Add interpolation model information to all classification proto models (#4688)

* adding interpolation model information to all classification prototype models

* fixing lint errors
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

torchvision/prototype/models/alexnet.py

@@ -2,6 +2,8 @@ import warnings
 from functools import partial
 from typing import Any, Optional
 
+from torchvision.transforms.functional import InterpolationMode
+
 from ...models.alexnet import AlexNet
 from ..transforms.presets import ImageNetEval
 from ._api import Weights, WeightEntry
@@ -11,10 +13,7 @@ from ._meta import _IMAGENET_CATEGORIES
 __all__ = ["AlexNet", "AlexNetWeights", "alexnet"]
 
 
-_common_meta = {
-    "size": (224, 224),
-    "categories": _IMAGENET_CATEGORIES,
-}
+_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
 
 
 class AlexNetWeights(Weights):

torchvision/prototype/models/densenet.py

@@ -4,6 +4,7 @@ from functools import partial
 from typing import Any, Optional, Tuple
 
 import torch.nn as nn
+from torchvision.transforms.functional import InterpolationMode
 
 from ...models.densenet import DenseNet
 from ..transforms.presets import ImageNetEval
@@ -62,10 +63,7 @@ def _densenet(
     return model
 
 
-_common_meta = {
-    "size": (224, 224),
-    "categories": _IMAGENET_CATEGORIES,
-}
+_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
 
 
 class DenseNet121Weights(Weights):

torchvision/prototype/models/resnet.py

@@ -2,6 +2,8 @@ import warnings
 from functools import partial
 from typing import Any, List, Optional, Type, Union
 
+from torchvision.transforms.functional import InterpolationMode
+
 from ...models.resnet import BasicBlock, Bottleneck, ResNet
 from ..transforms.presets import ImageNetEval
 from ._api import Weights, WeightEntry
@@ -49,10 +51,7 @@ def _resnet(
     return model
 
 
-_common_meta = {
-    "size": (224, 224),
-    "categories": _IMAGENET_CATEGORIES,
-}
+_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
 
 
 class ResNet18Weights(Weights):

torchvision/prototype/models/vgg.py

@@ -2,6 +2,8 @@ import warnings
 from functools import partial
 from typing import Any, Optional
 
+from torchvision.transforms.functional import InterpolationMode
+
 from ...models.vgg import VGG, make_layers, cfgs
 from ..transforms.presets import ImageNetEval
 from ._api import Weights, WeightEntry
@@ -38,10 +40,7 @@ def _vgg(arch: str, cfg: str, batch_norm: bool, weights: Optional[Weights], prog
     return model
 
 
-_common_meta = {
-    "size": (224, 224),
-    "categories": _IMAGENET_CATEGORIES,
-}
+_common_meta = {"size": (224, 224), "categories": _IMAGENET_CATEGORIES, "interpolation": InterpolationMode.BILINEAR}
 
 
 class VGG11Weights(Weights):