From 27051aed43b2e727fc95098779e314ab66e91842 Mon Sep 17 00:00:00 2001 From: MaxJa4 <74194322+MaxJa4@users.noreply.github.com> Date: Mon, 20 Nov 2023 18:17:37 +0100 Subject: [PATCH] 54 - Add model evaluation experiment for perception (#71) --- .../experiments/model_evaluation/README.md | 198 ++++++++++++++++++ .../1619_PT_fasterrcnn_resnet50_fpn_v2.jpg | Bin 0 -> 359855 bytes .../asset-copies/1619_TF_faster-rcnn.jpg | Bin 0 -> 180433 bytes .../asset-copies/1619_yolo_nas_l.jpg | Bin 0 -> 117005 bytes .../asset-copies/1619_yolo_rtdetr_x.jpg | Bin 0 -> 117385 bytes .../asset-copies/1619_yolov8x.jpg | Bin 0 -> 116043 bytes .../asset-copies/1619_yolov8x_seg.jpg | Bin 0 -> 89499 bytes .../experiments/model_evaluation/globals.py | 12 ++ .../experiments/model_evaluation/pt.py | 90 ++++++++ .../experiments/model_evaluation/pylot.py | 120 +++++++++++ .../model_evaluation/requirements.txt | 6 + .../experiments/model_evaluation/yolo.py | 40 ++++ 12 files changed, 466 insertions(+) create mode 100644 doc/06_perception/experiments/model_evaluation/README.md create mode 100644 doc/06_perception/experiments/model_evaluation/asset-copies/1619_PT_fasterrcnn_resnet50_fpn_v2.jpg create mode 100644 doc/06_perception/experiments/model_evaluation/asset-copies/1619_TF_faster-rcnn.jpg create mode 100644 doc/06_perception/experiments/model_evaluation/asset-copies/1619_yolo_nas_l.jpg create mode 100644 doc/06_perception/experiments/model_evaluation/asset-copies/1619_yolo_rtdetr_x.jpg create mode 100644 doc/06_perception/experiments/model_evaluation/asset-copies/1619_yolov8x.jpg create mode 100644 doc/06_perception/experiments/model_evaluation/asset-copies/1619_yolov8x_seg.jpg create mode 100644 doc/06_perception/experiments/model_evaluation/globals.py create mode 100644 doc/06_perception/experiments/model_evaluation/pt.py create mode 100644 doc/06_perception/experiments/model_evaluation/pylot.py create mode 100644 doc/06_perception/experiments/model_evaluation/requirements.txt create mode 100644 doc/06_perception/experiments/model_evaluation/yolo.py diff --git a/doc/06_perception/experiments/model_evaluation/README.md b/doc/06_perception/experiments/model_evaluation/README.md new file mode 100644 index 00000000..3512f6f3 --- /dev/null +++ b/doc/06_perception/experiments/model_evaluation/README.md @@ -0,0 +1,198 @@ +# Model Evaluation Experiment + +This experiment was conducted to evaluate different kinds of object detection models for usage in the CARLA simulator. + +## Installation + +1. Create a virtual environment with `python -m venv venv` while being inside the project's folder. +2. Install the requirements with `pip install -r requirements.txt` + 1. If you have issues, check out the [Tensorflow-Docs](https://www.tensorflow.org/hub/installation), [PyTorch-Docs](https://pytorch.org/get-started/locally/) or [YOLO-Docs](https://docs.ultralytics.com/quickstart/) for installation +3. For the PyLot models (using Tensorflow), also run the following commands ([source](https://www.tensorflow.org/hub/tutorials/tf2_object_detection)): + +```shell +git clone --depth 1 https://github.com/tensorflow/models +sudo apt install -y protobuf-compiler +cd models/research/ +protoc object_detection/protos/*.proto --python_out=. +cp object_detection/packages/tf2/setup.py . +python -m pip install . +``` + +For the YOLO-NAS models, also run `pip install super-gradients==3.1.1`. It is recommended to run these models in a seperate venv, e.g. with `python -m venv venv_yolo` to not mix package versions + +## Usage + +Simply start `yolo.py`, `pt.py` or `pylot.py` to run the respective models and let it save the results. +`globals.py` only holds some global common constants. + +## Results + +For all models, 8 image directly captured from the CARLA