Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option. @@ -204,53 +211,54 @@ def videomme_doc_to_text(doc, model_specific_prompt_kwargs=None): # 【question】 # The best answer is: -def videomme_doc_to_text_subtitle(doc, model_specific_prompt_kwargs=None): + +def videomme_doc_to_text_subtitle(doc, lmms_eval_specific_kwargs=None): cache_dir = os.path.join(base_cache_dir, cache_name) video_path = doc["videoID"] + ".mp4" - subtitle_path=os.path.join(cache_dir,"subtitle",doc["videoID"]+".srt") + subtitle_path = os.path.join(cache_dir, "subtitle", doc["videoID"] + ".srt") video_path = os.path.join(cache_dir, video_path) - if os.path.exists(subtitle_path): #Denote have subtitle - subtitle=open(subtitle_path).readlines() + if os.path.exists(subtitle_path): # Denote have subtitle + subtitle = open(subtitle_path).readlines() else: - subtitle="" - subtitles_prompt="This video's subtitles are listed below: \n" - if subtitle=="": - subtitle="No subtitles available" + subtitle = "" + subtitles_prompt = "This video's subtitles are listed below: \n" + if subtitle == "": + subtitle = "No subtitles available" else: - if "gemini_api_flag" in model_specific_prompt_kwargs: #specific for gemini_api - if model_specific_prompt_kwargs['gemini_api_flag']=="full subtitle": - textlist=[] + if "gemini_api_flag" in lmms_eval_specific_kwargs: # specific for gemini_api + if lmms_eval_specific_kwargs["gemini_api_flag"] == "full subtitle": + textlist = [] for ele in subtitle: pattern = r'(.*?)' matches = re.findall(pattern, ele) if matches: textlist.append(matches[0]) - subtitle_text="\n".join(textlist) + subtitle_text = "\n".join(textlist) else: - if "frame_num" in model_specific_prompt_kwargs: - frame_num=model_specific_prompt_kwargs['frame_num'] - subtitle_by_frame,total_frame=extract_subtitles(video_path,subtitle_path) + if "frame_num" in lmms_eval_specific_kwargs: + frame_num = lmms_eval_specific_kwargs["frame_num"] + subtitle_by_frame, total_frame = extract_subtitles(video_path, subtitle_path) uniform_sampled_frames = np.linspace(0, total_frame - 1, frame_num, dtype=int).tolist() - - subtitle_by_frame_idx=[] + + subtitle_by_frame_idx = [] for frame_idx in uniform_sampled_frames: - for idx,title in enumerate(subtitle_by_frame): - if frame_idx=title[0]: + for idx, title in enumerate(subtitle_by_frame): + if frame_idx < title[1] and frame_idx >= title[0]: subtitle_by_frame_idx.append(idx) - subtitle_by_frame_idx=list(set(subtitle_by_frame_idx)) + subtitle_by_frame_idx = list(set(subtitle_by_frame_idx)) - textlist=[] + textlist = [] for idx in subtitle_by_frame_idx: pattern = r'(.*?)' - raw_text=re.findall(pattern, subtitle_by_frame[idx][2]) + raw_text = re.findall(pattern, subtitle_by_frame[idx][2]) try: textlist.append(raw_text[0]) except: continue - subtitle_text="\n".join(textlist) - subtitle=subtitle_text + subtitle_text = "\n".join(textlist) + subtitle = subtitle_text - option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option." + option_prompt = "Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option." question = doc["question"] option = str(doc["options"]) # option = "\n".join([f"{opt}" for i, opt in enumerate(doc["options"])]) @@ -272,7 +280,7 @@ def videomme_doc_to_text_subtitle(doc, model_specific_prompt_kwargs=None): # The best answer is: -def videomme_doc_to_text_subtitle(doc, model_specific_prompt_kwargs=None): +def videomme_doc_to_text_subtitle(doc, lmms_eval_specific_kwargs=None): cache_dir = os.path.join(base_cache_dir, cache_name) video_path = doc["videoID"] + ".mp4" subtitle_path = os.path.join(cache_dir, "subtitle", doc["videoID"] + ".srt") @@ -285,8 +293,8 @@ def videomme_doc_to_text_subtitle(doc, model_specific_prompt_kwargs=None): if subtitle == "": subtitle = "No subtitles available" else: - if "gemini_api_flag" in model_specific_prompt_kwargs: # specific for gemini_api - if model_specific_prompt_kwargs["gemini_api_flag"] == "full subtitle": + if "gemini_api_flag" in lmms_eval_specific_kwargs: # specific for gemini_api + if lmms_eval_specific_kwargs["gemini_api_flag"] == "full subtitle": textlist = [] for ele in subtitle: pattern = r'(.*?)' @@ -295,8 +303,8 @@ def videomme_doc_to_text_subtitle(doc, model_specific_prompt_kwargs=None): textlist.append(matches[0]) subtitle_text = "\n".join(textlist) else: - if "frame_num" in model_specific_prompt_kwargs: - frame_num = model_specific_prompt_kwargs["frame_num"] + if "frame_num" in lmms_eval_specific_kwargs: + frame_num = lmms_eval_specific_kwargs["frame_num"] subtitle_by_frame, total_frame = extract_subtitles(video_path, subtitle_path) uniform_sampled_frames = np.linspace(0, total_frame - 1, frame_num, dtype=int).tolist() diff --git a/lmms_eval/tasks/videomme/videomme.yaml b/lmms_eval/tasks/videomme/videomme.yaml index 7689da73..08f3de19 100644 --- a/lmms_eval/tasks/videomme/videomme.yaml +++ b/lmms_eval/tasks/videomme/videomme.yaml @@ -23,10 +23,10 @@ metric_list: - metric: videomme_percetion_score aggregation: !function utils.videomme_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" - post_prompt: "\nAnswer the question with A, B, C, or D." + post_prompt: "\nAnswer with the option's letter from the given choices directly." gpt4v: pre_prompt: "" post_prompt: "\nAnswer the question with A, B, C, or D." diff --git a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml index cfa5f070..938bb2f2 100644 --- a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml +++ b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml @@ -23,7 +23,7 @@ metric_list: - metric: videomme_percetion_score aggregation: !function utils.videomme_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: frame_num: 32 pre_prompt: "" diff --git a/lmms_eval/tasks/vitatecs/_default_template_yaml b/lmms_eval/tasks/vitatecs/_default_template_yaml index e93ce7d5..67547982 100644 --- a/lmms_eval/tasks/vitatecs/_default_template_yaml +++ b/lmms_eval/tasks/vitatecs/_default_template_yaml @@ -3,7 +3,7 @@ dataset_kwargs: token: True video: True cache_dir: vitatecs -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nPlease response with a single letter (A or B):" \ No newline at end of file diff --git a/lmms_eval/tasks/vitatecs/utils.py b/lmms_eval/tasks/vitatecs/utils.py index b2adcaa9..af2b09ab 100644 --- a/lmms_eval/tasks/vitatecs/utils.py +++ b/lmms_eval/tasks/vitatecs/utils.py @@ -62,15 +62,15 @@ def vitatecs_doc_to_visual(doc): # This is the place where you format your question -def vitatecs_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def vitatecs_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] question, _, _ = format_question_and_answer(doc) return f"{pre_prompt}{question}{post_prompt}" diff --git a/lmms_eval/tasks/vizwiz_vqa/_default_template_vqa_yaml b/lmms_eval/tasks/vizwiz_vqa/_default_template_vqa_yaml index b7dd4137..248c763a 100755 --- a/lmms_eval/tasks/vizwiz_vqa/_default_template_vqa_yaml +++ b/lmms_eval/tasks/vizwiz_vqa/_default_template_vqa_yaml @@ -8,7 +8,7 @@ generation_kwargs: - "ASSISTANT:" metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/vizwiz_vqa/utils.py b/lmms_eval/tasks/vizwiz_vqa/utils.py index 9be1f574..b17cf8d1 100755 --- a/lmms_eval/tasks/vizwiz_vqa/utils.py +++ b/lmms_eval/tasks/vizwiz_vqa/utils.py @@ -48,15 +48,15 @@ def vizwiz_vqa_process_results(doc, result): } -def vizwiz_vqa_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def vizwiz_vqa_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] text = f"{pre_prompt}{doc['question'].capitalize()}{post_prompt}" return text diff --git a/lmms_eval/tasks/vqav2/_default_template_vqav2_yaml b/lmms_eval/tasks/vqav2/_default_template_vqav2_yaml index d3ce20f7..ef0a7c8b 100755 --- a/lmms_eval/tasks/vqav2/_default_template_vqav2_yaml +++ b/lmms_eval/tasks/vqav2/_default_template_vqav2_yaml @@ -9,7 +9,7 @@ generation_kwargs: max_new_tokens: 16 metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." \ No newline at end of file diff --git a/lmms_eval/tasks/vqav2/utils.py b/lmms_eval/tasks/vqav2/utils.py index f70a3965..71518e0c 100755 --- a/lmms_eval/tasks/vqav2/utils.py +++ b/lmms_eval/tasks/vqav2/utils.py @@ -68,15 +68,15 @@ def vqav2_process_results_val(doc, result): } -def vqav2_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def vqav2_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] return f"{pre_prompt}{doc['question']}{post_prompt}" diff --git a/lmms_eval/tasks/wild_vision_bench/_default_template_yaml b/lmms_eval/tasks/wild_vision_bench/_default_template_yaml index 7ce709dc..33d18d39 100644 --- a/lmms_eval/tasks/wild_vision_bench/_default_template_yaml +++ b/lmms_eval/tasks/wild_vision_bench/_default_template_yaml @@ -5,6 +5,7 @@ output_type: generate_until doc_to_visual: !function utils.wild_vision_doc_to_visual doc_to_text: !function utils.wild_vision_doc_to_text doc_to_target: !function utils.wild_vision_doc_to_target +process_results_use_image: true generation_kwargs: max_new_tokens: 4096 temperature: 0 diff --git a/lmms_eval/tasks/wild_vision_bench/utils.py b/lmms_eval/tasks/wild_vision_bench/utils.py index dbbbc2bc..3913a151 100644 --- a/lmms_eval/tasks/wild_vision_bench/utils.py +++ b/lmms_eval/tasks/wild_vision_bench/utils.py @@ -69,6 +69,7 @@ prompt_template = "<|User Prompt|>\n{question_1}\n\n<|The Start of Assistant A's Answer|>\n{answer_1}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{answer_2}\n<|The End of Assistant B's Answer|>" + def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10): headers = { "Authorization": f"Bearer {API_KEY}", @@ -96,6 +97,7 @@ def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10): response = requests.post(API_URL, headers=headers, json=payload, timeout=60) response.raise_for_status() response_data = response.json() + print(response_data) return response_data["choices"][0]["message"]["content"], GPT_EVAL_MODEL_NAME except requests.exceptions.RequestException as e: print(f"Request failed on attempt {attempt+1}: {e}") @@ -112,6 +114,7 @@ def image_to_base64(pil_image): pil_image.save(buffered, format="PNG") return base64.b64encode(buffered.getvalue()).decode("utf-8") + def get_score(judgement, pattern, pairwise=True): matches = pattern.findall(judgement) matches = [m for m in matches if m != ""] @@ -129,14 +132,15 @@ def wild_vision_doc_to_visual(doc): return [doc["image"].convert("RGB")] -def wild_vision_doc_to_text(doc, model_specific_prompt_kwargs=None): +def wild_vision_doc_to_text(doc, lmms_eval_specific_kwargs=None): question = doc["instruction"].strip() - if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "": - question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}" - if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "": - question = f"{question}{model_specific_prompt_kwargs['post_prompt']}" + if "pre_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["pre_prompt"] != "": + question = f"{lmms_eval_specific_kwargs['pre_prompt']}{question}" + if "post_prompt" in lmms_eval_specific_kwargs and lmms_eval_specific_kwargs["post_prompt"] != "": + question = f"{question}{lmms_eval_specific_kwargs['post_prompt']}" return question + def wild_vision_doc_to_target(doc): return doc[BASELINE_MODEL_NAME] diff --git a/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml b/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml index 93576768..88ba5690 100644 --- a/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml +++ b/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml @@ -3,7 +3,7 @@ dataset_name: release_bench_0617_with_modelresponse test_split: test500 output_type: generate_until include: _default_template_yaml -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/worldqa/utils.py b/lmms_eval/tasks/worldqa/utils.py index bd9b89ef..42d4059f 100755 --- a/lmms_eval/tasks/worldqa/utils.py +++ b/lmms_eval/tasks/worldqa/utils.py @@ -132,15 +132,15 @@ def worldqa_doc_to_visual(doc): # This is the place where you format your question -def worldqa_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs is None: - model_specific_prompt_kwargs = {} +def worldqa_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs is None: + lmms_eval_specific_kwargs = {} pre_prompt = "" post_prompt = "" - if "pre_prompt" in model_specific_prompt_kwargs: - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] - if "post_prompt" in model_specific_prompt_kwargs: - post_prompt = model_specific_prompt_kwargs["post_prompt"] + if "pre_prompt" in lmms_eval_specific_kwargs: + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] + if "post_prompt" in lmms_eval_specific_kwargs: + post_prompt = lmms_eval_specific_kwargs["post_prompt"] question = doc["question"] if "option" in doc: diff --git a/lmms_eval/tasks/worldqa/worldqa_generation.yaml b/lmms_eval/tasks/worldqa/worldqa_generation.yaml index f5ed41e9..78db45bc 100755 --- a/lmms_eval/tasks/worldqa/worldqa_generation.yaml +++ b/lmms_eval/tasks/worldqa/worldqa_generation.yaml @@ -13,7 +13,7 @@ metric_list: - metric: gpt_eval aggregation: !function utils.worldq_gen_gpt_eval higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/worldqa/worldqa_mc.yaml b/lmms_eval/tasks/worldqa/worldqa_mc.yaml index 821db87f..1ef4ace3 100755 --- a/lmms_eval/tasks/worldqa/worldqa_mc.yaml +++ b/lmms_eval/tasks/worldqa/worldqa_mc.yaml @@ -10,7 +10,7 @@ metric_list: - metric: gpt_eval aggregation: !function utils.worldqa_aggregate_mc_eval higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." diff --git a/lmms_eval/tasks/worldqa/worldqa_mcppl.yaml b/lmms_eval/tasks/worldqa/worldqa_mcppl.yaml index f954bf4c..283f1864 100755 --- a/lmms_eval/tasks/worldqa/worldqa_mcppl.yaml +++ b/lmms_eval/tasks/worldqa/worldqa_mcppl.yaml @@ -8,7 +8,7 @@ doc_to_target: !function utils.worldqa_doc_to_answer_mc_ppl doc_to_choice: !function utils.worldqa_doc_to_choice metric_list: - metric: acc -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/youcook2/utils.py b/lmms_eval/tasks/youcook2/utils.py index f4e9af91..2a116f1a 100644 --- a/lmms_eval/tasks/youcook2/utils.py +++ b/lmms_eval/tasks/youcook2/utils.py @@ -44,9 +44,9 @@ def youcook2_doc_to_visual(doc): return [os.path.join(cache_dir, doc["video_path"])] -def youcook2_doc_to_text(doc, model_specific_prompt_kwargs=None): - if model_specific_prompt_kwargs and "prompt" in model_specific_prompt_kwargs: - return model_specific_prompt_kwargs["prompt"] +def youcook2_doc_to_text(doc, lmms_eval_specific_kwargs=None): + if lmms_eval_specific_kwargs and "prompt" in lmms_eval_specific_kwargs: + return lmms_eval_specific_kwargs["prompt"] else: return "Provide a one-sentence caption for the provided video." diff --git a/lmms_eval/tasks/youcook2/youcook2_val.yaml b/lmms_eval/tasks/youcook2/youcook2_val.yaml index f574e6a3..6668abe0 100644 --- a/lmms_eval/tasks/youcook2/youcook2_val.yaml +++ b/lmms_eval/tasks/youcook2/youcook2_val.yaml @@ -31,7 +31,7 @@ metric_list: # aggregation : !function utils.youcook2_spice # higher_is_better : true include: _default_template_yaml -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: prompt: Provide a one-sentence caption for the provided video. gemini_api: diff --git a/pyproject.toml b/pyproject.toml index da2f2120..cb9f04e6 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ dependencies = [ "pytablewriter", "sacrebleu>=1.5.0", "scikit-learn>=0.24.1", - "sqlitedict", + "sqlitedict==2.1.0", "torch>=2.1.0", # to enable sdpa mode for running 34B model on one 80GB GPU "torchvision>=0.16.0", "timm", @@ -75,6 +75,7 @@ dependencies = [ "anls", "rouge", "capture_metric", + "protobuf==3.20", ] [project.optional-dependencies] @@ -84,9 +85,14 @@ vila = [ gemini = [ "google-generativeai", ] +reka = [ + "httpx==0.23.3", + "reka-api", +] all = [ "vila", "gemini", + "reka", ] [tool.setuptools.packages.find] diff --git a/tools/live_bench/create_dataset.py b/tools/live_bench/create_dataset.py index c02d811c..8400d6c6 100644 --- a/tools/live_bench/create_dataset.py +++ b/tools/live_bench/create_dataset.py @@ -4,9 +4,9 @@ if __name__ == "__main__": website = load_websites() - dataset = LiveBench(force_clear=False, name="2024-06") - dataset.capture(websites=website, driver_kwargs={"headless": True}, screen_shoter="single_screen", shoter_kwargs={"screen_size": (1024, 1024)}, qa_generator="gpt4v", scorer="gpt4v", checker="gemini") + dataset = LiveBench() + dataset.capture(websites=website, driver_kwargs={"headless": True}, screen_shoter="single_screen", shoter_kwargs={"screen_size": (1024, 1024)}, qa_generator="gpt4v", scorer="claude", checker="gemini") website = load_websites_from_file("/data/pufanyi/project/lmms-eval/temp/images") - dataset.capture(websites=website, screen_shoter="human", qa_generator="gpt4v", scorer="gpt4v", checker="gemini", driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}) + dataset.capture(websites=website, screen_shoter="human", qa_generator="gpt4v", scorer="claude", checker="gemini", driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}) dataset.upload() diff --git a/tools/live_bench/data_summary.ipynb b/tools/live_bench/data_summary.ipynb index 43048195..291aeff0 100644 --- a/tools/live_bench/data_summary.ipynb +++ b/tools/live_bench/data_summary.ipynb @@ -2,29 +2,30 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/data/pufanyi/anaconda3/anacondabin/envs/live_bench/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" + "Downloading readme: 100%|██████████| 2.45k/2.45k [00:00<00:00, 5.76MB/s]\n", + "Downloading data: 100%|██████████| 182M/182M [00:12<00:00, 14.3MB/s] \n", + "Generating test split: 100%|██████████| 404/404 [00:01<00:00, 364.97 examples/s]\n" ] } ], "source": [ - "from datasets import load_dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "data = load_dataset(\"lmms-lab/LiveBench\", \"2024-06\")" + "data = load_dataset(\"lmms-lab/LiveBench\", \"2024-07\")" ] }, { @@ -36,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -48,70 +49,59 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "False" + "subtask\n", + "Contextual Analysis 103\n", + "Deeper Implications 97\n", + "Broader Implications 79\n", + "Basic Understanding 59\n", + "Further Insights 55\n", + "Name: count, dtype: int64" ] }, - "execution_count": 4, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.iloc[620][\"checker\"] is None" + "df[\"subtask\"].value_counts()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "subtask\n", - "Contextual Analysis 477\n", - "Deeper Implications 132\n", - "Basic Understanding 118\n", - "Further Insights 63\n", - "Broader Implications 52\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df[\"subtask\"].value_counts()" + "df = df.sample(frac=1).reset_index(drop=True)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "df = df.sample(frac=1).reset_index(drop=True)" + "df = df[df[\"id\"] != 35]" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_303711/5529174.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " top_50_per_subtask = df.groupby('subtask').apply(lambda x: x.nlargest(50, 'score'))\n" + "/tmp/ipykernel_425178/992929014.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " top_50_per_subtask = df.groupby(\"subtask\").apply(lambda x: x.nlargest(50, \"score\"))\n" ] }, { @@ -120,7 +110,7 @@ "250" ] }, - "execution_count": 7, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -133,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -148,7 +138,7 @@ "Name: count, dtype: int64" ] }, - "execution_count": 8, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -159,16 +149,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "9.276" + "9.128" ] }, - "execution_count": 9, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -181,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -220,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -232,7 +222,7 @@ "})" ] }, - "execution_count": 11, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -243,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -266,7 +256,7 @@ " 'scorer_name': Value(dtype='string', id=None)}" ] }, - "execution_count": 12, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -284,31 +274,31 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Map: 100%|██████████| 250/250 [00:00<00:00, 273.33 examples/s]it/s]\n", - "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 4.98ba/s]\n", - "Uploading the dataset shards: 100%|██████████| 1/1 [00:18<00:00, 18.34s/it]\n" + "Map: 100%|██████████| 250/250 [00:00<00:00, 314.90 examples/s]it/s]\n", + "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 5.14ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:19<00:00, 19.84s/it]\n" ] }, { "data": { "text/plain": [ - "CommitInfo(commit_url='https://huggingface.co/datasets/lmms-lab/LiveBench/commit/89ce4e07cf0988b2305b7227ac2677d481e6112a', commit_message='Upload dataset', commit_description='', oid='89ce4e07cf0988b2305b7227ac2677d481e6112a', pr_url=None, pr_revision=None, pr_num=None)" + "CommitInfo(commit_url='https://huggingface.co/datasets/lmms-lab/LiveBench/commit/19dd279ffe05a6911e11f93a1e9699b6daf7be4e', commit_message='Upload dataset', commit_description='', oid='19dd279ffe05a6911e11f93a1e9699b6daf7be4e', pr_url=None, pr_revision=None, pr_num=None)" ] }, - "execution_count": 13, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data.push_to_hub(\"lmms-lab/LiveBench\", \"2024-06\", split=\"test\")" + "data.push_to_hub(\"lmms-lab/LiveBench\", \"2024-07\", split=\"test\")" ] }, { diff --git a/tools/live_bench/example.ipynb b/tools/live_bench/example.ipynb new file mode 100644 index 00000000..92b9df73 --- /dev/null +++ b/tools/live_bench/example.ipynb @@ -0,0 +1,481 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/data/pufanyi/anaconda3/anacondabin/envs/live_bench/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from random import sample\n", + "\n", + "from live_bench.websites.website import DefaultWebsite\n", + "from live_bench.websites import load_websites\n", + "\n", + "# website = load_websites()\n", + "# website = sample(website, 1)\n", + "# website[0].url\n", + "website = [DefaultWebsite(url=\"https://www.asahi.com/\")] # , DefaultWebsite(url=\"https://www.bbc.com/sport\"), DefaultWebsite(url=\"https://www.bbc.com/business\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "could not detect version_main.therefore, we are assuming it is chrome 108 or higher\n" + ] + } + ], + "source": [ + "from live_bench.data_generator.utils.extract_infomation import InfomationExtractor\n", + "from live_bench.screen_shoter import get_shoter\n", + "from live_bench.driver import load_driver\n", + "\n", + "shoter = get_shoter(\"single_screen\")\n", + "driver = load_driver()\n", + "w = shoter(driver, website[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "extractor = InfomationExtractor()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "response = extractor.extract_infomation(w)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Here is something you can take as reference.**\n", + "\n", + "## Text Extracted in the HTML\n", + "\n", + "Below is the text extracted from the website for you to take reference:\n", + "BBC Home - Breaking News, World News, US News, Sports, Business, Innovation, Climate, Culture, Travel, Video & Audio\n", + "\n", + "What son's conviction means for President Biden\n", + "The guilty verdict is unlikely to change voters' minds, but it will be a personal blow for the US president.\n", + "1 hr ago | US & Canada\n", + "\n", + "Hunter Biden found guilty on all counts in gun trial\n", + "The US president's son is found guilty of lying about his drug use when buying a handgun in 2018.\n", + "- The struggles and scandals of Hunter Biden\n", + "\n", + "Blinken says fate of ceasefire plan down to Hamas\n", + "The US diplomat says Israel's prime minister \"reaffirmed his commitment\" to a Gaza ceasefire plan.\n", + "2 hrs ago | Middle East\n", + "\n", + "Ukraine 'hits missile launch sites in Russia'\n", + "The mayor of the city of Kharkiv says the situation there is \"calmer\" as Russia has been shelling less.\n", + "1 hr ago | Europe\n", + "\n", + "Four US college instructors stabbed in public park in China\n", + "The instructors were on a daytime visit to a public park when they were attacked, Cornell College says.\n", + "4 hrs ago | Asia\n", + "\n", + "Animal-rights protesters attack portrait of King\n", + "Animal-rights protesters attack a portrait of King Charles III, in a London gallery.\n", + "2 hrs ago | UK\n", + "\n", + "Warning shots from South as NK soldiers cross border\n", + "The incident at the DMZ comes at a time of heightened tensions between the two Koreas.\n", + "11 hrs ago | Asia\n", + "\n", + "## Image Features\n", + "\n", + "From the screenshot of the news website you provided, here is the analysis based on the images displayed alongside the corresponding news headings and text:\n", + "\n", + "1. **Image Associated with Hunter Biden's Conviction**:\n", + " - **Description**: The image depicts Hunter Biden escorted by, possibly, security personnel or aides. He seems to be dressed in a formal dark suit and appears to be descending stairs or possibly exiting a vehicle. He carries an air of seriousness, likely reflective of the gravity of his legal situation.\n", + " - **Relevance**: This image effectively captures the serious, personal, and public nature of the judicial proceedings against the President's son, making the situation more relatable to the audience. It directly ties to the news confirming Hunter Biden’s guilty verdict in a gun trial related to lying about drug use.\n", + "\n", + "2. **Image Accompanying the Article on Biden's Supporters**:\n", + " - **Description**: The accompanying image shows a group of enthusiastic supporters holding signs, with one prominently reading \"Say Yes to Biden,\" suggesting a political rally or campaign event. The participants display expressions of support and enthusiasm.\n", + " - **Relevance**: This image provides a visual contrast to the first, highlighting the ongoing support for the Biden family or campaign despite the legal issues faced by Hunter Biden. It serves to illustrate the political backdrop and public opinion dynamic mentioned in the news headlines.\n", + "\n", + "These images serve different purposes:\n", + "- The first image personalizes the news story, putting a face to the name in a high-stakes legal controversy. It underlines the personal and public challenges faced by the Biden family due to the conviction.\n", + "- The second image contextualizes the broader political support for the Biden family, suggesting that despite personal legal woes, there is a segment of the populace fervently supporting them.\n", + "\n", + "The clear connection between the images and the corresponding textual content on the news site helps readers visualize and better understand the unfolding events, enhancing the impact of the news storytelling.\n", + "\n", + "## Interesting Points\n", + "\n", + "The BBC news website, as demonstrated through the detailed examination of its content, offers a dynamic and visually engaging approach to news presentation. Here’s a deeper analysis of how it distinguishes itself:\n", + "\n", + "1. **Comprehensive and Geographically Diverse News Coverage**:\n", + " - The content spans a wide range of geographical locations including the US, Middle East, Europe, Asia, and the UK. Each news piece targets a major recent event, reflecting the website’s commitment to global news coverage. This expansive geographic focus ensures that readers have access to a broad spectrum of significant, impactful news.\n", + "\n", + "2. **Varied Content Themes**: \n", + " - The news themes are diverse, covering political, social, and cultural issues. From the legal troubles of a high-profile political figure’s son in the US to a ceasefire plan in the Middle East and violent incidents in Asia, the website covers a wide array of topics. This variety meets different readers' interests and keeps the content engaging.