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import argparse |
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from collections import OrderedDict |
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import torch |
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convert_dict_s = { |
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'model.0': 'backbone.stem', |
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'model.1': 'backbone.stage1.0', |
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'model.2': 'backbone.stage1.1', |
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'model.3': 'backbone.stage2.0', |
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'model.4': 'backbone.stage2.1', |
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'model.5': 'backbone.stage3.0', |
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'model.6': 'backbone.stage3.1', |
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'model.7': 'backbone.stage4.0', |
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'model.8': 'backbone.stage4.1', |
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'model.9': 'backbone.stage4.2', |
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'model.12': 'neck.top_down_layers.0', |
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'model.15': 'neck.top_down_layers.1', |
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'model.16': 'neck.downsample_layers.0', |
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'model.18': 'neck.bottom_up_layers.0', |
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'model.19': 'neck.downsample_layers.1', |
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'model.21': 'neck.bottom_up_layers.1', |
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'model.22': 'bbox_head.head_module', |
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} |
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def convert(src, dst): |
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"""Convert keys in pretrained YOLOv8 models to mmyolo style.""" |
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convert_dict = convert_dict_s |
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try: |
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yolov8_model = torch.load(src)['model'] |
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blobs = yolov8_model.state_dict() |
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except ModuleNotFoundError: |
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raise RuntimeError( |
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'This script must be placed under the ultralytics repo,' |
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' because loading the official pretrained model need' |
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' `model.py` to build model.' |
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'Also need to install hydra-core>=1.2.0 and thop>=0.1.1') |
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state_dict = OrderedDict() |
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for key, weight in blobs.items(): |
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num, module = key.split('.')[1:3] |
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prefix = f'model.{num}' |
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new_key = key.replace(prefix, convert_dict[prefix]) |
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if '.m.' in new_key: |
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new_key = new_key.replace('.m.', '.blocks.') |
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new_key = new_key.replace('.cv', '.conv') |
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elif 'bbox_head.head_module.proto.cv' in new_key: |
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new_key = new_key.replace( |
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'bbox_head.head_module.proto.cv', |
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'bbox_head.head_module.proto_preds.conv') |
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elif 'bbox_head.head_module.proto' in new_key: |
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new_key = new_key.replace('bbox_head.head_module.proto', |
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'bbox_head.head_module.proto_preds') |
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elif 'bbox_head.head_module.cv4.' in new_key: |
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new_key = new_key.replace( |
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'bbox_head.head_module.cv4', |
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'bbox_head.head_module.mask_coeff_preds') |
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new_key = new_key.replace('.2.weight', '.2.conv.weight') |
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new_key = new_key.replace('.2.bias', '.2.conv.bias') |
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elif 'bbox_head.head_module' in new_key: |
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new_key = new_key.replace('.cv2', '.reg_preds') |
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new_key = new_key.replace('.cv3', '.cls_preds') |
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elif 'backbone.stage4.2' in new_key: |
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new_key = new_key.replace('.cv', '.conv') |
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else: |
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new_key = new_key.replace('.cv1', '.main_conv') |
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new_key = new_key.replace('.cv2', '.final_conv') |
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if 'bbox_head.head_module.dfl.conv.weight' == new_key: |
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print('Drop "bbox_head.head_module.dfl.conv.weight", ' |
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'because it is useless') |
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continue |
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state_dict[new_key] = weight |
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print(f'Convert {key} to {new_key}') |
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checkpoint = dict() |
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checkpoint['state_dict'] = state_dict |
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torch.save(checkpoint, dst) |
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def main(): |
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parser = argparse.ArgumentParser(description='Convert model keys') |
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parser.add_argument( |
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'--src', default='yolov8s.pt', help='src YOLOv8 model path') |
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parser.add_argument('--dst', default='mmyolov8s.pth', help='save path') |
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args = parser.parse_args() |
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convert(args.src, args.dst) |
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if __name__ == '__main__': |
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main() |
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