101 lines
3.8 KiB
Python
101 lines
3.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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"""convert the weights of efficientnetv2 in
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timm(https://github.com/rwightman/pytorch-image-models) to mmpretrain
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format."""
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import argparse
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import os.path as osp
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import mmengine
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import torch
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from mmengine.runner import CheckpointLoader
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def convert_from_efficientnetv2_timm(param):
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# main change_key
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param_lst = list(param.keys())
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op = str(int(param_lst[-9][7]) + 2)
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new_key = dict()
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for name in param_lst:
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data = param[name]
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if 'blocks' not in name:
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if 'conv_stem' in name:
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name = name.replace('conv_stem', 'backbone.layers.0.conv')
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if 'bn1' in name:
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name = name.replace('bn1', 'backbone.layers.0.bn')
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if 'conv_head' in name:
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# if efficientnet-v2_s/base/b1/b2/b3，op = 7，
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# if for m/l/xl , op = 8
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name = name.replace('conv_head', f'backbone.layers.{op}.conv')
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if 'bn2' in name:
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name = name.replace('bn2', f'backbone.layers.{op}.bn')
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if 'classifier' in name:
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name = name.replace('classifier', 'head.fc')
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else:
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operator = int(name[7])
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if operator == 0:
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name = name[:7] + str(operator + 1) + name[8:]
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name = name.replace('blocks', 'backbone.layers')
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if 'conv' in name:
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name = name.replace('conv', 'conv')
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if 'bn1' in name:
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name = name.replace('bn1', 'bn')
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elif operator < 3:
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name = name[:7] + str(operator + 1) + name[8:]
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name = name.replace('blocks', 'backbone.layers')
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if 'conv_exp' in name:
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name = name.replace('conv_exp', 'conv1.conv')
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if 'conv_pwl' in name:
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name = name.replace('conv_pwl', 'conv2.conv')
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if 'bn1' in name:
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name = name.replace('bn1', 'conv1.bn')
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if 'bn2' in name:
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name = name.replace('bn2', 'conv2.bn')
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else:
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name = name[:7] + str(operator + 1) + name[8:]
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name = name.replace('blocks', 'backbone.layers')
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if 'conv_pwl' in name:
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name = name.replace('conv_pwl', 'linear_conv.conv')
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if 'conv_pw' in name:
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name = name.replace('conv_pw', 'expand_conv.conv')
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if 'conv_dw' in name:
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name = name.replace('conv_dw', 'depthwise_conv.conv')
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if 'bn1' in name:
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name = name.replace('bn1', 'expand_conv.bn')
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if 'bn2' in name:
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name = name.replace('bn2', 'depthwise_conv.bn')
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if 'bn3' in name:
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name = name.replace('bn3', 'linear_conv.bn')
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if 'se.conv_reduce' in name:
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name = name.replace('se.conv_reduce', 'se.conv1.conv')
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if 'se.conv_expand' in name:
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name = name.replace('se.conv_expand', 'se.conv2.conv')
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new_key[name] = data
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return new_key
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def main():
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parser = argparse.ArgumentParser(
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description='Convert pretrained efficientnetv2 '
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'models in timm to mmpretrain style.')
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parser.add_argument('src', help='src model path or url')
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# The dst path must be a full path of the new checkpoint.
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parser.add_argument('dst', help='save path')
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args = parser.parse_args()
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checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
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if 'state_dict' in checkpoint:
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state_dict = checkpoint['state_dict']
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else:
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state_dict = checkpoint
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weight = convert_from_efficientnetv2_timm(state_dict)
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mmengine.mkdir_or_exist(osp.dirname(args.dst))
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torch.save(weight, args.dst)
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print('Done!!')
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if __name__ == '__main__':
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main()
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