Merge pull request #714 from rwightman/vit_and_bit_test_fixes

Fix a few issues loading pretrained vit/bit npz weights...

Author: rwightman

README.md

@@ -23,6 +23,9 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 
 ## What's New
 
+### June 23, 2021
+* Reproduce gMLP model training, `gmlp_s16_224` trained to 79.6 top-1, matching [paper](https://arxiv.org/abs/2105.08050).
+
 ### June 20, 2021
 * Release Vision Transformer 'AugReg' weights from [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://arxiv.org/abs/2106.10270)
   * .npz weight loading support added, can load any of the 50K+ weights from the [AugReg series](https://console.cloud.google.com/storage/browser/vit_models/augreg)

tests/test_models.py

@@ -147,6 +147,15 @@ def test_model_default_cfgs(model_name, batch_size):
             # FIXME mobilenetv3/ghostnet forward_features vs removed pooling differ
             assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
 
+        if 'pruned' not in model_name:  # FIXME better pruned model handling
+            # test classifier + global pool deletion via __init__
+            model = create_model(model_name, pretrained=False, num_classes=0, global_pool='').eval()
+            outputs = model.forward(input_tensor)
+            assert len(outputs.shape) == 4
+            if not isinstance(model, timm.models.MobileNetV3) and not isinstance(model, timm.models.GhostNet):
+                # FIXME mobilenetv3/ghostnet forward_features vs removed pooling differ
+                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)):
@@ -193,6 +202,13 @@ def test_model_default_cfgs_non_std(model_name, batch_size):
     assert len(outputs.shape) == 2
     assert outputs.shape[1] == model.num_features
 
+    model = create_model(model_name, pretrained=False, num_classes=0).eval()
+    outputs = model.forward(input_tensor)
+    if isinstance(outputs, tuple):
+        outputs = outputs[0]
+    assert len(outputs.shape) == 2
+    assert outputs.shape[1] == model.num_features
+
     # check classifier name matches default_cfg
     classifier = cfg['classifier']
     if not isinstance(classifier, (tuple, list)):
@@ -217,6 +233,7 @@ def test_model_load_pretrained(model_name, batch_size):
         """Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
         in_chans = 3 if 'pruned' in model_name else 1  # pruning not currently supported with in_chans change
         create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
+        create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=0)
 
     @pytest.mark.timeout(120)
     @pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))

timm/models/ghostnet.py

@@ -182,7 +182,7 @@ def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2, in_chans=3, o
         self.conv_head = nn.Conv2d(prev_chs, out_chs, 1, 1, 0, bias=True)
         self.act2 = nn.ReLU(inplace=True)
         self.flatten = nn.Flatten(1) if global_pool else nn.Identity()  # don't flatten if pooling disabled
-        self.classifier = Linear(out_chs, num_classes)
+        self.classifier = Linear(out_chs, num_classes) if num_classes > 0 else nn.Identity()
 
     def get_classifier(self):
         return self.classifier

timm/models/levit.py

@@ -542,7 +542,7 @@ def checkpoint_filter_fn(state_dict, model):
         state_dict = state_dict['model']
     D = model.state_dict()
     for k in state_dict.keys():
-        if D[k].ndim == 4 and state_dict[k].ndim == 2:
+        if k in D and D[k].ndim == 4 and state_dict[k].ndim == 2:
             state_dict[k] = state_dict[k][:, :, None, None]
     return state_dict
 

timm/models/mlp_mixer.py

@@ -129,7 +129,9 @@ def _cfg(url='', **kwargs):
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
 
     gmlp_ti16_224=_cfg(),
-    gmlp_s16_224=_cfg(),
+    gmlp_s16_224=_cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmlp_s16_224_raa-10536d42.pth',
+    ),
     gmlp_b16_224=_cfg(),
 )
 
@@ -266,7 +268,7 @@ def __init__(
                 act_layer=act_layer, drop=drop_rate, drop_path=drop_path_rate)
             for _ in range(num_blocks)])
         self.norm = norm_layer(embed_dim)
-        self.head = nn.Linear(embed_dim, self.num_classes)  # zero init
+        self.head = nn.Linear(embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
 
         self.init_weights(nlhb=nlhb)
 

timm/models/resnetv2.py

@@ -424,7 +424,8 @@ 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 model.head.fc.weight.shape[0] == weights[f'{prefix}head/conv2d/kernel'].shape[-1]:
+    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']))
     for i, (sname, stage) in enumerate(model.stages.named_children()):

timm/models/visformer.py

@@ -237,7 +237,6 @@ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, in
         self.num_features = embed_dim if self.vit_stem else embed_dim * 2
         self.norm = norm_layer(self.num_features)
         self.global_pool, self.head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
-        self.head = nn.Linear(self.num_features, num_classes)
 
         # weights init
         if self.pos_embed:

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
 
@@ -448,9 +448,12 @@ def _n2p(w, t=True):
     model.pos_embed.copy_(pos_embed_w)
     model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
     model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
-    if model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
+    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)