Add Medium and Large weights

Author: datumbox

references/classification/README.md

@@ -124,12 +124,14 @@ torchrun --nproc_per_node=8 train.py \
 --train-crop-size $TRAIN_SIZE --model-ema --val-crop-size $EVAL_SIZE --val-resize-size $EVAL_SIZE \
 --ra-sampler --ra-reps 4
 ```
-Here `$MODEL` is one of `efficientnet_v2_s`, `efficientnet_v2_m` and `efficientnet_v2_l`. 
-Note that the Small variant had a `$TRAIN_SIZE` of `300` and a `$EVAL_SIZE` of `384`, while the other variants `384` and `480` respectively.
+Here `$MODEL` is one of `efficientnet_v2_s` and `efficientnet_v2_m`. 
+Note that the Small variant had a `$TRAIN_SIZE` of `300` and a `$EVAL_SIZE` of `384`, while the Medium `384` and `480` respectively.
 
 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 16`.
+For generatring the pre-trained weights, we trained with 4 nodes, each with 8 GPUs (for a total of 32 GPUs),
+and `--batch_size 32`.
+
+The weights of the Large variant are ported from the original paper rather than trained from scratch. See the `EfficientNet_V2_L_Weights` entry for their exact preprocessing transforms.
 
 
 ### RegNet

torchvision/models/efficientnet.py

@@ -44,6 +44,9 @@
     "efficientnet_b7": "https://download.pytorch.org/models/efficientnet_b7_lukemelas-dcc49843.pth",
     # Weights trained with TorchVision
     "efficientnet_v2_s": "https://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pth",
+    "efficientnet_v2_m": "https://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pth",
+    # Weights ported from TF
+    "efficientnet_v2_l": "https://download.pytorch.org/models/efficientnet_v2_l-59c71312.pth",
 }
 
 

torchvision/prototype/models/efficientnet.py

@@ -236,11 +236,45 @@ class EfficientNet_V2_S_Weights(WeightsEnum):
 
 
 class EfficientNet_V2_M_Weights(WeightsEnum):
-    pass
+    IMAGENET1K_V1 = Weights(
+        url="https://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pth",
+        transforms=partial(
+            ImageNetEval,
+            crop_size=480,
+            resize_size=480,
+            interpolation=InterpolationMode.BILINEAR,
+        ),
+        meta={
+            **_COMMON_META_V2,
+            "num_params": 54139356,
+            "size": (480, 480),
+            "acc@1": 85.119,
+            "acc@5": 97.151,
+        },
+    )
+    DEFAULT = IMAGENET1K_V1
 
 
 class EfficientNet_V2_L_Weights(WeightsEnum):
-    pass
+    IMAGENET1K_V1 = Weights(
+        url="https://download.pytorch.org/models/efficientnet_v2_l-59c71312.pth",
+        transforms=partial(
+            ImageNetEval,
+            crop_size=480,
+            resize_size=480,
+            interpolation=InterpolationMode.BICUBIC,
+            mean=(0.5, 0.5, 0.5),
+            std=(0.5, 0.5, 0.5),
+        ),
+        meta={
+            **_COMMON_META_V2,
+            "num_params": 118515272,
+            "size": (480, 480),
+            "acc@1": 85.808,
+            "acc@5": 97.788,
+        },
+    )
+    DEFAULT = IMAGENET1K_V1
 
 
 @handle_legacy_interface(weights=("pretrained", EfficientNet_B0_Weights.IMAGENET1K_V1))
@@ -365,7 +399,7 @@ def efficientnet_v2_s(
     )
 
 
-@handle_legacy_interface(weights=("pretrained", None))
+@handle_legacy_interface(weights=("pretrained", EfficientNet_V2_M_Weights.IMAGENET1K_V1))
 def efficientnet_v2_m(
     *, weights: Optional[EfficientNet_V2_M_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet:
@@ -383,7 +417,7 @@ def efficientnet_v2_m(
     )
 
 
-@handle_legacy_interface(weights=("pretrained", None))
+@handle_legacy_interface(weights=("pretrained", EfficientNet_V2_L_Weights.IMAGENET1K_V1))
 def efficientnet_v2_l(
     *, weights: Optional[EfficientNet_V2_L_Weights] = None, progress: bool = True, **kwargs: Any
 ) -> EfficientNet: