Refactor preset transforms (#5562)

* Refactor preset transforms

* Making presets public.

references/classification/train.py

@@ -163,7 +163,7 @@ def load_data(traindir, valdir, args):
                 weights = prototype.models.get_weight(args.weights)
                 preprocessing = weights.transforms()
             else:
-                preprocessing = prototype.transforms.ImageNetEval(
+                preprocessing = prototype.transforms.ImageClassificationEval(
                     crop_size=val_crop_size, resize_size=val_resize_size, interpolation=interpolation
                 )
 

references/detection/train.py

@@ -57,7 +57,7 @@ def get_transform(train, args):
             weights = prototype.models.get_weight(args.weights)
             return weights.transforms()
         else:
-            return prototype.transforms.CocoEval()
+            return prototype.transforms.ObjectDetectionEval()
 
 
 def get_args_parser(add_help=True):

references/optical_flow/train.py

@@ -137,7 +137,7 @@ def validate(model, args):
             weights = prototype.models.get_weight(args.weights)
             preprocessing = weights.transforms()
         else:
-            preprocessing = prototype.transforms.RaftEval()
+            preprocessing = prototype.transforms.OpticalFlowEval()
     else:
         preprocessing = OpticalFlowPresetEval()
 

references/segmentation/train.py

@@ -42,7 +42,7 @@ def get_transform(train, args):
             weights = prototype.models.get_weight(args.weights)
             return weights.transforms()
         else:
-            return prototype.transforms.VocEval(resize_size=520)
+            return prototype.transforms.SemanticSegmentationEval(resize_size=520)
 
 
 def criterion(inputs, target):

references/video_classification/train.py

@@ -157,7 +157,7 @@ def main(args):
             weights = prototype.models.get_weight(args.weights)
             transform_test = weights.transforms()
         else:
-            transform_test = prototype.transforms.Kinect400Eval(crop_size=(112, 112), resize_size=(128, 171))
+            transform_test = prototype.transforms.VideoClassificationEval(crop_size=(112, 112), resize_size=(128, 171))
 
     if args.cache_dataset and os.path.exists(cache_path):
         print(f"Loading dataset_test from {cache_path}")

torchvision/prototype/models/alexnet.py

 from functools import partial
 from typing import Any, Optional
 
-from torchvision.prototype.transforms import ImageNetEval
+from torchvision.prototype.transforms import ImageClassificationEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ...models.alexnet import AlexNet
@@ -16,7 +16,7 @@ __all__ = ["AlexNet", "AlexNet_Weights", "alexnet"]
 class AlexNet_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/alexnet-owt-7be5be79.pth",
-        transforms=partial(ImageNetEval, crop_size=224),
+        transforms=partial(ImageClassificationEval, crop_size=224),
         meta={
             "task": "image_classification",
             "architecture": "AlexNet",

torchvision/prototype/models/convnext.py

 from functools import partial
 from typing import Any, List, Optional
 
-from torchvision.prototype.transforms import ImageNetEval
+from torchvision.prototype.transforms import ImageClassificationEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ...models.convnext import ConvNeXt, CNBlockConfig
@@ -56,7 +56,7 @@ _COMMON_META = {
 class ConvNeXt_Tiny_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/convnext_tiny-983f1562.pth",
-        transforms=partial(ImageNetEval, crop_size=224, resize_size=236),
+        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=236),
         meta={
             **_COMMON_META,
             "num_params": 28589128,
@@ -70,7 +70,7 @@ class ConvNeXt_Tiny_Weights(WeightsEnum):
 class ConvNeXt_Small_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/convnext_small-0c510722.pth",
-        transforms=partial(ImageNetEval, crop_size=224, resize_size=230),
+        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=230),
         meta={
             **_COMMON_META,
             "num_params": 50223688,
@@ -84,7 +84,7 @@ class ConvNeXt_Small_Weights(WeightsEnum):
 class ConvNeXt_Base_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/convnext_base-6075fbad.pth",
-        transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 88591464,
@@ -98,7 +98,7 @@ class ConvNeXt_Base_Weights(WeightsEnum):
 class ConvNeXt_Large_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/convnext_large-ea097f82.pth",
-        transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 197767336,

torchvision/prototype/models/densenet.py

@@ -3,7 +3,7 @@ from functools import partial
 from typing import Any, Optional, Tuple
 
