36 lines
1.6 KiB
Python
36 lines
1.6 KiB
Python
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import warnings
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warnings.filterwarnings('ignore')
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import torch
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from ultralytics.nn.tasks import DetectionModel
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if __name__ == '__main__':
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model_PGI_weights_path = 'runs/train/yolov8n-PGI/weights/best.pt'
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model_cfg_path = "ultralytics/cfg/models/v8/yolov8n.yaml"
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layer_num, pgi_layer_num = 22, 38
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device = torch.device("cpu")
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model_PGI = torch.load(model_PGI_weights_path, map_location='cpu')
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model_name_key = 'model' if model_PGI['model'] is not None else 'ema'
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model_PGI_dict = model_PGI[model_name_key].model.state_dict()
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model_PGI_head = model_PGI[model_name_key].model[-1]
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model_name = model_PGI[model_name_key].names
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model = DetectionModel(model_cfg_path, nc=model_PGI_head.nc)
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model.names = model_name
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model_dict = model.state_dict()
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new_dict = {}
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for name in model_PGI_dict:
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layer_id = int(name.split('.')[0]) - 1
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new_name = f'.'.join(['model', str(layer_id)] + name.split('.')[1:])
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if new_name in model_dict and model_PGI_dict[name].size() == model_dict[new_name].size():
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new_dict[new_name] = model_PGI_dict[name]
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if (layer_id + 1) == pgi_layer_num:
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new_name = f'.'.join(['model', str(layer_num)] + name.split('.')[1:])
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if new_name in model_dict and model_PGI_dict[name].size() == model_dict[new_name].size():
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new_dict[new_name] = model_PGI_dict[name]
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print(len(new_dict), len(model_dict))
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model.load_state_dict(new_dict)
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model.eval()
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torch.save({'model':model.half()}, f'{model_PGI_weights_path[:model_PGI_weights_path.rfind(".")]}_rep.pt')
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