RGB camera were used which depict different traffic scenarios. + +*Legend for tables below*: ++, +, /, -, -- (best to worst) + +### Pylot Models (Tensorflow) + +The following models were evaluated (sorted descending by recognition performance): + +1. faster-rcnn +2. ssd-mobilenet-v1-fpn +3. ssd-mobilenet-fpn-640 +4. ssd-mobilenet-v1 +5. ssdlite-mobilenet-v2 + +Images with boundary boxes: [Google Drive](https://drive.google.com/drive/folders/1Jk0KNf1-inO1LN8YWoX0nd796w44zp7_?usp=sharing) + +#### Summary + +| | Cyclists | Traffic lights | Cars | Noise | Speed | +|-----------------------|----------|----------------|------|-------|-------| +| faster-rcnn | -- | -- | ++ | ++ | -- | +| ssd-mobilenet-v1-fpn | -- | -- | + | ++ | - | +| ssd-mobilenet-fpn-640 | -- | -- | - | ++ | + | +| ssd-mobilenet-v1 | -- | -- | - | ++ | ++ | +| ssdlite-mobilenet-v2 | -- | -- | -- | ++ | ++ | + +#### Recognition + +Overall, `faster-rcnn` performed the best out of these 5 Pylot models. It was able to recognize most cars reliably. The only downside was, pretty much nothing else was detected - like cyclists, traffic lights or construction sites. + +`ssd-mobilenet-v1-fpn` recognized significantly less then `faster-rcnn`, but still better than the rest of the Pylot models. + +The difference between `ssd-mobilenet-fpn-640` and `ssd-mobilenet-v1` isn't very noticable, both are rather mediocre. + +`ssdlite-mobilenet-v2` only recognized the most obvious cars and tended to group multiple cars into one bounding box. + +#### Computation speed + +These values are meant to be compared between the models, not as a representative performance indicator in general. +Only the inference time was measured. + +| Model | Time | FPS | +|---------------------------|-------|------| +| ssd-mobilenet-v1 | ~4ms | 250 | +| ssdlite-mobilenet-v2 | ~6ms | ~166 | +| ssd-mobilenet-fpn-640 | ~9ms | ~111 | +| ssd-mobilenet-v1-fpn | ~10ms | 100 | +| faster-rcnn | ~26ms | ~38 | + +### PyTorch Models + +The following models were evaluated (sorted descending by recognition performance): + +1. fasterrcnn_resnet50_fpn_v2 +2. fasterrcnn_mobilenet_v3_large_320_fpn +3. retinanet_resnet50_fpn_v2 + +Images with boundary boxes: [Google Drive](https://drive.google.com/drive/folders/1AnTCIx351_aXWTcdGusIR4kTUOcomcy-?usp=sharing) + +#### Summary + +| Model | Cyclists | Traffic lights | Cars | Noise | Speed | +|---------------------------------------|----------|----------------|------|-------|-------| +| fasterrcnn_resnet50_fpn_v2 | ++ | ++ | ++ | - | -- | +| fasterrcnn_mobilenet_v3_large_320_fpn | ++ | + | ++ | / | ++ | +| retinanet_resnet50_fpn_v2 | ++ | ++ | ++ | -- | - | + +#### Recognition + +Although, all models performed very well at recognizing all kinds of objects, especially `retinanet_resnet50_fpn_v2` showed a **lot** of noise (see images [above](#pytorch-models)). +When filtering these results for a higher minimum for detection score, these noise issues for all models could perhaps be reduced while keeping the good recogintion performance. + +#### Computation speed + +These values are meant to be compared between the models, not as a representative performance indicator in general. +Only the inference time was measured. + +| Model | Time | FPS | +|---------------------------------------|-------|------| +| fasterrcnn_mobilenet_v3_large_320_fpn | ~6ms | ~166 | +| retinanet_resnet50_fpn_v2 | ~36ms | ~27 | +| fasterrcnn_resnet50_fpn_v2 | ~45ms | ~22 | + +### YOLOv8 + +The model versions are different sizes of the same model. +The following models were evaluated (sorted descending by recognition performance): + +1. yolo-rtdetr-x +2. yolo-rtdetr-l +3. yolo-nas-l +4. yolo-nas-m +5. yolo-nas-s +6. yolov8x / yolov8x-seg +7. yolov8l +8. yolov8m +9. yolov8s +10. yolov8n + +Images with boundary boxes: [Google Drive](https://drive.google.com/drive/folders/1u6T0Q3kd9FqjiBWMqzlT-3-fglMqlkBB?usp=sharing) + +#### Summary + +| Model | Cyclists | Traffic lights | Cars | Noise | Speed | +|---------------|----------|----------------|------|-------|-------| +| yolo-rtdetr-x | ++ | ++ | ++ | + | - | +| yolo-rtdetr-l | ++ | ++ | ++ | + | - | +| yolo-nas-l | ++ | ++ | ++ | ++ | + | +| yolo-nas-m | ++ | ++ | ++ | ++ | + | +| yolo-nas-s | ++ | ++ | ++ | ++ | + | +| yolov8x/-seg | ++ | ++ | ++ | ++ | + | +| yolov8l | ++ | ++ | ++ | ++ | + | +| yolov8m | ++ | ++ | ++ | + | ++ | +| yolov8s | + | + | ++ | ++ | ++ | +| yolov8n | + | - | + | ++ | ++ | + +#### Recognition + +All model version performed very well. Only the smallest (`v8n`) version missed some cars. The v8s version was already visibly better, although the `v8x`, `v8l` and `v8m` versions recognized more details - like instead of just a person, they saw a person and a bicycle underneath. + +The same can be said for traffic lights - `v8x`, `v8l` and `v8m` saw them from a larger distance, while `v8n` and `v8s` needed more proximity. + +The `YOLO-NAS` family of models are similar to the best `v8` version but with higher confidence scores. + +`RT-DETR` recognized a little more details and with higher confidence. But at the same time, they have more noise and see irrelevant objects (can be filtered though). + +Throughout all versions, almost no noise (random wrong/duplicate predictions ) was present, without tweaking any values - only some noise with `v8m`. + +#### Computation speed + +These values are meant to be compared between the models, not as a representative performance indicator in general. +Only the inference time was measured. + +| Model | Time | FPS | +|---------------|--------|------| +| yolov8n | ~2ms | 500 | +| yolov8s | ~2ms | 500 | +| yolov8m | ~3ms | ~333 | +| yolov8l | ~4ms | 250 | +| yolov8x/-seg | ~6/7ms | ~166 | +| yolo-nas-l | ~6ms | ~166 | +| yolo-nas-m | ~6ms | ~166 | +| yolo-nas-s | ~7ms | ~142 | +| yolo-rtdetr-l | ~13ms | ~77 | +| yolo-rtdetr-x | ~16ms | ~62 | + +## Conclusion + +Comparing all models with each other, YOLOv8 is currently by far the winner of the comparison. It has the best speed, lowest noise, most detail and reliable recognition - while also being the easiest to use and configure (compare the .py files yourself). + +Since the `v8m` version is sometimes to sensitive and the `v8x` version is the largest/slowest, the `v8l` version is a good middle ground, if performance is important. + +If the best detection results are the most important, the `v8x` version and `nas` family should be analyzed further with more images and situations. + +For segmentation, also `sam` and `fast-sam` was tested. `Sam` needs multiple seconds for inference and is like `fast-sam` not suitable at all for Carla, as they segment the entire image and e.g. segment individual windows of a car or building. + +| ![1619_TF_faster-rcnn.jpg](asset-copies/1619_TF_faster-rcnn.jpg) | +|:--:| +| ^ *Pylot - Faster RCNN (26ms)* ^ | +| ![1619_PT_fasterrcnn_resnet50_fpn_v2.jpg](asset-copies/1619_PT_fasterrcnn_resnet50_fpn_v2.jpg) | +| ^ *Pytorch - Faster RCNN Resnet50 FPN V2 (45ms)* 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+++ b/doc/06_perception/experiments/model_evaluation/pt.py @@ -0,0 +1,90 @@ +''' +Docs: https://pytorch.org/vision/stable/models.html#object-detection +''' + +import os +from time import perf_counter +import torch +import torchvision +from torchvision.models.detection.faster_rcnn import FasterRCNN_MobileNet_V3_Large_320_FPN_Weights, FasterRCNN_ResNet50_FPN_V2_Weights +from torchvision.models.detection.retinanet import RetinaNet_ResNet50_FPN_V2_Weights +from globals import IMAGE_BASE_FOLDER, IMAGES_FOR_TEST +from torchvision.utils import draw_bounding_boxes +from pathlib import Path +import matplotlib.pyplot as plt +from PIL import Image +from torchvision import transforms +from torchvision.transforms.functional import to_pil_image + +ALL_MODELS = { + 'fasterrcnn_mobilenet_v3_large_320_fpn': FasterRCNN_MobileNet_V3_Large_320_FPN_Weights, + 'fasterrcnn_resnet50_fpn_v2': FasterRCNN_ResNet50_FPN_V2_Weights, + 'retinanet_resnet50_fpn_v2': RetinaNet_ResNet50_FPN_V2_Weights, +} + + +def load_model(model_name): + print('Selected model: ' + model_name) + print('Loading model...', end='') + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + weights = ALL_MODELS[model_name].DEFAULT + model = torchvision.models.detection.__dict__[model_name](weights=weights).to(device) + model.eval() + return model, weights, device + + +def load_image(image_path, model_weights, device): + img = Image.open(image_path) + img = img.convert('RGB') + img = transforms.Compose([transforms.PILToTensor()])(img) + img = model_weights.transforms()(img) + img = img.unsqueeze_(0) + img = img.to(device) + + return img + + +first_gen = True + +for m in ALL_MODELS: + model, weights, device = load_model(m) + + for p in IMAGES_FOR_TEST: + image_path = os.path.join(IMAGE_BASE_FOLDER, IMAGES_FOR_TEST[p]) + image_np = load_image(image_path, weights, device) + + if first_gen: + print('Running warmup inference...') + model(image_np) + first_gen = False + + print(f'Running inference for {p}... ') + + start_time = perf_counter() + + # running inference + results = model(image_np) + + elapsed_time = perf_counter() - start_time + + # different object detection models have additional results + # all of them are explained in the documentation + result = results[0] + + label_id_offset = -1 + + image_np_with_detections = torch.tensor(image_np * 255, dtype=torch.uint8) + boxes = result['boxes'] + scores = result['scores'] + labels = [weights.meta["categories"][i] for i in result['labels']] + + box = draw_bounding_boxes(image_np_with_detections[0], boxes, labels, colors='red', width=2) + box_img = to_pil_image(box) + + file_name = Path(image_path).stem + + plt.figure(figsize=(32, 18)) + plt.title(f'PyTorch - {m} - {p} - {elapsed_time*1000:.0f}ms', fontsize=30) + plt.imshow(box_img) + plt.savefig(f'{IMAGE_BASE_FOLDER}/result/{file_name}_PT_{m}.jpg') + plt.close() diff --git a/doc/06_perception/experiments/model_evaluation/pylot.py b/doc/06_perception/experiments/model_evaluation/pylot.py new file mode 100644 index 00000000..f3a670c6 --- /dev/null +++ b/doc/06_perception/experiments/model_evaluation/pylot.py @@ -0,0 +1,120 @@ +''' +Docs: https://www.tensorflow.org/hub/tutorials/tf2_object_detection, https://pylot.readthedocs.io/en/latest/perception.detection.html +''' + +from globals import IMAGE_BASE_FOLDER, IMAGES_FOR_TEST + +import os +from time import perf_counter +import tensorflow as tf +import tensorflow_hub as hub + +import numpy as np +from six import BytesIO +from PIL import Image + +from pathlib import Path +import matplotlib +import matplotlib.pyplot as plt + +from object_detection.utils import visualization_utils as viz_utils + +matplotlib.use('TkAgg') + +tf.get_logger().setLevel('ERROR') + +ALL_MODELS = [ + 'faster-rcnn', + 'ssdlite-mobilenet-v2', + 'ssd-mobilenet-fpn-640', + 'ssd-mobilenet-v1', + 'ssd-mobilenet-v1-fpn' +] + +MODEL_BASE_FOLDER = '/home/maxi/Downloads/models/obstacle_detection' + +LABEL_FILE = '/home/maxi/Downloads/pylot.names' + + +def load_image_into_numpy_array(path): + image_data = tf.io.gfile.GFile(path, 'rb').read() + image = Image.open(BytesIO(image_data)) + + (im_width, im_height) = image.size + return np.array(image.convert('RGB').getdata()).reshape( + (1, im_height, im_width, 3)).astype(np.uint8) + + +def