[ViT] Graduate ViT from prototype (#5173)

* graduate vit from prototype

* nit

* add vit to docs and hubconf

* ufmt

* re-correct ufmt

* again

* fix linter

docs/source/models.rst

@@ -40,6 +40,7 @@ architectures for image classification:
 -  `MNASNet`_
 -  `EfficientNet`_
 -  `RegNet`_
+-  `VisionTransformer`_
 
 You can construct a model with random weights by calling its constructor:
 
@@ -82,6 +83,10 @@ You can construct a model with random weights by calling its constructor:
     regnet_x_8gf = models.regnet_x_8gf()
     regnet_x_16gf = models.regnet_x_16gf()
     regnet_x_32gf = models.regnet_x_32gf()
+    vit_b_16 = models.vit_b_16()
+    vit_b_32 = models.vit_b_32()
+    vit_l_16 = models.vit_l_16()
+    vit_l_32 = models.vit_l_32()
 
 We provide pre-trained models, using the PyTorch :mod:`torch.utils.model_zoo`.
 These can be constructed by passing ``pretrained=True``:
@@ -125,6 +130,10 @@ These can be constructed by passing ``pretrained=True``:
     regnet_x_8gf = models.regnet_x_8gf(pretrained=True)
     regnet_x_16gf = models.regnet_x_16gf(pretrainedTrue)
     regnet_x_32gf = models.regnet_x_32gf(pretrained=True)
+    vit_b_16 = models.vit_b_16(pretrained=True)
+    vit_b_32 = models.vit_b_32(pretrained=True)
+    vit_l_16 = models.vit_l_16(pretrained=True)
+    vit_l_32 = models.vit_l_32(pretrained=True)
 
 Instancing a pre-trained model will download its weights to a cache directory.
 This directory can be set using the `TORCH_HOME` environment variable. See
@@ -233,6 +242,10 @@ regnet_y_3_2gf                    78.948          94.576
 regnet_y_8gf                      80.032          95.048
 regnet_y_16gf                     80.424          95.240
 regnet_y_32gf                     80.878          95.340
+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
 ================================  =============   =============
 
 
@@ -250,6 +263,7 @@ regnet_y_32gf                     80.878          95.340
 .. _MNASNet: https://arxiv.org/abs/1807.11626
 .. _EfficientNet: https://arxiv.org/abs/1905.11946
 .. _RegNet: https://arxiv.org/abs/2003.13678
+.. _VisionTransformer: https://arxiv.org/abs/2010.11929
 
 .. currentmodule:: torchvision.models
 
@@ -433,6 +447,18 @@ RegNet
     regnet_x_16gf
     regnet_x_32gf
 
+VisionTransformer
+-----------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    vit_b_16
+    vit_b_32
+    vit_l_16
+    vit_l_32
+
 Quantized Models
 ----------------
 

hubconf.py

@@ -1,7 +1,6 @@
 # Optional list of dependencies required by the package
 dependencies = ["torch"]
 
-# classification
 from torchvision.models.alexnet import alexnet
 from torchvision.models.densenet import densenet121, densenet169, densenet201, densenet161
 from torchvision.models.efficientnet import (
@@ -47,8 +46,6 @@
     wide_resnet50_2,
     wide_resnet101_2,
 )
-
-# segmentation
 from torchvision.models.segmentation import (
     fcn_resnet50,
     fcn_resnet101,
@@ -60,3 +57,9 @@
 from torchvision.models.shufflenetv2 import shufflenet_v2_x0_5, shufflenet_v2_x1_0
 from torchvision.models.squeezenet import squeezenet1_0, squeezenet1_1
 from torchvision.models.vgg import vgg11, vgg13, vgg16, vgg19, vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn
+from torchvision.models.vision_transformer import (
+    vit_b_16,
+    vit_b_32,
+    vit_l_16,
+    vit_l_32,
+)

torchvision/models/__init__.py

@@ -10,6 +10,7 @@
 from .shufflenetv2 import *
 from .efficientnet import *
 from .regnet import *
+from .vision_transformer import *
 from . import detection
 from . import feature_extraction
 from . import optical_flow