Add some ViT comments and fix a few minor issues.

rwightman · rwightman · commit c16e9650371d · 2021-01-24T23:18:35.000-08:00
diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py
@@ -100,10 +100,10 @@ def _cfg(url='', **kwargs):
     # hybrid models (weights ported from official Google JAX impl)
     'vit_base_resnet50_224_in21k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
-        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9),
+        num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'),
     'vit_base_resnet50_384': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
-        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'),
 
     # hybrid models (my experiments)
     'vit_small_resnet26d_224': _cfg(),
@@ -256,11 +256,33 @@ def forward(self, x):
 
 
 class VisionTransformer(nn.Module):
-    """ Vision Transformer with support for patch or hybrid CNN input stage
+    """ Vision Transformer
+
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
+        https://arxiv.org/abs/2010.11929
     """
     def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                  num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            num_classes (int): number of classes for classification head
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
+            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
+            drop_rate (float): dropout rate
+            attn_drop_rate (float): attention dropout rate
+            drop_path_rate (float): stochastic depth rate
+            hybrid_backbone (nn.Module): CNN backbone to use in-place of PatchEmbed module
+            norm_layer: (nn.Module): normalization layer
+        """
         super().__init__()
         self.num_classes = num_classes
         self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
@@ -346,8 +368,7 @@ def forward(self, x):
 
 
 def resize_pos_embed(posemb, posemb_new):
-    # Rescale the grid of position embeddings when loading from state_dict
-    # Adapted from
+    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
     # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
     _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
     ntok_new = posemb_new.shape[1]
@@ -363,22 +384,21 @@ def resize_pos_embed(posemb, posemb_new):
     posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bilinear')
     posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1)
     posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
-    state_dict['pos_embed'] = posemb
-    return state_dict
+    return posemb
 
 
 def checkpoint_filter_fn(state_dict, model):
     """ convert patch embedding weight from manual patchify + linear proj to conv"""
     out_dict = {}
     if 'model' in state_dict:
-        # for deit models
+        # For deit models
         state_dict = state_dict['model']
     for k, v in state_dict.items():
         if 'patch_embed.proj.weight' in k and len(v.shape) < 4:
-            # for old models that I trained prior to conv based patchification
+            # For old models that I trained prior to conv based patchification
             v = v.reshape(model.patch_embed.proj.weight.shape)
         elif k == 'pos_embed' and v.shape != model.pos_embed.shape:
-            # to resize pos embedding when using model at different size from pretrained weights
+            # To resize pos embedding when using model at different size from pretrained weights
             v = resize_pos_embed(v, model.pos_embed)
         out_dict[k] = v
     return out_dict
@@ -393,8 +413,9 @@ def _create_vision_transformer(variant, pretrained=False, **kwargs):
     img_size = kwargs.pop('img_size', default_img_size)
     repr_size = kwargs.pop('representation_size', None)
     if repr_size is not None and num_classes != default_num_classes:
-        # remove representation layer if fine-tuning
-        _logger.info("Removing representation layer for fine-tuning.")
+        # Remove representation layer if fine-tuning. This may not always be the desired action,
+        # but I feel better than doing nothing by default for fine-tuning. Perhaps a better interface?
+        _logger.warning("Removing representation layer for fine-tuning.")
         repr_size = None
 
     model = VisionTransformer(img_size=img_size, num_classes=num_classes, representation_size=repr_size, **kwargs)
@@ -409,6 +430,7 @@ def _create_vision_transformer(variant, pretrained=False, **kwargs):
 
 @register_model
 def vit_small_patch16_224(pretrained=False, **kwargs):
+    """ My custom 'small' ViT model. Depth=8, heads=8= mlp_ratio=3."""
     model_kwargs = dict(
         patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.,
         qkv_bias=False, norm_layer=nn.LayerNorm, **kwargs)
@@ -421,27 +443,38 @@ def vit_small_patch16_224(pretrained=False, **kwargs):
 
 @register_model
 def vit_base_patch16_224(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
+    """
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_base_patch32_224(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
+    """
     model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_base_patch16_384(pretrained=False, **kwargs):
+    """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+    """
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_base_patch32_384(pretrained=False, **kwargs):
+    """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+    """
     model_kwargs = dict(
         patch_size=32, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
         norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
@@ -451,35 +484,48 @@ def vit_base_patch32_384(pretrained=False, **kwargs):
 
