154 lines
4.8 KiB
Python
154 lines
4.8 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_eva02(ckpt):
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new_ckpt = OrderedDict()
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qkv_proj = {}
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qkv_bias = {}
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w12_weight = {}
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w12_bias = {}
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banned = {
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'mask_token',
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'lm_head.weight',
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'lm_head.bias',
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'norm.weight',
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'norm.bias',
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}
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for k, v in list(ckpt.items()):
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if k in banned:
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continue
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if k.startswith('head'):
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new_k = k.replace('head.', 'head.fc.')
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new_ckpt[new_k] = v
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else:
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if k.startswith('patch_embed'):
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new_k = k.replace('proj.', 'projection.')
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elif k.startswith('fc_norm') or k.startswith('norm'):
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new_k = k.replace('norm.', 'ln2.')
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new_k = k.replace('fc_norm.', 'ln2.')
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elif k.startswith('blocks'):
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new_k = k.replace('blocks.', 'layers.')
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if 'mlp' in new_k:
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if 'w1.' in new_k or 'w2.' in new_k:
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# For base and large version, mlp is implemented with
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# 2 linears, where w1 and w2 are required to integrate
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# into w12.
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s = new_k.split('.') # e.g. layers.0.mlp.w1.weight
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idx = s[1]
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if 'weight' in new_k:
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# w1.weight or w2.weight
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if idx not in w12_weight:
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w12_weight[idx] = {}
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w12_weight[idx][s[-2]] = v
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else:
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# w1.bias or w2.bias
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if idx not in w12_bias:
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w12_bias[idx] = {}
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w12_bias[idx][s[-2]] = v
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continue
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if 'ffn_ln' in new_k:
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new_k = new_k.replace('ffn_ln.', 'norm.')
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elif 'attn' in new_k:
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if 'q_proj.weight' in new_k or \
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'k_proj.weight' in new_k or \
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'v_proj.weight' in new_k:
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# For base and large version, qkv projection is
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# implemented with three linear layers,
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s = new_k.split('.')
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idx = s[1]
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if idx not in qkv_proj:
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qkv_proj[idx] = {}
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qkv_proj[idx][s[-2]] = v
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continue
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if 'q_bias' in new_k or 'v_bias' in new_k:
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# k_bias is 0
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s = new_k.split('.')
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idx = s[1]
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if idx not in qkv_bias:
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qkv_bias[idx] = {}
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qkv_bias[idx][s[-1]] = v
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continue
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else:
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new_k = k
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new_k = 'backbone.' + new_k
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new_ckpt[new_k] = v
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for idx in qkv_proj:
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q_proj = qkv_proj[idx]['q_proj']
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k_proj = qkv_proj[idx]['k_proj']
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v_proj = qkv_proj[idx]['v_proj']
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weight = torch.cat((q_proj, k_proj, v_proj))
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new_k = f'backbone.layers.{idx}.attn.qkv.weight'
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new_ckpt[new_k] = weight
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for idx in qkv_bias:
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q_bias = qkv_bias[idx]['q_bias']
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k_bias = torch.zeros_like(q_bias)
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v_bias = qkv_bias[idx]['v_bias']
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weight = torch.cat((q_bias, k_bias, v_bias))
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new_k = f'backbone.layers.{idx}.attn.qkv.bias'
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new_ckpt[new_k] = weight
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for idx in w12_weight:
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w1 = w12_weight[idx]['w1']
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w2 = w12_weight[idx]['w2']
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weight = torch.cat((w1, w2))
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new_k = f'backbone.layers.{idx}.mlp.w12.weight'
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new_ckpt[new_k] = weight
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for idx in w12_bias:
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w1 = w12_bias[idx]['w1']
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w2 = w12_bias[idx]['w2']
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weight = torch.cat((w1, w2))
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new_k = f'backbone.layers.{idx}.mlp.w12.bias'
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new_ckpt[new_k] = weight
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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 eva02 '
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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 'module' in checkpoint:
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state_dict = checkpoint['module']
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else:
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state_dict = checkpoint
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weight = convert_eva02(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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