\n", + "\n", + "3. **Immediate Relevance**:\n", + " - The website's content is timely, as indicated by timestamps such as “1 hr ago” and “2 hrs ago.” This reflects the website’s commitment to providing the latest news, which is crucial for maintaining reader engagement and trust in a digital age where current information is highly valued.\n", + "\n", + "4. **Stylistic and Engaging Visual Design**:\n", + " - The use of compelling images alongside the news articles plays a critical role in storytelling. For instance, the image of Hunter Biden descending steps with a serious demeanor visually reinforces the gravity of the news about his conviction. \n", + " - Meanwhile, the image of supporters holding \"Say Yes to Biden\" signs juxtaposed with Hunter Biden's legal news offers a visual narrative of continued support amidst political strife, underscoring the complexity and depth of public and personal life in politics.\n", + "\n", + "5. **Interactive and Multimedia Features**:\n", + " - The use of tags such as \"OLIVE\" beside the breaking story of Hunter Biden indicates an interactive or breaking news feature that likely offers real-time updates and extensive coverage. This kind of multimedia integration enhances user interaction and engagement.\n", + "\n", + "In summary, the BBC news website sets itself apart through a combination of up-to-date, visually engaging, and comprehensively covered news items that cater to a global audience with varied interests. The effective use of images not only contextualizes the stories but also adds a layer of emotional and visual impact, making the news relatable and striking.\n" + ] + } + ], + "source": [ + "print(str(response))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This screenshot from a news website contains several images corresponding to different news stories. Let's examine each image and extract relevant details:\n", + "\n", + "1. **Image associated with the Michael Mosley news story:**\n", + " - The image depicts a middle-aged couple, smiling warmly at each other in a sunny, natural outdoor setting. This photo likely portrays Dr. Michael Mosley with his wife, representing a human interest angle to the story about Dr. Mosley's disappearance on the Greek island of Symi. The caption, \"'We will not lose hope,' says Michael Mosley's wife,\" implies a context of hope and determination amidst difficult circumstances.\n", + "\n", + "2. **Image linked to the news about the hostages freed in Gaza:**\n", + " - This image features a group of soldiers with one individual in civilian clothing at the center, being lifted or celebrated, possibly right after a rescue scenario. The setting appears to be a rugged outdoor area, suggestive of a conflict or military zone, which aligns with the news story about hostages being freed in Gaza. The inclusion of armed personnel and a jubilant expression on the civilian's face highlights the relief and successful outcome of a dangerous operation.\n", + "\n", + "3. **Image for the Nova festival hostages news:**\n", + " - This image depicts a motorboat on clear water under bright skies, possibly implying the geographic setting related to Michael Mosley’s disappearance near the Greek island of Symi. The serene environment contrasts starkly with the concerning news of his disappearance during what might have been a routine outing or travel.\n", + "\n", + "These images serve as visual supplements to the written content, providing readers with a clearer, more immediate understanding of the stories. They help bridge the emotional and contextual gaps that pure text might leave, allowing readers to engage more deeply with the news events. Each image is carefully selected to evoke specific sentiments and to provide visual context to the news headlines and summaries.\n" + ] + } + ], + "source": [ + "print(response[\"features\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from live_bench import LiveBench" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = LiveBench(force_clear=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2024-06'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.name" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "could not detect version_main.therefore, we are assuming it is chrome 108 or higher\n", + "Capturing websites: 0%| | 0/1 [00:00 Tuple[List[LiveBenchData], Response]: images = screen_shoter.capture(driver, website) qa_data, logs = get_qa_data(images, qa_generator, test=test, infomation_getter=infomation_getter) data = [] for qa in qa_data: - item = LiveBenchData(screen=images, question=qa.question, answer=qa.answer, subtask=qa.subtask, criteria=qa.criteria, data_generator=qa_generator.get_name(), checker=checker, scorer=scorer) + # qa_data = question_finalizer.finalize_question(qa, images.images) + item = LiveBenchData(screen=images, question=qa.question, answer=qa.answer, subtask=qa.subtask, criteria=qa.criteria, data_generator=qa_generator.get_name(), checker=checker, scorer=scorer, finalizer=question_finalizer) if score_threshold and (not item.score or item.score < score_threshold): continue data.append(item) @@ -94,7 +96,23 @@ def add(self, data: LiveBenchData, id: int = None): organized_data["id"] = id self.hf_data = self.hf_data.add_item(organized_data) - def capture(self, websites: List[Website] = None, *, screen_shoter="single_screen", qa_generator=None, checker=None, driver=None, scorer=None, test=False, driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}, log_folder="./logs"): + def capture( + self, + websites: List[Website] = None, + *, + screen_shoter="single_screen", + qa_generator=None, + checker=None, + driver=None, + scorer=None, + question_finalizer=None, + test=False, + driver_kwargs={}, + shoter_kwargs={}, + generator_kwargs={}, + question_finalizer_kwargs={}, + log_folder="./logs", + ): can_quit_driver = False if driver is None and screen_shoter != "human": driver = load_driver(**driver_kwargs) @@ -112,11 +130,13 @@ def capture(self, websites: List[Website] = None, *, screen_shoter="single_scree scorer = get_score_getter(scorer) elif scorer is None: scorer = get_random_score_getter() + if question_finalizer is None: + question_finalizer = QuestionFinalizer(**question_finalizer_kwargs) logs = [] infomation_getter = InfomationExtractor() for website in tqdm(websites, desc="Capturing websites"): try: - data, log = get_live_bench_data(driver, website, screen_shoter, qa_generator, checker, test=test, scorer=scorer, infomation_getter=infomation_getter) + data, log = get_live_bench_data(driver, website, screen_shoter, qa_generator, checker, test=test, scorer=scorer, infomation_getter=infomation_getter, question_finalizer=question_finalizer) logs.append(log.to_dict()) for d in data: self.add(d) diff --git a/tools/live_bench/live_bench/data_generator/live_bench_data.py b/tools/live_bench/live_bench/data_generator/live_bench_data.py new file mode 100644 index 00000000..3e53f4ca --- /dev/null +++ b/tools/live_bench/live_bench/data_generator/live_bench_data.py @@ -0,0 +1,139 @@ +from live_bench.screen_shoter.screen import ScreenImage +from live_bench.data_generator.utils.extract_infomation import ImageInfomation +from live_bench.data_generator.qa_generator import QAGenerator +from live_bench.data_generator.question_finalizer import QuestionFinalizer +import datasets + + +class LiveBenchData(object): + SUBTASKS = ("Basic Understanding", "Contextual Analysis", "Deeper Implications", "Broader Implications", "Further Insights") + + features = datasets.Features( + { + "id": datasets.Value("int32"), + "images": datasets.Sequence(datasets.Image()), + "website": datasets.Value("string"), + "question": datasets.Value("string"), + "answer": datasets.Value("string"), + "criteria": datasets.Value("string"), + "subtask": datasets.Value("string"), + "data_generator": datasets.Value("string"), + "checker": datasets.Value("string"), + "date_time": datasets.Value("string"), + "screen_shoter": datasets.Value("string"), + "screen_size": datasets.Value("string"), + "score": datasets.Value("int32"), + "reason": datasets.Value("string"), + "scorer_name": datasets.Value("string"), + } + ) + + def __init__( + self, + *, + screen: ScreenImage, + question: str, + answer: str, + criteria: str, + subtask: str, + data_generator: str, + infomation: ImageInfomation = None, + score: int = None, + reason: str = None, + checker: QAGenerator = None, + finalizer: QuestionFinalizer = None, + scorer_name=None, + scorer=None, + ): + self.screen = screen + self.question = question + self.answer = answer + self.criteria = criteria + self.subtask = subtask + self.data_generator = data_generator + self.infomation = infomation + self.checker = None + if checker: + response = checker.check(screen, question, answer, criteria, subtask, infomation=infomation) + if response.success: + formatted_response = checker.format_checked_response(response) + if formatted_response.question and formatted_response.answer and formatted_response.criteria: + self.question = formatted_response.question + self.answer = formatted_response.answer + self.criteria = formatted_response.criteria + if formatted_response.subtask: + self.subtask = formatted_response.subtask + else: + self.subtask = subtask + self.checker = checker.get_name() + if finalizer: + try: + qa = finalizer.finalize_question(self.question, self.answer, self.criteria, self.screen.images) + except Exception as e: + raise e + self.question = qa["question"] + self.answer = qa["answer"] + self.criteria = qa["criteria"] + if self.subtask: + for sub in LiveBenchData.SUBTASKS: + if sub.lower() in self.subtask.lower(): + self.subtask = sub + break + else: + self.subtask = "Further Insights" + else: + self.subtask = "Not Specified" + if score is not None: + self.score = score + self.reason = reason + self.scorer_name = scorer_name + else: + score = scorer.get_score(question, answer, screen.images) + self.score = score.score + self.reason = score.reason + self.scorer_name = scorer.get_name() + + def to_dict(self): + images = self.screen.images + website = self.screen.website.get_info() + question = self.question + answer = self.answer + subtask = self.subtask + data_generator = self.data_generator + date_time = self.screen.capture_datetime + screen_shoter = self.screen.shoter + screen_size = self.screen.screen_size + criteria = self.criteria + return { + "images": images, + "website": website, + "question": question, + "answer": answer, + "criteria": criteria, + "subtask": subtask, + "data_generator": data_generator, + "checker": self.checker, + "date_time": date_time, + "screen_shoter": screen_shoter, + "screen_size": screen_size, + "score": self.score, + "reason": self.reason, + "scorer_name": self.scorer_name, + } + + def to_hf_dict(self): + return self.features.encode_example(self.to_dict()) + + def to_output_dict(self): + return { + "screen": self.screen.to_output_dict(), + "question": self.question, + "answer": self.answer, + "criteria": self.criteria, + "subtask": self.subtask, + "data_generator": self.data_generator, + "checker": self.checker, + "score": self.score, + "reason": self.reason, + "scorer_name": self.scorer_name, + } diff --git a/tools/live_bench/live_bench/data_generator/prompt.md b/tools/live_bench/live_bench/data_generator/prompt.md index 10835d73..faa2fa88 100644 --- a/tools/live_bench/live_bench/data_generator/prompt.md +++ b/tools/live_bench/live_bench/data_generator/prompt.md @@ -1,4 +1,4 @@ -I would like you to act as a quizmaster who designs questions based on a provided image that would challenge adults to think critically. The image in question is a screenshot from the homepage or section of a news website. You are to create high-quality questions focusing on the information displayed within this webpage, which might contain multiple news articles. Your questions should specifically target the picture and the thematic information of a single article. Your question should be answerable, and checkable. Please disregard redundant elements of the website such as headers, and focus on the events depicted in the images themselves. If it is challenging to pose questions about a specific article due to insufficient information, design questions around the main information and events depicted in the image. +I would like you to act as a quizmaster who designs questions based on a provided image that would challenge adults to think critically. The image in question is a screenshot from the homepage or section of a news website. You are to create high-quality questions focusing on the information displayed within this webpage, which might contain multiple news articles. Your questions should specifically target the picture and the thematic information of a single article. Your question should be answerable, and checkable. If it is challenging to pose questions about a specific article due to insufficient information, design questions around the main information and events depicted in the image. Within your capabilities, try to make the questions more challenging. A well-crafted question about an event should allow respondents to gain deeper insights by observing and analyzing the event, paying attention to the following aspects: @@ -8,7 +8,7 @@ A well-crafted question about an event should allow respondents to gain deeper i - Broader Implications: Questions that extend the discussion beyond the immediate context of the image to its wider impact on society, other fields, or global issues. - Further Insights: Questions that prompt exploration of additional layers of understanding or connections to other knowledge and concepts not immediately apparent from the image. -Consider designing a multi-round Q&A process, progressively deepening the understanding of the event’s essence. Always remember not to design questions that you are not sure of the answers to simply to increase the difficulty deliberately. +Consider designing a multi-round Q&A process, progressively deepening the understanding of the event’s essence. Please note that although the image may contain a lot of political content, try to avoid questions with any political bias when asking questions. Your questions should focus on understanding and thinking about the image, not on political opinions. diff --git a/tools/live_bench/live_bench/data_generator/qa_generator.py b/tools/live_bench/live_bench/data_generator/qa_generator.py new file mode 100644 index 00000000..24989277 --- /dev/null +++ b/tools/live_bench/live_bench/data_generator/qa_generator.py @@ -0,0 +1,522 @@ +import io +import re +import os +import openai +import anthropic +import base64 +import json +import random +import logging +import google.generativeai as genai +from time import sleep +from PIL import Image +from typing import List +from abc import ABC, abstractmethod +from live_bench.data_generator.response import Response +from live_bench.screen_shoter import ScreenImage +from live_bench.data_generator.utils.gpt4v import format_gpt4v_images, gpt4v_generate_response +from live_bench.data_generator.utils.claude import format_claude_images, claude_generate_response +from live_bench.data_generator.utils.gemini import gemini_generate_response +from live_bench.data_generator.utils.extract_infomation import InfomationExtractor, ImageInfomation + +logger = logging.getLogger("lmms-eval") + +SUBTASKS = {"Basic Understanding", "Contextual Analysis", "Deeper Implications", "Broader Implications", "Further Insights"} + + +class QAData(object): + def __init__(self, question: str = None, answer: str = None, criteria: str = None, subtask: str = None): + self.question = question + self.answer = answer + self.criteria = criteria + self.subtask = subtask + + def parse_subtask(subtask: str) -> str: + subtask = subtask.strip().lower() + for valid_subtask in SUBTASKS: + if valid_subtask.lower() in subtask.lower(): + return valid_subtask + return "Unknown Subtask" + + def set_subtask(self, subtask: str): + """ + Set the subtask for the QAData instance after parsing it. + + Args: + subtask (str): The subtask string to be set. + """ + self.subtask = self.parse_subtask(subtask) + + def to_dict(self): + return {"question": self.question, "answer": self.answer} + + +class QAGenerator(ABC): + def __init__(self, prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md")): + self.prompt_file = prompt_file + self.prompt = self._load_prompt() + + def _load_prompt(self): + with open(self.prompt_file, "r") as f: + return f.read() + + def __call__(self, images: ScreenImage, *args, **kwargs): + return self.generate(images, *args, **kwargs) + + def generate(self, images: ScreenImage, *, test=False, infomation=None, **kwargs) -> Response: + if test: + return Response(success=True, content="This is a test response.", full_log={}) + return self._generate(images, infomation=infomation, test=test, **kwargs) + + def check(self, images: ScreenImage, question, answer, criteria, subtask, *, infomation=None, test=False, **kwargs) -> Response: + if test: + return Response(success=True, content="This is a test response.", full_log={}) + return self._check(images, question, answer, criteria, subtask, infomation=infomation, **kwargs) + + @abstractmethod + def _generate(self, images: ScreenImage, **kwargs) -> Response: + raise NotImplementedError("_generate not implemented") + + @abstractmethod + def _check(self, images: ScreenImage, question, answer, criteria, subtask, **kwargs) -> Response: + raise NotImplementedError("_check not implemented") + + def format_response(self, response: Response) -> QAData: + if response.success: + qa_data = self._format_response(response) + if qa_data is None: + return [] + else: + return qa_data + else: + return [] + + @abstractmethod + def _format_response(self, response: Response) -> str: + raise NotImplementedError("format_response not implemented") + + @abstractmethod + def format_checked_response(self, response: Response) -> QAData: + raise NotImplementedError("format_checked_response not implemented") + + def get_name(self) -> str: + raise NotImplementedError("get_name not implemented") + + +class GeneratorRegistry: + def __init__(self): + self.generators = {} + + def register_generator(self, name): + def decorator(cls): + self.generators[name] = cls + cls.get_name = lambda self: name + return cls + + return decorator + + def get_generator(self, name) -> QAGenerator: + return self.generators[name] + + def get_random_generator(self) -> QAGenerator: + return random.choice(list(self.generators.values())) + + +generator_registry = GeneratorRegistry() + + +def register_generator(name): + return generator_registry.register_generator(name) + + +def get_generator(name, *args, **kwargs) -> QAGenerator: + return generator_registry.get_generator(name)(*args, **kwargs) + + +def get_random_generator(*args, **kwargs) -> QAGenerator: + return generator_registry.get_random_generator()(*args, **kwargs) + + +@register_generator("gpt4v") +class GPT4Generator(QAGenerator): + def __init__( + self, + prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md"), + model="gpt-4o", + example_path=os.path.join(os.path.dirname(__file__), "example"), + check_prompt=os.path.join(os.path.dirname(__file__), "check_prompt.md"), + ): + super().__init__(prompt_file) + API_KEY = os.getenv("OPENAI_API_KEY") + if not API_KEY: + raise ValueError("OPENAI_API_KEY environment variable not set.") + self.api_key = API_KEY + self.client = openai.OpenAI(api_key=self.api_key) + self.model = model + if os.path.exists(example_path): + self.example_path = example_path + else: + self.example_path = None + if os.path.exists(check_prompt): + with open(check_prompt, "r") as f: + self.check_prompt = f.read() + else: + self.check_prompt = check_prompt + + def format_messages(self, images: List[Image.Image], example_image: Image.Image, example_output: str, infomation: ImageInfomation): + example = [ + { + "type": "text", + "text": "Here are few examples about the task and the expected output format. You can take these as examples to generate your own questions.", + }, + format_gpt4v_images(example_image), + { + "type": "text", + "text": example_output, + }, + ] + content = example + [format_gpt4v_images(image) for image in images] + if infomation: + content.append({"type": "text", "text": str(infomation)}) + content.append( + { + "type": "text", + "text": "Please generate high-quality questions focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.", + }, + ) + messages = [ + { + "role": "system", + "content": self.prompt, + }, + { + "role": "user", + "content": content, + }, + ] + return messages + + def _generate(self, images: ScreenImage, *, max_tokens=4096, max_try_times=5, infomation=None, **kwargs): + if self.example_path: + example_image_path = os.path.join(self.example_path, "example_website.png") + example_output_path = os.path.join(self.example_path, "example_output.json") + example_image = Image.open(example_image_path) + with open(example_output_path, "r") as f: + example_output = json.load(f) + example_output = json.dumps(example_output, indent=4) + + messages = self.format_messages(images.images, example_image, example_output, infomation) + + return gpt4v_generate_response(client=self.client, model=self.model, messages=messages, max_tokens=max_tokens, max_try_times=max_try_times, json_format=True, **kwargs) + + def get_check_prompt(self, question: str, answer: str, criteria, subtask, images: List[Image.Image], infomation: ImageInfomation = None): + messages = [ + { + "role": "system", + "content": self.check_prompt, + } + ] + content = [] + for img in images: + content.append(format_gpt4v_images(img)) + content.append( + { + "type": "text", + "text": f"Question: {question}\nQuestioner's Answer: {answer}\nCriteria: {criteria}\nSubtask: {subtask}", + }, + ) + if infomation: + content.append( + { + "type": "text", + "text": str(infomation), + }, + ) + content.append( + { + "type": "text", + "text": "Please rephrase or rewrite the high-quality question focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.", + }, + ) + messages.append( + { + "role": "user", + "content": content, + } + ) + return messages + + def _check(self, images: ScreenImage, question, answer, criteria, subtask, *, max_tokens=4096, max_try_times=5, **kwargs): + messages = self.get_check_prompt(question, answer, criteria, subtask, images.images) + return gpt4v_generate_response(client=self.client, model=self.model, messages=messages, max_tokens=max_tokens, max_try_times=max_try_times, json_format=True, **kwargs) + + def format_checked_response(self, response: Response): + data = json.loads(response.content) + question = data.get("question", None) + answer = data.get("answer", None) + criteria = data.get("criteria", None) + subtask = data.get("subtask", None) + return QAData(question=question, answer=answer, criteria=criteria, subtask=subtask) + + def _format_response(self, response: Response) -> List[QAData]: + try: + qa_data = [] + content = json.loads(response.content) + for subtask, messages in content.items(): + subtask = subtask.lower() + for message in messages: + message_lower = {k.lower(): v for k, v in message.items()} + try: + question = message_lower["question"] + answer = message_lower["answer"] + criteria = message_lower["criteria"] + qa_data.append(QAData(question=question, answer=answer, criteria=criteria, subtask=subtask)) + except KeyError as e: + logger.error(f"Failed to parse response: {message}") + logger.error(f"Error: {e}") + return qa_data + except Exception as e: + logger.error(f"Failed to format response: {e}") + return [] + + +@register_generator("gemini") +class GeminiGenerator(QAGenerator): + def __init__( + self, + prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md"), + model="gemini-1.5-pro-latest", + example_path=os.path.join(os.path.dirname(__file__), "example"), + check_prompt=os.path.join(os.path.dirname(__file__), "check_prompt.md"), + ): + super().__init__(prompt_file) + GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") + if not GOOGLE_API_KEY: + raise ValueError("GOOGLE_API_KEY environment variable not set.") + genai.configure(api_key=GOOGLE_API_KEY) + + self.api_key = GOOGLE_API_KEY + self.model = model + self.client = genai.GenerativeModel(model) + if os.path.exists(example_path): + self.example_path = example_path + else: + self.example_path = None + if os.path.exists(check_prompt): + with open(check_prompt, "r") as f: + self.check_prompt = f.read() + else: + self.check_prompt = check_prompt + + def format_messages(self, images: List[Image.Image], example_image: Image.Image, example_output: str, infomation: ImageInfomation = None): + content = [self.prompt, "\n", "Example Image:", example_image, "\n", "Example Output:", example_output] + content.extend(images) + content.append(str(infomation)) + content.append("Please generate high-quality questions focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.") + return content + + def _generate(self, images: ScreenImage, *, max_tokens=4096, max_try_times=5, infomation: ImageInfomation = None, **kwargs): + if self.example_path: + example_image_path = os.path.join(self.example_path, "example_website.png") + example_output_path = os.path.join(self.example_path, "example_output.json") + example_image = Image.open(example_image_path) + with open(example_output_path, "r") as f: + # example_output = f.read() + example_output = json.load(f) + example_output = json.dumps(example_output, indent=4) + + messages = self.format_messages(images.images, example_image, example_output, infomation) + + return gemini_generate_response(self.client, messages, max_tokens, max_try_times, **kwargs) + + def get_check_prompt(self, question: str, answer: str, criteria, subtask, images: List[Image.Image], infomation: ImageInfomation = None): + content = [self.check_prompt] + images + content.append(f"Question: {question}\nQuestioner's Answer: {answer}\nCriteria: {criteria}, Subtask: {subtask}") + content.append("Your response should be strictly in the below format:\n\nQuestion: \nAnswer: \nCriteria: \nSubtask: ") + if infomation: + content.append(str(infomation)) + return content + + def _check(self, images: ScreenImage, question, answer, criteria, subtask, *, max_tokens=4096, max_try_times=5, infomation: ImageInfomation = None, **kwargs): + messages = self.get_check_prompt(question, answer, criteria, subtask, images.images, infomation) + return gemini_generate_response(self.client, messages, max_tokens, max_try_times, **kwargs) + + def format_checked_response(self, response: Response): + # Extract the question, answer, and subtask from the normalized content + question_match = re.search(r"question:\s*(.*?)\nAnswer:", response.content, re.IGNORECASE | re.DOTALL) + answer_match = re.search(r"answer:\s*(.*?)\nCriteria", response.content, re.IGNORECASE | re.DOTALL) + criteria_match = re.search(r"criteria:\s*(.*?)\n(Subtask:|$)", response.content, re.IGNORECASE | re.DOTALL) + subtask_match = re.search(r"subtask:\s*(.*)", response.content, re.IGNORECASE) + + question = answer = subtask = None + + if question_match: + # Extract the matched groups + question = question_match.group(1).strip() + if answer_match: + answer = answer_match.group(1).strip() + if criteria_match: + criteria = criteria_match.group(1).strip() + if subtask_match: + subtask = subtask_match.group(1).strip() + + return QAData(question=question, answer=answer, criteria=criteria, subtask=subtask) + + def _format_response(self, response: Response) -> List[QAData]: + try: + qa_data = [] + content = json.loads(response.content) + for subtask, message in content.items(): + subtask = subtask.lower() + message_lower = {k.lower(): v for k, v in message.items()} + try: + question = message_lower["question"] + answer = message_lower["answer"] + qa_data.append(QAData(question=question, answer=answer, subtask=subtask)) + except KeyError as e: + logger.error(f"Failed to parse response: {message}") + logger.error(f"Error: {e}") + return qa_data + except Exception as e: + logger.error(f"Failed to format response: {e}") + return [] + + +@register_generator("claude") +class ClaudeGenerator(QAGenerator): + def __init__( + self, + prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md"), + model="claude-3-5-sonnet-20240620", + example_path=os.path.join(os.path.dirname(__file__), "example"), + check_prompt=os.path.join(os.path.dirname(__file__), "check_prompt.md"), + ): + super().