 import torch.nn as nn
-from torchvision.prototype.transforms import ImageNetEval
+from torchvision.prototype.transforms import ImageClassificationEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ...models.densenet import DenseNet
@@ -78,7 +78,7 @@ _COMMON_META = {
 class DenseNet121_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
-        transforms=partial(ImageNetEval, crop_size=224),
+        transforms=partial(ImageClassificationEval, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 7978856,
@@ -92,7 +92,7 @@ class DenseNet121_Weights(WeightsEnum):
 class DenseNet161_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
-        transforms=partial(ImageNetEval, crop_size=224),
+        transforms=partial(ImageClassificationEval, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 28681000,
@@ -106,7 +106,7 @@ class DenseNet161_Weights(WeightsEnum):
 class DenseNet169_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
-        transforms=partial(ImageNetEval, crop_size=224),
+        transforms=partial(ImageClassificationEval, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 14149480,
@@ -120,7 +120,7 @@ class DenseNet169_Weights(WeightsEnum):
 class DenseNet201_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet201-c1103571.pth",
-        transforms=partial(ImageNetEval, crop_size=224),
+        transforms=partial(ImageClassificationEval, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 20013928,

torchvision/prototype/models/detection/faster_rcnn.py

 from typing import Any, Optional, Union
 
 from torch import nn
-from torchvision.prototype.transforms import CocoEval
+from torchvision.prototype.transforms import ObjectDetectionEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ....models.detection.faster_rcnn import (
@@ -43,7 +43,7 @@ _COMMON_META = {
 class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             **_COMMON_META,
             "num_params": 41755286,
@@ -57,7 +57,7 @@ class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
 class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             **_COMMON_META,
             "num_params": 19386354,
@@ -71,7 +71,7 @@ class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
 class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             **_COMMON_META,
             "num_params": 19386354,

torchvision/prototype/models/detection/fcos.py

 from typing import Any, Optional
 
 from torch import nn
-from torchvision.prototype.transforms import CocoEval
+from torchvision.prototype.transforms import ObjectDetectionEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ....models.detection.fcos import (
@@ -27,7 +27,7 @@ __all__ = [
 class FCOS_ResNet50_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fcos_resnet50_fpn_coco-99b0c9b7.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             "task": "image_object_detection",
             "architecture": "FCOS",

torchvision/prototype/models/detection/keypoint_rcnn.py

 from typing import Any, Optional
 
 from torch import nn
-from torchvision.prototype.transforms import CocoEval
+from torchvision.prototype.transforms import ObjectDetectionEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ....models.detection.keypoint_rcnn import (
@@ -37,7 +37,7 @@ _COMMON_META = {
 class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
     COCO_LEGACY = Weights(
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-9f466800.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             **_COMMON_META,
             "num_params": 59137258,
@@ -48,7 +48,7 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
     )
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             **_COMMON_META,
             "num_params": 59137258,

torchvision/prototype/models/detection/mask_rcnn.py

 from typing import Any, Optional
 
 from torch import nn
-from torchvision.prototype.transforms import CocoEval
+from torchvision.prototype.transforms import ObjectDetectionEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ....models.detection.mask_rcnn import (
@@ -27,7 +27,7 @@ __all__ = [
 class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             "task": "image_object_detection",
             "architecture": "MaskRCNN",

torchvision/prototype/models/detection/retinanet.py

 from typing import Any, Optional
 
 from torch import nn
-from torchvision.prototype.transforms import CocoEval
+from torchvision.prototype.transforms import ObjectDetectionEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ....models.detection.retinanet import (
@@ -28,7 +28,7 @@ __all__ = [
 class RetinaNet_ResNet50_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/retinanet_resnet50_fpn_coco-eeacb38b.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             "task": "image_object_detection",
             "architecture": "RetinaNet",

torchvision/prototype/models/detection/ssd.py

 import warnings
 from typing import Any, Optional
 
-from torchvision.prototype.transforms import CocoEval
+from torchvision.prototype.transforms import ObjectDetectionEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ....models.detection.ssd import (
@@ -25,7 +25,7 @@ __all__ = [
 class SSD300_VGG16_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/ssd300_vgg16_coco-b556d3b4.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             "task": "image_object_detection",
             "architecture": "SSD",

torchvision/prototype/models/detection/ssdlite.py

@@ -3,7 +3,7 @@ from functools import partial
 from typing import Any, Callable, Optional
 
 from torch import nn
-from torchvision.prototype.transforms import CocoEval
+from torchvision.prototype.transforms import ObjectDetectionEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ....models.detection.ssdlite import (
@@ -30,7 +30,7 @@ __all__ = [
 class SSDLite320_MobileNet_V3_Large_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/ssdlite320_mobilenet_v3_large_coco-a79551df.pth",
-        transforms=CocoEval,
+        transforms=ObjectDetectionEval,
         meta={
             "task": "image_object_detection",
             "architecture": "SSDLite",

