Add Dino pretrained weights (no head) for vit models. Add support to tests and helpers for models w/ no classifier (num_classes=0 in pretrained cfg)

tests/test_models.py

@@ -170,11 +170,12 @@ def test_model_default_cfgs(model_name, batch_size):
                 assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
 
     # check classifier name matches default_cfg
-    classifier = cfg['classifier']
-    if not isinstance(classifier, (tuple, list)):
-        classifier = classifier,
-    for c in classifier:
-        assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
+    if cfg.get('num_classes', None):
+        classifier = cfg['classifier']
+        if not isinstance(classifier, (tuple, list)):
+            classifier = classifier,
+        for c in classifier:
+            assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
 
     # check first conv(s) names match default_cfg
     first_conv = cfg['first_conv']
@@ -222,11 +223,12 @@ def test_model_default_cfgs_non_std(model_name, batch_size):
     assert outputs.shape[1] == model.num_features
 
     # check classifier name matches default_cfg
-    classifier = cfg['classifier']
-    if not isinstance(classifier, (tuple, list)):
-        classifier = classifier,
-    for c in classifier:
-        assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
+    if cfg.get('num_classes', None):
+        classifier = cfg['classifier']
+        if not isinstance(classifier, (tuple, list)):
+            classifier = classifier,
+        for c in classifier:
+            assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
 
     # check first conv(s) names match default_cfg
     first_conv = cfg['first_conv']

timm/models/helpers.py

@@ -221,8 +221,8 @@ def load_pretrained(model, default_cfg=None, num_classes=1000, in_chans=3, filte
         if num_classes != default_cfg['num_classes']:
             for classifier_name in classifiers:
                 # completely discard fully connected if model num_classes doesn't match pretrained weights
-                del state_dict[classifier_name + '.weight']
-                del state_dict[classifier_name + '.bias']
+                state_dict.pop(classifier_name + '.weight', None)
+                state_dict.pop(classifier_name + '.bias', None)
             strict = False
         elif label_offset > 0:
             for classifier_name in classifiers:

timm/models/vision_transformer.py

@@ -140,11 +140,25 @@ default_cfgs = {
         num_classes=21843),
 
     # SAM trained models (https://arxiv.org/abs/2106.01548)
-    'vit_base_patch32_sam_224': _cfg(
+    'vit_base_patch32_224_sam': _cfg(
         url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz'),
-    'vit_base_patch16_sam_224': _cfg(
+    'vit_base_patch16_224_sam': _cfg(
         url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz'),
 
+    # DINO pretrained - https://arxiv.org/abs/2104.14294 (no classifier head, for fine-tune only)
+    'vit_small_patch16_224_dino': _cfg(
+        url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
+    'vit_small_patch8_224_dino': _cfg(
+        url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
+    'vit_base_patch16_224_dino': _cfg(
+        url='https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
+    'vit_base_patch8_224_dino': _cfg(
+        url='https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
+
     # deit models (FB weights)
     'deit_tiny_patch16_224': _cfg(
         url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth',
@@ -699,26 +713,6 @@ def vit_large_patch16_384(pretrained=False, **kwargs):
     return model
 
 
-@register_model
-def vit_base_patch16_sam_224(pretrained=False, **kwargs):
-    """ ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
-    """
-    # NOTE original SAM weights release worked with representation_size=768
-    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=0, **kwargs)
-    model = _create_vision_transformer('vit_base_patch16_sam_224', pretrained=pretrained, **model_kwargs)
-    return model
-
-
-@register_model
-def vit_base_patch32_sam_224(pretrained=False, **kwargs):
-    """ ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
-    """
-    # NOTE original SAM weights release worked with representation_size=768
-    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=0, **kwargs)
-    model = _create_vision_transformer('vit_base_patch32_sam_224', pretrained=pretrained, **model_kwargs)
-    return model
-
-
 @register_model
 def vit_huge_patch14_224(pretrained=False, **kwargs):
     """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
@@ -851,6 +845,62 @@ def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_base_patch16_224_sam(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
+    """
+    # NOTE original SAM weights release worked with representation_size=768
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model = _create_vision_transformer('vit_base_patch16_224_sam', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_patch32_224_sam(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
+    """
+    # NOTE original SAM weights release worked with representation_size=768
+    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model = _create_vision_transformer('vit_base_patch32_224_sam', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_patch16_224_dino(pretrained=False, **kwargs):
+    """ ViT-Small (ViT-S/16) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
+    """
+    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model = _create_vision_transformer('vit_small_patch16_224_dino', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_small_patch8_224_dino(pretrained=False, **kwargs):
+    """ ViT-Small (ViT-S/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
+    """
+    model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
+    model = _create_vision_transformer('vit_small_patch8_224_dino', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_patch16_224_dino(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/16) /w DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
+    """
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model = _create_vision_transformer('vit_base_patch16_224_dino', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_patch8_224_dino(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/8) w/ DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294
+    """
+    model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model = _create_vision_transformer('vit_base_patch8_224_dino', pretrained=pretrained, **model_kwargs)
+    return model
+
+
 @register_model
 def deit_tiny_patch16_224(pretrained=False, **kwargs):
     """ DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).