__init__(prompt_file) + API_KEY = os.getenv("ANTHROPIC_API_KEY") + if not API_KEY: + raise ValueError("ANTHROPIC_API_KEY environment variable not set.") + self.api_key = API_KEY + self.client = anthropic.Anthropic(api_key=self.api_key) + self.model = model + if os.path.exists(example_path): + self.example_path = example_path + else: + self.example_path = None + if os.path.exists(check_prompt): + with open(check_prompt, "r") as f: + self.check_prompt = f.read() + else: + self.check_prompt = check_prompt + + def format_messages(self, images: List[Image.Image], example_image: Image.Image, example_output: str, infomation: ImageInfomation): + example = [ + { + "type": "text", + "text": "Here are few examples about the task and the expected output format. You can take these as examples to generate your own questions.", + }, + format_claude_images(example_image), + { + "type": "text", + "text": example_output, + }, + ] + content = example + [format_claude_images(image) for image in images] + if infomation: + content.append({"type": "text", "text": str(infomation)}) + content.append( + { + "type": "text", + "text": "Please generate high-quality questions focusing on the information displayed within this webpage. Ensure your response adheres to the examples provided above and is structured in JSON format, incorporating regular expressions to validate the format.", + }, + ) + messages = [ + { + "role": "user", + "content": content, + }, + ] + return messages + + def _generate(self, images: ScreenImage, *, max_tokens=4096, max_try_times=5, infomation=None, **kwargs): + if self.example_path: + example_image_path = os.path.join(self.example_path, "example_website.png") + example_output_path = os.path.join(self.example_path, "example_output.json") + example_image = Image.open(example_image_path) + with open(example_output_path, "r") as f: + # example_output = f.read() + example_output = json.load(f) + example_output = json.dumps(example_output, indent=4) + + messages = self.format_messages(images.images, example_image, example_output, infomation) + + return claude_generate_response(client=self.client, model=self.model, messages=messages, max_tokens=max_tokens, max_try_times=max_try_times, json_format=True, system=self.prompt, **kwargs) + + def get_check_prompt(self, question: str, answer: str, criteria, subtask, images: List[Image.Image], infomation: ImageInfomation = None): + messages = [ + { + "role": "system", + "content": self.check_prompt, + } + ] + content = [] + for img in images: + content.append(format_claude_images(img)) + content.append( + { + "type": "text", + "text": f"Question: {question}\nQuestioner's Answer: {answer}\nCriteria: {criteria}\nSubtask: {subtask}", + }, + ) + if infomation: + content.append( + { + "type": "text", + "text": str(infomation), + }, + ) + content.append( + { + "type": "text", + "text": "Please rephrase or rewrite the high-quality question focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.", + }, + ) + messages.append( + { + "role": "user", + "content": content, + } + ) + return messages + + def _check(self, images: ScreenImage, question, answer, criteria, subtask, *, max_tokens=4096, max_try_times=5, **kwargs): + messages = self.get_check_prompt(question, answer, criteria, subtask, images.images) + return claude_generate_response(client=self.client, model=self.model, messages=messages, max_tokens=max_tokens, max_try_times=max_try_times, json_format=True, **kwargs) + + def format_checked_response(self, response: Response): + data = json.loads(response.content) + question = data.get("question", None) + answer = data.get("answer", None) + criteria = data.get("criteria", None) + subtask = data.get("subtask", None) + return QAData(question=question, answer=answer, criteria=criteria, subtask=subtask) + + def _format_response(self, response: Response) -> List[QAData]: + try: + qa_data = [] + content = json.loads(response.content) + for subtask, messages in content.items(): + subtask = subtask.lower() + for message in messages: + message_lower = {k.lower(): v for k, v in message.items()} + try: + question = message_lower["question"] + answer = message_lower["answer"] + criteria = message_lower["criteria"] + qa_data.append(QAData(question=question, answer=answer, criteria=criteria, subtask=subtask)) + except KeyError as e: + logger.error(f"Failed to parse response: {message}") + logger.error(f"Error: {e}") + return qa_data + except Exception as e: + logger.error(f"Failed to format response: {e}") + return [] diff --git a/tools/live_bench/live_bench/data_generator/question_finalizer.py b/tools/live_bench/live_bench/data_generator/question_finalizer.py new file mode 100644 index 00000000..86a625ef --- /dev/null +++ b/tools/live_bench/live_bench/data_generator/question_finalizer.py @@ -0,0 +1,132 @@ +import os +import json +import openai +import anthropic +import google.generativeai as genai +from PIL import Image +from typing import List +from abc import ABC, abstractmethod +from live_bench.data_generator.qa_generator import QAData +from live_bench.data_generator.utils.gpt4v import format_gpt4v_images, gpt4v_generate_response +from live_bench.data_generator.utils.claude import format_claude_images, claude_generate_response +from live_bench.data_generator.utils.gemini import gemini_generate_response + + +REJECT_TO_ANSWER = "Reject to answer" + + +class AnswerGetter(ABC): + @abstractmethod + def get_answer(self, question: str, images: List[Image.Image]): + raise NotImplementedError("get_answer not implemented") + + +class GPT4VAnswerGetter(AnswerGetter): + def __init__(self, model: str = "gpt-4o", api_key=None): + self.model = model + if api_key is None: + self.api_key = os.getenv("OPENAI_API_KEY", None) + else: + self.api_key = api_key + self.client = openai.OpenAI(api_key=self.api_key) + + def get_answer(self, question: str, images: List[Image.Image]): + messages = [{"role": "user", "content": format_gpt4v_images(images) + [{"type": "text", "text": question}]}] + response = gpt4v_generate_response(messages=messages, client=self.client, model=self.model) + if response.success: + return response.content + else: + return REJECT_TO_ANSWER + + +class ClaudeAnswerGetter(AnswerGetter): + def __init__(self, model: str = "claude-3-5-sonnet-20240620", api_key=None): + self.model = model + if api_key is None: + self.api_key = os.getenv("ANTHROPIC_API_KEY", None) + else: + self.api_key = api_key + self.client = anthropic.Anthropic(api_key=self.api_key) + + def get_answer(self, question: str, images: List[Image.Image]): + messages = [{"role": "user", "content": format_claude_images(images) + [{"type": "text", "text": question}]}] + response = claude_generate_response(self.client, self.model, messages) + if response.success: + return response.content + else: + return REJECT_TO_ANSWER + + +class GeminiAnswerGetter(AnswerGetter): + def __init__(self, model: str = "gemini-1.5-pro", api_key=None): + self.model = model + self.client = genai.GenerativeModel(model) + + def get_answer(self, question: str, images: List[Image.Image]): + response = gemini_generate_response(self.client, images + [question], max_tokens=2048) + if response.success: + return response.content + else: + return REJECT_TO_ANSWER + + +QUESTION_FINALIZER_PROMPT = """\ +You are a question setter, and your task is to modify the following question, answer, and scoring criteria to ensure: + +1. The question is clear and unambiguous. +2. The answer is correct and reasonable (although the original ground truth answer is mostly correct, it may not be perfect, and sometimes the answer maybe incorrect). +3. The scoring criteria are rational and facilitate the accurate assessment of responses. +4. The full score for the scoring criteria must be 10 points, and it must directly relate to the specific answer. +5. Except for some multiple-choice questions or other questions with only one possible answer, the scoring criteria should not be an all-or-nothing system. Partially correct answers should receive proportional points. +6. Ensure that the scoring system is flexible enough to accommodate slight variations in correct answers while still maintaining a standard for what is considered an acceptable answer. +7. Clearly define what constitutes a full score, partial score, and zero score. +8. The criteria should be as detailed as possible, even if a LLM without image understanding capabilities could still score the answer based on the criteria and ground truth answer correctly. + +Your task is to finalize these standards, thus ensuring the correctness of the answer and the rationality of the scoring criteria. + +Some tips: + +1. For some extremely hard open-ended questions where answers may vary, hitting all points perfectly may not be realistic. In such cases, you can relax the criteria slightly. For example, if there are five possible points in an answer, but answering three adequately could merit full points. An other option is to change the question to a multiple-choice / multi-select question. But remember, it only applies to extremely hard open-ended questions which are impossible to answer perfectly. +2. For some questions, changing the format might be beneficial. You can consider transforming them into different types of questions such as essay, fill-in-the-blank, multiple-choice (or multiple-select), true/false, ranking (e.g., based on time, importance, etc.), or matching questions to enhance the difficulty and rationality of the scoring criteria. +""" + +FINALIZER_OUTPUT_FORMAT_PROMPT = """\ +Please provide the final question, answer, and scoring criteria in the following json format: +{ + "question": "The final question", + "answer": "The final answer", + "criteria": "The final scoring criteria" +} + +One thing as a reminder is that if you want to add a new line in the json string, you should use the escape character "\\n" instead to represent the new line. +""" + + +class QuestionFinalizer(object): + def __init__(self, gpt4v_model: str = "gpt-4o", claude_model: str = "claude-3-5-sonnet-20240620", gemini_model: str = "gemini-1.5-pro"): + self.models = {"GPT4V": GPT4VAnswerGetter(gpt4v_model), "Claude": ClaudeAnswerGetter(claude_model), "Gemini": GeminiAnswerGetter(gemini_model)} + self.client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY", None)) + # self.client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY", None)) + + def finalize_question(self, question, answer, criteria, images: List[Image.Image]): + information = [f"[Original Question]\n{question}", f"[Original Answer]\n{answer}", f"[Original Criteria]\n{criteria}"] + information.append( + "Below are answers from three candidates for reference. These answers may not be correct but are reasonably credible (but mostly correct). If any candidate rejects to answer, consider whether there is an issue with the question (such as containing violent or graphic content, or having a clear political bias). If so, please make necessary modifications. For open-ended questions, also consider the reasonableness of these answers. If they are reasonable, you may need to adjust the scoring criteria or the answer itself." + ) + for model_name, model in self.models.items(): + information.append(f"[{model_name} Answer]\n{model.get_answer(question, images)}") + information.append(FINALIZER_OUTPUT_FORMAT_PROMPT) + prompt = "\n\n".join(information) + messages = [{"role": "user", "content": format_gpt4v_images(images) + [{"type": "text", "text": prompt}]}] + try: + response = gpt4v_generate_response(client=self.client, model="gpt-4o", messages=messages, system=QUESTION_FINALIZER_PROMPT) + if response.success: + data = json.loads(response.content) + return { + "question": data["question"], + "answer": data["answer"], + "criteria": data["criteria"], + } + except Exception as e: + print(f"Failed to generate response: {e}") + return None diff --git a/tools/live_bench/live_bench/data_generator/response.py b/tools/live_bench/live_bench/data_generator/response.py new file mode 100644 index 00000000..9eed882b --- /dev/null +++ b/tools/live_bench/live_bench/data_generator/response.py @@ -0,0 +1,12 @@ +class Response(object): + def __init__(self, success: bool, content: str, full_log: dict): + self.success = success + self.content = content + self.full_log = full_log + + def to_dict(self): + return { + "success": self.success, + "content": self.content, + "full_log": self.full_log, + } diff --git a/tools/live_bench/live_bench/data_generator/score_getter.py b/tools/live_bench/live_bench/data_generator/score_getter.py new file mode 100644 index 00000000..41298928 --- /dev/null +++ b/tools/live_bench/live_bench/data_generator/score_getter.py @@ -0,0 +1,157 @@ +import os +import json +import random +import openai +import anthropic +from abc import ABC, abstractmethod +from typing import List +from PIL import Image +from live_bench.screen_shoter import ScreenImage +from live_bench.data_generator.qa_generator import Response +from live_bench.data_generator.utils.gpt4v import format_gpt4v_images, gpt4v_generate_response +from live_bench.data_generator.utils.claude import format_claude_images, claude_generate_response + + +class Score(object): + def __init__(self, score: int, reason: str): + self.score = score + self.reason = reason + + +class ScoreGetter(ABC): + def get_name(self): + return self.name + + @abstractmethod + def get_score(self, question: str, answer: str, images: ScreenImage): + raise NotImplementedError("get_score not implemented") + + def __call__(self, question: str, answer: str, images: ScreenImage, **kwargs): + return self.get_score(question, answer, images, **kwargs) + + +class ScoreGetterRegistry: + def __init__(self): + self.score_getters = {} + + def register_score_getter(self, name): + def decorator(cls): + self.score_getters[name] = cls + cls.name = name + return cls + + return decorator + + def get_score_getter(self, name) -> ScoreGetter: + return self.score_getters[name] + + def get_random_score_getter(self) -> ScoreGetter: + return random.choice(list(self.score_getters.values())) + + +generator_registry = ScoreGetterRegistry() + + +def register_score_getter(name): + return generator_registry.register_score_getter(name) + + +def get_score_getter(name, *args, **kwargs) -> ScoreGetter: + return generator_registry.get_score_getter(name)(*args, **kwargs) + + +def get_random_score_getter(*args, **kwargs) -> ScoreGetter: + return generator_registry.get_random_score_getter()(*args, **kwargs) + + +@register_score_getter("gpt4v") +class GPT4VScoreGetter(ScoreGetter): + def __init__(self, prompt: str = os.path.join(os.path.dirname(__file__), "score_prompt.md"), model="gpt-4o", example_path=os.path.join(os.path.dirname(__file__), "example")): + super().__init__() + if os.path.exists(prompt): + with open(prompt, "r") as f: + self.prompt = f.read() + else: + self.prompt = prompt + API_KEY = os.getenv("OPENAI_API_KEY") + if not API_KEY: + raise ValueError("OPENAI_API_KEY environment variable not set.") + self.api_key = API_KEY + self.client = openai.OpenAI(api_key=self.api_key) + self.model = model + if os.path.exists(example_path) and os.path.isfile(os.path.join(example_path, "example_score_input.md")): + with open(example_path, "r") as f: + self.example = f.read() + else: + self.example = None + + def _format_prompt(self, question: str, answer: str, images: List[Image.Image]): + prompt = [{"role": "system", "content": self.prompt}] + messages = [] + for image in images: + messages.append(format_gpt4v_images(image)) + messages.append({"type": "text", "text": f"Question: {question}\nQuestioner's Answer: {answer}"}) + messages.append({"type": "text", "text": 'You should format you answer into json format like this: {"reason": "some reason", "score": 10}'}) + prompt.append({"role": "user", "content": messages}) + return prompt + + def get_score(self, question: str, answer: str, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs) -> Score: + prompt = self._format_prompt(question, answer, images) + try: + response = gpt4v_generate_response(client=self.client, model=self.model, messages=prompt, max_tokens=max_tokens, max_try_times=max_try_times, json_format=True, **kwargs) + if response.success: + content = json.loads(response.content) + score = content.get("score", None) + reason = content.get("reason", None) + return Score(score=score, reason=reason) + else: + return Score(score=None, reason=response.content) + except Exception as e: + return Score(score=None, reason=str(e)) + + +@register_score_getter("claude") +class ClaudeScoreGetter(ScoreGetter): + def __init__(self, prompt: str = os.path.join(os.path.dirname(__file__), "score_prompt.md"), model="claude-3-5-sonnet-20240620", example_path=os.path.join(os.path.dirname(__file__), "example")): + super().__init__() + if os.path.exists(prompt): + with open(prompt, "r") as f: + self.prompt = f.read() + else: + self.prompt = prompt + API_KEY = os.getenv("ANTHROPIC_API_KEY") + if not API_KEY: + raise ValueError("ANTHROPIC_API_KEY environment variable not set.") + self.api_key = API_KEY + self.client = anthropic.Anthropic(api_key=self.api_key) + self.model = model + if os.path.exists(example_path) and os.path.isfile(os.path.join(example_path, "example_score_input.md")): + with open(example_path, "r") as f: + self.example = f.read() + else: + self.example = None + + def _format_prompt(self, question: str, answer: str, images: List[Image.Image]): + # prompt = [{"role": "system", "content": self.prompt}] + prompt = [] + messages = [] + for image in images: + messages.append(format_claude_images(image)) + messages.append({"type": "text", "text": f"Question: {question}\nQuestioner's Answer: {answer}"}) + messages.append({"type": "text", "text": 'You should format you answer into JSON format like this: { "reason": "some reason", "score": 10 }'}) + prompt.append({"role": "user", "content": messages}) + return prompt + + def get_score(self, question: str, answer: str, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs) -> Score: + prompt = self._format_prompt(question, answer, images) + try: + response = claude_generate_response(client=self.client, model=self.model, messages=prompt, system=self.prompt, max_tokens=max_tokens, max_try_times=max_try_times, **kwargs) + if response.success: + content = json.loads(response.content) + score = content.get("score", None) + reason = content.get("reason", None) + return Score(score=score, reason=reason) + else: + return Score(score=None, reason=response.content) + except Exception as e: + return Score(score=None, reason=str(e)) diff --git a/tools/live_bench/live_bench/data_generator/utils/claude.py b/tools/live_bench/live_bench/data_generator/utils/claude.py new file mode 100644 index 00000000..ccd0b381 --- /dev/null +++ b/tools/live_bench/live_bench/data_generator/utils/claude.py @@ -0,0 +1,68 @@ +from PIL import Image +import io +import base64 +from live_bench.data_generator.response import Response +import anthropic +import logging +from time import sleep +from typing import Union, List + +logger = logging.getLogger("lmms-eval") + + +def format_claude_images(image: Union[Image.Image, List[Image.Image]]): + if isinstance(image, list): + return [format_claude_images(img) for img in image] + buffered = io.BytesIO() + image.save(buffered, format="PNG") + img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") + return { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": img_str, + }, + } + + +def claude_generate_response(client: anthropic.Anthropic, model, messages, max_tokens: int = 4096, max_try_times=5, system=None, json_format="auto", test=False, **kwargs): + if json_format == "auto": + json_format = False + for message in messages: + if message.get("role") == "user": + contents = message.get("content", []) + if isinstance(contents, str): + if "json" in contents: + json_format = True + break + else: + for content in contents: + if content.get("type", None) == "text" and "json" in content.get("text", ""): + json_format = True + break + + if json_format: + messages.append({"role": "assistant", "content": "{"}) + + def _generate(): + if system: + return client.messages.create(model=model, messages=messages, max_tokens=max_tokens, system=system, **kwargs) + else: + return client.messages.create(model=model, messages=messages, max_tokens=max_tokens, **kwargs) + + for times in range(max_try_times): + try: + response = _generate() + response_str = response.content[0].text + if json_format: + response_str = "{" + response_str + return Response(success=True, content=response_str, full_log={"input": messages, "output": response.to_dict()}) + except Exception as e: + logger.error(f"Failed to generate response: {e}") + if times < max_try_times - 1: + logger.info(f"Retrying... ({times+1}/{max_try_times})") + sleep(3) + else: + logger.error("Failed to generate response after retrying.") + return Response(success=False, content=str(e), full_log={"input": messages, "output": None}) diff --git a/tools/live_bench/live_bench/data_generator/utils/extract_infomation.py b/tools/live_bench/live_bench/data_generator/utils/extract_infomation.py index 6946c815..f76a6b29 100644 --- a/tools/live_bench/live_bench/data_generator/utils/extract_infomation.py +++ b/tools/live_bench/live_bench/data_generator/utils/extract_infomation.py @@ -1,9 +1,11 @@ import os +import anthropic import openai import logging from bs4 import BeautifulSoup import requests from live_bench.data_generator.utils.gpt4v import gpt4v_generate_response, format_gpt4v_images +from live_bench.data_generator.utils.claude import claude_generate_response, format_claude_images from live_bench.screen_shoter import ScreenImage from live_bench.websites import Website from live_bench.data_generator.response import Response @@ -11,17 +13,17 @@ logger = logging.getLogger("live-bench") -GPT4V_EXTRACT_TEXT_PROMPT: str = """\ +EXTRACT_TEXT_PROMPT: str = """\ These are the images of the website that we have captured. Please extract the text from the website. You should extract the text from the website as detailed as possible. Only output the text extracted from the website, do not include any other information. """ -GPT4V_FIND_IMAGES_FEATURES_PROMPT: str = """\ +FIND_IMAGES_FEATURES_PROMPT: str = """\ This is a screenshot from a news website. Your task is to identify the meaningful images in this screenshot and extract relevant information about these images, such as the environment depicted, the actions and expressions of the people, and the connection between these images and the corresponding text. You need to think deeply about these images and provide as much detailed and useful information as possible. """ -GPT4V_THINK_DIFFERENTLY_PROMPT: str = """\ +THINK_DIFFERENTLY_PROMPT: str = """\ What makes this website different from other websites? What is special about its news? Since it is a news website, where is the "new" reflected? Do not give a generalized answer; you need to provide detailed answers based on the specific content of each news article and the accompanying illustrations. """ @@ -54,22 +56,28 @@ def get_info(self): class InfomationExtractor(object): - def __init__(self, model="gpt-4-turbo", openai_api_key=None): + def __init__(self, model="claude-3-5-sonnet-20240620", openai_api_key=None, anthropic_api_key=None): + if not anthropic_api_key: + anthropic_api_key = os.getenv("ANTHROPIC_API_KEY", None) if not openai_api_key: - openai_api_key = os.getenv("OPENAI_API_KEY") - if not openai_api_key: - raise ValueError("OPENAI_API_KEY environment variable not set.") - self.client = openai.OpenAI(api_key=openai_api_key) + openai_api_key = os.getenv("OPENAI_API_KEY", None) + if "gpt" in model: + self.client = openai.OpenAI(api_key=openai_api_key) + self.generate_response = gpt4v_generate_response + self.format_images = format_gpt4v_images + elif "claude" in model: + self.client = anthropic.Anthropic(api_key=anthropic_api_key) + self.generate_response = claude_generate_response + self.format_images = format_claude_images self.model = model def extract_text_from_html(self, url): response = requests.get(url) soup = BeautifulSoup(response.text, "html.parser") - text = "\n".join(soup.stripped_strings) return text - def extract_text_from_html_using_gpt4v(self, screen_image: ScreenImage, **kwargs) -> Response: + def extract_text_from_html_from_gpt(self, screen_image: ScreenImage, **kwargs) -> Response: website: Website = screen_image.website if website.url: url = website.url @@ -77,18 +85,18 @@ def extract_text_from_html_using_gpt4v(self, screen_image: ScreenImage, **kwargs text = f"Below is the text extracted from the website {url} for you to take reference:\n{text}" else: text = "" - text = f"{GPT4V_EXTRACT_TEXT_PROMPT}\n{text}" + text = f"{EXTRACT_TEXT_PROMPT}\n{text}" messages = [ { "role": "user", - "content": [{"type": "text", "text": text}] + format_gpt4v_images(screen_image.images), + "content": [{"type": "text", "text": text}] + self.format_images(screen_image.images), } ] - response = gpt4v_generate_response(messages, model=self.model, client=self.client, json_format=False, **kwargs) + response = self.generate_response(messages=messages, model=self.model, client=self.client, json_format=False, **kwargs) return response def extract_infomation(self, screen_image: ScreenImage, **kwargs) -> ImageInfomation: - ocrs = self.extract_text_from_html_using_gpt4v(screen_image) + ocrs = self.extract_text_from_html_from_gpt(screen_image) infomation = ImageInfomation() if ocrs.success: ocrs = f"Below is the text extracted from the website for you to take reference:\n{ocrs.content}" @@ -98,19 +106,19 @@ def extract_infomation(self, screen_image: ScreenImage, **kwargs) -> ImageInfoma messages = [ { "role": "user", - "content": [{"type": "text", "text": f"{GPT4V_FIND_IMAGES_FEATURES_PROMPT}\n{ocrs}"}] + format_gpt4v_images(screen_image.images), + "content": [{"type": "text", "text": f"{FIND_IMAGES_FEATURES_PROMPT}\n{ocrs}"}] + self.format_images(screen_image.images), } ] - response = gpt4v_generate_response(messages, model=self.model, client=self.client, json_format=False, **kwargs) + response = self.generate_response(messages=messages, model=self.model, client=self.client, json_format=False, **kwargs) if response.success: infomation.image_features = response.content messages = [ { "role": "user", - "content": [{"type": "text", "text": f"{GPT4V_THINK_DIFFERENTLY_PROMPT}\n\n{str(infomation)}"}] + format_gpt4v_images(screen_image.images), + "content": [{"type": "text", "text": f"{THINK_DIFFERENTLY_PROMPT}\n\n{str(infomation)}"}] + self.format_images(screen_image.images), } ] - response = gpt4v_generate_response(messages, model=self.model, client=self.client, json_format=False, **kwargs) + response = self.generate_response(messages=messages, model=self.model, client=self.client, json_format=False, **kwargs) if response.success: infomation.differnt_points = response.content return infomation diff --git a/tools/live_bench/live_bench/data_generator/utils/gemini.py b/tools/live_bench/live_bench/data_generator/utils/gemini.py new file mode 100644 index 00000000..57b77536 --- /dev/null +++ b/tools/live_bench/live_bench/data_generator/utils/gemini.py @@ -0,0 +1,37 @@ +import google.generativeai as genai +from time import sleep +from