Add DINOv2 models with register tokens. Convert pos embed to non-over…

…lapping for consistency.

timm/models/vision_transformer.py

@@ -567,7 +567,11 @@ def get_classifier(self):
     def reset_classifier(self, num_classes: int, global_pool=None):
         self.num_classes = num_classes
         if global_pool is not None:
-            assert global_pool in ('', 'avg', 'token')
+            assert global_pool in ('', 'avg', 'token', 'map')
+            if global_pool == 'map' and self.attn_pool is None:
+                assert False, "Cannot currently add attention pooling in reset_classifier()."
+            elif global_pool != 'map ' and self.attn_pool is not None:
+                self.attn_pool = None  # remove attention pooling
             self.global_pool = global_pool
         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
 
@@ -937,10 +941,14 @@ def _convert_openai_clip(state_dict, model):
 def _convert_dinov2(state_dict, model):
     import re
     out_dict = {}
+    state_dict.pop("mask_token", None)
+    if 'register_tokens' in state_dict:
+        # convert dinov2 w/ registers to no_embed_class timm model (neither cls or reg tokens overlap pos embed)
+        out_dict['reg_token'] = state_dict.pop('register_tokens')
+        out_dict['cls_token'] = state_dict.pop('cls_token') + state_dict['pos_embed'][:, 0]
+        out_dict['pos_embed'] = state_dict.pop('pos_embed')[:, 1:]
     for k, v in state_dict.items():
-        if k == "mask_token":
-            continue
-        elif re.match(r"blocks\.(\d+)\.mlp\.w12\.(?:weight|bias)", k):
+        if re.match(r"blocks\.(\d+)\.mlp\.w12\.(?:weight|bias)", k):
             out_dict[k.replace("w12", "fc1")] = v
             continue
         elif re.match(r"blocks\.(\d+)\.mlp\.w3\.(?:weight|bias)", k):
@@ -1229,6 +1237,32 @@ def _cfg(url='', **kwargs):
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
         input_size=(3, 518, 518), crop_pct=1.0),
 
+    # DINOv2 pretrained w/ registers - https://arxiv.org/abs/2309.16588 (no classifier head, for fine-tune/features only)
+    'vit_small_patch14_reg4_dinov2.lvd142m': _cfg(
+        url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_reg4_pretrain.pth',
+        # hf_hub_id='timm/',
+        license='apache-2.0',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
+        input_size=(3, 518, 518), crop_pct=1.0),
+    'vit_base_patch14_reg4_dinov2.lvd142m': _cfg(
+        url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_reg4_pretrain.pth',
+        # hf_hub_id='timm/',
+        license='apache-2.0',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
+        input_size=(3, 518, 518), crop_pct=1.0),
+    'vit_large_patch14_reg4_dinov2.lvd142m': _cfg(
+        url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_reg4_pretrain.pth',
+        # hf_hub_id='timm/',
+        license='apache-2.0',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
+        input_size=(3, 518, 518), crop_pct=1.0),
+    'vit_giant_patch14_reg4_dinov2.lvd142m': _cfg(
+        url='https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_reg4_pretrain.pth',
+        # hf_hub_id='timm/',
+        license='apache-2.0',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
+        input_size=(3, 518, 518), crop_pct=1.0),
+
     # ViT ImageNet-21K-P pretraining by MILL
     'vit_base_patch16_224_miil.in21k': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_in21k_miil-887286df.pth',
@@ -2173,9 +2207,7 @@ def vit_huge_patch14_xp_224(pretrained=False, **kwargs) -> VisionTransformer:
 def vit_small_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
     """ ViT-S/14 for DINOv2
     """
-    model_args = dict(
-        patch_size=14, embed_dim=384, depth=12, num_heads=6, init_values=1e-5, img_size=518,
-    )
+    model_args = dict(patch_size=14, embed_dim=384, depth=12, num_heads=6, init_values=1e-5, img_size=518)
     model = _create_vision_transformer(
         'vit_small_patch14_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
     return model
@@ -2185,9 +2217,7 @@ def vit_small_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
 def vit_base_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
     """ ViT-B/14 for DINOv2
     """
-    model_args = dict(
-        patch_size=14, embed_dim=768, depth=12, num_heads=12, init_values=1e-5, img_size=518,
-    )
+    model_args = dict(patch_size=14, embed_dim=768, depth=12, num_heads=12, init_values=1e-5, img_size=518)
     model = _create_vision_transformer(
         'vit_base_patch14_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
     return model
@@ -2197,9 +2227,7 @@ def vit_base_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
 def vit_large_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
     """ ViT-L/14 for DINOv2
     """
-    model_args = dict(
-        patch_size=14, embed_dim=1024, depth=24, num_heads=16, init_values=1e-5, img_size=518,
-    )
+    model_args = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, init_values=1e-5, img_size=518)
     model = _create_vision_transformer(
         'vit_large_patch14_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
     return model
@@ -2209,12 +2237,10 @@ def vit_large_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
 def vit_giant_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
     """ ViT-G/14 for DINOv2
     """
-
     # The hidden_features of SwiGLU is calculated by:
     # hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
     # When embed_dim=1536, hidden_features=4096
     # With SwiGLUPacked, we need to set hidden_features = 2 * 4096 = 8192
-
     model_args = dict(
         patch_size=14, embed_dim=1536, depth=40, num_heads=24, init_values=1e-5,
         mlp_ratio=2.66667 * 2, mlp_layer=SwiGLUPacked, img_size=518, act_layer=nn.SiLU
@@ -2224,6 +2250,62 @@ def vit_giant_patch14_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
     return model
 