 @register_model
 def vit_large_patch16_224(pretrained=False, **kwargs):
+    """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
+    """
     model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_large_patch32_224(pretrained=False, **kwargs):
+    """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
+    """
     model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_large_patch16_384(pretrained=False, **kwargs):
+    """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+    """
     model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
-    model_kwargs = dict(
-        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, representation_size=768, **kwargs)
-    model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+def vit_large_patch32_384(pretrained=False, **kwargs):
+    """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+    """
+    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, **kwargs)
+    model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
-def vit_base_patch16_384_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.
+    """
     model_kwargs = dict(
         patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, representation_size=768, **kwargs)
     model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
@@ -488,6 +534,9 @@ def vit_base_patch16_384_in21k(pretrained=False, **kwargs):
 
 @register_model
 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.
+    """
     model_kwargs = dict(
         patch_size=32, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, representation_size=768, **kwargs)
     model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
@@ -496,22 +545,20 @@ def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
 
 @register_model
 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.
+    """
     model_kwargs = dict(
         patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, representation_size=1024, **kwargs)
     model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
     return model
 
 
-# @register_model
-# def vit_large_patch16_384_in21k(pretrained=False, **kwargs):
-#     model_kwargs = dict(
-#         patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, representation_size=1024, **kwargs)
-#     model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
-#     return model
-
-
 @register_model
 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.
+    """
     model_kwargs = dict(
         patch_size=32, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, representation_size=1024, **kwargs)
     model = _create_vision_transformer('vit_large_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
@@ -520,6 +567,10 @@ def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
 
 @register_model
 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.
+    """
     model_kwargs = dict(
         patch_size=14, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, representation_size=1280, **kwargs)
     model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)
@@ -528,9 +579,13 @@ def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
 
 @register_model
 def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
+    """ R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+    """
     # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
     backbone = ResNetV2(
-        layers=(3, 4, 9), preact=False, stem_type='same', conv_layer=StdConv2dSame, num_classes=0, global_pool='')
+        layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
+        preact=False, stem_type='same', conv_layer=StdConv2dSame)
     model_kwargs = dict(
         embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone,
         representation_size=768, **kwargs)
@@ -540,73 +595,93 @@ def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
 
 @register_model
 def vit_base_resnet50_384(pretrained=False, **kwargs):
+    """ R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
+    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+    """
     # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
     backbone = ResNetV2(
-        layers=(3, 4, 9), preact=False, stem_type='same', conv_layer=StdConv2dSame, num_classes=0, global_pool='')
+        layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
+        preact=False, stem_type='same', conv_layer=StdConv2dSame)
     model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
     model = _create_vision_transformer('vit_base_resnet50_384', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_small_resnet26d_224(pretrained=False, **kwargs):
-    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
-    backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
+    """ Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.
+    """
+    backbone = resnet26d(pretrained=pretrained, features_only=True, out_indices=[4])
     model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
     model = _create_vision_transformer('vit_small_resnet26d_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
-    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
-    backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[3])
+    """ Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.
+    """
+    backbone = resnet50d(pretrained=pretrained, features_only=True, out_indices=[3])
     model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
     model = _create_vision_transformer('vit_small_resnet50d_s3_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_base_resnet26d_224(pretrained=False, **kwargs):
-    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
-    backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
+    """ Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights.
+    """
+    backbone = resnet26d(pretrained=pretrained, features_only=True, out_indices=[4])
     model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
     model = _create_vision_transformer('vit_base_resnet26d_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_base_resnet50d_224(pretrained=False, **kwargs):
-    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
-    backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
+    """ Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights.
+    """
+    backbone = resnet50d(pretrained=pretrained, features_only=True, out_indices=[4])
     model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
     model = _create_vision_transformer('vit_base_resnet50d_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_deit_tiny_patch16_224(pretrained=False, **kwargs):
+    """ DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
     model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_deit_small_patch16_224(pretrained=False, **kwargs):
+    """ DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
     model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_deit_small_patch16_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_deit_base_patch16_224(pretrained=False, **kwargs):
+    """ DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_deit_base_patch16_224', pretrained=pretrained, **model_kwargs)
     return model
 
 
 @register_model
 def vit_deit_base_patch16_384(pretrained=False, **kwargs):
+    """ DeiT base model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
+    ImageNet-1k weights from https://github.com/facebookresearch/deit.
+    """
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, **kwargs)
     model = _create_vision_transformer('vit_deit_base_patch16_384', pretrained=pretrained, **model_kwargs)
     return model
