Merge branch 'master' into tests-sbu

Author: datumbox

test/test_transforms_video.py

@@ -1,16 +1,21 @@
 import torch
-import torchvision.transforms._transforms_video as transforms
 from torchvision.transforms import Compose
 import unittest
 import random
 import numpy as np
+import warnings
 
 try:
     from scipy import stats
 except ImportError:
     stats = None
 
 
+with warnings.catch_warnings(record=True):
+    warnings.simplefilter("always")
+    import torchvision.transforms._transforms_video as transforms
+
+
 class TestVideoTransforms(unittest.TestCase):
 
     def test_random_crop_video(self):

torchvision/models/mobilenetv2.py

@@ -1,3 +1,4 @@
+import torch
 from torch import nn
 from torch import Tensor
 from .utils import load_state_dict_from_url
@@ -189,8 +190,9 @@ def _forward_impl(self, x: Tensor) -> Tensor:
         # This exists since TorchScript doesn't support inheritance, so the superclass method
         # (this one) needs to have a name other than `forward` that can be accessed in a subclass
         x = self.features(x)
-        # Cannot use "squeeze" as batch-size can be 1 => must use reshape with x.shape[0]
-        x = nn.functional.adaptive_avg_pool2d(x, (1, 1)).reshape(x.shape[0], -1)
+        # Cannot use "squeeze" as batch-size can be 1
+        x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
+        x = torch.flatten(x, 1)
         x = self.classifier(x)
         return x
 

torchvision/transforms/_functional_video.py

@@ -1,4 +1,10 @@
 import torch
+import warnings
+
+
+warnings.warn(
+    "The _functional_video module is deprecated. Please use the functional module instead."
+)
 
 
 def _is_tensor_video_clip(clip):

torchvision/transforms/_transforms_video.py

@@ -2,6 +2,7 @@
 
 import numbers
 import random
+import warnings
 
 from torchvision.transforms import (
     RandomCrop,
@@ -21,6 +22,11 @@
 ]
 
 
+warnings.warn(
+    "The _transforms_video module is deprecated. Please use the transforms module instead."
+)
+
+
 class RandomCropVideo(RandomCrop):
     def __init__(self, size):
         if isinstance(size, numbers.Number):