torchvision/prototype/models/efficientnet.py

@@ -2,7 +2,7 @@ from functools import partial
 from typing import Any, Optional, Sequence, Union
 
 from torch import nn
-from torchvision.prototype.transforms import ImageNetEval
+from torchvision.prototype.transforms import ImageClassificationEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ...models.efficientnet import EfficientNet, MBConvConfig, FusedMBConvConfig, _efficientnet_conf
@@ -85,7 +85,9 @@ _COMMON_META_V2 = {
 class EfficientNet_B0_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth",
-        transforms=partial(ImageNetEval, crop_size=224, resize_size=256, interpolation=InterpolationMode.BICUBIC),
+        transforms=partial(
+            ImageClassificationEval, crop_size=224, resize_size=256, interpolation=InterpolationMode.BICUBIC
+        ),
         meta={
             **_COMMON_META_V1,
             "num_params": 5288548,
@@ -100,7 +102,9 @@ class EfficientNet_B0_Weights(WeightsEnum):
 class EfficientNet_B1_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b1_rwightman-533bc792.pth",
-        transforms=partial(ImageNetEval, crop_size=240, resize_size=256, interpolation=InterpolationMode.BICUBIC),
+        transforms=partial(
+            ImageClassificationEval, crop_size=240, resize_size=256, interpolation=InterpolationMode.BICUBIC
+        ),
         meta={
             **_COMMON_META_V1,
             "num_params": 7794184,
@@ -111,7 +115,9 @@ class EfficientNet_B1_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b1-c27df63c.pth",
-        transforms=partial(ImageNetEval, crop_size=240, resize_size=255, interpolation=InterpolationMode.BILINEAR),
+        transforms=partial(
+            ImageClassificationEval, crop_size=240, resize_size=255, interpolation=InterpolationMode.BILINEAR
+        ),
         meta={
             **_COMMON_META_V1,
             "num_params": 7794184,
@@ -128,7 +134,9 @@ class EfficientNet_B1_Weights(WeightsEnum):
 class EfficientNet_B2_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth",
-        transforms=partial(ImageNetEval, crop_size=288, resize_size=288, interpolation=InterpolationMode.BICUBIC),
+        transforms=partial(
+            ImageClassificationEval, crop_size=288, resize_size=288, interpolation=InterpolationMode.BICUBIC
+        ),
         meta={
             **_COMMON_META_V1,
             "num_params": 9109994,
@@ -143,7 +151,9 @@ class EfficientNet_B2_Weights(WeightsEnum):
 class EfficientNet_B3_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b3_rwightman-cf984f9c.pth",
-        transforms=partial(ImageNetEval, crop_size=300, resize_size=320, interpolation=InterpolationMode.BICUBIC),
+        transforms=partial(
+            ImageClassificationEval, crop_size=300, resize_size=320, interpolation=InterpolationMode.BICUBIC
+        ),
         meta={
             **_COMMON_META_V1,
             "num_params": 12233232,
@@ -158,7 +168,9 @@ class EfficientNet_B3_Weights(WeightsEnum):
 class EfficientNet_B4_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b4_rwightman-7eb33cd5.pth",
-        transforms=partial(ImageNetEval, crop_size=380, resize_size=384, interpolation=InterpolationMode.BICUBIC),
+        transforms=partial(
+            ImageClassificationEval, crop_size=380, resize_size=384, interpolation=InterpolationMode.BICUBIC
+        ),
         meta={
             **_COMMON_META_V1,
             "num_params": 19341616,
@@ -173,7 +185,9 @@ class EfficientNet_B4_Weights(WeightsEnum):
 class EfficientNet_B5_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b5_lukemelas-b6417697.pth",
-        transforms=partial(ImageNetEval, crop_size=456, resize_size=456, interpolation=InterpolationMode.BICUBIC),
+        transforms=partial(
+            ImageClassificationEval, crop_size=456, resize_size=456, interpolation=InterpolationMode.BICUBIC
+        ),
         meta={
             **_COMMON_META_V1,
             "num_params": 30389784,
@@ -188,7 +202,9 @@ class EfficientNet_B5_Weights(WeightsEnum):
 class EfficientNet_B6_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b6_lukemelas-c76e70fd.pth",
-        transforms=partial(ImageNetEval, crop_size=528, resize_size=528, interpolation=InterpolationMode.BICUBIC),
+        transforms=partial(
+            ImageClassificationEval, crop_size=528, resize_size=528, interpolation=InterpolationMode.BICUBIC
+        ),
         meta={
             **_COMMON_META_V1,
             "num_params": 43040704,
@@ -203,7 +219,9 @@ class EfficientNet_B6_Weights(WeightsEnum):
 class EfficientNet_B7_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b7_lukemelas-dcc49843.pth",
-        transforms=partial(ImageNetEval, crop_size=600, resize_size=600, interpolation=InterpolationMode.BICUBIC),
+        transforms=partial(
+            ImageClassificationEval, crop_size=600, resize_size=600, interpolation=InterpolationMode.BICUBIC
+        ),
         meta={
             **_COMMON_META_V1,
             "num_params": 66347960,
@@ -219,7 +237,7 @@ class EfficientNet_V2_S_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pth",
         transforms=partial(
-            ImageNetEval,
+            ImageClassificationEval,
             crop_size=384,
             resize_size=384,
             interpolation=InterpolationMode.BILINEAR,
@@ -239,7 +257,7 @@ class EfficientNet_V2_M_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pth",
         transforms=partial(
-            ImageNetEval,
+            ImageClassificationEval,
             crop_size=480,
             resize_size=480,
             interpolation=InterpolationMode.BILINEAR,
@@ -259,7 +277,7 @@ class EfficientNet_V2_L_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_v2_l-59c71312.pth",
         transforms=partial(
-            ImageNetEval,
+            ImageClassificationEval,
             crop_size=480,
             resize_size=480,
             interpolation=InterpolationMode.BICUBIC,

