Merge branch 'master' into phototour-nit

test/test_datasets.py

@@ -988,7 +988,7 @@ def _create_lmdb(self, root, cls):
         folder = f"{cls}_lmdb"
 
         num_images = torch.randint(1, 4, size=()).item()
-        format = "webp"
+        format = "png"
         files = datasets_utils.create_image_folder(root, folder, lambda idx: f"{idx}.{format}", num_images)
 
         with lmdb.open(str(root / folder)) as env, env.begin(write=True) as txn:

test/test_transforms.py

@@ -1991,6 +1991,9 @@ def test_random_erasing(self):
         p_value = stats.binom_test(count_bigger_then_ones, trial, p=0.5)
         self.assertGreater(p_value, 0.0001)
 
+        # Checking if RandomErasing can be printed as string
+        t.__repr__()
+
 
 if __name__ == '__main__':
     unittest.main()

torchvision/transforms/functional_tensor.py

@@ -227,7 +227,6 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
     result = (gain * result ** gamma).clamp(0, 1)
 
     result = convert_image_dtype(result, dtype)
-    result = result.to(dtype)
     return result
 
 

torchvision/transforms/transforms.py

@@ -1630,6 +1630,14 @@ def forward(self, img):
             return F.erase(img, x, y, h, w, v, self.inplace)
         return img
 
+    def __repr__(self):
+        s = '(p={}, '.format(self.p)
+        s += 'scale={}, '.format(self.scale)
+        s += 'ratio={}, '.format(self.ratio)
+        s += 'value={}, '.format(self.value)
+        s += 'inplace={})'.format(self.inplace)
+        return self.__class__.__name__ + s
+
 
 class GaussianBlur(torch.nn.Module):
     """Blurs image with randomly chosen Gaussian blur.