 
+@register_model
+def vit_small_patch14_reg4_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
+    """ ViT-S/14 for DINOv2 w/ 4 registers
+    """
+    model_args = dict(
+        patch_size=14, embed_dim=384, depth=12, num_heads=6, init_values=1e-5,
+        reg_tokens=4, no_embed_class=True,
+    )
+    model = _create_vision_transformer(
+        'vit_small_patch14_reg4_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_base_patch14_reg4_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
+    """ ViT-B/14 for DINOv2 w/ 4 registers
+    """
+    model_args = dict(
+        patch_size=14, embed_dim=768, depth=12, num_heads=12, init_values=1e-5,
+        reg_tokens=4, no_embed_class=True,
+    )
+    model = _create_vision_transformer(
+        'vit_base_patch14_reg4_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_large_patch14_reg4_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
+    """ ViT-L/14 for DINOv2 w/ 4 registers
+    """
+    model_args = dict(
+        patch_size=14, embed_dim=1024, depth=24, num_heads=16, init_values=1e-5,
+        reg_tokens=4, no_embed_class=True,
+    )
+    model = _create_vision_transformer(
+        'vit_large_patch14_reg4_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
+@register_model
+def vit_giant_patch14_reg4_dinov2(pretrained=False, **kwargs) -> VisionTransformer:
+    """ ViT-G/14 for DINOv2
+    """
+    # The hidden_features of SwiGLU is calculated by:
+    # hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+    # When embed_dim=1536, hidden_features=4096
+    # With SwiGLUPacked, we need to set hidden_features = 2 * 4096 = 8192
+    model_args = dict(
+        patch_size=14, embed_dim=1536, depth=40, num_heads=24, init_values=1e-5, mlp_ratio=2.66667 * 2,
+        mlp_layer=SwiGLUPacked, act_layer=nn.SiLU, reg_tokens=4, no_embed_class=True,
+    )
+    model = _create_vision_transformer(
+        'vit_giant_patch14_reg4_dinov2', pretrained=pretrained, **dict(model_args, **kwargs))
+    return model
+
+
 @register_model
 def vit_base_patch16_siglip_224(pretrained=False, **kwargs) -> VisionTransformer:
     model_args = dict(