init

finetune/tools/model_converters/vit2mmseg.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os.path as osp
+from collections import OrderedDict
+
+import mmengine
+import torch
+from mmengine.runner import CheckpointLoader
+
+
+def convert_vit(ckpt):
+
+    new_ckpt = OrderedDict()
+
+    for k, v in ckpt.items():
+        if k.startswith('head'):
+            continue
+        if k.startswith('norm'):
+            new_k = k.replace('norm.', 'ln1.')
+        elif k.startswith('patch_embed'):
+            if 'proj' in k:
+                new_k = k.replace('proj', 'projection')
+            else:
+                new_k = k
+        elif k.startswith('blocks'):
+            if 'norm' in k:
+                new_k = k.replace('norm', 'ln')
+            elif 'mlp.fc1' in k:
+                new_k = k.replace('mlp.fc1', 'ffn.layers.0.0')
+            elif 'mlp.fc2' in k:
+                new_k = k.replace('mlp.fc2', 'ffn.layers.1')
+            elif 'attn.qkv' in k:
+                new_k = k.replace('attn.qkv.', 'attn.attn.in_proj_')
+            elif 'attn.proj' in k:
+                new_k = k.replace('attn.proj', 'attn.attn.out_proj')
+            else:
+                new_k = k
+            new_k = new_k.replace('blocks.', 'layers.')
+        else:
+            new_k = k
+        new_ckpt[new_k] = v
+
+    return new_ckpt
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Convert keys in timm pretrained vit models to '
+        'MMSegmentation style.')
+    parser.add_argument('src', help='src model path or url')
+    # The dst path must be a full path of the new checkpoint.
+    parser.add_argument('dst', help='save path')
+    args = parser.parse_args()
+
+    checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
+    if 'state_dict' in checkpoint:
+        # timm checkpoint
+        state_dict = checkpoint['state_dict']
+    elif 'model' in checkpoint:
+        # deit checkpoint
+        state_dict = checkpoint['model']
+    else:
+        state_dict = checkpoint
+    weight = convert_vit(state_dict)
+    mmengine.mkdir_or_exist(osp.dirname(args.dst))
+    torch.save(weight, args.dst)
+
+
+if __name__ == '__main__':
+    main()