Fix ViT in21k representation (pre_logits) layer handling across old and new npz checkpoints

Author: rwightman

timm/models/resnetv2.py

@@ -424,7 +424,7 @@ def t2p(conv_weights):
     model.stem.conv.weight.copy_(stem_conv_w)
     model.norm.weight.copy_(t2p(weights[f'{prefix}group_norm/gamma']))
     model.norm.bias.copy_(t2p(weights[f'{prefix}group_norm/beta']))
-    if isinstance(model.head.fc, nn.Conv2d) and \
+    if isinstance(getattr(model.head, 'fc', None), nn.Conv2d) and \
             model.head.fc.weight.shape[0] == weights[f'{prefix}head/conv2d/kernel'].shape[-1]:
         model.head.fc.weight.copy_(t2p(weights[f'{prefix}head/conv2d/kernel']))
         model.head.fc.bias.copy_(t2p(weights[f'{prefix}head/conv2d/bias']))

timm/models/vision_transformer.py

@@ -6,7 +6,7 @@
     - https://arxiv.org/abs/2010.11929
 
 `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
-    - https://arxiv.org/abs/2106.TODO
+    - https://arxiv.org/abs/2106.10270
 
 The official jax code is released and available at https://github.com/google-research/vision_transformer
 
@@ -451,6 +451,9 @@ def _n2p(w, t=True):
     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
+    if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
+        model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
+        model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
     for i, block in enumerate(model.blocks.children()):
         block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
         mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
@@ -673,6 +676,7 @@ def vit_large_patch16_384(pretrained=False, **kwargs):
 def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
     """ ViT-Tiny (Vit-Ti/16).
     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
     """
     model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
     model = _create_vision_transformer('vit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
@@ -683,6 +687,7 @@ def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
 def vit_small_patch32_224_in21k(pretrained=False, **kwargs):
     """ ViT-Small (ViT-S/16)
     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
     """
     model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
     model = _create_vision_transformer('vit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
@@ -693,6 +698,7 @@ def vit_small_patch32_224_in21k(pretrained=False, **kwargs):
 def vit_small_patch16_224_in21k(pretrained=False, **kwargs):
     """ ViT-Small (ViT-S/16)
     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
     """
     model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
     model = _create_vision_transformer('vit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
@@ -703,9 +709,10 @@ def vit_small_patch16_224_in21k(pretrained=False, **kwargs):
 def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
     """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
     """
     model_kwargs = dict(
-        patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
+        patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
     model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
     return model
 
@@ -714,9 +721,10 @@ def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
 def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
     """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
     """
     model_kwargs = dict(
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
     model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
     return model
 
@@ -725,6 +733,7 @@ def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
 def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
     """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+    NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
     """
     model_kwargs = dict(
         patch_size=32, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
@@ -736,9 +745,10 @@ def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
 def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
     """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
     """
     model_kwargs = dict(
-        patch_size=16, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
     model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
     return model
 
@@ -747,7 +757,7 @@ def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
 def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
     """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
     ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
-    NOTE: converted weights not currently available, too large for github release hosting.
+    NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
     """
     model_kwargs = dict(
         patch_size=14, embed_dim=1280, depth=32, num_heads=16, representation_size=1280, **kwargs)