load_model(model_name): + model_handle = os.path.join(MODEL_BASE_FOLDER, model_name) + + print('Selected model: ' + model_name) + + print('Loading model...', end='') + hub_model = hub.load(model_handle) + print(' done!') + + return hub_model + + +def get_category_index(label_file): + with open(label_file, 'r') as f: + labels = f.readlines() + labels = [label.strip() for label in labels] + category_index = {i: {'id': i, 'name': name} for i, name in enumerate(labels)} + return category_index + + +if not os.path.exists(f'{IMAGE_BASE_FOLDER}/result'): + os.makedirs(f'{IMAGE_BASE_FOLDER}/result') + +category_index = get_category_index(LABEL_FILE) + +for m in ALL_MODELS: + model = load_model(m) + first_gen = True + + for p in IMAGES_FOR_TEST: + image_path = os.path.join(IMAGE_BASE_FOLDER, IMAGES_FOR_TEST[p]) + image_np = load_image_into_numpy_array(image_path) + image_tensor = tf.convert_to_tensor(image_np) + + if first_gen: + print('Running warmup inference...') + model.signatures['serving_default'](image_tensor) + first_gen = False + + print(f'Running inference for {p}... ') + + start_time = perf_counter() + + # running inference + results = model.signatures['serving_default'](image_tensor) + + elapsed_time = perf_counter() - start_time + + # different object detection models have additional results + # all of them are explained in the documentation + result = {key: value.numpy() for key, value in results.items()} + + label_id_offset = -1 + image_np_with_detections = image_np.copy() + + viz_utils.visualize_boxes_and_labels_on_image_array( + image_np_with_detections[0], + result['boxes'][0], + (result['classes'][0] + label_id_offset).astype(int), + result['scores'][0], + category_index, + use_normalized_coordinates=True, + max_boxes_to_draw=200, + min_score_thresh=.10, + agnostic_mode=False) + + file_name = Path(image_path).stem + + plt.figure(figsize=(32, 18)) + plt.title(f'Pylot (TF) - {m} - {p} - {elapsed_time*1000:.0f}ms', fontsize=30) + plt.imshow(image_np_with_detections[0]) + plt.savefig(f'{IMAGE_BASE_FOLDER}/result/{file_name}_TF_{m}.jpg') + plt.close() diff --git a/doc/06_perception/experiments/model_evaluation/requirements.txt b/doc/06_perception/experiments/model_evaluation/requirements.txt new file mode 100644 index 00000000..89162b18 --- /dev/null +++ b/doc/06_perception/experiments/model_evaluation/requirements.txt @@ -0,0 +1,6 @@ +tensorflow[and-cuda] +torch +torchvision +torchaudio +ultralytics +six \ No newline at end of file diff --git a/doc/06_perception/experiments/model_evaluation/yolo.py b/doc/06_perception/experiments/model_evaluation/yolo.py new file mode 100644 index 00000000..f7ff342d --- /dev/null +++ b/doc/06_perception/experiments/model_evaluation/yolo.py @@ -0,0 +1,40 @@ +''' +Docs: https://docs.ultralytics.com/modes/predict/, https://docs.ultralytics.com/tasks/detect/#models, https://docs.ultralytics.com/models/yolo-nas +''' + +import os +from globals import IMAGE_BASE_FOLDER, IMAGES_FOR_TEST +from ultralytics import NAS, YOLO, RTDETR, SAM, FastSAM +from PIL import Image +import torch + +ALL_MODELS = { + 'yolov8n': YOLO, + 'yolov8s': YOLO, + 'yolov8m': YOLO, + 'yolov8l': YOLO, + 'yolov8x': YOLO, + 'yolo_nas_l': NAS, + 'yolo_nas_m': NAS, + 'yolo_nas_s': NAS, + 'rtdetr-l': RTDETR, + 'rtdetr-x': RTDETR, + 'yolov8x-seg': YOLO, + 'sam-l': SAM, + 'FastSAM-x': FastSAM, +} + + +with torch.inference_mode(): + for m, wrapper in ALL_MODELS.items(): + print('Selected model: ' + m) + model_path = os.path.join('yolo', m + '.pt') + model = wrapper(model_path) + + for p in IMAGES_FOR_TEST: + image_path = os.path.join(IMAGE_BASE_FOLDER, IMAGES_FOR_TEST[p]) + img = Image.open(image_path) + + _ = model.predict(source=img, save=True, save_conf=True, line_width=1, half=True) + + del model