Use bigger images

pytorch · Jul 25, 2023 · 45bf28c · 45bf28c
1 parent e91e879
commit 45bf28c
Show file tree

Hide file tree

Showing 2 changed files with 6 additions and 6 deletions.
diff --git a/test/test_transforms_v2_refactored.py b/test/test_transforms_v2_refactored.py
@@ -1647,8 +1647,8 @@ def __init__(self, size, one_hot, num_categories):
             assert size < num_categories
 
         def __getitem__(self, idx):
-            img = torch.rand(3, 12, 12)
-            label = idx
+            img = torch.rand(3, 100, 100)
+            label = idx  # This ensures all labels in a batch are unique and makes testing easier
             if self.one_hot:
                 label = torch.nn.functional.one_hot(torch.tensor(label), num_classes=self.num_categories)
             return img, label
@@ -1672,7 +1672,6 @@ def test_supported_input_structure(self, T, one_hot):
                 cutmix_mixup = transforms.Compose([cutmix, mixup])
             else:
                 cutmix_mixup = transforms.Compose([mixup, cutmix])
-            # When both CutMix and MixUp
             expected_num_non_zero_labels = 3
         else:
             cutmix_mixup = T(alpha=0.5, num_categories=num_categories)
@@ -1682,11 +1681,12 @@ def test_supported_input_structure(self, T, one_hot):
 
         # Input sanity checks
         img, target = next(iter(dl))
+        input_img_size = img.shape[-3:]
         assert isinstance(img, torch.Tensor) and isinstance(target, torch.Tensor)
         assert target.shape == (batch_size, num_categories) if one_hot else (batch_size,)
 
         def check_output(img, target):
-            assert img.shape == (batch_size, 3, 12, 12)
+            assert img.shape == (batch_size, *input_img_size)
             assert target.shape == (batch_size, num_categories)
             torch.testing.assert_close(target.sum(axis=-1), torch.ones(batch_size))
             num_non_zero_labels = (target != 0).sum(axis=-1)

diff --git a/torchvision/transforms/v2/_augment.py b/torchvision/transforms/v2/_augment.py
@@ -243,8 +243,8 @@ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
 
         H, W = query_spatial_size(flat_inputs)
 
-        r_x = torch.randint(W, ())
-        r_y = torch.randint(H, ())
+        r_x = torch.randint(W, size=(1,))
+        r_y = torch.randint(H, size=(1,))
 
         r = 0.5 * math.sqrt(1.0 - lam)
         r_w_half = int(r * W)