live_bench.data_generator.response import Response +import logging +from google.generativeai.types import HarmCategory, HarmBlockThreshold + +logger = logging.getLogger("lmms-eval") + + +def gemini_generate_response(client: genai.GenerativeModel, messages, max_tokens: int, max_try_times: int = 5, **kwargs): + generation_config = genai.GenerationConfig(max_output_tokens=max_tokens) + + def _generate(): + return client.generate_content( + messages, + generation_config=generation_config, + safety_settings={ + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, + }, + **kwargs, + ) + + for times in range(max_try_times): + try: + response = _generate() + return Response(success=True, content=response.text, full_log={"input": messages, "output": response}) + except Exception as e: + logger.error(f"Failed to generate response: {e}") + if times < max_try_times - 1: + logger.info(f"Retrying... ({times+1}/{max_try_times})") + sleep(3) + else: + logger.error("Failed to generate response after retrying.") + return Response(success=False, content=str(e), full_log={"input": messages, "output": None}) diff --git a/tools/live_bench/live_bench/data_generator/utils/gpt4v.py b/tools/live_bench/live_bench/data_generator/utils/gpt4v.py index 2ee70794..270a76ea 100644 --- a/tools/live_bench/live_bench/data_generator/utils/gpt4v.py +++ b/tools/live_bench/live_bench/data_generator/utils/gpt4v.py @@ -34,7 +34,10 @@ def format_printable_messages(messages): return messages -def gpt4v_generate_response(messages, *, client=None, model="gpt-4-turbo", max_tokens: int = 4096, max_try_times: int = 5, json_format="auto", test=False, **kwargs) -> Response: +def gpt4v_generate_response(messages, *, client=None, model="gpt-4o", max_tokens: int = 4096, max_try_times: int = 5, json_format="auto", test=False, system=None, **kwargs) -> Response: + if system: + messages = [{"role": "system", "content": system}] + messages + if json_format == "auto": json_format = False for message in messages: diff --git a/tools/live_bench/live_bench/driver/.gitignore b/tools/live_bench/live_bench/driver/.gitignore new file mode 100644 index 00000000..0ef18421 --- /dev/null +++ b/tools/live_bench/live_bench/driver/.gitignore @@ -0,0 +1 @@ +extensions/ diff --git a/tools/live_bench/live_bench/driver/load_driver.py b/tools/live_bench/live_bench/driver/load_driver.py new file mode 100644 index 00000000..c0ca7528 --- /dev/null +++ b/tools/live_bench/live_bench/driver/load_driver.py @@ -0,0 +1,71 @@ +import os +import zipfile +import requests +from selenium import webdriver +from webdriver_manager.chrome import ChromeDriverManager +from webdriver_manager.core.os_manager import ChromeType +from selenium.webdriver.chrome.options import Options + +import undetected_chromedriver as uc + + +def load_driver( + window_size="auto", + headless=True, + driver="undetected_chromedriver", + driver_version=None, + chrome_type="CHROME", + adblock=True, + adblock_version="6.0.2-mv3", + extension_cache_dir=os.path.join(os.path.dirname(__file__), "extensions"), + *, + service=None, + additional_options=None, +): + options = Options() + if service is None: + chrome_type = chrome_type.upper() + if chrome_type == "CHROMIUM": + chrome_type = ChromeType.CHROMIUM + elif chrome_type == "CHROME": + chrome_type = ChromeType.GOOGLE + elif chrome_type == "BRAVE": + chrome_type = ChromeType.BRAVE + service = ChromeDriverManager(driver_version=driver_version, chrome_type=chrome_type).install() + if headless: + options.add_argument("--headless") + if adblock: + try: + adblock_url = f"https://code.getadblock.com/releases/adblockchrome-{adblock_version}.zip" + adblock_path = os.path.join(extension_cache_dir, f"adblockchrome-{adblock_version}") + if not os.path.isdir(adblock_path): + os.makedirs(os.path.join(adblock_path, ".."), exist_ok=True) + # Download the adblock zip file + response = requests.get(adblock_url) + with open(f"{adblock_path}.zip", "wb") as file: + file.write(response.content) + # Unzip the downloaded file + with zipfile.ZipFile(f"{adblock_path}.zip", "r") as zip_ref: + zip_ref.extractall(adblock_path) + # Remove the zip file after extraction + os.remove(f"{adblock_path}.zip") + options.add_argument(f"--load-extension={os.path.abspath(adblock_path)}") + except Exception as e: + print(f"Error loading adblock extension: {e}") + if driver == "undetected_chromedriver": + driver = uc.Chrome(headless=headless, options=options, driver_executable_path=service) + if window_size != "auto": + driver.set_window_size(*window_size) + return driver + elif driver == "chrome": + options = Options() + if additional_options is not None: + for option in additional_options: + options.add_argument(option) + service = webdriver.chrome.service.Service(service) + driver = webdriver.Chrome(service=service, options=options) + if window_size != "auto": + driver.set_window_size(*window_size) + return driver + else: + raise ValueError(f"Unknown driver: {driver}") diff --git a/tools/live_bench/live_bench/screen_shoter/screen.py b/tools/live_bench/live_bench/screen_shoter/screen.py new file mode 100644 index 00000000..5817b8cc --- /dev/null +++ b/tools/live_bench/live_bench/screen_shoter/screen.py @@ -0,0 +1,30 @@ +import io +import base64 + +from PIL import Image +from typing import List, Tuple + +from live_bench.websites import Website + + +def image_to_base64(image: Image.Image) -> str: + buffered = io.BytesIO() + image.save(buffered, format="PNG") + return base64.b64encode(buffered.getvalue()).decode("utf-8") + + +class ScreenImage(object): + def __init__(self, images: List[Image.Image], website: Website, shoter: str, screen_size: Tuple[int, int], capture_datetime: str): + self.images = images + self.website = website + self.shoter = shoter + self.screen_size = screen_size + self.capture_datetime = capture_datetime + + def to_dict(self): + return {"images": self.images, "website": self.website.get_info(), "shoter": self.shoter, "screen_size": self.screen_size, "capture_datetime": self.capture_datetime} + + def to_output_dict(self): + output = self.to_dict() + output["images"] = [image_to_base64(image) for image in self.images] + return output diff --git a/tools/live_bench/live_bench/screen_shoter/screen_shoter.py b/tools/live_bench/live_bench/screen_shoter/screen_shoter.py new file mode 100644 index 00000000..6069cd26 --- /dev/null +++ b/tools/live_bench/live_bench/screen_shoter/screen_shoter.py @@ -0,0 +1,141 @@ +from selenium import webdriver +from PIL import Image +from live_bench.websites import Website +from live_bench.screen_shoter.screen import ScreenImage +from typing import List +from abc import ABC, abstractmethod +from datetime import datetime +from PIL import Image +import os +import io +import logging + +logger = logging.getLogger("lmms-eval") + + +class ScreenShoter(ABC): + def __init__(self, screen_size=(1024, 1024)): + self.screen_size = screen_size + + def capture(self, driver: webdriver.Chrome, website: Website) -> ScreenImage: + if driver is not None: + website.visit(driver) + if self.screen_size != "auto": + driver.set_window_size(self.screen_size[0], self.screen_size[1]) + else: + driver.set_window_size(1024, 1024) + page_width = driver.execute_script("return document.body.scrollWidth") + driver.set_window_size(page_width, 1024) + # print("Screen size:", driver.get_window_size()) + images = self.get_screenshot(driver) + return ScreenImage(images, website, self.get_name(), self.screen_size, datetime.now().strftime("%Y-%m-%d %H:%M:%S")) + + def __call__(self, driver: webdriver.Chrome, website: Website) -> List[Image.Image]: + return self.capture(driver, website) + + def get_name(self) -> str: + raise NotImplementedError("get_name not implemented") + + @abstractmethod + def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]: + pass + + +class ScreenShoterRegistry: + def __init__(self): + self.shoters = {} + + def register_shoter(self, name): + def decorator(cls): + self.shoters[name] = cls + cls.get_name = lambda self: name + return cls + + return decorator + + def get_shoter(self, name) -> ScreenShoter: + return self.shoters[name] + + +shoter_registry = ScreenShoterRegistry() + + +def register_shoter(name): + return shoter_registry.register_shoter(name) + + +def get_shoter(name, *args, **kwargs) -> ScreenShoter: + return shoter_registry.get_shoter(name)(*args, **kwargs) + + +@register_shoter("human") +class HumanScreenShoter(ScreenShoter): + def __init__(self, screen_size=None): + super().__init__(screen_size) + + def capture(self, driver: webdriver.Chrome, website: Website) -> ScreenImage: + path = website.get_path() + images = [] + + def get_image(path): + try: + with open(path, "rb") as f: + image_data = f.read() + image = Image.open(io.BytesIO(image_data)) + images.append(image) + except Exception as e: + logger.error(f"Error loading image {path}: {e}") + + if os.path.isdir(path): + for root, dirs, files in os.walk(path): + for file_name in files: + get_image(os.path.join(root, file_name)) + else: + try: + get_image(path) + except Exception as e: + logger.error(f"Error loading image {path}: {e}") + if not images: + raise ValueError(f"No images found in {path}") + return ScreenImage(images, website, self.get_name(), self.screen_size, datetime.now().strftime("%Y-%m-%d %H:%M:%S")) + + def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]: + return [] + + +@register_shoter("single_screen") +class SingleScreenShoter(ScreenShoter): + def __init__(self, screen_size=(1024, 1024)): + super().__init__(screen_size) + + def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]: + screenshot = driver.get_screenshot_as_png() + return [Image.open(io.BytesIO(screenshot))] + + +@register_shoter("rolling_screen") +class RollingScreenShoter(ScreenShoter): + def __init__(self, screen_size=(1024, 1024)): + super().__init__(screen_size) + + def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]: + screenshots = [] + # Scroll to the top of the page before taking the first screenshot + driver.execute_script("window.scrollTo(0, 0)") + # Get the total height of the web page + total_height = driver.execute_script("return document.body.parentNode.scrollHeight") + # Get the viewport height + viewport_height = driver.execute_script("return window.innerHeight") + # Initialize the current scroll position + current_scroll_position = 0 + + # Scroll through the page and take screenshots + while current_scroll_position < total_height: + # Take screenshot and append to the list + screenshot = driver.get_screenshot_as_png() + screenshots.append(Image.open(io.BytesIO(screenshot))) + # Scroll down by the viewport height + current_scroll_position += viewport_height + driver.execute_script(f"window.scrollTo(0, {current_scroll_position})") + + return screenshots diff --git a/tools/live_bench/live_bench/view.ipynb b/tools/live_bench/live_bench/view.ipynb index 71d99d91..5a4addac 100644 --- a/tools/live_bench/live_bench/view.ipynb +++ b/tools/live_bench/live_bench/view.ipynb @@ -2,27 +2,30 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Downloading data: 100%|██████████| 470k/470k [00:00<00:00, 575kB/s]\n", - "Generating test split: 100%|██████████| 9/9 [00:00<00:00, 341.15 examples/s]\n" + "/data/pufanyi/anaconda3/anacondabin/envs/live_bench/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "Downloading readme: 100%|██████████| 2.45k/2.45k [00:00<00:00, 5.08MB/s]\n", + "Downloading data: 100%|██████████| 88.6M/88.6M [00:04<00:00, 19.6MB/s]\n", + "Generating test split: 100%|██████████| 320/320 [00:00<00:00, 384.23 examples/s]\n" ] } ], "source": [ "from datasets import load_dataset\n", "\n", - "dataset = load_dataset(\"lmms-lab/LiveBench\", \"2024-06\")" + "dataset = load_dataset(\"lmms-lab/LiveBench\", \"2024-07\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -30,13 +33,13 @@ "text/plain": [ "DatasetDict({\n", " test: Dataset({\n", - " features: ['id', 'images', 'website', 'question', 'answer', 'subtask', 'data_generator', 'checker', 'date_time', 'screen_shoter', 'screen_size', 'score', 'reason', 'scorer_name'],\n", - " num_rows: 9\n", + " features: ['id', 'images', 'website', 'question', 'answer', 'criteria', 'subtask', 'data_generator', 'checker', 'date_time', 'screen_shoter', 'screen_size', 'score', 'reason', 'scorer_name'],\n", + " num_rows: 320\n", " })\n", "})" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -47,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -76,6 +79,7 @@ " website\n", " question\n", " answer\n", + " criteria\n", " subtask\n", " data_generator\n", " checker\n", @@ -92,228 +96,301 @@ " 0\n", " 0\n", " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", - " {'url': 'https://www.bbc.com/'}\n", - " Look at the image provided. Two of the article...\n", - " The shared theme is the legal situation of Hun...\n", - " Contextual Analysis\n", - " gpt4v\n", + " {'url': 'https://www.bbc.com/news'}\n", + " Examine the top menu bar of the BBC website di...\n", + " C) Weather\n", + " Award 10 points for selecting option C) Weathe...\n", + " Basic Understanding\n", + " claude\n", " gemini\n", - " 2024-06-12 01:14:15\n", + " 2024-07-20 14:02:22\n", " single_screen\n", " (1024, 1024)\n", " 10\n", - " The answer accurately identifies the article d...\n", - " gpt4v\n", + " The answer is correct and can be directly veri...\n", + " claude\n", " \n", " \n", " 1\n", " 1\n", " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", - " {'url': 'https://www.bbc.com/'}\n", - " Based on the provided image, what is the main ...\n", - " Ukraine has launched missiles at sites within ...\n", - " Basic Understanding\n", - " gpt4v\n", + " {'url': 'https://www.bbc.com/news'}\n", + " Based on the image accompanying the article ab...\n", + " The image depicts a crowded airport setting, l...\n", + " {'10 points': 'The answer correctly identifies...\n", + " Deeper Implications\n", + " claude\n", " gemini\n", - " 2024-06-12 01:14:15\n", + " 2024-07-20 14:02:22\n", " single_screen\n", " (1024, 1024)\n", " 10\n", - " The information provided in the answer directl...\n", - " gpt4v\n", + " The image clearly shows that the news article ...\n", + " claude\n", " \n", " \n", " 2\n", " 2\n", " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", - " {'url': 'https://www.bbc.com/'}\n", - " Based on the juxtaposition of the articles on ...\n", - " The two narratives are the personal and legal ...\n", + " {'url': 'https://www.bbc.com/news'}\n", + " Based on the photograph accompanying the headl...\n", + " The photograph shows a crowded scene inside an...\n", + " {'2 points': 'Interprets the overall scene as ...\n", " Contextual Analysis\n", - " gpt4v\n", + " claude\n", " gemini\n", - " 2024-06-12 01:14:15\n", + " 2024-07-20 14:02:22\n", " single_screen\n", " (1024, 1024)\n", - " 8\n", - " The answer is mostly authentic as the convicti...\n", - " gpt4v\n", + " 10\n", + " The answer accurately describes the image and ...\n", + " claude\n", " \n", " \n", " 3\n", " 3\n", " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", - " {'url': 'https://www.bbc.com/'}\n", - " Examining the headline and image, what visual ...\n", - " The image showcasing a relatively unscathed st...\n", - " Contextual Analysis\n", - " gpt4v\n", - " gemini\n", - " 2024-06-12 01:14:15\n", + " {'url': 'https://www.bbc.com/news'}\n", + " Considering the headline 'Global services slow...\n", + " This incident underscores several critical iss...\n", + " Award up to 10 marks: 1.5 points for discussin...\n", + " Deeper Implications\n", + " claude\n", + " None\n", + " 2024-07-20 14:02:22\n", " single_screen\n", " (1024, 1024)\n", - " 8\n", - " The answer is relevant and provides a plausibl...\n", - " gpt4v\n", + " 10\n", + " The answer provides an insightful analysis of ...\n", + " claude\n", " \n", " \n", " 4\n", " 4\n", " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", - " {'url': 'https://www.bbc.com/'}\n", - " Based on the juxtaposition of the articles on ...\n", - " The audience might infer a potential media bia...\n", - " Deeper Implications\n", - " gpt4v\n", + " {'url': 'https://www.bbc.com/news'}\n", + " Analyze the layout and image selection of the ...\n", + " The layout of the BBC News homepage utilizes v...\n", + " {'10 points': 'The answer provides a detailed ...\n", + " Contextual Analysis\n", + " claude\n", " gemini\n", - " 2024-06-12 01:14:15\n", + " 2024-07-20 14:02:22\n", " single_screen\n", " (1024, 1024)\n", " 8\n", - " The response provides a reasoned inference bas...\n", - " gpt4v\n", + " The ranking provided is generally accurate and...\n", + " claude\n", " \n", " \n", - " 5\n", - " 5\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 315\n", + " 315\n", " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", - " {'url': 'https://www.bbc.com/'}\n", - " Examining the image and the headline \"What son...\n", - " Potential narratives could include: the impact...\n", - " Broader Implications\n", + " {'name': 'bloomberg.comeconomics.png'}\n", + " Analyze the image accompanying the article 'Xi...\n", + " The image accompanying the article suggests a ...\n", + " Scoring Criteria (Total: 10 points)\\n\\n- Ident...\n", + " Contextual Analysis\n", " gpt4v\n", " gemini\n", - " 2024-06-12 01:14:15\n", - " single_screen\n", - " (1024, 1024)\n", - " 7\n", - " Authenticity (4/5): The information about it b...\n", - " gpt4v\n", + " 2024-07-21 20:23:39\n", + " human\n", + " None\n", + " 6\n", + " While the question is thoughtful and relevant ...\n", + " claude\n", " \n", " \n", - " 6\n", - " 6\n", + " 316\n", + " 316\n", " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", - " {'url': 'https://www.bbc.com/'}\n", - " Based on the visual framing and placement of t...\n", - " The BBC seemingly prioritizes Hunter Biden's t...\n", + " {'name': 'wsj.comworldafrica.png'}\n", + " Analyze the image accompanying the article 'Qu...\n", + " The image contains several key elements that s...\n", + " Scoring Criteria (Total: 10 points):\\n\\n1. Ide...\n", " Contextual Analysis\n", " gpt4v\n", " gemini\n", - " 2024-06-12 01:14:15\n", - " single_screen\n", - " (1024, 1024)\n", - " 8\n", - " The answer effectively compares domestic polit...\n", - " gpt4v\n", + " 2024-07-21 20:27:57\n", + " human\n", + " None\n", + " 6\n", + " While the question is based on information pro...\n", + " claude\n", " \n", " \n", - " 7\n", - " 7\n", + " 317\n", + " 317\n", " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", - " {'url': 'https://www.bbc.com/'}\n", - " Considering the use of the word \"LIVE\" in the ...\n", - " The use of \"LIVE\" could lead the public to per...\n", - " Deeper Implications\n", + " {'name': 'wsj.comworldafrica.png'}\n", + " Based on the articles shown on the Wall Street...\n", + " The two most closely related news stories are:...\n", + " Scoring Criteria (Total 10 points):\\n1. Correc...\n", + " Contextual Analysis\n", " gpt4v\n", " gemini\n", - " 2024-06-12 01:14:15\n", - " single_screen\n", - " (1024, 1024)\n", - " 10\n", - " The answer provided is directly related to the...\n", - " gpt4v\n", + " 2024-07-21 20:27:57\n", + " human\n", + " None\n", + " 7\n", + " This question requires analysis and interpreta...\n", + " claude\n", " \n", " \n", - " 8\n", - " 8\n", + " 318\n", + " 318\n", " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", - " {'url': 'https://www.bbc.com/'}\n", - " Based on the image alone, what elements sugges...\n", - " The juxtaposition of the image depicting Presi...\n", - " Deeper Implications\n", + " {'name': 'wsj.comworldafrica.png'}\n", + " Analyze the image and headlines related to the...\n", + " The image and headlines present a complex narr...\n", + " Scoring Criteria (Total: 10 points)\\n\\n1. Anal...\n", + " Contextual Analysis\n", " gpt4v\n", " gemini\n", - " 2024-06-12 01:14:15\n", - " single_screen\n", - " (1024, 1024)\n", - " 8\n", - " The answer effectively discusses the potential...\n", + " 2024-07-21 20:27:57\n", + " human\n", + " None\n", + " 6\n", + " This question and answer are not directly supp...\n", + " claude\n", + " \n", + " \n", + " 319\n", + " 319\n", + " [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...\n", + " {'name': 'wsj.comworldafrica.png'}\n", + " Analyze the image accompanying the article tit...\n", + " The image contains several key visual elements...\n", + " Scoring Criteria (Total 10 points):\\n\\n- Ident...\n", + " Basic Understanding\n", " gpt4v\n", + " gemini\n", + " 2024-07-21 20:27:57\n", + " human\n", + " None\n", + " 5\n", + " This answer is not directly supported by the i...\n", + " claude\n", " \n", " \n", "\n", + "

    320 rows × 15 columns

    \n", "" ], "text/plain": [ - " id images \\\n", - "0 0 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", - "1 1 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", - "2 2 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", - "3 3 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", - "4 4 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", - "5 5 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", - "6 6 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", - "7 7 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", - "8 8 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + " id images \\\n", + "0 0 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + "1 1 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + "2 2 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + "3 3 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + "4 4 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + ".. ... ... \n", + "315 315 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + "316 316 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + "317 317 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + "318 318 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + "319 319 [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH... \n", + "\n", + " website \\\n", + "0 {'url': 'https://www.bbc.com/news'} \n", + "1 {'url': 'https://www.bbc.com/news'} \n", + "2 {'url': 'https://www.bbc.com/news'} \n", + "3 {'url': 'https://www.bbc.com/news'} \n", + "4 {'url': 'https://www.bbc.com/news'} \n", + ".. ... \n", + "315 {'name': 'bloomberg.comeconomics.png'} \n", + "316 {'name': 'wsj.comworldafrica.png'} \n", + "317 {'name': 'wsj.comworldafrica.png'} \n", + "318 {'name': 'wsj.comworldafrica.png'} \n", + "319 {'name': 'wsj.comworldafrica.png'} \n", + "\n", + " question \\\n", + "0 Examine the top menu bar of the BBC website di... \n", + "1 Based on the image accompanying the article ab... \n", + "2 Based on the photograph accompanying the headl... \n", + "3 Considering the headline 'Global services slow... \n", + "4 Analyze the layout and image selection of the ... \n", + ".. ... \n", + "315 Analyze the image accompanying the article 'Xi... \n", + "316 Analyze the image accompanying the article 'Qu... \n", + "317 Based on the articles shown on the Wall Street... \n", + "318 Analyze the image and headlines related to the... \n", + "319 Analyze the image accompanying the article tit... \n", "\n", - " website \\\n", - "0 {'url': 'https://www.bbc.com/'} \n", - "1 {'url': 'https://www.bbc.com/'} \n", - "2 {'url': 'https://www.bbc.com/'} \n", - "3 {'url': 'https://www.bbc.com/'} \n", - "4 {'url': 'https://www.bbc.com/'} \n", - "5 {'url': 'https://www.bbc.com/'} \n", - "6 {'url': 'https://www.bbc.com/'} \n", - "7 {'url': 'https://www.bbc.com/'} \n", - "8 {'url': 'https://www.bbc.com/'} \n", + " answer \\\n", + "0 C) Weather \n", + "1 The image depicts a crowded airport setting, l... \n", + "2 The photograph shows a crowded scene inside an... \n", + "3 This incident underscores several critical iss... \n", + "4 The layout of the BBC News homepage utilizes v... \n", + ".. ... \n", + "315 The image accompanying the article suggests a ... \n", + "316 The image contains several key elements that s... \n", + "317 The two most closely related news stories are:... \n", + "318 The image and headlines present a complex narr... \n", + "319 The image contains several key visual elements... \n", "\n", - " question \\\n", - "0 Look at the image provided. Two of the article... \n", - "1 Based on the provided image, what is the main ... \n", - "2 Based on the juxtaposition of the articles on ... \n", - "3 Examining the headline and image, what visual ... \n", - "4 Based on the juxtaposition of the articles on ... \n", - "5 Examining the image and the headline \"What son... \n", - "6 Based on the visual framing and placement of t... \n", - "7 Considering the use of the word \"LIVE\" in the ... \n", - "8 Based on the image alone, what elements sugges... \n", + " criteria subtask \\\n", + "0 Award 10 points for selecting option C) Weathe... Basic Understanding \n", + "1 {'10 points': 'The answer correctly identifies... Deeper Implications \n", + "2 {'2 points': 'Interprets the overall scene as ... Contextual Analysis \n", + "3 Award up to 10 marks: 1.5 points for discussin... Deeper Implications \n", + "4 {'10 points': 'The answer provides a detailed ... Contextual Analysis \n", + ".. ... ... \n", + "315 Scoring Criteria (Total: 10 points)\\n\\n- Ident... Contextual Analysis \n", + "316 Scoring Criteria (Total: 10 points):\\n\\n1. Ide... Contextual Analysis \n", + "317 Scoring Criteria (Total 10 points):\\n1. Correc... Contextual Analysis \n", + "318 Scoring Criteria (Total: 10 points)\\n\\n1. Anal... Contextual Analysis \n", + "319 Scoring Criteria (Total 10 points):\\n\\n- Ident... Basic Understanding \n", "\n", - " answer subtask \\\n", - "0 The shared theme is the legal situation of Hun... Contextual Analysis \n", - "1 Ukraine has launched missiles at sites within ... Basic Understanding \n", - "2 The two narratives are the personal and legal ... Contextual Analysis \n", - "3 The image showcasing a relatively unscathed st... Contextual Analysis \n", - "4 The audience might infer a potential media bia... Deeper Implications \n", - "5 Potential narratives could include: the impact... Broader Implications \n", - "6 The BBC seemingly prioritizes Hunter Biden's t... Contextual Analysis \n", - "7 The use of \"LIVE\" could lead the public to per... Deeper Implications \n", - "8 The juxtaposition of the image depicting Presi... Deeper Implications \n", + " data_generator checker date_time screen_shoter screen_size \\\n", + "0 claude gemini 2024-07-20 14:02:22 single_screen (1024, 1024) \n", + "1 claude gemini 2024-07-20 14:02:22 single_screen (1024, 1024) \n", + "2 claude gemini 2024-07-20 14:02:22 single_screen (1024, 1024) \n", + "3 claude None 2024-07-20 14:02:22 single_screen (1024, 1024) \n", + "4 claude gemini 2024-07-20 14:02:22 single_screen (1024, 1024) \n", + ".. ... ... ... ... ... \n", + "315 gpt4v gemini 2024-07-21 20:23:39 human None \n", + "316 gpt4v gemini 2024-07-21 20:27:57 human None \n", + "317 gpt4v gemini 2024-07-21 20:27:57 human None \n", + "318 gpt4v gemini 2024-07-21 20:27:57 human None \n", + "319 gpt4v gemini 2024-07-21 20:27:57 human None \n", "\n", - " data_generator checker date_time screen_shoter screen_size \\\n", - "0 gpt4v gemini 2024-06-12 01:14:15 single_screen (1024, 1024) \n", - "1 gpt4v gemini 2024-06-12 01:14:15 single_screen (1024, 1024) \n", - "2 gpt4v gemini 2024-06-12 01:14:15 single_screen (1024, 1024) \n", - "3 gpt4v gemini 2024-06-12 01:14:15 single_screen (1024, 1024) \n", - "4 gpt4v gemini 2024-06-12 01:14:15 single_screen (1024, 1024) \n", - "5 gpt4v gemini 2024-06-12 01:14:15 single_screen (1024, 1024) \n", - "6 gpt4v gemini 2024-06-12 01:14:15 single_screen (1024, 1024) \n", - "7 gpt4v gemini 2024-06-12 01:14:15 single_screen (1024, 1024) \n", - "8 gpt4v gemini 2024-06-12 01:14:15 single_screen (1024, 1024) \n", + " score reason scorer_name \n", + "0 10 The answer is correct and can be directly veri... claude \n", + "1 10 The image clearly shows that the news article ... claude \n", + "2 10 The answer accurately describes the image and ... claude \n", + "3 10 The answer provides an insightful analysis of ... claude \n", + "4 8 The ranking provided is generally accurate and... claude \n", + ".. ... ... ... \n", + "315 6 While the question is thoughtful and relevant ... claude \n", + "316 6 While the question is based on information pro... claude \n", + "317 7 This question requires analysis and interpreta... claude \n", + "318 6 This question and answer are not directly supp... claude \n", + "319 5 This answer is not directly supported by the i... claude \n", "\n", - " score reason scorer_name \n", - "0 10 The answer accurately identifies the article d... gpt4v \n", - "1 10 The information provided in the answer directl... gpt4v \n", - "2 8 The answer is mostly authentic as the convicti... gpt4v \n", - "3 8 The answer is relevant and provides a plausibl... gpt4v \n", - "4 8 The response provides a reasoned inference bas... gpt4v \n", - "5 7 Authenticity (4/5): The information about it b... gpt4v \n", - "6 8 The answer effectively compares domestic polit... gpt4v \n", - "7 10 The answer provided is directly related to the... gpt4v \n", - "8 8 The answer effectively discusses the potential... gpt4v " + "[320 rows x 15 columns]" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } diff --git a/tools/live_bench/live_bench/websites/load_website.py b/tools/live_bench/live_bench/websites/load_website.py new file mode 100644 index 00000000..10976db7 --- /dev/null +++ b/tools/live_bench/live_bench/websites/load_website.py @@ -0,0 +1,34 @@ +import yaml +import os +from random import sample +from live_bench.websites.website import Website, DefaultWebsite, HumanScreenShotWebsite + + +def get_website(website_dict): + if "website_class" not in website_dict: + website_class = DefaultWebsite + else: + website_class = website_dict["website_class"] + url = website_dict["url"] + if "args" in website_dict: + return website_class(url, **website_dict["args"]) + else: + return website_class(url) + + +def load_websites(num_sample: int = -1): + website_list_path = os.path.join(os.path.dirname(__file__), "website_list.yaml") + with open(website_list_path, "r") as f: + website_list = yaml.full_load(f)["websites"] + if num_sample > 0: + website_list = sample(website_list, num_sample) + return [get_website(website_dict) for website_dict in website_list] + + +def load_websites_from_file(file_path): + names = os.listdir(file_path) + websites = [] + for name in names: + path = os.path.join(file_path, name) + websites.append(HumanScreenShotWebsite(path=path, name=name)) + return websites diff --git a/tools/live_bench/live_bench/websites/website.py b/tools/live_bench/live_bench/websites/website.py new file mode 100644 index 00000000..327bad37 --- /dev/null +++ b/tools/live_bench/live_bench/websites/website.py @@ -0,0 +1,62 @@ +import time +import os + +from webdriver_manager.core.driver import Driver +from abc import ABC, abstractmethod + + +class Website(ABC): + def __init__(self, url=None, name=None, path=None): + self.url = url + self.name = name + self.path = path + assert self.url is not None or self.path is not None, "Either url or path must be provided" + + def get_path(self): + if self.url: + return self.url + else: + return self.path + + def visit(self, driver: Driver): + self.pre_visit(driver) + driver.get(self.url) + self.post_visit(driver) + + def get_info(self): + info = {} + if self.url: + info["url"] = self.url + if self.name: + info["name"] = self.name + return info + + @abstractmethod + def pre_visit(self, driver: Driver): + raise NotImplementedError("pre_action not implemented") + + @abstractmethod + def post_visit(self, driver: Driver): + raise NotImplementedError("post_action not implemented") + + +class DefaultWebsite(Website): + def __init__(self, url, name=None): + super().__init__(url, name) + + def pre_visit(self, driver: Driver): + pass + + def post_visit(self, driver: Driver): + time.sleep(5) # Wait for 5 seconds to allow adblock to finish + + +class HumanScreenShotWebsite(Website): + def __init__(self, name=None, path=None): + super().__init__(name=name, path=path) + + def pre_visit(self, driver: Driver): + pass + + def post_visit(self, driver: Driver): + pass diff --git a/tools/live_bench/live_bench/websites/website_list.yaml b/tools/live_bench/live_bench/websites/website_list.yaml new file mode 100644 index 00000000..c85605d7 --- /dev/null +++ b/tools/live_bench/live_bench/websites/website_list.yaml @@ -0,0 +1,78 @@ +websites: +- url: https://www.bbc.com/ + # can add below line to specify the class to use for this website + # website_class: !constructor website.DefaultWebsite + # can add args tag to specify the arguments to pass to the class constructor + # args: + # arg1: value1 + # arg2: value2 +# - url: https://www.bbc.com/news +# - url: https://www.bbc.com/sport +# - url: https://www.bbc.com/business +# - url: https://www.bbc.com/innovation +# - url: https://www.bbc.com/culture +# - url: https://www.bbc.com/travel +# - url: https://www.bbc.com/future-planet +# - url: https://edition.cnn.com/ +# - url: https://edition.cnn.com/politics +# - url: https://edition.cnn.com/entertainment +# - url: https://edition.cnn.com/style +# - url: https://www.bloomberg.com/economics +# - url: https://www.bloomberg.com/industries +# - url: https://www.bloomberg.com/technology +# - url: https://www.bloomberg.com/politics +# - url: https://www.bloomberg.com/opinion +# - url: https://www.wsj.com/ +# - url: https://www.wsj.com/world/africa?mod=nav_top_subsection +# - url: https://www.wsj.com/world/americas?mod=nav_top_subsection +# - url: https://www.wsj.com/world/asia?mod=nav_top_subsection +# - url: https://www.wsj.com/world/china?mod=nav_top_subsection +# - url: https://www.wsj.com/world/europe?mod=nav_top_subsection +# - url: https://www.wsj.com/world/middle-east?mod=nav_top_subsection +# - url: https://www.wsj.com/world/india?mod=nav_top_subsection +# - url: https://www.wsj.com/world/oceania?mod=nav_top_subsection +# - url: https://www.wsj.com/world/russia?mod=nav_top_subsection +# - url: https://www.wsj.com/world/uk?mod=nav_top_subsection +# - url: https://www.wsj.com/science?mod=nav_top_subsection +# - url: https://www.wsj.com/science/archaeology?mod=nav_top_subsection +# - url: https://www.wsj.com/science/biology?mod=nav_top_subsection +# - url: https://www.wsj.com/science/environment?mod=nav_top_subsection +# - url: https://www.wsj.com/science/physics?mod=nav_top_subsection +# - url: https://www.wsj.com/science/space-astronomy?mod=nav_top_subsection +# - url: https://www.wsj.com/economy/central-banking?mod=nav_top_subsection +# - url: https://www.wsj.com/economy/consumers?mod=nav_top_subsection +# - url: https://www.wsj.com/economy/housing?mod=nav_top_subsection +# - url: https://www.wsj.com/economy/jobs?mod=nav_top_subsection +# - url: https://www.wsj.com/economy/trade?mod=nav_top_subsection +# - url: https://www.wsj.com/economy/global +# - url: https://www.wsj.com/tech/ai?mod=nav_top_subsection +# - url: https://www.wsj.com/tech/biotech +# - url: https://www.wsj.com/tech/cybersecurity?mod=nav_top_subsection +# - url: https://www.wsj.com/tech/personal-tech?mod=nav_top_subsection +# - url: https://www.reuters.com/ +# - url: https://www.reuters.com/business/aerospace-defense/ +# - url: https://www.reuters.com/business/autos-transportation/ +# - url: https://www.reuters.com/business/davos/ +# - url: https://www.reuters.com/business/energy/ +# - url: https://www.reuters.com/business/environment/ +# - url: https://www.reuters.com/business/finance/ +# - url: https://www.reuters.com/business/healthcare-pharmaceuticals/ +# - url: https://www.reuters.com/business/media-telecom/ +# - url: https://www.reuters.com/business/retail-consumer/ +# - url: https://www.reuters.com/business/future-of-health/ +# - url: https://www.reuters.com/business/future-of-money/ +# - url: https://www.reuters.com/business/take-five/ +# - url: https://www.reuters.com/business/world-at-work/ +# - url: https://www.reuters.com/breakingviews/ +# - url: https://www.reuters.com/technology/ +# - url: https://www.reuters.com/technology/cybersecurity/ +# - url: https://www.reuters.com/technology/space/ +# - url: https://www.reuters.com/technology/disrupted/ +# - url: https://www.reuters.com/technology/reuters-momentum/ +# - url: https://www.reuters.com/investigations/ +# - url: https://a16z.com/news-content/#latest +# - url: https://news.ycombinator.com/ +# - url: https://www.reddit.com/?rdt=48006 +# - url: https://news.crunchbase.com/ +# - url: https://www.cctv.com/ +# - url: https://sports.cctv.com/ diff --git a/tools/live_bench/pyproject.toml b/tools/live_bench/pyproject.toml old mode 100755 new mode 100644 diff --git a/tools/live_bench/refine_all_results.py b/tools/live_bench/refine_all_results.py new file mode 100644 index 00000000..fcc5beb3 --- /dev/null +++ b/tools/live_bench/refine_all_results.py @@ -0,0 +1,36 @@ +from tqdm import tqdm +from datasets import Dataset, load_dataset +from live_bench.data_generator.question_finalizer import QuestionFinalizer + + +if __name__ == "__main__": + hf_data = load_dataset("lmms-lab/LiveBench", "2024-07", split="test") + finalizer = QuestionFinalizer() + + def load_results(): + for item in tqdm(hf_data): + try: + res = finalizer.finalize_question(question=item["question"], answer=item["answer"], criteria=item["criteria"], images=item["images"]) + final_answer = item.copy() + final_answer["question"] = res["question"] + final_answer["answer"] = res["answer"] + final_answer["criteria"] = res["criteria"] + print(item) + print(final_answer) + except Exception as e: + print(f"Error in {item['id']}: {e}") + final_answer = item + + yield final_answer + # break + + final_data = {} + for data in load_results(): + for item, value in data.items(): + if item not in final_data: + final_data[item] = [] + final_data[item].append(value) + # final_data = Dataset.from_generator(load_results) + final_data = Dataset.from_dict(final_data, features=hf_data.features) + final_data.save_to_disk("logs/2024-07-final") + final_data.push_to_hub("lmms-lab/LiveBench", "2024-07") diff --git a/tools/live_bench/script/README.md b/tools/live_bench/script/README.md new file mode 100644 index 00000000..49250cfc --- /dev/null +++ b/tools/live_bench/script/README.md @@ -0,0 +1,15 @@ +## Usage + +### Upload Results + +```sh +python upload_results.py -f -m [-F] +``` + +`[-F]` means the script will automatically upload the results without human checking. Otherwise, the script will print the results and ask for confirmation before uploading. + +Example: + +```sh +python upload_results.py -f logs/0706_0959_model_outputs_gpt4v_model_args_c974bc -m gpt-4o -F +``` diff --git a/tools/live_bench/script/modify.ipynb b/tools/live_bench/script/modify.ipynb new file mode 100644 index 00000000..22944f1e --- /dev/null +++ b/tools/live_bench/script/modify.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading data: 100%|██████████| 4.62k/4.62k [00:01<00:00, 4.14kB/s]\n", + "Generating test split: 100%|██████████| 8/8 [00:00<00:00, 933.60 examples/s]\n" + ] + } + ], + "source": [ + "import datasets\n", + "\n", + "data = datasets.load_dataset(\"lmms-lab/LiveBenchResults\", \"2024-07\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df = data[\"test\"].to_pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(\"2024-07.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"2024-07.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    Model NameTotalBasic UnderstandingContextual AnalysisDeeper ImplicationsBroader ImplicationsFurther Insights
    0gpt-4o-mini86.24000089.081.089.687.60000084.0
    1gemini-1.5-flash80.76000084.469.281.790.30000078.2
    2gpt-4o91.30000092.283.494.194.40000092.4
    3gemini-1.5-pro86.28000090.676.685.691.60000087.0
    4llama3-llava-next-8b62.65060260.161.474.863.67346953.3
    5llava-1.5-7b41.94000038.634.558.842.80000035.0
    6Idefics2-8B25.86000018.016.343.827.00000024.2
    7InternVL2-2B56.84000065.849.964.255.80000048.5
    \n", + "
    " + ], + "text/plain": [ + " Model Name Total Basic Understanding Contextual Analysis \\\n", + "0 gpt-4o-mini 86.240000 89.0 81.0 \n", + "1 gemini-1.5-flash 80.760000 84.4 69.2 \n", + "2 gpt-4o 91.300000 92.2 83.4 \n", + "3 gemini-1.5-pro 86.280000 90.6 76.6 \n", + "4 llama3-llava-next-8b 62.650602 60.1 61.4 \n", + "5 llava-1.5-7b 41.940000 38.6 34.5 \n", + "6 Idefics2-8B 25.860000 18.0 16.3 \n", + "7 InternVL2-2B 56.840000 65.8 49.9 \n", + "\n", + " Deeper Implications Broader Implications Further Insights \n", + "0 89.6 87.600000 84.0 \n", + "1 81.7 90.300000 78.2 \n", + "2 94.1 94.400000 92.4 \n", + "3 85.6 91.600000 87.0 \n", + "4 74.8 63.673469 53.3 \n", + "5 58.8 42.800000 35.0 \n", + "6 43.8 27.000000 24.2 \n", + "7 64.2 55.800000 48.5 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data = datasets.Dataset.from_pandas(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Creating parquet from Arrow format: 100%|██████████| 1/1 [00:00<00:00, 473.45ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.17s/it]\n" + ] + }, + { + "data": { + "text/plain": [ + "CommitInfo(commit_url='https://huggingface.co/datasets/lmms-lab/LiveBenchResults/commit/a29f8ecb399dbd7ab7475f0de2c48ee54affbff9', commit_message='Upload dataset', commit_description='', oid='a29f8ecb399dbd7ab7475f0de2c48ee54affbff9', pr_url=None, pr_revision=None, pr_num=None)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.push_to_hub(\"lmms-lab/LiveBenchResults\", \"2024-07\", split=\"test\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "data = datasets.load_dataset(\"lmms-lab/LiveBenchDetailedResults\", \"2024-07\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "data[\"Idefics2_8B\"] = data[\"idefics2\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " gpt_4o_mini: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " gemini_1.5_flash: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " gpt_4o: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " gemini_1.5_pro: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " llama3_llava_next_8b: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " llava_1.5_7b: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " idefics2: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " InternVL2_2B: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " Idefics2_8B: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + "})" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "new_data = {}\n", + "for k, v in data.items():\n", + " if k == \"idefics2\":\n", + " continue\n", + " new_data[k] = v\n", + "data = datasets.DatasetDict(new_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " gpt_4o_mini: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " gemini_1.5_flash: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " gpt_4o: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " gemini_1.5_pro: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " llama3_llava_next_8b: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " llava_1.5_7b: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " InternVL2_2B: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + " Idefics2_8B: Dataset({\n", + " features: ['id', 'images', 'question', 'ground_truth', 'criteria', 'subtask', 'response', 'score', 'reason'],\n", + " num_rows: 250\n", + " })\n", + "})" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Map: 100%|██████████| 250/250 [00:00<00:00, 347.35 examples/s]it/s]\n", + "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 12.58ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.63s/it]\n", + "Map: 100%|██████████| 250/250 [00:00<00:00, 363.40 examples/s]it/s]\n", + "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 12.70ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.59s/it]\n", + "Map: 100%|██████████| 250/250 [00:00<00:00, 472.60 examples/s]it/s]\n", + "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 12.62ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.43s/it]\n", + "Map: 100%|██████████| 250/250 [00:00<00:00, 352.11 examples/s]it/s]\n", + "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 11.55ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.63s/it]\n", + "Map: 100%|██████████| 250/250 [00:00<00:00, 475.90 examples/s]it/s]\n", + "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 11.38ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.46s/it]\n", + "Map: 100%|██████████| 250/250 [00:00<00:00, 364.89 examples/s]it/s]\n", + "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 10.94ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.59s/it]\n", + "Map: 100%|██████████| 250/250 [00:00<00:00, 529.96 examples/s]it/s]\n", + "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 13.51ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.33s/it]\n", + "Map: 100%|██████████| 250/250 [00:00<00:00, 349.67 examples/s]it/s]\n", + "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 12.74ba/s]\n", + "Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 1.57s/it]\n" + ] + }, + { + "data": { + "text/plain": [ + "CommitInfo(commit_url='https://huggingface.co/datasets/lmms-lab/LiveBenchDetailedResults/commit/047d6dc66759e0a8b57b4e6015db6208da1cd4da', commit_message='Upload dataset', commit_description='', oid='047d6dc66759e0a8b57b4e6015db6208da1cd4da', pr_url=None, pr_revision=None, pr_num=None)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.push_to_hub(\"lmms-lab/LiveBenchDetailedResults\", \"2024-07\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "live_bench", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/live_bench/script/upload_results.py b/tools/live_bench/script/upload_results.py new file mode 100644 index 00000000..b8e608ff --- /dev/null +++ b/tools/live_bench/script/upload_results.py @@ -0,0 +1,178 @@ +import os +import json +import argparse +import datasets +import numpy as np +import pandas as pd +from datasets import Dataset, load_dataset + +from PIL import Image +from typing import Dict, List, Union +from tqdm import tqdm + + +EPS = 1e-6 + +RESULT_FEATURES = { + "id": datasets.Value("int32"), + "images": datasets.Sequence(datasets.Image()), + "question": datasets.Value("string"), + "ground_truth": datasets.Value("string"), + "criteria": datasets.Value("string"), + "subtask": datasets.Value("string"), + "response": datasets.Value("string"), + "score": datasets.Value("float32"), + "reason": datasets.Value("string"), +} + +SUBTASKS = [ + "Basic Understanding", + "Contextual Analysis", + "Deeper Implications", + "Broader Implications", + "Further Insights", +] + + +def load_images(config) -> Dict[int, List[Image.Image]]: + dataset = datasets.load_dataset(config["dataset_path"], config["dataset_name"], split=config["test_split"]) + images = {} + for data in tqdm(dataset, desc="Loading images"): + images[data["id"]] = data["images"] + return images + + +def get_hf_results(results, detailed_results): + live_bench_images = load_images(results["configs"]["live_bench"]) + + def load_results(): + for result in tqdm(detailed_results["logs"], desc="Loading results"): + doc = result["doc"] + res = {} + res["id"] = doc["id"] + res["images"] = live_bench_images[doc["id"]] + res["question"] = doc["question"] + res["ground_truth"] = doc["answer"] + res["criteria"] = doc["criteria"] + res["subtask"] = doc["subtask"] + res["response"] = result["filtered_resps"][0] + res["score"] = result["gpt4_eval_score"]["rating"] + res["reason"] = result["gpt4_eval_score"]["explanation"] + yield res + + result_dataset = Dataset.from_generator(load_results, features=datasets.Features(RESULT_FEATURES)) + + return result_dataset + + +def preview_results(results, heading: str): + HEADING = "=" * 15 + " " + heading + " " + "=" * 15 + ENDING = "=" * len(HEADING) + print(HEADING) + print(results) + print(ENDING) + + +def calculate_score(results: Dataset): + results = results.to_pandas() + + sum_score, count = 0, 0 + score = {} + for subtask in SUBTASKS: + score[subtask] = [] + for index, result in tqdm(results.iterrows(), total=len(results), desc="Calculating score"): + if result["score"] == -1: + continue + sum_score += result["score"] / 10 + count += 1 + subtask = result["subtask"] + if subtask not in SUBTASKS: + subtask = "Further Insights" + score[result["subtask"]].append(result["score"] / 10) + res = [(subtask, len(score[subtask]), np.mean(score[subtask]) * 100) for subtask in SUBTASKS] + res.append(("Total", count, sum_score / count * 100)) + res = pd.DataFrame(res, columns=["Subtask", "Count", "Score"]) + + return res + + +def get_results(folder): + detailed_file = os.path.join(folder, "live_bench.json") + results_file = os.path.join(folder, "results.json") + + with open(results_file, "r") as f: + results = json.load(f) + + assert "live_bench" in results["configs"], "No live_bench config found in results.json" + final_score = results["results"]["live_bench"]["gpt4_eval_score,none"] + model_configs = results["model_configs"] + version = results["configs"]["live_bench"]["metadata"]["version"] + + assert model_configs["limit"] is None, "Model limit is not None, please check if the model is tested on the full dataset" + + with open(detailed_file, "r") as f: + detailed_results = json.load(f) + + hf_results = get_hf_results(results, detailed_results) + preview_results(hf_results.to_pandas().iloc[0], "Detailed Results") + score = calculate_score(hf_results) + preview_results(score, "Final Score") + + assert ( + abs(score[score["Subtask"] == "Total"]["Score"] - final_score) <= EPS + ).all(), f"Final score does not match the calculated score, the score calculated by the script is {score[score['Subtask'] == 'Total']['Score'].values[0]} and the final score in the log is {final_score}." + + return hf_results, score, version + + +def upload_results( + hf_results: Dataset, + score: pd.DataFrame, + model_name, + dataset_version, + log_folder="logs", +): + hf_results.push_to_hub( + "lmms-lab/LiveBenchDetailedResults", + config_name=dataset_version, + split=model_name.replace("-", "_"), + ) + if not os.path.exists(log_folder): + os.makedirs(log_folder) + score_path = os.path.abspath(os.path.join(log_folder, f"{dataset_version}_{model_name}.csv")) + score.to_csv(score_path, index=False) + print(f"Results saved to {score_path}") + score_dict = {item["Subtask"]: item["Score"] for index, item in score.iterrows()} + score_dict["Model Name"] = model_name + try: + hf_score = datasets.load_dataset("lmms-lab/LiveBenchResults", dataset_version, split="test") + except: + hf_score = Dataset.from_dict({subtask: [] for subtask in ["Model Name", "Total"] + SUBTASKS}) + hf_score = hf_score.add_item(score_dict) + df_score = pd.DataFrame(hf_score) + df_score = df_score.drop_duplicates(subset=["Model Name"], keep="last") + df_score = df_score[["Model Name", "Total"] + SUBTASKS] + hf_score = Dataset.from_pandas(df_score) + hf_score.push_to_hub("lmms-lab/LiveBenchResults", dataset_version, split="test") + + +if __name__ == "__main__": + argparse = argparse.ArgumentParser() + argparse.add_argument("--folder", "-f", type=str, required=True, help="Results folder") + argparse.add_argument("--name", "-m", type=str, required=True, help="Model name") + argparse.add_argument("--log_folder", "-l", type=str, default="logs", help="Log folder") + argparse.add_argument("--force", "-F", action="store_true", help="Force upload") + args = argparse.parse_args() + hf_results, score, version = get_results(args.folder) + print(f"Results will be uploaded with model name {args.name} and model version {version}") + if args.force is False: + print("Are you sure you want to upload the results? (y/n)", end=" ") + while True: + choice = input().lower() + if choice == "y": + break + elif choice == "n": + exit() + else: + print("Invalid choice, please enter 'y' or 'n'") + upload_results(hf_results, score, args.name, version, args.log_folder) diff --git a/tools/live_bench/setup.py b/tools/live_bench/setup.py old mode 100755 new mode 100644 From 2af043ef4521d93d012899dc27d03858a77515ef Mon Sep 17 00:00:00 2001 From: Li Bo Date: Wed, 7 Aug 2024 14:12:01 +1000 Subject: [PATCH 176/196] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ec054abd..b116e3a5 100755 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ --- ## Annoucement -- [2024-07] 🎉🎉 We welcome the new tasks [LongVideoBench](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/117), [MMStar](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/158), new models [Mantis](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/162). +- [2024-08] 🎉🎉 We welcome the new model [llava-onevision](https://huggingface.co/papers/2408.03326), [Mantis](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/162), new tasks [MVBench](https://huggingface.co/datasets/OpenGVLab/MVBench), [LongVideoBench](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/117), [MMStar](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/158). - [2024-07] 🎉🎉 We have released the [technical report](https://arxiv.org/abs/2407.12772) and [LiveBench](https://huggingface.co/spaces/lmms-lab/LiveBench)! - [2024-07] 👨‍💻👨‍💻 The `lmms-eval/v0.2.1` has been upgraded to support more models, including [LongVA](https://github.com/EvolvingLMMs-Lab/LongVA), [InterVL-2](https://github.com/OpenGVLab/InternVL), [VILA](https://github.com/NVlabs/VILA), and many more evaluation tasks, e.g. [Details Captions](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/136), [MLVU](https://arxiv.org/abs/2406.04264), [WildVision-Bench](https://huggingface.co/datasets/WildVision/wildvision-arena-data), [VITATECS](https://github.com/lscpku/VITATECS) and [LLaVA-Interleave-Bench](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/). From 57a7f79ed5b3ba7d43d41be77ec2d6143a91c38b Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Thu, 8 Aug 2024 09:53:37 +0800 Subject: [PATCH 177/196] upload --- README.md | 7 ++- lmms_eval/api/samplers.py | 4 +- lmms_eval/models/__init__.py | 2 +- lmms_eval/models/llava_hf.py | 2 +- lmms_eval/models/llava_vid.py | 2 + lmms_eval/models/mantis.py | 46 +++++++++---------- lmms_eval/models/xcomposer2d5.py | 6 +-- lmms_eval/tasks/mirb/utils.py | 55 ++++++++++++----------- lmms_eval/tasks/mmstar/utils.py | 2 +- lmms_eval/tasks/seedbench_2_plus/utils.py | 4 +- pyproject.toml | 5 +-- 11 files changed, 70 insertions(+), 65 deletions(-) diff --git a/README.md b/README.md index b116e3a5..08d65dea 100755 --- a/README.md +++ b/README.md @@ -49,7 +49,7 @@ cd lmms-eval pip install -e . ``` -If you wanted to test llava, you will have to clone their repo from [LLaVA](https://github.com/haotian-liu/LLaVA) and +If you want to test LLaVA, you will have to clone their repo from [LLaVA](https://github.com/haotian-liu/LLaVA) and ```bash # for llava 1.5 # git clone https://github.com/haotian-liu/LLaVA @@ -93,6 +93,11 @@ We also provide the raw data exported from Weights & Biases for the detailed res
