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import os |
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import sys |
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import argparse |
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import numpy as np |
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import cv2 as cv |
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from datasets import DATASETS |
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if "PYTHONPATH" in os.environ: |
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root_dir = os.environ["PYTHONPATH"] |
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else: |
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root_dir = os.path.join("..", "..") |
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sys.path.append(root_dir) |
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from models import MODELS |
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parser = argparse.ArgumentParser("Evaluation with OpenCV on different models in the zoo.") |
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parser.add_argument("--model", "-m", type=str, required=True, help="model name") |
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parser.add_argument("--dataset", "-d", type=str, required=True, help="Dataset name") |
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parser.add_argument("--dataset_root", "-dr", type=str, required=True, help="Root directory of given dataset") |
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args = parser.parse_args() |
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models = dict( |
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mobilenetv1=dict( |
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name="MobileNet", |
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topic="image_classification", |
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modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr.onnx"), |
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topK=5, |
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loadLabel=False), |
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mobilenetv1_q=dict( |
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name="MobileNet", |
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topic="image_classification", |
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modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr_int8.onnx"), |
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topK=5, |
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loadLabel=False), |
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mobilenetv1_bq=dict( |
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name="MobileNet", |
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topic="image_classification", |
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modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr_int8bq.onnx"), |
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topK=5, |
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loadLabel=False), |
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mobilenetv2=dict( |
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name="MobileNet", |
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topic="image_classification", |
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modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr.onnx"), |
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topK=5, |
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loadLabel=False), |
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mobilenetv2_q=dict( |
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name="MobileNet", |
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topic="image_classification", |
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modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr_int8.onnx"), |
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topK=5, |
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loadLabel=False), |
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mobilenetv2_bq=dict( |
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name="MobileNet", |
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topic="image_classification", |
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modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr_int8bq.onnx"), |
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topK=5, |
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loadLabel=False), |
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ppresnet=dict( |
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name="PPResNet", |
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topic="image_classification", |
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modelPath=os.path.join(root_dir, "models/image_classification_ppresnet/image_classification_ppresnet50_2022jan.onnx"), |
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topK=5, |
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loadLabel=False), |
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ppresnet_q=dict( |
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name="PPResNet", |
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topic="image_classification", |
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modelPath=os.path.join(root_dir, "models/image_classification_ppresnet/image_classification_ppresnet50_2022jan_int8.onnx"), |
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topK=5, |
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loadLabel=False), |
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ppresnet_bq=dict( |
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name="PPResNet", |
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topic="image_classification", |
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modelPath=os.path.join(root_dir, "models/image_classification_ppresnet/image_classification_ppresnet50_2022jan_int8bq.onnx"), |
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topK=5, |
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loadLabel=False), |
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yunet=dict( |
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name="YuNet", |
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topic="face_detection", |
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modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2023mar.onnx"), |
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topK=5000, |
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confThreshold=0.3, |
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nmsThreshold=0.45), |
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yunet_q=dict( |
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name="YuNet", |
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topic="face_detection", |
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modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2023mar_int8.onnx"), |
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topK=5000, |
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confThreshold=0.3, |
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nmsThreshold=0.45), |
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yunet_bq=dict( |
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name="YuNet", |
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topic="face_detection", |
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modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2023mar_int8bq.onnx"), |
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topK=5000, |
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confThreshold=0.3, |
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nmsThreshold=0.45), |
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sface=dict( |
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name="SFace", |
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topic="face_recognition", |
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modelPath=os.path.join(root_dir, "models/face_recognition_sface/face_recognition_sface_2021dec.onnx")), |
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sface_q=dict( |
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name="SFace", |
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topic="face_recognition", |
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modelPath=os.path.join(root_dir, "models/face_recognition_sface/face_recognition_sface_2021dec_int8.onnx")), |
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sface_bq=dict( |
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name="SFace", |
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topic="face_recognition", |
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modelPath=os.path.join(root_dir, "models/face_recognition_sface/face_recognition_sface_2021dec_int8bq.onnx")), |
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crnn_en=dict( |
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name="CRNN", |
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topic="text_recognition", |
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modelPath=os.path.join(root_dir, "models/text_recognition_crnn/text_recognition_CRNN_EN_2021sep.onnx")), |
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crnn_en_q=dict( |
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name="CRNN", |
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topic="text_recognition", |
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modelPath=os.path.join(root_dir, "models/text_recognition_crnn/text_recognition_CRNN_EN_2022oct_int8.onnx")), |
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pphumanseg=dict( |
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name="PPHumanSeg", |
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topic="human_segmentation", |
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modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2023mar.onnx")), |
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pphumanseg_q=dict( |
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name="PPHumanSeg", |
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topic="human_segmentation", |
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modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2023mar_int8.onnx")), |
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pphumanseg_bq=dict( |
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name="PPHumanSeg", |
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topic="human_segmentation", |
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modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2023mar_int8bq.onnx")), |
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) |
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datasets = dict( |
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imagenet=dict( |
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name="ImageNet", |
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topic="image_classification", |
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size=224), |
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widerface=dict( |
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name="WIDERFace", |
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topic="face_detection"), |
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lfw=dict( |
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name="LFW", |
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topic="face_recognition", |
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target_size=112), |
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icdar=dict( |
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name="ICDAR", |
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topic="text_recognition"), |
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iiit5k=dict( |
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name="IIIT5K", |
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topic="text_recognition"), |
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mini_supervisely=dict( |
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name="MiniSupervisely", |
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topic="human_segmentation"), |
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) |
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def main(args): |
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model_key = args.model.lower() |
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assert model_key in models |
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model_name = models[model_key].pop("name") |
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model_topic = models[model_key].pop("topic") |
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model_handler, _ = MODELS.get(model_name) |
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model = model_handler(**models[model_key]) |
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dataset_key = args.dataset.lower() |
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assert dataset_key in datasets |
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dataset_name = datasets[dataset_key].pop("name") |
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dataset_topic = datasets[dataset_key].pop("topic") |
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dataset = DATASETS.get(dataset_name)(root=args.dataset_root, **datasets[dataset_key]) |
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assert model_topic == dataset_topic |
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dataset.eval(model) |
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dataset.print_result() |
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if __name__ == "__main__": |
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main(args) |
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