Remove non-functional Transforms from presets. (#4952)

torchvision/prototype/models/video/resnet.py

@@ -62,7 +62,7 @@ _COMMON_META = {
 class R3D_18Weights(Weights):
     Kinetics400_RefV1 = WeightEntry(
         url="https://download.pytorch.org/models/r3d_18-b3b3357e.pth",
-        transforms=partial(Kinect400Eval, resize_size=(128, 171), crop_size=(112, 112)),
+        transforms=partial(Kinect400Eval, crop_size=(112, 112), resize_size=(128, 171)),
         meta={
             **_COMMON_META,
             "acc@1": 52.75,
@@ -74,7 +74,7 @@ class R3D_18Weights(Weights):
 class MC3_18Weights(Weights):
     Kinetics400_RefV1 = WeightEntry(
         url="https://download.pytorch.org/models/mc3_18-a90a0ba3.pth",
-        transforms=partial(Kinect400Eval, resize_size=(128, 171), crop_size=(112, 112)),
+        transforms=partial(Kinect400Eval, crop_size=(112, 112), resize_size=(128, 171)),
         meta={
             **_COMMON_META,
             "acc@1": 53.90,
@@ -86,7 +86,7 @@ class MC3_18Weights(Weights):
 class R2Plus1D_18Weights(Weights):
     Kinetics400_RefV1 = WeightEntry(
         url="https://download.pytorch.org/models/r2plus1d_18-91a641e6.pth",
-        transforms=partial(Kinect400Eval, resize_size=(128, 171), crop_size=(112, 112)),
+        transforms=partial(Kinect400Eval, crop_size=(112, 112), resize_size=(128, 171)),
         meta={
             **_COMMON_META,
             "acc@1": 57.50,

torchvision/prototype/transforms/_presets.py

@@ -3,8 +3,7 @@ from typing import Dict, Optional, Tuple
 import torch
 from torch import Tensor, nn
 
-from ... import transforms as T
-from ...transforms import functional as F
+from ...transforms import functional as F, InterpolationMode
 
 
 __all__ = ["CocoEval", "ImageNetEval", "Kinect400Eval", "VocEval"]
@@ -26,42 +25,47 @@ class ImageNetEval(nn.Module):
         resize_size: int = 256,
         mean: Tuple[float, ...] = (0.485, 0.456, 0.406),
         std: Tuple[float, ...] = (0.229, 0.224, 0.225),
-        interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     ) -> None:
         super().__init__()
-        self._resize = T.Resize(resize_size, interpolation=interpolation)
-        self._crop = T.CenterCrop(crop_size)
-        self._normalize = T.Normalize(mean=mean, std=std)
+        self._crop_size = [crop_size]
+        self._size = [resize_size]
+        self._mean = list(mean)
+        self._std = list(std)
+        self._interpolation = interpolation
 
     def forward(self, img: Tensor) -> Tensor:
-        img = self._crop(self._resize(img))
+        img = F.resize(img, self._size, interpolation=self._interpolation)
+        img = F.center_crop(img, self._crop_size)
         if not isinstance(img, Tensor):
             img = F.pil_to_tensor(img)
         img = F.convert_image_dtype(img, torch.float)
-        return self._normalize(img)
+        img = F.normalize(img, mean=self._mean, std=self._std)
+        return img
 
 
 class Kinect400Eval(nn.Module):
     def __init__(
         self,
-        resize_size: Tuple[int, int],
         crop_size: Tuple[int, int],
+        resize_size: Tuple[int, int],
         mean: Tuple[float, ...] = (0.43216, 0.394666, 0.37645),
         std: Tuple[float, ...] = (0.22803, 0.22145, 0.216989),
-        interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
     ) -> None:
         super().__init__()
-        self._convert = T.ConvertImageDtype(torch.float)
-        self._resize = T.Resize(resize_size, interpolation=interpolation)
-        self._normalize = T.Normalize(mean=mean, std=std)
-        self._crop = T.CenterCrop(crop_size)
+        self._crop_size = list(crop_size)
+        self._size = list(resize_size)
+        self._mean = list(mean)
+        self._std = list(std)
+        self._interpolation = interpolation
 
     def forward(self, vid: Tensor) -> Tensor:
         vid = vid.permute(0, 3, 1, 2)  # (T, H, W, C) => (T, C, H, W)
-        vid = self._convert(vid)
-        vid = self._resize(vid)
-        vid = self._normalize(vid)
-        vid = self._crop(vid)
+        vid = F.resize(vid, self._size, interpolation=self._interpolation)
+        vid = F.center_crop(vid, self._crop_size)
+        vid = F.convert_image_dtype(vid, torch.float)
+        vid = F.normalize(vid, mean=self._mean, std=self._std)
         return vid.permute(1, 0, 2, 3)  # (T, C, H, W) => (C, T, H, W)
 
 
@@ -71,8 +75,8 @@ class VocEval(nn.Module):
         resize_size: int,
         mean: Tuple[float, ...] = (0.485, 0.456, 0.406),
         std: Tuple[float, ...] = (0.229, 0.224, 0.225),
-        interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,
-        interpolation_target: T.InterpolationMode = T.InterpolationMode.NEAREST,
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+        interpolation_target: InterpolationMode = InterpolationMode.NEAREST,
     ) -> None:
         super().__init__()
         self._size = [resize_size]