    +If you want to test [VILA](https://github.com/NVlabs/VILA), you should install the following dependencies: + +```bash +pip install s2wrapper@git+https://github.com/bfshi/scaling_on_scales +``` Our Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub. diff --git a/lmms_eval/api/samplers.py b/lmms_eval/api/samplers.py index 2cecfe22..f77065e8 100755 --- a/lmms_eval/api/samplers.py +++ b/lmms_eval/api/samplers.py @@ -37,9 +37,7 @@ def get_context(self, doc, num_fewshot): + ( str(self.doc_to_target(doc)[0]) if type(self.doc_to_target(doc)) is list - else self.doc_to_target(doc) - if (self.config.doc_to_choice is None or type(self.doc_to_target(doc)) is str) - else str(self.doc_to_choice(doc)[self.doc_to_target(doc)]) + else self.doc_to_target(doc) if (self.config.doc_to_choice is None or type(self.doc_to_target(doc)) is str) else str(self.doc_to_choice(doc)[self.doc_to_target(doc)]) ) for doc in selected_docs ] diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index 4acc38a9..de1b533b 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -41,7 +41,7 @@ "video_llava": "VideoLLaVA", "vila": "VILA", "xcomposer2_4KHD": "XComposer2_4KHD", - "xcomposer2d5": "XComposer2D5" + "xcomposer2d5": "XComposer2D5", } for model_name, model_class in AVAILABLE_MODELS.items(): diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py index af0b554e..3c5791fe 100644 --- a/lmms_eval/models/llava_hf.py +++ b/lmms_eval/models/llava_hf.py @@ -320,7 +320,7 @@ def _collate(x): pad_token_id=self.tokenizer.eos_token_id, eos_token_id=self.specified_eot_token_id, ) - cont = cont[:, inputs["input_ids"].shape[-1]:] + cont = cont[:, inputs["input_ids"].shape[-1] :] except Exception as e: eval_logger.error(f"Error {e} in generating") cont = "" diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py index bdf7fb37..c1e1fd38 100755 --- a/lmms_eval/models/llava_vid.py +++ b/lmms_eval/models/llava_vid.py @@ -28,11 +28,13 @@ try: from llavavid.model.language_model.llava_qwen import LlavaQwenConfig + AutoConfig.register("llava_qwen", LlavaQwenConfig) except ImportError: eval_logger.debug("No Qwen for llava vid") from llavavid.model.language_model.llava_llama import LlavaConfig + AutoConfig.register("llava_llama", LlavaConfig) diff --git a/lmms_eval/models/mantis.py b/lmms_eval/models/mantis.py index fe1bb0b2..7b0a1569 100644 --- a/lmms_eval/models/mantis.py +++ b/lmms_eval/models/mantis.py @@ -27,10 +27,10 @@ from mantis.models.mllava import LlavaForConditionalGeneration, MLlavaProcessor from mantis.models.mfuyu import MFuyuForCausalLM, MFuyuProcessor from mantis.models.conversation import conv_mllava_v1 as default_conv, conv_templates - + except Exception as e: eval_logger.debug("Mantis is not installed. Please install Mantis to use this model.\nError: %s" % e) - + try: from transformers import AutoModelForVision2Seq, AutoProcessor except Exception as e: @@ -42,13 +42,14 @@ try: import flash_attn - + best_fit_attn_implementation = "flash_attention_2" except ImportError: best_fit_attn_implementation = "eager" DEFAULT_IMAGE_TOKEN = "" + @register_model("mantis") class Mantis(lmms): """ @@ -84,27 +85,27 @@ def __init__( else: self._device = torch.device(f"cuda:{accelerator.local_process_index}") self.device_map = f"cuda:{accelerator.local_process_index}" - + self._is_idefics = "idefics" in pretrained.lower() if isinstance(dtype, str) and dtype != "auto": dtype = getattr(torch, dtype) - + # Here we load the "non-idefics" Mantis model. if not self._is_idefics: - if 'fuyu' in pretrained.lower(): + if "fuyu" in pretrained.lower(): self._processor = MFuyuProcessor.from_pretrained(pretrained) self._model = MFuyuForCausalLM.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation, torch_dtype=dtype) else: self._processor = MLlavaProcessor.from_pretrained(pretrained) self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation, torch_dtype=dtype) - + else: self._processor = AutoProcessor.from_pretrained(pretrained) self._model = AutoModelForVision2Seq.from_pretrained(pretrained, device_map=self.device_map, torch_dtype=dtype) eval_logger.info(f"Using {type(self._model)} to instantiate the Mantis model.") - + self._tokenizer = self._processor.tokenizer - + self._config = self._model.config self.model.eval() self.model.tie_weights() @@ -112,7 +113,7 @@ def __init__( self.batch_size_per_gpu = int(batch_size) self.use_cache = use_cache self.truncate_context = truncate_context - + if accelerator.num_processes > 1: assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model @@ -222,7 +223,7 @@ def flatten(self, input): def generate_until(self, requests: List[Instance]) -> List[str]: res = [] - + def _collate(x): # the negative sign on len(toks) sorts descending - this has a few advantages: # - time estimates will always be over not underestimates, which is more useful for planning @@ -243,11 +244,11 @@ def _collate(x): for chunk in chunks: contexts, all_gen_kwargs, doc_to_visuals, doc_id, tasks, splits = zip(*chunk) visuals = [doc_to_visual(self.task_dict[task][split][ids]) for ids, task, split, doc_to_visual in zip(doc_id, tasks, splits, doc_to_visuals)] - + # we assume all gen kwargs in the batch are the same # this is safe to assume because the `grouper` object ensures it. gen_kwargs = all_gen_kwargs[0] - + until = gen_kwargs.pop("until", None) image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio", None) @@ -261,7 +262,7 @@ def _collate(x): prompts = [] for visual, context in zip(visuals, contexts): if self._is_idefics: - # Follow the idefics implementation: + # Follow the idefics implementation: content = [] if DEFAULT_IMAGE_TOKEN not in context: for _ in visual: @@ -274,17 +275,14 @@ def _collate(x): # We follow the Mantis code base: https://github.com/TIGER-AI-Lab/Mantis/blob/main/mantis/models/mllava/utils.py#L33 to make sure they are consistent # Users don't need to define chat template as it is done here if "llama-3" in self._model.language_model.name_or_path.lower(): - conv = conv_templates['llama_3'] - terminators = [ - self._processor.tokenizer.eos_token_id, - self._processor.tokenizer.convert_tokens_to_ids("<|eot_id|>") - ] + conv = conv_templates["llama_3"] + terminators = [self._processor.tokenizer.eos_token_id, self._processor.tokenizer.convert_tokens_to_ids("<|eot_id|>")] else: conv = default_conv terminators = None - + gen_kwargs["eos_token_id"] = terminators - + conv = conv.copy() conv.append_message(conv.roles[0], context) conv.append_message(conv.roles[1], "") @@ -292,9 +290,9 @@ def _collate(x): prompts.append(prompt) inputs = self._processor(images=visuals, text=prompts, return_tensors="pt", truncation=True) if "image_patches" in inputs.keys(): - inputs["image_patches"] = inputs["image_patches"][0] # FIXME: Fuyu model would return a list instead of a pytorch tensor. This weird behavior needs fixing. - inputs = {k: v.to(self.device) for k, v in inputs.items()} - + inputs["image_patches"] = inputs["image_patches"][0] # FIXME: Fuyu model would return a list instead of a pytorch tensor. This weird behavior needs fixing. + inputs = {k: v.to(self.device) for k, v in inputs.items()} + output_ids = self.model.generate(**inputs, **gen_kwargs) for output_id, input_id in zip(output_ids, inputs["input_ids"]): generated_id = output_id[len(input_id) :] diff --git a/lmms_eval/models/xcomposer2d5.py b/lmms_eval/models/xcomposer2d5.py index 17ed8fa2..8bfe6c65 100644 --- a/lmms_eval/models/xcomposer2d5.py +++ b/lmms_eval/models/xcomposer2d5.py @@ -40,7 +40,6 @@ def __init__( if not os.path.exists(self.tmp_folder): os.makedirs(self.tmp_folder) eval_logger.info(f"Using temporary folder: {self.tmp_folder}") - batch_size = int(batch_size) assert batch_size == 1, f"Batch size should be 1 for InternVL2, but got {batch_size}." @@ -57,7 +56,7 @@ def __init__( else: self._device = torch.device(f"cuda:{accelerator.local_process_index}") self.device_map = f"cuda:{accelerator.local_process_index}" - + self.path = pretrained self._model = AutoModel.from_pretrained(self.path, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map=self.device_map).half().eval() self._tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True) @@ -130,7 +129,6 @@ def rank(self): def world_size(self): return self._world_size - def flatten(self, input): new_list = [] for i in input: @@ -168,7 +166,7 @@ def generate_until(self, requests) -> List[str]: gen_kwargs["num_beams"] = 1 try: - with torch.autocast(device_type='cuda', dtype=torch.float16): + with torch.autocast(device_type="cuda", dtype=torch.float16): response, his = self.model.chat(self.tokenizer, contexts, image, do_sample=False, num_beams=1, use_meta=True, max_new_tokens=gen_kwargs["max_new_tokens"]) except Exception as e: eval_logger.error(f"Error : {e}") diff --git a/lmms_eval/tasks/mirb/utils.py b/lmms_eval/tasks/mirb/utils.py index 2ed447d0..174659dc 100644 --- a/lmms_eval/tasks/mirb/utils.py +++ b/lmms_eval/tasks/mirb/utils.py @@ -1,4 +1,3 @@ - from lmms_eval.filters.extraction import ExtendedRegexFilter from lmms_eval.filters.transformation import MapFilter import re @@ -6,32 +5,37 @@ import logging + eval_logger = logging.getLogger("lmms-eval") + def get_task_instruction(dataset): - if dataset in ['analogy', 'attribute', 'plot_code', 'visual_chain', 'sightseeing']: - instr = 'Answer with a single word.' - elif dataset in ['codeu', 'food', 'image_jigsaw']: - instr = 'Answer with the option symbol.' - elif dataset in ['arxiv']: - instr = 'Answer with the paper title.' - elif dataset in ['count']: - instr = 'Answer with a single number.' - elif dataset in ['3d_scene']: - instr = 'The following images are different views of the same 3D scene. Answer with a single number.' - + if dataset in ["analogy", "attribute", "plot_code", "visual_chain", "sightseeing"]: + instr = "Answer with a single word." + elif dataset in ["codeu", "food", "image_jigsaw"]: + instr = "Answer with the option symbol." + elif dataset in ["arxiv"]: + instr = "Answer with the paper title." + elif dataset in ["count"]: + instr = "Answer with a single number." + elif dataset in ["3d_scene"]: + instr = "The following images are different views of the same 3D scene. Answer with a single number." + return instr + def mirb_doc_to_text(doc, model_specific_prompt_kwargs=None): - subset, question = doc['subset'], doc["questions"] + subset, question = doc["subset"], doc["questions"] task_instruction = get_task_instruction(subset) post_prompt = model_specific_prompt_kwargs["post_prompt"] pre_prompt = model_specific_prompt_kwargs["pre_prompt"] return f"{pre_prompt}{task_instruction}{question}{post_prompt}" + def mirb_doc_to_visual(doc): image_list = [image.convert("RGB") for image in doc["image_list"]] - return image_list + return image_list + def mirb_doc_to_target(doc): return doc["answers"] @@ -60,6 +64,7 @@ def extract_numbers(string): all_numbers = numbers_with_commas + numbers_scientific + numbers_simple return all_numbers + def check_is_number(string): """ Check if the given string a number. @@ -72,6 +77,7 @@ def check_is_number(string): # check if there's comma inside return False + def normalize_str(string): """ Normalize the str to lower case and make them float numbers if possible. @@ -97,6 +103,7 @@ def normalize_str(string): return [" " + string, string + " "] # avoid trivial matches return [string] + def parse_multi_choice_response(response): # here, we assume we have a list, in which each element is # a list of model responses for some particular input/target pair. @@ -185,10 +192,11 @@ def get_key_subresponses(response): return pred_list + def mirb_process_results(doc, results): pred = results[0] subset, answer = doc["subset"], doc["answers"] - if answer in ['A', 'B', 'C', 'D', 'E']: # MCQ tasks + if answer in ["A", "B", "C", "D", "E"]: # MCQ tasks parsed_pred = parse_multi_choice_response(pred) else: parsed_pred = parse_open_response(pred) @@ -196,6 +204,7 @@ def mirb_process_results(doc, results): data_dict = {"question_id": doc["question_id"], "subset": task_type, "pred_answer": parsed_pred, "answers": doc["answers"]} return {f"mirb_score": data_dict} + def eval_multi_choice(gold_i, pred_i): """ Evaluate a multiple choice instance. @@ -242,6 +251,7 @@ def eval_open(gold_i, pred_i): break return correct + def mirb_aggregation(results): task_num = {} score = 0 @@ -251,7 +261,7 @@ def mirb_aggregation(results): task_score[result["subset"]] = 0 task_num[result["subset"]] = 0 - if result['answers'] in ['A', 'B', 'C', 'D', 'E']: # MCQ tasks + if result["answers"] in ["A", "B", "C", "D", "E"]: # MCQ tasks correct = eval_multi_choice(result["answers"], result["pred_answer"]) task_score[result["subset"]] += correct score += correct @@ -262,7 +272,7 @@ def mirb_aggregation(results): task_num[result["subset"]] += 1 avg_score = score / len(results) - task_score = {k : v / task_num[k] for k,v in task_score.items()} + task_score = {k: v / task_num[k] for k, v in task_score.items()} print("Performances for different subsets:") print("=" * 50) @@ -271,12 +281,7 @@ def mirb_aggregation(results): print("=" * 50) # print across evaluation dimension - groups = { - "Knowledge": ["food", "sightseeing"], - "Reasoning": ["codeu", "plot_code", "analogy", "3d_scene"], - "Perception": ["image_jigsaw", "count", "attribute"], - "Multi-Hop": ["visual_chain", "arxiv"] - } + groups = {"Knowledge": ["food", "sightseeing"], "Reasoning": ["codeu", "plot_code", "analogy", "3d_scene"], "Perception": ["image_jigsaw", "count", "attribute"], "Multi-Hop": ["visual_chain", "arxiv"]} # Compute the averages for each group averages_dict = compute_averages_from_task_scores(task_score, groups) @@ -287,7 +292,7 @@ def mirb_aggregation(results): for group, score in averages_dict.items(): print(f"{group} : {score:.2f}") print("=" * 50) - + return avg_score @@ -297,4 +302,4 @@ def compute_averages_from_task_scores(task_score, groups): for group, features in groups.items(): values = [task_score[feature] for feature in features] averages[group] = np.mean(values) - return averages \ No newline at end of file + return averages diff --git a/lmms_eval/tasks/mmstar/utils.py b/lmms_eval/tasks/mmstar/utils.py index aba6dd6a..5d3a8f6e 100644 --- a/lmms_eval/tasks/mmstar/utils.py +++ b/lmms_eval/tasks/mmstar/utils.py @@ -92,4 +92,4 @@ def mmstar_aggregate_results(results): eval_logger.info(f"{l2_category}: {avg_score:.2f}") avg_score = sum(l2_category_avg_score.values()) / len(l2_category_avg_score) - return avg_score \ No newline at end of file + return avg_score diff --git a/lmms_eval/tasks/seedbench_2_plus/utils.py b/lmms_eval/tasks/seedbench_2_plus/utils.py index 3182b4bc..b722d250 100755 --- a/lmms_eval/tasks/seedbench_2_plus/utils.py +++ b/lmms_eval/tasks/seedbench_2_plus/utils.py @@ -1,8 +1,10 @@ import json + def seed_doc_to_visual(doc): return [doc["image"].convert("RGB")] + def parse_choice_img(choice: str, img_token: str): if "jpg" in choice or "png" in choice: return img_token @@ -16,7 +18,7 @@ def seed_doc_to_text(doc, model_specific_kwargs=None): question += f"B. {parse_choice_img(doc['choice_B'], model_specific_kwargs['img_token'])}\n" question += f"C. {parse_choice_img(doc['choice_C'], model_specific_kwargs['img_token'])}\n" question += f"D. {parse_choice_img(doc['choice_D'], model_specific_kwargs['img_token'])}" - + return f"{question}\n{model_specific_kwargs['post_prompt']}" diff --git a/pyproject.toml b/pyproject.toml index cb9f04e6..13a05e17 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta" [project] name = "lmms_eval" -version = "0.2.1" +version = "0.2.2.dev0" authors = [ { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" }, ] @@ -79,9 +79,6 @@ dependencies = [ ] [project.optional-dependencies] -vila = [ - "s2wrapper@git+https://github.com/bfshi/scaling_on_scales" -] gemini = [ "google-generativeai", ] From 656fcce87aa873b1743a66717cac1c13519c0994 Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Thu, 8 Aug 2024 09:56:16 +0800 Subject: [PATCH 178/196] update version & fix typos --- README.md | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 08d65dea..9f6eaa67 100755 --- a/README.md +++ b/README.md @@ -69,7 +69,7 @@ You can check the [environment install script](miscs/repr_scripts.sh) and [torch -If you want to test on caption dataset such as `coco`, `refcoco`, and `nocaps`, you will need to have `java==1.8.0 ` to let pycocoeval api to work. If you don't have it, you can install by using conda +If you want to test on caption dataset such as `coco`, `refcoco`, and `nocaps`, you will need to have `java==1.8.0` to let pycocoeval api to work. If you don't have it, you can install by using conda ``` conda install openjdk=8 ``` diff --git a/pyproject.toml b/pyproject.toml index 13a05e17..fd8408c8 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta" [project] name = "lmms_eval" -version = "0.2.2.dev0" +version = "0.2.2" authors = [ { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" }, ] From a365bf72543639b6b2171a6732f00eea7e6bee2f Mon Sep 17 00:00:00 2001 From: Fanyi Pu Date: Thu, 8 Aug 2024 09:57:04 +0800 Subject: [PATCH 179/196] seems there is no 0.2.1 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index fd8408c8..b5512035 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta" [project] name = "lmms_eval" -version = "0.2.2" +version = "0.2.1" authors = [ { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" }, ] From 536b70b4e5797f8bc51d46bc658e05db1d2b3c3b Mon Sep 17 00:00:00 2001 From: kcz358 Date: Mon, 12 Aug 2024 05:25:34 +0000 Subject: [PATCH 180/196] Fix typo in model registry for llava onevision --- lmms_eval/models/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py index de1b533b..534c4e73 100755 --- a/lmms_eval/models/__init__.py +++ b/lmms_eval/models/__init__.py @@ -24,7 +24,7 @@ "llama_vid": "LLaMAVid", "llava": "Llava", "llava_hf": "LlavaHf", - "llava_onevision": "LlavaOneVision", + "llava_onevision": "Llava_OneVision", "llava_sglang": "LlavaSglang", "llava_vid": "LlavaVid", "longva": "LongVA", From 7d935fafdd4dbb14a5c1f3954778ce4cb9369381 Mon Sep 17 00:00:00 2001 From: Pu Fanyi Date: Wed, 14 Aug 2024 23:19:35 +0800 Subject: [PATCH 181/196] Update the blog link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9f6eaa67..b7816ca9 100755 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ > Accelerating the development of large multimodal models (LMMs) with `lmms-eval` -🏠 [LMMs-Lab Homepage](https://lmms-lab.github.io/) | 🎉 [Blog](https://lmms-lab.github.io/lmms-eval-blog/lmms-eval-0.1/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | Discord_Thread [discord/lmms-eval](https://discord.gg/zdkwKUqrPy) +🏠 [LMMs-Lab Homepage](https://lmms-lab.github.io/) | 🎉 [Blog](https://lmms-lab.framer.ai/blog/lmms-eval-blog) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | Discord_Thread [discord/lmms-eval](https://discord.gg/zdkwKUqrPy) --- From c514d30bbe2133afe7b8fc3a99b6a34dbc376b71 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Fri, 16 Aug 2024 22:15:38 +1000 Subject: [PATCH 182/196] Update README.md --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index b7816ca9..27cdacf8 100755 --- a/README.md +++ b/README.md @@ -4,6 +4,11 @@ # The Evaluation Suite of Large Multimodal Models +[![PyPI](https://img.shields.io/pypi/v/lmms-eval)](https://pypi.org/project/lmms-eval) +![PyPI - Downloads](https://img.shields.io/pypi/dm/lmms-eval) +[![issue resolution](https://img.shields.io/github/issues-closed-raw/EvolvingLMMs-Lab/lmms-eval)](https://github.com/EvolvingLMMs-Lab/lmms-eval/issues) +[![open issues](https://img.shields.io/github/issues-raw/EvolvingLMMs-Lab/lmms-eval)](https://github.com/EvolvingLMMs-Lab/lmms-eval/issues) + > Accelerating the development of large multimodal models (LMMs) with `lmms-eval` 🏠 [LMMs-Lab Homepage](https://lmms-lab.github.io/) | 🎉 [Blog](https://lmms-lab.framer.ai/blog/lmms-eval-blog) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | Discord_Thread [discord/lmms-eval](https://discord.gg/zdkwKUqrPy) From 67c232f4cd9e3d888d7804d90d417f964aa5554a Mon Sep 17 00:00:00 2001 From: Li Bo Date: Fri, 16 Aug 2024 22:19:07 +1000 Subject: [PATCH 183/196] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 27cdacf8..9a99b860 100755 --- a/README.md +++ b/README.md @@ -6,6 +6,7 @@ [![PyPI](https://img.shields.io/pypi/v/lmms-eval)](https://pypi.org/project/lmms-eval) ![PyPI - Downloads](https://img.shields.io/pypi/dm/lmms-eval) +![GitHub contributors](https://img.shields.io/github/contributors/EvolvingLMMs-Lab/lmms-eval) [![issue resolution](https://img.shields.io/github/issues-closed-raw/EvolvingLMMs-Lab/lmms-eval)](https://github.com/EvolvingLMMs-Lab/lmms-eval/issues) [![open issues](https://img.shields.io/github/issues-raw/EvolvingLMMs-Lab/lmms-eval)](https://github.com/EvolvingLMMs-Lab/lmms-eval/issues) From 385d03827a19c07df71764f57497f47311e0aa16 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Fri, 16 Aug 2024 22:20:14 +1000 Subject: [PATCH 184/196] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9a99b860..56c1b4cf 100755 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ > Accelerating the development of large multimodal models (LMMs) with `lmms-eval` -🏠 [LMMs-Lab Homepage](https://lmms-lab.github.io/) | 🎉 [Blog](https://lmms-lab.framer.ai/blog/lmms-eval-blog) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | Discord_Thread [discord/lmms-eval](https://discord.gg/zdkwKUqrPy) +🏠 [LMMs-Lab Homepage](https://lmms-lab.framer.ai) | 🎉 [Blog](https://lmms-lab.framer.ai/blog/lmms-eval-blog) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | Discord_Thread [discord/lmms-eval](https://discord.gg/zdkwKUqrPy) --- From 307e4184474cfdbde1702466906dcd619c557263 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Mon, 19 Aug 2024 10:52:28 +1000 Subject: [PATCH 185/196] Update commands.md --- docs/commands.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/commands.md b/docs/commands.md index e303fc64..3566e0f6 100755 --- a/docs/commands.md +++ b/docs/commands.md @@ -29,7 +29,7 @@ This mode supports a number of command-line arguments, the details of which can ```bash git clone https://github.com/EvolvingLMMs-Lab/sglang.git cd sglang; -git checkout dev/onevision; +git checkout dev/onevision_local; pip install -e "python[srt]" ``` From 6195d443f0712c7a27ace77eae7818c84658ed99 Mon Sep 17 00:00:00 2001 From: Li Bo Date: Mon, 19 Aug 2024 11:18:29 +1000 Subject: [PATCH 186/196] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 56c1b4cf..591075e0 100755 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ --- ## Annoucement -- [2024-08] 🎉🎉 We welcome the new model [llava-onevision](https://huggingface.co/papers/2408.03326), [Mantis](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/162), new tasks [MVBench](https://huggingface.co/datasets/OpenGVLab/MVBench), [LongVideoBench](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/117), [MMStar](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/158). +- [2024-08] 🎉🎉 We welcome the new model [LLaVA-OneVision](https://huggingface.co/papers/2408.03326), [Mantis](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/162), new tasks [MVBench](https://huggingface.co/datasets/OpenGVLab/MVBench), [LongVideoBench](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/117), [MMStar](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/158). We provide new feature of SGlang Runtime API for llava-onevision model, please refer the [doc](https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/docs/commands.md) for inference acceleration - [2024-07] 🎉🎉 We have released the [technical report](https://arxiv.org/abs/2407.12772) and [LiveBench](https://huggingface.co/spaces/lmms-lab/LiveBench)! - [2024-07] 👨‍💻👨‍💻 The `lmms-eval/v0.2.1` has been upgraded to support more models, including [LongVA](https://github.com/EvolvingLMMs-Lab/LongVA), [InterVL-2](https://github.com/OpenGVLab/InternVL), [VILA](https://github.com/NVlabs/VILA), and many more evaluation tasks, e.g. [Details Captions](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/136), [MLVU](https://arxiv.org/abs/2406.04264), [WildVision-Bench](https://huggingface.co/datasets/WildVision/wildvision-arena-data), [VITATECS](https://github.com/lscpku/VITATECS) and [LLaVA-Interleave-Bench](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/). From c2f73de2fbe0e6af0848094b07294c7de5a2c6f3 Mon Sep 17 00:00:00 2001 From: Kaichen Zhang - NTU Date: Tue, 20 Aug 2024 19:00:00 +0800 Subject: [PATCH 187/196] Bring back PR#52 (#198) * Draft of localization eval, using RefCOCO as first target * Other RefCOCO instances for grounding/REC --------- Co-authored-by: Hunter Heidenreich --- lmms_eval/api/task.py | 22 +- .../refcoco+/_default_template_bbox_rec_yaml | 34 +++ .../refcoco+/refcoco+_bbox_rec_testA.yaml | 4 + .../refcoco+/refcoco+_bbox_rec_testB.yaml | 4 + .../tasks/refcoco+/refcoco+_bbox_rec_val.yaml | 4 + lmms_eval/tasks/refcoco+/utils_rec.py | 221 ++++++++++++++++++ .../refcoco/_default_template_bbox_rec_yaml | 34 +++ .../tasks/refcoco/refcoco_bbox_rec_test.yaml | 4 + .../tasks/refcoco/refcoco_bbox_rec_testA.yaml | 4 + .../tasks/refcoco/refcoco_bbox_rec_testB.yaml | 4 + .../tasks/refcoco/refcoco_bbox_rec_val.yaml | 4 + lmms_eval/tasks/refcoco/utils_rec.py | 221 ++++++++++++++++++ .../refcocog/_default_template_bbox_rec_yaml | 34 +++ .../refcocog/refcocog_bbox_rec_test.yaml | 4 + .../tasks/refcocog/refcocog_bbox_rec_val.yaml | 4 + lmms_eval/tasks/refcocog/utils_rec.py | 221 ++++++++++++++++++ 16 files changed, 810 insertions(+), 13 deletions(-) create mode 100644 lmms_eval/tasks/refcoco+/_default_template_bbox_rec_yaml create mode 100644 lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_testA.yaml create mode 100644 lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_testB.yaml create mode 100644 lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_val.yaml create mode 100644 lmms_eval/tasks/refcoco+/utils_rec.py create mode 100644 lmms_eval/tasks/refcoco/_default_template_bbox_rec_yaml create mode 100644 lmms_eval/tasks/refcoco/refcoco_bbox_rec_test.yaml create mode 100644 lmms_eval/tasks/refcoco/refcoco_bbox_rec_testA.yaml create mode 100644 lmms_eval/tasks/refcoco/refcoco_bbox_rec_testB.yaml create mode 100644 lmms_eval/tasks/refcoco/refcoco_bbox_rec_val.yaml create mode 100644 lmms_eval/tasks/refcoco/utils_rec.py create mode 100644 lmms_eval/tasks/refcocog/_default_template_bbox_rec_yaml create mode 100644 lmms_eval/tasks/refcocog/refcocog_bbox_rec_test.yaml create mode 100644 lmms_eval/tasks/refcocog/refcocog_bbox_rec_val.yaml create mode 100644 lmms_eval/tasks/refcocog/utils_rec.py diff --git a/lmms_eval/api/task.py b/lmms_eval/api/task.py index 310b4bfb..422aaca4 100755 --- a/lmms_eval/api/task.py +++ b/lmms_eval/api/task.py @@ -874,13 +874,15 @@ def concat_tar_parts(tar_parts, output_tar): download_config=download_config, **dataset_kwargs if dataset_kwargs is not None else {}, ) - self.dataset_no_image = datasets.load_dataset( - path=self.DATASET_PATH, - name=self.DATASET_NAME, - download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, - download_config=download_config, - **dataset_kwargs if dataset_kwargs is not None else {}, - ) + if self.config.process_docs is not None: + for split in self.dataset: + if split in [ + self.config.training_split, self.config.validation_split, self.config.test_split, self.config.fewshot_split + ]: + self.dataset[split] = self.config.process_docs(self.dataset[split]) + + # copy dataset, remove image features + self.dataset_no_image = self.dataset.copy() for doc_name in self.dataset_no_image: remove_cols = [] features = self.dataset_no_image[doc_name].features @@ -913,20 +915,14 @@ def has_test_docs(self) -> bool: def training_docs(self) -> datasets.Dataset: if self.has_training_docs(): - if self.config.process_docs is not None: - return self.config.process_docs(self.dataset[self.config.training_split]) return self.dataset[self.config.training_split] def validation_docs(self) -> datasets.Dataset: if self.has_validation_docs(): - if self.config.process_docs is not None: - return self.config.process_docs(self.dataset[self.config.validation_split]) return self.dataset[self.config.validation_split] def test_docs(self) -> datasets.Dataset: if self.has_test_docs(): - if self.config.process_docs is not None: - return self.config.process_docs(self.dataset[self.config.test_split]) return self.dataset[self.config.test_split] def fewshot_docs(self): diff --git a/lmms_eval/tasks/refcoco+/_default_template_bbox_rec_yaml b/lmms_eval/tasks/refcoco+/_default_template_bbox_rec_yaml new file mode 100644 index 00000000..c369baee --- /dev/null +++ b/lmms_eval/tasks/refcoco+/_default_template_bbox_rec_yaml @@ -0,0 +1,34 @@ +dataset_path: lmms-lab/RefCOCOPlus +output_type: generate_until +process_docs: !function utils_rec.refcoco_bbox_rec_preprocess_dataset +doc_to_visual: !function utils_rec.refcoco_bbox_rec_doc_to_visual +doc_to_text: !function utils_rec.refcoco_bbox_rec_doc_to_text +doc_to_target: "bbox" +generation_kwargs: + until: + - "ASSISTANT:" +process_results: !function utils_rec.refcoco_bbox_rec_process_result +metric_list: + - metric: refcoco_IoU + aggregation : !function utils_rec.refcoco_bbox_rec_iou + higher_is_better : true + - metric: refcoco_ACC@0.1 + aggregation : !function utils_rec.refcoco_bbox_rec_acc01 + higher_is_better : true + - metric: refcoco_ACC@0.3 + aggregation : !function utils_rec.refcoco_bbox_rec_acc03 + higher_is_better : true + - metric: refcoco_ACC@0.5 + aggregation : !function utils_rec.refcoco_bbox_rec_acc05 + higher_is_better : true + - metric: refcoco_ACC@0.7 + aggregation : !function utils_rec.refcoco_bbox_rec_acc07 + higher_is_better : true + - metric: refcoco_ACC@0.9 + aggregation : !function utils_rec.refcoco_bbox_rec_acc09 + higher_is_better : true + - metric: refcoco_Center_ACC + aggregation : !function utils_rec.refcoco_bbox_rec_center_acc + higher_is_better : true +metadata: + version: '0.0' \ No newline at end of file diff --git a/lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_testA.yaml b/lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_testA.yaml new file mode 100644 index 00000000..0ebb6c0c --- /dev/null +++ b/lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_testA.yaml @@ -0,0 +1,4 @@ +group: refcoco+_bbox_rec +task: refcoco+_bbox_rec_testA +include: _default_template_bbox_rec_yaml +test_split: testA diff --git a/lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_testB.yaml b/lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_testB.yaml new file mode 100644 index 00000000..b347bce6 --- /dev/null +++ b/lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_testB.yaml @@ -0,0 +1,4 @@ +group: refcoco+_bbox_rec +task: refcoco+_bbox_rec_testB +include: _default_template_bbox_rec_yaml +test_split: testB diff --git a/lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_val.yaml b/lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_val.yaml new file mode 100644 index 00000000..890f588b --- /dev/null +++ b/lmms_eval/tasks/refcoco+/refcoco+_bbox_rec_val.yaml @@ -0,0 +1,4 @@ +group: refcoco+_bbox_rec +task: refcoco+_bbox_rec_val +include: _default_template_bbox_rec_yaml +test_split: val diff --git a/lmms_eval/tasks/refcoco+/utils_rec.py b/lmms_eval/tasks/refcoco+/utils_rec.py new file mode 100644 index 00000000..ec91bf9c --- /dev/null +++ b/lmms_eval/tasks/refcoco+/utils_rec.py @@ -0,0 +1,221 @@ +import re +import logging +from datasets import Dataset + +eval_logger = logging.getLogger("lmms-eval") + +COCO_REC_METRICS = ["IoU", "ACC@0.1", "ACC@0.3", "ACC@0.5", "ACC@0.7", "ACC@0.9", "Center_ACC"] + + +def refcoco_bbox_rec_preprocess_dataset(dataset: Dataset): + # PIL image stored in dataset['image'] + # add `image_width` and `image_height` to the dataset + dataset = dataset.map(lambda x: {"image_width": x["image"].width, "image_height": x["image"].height}) + + # Original bbox format (top x, top y, width, height) + # Convert to (top-left x, top-left y, bottom-right x, bottom-right y) + # Normalize the bounding box coordinates to be between 0 and 1 + # using the image width and height + dataset = dataset.map( + lambda x: {"bbox": [x["bbox"][0] / x["image_width"], + x["bbox"][1] / x["image_height"], + (x["bbox"][0] + x["bbox"][2]) / x["image_width"], + (x["bbox"][1] + x["bbox"][3]) / x["image_height"]]} + ) + + # currently, the dataset has `answer` as a list of strings + # each answer should be its own row + # we will explode the dataset to have one row per answer + # duplicate the other columns + def explode_answers(example): + answers = example.pop('answer') + return [{'answer': answer, **example} for answer in answers] + + # Apply the function to each element, collecting the results + exploded_rows = [] + for example in dataset: + exploded_rows.extend(explode_answers(example)) + + # Create a new dataset from the exploded rows + new_dataset = Dataset.from_list(exploded_rows) + print(f"Exploded dataset from {len(dataset)} to {len(new_dataset)} rows") + + return new_dataset + + +def refcoco_bbox_rec_doc_to_visual(doc): + # Image is presented as is + image = doc["image"].convert("RGB") + return [image.convert("RGB")] + + +def refcoco_bbox_rec_doc_to_text(doc): + assert isinstance(doc['answer'], str), "Answer must be a string" + return "Bounding box coordinates are specified in the format (top-left x, top-left y, bottom-right x, bottom-right y). All values are floating point numbers bounded between 0 and 1. Please provide the bounding box coordinate of the region this sentence describes: " + doc['answer'] + + +def parse_float_sequence_within(input_str): + """ + Extract the first sequence of four floating-point numbers within square brackets from a string. + + Args: + input_str (str): A string that may contain a sequence of four floats within square brackets. + + Returns: + list: A list of four floats if the pattern is found, or a list of four zeros if the pattern is not found. + """ + # Define the regex pattern to find the first instance of four floats within square brackets + pattern = r'\[\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?)\s*\]' + + # Use re.search to find the first match of the pattern in the input string + match = re.search(pattern, input_str) + + # If a match is found, convert the captured groups into a list of floats + if match: + return [float(match.group(i)) for i in range(1, 5)] + + # If the input does not contain the pattern, return the null float sequence + return [0, 0, 0, 0] + + +def refcoco_bbox_rec_process_result(doc, result): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name, value: metric value + """ + pred = result[0] if len(result) > 0 else "" + pred = parse_float_sequence_within(pred) + ann_id = doc["question_id"] + data_dict = {"answer": doc["answer"], "pred": pred, "ann_id": ann_id, 'bbox': doc['bbox']} + return {f"refcoco_{metric}": data_dict for metric in COCO_REC_METRICS} + + +def compute_iou(box1, box2): + """ + Compute the Intersection over Union (IoU) of two bounding boxes. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + + Returns: + - float: IoU of box1 and box2. + """ + # Determine the coordinates of the intersection rectangle + x_left = max(box1[0], box2[0]) + y_top = max(box1[1], box2[1]) + x_right = min(box1[2], box2[2]) + y_bottom = min(box1[3], box2[3]) + + # Compute the area of intersection + intersection_area = max(0, x_right - x_left) * max(0, y_bottom - y_top) + + # Compute the area of both bounding boxes + box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) + box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1]) + + # Compute the area of the union + union_area = box1_area + box2_area - intersection_area + + # Compute the Intersection over Union + iou = intersection_area / union_area + + return iou + + +def compute_accuracy(box1, box2, threshold=0.5): + """ + Compute the accuracy of two bounding boxes based on a specified threshold. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - threshold (float): Threshold for the IoU to consider the prediction correct. + + Returns: + - float: Accuracy of the prediction based on the IoU threshold. + """ + iou = compute_iou(box1, box2) + return iou >= threshold + + +def compute_center_accuracy(box1, box2): + """ + Compute if the center point of box 2 is within box 1. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + + Returns: + - bool: True if the center point of box 2 is within box 1, False otherwise. + """ + # Compute the center point of box 2 + center_x = (box2[0] + box2[2]) / 2 + center_y = (box2[1] + box2[3]) / 2 + + # Check if the center point is within box 1 + return box1[0] <= center_x <= box1[2] and box1[1] <= center_y <= box1[3] + + +def refcoco_bbox_rec_aggregation_result(results, metric): + """ + Aggregate the results of the RefCOCO evaluation task using the specified metric. + + Args: + - results (list of dict): List of result dictionaries. + - metric (str): Metric to use for aggregation. + + Returns: + - dict: Dictionary containing the aggregated results for the specified metric. + """ + scorers = { + 'IoU': compute_iou, + 'ACC@0.1': lambda x, y: compute_accuracy(x, y, 0.1), + 'ACC@0.3': lambda x, y: compute_accuracy(x, y, 0.3), + 'ACC@0.5': lambda x, y: compute_accuracy(x, y, 0.5), + 'ACC@0.7': lambda x, y: compute_accuracy(x, y, 0.7), + 'ACC@0.9': lambda x, y: compute_accuracy(x, y, 0.9), + 'Center_ACC': compute_center_accuracy + } + results_dict = {metric: []} + for result in results: + # Extract the ground truth and predicted bounding boxes + gt_bbox = result['bbox'] + pred_bbox = result['pred'] + # Compute the specified metric between the ground truth and predicted bounding boxes + score = scorers[metric](gt_bbox, pred_bbox) + results_dict[metric].append(score) + results_dict[metric] = sum(results_dict[metric]) / len(results_dict[metric]) + print(f"Aggregated {metric} score: {results_dict[metric]}") + return results_dict[metric] + + +def refcoco_bbox_rec_iou(results): + return refcoco_bbox_rec_aggregation_result(results, "IoU") + + +def refcoco_bbox_rec_acc01(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.1") + +def refcoco_bbox_rec_acc03(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.3") + + +def refcoco_bbox_rec_acc05(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.5") + + +def refcoco_bbox_rec_acc07(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.7") + + +def refcoco_bbox_rec_acc09(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.9") + + +def refcoco_bbox_rec_center_acc(results): + return refcoco_bbox_rec_aggregation_result(results, "Center_ACC") diff --git a/lmms_eval/tasks/refcoco/_default_template_bbox_rec_yaml b/lmms_eval/tasks/refcoco/_default_template_bbox_rec_yaml new file mode 100644 index 00000000..3b5ca100 --- /dev/null +++ b/lmms_eval/tasks/refcoco/_default_template_bbox_rec_yaml @@ -0,0 +1,34 @@ +dataset_path: lmms-lab/RefCOCO +output_type: generate_until +process_docs: !function utils_rec.refcoco_bbox_rec_preprocess_dataset +doc_to_visual: !function utils_rec.refcoco_bbox_rec_doc_to_visual +doc_to_text: !function utils_rec.refcoco_bbox_rec_doc_to_text +doc_to_target: "bbox" +generation_kwargs: + until: + - "ASSISTANT:" +process_results: !function utils_rec.refcoco_bbox_rec_process_result +metric_list: + - metric: refcoco_IoU + aggregation : !function utils_rec.refcoco_bbox_rec_iou + higher_is_better : true + - metric: refcoco_ACC@0.1 + aggregation : !function utils_rec.refcoco_bbox_rec_acc01 + higher_is_better : true + - metric: refcoco_ACC@0.3 + aggregation : !function utils_rec.refcoco_bbox_rec_acc03 + higher_is_better : true + - metric: refcoco_ACC@0.5 + aggregation : !function utils_rec.refcoco_bbox_rec_acc05 + higher_is_better : true + - metric: refcoco_ACC@0.7 + aggregation : !function utils_rec.refcoco_bbox_rec_acc07 + higher_is_better : true + - metric: refcoco_ACC@0.9 + aggregation : !function utils_rec.refcoco_bbox_rec_acc09 + higher_is_better : true + - metric: refcoco_Center_ACC + aggregation : !function utils_rec.refcoco_bbox_rec_center_acc + higher_is_better : true +metadata: + version: '0.0' \ No newline at end of file diff --git a/lmms_eval/tasks/refcoco/refcoco_bbox_rec_test.yaml b/lmms_eval/tasks/refcoco/refcoco_bbox_rec_test.yaml new file mode 100644 index 00000000..896ed4ac --- /dev/null +++ b/lmms_eval/tasks/refcoco/refcoco_bbox_rec_test.yaml @@ -0,0 +1,4 @@ +group: refcoco_bbox_rec +task: refcoco_bbox_rec_test +test_split: test +include: _default_template_bbox_rec_yaml diff --git a/lmms_eval/tasks/refcoco/refcoco_bbox_rec_testA.yaml b/lmms_eval/tasks/refcoco/refcoco_bbox_rec_testA.yaml new file mode 100644 index 00000000..191268a6 --- /dev/null +++ b/lmms_eval/tasks/refcoco/refcoco_bbox_rec_testA.yaml @@ -0,0 +1,4 @@ +group: refcoco_bbox_rec +task: refcoco_bbox_rec_testA +test_split: testA +include: _default_template_bbox_rec_yaml diff --git a/lmms_eval/tasks/refcoco/refcoco_bbox_rec_testB.yaml b/lmms_eval/tasks/refcoco/refcoco_bbox_rec_testB.yaml new file mode 100644 index 00000000..39b29071 --- /dev/null +++ b/lmms_eval/tasks/refcoco/refcoco_bbox_rec_testB.yaml @@ -0,0 +1,4 @@ +group: refcoco_bbox_rec +task: refcoco_bbox_rec_testB +test_split: testB +include: _default_template_bbox_rec_yaml diff --git a/lmms_eval/tasks/refcoco/refcoco_bbox_rec_val.yaml b/lmms_eval/tasks/refcoco/refcoco_bbox_rec_val.yaml new file mode 100644 index 00000000..f5da6c5e --- /dev/null +++ b/lmms_eval/tasks/refcoco/refcoco_bbox_rec_val.yaml @@ -0,0 +1,4 @@ +group: refcoco_bbox_rec +task: refcoco_bbox_rec_val +test_split: val +include: _default_template_bbox_rec_yaml diff --git a/lmms_eval/tasks/refcoco/utils_rec.py b/lmms_eval/tasks/refcoco/utils_rec.py new file mode 100644 index 00000000..ec91bf9c --- /dev/null +++ b/lmms_eval/tasks/refcoco/utils_rec.py @@ -0,0 +1,221 @@ +import re +import logging +from datasets import Dataset + +eval_logger = logging.getLogger("lmms-eval") + +COCO_REC_METRICS = ["IoU", "ACC@0.1", "ACC@0.3", "ACC@0.5", "ACC@0.7", "ACC@0.9", "Center_ACC"] + + +def refcoco_bbox_rec_preprocess_dataset(dataset: Dataset): + # PIL image stored in dataset['image'] + # add `image_width` and `image_height` to the dataset + dataset = dataset.map(lambda x: {"image_width": x["image"].width, "image_height": x["image"].height}) + + # Original bbox format (top x, top y, width, height) + # Convert to (top-left x, top-left y, bottom-right x, bottom-right y) + # Normalize the bounding box coordinates to be between 0 and 1 + # using the image width and height + dataset = dataset.map( + lambda x: {"bbox": [x["bbox"][0] / x["image_width"], + x["bbox"][1] / x["image_height"], + (x["bbox"][0] + x["bbox"][2]) / x["image_width"], + (x["bbox"][1] + x["bbox"][3]) / x["image_height"]]} + ) + + # currently, the dataset has `answer` as a list of strings + # each answer should be its own row + # we will explode the dataset to have one row per answer + # duplicate the other columns + def explode_answers(example): + answers = example.pop('answer') + return [{'answer': answer, **example} for answer in answers] + + # Apply the function to each element, collecting the results + exploded_rows = [] + for example in dataset: + exploded_rows.extend(explode_answers(example)) + + # Create a new dataset from the exploded rows + new_dataset = Dataset.from_list(exploded_rows) + print(f"Exploded dataset from {len(dataset)} to {len(new_dataset)} rows") + + return new_dataset + + +def refcoco_bbox_rec_doc_to_visual(doc): + # Image is presented as is + image = doc["image"].convert("RGB") + return [image.convert("RGB")] + + +def refcoco_bbox_rec_doc_to_text(doc): + assert isinstance(doc['answer'], str), "Answer must be a string" + return "Bounding box coordinates are specified in the format (top-left x, top-left y, bottom-right x, bottom-right y). All values are floating point numbers bounded between 0 and 1. Please provide the bounding box coordinate of the region this sentence describes: " + doc['answer'] + + +def parse_float_sequence_within(input_str): + """ + Extract the first sequence of four floating-point numbers within square brackets from a string. + + Args: + input_str (str): A string that may contain a sequence of four floats within square brackets. + + Returns: + list: A list of four floats if the pattern is found, or a list of four zeros if the pattern is not found. + """ + # Define the regex pattern to find the first instance of four floats within square brackets + pattern = r'\[\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?)\s*\]' + + # Use re.search to find the first match of the pattern in the input string + match = re.search(pattern, input_str) + + # If a match is found, convert the captured groups into a list of floats + if match: + return [float(match.group(i)) for i in range(1, 5)] + + # If the input does not contain the pattern, return the null float sequence + return [0, 0, 0, 0] + + +def refcoco_bbox_rec_process_result(doc, result): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name, value: metric value + """ + pred = result[0] if len(result) > 0 else "" + pred = parse_float_sequence_within(pred) + ann_id = doc["question_id"] + data_dict = {"answer": doc["answer"], "pred": pred, "ann_id": ann_id, 'bbox': doc['bbox']} + return {f"refcoco_{metric}": data_dict for metric in COCO_REC_METRICS} + + +def compute_iou(box1, box2): + """ + Compute the Intersection over Union (IoU) of two bounding boxes. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + + Returns: + - float: IoU of box1 and box2. + """ + # Determine the coordinates of the intersection rectangle + x_left = max(box1[0], box2[0]) + y_top = max(box1[1], box2[1]) + x_right = min(box1[2], box2[2]) + y_bottom = min(box1[3], box2[3]) + + # Compute the area of intersection + intersection_area = max(0, x_right - x_left) * max(0, y_bottom - y_top) + + # Compute the area of both bounding boxes + box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) + box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1]) + + # Compute the area of the union + union_area = box1_area + box2_area - intersection_area + + # Compute the Intersection over Union + iou = intersection_area / union_area + + return iou + + +def compute_accuracy(box1, box2, threshold=0.5): + """ + Compute the accuracy of two bounding boxes based on a specified threshold. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - threshold (float): Threshold for the IoU to consider the prediction correct. + + Returns: + - float: Accuracy of the prediction based on the IoU threshold. + """ + iou = compute_iou(box1, box2) + return iou >= threshold + + +def compute_center_accuracy(box1, box2): + """ + Compute if the center point of box 2 is within box 1. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + + Returns: + - bool: True if the center point of box 2 is within box 1, False otherwise. + """ + # Compute the center point of box 2 + center_x = (box2[0] + box2[2]) / 2 + center_y = (box2[1] + box2[3]) / 2 + + # Check if the center point is within box 1 + return box1[0] <= center_x <= box1[2] and box1[1] <= center_y <= box1[3] + + +def refcoco_bbox_rec_aggregation_result(results, metric): + """ + Aggregate the results of the RefCOCO evaluation task using the specified metric. + + Args: + - results (list of dict): List of result dictionaries. + - metric (str): Metric to use for aggregation. + + Returns: + - dict: Dictionary containing the aggregated results for the specified metric. + """ + scorers = { + 'IoU': compute_iou, + 'ACC@0.1': lambda x, y: compute_accuracy(x, y, 0.1), + 'ACC@0.3': lambda x, y: compute_accuracy(x, y, 0.3), + 'ACC@0.5': lambda x, y: compute_accuracy(x, y, 0.5), + 'ACC@0.7': lambda x, y: compute_accuracy(x, y, 0.7), + 'ACC@0.9': lambda x, y: compute_accuracy(x, y, 0.9), + 'Center_ACC': compute_center_accuracy + } + results_dict = {metric: []} + for result in results: + # Extract the ground truth and predicted bounding boxes + gt_bbox = result['bbox'] + pred_bbox = result['pred'] + # Compute the specified metric between the ground truth and predicted bounding boxes + score = scorers[metric](gt_bbox, pred_bbox) + results_dict[metric].append(score) + results_dict[metric] = sum(results_dict[metric]) / len(results_dict[metric]) + print(f"Aggregated {metric} score: {results_dict[metric]}") + return results_dict[metric] + + +def refcoco_bbox_rec_iou(results): + return refcoco_bbox_rec_aggregation_result(results, "IoU") + + +def refcoco_bbox_rec_acc01(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.1") + +def refcoco_bbox_rec_acc03(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.3") + + +def refcoco_bbox_rec_acc05(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.5") + + +def refcoco_bbox_rec_acc07(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.7") + + +def refcoco_bbox_rec_acc09(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.9") + + +def refcoco_bbox_rec_center_acc(results): + return refcoco_bbox_rec_aggregation_result(results, "Center_ACC") diff --git a/lmms_eval/tasks/refcocog/_default_template_bbox_rec_yaml b/lmms_eval/tasks/refcocog/_default_template_bbox_rec_yaml new file mode 100644 index 00000000..ce95812b --- /dev/null +++ b/lmms_eval/tasks/refcocog/_default_template_bbox_rec_yaml @@ -0,0 +1,34 @@ +dataset_path: lmms-lab/RefCOCOg +output_type: generate_until +process_docs: !function utils_rec.refcoco_bbox_rec_preprocess_dataset +doc_to_visual: !function utils_rec.refcoco_bbox_rec_doc_to_visual +doc_to_text: !function utils_rec.refcoco_bbox_rec_doc_to_text +doc_to_target: "bbox" +generation_kwargs: + until: + - "ASSISTANT:" +process_results: !function utils_rec.refcoco_bbox_rec_process_result +metric_list: + - metric: refcoco_IoU + aggregation : !function utils_rec.refcoco_bbox_rec_iou + higher_is_better : true + - metric: refcoco_ACC@0.1 + aggregation : !function utils_rec.refcoco_bbox_rec_acc01 + higher_is_better : true + - metric: refcoco_ACC@0.3 + aggregation : !function utils_rec.refcoco_bbox_rec_acc03 + higher_is_better : true + - metric: refcoco_ACC@0.5 + aggregation : !function utils_rec.refcoco_bbox_rec_acc05 + higher_is_better : true + - metric: refcoco_ACC@0.7 + aggregation : !function utils_rec.refcoco_bbox_rec_acc07 + higher_is_better : true + - metric: refcoco_ACC@0.9 + aggregation : !function utils_rec.refcoco_bbox_rec_acc09 + higher_is_better : true + - metric: refcoco_Center_ACC + aggregation : !function utils_rec.refcoco_bbox_rec_center_acc + higher_is_better : true +metadata: + version: '0.0' \ No newline at end of file diff --git a/lmms_eval/tasks/refcocog/refcocog_bbox_rec_test.yaml b/lmms_eval/tasks/refcocog/refcocog_bbox_rec_test.yaml new file mode 100644 index 00000000..2f435979 --- /dev/null +++ b/lmms_eval/tasks/refcocog/refcocog_bbox_rec_test.yaml @@ -0,0 +1,4 @@ +group: refcocog_bbox_rec +task: refcocog_bbox_rec_test +include: _default_template_bbox_rec_yaml +test_split: test diff --git a/lmms_eval/tasks/refcocog/refcocog_bbox_rec_val.yaml b/lmms_eval/tasks/refcocog/refcocog_bbox_rec_val.yaml new file mode 100644 index 00000000..5e19397a --- /dev/null +++ b/lmms_eval/tasks/refcocog/refcocog_bbox_rec_val.yaml @@ -0,0 +1,4 @@ +group: refcocog_bbox_rec +task: refcocog_bbox_rec_val +include: _default_template_bbox_rec_yaml +test_split: val diff --git a/lmms_eval/tasks/refcocog/utils_rec.py b/lmms_eval/tasks/refcocog/utils_rec.py new file mode 100644 index 00000000..ec91bf9c --- /dev/null +++ b/lmms_eval/tasks/refcocog/utils_rec.py @@ -0,0 +1,221 @@ +import re +import logging +from datasets import Dataset + +eval_logger = logging.getLogger("lmms-eval") + +COCO_REC_METRICS = ["IoU", "ACC@0.1", "ACC@0.3", "ACC@0.5", "ACC@0.7", "ACC@0.9", "Center_ACC"] + + +def refcoco_bbox_rec_preprocess_dataset(dataset: Dataset): + # PIL image stored in dataset['image'] + # add `image_width` and `image_height` to the dataset + dataset = dataset.map(lambda x: {"image_width": x["image"].width, "image_height": x["image"].height}) + + # Original bbox format (top x, top y, width, height) + # Convert to (top-left x, top-left y, bottom-right x, bottom-right y) + # Normalize the bounding box coordinates to be between 0 and 1 + # using the image width and height + dataset = dataset.map( + lambda x: {"bbox": [x["bbox"][0] / x["image_width"], + x["bbox"][1] / x["image_height"], + (x["bbox"][0] + x["bbox"][2]) / x["image_width"], + (x["bbox"][1] + x["bbox"][3]) / x["image_height"]]} + ) + + # currently, the dataset has `answer` as a list of strings + # each answer should be its own row + # we will explode the dataset to have one row per answer + # duplicate the other columns + def explode_answers(example): + answers = example.pop('answer') + return [{'answer': answer, **example} for answer in answers] + + # Apply the function to each element, collecting the results + exploded_rows = [] + for example in dataset: + exploded_rows.extend(explode_answers(example)) + + # Create a new dataset from the exploded rows + new_dataset = Dataset.from_list(exploded_rows) + print(f"Exploded dataset from {len(dataset)} to {len(new_dataset)} rows") + + return new_dataset + + +def refcoco_bbox_rec_doc_to_visual(doc): + # Image is presented as is + image = doc["image"].convert("RGB") + return [image.convert("RGB")] + + +def refcoco_bbox_rec_doc_to_text(doc): + assert isinstance(doc['answer'], str), "Answer must be a string" + return "Bounding box coordinates are specified in the format (top-left x, top-left y, bottom-right x, bottom-right y). All values are floating point numbers bounded between 0 and 1. Please provide the bounding box coordinate of the region this sentence describes: " + doc['answer'] + + +def parse_float_sequence_within(input_str): + """ + Extract the first sequence of four floating-point numbers within square brackets from a string. + + Args: + input_str (str): A string that may contain a sequence of four floats within square brackets. + + Returns: + list: A list of four floats if the pattern is found, or a list of four zeros if the pattern is not found. + """ + # Define the regex pattern to find the first instance of four floats within square brackets + pattern = r'\[\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?)\s*\]' + + # Use re.search to find the first match of the pattern in the input string + match = re.search(pattern, input_str) + + # If a match is found, convert the captured groups into a list of floats + if match: + return [float(match.group(i)) for i in range(1, 5)] + + # If the input does not contain the pattern, return the null float sequence + return [0, 0, 0, 0] + + +def refcoco_bbox_rec_process_result(doc, result): + """ + Args: + doc: a instance of the eval dataset + results: [pred] + Returns: + a dictionary with key: metric name, value: metric value + """ + pred = result[0] if len(result) > 0 else "" + pred = parse_float_sequence_within(pred) + ann_id = doc["question_id"] + data_dict = {"answer": doc["answer"], "pred": pred, "ann_id": ann_id, 'bbox': doc['bbox']} + return {f"refcoco_{metric}": data_dict for metric in COCO_REC_METRICS} + + +def compute_iou(box1, box2): + """ + Compute the Intersection over Union (IoU) of two bounding boxes. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + + Returns: + - float: IoU of box1 and box2. + """ + # Determine the coordinates of the intersection rectangle + x_left = max(box1[0], box2[0]) + y_top = max(box1[1], box2[1]) + x_right = min(box1[2], box2[2]) + y_bottom = min(box1[3], box2[3]) + + # Compute the area of intersection + intersection_area = max(0, x_right - x_left) * max(0, y_bottom - y_top) + + # Compute the area of both bounding boxes + box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) + box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1]) + + # Compute the area of the union + union_area = box1_area + box2_area - intersection_area + + # Compute the Intersection over Union + iou = intersection_area / union_area + + return iou + + +def compute_accuracy(box1, box2, threshold=0.5): + """ + Compute the accuracy of two bounding boxes based on a specified threshold. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - threshold (float): Threshold for the IoU to consider the prediction correct. + + Returns: + - float: Accuracy of the prediction based on the IoU threshold. + """ + iou = compute_iou(box1, box2) + return iou >= threshold + + +def compute_center_accuracy(box1, box2): + """ + Compute if the center point of box 2 is within box 1. + + Parameters: + - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max]. + + Returns: + - bool: True if the center point of box 2 is within box 1, False otherwise. + """ + # Compute the center point of box 2 + center_x = (box2[0] + box2[2]) / 2 + center_y = (box2[1] + box2[3]) / 2 + + # Check if the center point is within box 1 + return box1[0] <= center_x <= box1[2] and box1[1] <= center_y <= box1[3] + + +def refcoco_bbox_rec_aggregation_result(results, metric): + """ + Aggregate the results of the RefCOCO evaluation task using the specified metric. + + Args: + - results (list of dict): List of result dictionaries. + - metric (str): Metric to use for aggregation. + + Returns: + - dict: Dictionary containing the aggregated results for the specified metric. + """ + scorers = { + 'IoU': compute_iou, + 'ACC@0.1': lambda x, y: compute_accuracy(x, y, 0.1), + 'ACC@0.3': lambda x, y: compute_accuracy(x, y, 0.3), + 'ACC@0.5': lambda x, y: compute_accuracy(x, y, 0.5), + 'ACC@0.7': lambda x, y: compute_accuracy(x, y, 0.7), + 'ACC@0.9': lambda x, y: compute_accuracy(x, y, 0.9), + 'Center_ACC': compute_center_accuracy + } + results_dict = {metric: []} + for result in results: + # Extract the ground truth and predicted bounding boxes + gt_bbox = result['bbox'] + pred_bbox = result['pred'] + # Compute the specified metric between the ground truth and predicted bounding boxes + score = scorers[metric](gt_bbox, pred_bbox) + results_dict[metric].append(score) + results_dict[metric] = sum(results_dict[metric]) / len(results_dict[metric]) + print(f"Aggregated {metric} score: {results_dict[metric]}") + return results_dict[metric] + + +def refcoco_bbox_rec_iou(results): + return refcoco_bbox_rec_aggregation_result(results, "IoU") + + +def refcoco_bbox_rec_acc01(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.1") + +def refcoco_bbox_rec_acc03(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.3") + + +def refcoco_bbox_rec_acc05(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.5") + + +def refcoco_bbox_rec_acc07(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.7") + + +def refcoco_bbox_rec_acc09(results): + return refcoco_bbox_rec_aggregation_result(results, "ACC@0.9") + + +def refcoco_bbox_rec_center_acc(results): + return refcoco_bbox_rec_aggregation_result(results, "Center_ACC") From 22ed307d842f859eb5c7513e4355992f5b138ba0 Mon Sep 17 00:00:00 2001 From: Bo Li Date: Sat, 24 Aug 2024 07:16:45 +0000 Subject: [PATCH 188/196] fix: update from previous model_specific_prompt to current lmms_eval_specific_kwargs --- docs/task_guide.md | 4 ++-- lmms_eval/tasks/ai2d/ai2d_lite.yaml | 2 +- lmms_eval/tasks/chartqa/chartqa_lite.yaml | 2 +- lmms_eval/tasks/docvqa/docvqa_val_lite.yaml | 2 +- lmms_eval/tasks/gqa/gqa_lite.yaml | 2 +- lmms_eval/tasks/infovqa/infovqa_val_lite.yaml | 2 +- lmms_eval/tasks/mirb/mirb.yaml | 2 +- lmms_eval/tasks/mirb/utils.py | 6 +++--- lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml | 2 +- lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml | 2 +- lmms_eval/tasks/ok_vqa/ok_vqa_val2014_lite.yaml | 2 +- lmms_eval/tasks/seedbench_2_plus/seedbench_2_plus.yaml | 2 +- lmms_eval/tasks/textcaps/textcaps_val_lite.yaml | 2 +- lmms_eval/tasks/textvqa/textvqa_val_lite.yaml | 2 +- lmms_eval/tasks/vibe_eval/utils.py | 2 ++ lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml | 2 +- lmms_eval/tasks/vqav2/vqav2_val_lite.yaml | 2 +- 17 files changed, 21 insertions(+), 19 deletions(-) diff --git a/docs/task_guide.md b/docs/task_guide.md index 1376bc22..1e7d3a9d 100755 --- a/docs/task_guide.md +++ b/docs/task_guide.md @@ -40,7 +40,7 @@ metric_list: - metric: mme_cognition_score aggregation: !function utils.mme_aggregate_results higher_is_better: true -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." @@ -52,7 +52,7 @@ metadata: ``` You can pay special attention to the `process_results` and `metric_list` fields, which are used to define how the model output is post-processed and scored. -Also, the `model_specific_prompt_kwargs` field is used to define model-specific prompt configurations. The default is set to follow Llava. +Also, the `lmms_eval_specific_kwargs` field is used to define model-specific prompt configurations. The default is set to follow Llava. PPL-based tasks: - Seedbench (`lmms_eval/tasks/seedbench/seedbench_ppl.yaml`) diff --git a/lmms_eval/tasks/ai2d/ai2d_lite.yaml b/lmms_eval/tasks/ai2d/ai2d_lite.yaml index bdeb9724..b71abe80 100644 --- a/lmms_eval/tasks/ai2d/ai2d_lite.yaml +++ b/lmms_eval/tasks/ai2d/ai2d_lite.yaml @@ -9,7 +9,7 @@ doc_to_visual: !function utils.ai2d_doc_to_visual doc_to_text: !function utils.ai2d_doc_to_text doc_to_target: !function utils.ai2d_doc_to_target -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: prompt_format: mcq pre_prompt: "" diff --git a/lmms_eval/tasks/chartqa/chartqa_lite.yaml b/lmms_eval/tasks/chartqa/chartqa_lite.yaml index 4fbce497..96daff5f 100644 --- a/lmms_eval/tasks/chartqa/chartqa_lite.yaml +++ b/lmms_eval/tasks/chartqa/chartqa_lite.yaml @@ -25,7 +25,7 @@ metric_list: higher_is_better: true metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question with a single word." diff --git a/lmms_eval/tasks/docvqa/docvqa_val_lite.yaml b/lmms_eval/tasks/docvqa/docvqa_val_lite.yaml index afda5eb5..95d065df 100644 --- a/lmms_eval/tasks/docvqa/docvqa_val_lite.yaml +++ b/lmms_eval/tasks/docvqa/docvqa_val_lite.yaml @@ -16,7 +16,7 @@ generation_kwargs: max_new_tokens: 32 temperature: 0 do_sample: False -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/gqa/gqa_lite.yaml b/lmms_eval/tasks/gqa/gqa_lite.yaml index 81042041..7f432fc4 100644 --- a/lmms_eval/tasks/gqa/gqa_lite.yaml +++ b/lmms_eval/tasks/gqa/gqa_lite.yaml @@ -23,7 +23,7 @@ metric_list: metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/infovqa/infovqa_val_lite.yaml b/lmms_eval/tasks/infovqa/infovqa_val_lite.yaml index eab75120..f52ded6e 100644 --- a/lmms_eval/tasks/infovqa/infovqa_val_lite.yaml +++ b/lmms_eval/tasks/infovqa/infovqa_val_lite.yaml @@ -16,7 +16,7 @@ generation_kwargs: max_new_tokens: 32 temperature: 0 do_sample: False -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." \ No newline at end of file diff --git a/lmms_eval/tasks/mirb/mirb.yaml b/lmms_eval/tasks/mirb/mirb.yaml index b42e3c84..099e578c 100644 --- a/lmms_eval/tasks/mirb/mirb.yaml +++ b/lmms_eval/tasks/mirb/mirb.yaml @@ -10,7 +10,7 @@ doc_to_text: !function utils.mirb_doc_to_text doc_to_target: !function utils.mirb_doc_to_target process_results: !function utils.mirb_process_results -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "" diff --git a/lmms_eval/tasks/mirb/utils.py b/lmms_eval/tasks/mirb/utils.py index 174659dc..3e675d39 100644 --- a/lmms_eval/tasks/mirb/utils.py +++ b/lmms_eval/tasks/mirb/utils.py @@ -24,11 +24,11 @@ def get_task_instruction(dataset): return instr -def mirb_doc_to_text(doc, model_specific_prompt_kwargs=None): +def mirb_doc_to_text(doc, lmms_eval_specific_kwargs=None): subset, question = doc["subset"], doc["questions"] task_instruction = get_task_instruction(subset) - post_prompt = model_specific_prompt_kwargs["post_prompt"] - pre_prompt = model_specific_prompt_kwargs["pre_prompt"] + post_prompt = lmms_eval_specific_kwargs["post_prompt"] + pre_prompt = lmms_eval_specific_kwargs["pre_prompt"] return f"{pre_prompt}{task_instruction}{question}{post_prompt}" diff --git a/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml b/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml index 4fc38f96..78b36a65 100644 --- a/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_cn_dev_lite.yaml @@ -22,7 +22,7 @@ generation_kwargs: num_beams: 1 do_sample: false process_results: !function cn_utils.mmbench_process_results -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\n请直接使用所提供的选项字母作为答案回答。" diff --git a/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml b/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml index 60b9574e..226c4843 100644 --- a/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml +++ b/lmms_eval/tasks/mmbench/mmbench_en_dev_lite.yaml @@ -5,7 +5,7 @@ dataset_name: mmbench_en_dev dataset_kwargs: token: True doc_to_target: "answer" -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer with the option's letter from the given choices directly." diff --git a/lmms_eval/tasks/ok_vqa/ok_vqa_val2014_lite.yaml b/lmms_eval/tasks/ok_vqa/ok_vqa_val2014_lite.yaml index 60b76f73..7567e696 100644 --- a/lmms_eval/tasks/ok_vqa/ok_vqa_val2014_lite.yaml +++ b/lmms_eval/tasks/ok_vqa/ok_vqa_val2014_lite.yaml @@ -20,7 +20,7 @@ metric_list: aggregation: !function utils.ok_vqa_aggregate_submissions higher_is_better: true process_results: !function utils.ok_vqa_process_results -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/seedbench_2_plus/seedbench_2_plus.yaml b/lmms_eval/tasks/seedbench_2_plus/seedbench_2_plus.yaml index c47a3dba..71620676 100755 --- a/lmms_eval/tasks/seedbench_2_plus/seedbench_2_plus.yaml +++ b/lmms_eval/tasks/seedbench_2_plus/seedbench_2_plus.yaml @@ -31,7 +31,7 @@ metric_list: metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: llava : img_token : post_prompt : "Answer with the option's letter from the given choices directly." diff --git a/lmms_eval/tasks/textcaps/textcaps_val_lite.yaml b/lmms_eval/tasks/textcaps/textcaps_val_lite.yaml index 95525e07..a72a40b6 100644 --- a/lmms_eval/tasks/textcaps/textcaps_val_lite.yaml +++ b/lmms_eval/tasks/textcaps/textcaps_val_lite.yaml @@ -43,6 +43,6 @@ metric_list: # higher_is_better : true metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: prompt: Provide a one-sentence caption for the provided image. \ No newline at end of file diff --git a/lmms_eval/tasks/textvqa/textvqa_val_lite.yaml b/lmms_eval/tasks/textvqa/textvqa_val_lite.yaml index 5438228a..dc18a09f 100644 --- a/lmms_eval/tasks/textvqa/textvqa_val_lite.yaml +++ b/lmms_eval/tasks/textvqa/textvqa_val_lite.yaml @@ -19,7 +19,7 @@ generation_kwargs: until: - "ASSISTANT:" process_results: !function utils.textvqa_process_results -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/vibe_eval/utils.py b/lmms_eval/tasks/vibe_eval/utils.py index 09b0c58a..71ad4afa 100644 --- a/lmms_eval/tasks/vibe_eval/utils.py +++ b/lmms_eval/tasks/vibe_eval/utils.py @@ -8,6 +8,8 @@ import os from copy import deepcopy +from loguru import logger as eval_logger + try: from reka import ChatMessage from reka.client import Reka diff --git a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml index d686d3ff..915fa7cd 100644 --- a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml +++ b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val_lite.yaml @@ -11,7 +11,7 @@ generation_kwargs: - "ASSISTANT:" metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nWhen the provided information is insufficient, respond with 'Unanswerable'.\nAnswer the question using a single word or phrase." diff --git a/lmms_eval/tasks/vqav2/vqav2_val_lite.yaml b/lmms_eval/tasks/vqav2/vqav2_val_lite.yaml index 1446c8df..c0211c62 100644 --- a/lmms_eval/tasks/vqav2/vqav2_val_lite.yaml +++ b/lmms_eval/tasks/vqav2/vqav2_val_lite.yaml @@ -11,7 +11,7 @@ generation_kwargs: max_new_tokens: 16 metadata: - version: 0.0 -model_specific_prompt_kwargs: +lmms_eval_specific_kwargs: default: pre_prompt: "" post_prompt: "\nAnswer the question using a single word or phrase." From 3726b4f4714b5825bef9028828e985bd878a405d Mon Sep 17 00:00:00 2001 From: Zhijian Liu <5782437+zhijian-liu@users.noreply.github.com> Date: Sat, 24 Aug 2024 21:43:02 -0400 Subject: [PATCH 189/196] Fix `videomme` evaluation --- lmms_eval/api/task.py | 1 + lmms_eval/tasks/videomme/utils.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/lmms_eval/api/task.py b/lmms_eval/api/task.py index 422aaca4..09628110 100755 --- a/lmms_eval/api/task.py +++ b/lmms_eval/api/task.py @@ -780,6 +780,7 @@ def _download_from_youtube(path): if "video" in dataset_kwargs and dataset_kwargs["video"]: hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/") + hf_home = os.path.expanduser(hf_home) cache_dir = dataset_kwargs["cache_dir"] cache_dir = os.path.join(hf_home, cache_dir) accelerator = Accelerator() diff --git a/lmms_eval/tasks/videomme/utils.py b/lmms_eval/tasks/videomme/utils.py index d0c7e93c..42861fc2 100644 --- a/lmms_eval/tasks/videomme/utils.py +++ b/lmms_eval/tasks/videomme/utils.py @@ -177,7 +177,7 @@ def extract_subtitles(video_path, subtitle_path): def videomme_doc_to_visual(doc): cache_dir = os.path.join(base_cache_dir, cache_name) video_path = doc["videoID"] + ".mp4" - video_path = os.path.join(cache_dir, video_path) + video_path = os.path.join(cache_dir, "data", video_path) if os.path.exists(video_path): video_path = video_path elif os.path.exists(video_path.replace("mp4", "MP4")): From cbc8599b9f72d9f118db02175c24d5907c67438b Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 27 Aug 2024 03:31:51 +0000 Subject: [PATCH 190/196] Add sglang launch server in srt init process --- lmms_eval/models/srt_api.py | 31 ++++++++++++++++++++++++++++--- 1 file changed, 28 insertions(+), 3 deletions(-) diff --git a/lmms_eval/models/srt_api.py b/lmms_eval/models/srt_api.py index ca9d9e90..d6e4fb3b 100755 --- a/lmms_eval/models/srt_api.py +++ b/lmms_eval/models/srt_api.py @@ -18,6 +18,12 @@ from loguru import logger as eval_logger +from sglang.srt.utils import kill_child_process +from sglang.test.test_utils import ( + DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, + popen_launch_server, +) + NUM_SECONDS_TO_SLEEP = 5 @@ -25,13 +31,16 @@ class SRT_API(lmms): def __init__( self, - api_key: str = "EMPTY", - model_version: str = "default", + api_key: str = "sk-123456", + model_version: str = "lmms-lab/llava-onevision-qwen2-72b-ov", modality: str = "video", host: str = "127.0.0.1", port: int = 30000, max_frames_num: int = 32, timeout: int = 60, + chat_template: str = "chatml-llava", + tp: int = 8, + chunked_prefill_size: int = 16384, continual_mode: bool = False, response_persistent_folder: str = None, **kwargs, @@ -63,7 +72,23 @@ def __init__( self.cache_mode = "start" accelerator = Accelerator() - self.client = OpenAI(api_key="EMPTY", base_url="http://127.0.0.1:30000/v1") + self.model = model_version + self.base_url = f"http://{host}:{port}" + self.api_key = api_key + self.chat_template = chat_template + other_args = [] + other_args.extend(["--chunked-prefill-size", str(chunked_prefill_size)]) + other_args.extend(["--tensor-parallel-size", str(tp)]) + other_args.extend(["--chat-template", self.chat_template]) + self.process = popen_launch_server( + self.model, + self.base_url, + timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, + api_key=self.api_key, + other_args=other_args, + ) + self.base_url += "/v1" + self.client = OpenAI(api_key=self.api_key, base_url=self.base_url) # assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue." if accelerator.num_processes > 1: assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." From 0d02bada2f8bf84a70dc244fe948677cc6e7c4fc Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 27 Aug 2024 05:24:02 +0000 Subject: [PATCH 191/196] Add async support for openai server to enable faster speed --- lmms_eval/models/srt_api.py | 165 ++++++++++++++++-------------------- 1 file changed, 75 insertions(+), 90 deletions(-) diff --git a/lmms_eval/models/srt_api.py b/lmms_eval/models/srt_api.py index d6e4fb3b..55a1ae65 100755 --- a/lmms_eval/models/srt_api.py +++ b/lmms_eval/models/srt_api.py @@ -1,10 +1,12 @@ from accelerate import Accelerator, DistributedType +import asyncio import base64 from io import BytesIO from copy import deepcopy from decord import VideoReader, cpu import numpy as np -from openai import OpenAI +from multiprocessing import cpu_count +from openai import AsyncOpenAI from PIL import Image import os import json @@ -43,6 +45,7 @@ def __init__( chunked_prefill_size: int = 16384, continual_mode: bool = False, response_persistent_folder: str = None, + num_processes: int = cpu_count() // 2, **kwargs, ) -> None: super().__init__() @@ -88,7 +91,8 @@ def __init__( other_args=other_args, ) self.base_url += "/v1" - self.client = OpenAI(api_key=self.api_key, base_url=self.base_url) + self.client = AsyncOpenAI(api_key=self.api_key, base_url=self.base_url) + self.num_processes = num_processes # assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue." if accelerator.num_processes > 1: assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." @@ -155,100 +159,81 @@ def flatten(self, input): new_list.append(j) return new_list + async def generate(self, request): + contexts, gen_kwargs, doc_to_visual, doc_id, task, split = request.args + visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] + visuals = self.flatten(visuals) + imgs = [] # multiple images or frames for video + for visual in visuals: + if self.modality == "image": + img = self.encode_image(visual) + imgs.append(img) + elif self.modality == "video": + try: + frames = self.encode_video(visual, self.max_frames_num) + imgs.extend(frames) + except Exception as e: + eval_logger.error(f"Exception : {e} \n When loading video {visual}") + imgs = None + break + + # Handling video decode error + # If we can't even load using pyav, then we will skip + if imgs is None: + resps = "" + return resps + + messages = [] + + # put the images in the first place + content = [] + for img in imgs: + content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}) + + content.append({"type": "text", "text": contexts}) + messages.append({"role": "user", "content": content}) + + if "max_new_tokens" not in gen_kwargs: + gen_kwargs["max_new_tokens"] = 1024 + + if "temperature" not in gen_kwargs: + gen_kwargs["temperature"] = 0 + + for attempt in range(5): + try: + response = await self.client.chat.completions.create(model=self.model_version, messages=messages, temperature=gen_kwargs["temperature"], max_tokens=gen_kwargs["max_new_tokens"], timeout=self.timeout) + response_text = response.choices[0].message.content.strip() + break # If successful, break out of the loop + + except Exception as e: + eval_logger.info(f"Attempt {attempt + 1} failed with error: {str(e)}.") + if attempt < 4: + time.sleep(NUM_SECONDS_TO_SLEEP) + else: # If this was the last attempt, log and return empty string + eval_logger.error(f"All 5 attempts failed. Last error message: {str(e)}.") + response_text = "" + + return response_text + def generate_until(self, requests) -> List[str]: res = [] pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") - for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: - if self.continual_mode is True and self.cache_mode == "resume": - doc_uuid = f"{task}___{split}___{doc_id}" - if doc_uuid in self.response_cache: - response_text = self.response_cache[doc_uuid] - if response_text: - res.append(response_text) - pbar.update(1) - continue - - visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] - visuals = self.flatten(visuals) - imgs = [] # multiple images or frames for video - for visual in visuals: - if self.modality == "image": - img = self.encode_image(visual) - imgs.append(img) - elif self.modality == "video": - try: - frames = self.encode_video(visual, self.max_frames_num) - imgs.extend(frames) - except Exception as e: - eval_logger.error(f"Exception : {e} \n When loading video {visual}") - imgs = None - break - - # Handling video decode error - # If we can't even load using pyav, then we will skip - if imgs is None: - resps = "" - res.append(resps) + async def run(requests): + sem = asyncio.Semaphore(self.num_processes) + + async def _process(request): + async with sem: + return await self.generate(request) + + tasks = [asyncio.create_task(_process(request)) for request in requests] + for completed_task in asyncio.as_completed(tasks): + result = await completed_task + res.append(result) pbar.update(1) - continue - - messages = [] - - # put the images in the first place - content = [] - for img in imgs: - content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}) - - content.append({"type": "text", "text": contexts}) - messages.append({"role": "user", "content": content}) - # if self.image_token not in contexts: # single image format - # content = [] - # for img in imgs: - # content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}) - - # content.append({"type": "text", "text": contexts}) - # messages.append({"role": "user", "content": content}) - # else: # interleaved format - # contexts = contexts.split(self.image_token) - # for idx, img in enumerate(imgs): - # content = [ - # {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}, - # {"type": "text", "text": contexts[idx]}, - # ] - # messages.append({"role": "user", "content": content}) - # messages.append({"role": "user", "content": [{"type": "text", "text": contexts[-1]}]}) - - if "max_new_tokens" not in gen_kwargs: - gen_kwargs["max_new_tokens"] = 1024 - - if "temperature" not in gen_kwargs: - gen_kwargs["temperature"] = 0 - - for attempt in range(5): - try: - response = self.client.chat.completions.create(model=self.model_version, messages=messages, temperature=gen_kwargs["temperature"], max_tokens=gen_kwargs["max_new_tokens"], timeout=self.timeout) - response_text = response.choices[0].message.content.strip() - break # If successful, break out of the loop - except Exception as e: - eval_logger.info(f"Attempt {attempt + 1} failed with error: {str(e)}.") - if attempt < 4: - time.sleep(NUM_SECONDS_TO_SLEEP) - else: # If this was the last attempt, log and return empty string - eval_logger.error(f"All 5 attempts failed. Last error message: {str(e)}.") - response_text = "" - - res.append(response_text) - pbar.update(1) - - if self.continual_mode is True: # Cache the response - doc_uuid = f"{task}___{split}___{doc_id}" - self.response_cache[doc_uuid] = response_text - with open(self.response_persistent_file, "w") as f: - json.dump(self.response_cache, f) - - pbar.close() + asyncio.run(run(requests)) + return res def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: From 1dfea2e2a4a2f4bfd869b8165bcdde6540fd8402 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 27 Aug 2024 05:31:12 +0000 Subject: [PATCH 192/196] Update srt commands --- docs/commands.md | 29 +++++++++++++++++++++++------ 1 file changed, 23 insertions(+), 6 deletions(-) diff --git a/docs/commands.md b/docs/commands.md index 3566e0f6..8a15b09c 100755 --- a/docs/commands.md +++ b/docs/commands.md @@ -27,20 +27,38 @@ This mode supports a number of command-line arguments, the details of which can > install sglang ```bash -git clone https://github.com/EvolvingLMMs-Lab/sglang.git +git clone https://github.com/sgl-project/sglang.git +# Current version is tested on #1222 cd sglang; -git checkout dev/onevision_local; pip install -e "python[srt]" + +# Install FlashInfer CUDA kernels +pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ ``` > run sglang backend service with the following command ```bash -# backend service -python -m sglang.launch_server --model-path "\path\to\onevision" --tokenizer-path lmms-lab/llavanext-qwen-siglip-tokenizer --port=30000 --host=127.0.0.1 --tp-size=8 --chat-template=chatml-llava +# After update, there is no need to use an extra command to setup backend server +# the server will be initialized in the init process # launch lmms-eval srt_api model -python -m accelerate.commands.launch --main_process_port=12580 --num_processes=1 lmms_eval --model=srt_api --model_args=modality=image,host=127.0.0.1,port=30000 --tasks=ai2d --batch_size=1 --log_samples --log_samples_suffix=debug --output_path=./logs/ --verbosity=DEBUG +CKPT_PATH=$1 +TASK=$2 +MODALITY=$3 +TP_SIZE=$4 +echo $TASK +TASK_SUFFIX="${TASK//,/_}" +echo $TASK_SUFFIX + +python3 -m lmms_eval \ + --model srt_api \ + --model_args modality=$MODALITY,model_version=$CKPT_PATH,tp=$TP_SIZE,host=127.0.0.1,port=30000,timeout=600 \ + --tasks $TASK \ + --batch_size 1 \ + --log_samples \ + --log_samples_suffix $TASK_SUFFIX \ + --output_path ./logs/ ``` You may need to install some dependencies for the above command to work (if you encounter some errors). @@ -48,7 +66,6 @@ You may need to install some dependencies for the above command to work (if you ```bash pip install httpx==0.23.3 pip install protobuf==3.20 -pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/ ``` From f3c73b83d5f59f475c3b72a6b6a0caffbc7cd006 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 27 Aug 2024 05:31:32 +0000 Subject: [PATCH 193/196] Update srt run_examples --- docs/run_examples.md | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/docs/run_examples.md b/docs/run_examples.md index 99c2e716..15c5715b 100644 --- a/docs/run_examples.md +++ b/docs/run_examples.md @@ -209,6 +209,7 @@ accelerate launch --num_processes 8 --main_process_port 12345 -m lmms_eval \ ### SRT API MODEL To enable faster testing speed for larger llava model, you can use this srt api model to enable testing through sglang. +You will need to first glone sglang from "https://github.com/sgl-project/sglang". Current version is tested on the commit #1222 of sglang Here are the scripts if you want to test the result in one script. ```bash @@ -223,22 +224,16 @@ python3 -m pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/ CKPT_PATH=$1 -TOK_PATH=$2 -TASK=$3 -MODALITY=$4 +TASK=$2 +MODALITY=$3 +TP_SIZE=$4 echo $TASK TASK_SUFFIX="${TASK//,/_}" echo $TASK_SUFFIX -# Serve sglang in backend -python3 -m sglang.launch_server --model-path ${CKPT_PATH} --tokenizer-path ${TOK_PATH} --port=30000 --host=127.0.0.1 --tp-size=8 --chat-template=chatml-llava & - -# Wait till the server is ready -sleep 360; - python3 -m lmms_eval \ --model srt_api \ - --model_args modality=$MODALITY,host=127.0.0.1,port=30000,timeout=600 \ + --model_args modality=$MODALITY,model_version=$CKPT_PATH,tp=$TP_SIZE,host=127.0.0.1,port=30000,timeout=600 \ --tasks $TASK \ --batch_size 1 \ --log_samples \ From 7216f1b05ca8fe4617b88e66d4468dc27385413f Mon Sep 17 00:00:00 2001 From: Kaichen Zhang - NTU Date: Tue, 27 Aug 2024 13:52:21 +0800 Subject: [PATCH 194/196] Add kill sglang process (#213) --- lmms_eval/models/srt_api.py | 1 + 1 file changed, 1 insertion(+) diff --git a/lmms_eval/models/srt_api.py b/lmms_eval/models/srt_api.py index 55a1ae65..286beb43 100755 --- a/lmms_eval/models/srt_api.py +++ b/lmms_eval/models/srt_api.py @@ -233,6 +233,7 @@ async def _process(request): pbar.update(1) asyncio.run(run(requests)) + kill_child_process(self.process.pid) return res From b68f8130bd80ca715e4a2d940e8f484e99c88573 Mon Sep 17 00:00:00 2001 From: kcz358 Date: Tue, 27 Aug 2024 15:31:36 +0000 Subject: [PATCH 195/196] [Minor] Fix llava_vid small bugs --- lmms_eval/models/llava_vid.py | 1 + 1 file changed, 1 insertion(+) diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py index c1e1fd38..24edb939 100755 --- a/lmms_eval/models/llava_vid.py +++ b/lmms_eval/models/llava_vid.py @@ -96,6 +96,7 @@ def __init__( self.mm_spatial_pool_mode = mm_spatial_pool_mode self.max_frames_num = int(max_frames_num) self.mm_resampler_location = mm_pooling_position + self.mm_newline_position = mm_newline_position self.delay_load = delay_load if self.overwrite == True: From a826399b1158341f18da35ae2f357038b94b81d6 Mon Sep 17 00:00:00 2001 From: CaraJ7 <1350074492@qq.com> Date: Wed, 28 Aug 2024 09:12:15 +0800 Subject: [PATCH 196/196] Support textonly inference for LLaVA-OneVision. --- lmms_eval/models/llava_onevision.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/llava_onevision.py index 3e768225..064d40d7 100644 --- a/lmms_eval/models/llava_onevision.py +++ b/lmms_eval/models/llava_onevision.py @@ -453,6 +453,10 @@ def _collate(x): if len(visual) > 1 or "image_aspect_ratio" not in self._config.__dict__: # for multi image case, we treat per image aspect ratio as "pad" by default. self._config.image_aspect_ratio = getattr(gen_kwargs, "image_aspect_ratio", "pad") eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") + + if len(visual) == 0: # For textonly task + image_tensor = None + task_type = 'textonly' if type(visual[0]) == PIL.Image.Image and "task_type" not in metadata and "sample_frames" not in metadata: # For image task image_tensor = process_images(visual, self._image_processor, self._config)

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