88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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from collections import OrderedDict
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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_davit(ckpt):
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new_ckpt = OrderedDict()
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for k, v in list(ckpt.items()):
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new_v = v
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if k.startswith('patch_embeds.0'):
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new_k = k.replace('patch_embeds.0', 'patch_embed')
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new_k = new_k.replace('proj', 'projection')
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elif k.startswith('patch_embeds'):
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if k.startswith('patch_embeds.1'):
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new_k = k.replace('patch_embeds.1', 'stages.0.downsample')
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elif k.startswith('patch_embeds.2'):
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new_k = k.replace('patch_embeds.2', 'stages.1.downsample')
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elif k.startswith('patch_embeds.3'):
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new_k = k.replace('patch_embeds.3', 'stages.2.downsample')
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new_k = new_k.replace('proj', 'projection')
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elif k.startswith('main_blocks'):
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new_k = k.replace('main_blocks', 'stages')
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for num_stages in range(4):
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for num_blocks in range(9):
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if f'{num_stages}.{num_blocks}.0' in k:
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new_k = new_k.replace(
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f'{num_stages}.{num_blocks}.0',
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f'{num_stages}.blocks.{num_blocks}.spatial_block')
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elif f'{num_stages}.{num_blocks}.1' in k:
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new_k = new_k.replace(
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f'{num_stages}.{num_blocks}.1',
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f'{num_stages}.blocks.{num_blocks}.channel_block')
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if 'cpe.0' in k:
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new_k = new_k.replace('cpe.0', 'cpe1')
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elif 'cpe.1' in k:
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new_k = new_k.replace('cpe.1', 'cpe2')
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if 'mlp' in k:
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new_k = new_k.replace('mlp.fc1', 'ffn.layers.0.0')
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new_k = new_k.replace('mlp.fc2', 'ffn.layers.1')
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if 'spatial_block.attn' in new_k:
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new_k = new_k.replace('spatial_block.attn',
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'spatial_block.attn.w_msa')
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elif k.startswith('norms'):
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new_k = k.replace('norms', 'norm3')
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elif k.startswith('head'):
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new_k = k.replace('head', 'head.fc')
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else:
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new_k = k
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if not new_k.startswith('head'):
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new_k = 'backbone.' + new_k
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new_ckpt[new_k] = new_v
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return new_ckpt
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def main():
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parser = argparse.ArgumentParser(
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description='Convert keys in pretrained davit '
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'models 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_davit(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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