Adding pretrained ViT weights

references/classification/README.md

@@ -143,6 +143,60 @@ torchrun --nproc_per_node=8 train.py\
 ```
 Here `$MODEL` is one of `regnet_x_32gf`, `regnet_y_16gf` and `regnet_y_32gf`.
 
+### Vision Transformer
+
+#### vit_b_16
+```
+torchrun --nproc_per_node=8 train.py\
+    --model vit_b_16 --epochs 300 --batch-size 512 --opt adamw --lr 0.003 --wd 0.3\
+    --lr-scheduler cosineannealinglr --lr-warmup-method linear --lr-warmup-epochs 30\
+    --lr-warmup-decay 0.033 --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment ra\
+    --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema
+```
+
+Note that the above command corresponds to training on a single node with 8 GPUs.
+For generatring the pre-trained weights, we trained with 8 nodes, each with 8 GPUs (for a total of 64 GPUs),
+and `--batch_size 64`.
+
+#### vit_b_32
+```
+torchrun --nproc_per_node=8 train.py\
+    --model vit_b_32 --epochs 300 --batch-size 512 --opt adamw --lr 0.003 --wd 0.3\
+    --lr-scheduler cosineannealinglr --lr-warmup-method linear --lr-warmup-epochs 30\
+    --lr-warmup-decay 0.033 --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment imagenet\
+    --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema
+```
+
+Note that the above command corresponds to training on a single node with 8 GPUs.
+For generatring the pre-trained weights, we trained with 2 nodes, each with 8 GPUs (for a total of 16 GPUs),
+and `--batch_size 256`.
+
+#### vit_l_16
+```
+torchrun --nproc_per_node=8 train.py\
+    --model vit_l_16 --epochs 600 --batch-size 128 --lr 0.5 --lr-scheduler cosineannealinglr\
+    --lr-warmup-method linear --lr-warmup-epochs 5 --label-smoothing 0.1 --mixup-alpha 0.2\
+    --auto-augment ta_wide --random-erase 0.1 --weight-decay 0.00002 --norm-weight-decay 0.0\
+    --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema --val-resize-size 232
+```
+
+Note that the above command corresponds to training on a single node with 8 GPUs.
+For generatring the pre-trained weights, we trained with 2 nodes, each with 8 GPUs (for a total of 16 GPUs),
+and `--batch_size 64`.
+
+#### vit_l_32
+```
+torchrun --nproc_per_node=8 train.py\
+    --model vit_l_32 --epochs 300 --batch-size 512 --opt adamw --lr 0.003 --wd 0.3\
+    --lr-scheduler cosineannealinglr --lr-warmup-method linear --lr-warmup-epochs 30\
+    --lr-warmup-decay 0.033 --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment ra\
+    --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema
+```
+
+Note that the above command corresponds to training on a single node with 8 GPUs.
+For generatring the pre-trained weights, we trained with 8 nodes, each with 8 GPUs (for a total of 64 GPUs),
+and `--batch_size 64`.
+
 ## Mixed precision training
 Automatic Mixed Precision (AMP) training on GPU for Pytorch can be enabled with the [torch.cuda.amp](https://pytorch.org/docs/stable/amp.html?highlight=amp#module-torch.cuda.amp).
 

torchvision/prototype/models/vision_transformer.py

@@ -10,12 +10,14 @@
 import torch
 import torch.nn as nn
 from torch import Tensor
+from torchvision.prototype.transforms import ImageNetEval
+from torchvision.transforms.functional import InterpolationMode
 
 from ...utils import _log_api_usage_once
-from ._api import WeightsEnum
+from ._api import WeightsEnum, Weights
+from ._meta import _IMAGENET_CATEGORIES
 from ._utils import handle_legacy_interface
 
-
 __all__ = [
     "VisionTransformer",
     "ViT_B_16_Weights",
@@ -233,24 +235,70 @@ def forward(self, x: torch.Tensor):
         return x
 
 
+_COMMON_META = {
+    "categories": _IMAGENET_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+}
+
+
 class ViT_B_16_Weights(WeightsEnum):
-    # If a default model is added here the corresponding changes need to be done in vit_b_16
-    pass
+    ImageNet1K_V1 = Weights(
+        url="https://download.pytorch.org/models/vit_b_16-c867db91.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_COMMON_META,
+            "size": (224, 224),
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_16",
+            "acc@1": 81.072,
+            "acc@5": 95.318,
+        },
+    )
+    default = ImageNet1K_V1
 
 
 class ViT_B_32_Weights(WeightsEnum):
-    # If a default model is added here the corresponding changes need to be done in vit_b_32
-    pass
+    ImageNet1K_V1 = Weights(
+        url="https://download.pytorch.org/models/vit_b_32-d86f8d99.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_COMMON_META,
+            "size": (224, 224),
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_b_32",
+            "acc@1": 75.912,
+            "acc@5": 92.466,
+        },
+    )
+    default = ImageNet1K_V1
 
 
 class ViT_L_16_Weights(WeightsEnum):
-    # If a default model is added here the corresponding changes need to be done in vit_l_16
-    pass
+    ImageNet1K_V1 = Weights(
+        url="https://download.pytorch.org/models/vit_l_16-852ce7e3.pth",
+        transforms=partial(ImageNetEval, crop_size=224, resize_size=242),
+        meta={
+            **_COMMON_META,
+            "size": (224, 224),
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_16",
+            "acc@1": 79.662,
+            "acc@5": 94.638,
+        },
+    )
+    default = ImageNet1K_V1
 
 
 class ViT_L_32_Weights(WeightsEnum):
-    # If a default model is added here the corresponding changes need to be done in vit_l_32
-    pass
+    ImageNet1K_V1 = Weights(
+        url="https://download.pytorch.org/models/vit_l_32-c7638314.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_COMMON_META,
+            "size": (224, 224),
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#vit_l_32",
+            "acc@1": 76.972,
+            "acc@5": 93.07,
+        },
+    )
+    default = ImageNet1K_V1
 
 
 def _vision_transformer(
@@ -281,7 +329,7 @@ def _vision_transformer(
     return model
 
 
-@handle_legacy_interface(weights=("pretrained", None))
+@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:
     """
     Constructs a vit_b_16 architecture from
@@ -306,7 +354,7 @@ def vit_b_16(*, weights: Optional[ViT_B_16_Weights] = None, progress: bool = Tru
     )
 
 
-@handle_legacy_interface(weights=("pretrained", None))
+@handle_legacy_interface(weights=("pretrained", ViT_B_32_Weights.ImageNet1K_V1))
 def vit_b_32(*, weights: Optional[ViT_B_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     """
     Constructs a vit_b_32 architecture from
@@ -331,7 +379,7 @@ def vit_b_32(*, weights: Optional[ViT_B_32_Weights] = None, progress: bool = Tru
     )
 
 
-@handle_legacy_interface(weights=("pretrained", None))
+@handle_legacy_interface(weights=("pretrained", ViT_L_16_Weights.ImageNet1K_V1))
 def vit_l_16(*, weights: Optional[ViT_L_16_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     """
     Constructs a vit_l_16 architecture from
@@ -356,7 +404,7 @@ def vit_l_16(*, weights: Optional[ViT_L_16_Weights] = None, progress: bool = Tru
     )
 
 
-@handle_legacy_interface(weights=("pretrained", None))
+@handle_legacy_interface(weights=("pretrained", ViT_L_32_Weights.ImageNet1K_V1))
 def vit_l_32(*, weights: Optional[ViT_L_32_Weights] = None, progress: bool = True, **kwargs: Any) -> VisionTransformer:
     """
     Constructs a vit_l_32 architecture from