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main.py
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main.py
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#coding: utf-8
import os
import json
import utils
import torch
import numpy as np
from argparse import ArgumentParser
from run_span import NerArgumentParser, CailNerProcessor, predict_decode_batch
infile = "/input/input.json"
outfile = "/output/output.json"
def main():
parser = ArgumentParser()
parser.add_argument("--local_debug", action="store_true", default=False)
parser.add_argument("--test_file", type=str, default="dev.all.json")
run_args = parser.parse_args()
local_debug = run_args.local_debug
# WARNING：以下配置需要在提交前指定！！！
span_proba_thresh = 0.0
# span_proba_thresh = 0.3
# version = "baseline"
# model_type = "bert_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# --------------------------
# version = "rdrop0.1-fgm1.0"
# model_type = "bert_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# --------------------------
# version = "nezha_rdrop0.1-fgm1.0"
# model_type = "nezha_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# --------------------------
# version = "nezha-fgm1.0"
# model_type = "nezha_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# --------------------------
# version = "nezha-rdrop0.1-fgm1.0-aug_ctx0.15"
# model_type = "nezha_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# --------------------------
# version = "nezha-fgm1.0-lsr0.1"
# model_type = "nezha_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# --------------------------
version = "nezha-legal-fgm1.0-lsr0.1"
model_type = "nezha_span"
dataset_name = "cail_ner"
n_splits = 5
seed=42
# --------------------------
# version = "nezha-legal-fgm1.0-lsr0.1-ema3"
# model_type = "nezha_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# --------------------------
# version = "nezha-legal-100k-fgm1.0-lsr0.1"
# model_type = "nezha_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# --------------------------
# version = "nezha-legal-fgm2.0-lsr0.1"
# model_type = "nezha_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# # --------------------------
# version = "nezha-legal-fgm1.0-lsr0.1-v2"
# model_type = "nezha_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
# seed=32
# seed=12345
# --------------------------
# version = "nezha-legal-fgm1.0-lsr0.1-v2-pseudo_t0.9"
# model_type = "nezha_span"
# dataset_name = "cail_ner"
# n_splits = 5
# seed=42
test_examples = None
test_batches = None
# for k in range(n_splits):
# model_path = f"./output/ner-{dataset_name}-{model_type}-{version}-fold{k}-{seed}/"
model_paths = [f"./output/ner-{dataset_name}-{model_type}-{version}-fold{k}-{seed}/" for k in range(n_splits)]
for k in range(len(model_paths)):
model_path = model_paths[k]
# 生成测试运行参数
json_file = os.path.join(model_path, "training_args.json")
parser = NerArgumentParser()
args = parser.parse_args_from_json(json_file)
args.do_train, args.do_eval, args.do_predict = False, False, True
args.per_gpu_eval_batch_size = 1
args.fp16 = False
# 生成测试数据集
if not local_debug:
raw_samples = utils.load_raw(infile)
utils.save_samples(os.path.join(args.data_dir, "test.json"), raw_samples)
args.test_file = "test.json"
else:
args.test_file = run_args.test_file
parser.save_args_to_json(f"./args/pred.{k}.json", args)
# 确保目录下预测输出文件被清除
os.system(f"rm -rf {os.path.join(model_path, 'test_*')}")
if local_debug:
# 线下预测测试
os.system(f"python run_span.py ./args/pred.{k}.json")
else:
# 线上预测阶段
os.system(f"sudo /home/user/miniconda/bin/python3 run_span.py ./args/pred.{k}.json")
# 读取预测输出，并集成
test_examples_ = torch.load(os.path.join(model_path, 'test_examples.pkl'))
test_batches_ = torch.load(os.path.join(model_path, 'test_batches.pkl'))
if test_examples is None:
test_examples, test_batches = test_examples_, test_batches_
else:
for i, (batch, batch_) in enumerate(zip(test_batches, test_batches_)):
test_batches[i]["logits"] = batch["logits"] + batch_["logits"]
# 集成结果预测
results = []
for i, (example, batch) in enumerate(zip(test_examples, test_batches)):
results.append(predict_decode_batch(example[1], batch, CailNerProcessor().id2label,
thresh=span_proba_thresh, post_process=True))
# 保存结果
output_predict_file = "output.json" if local_debug else outfile
with open(output_predict_file, "w") as writer:
for record in results:
writer.write(json.dumps(record) + '\n')
if local_debug:
tmp = run_args.test_file.split(".")
tmp.insert(1, "gt")
gt_file = ".".join(tmp)
os.system(f"python evaluate.py data/ner-ctx0-5fold-seed42/{gt_file} output.json")
if __name__ == '__main__':
main()