torchvision/prototype/models/googlenet.py

@@ -2,7 +2,7 @@ import warnings
 from functools import partial
 from typing import Any, Optional
 
-from torchvision.prototype.transforms import ImageNetEval
+from torchvision.prototype.transforms import ImageClassificationEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ...models.googlenet import GoogLeNet, GoogLeNetOutputs, _GoogLeNetOutputs
@@ -17,7 +17,7 @@ __all__ = ["GoogLeNet", "GoogLeNetOutputs", "_GoogLeNetOutputs", "GoogLeNet_Weig
 class GoogLeNet_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/googlenet-1378be20.pth",
-        transforms=partial(ImageNetEval, crop_size=224),
+        transforms=partial(ImageClassificationEval, crop_size=224),
         meta={
             "task": "image_classification",
             "architecture": "GoogLeNet",

torchvision/prototype/models/inception.py

 from functools import partial
 from typing import Any, Optional
 
-from torchvision.prototype.transforms import ImageNetEval
+from torchvision.prototype.transforms import ImageClassificationEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ...models.inception import Inception3, InceptionOutputs, _InceptionOutputs
@@ -16,7 +16,7 @@ __all__ = ["Inception3", "InceptionOutputs", "_InceptionOutputs", "Inception_V3_
 class Inception_V3_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth",
-        transforms=partial(ImageNetEval, crop_size=299, resize_size=342),
+        transforms=partial(ImageClassificationEval, crop_size=299, resize_size=342),
         meta={
             "task": "image_classification",
             "architecture": "InceptionV3",

torchvision/prototype/models/mnasnet.py

 from functools import partial
 from typing import Any, Optional
 
-from torchvision.prototype.transforms import ImageNetEval
+from torchvision.prototype.transforms import ImageClassificationEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ...models.mnasnet import MNASNet
@@ -38,7 +38,7 @@ _COMMON_META = {
 class MNASNet0_5_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth",
-        transforms=partial(ImageNetEval, crop_size=224),
+        transforms=partial(ImageClassificationEval, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 2218512,
@@ -57,7 +57,7 @@ class MNASNet0_75_Weights(WeightsEnum):
 class MNASNet1_0_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth",
-        transforms=partial(ImageNetEval, crop_size=224),
+        transforms=partial(ImageClassificationEval, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 4383312,

torchvision/prototype/models/mobilenetv2.py

 from functools import partial
 from typing import Any, Optional
 
-from torchvision.prototype.transforms import ImageNetEval
+from torchvision.prototype.transforms import ImageClassificationEval
 from torchvision.transforms.functional import InterpolationMode
 
 from ...models.mobilenetv2 import MobileNetV2
@@ -28,7 +28,7 @@ _COMMON_META = {
 class MobileNet_V2_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
-        transforms=partial(ImageNetEval, crop_size=224),
+        transforms=partial(ImageClassificationEval, crop_size=224),
         meta={
             **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
@@ -38,7 +38,7 @@ class MobileNet_V2_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth",
-        transforms=partial(ImageNetEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning",