Adding the huge vision transformer from SWAG (#5721)

* Add vit_b_16_swag

* Better handling idiom for image_size, edit test_extended_model to handle case where number of param differ from default due to different image size input

* Update the accuracy to the experiment result on torchvision model

* Fix typo missing underscore

* raise exception instead of torch._assert, add back publication year (accidentally deleted)

* Add license information on meta and readme

* Improve wording and fix typo for pretrained model license in readme

* Add vit_l_16 weight

* Update README.rst
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

* Update the accuracy meta on vit_l_16_swag model to result from our experiment

* Add vit_h_14_swag model

* Add accuracy from experiments

* Add to vit_h_16 model to hubconf.py

* Add docs and expected pkl file for test

* Remove legacy compatibility for ViT_H_14 model

Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply....

docs/source/models.rst

@@ -92,6 +92,7 @@ You can construct a model with random weights by calling its constructor:
     vit_b_32 = models.vit_b_32()
     vit_l_16 = models.vit_l_16()
     vit_l_32 = models.vit_l_32()
+    vit_h_14 = models.vit_h_14()
     convnext_tiny = models.convnext_tiny()
     convnext_small = models.convnext_small()
     convnext_base = models.convnext_base()
@@ -213,6 +214,7 @@ vit_b_16                          81.072          95.318
 vit_b_32                          75.912          92.466
 vit_l_16                          79.662          94.638
 vit_l_32                          76.972          93.070
+vit_h_14                          88.552          98.694 
 convnext_tiny                     82.520          96.146
 convnext_small                    83.616          96.650
 convnext_base                     84.062          96.870
@@ -434,6 +436,7 @@ VisionTransformer
     vit_b_32
     vit_l_16
     vit_l_32
+    vit_h_14
 
 ConvNeXt
 --------

hubconf.py

@@ -67,4 +67,5 @@ from torchvision.models.vision_transformer import (
     vit_b_32,
     vit_l_16,
     vit_l_32,
+    vit_h_14,
 )

test/test_models.py

@@ -280,6 +280,10 @@ _model_params = {
         "rpn_pre_nms_top_n_test": 1000,
         "rpn_post_nms_top_n_test": 1000,
     },
+    "vit_h_14": {
+        "image_size": 56,
+        "input_shape": (1, 3, 56, 56),
+    },
 }
 # speeding up slow models:
 slow_models = [

torchvision/models/vision_transformer.py

@@ -20,10 +20,12 @@ __all__ = [
     "ViT_B_32_Weights",
     "ViT_L_16_Weights",
     "ViT_L_32_Weights",
+    "ViT_H_14_Weights",
     "vit_b_16",
     "vit_b_32",
     "vit_l_16",
     "vit_l_32",
+    "vit_h_14",
 ]
 
 
@@ -435,6 +437,27 @@ class ViT_L_32_Weights(WeightsEnum):
     DEFAULT = IMAGENET1K_V1
 
 
+class ViT_H_14_Weights(WeightsEnum):
+    IMAGENET1K_SWAG_V1 = Weights(
+        url="https://download.pytorch.org/models/vit_h_14_swag-80465313.pth",
+        transforms=partial(
+            ImageClassification,
+            crop_size=518,
+            resize_size=518,
+            interpolation=InterpolationMode.BICUBIC,
+        ),
+        meta={
+            **_COMMON_SWAG_META,
+            "num_params": 633470440,
+            "size": (518, 518),
+            "min_size": (518, 518),
+            "acc@1": 88.552,
+            "acc@5": 98.694,
+        },
+    )
+    DEFAULT = IMAGENET1K_SWAG_V1
+
+
 @handle_legacy_interface(weights=("pretrained", ViT_B_16_Weights.IMAGENET1K_V1))
 def vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     """
@@ -531,6 +554,29 @@ def vit_l_32(*, weights: Optional[ViT_L_32_Weights] = None, progress: bool = Tru
     )
 
 
+def vit_h_14(*, weights: Optional[ViT_H_14_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
+    """
+    Constructs a vit_h_14 architecture from
+    `"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" <https://arxiv.org/abs/2010.11929>`_.
+
+    Args:
+        weights (ViT_H_14_Weights, optional): The pretrained weights for the model
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    weights = ViT_H_14_Weights.verify(weights)
+
+    return _vision_transformer(
+        patch_size=14,
+        num_layers=32,
+        num_heads=16,
+        hidden_dim=1280,
+        mlp_dim=5120,
+        weights=weights,
+        progress=progress,
+        **kwargs,
+    )
+
+
 def interpolate_embeddings(
     image_size: int,
     patch_size: int,