2024-11-28 nightly release (19fef3d)

.gitignore

@@ -16,6 +16,7 @@ docs/source/auto_examples/
 docs/source/gen_modules/
 docs/source/generated/
 docs/source/models/generated/
+docs/source/sg_execution_times.rst
 # pytorch-sphinx-theme gets installed here
 docs/src
 
@@ -42,3 +43,5 @@ xcuserdata/
 # direnv
 .direnv
 .envrc
+
+scripts/release_notes/data.json

references/depth/stereo/utils/losses.py

@@ -13,7 +13,7 @@ def make_gaussian_kernel(kernel_size: int, sigma: float) -> torch.Tensor:
     y = torch.arange(kernel_size, dtype=torch.float32)
     x = x - (kernel_size - 1) / 2
     y = y - (kernel_size - 1) / 2
-    x, y = torch.meshgrid(x, y)
+    x, y = torch.meshgrid(x, y, indexing="ij")
     grid = (x**2 + y**2) / (2 * sigma**2)
     kernel = torch.exp(-grid)
     kernel = kernel / kernel.sum()

references/video_classification/train.py

@@ -164,7 +164,7 @@ def main(args):
 
     if args.cache_dataset and os.path.exists(cache_path):
         print(f"Loading dataset_train from {cache_path}")
-        dataset, _ = torch.load(cache_path, weights_only=True)
+        dataset, _ = torch.load(cache_path, weights_only=False)
         dataset.transform = transform_train
     else:
         if args.distributed:
@@ -201,7 +201,7 @@ def main(args):
 
     if args.cache_dataset and os.path.exists(cache_path):
         print(f"Loading dataset_test from {cache_path}")
-        dataset_test, _ = torch.load(cache_path, weights_only=True)
+        dataset_test, _ = torch.load(cache_path, weights_only=False)
         dataset_test.transform = transform_test
     else:
         if args.distributed:

torchvision/datasets/lsun.py

@@ -55,7 +55,7 @@ def __len__(self) -> int:
 
 
 class LSUN(VisionDataset):
-    """`LSUN <https://www.yf.io/p/lsun>`_ dataset.
+    """`LSUN <https://paperswithcode.com/dataset/lsun>`_ dataset.
 
     You will need to install the ``lmdb`` package to use this dataset: run
     ``pip install lmdb``

torchvision/models/googlenet.py

@@ -171,7 +171,7 @@ def eager_outputs(self, x: Tensor, aux2: Tensor, aux1: Optional[Tensor]) -> Goog
 
     def forward(self, x: Tensor) -> GoogLeNetOutputs:
         x = self._transform_input(x)
-        x, aux1, aux2 = self._forward(x)
+        x, aux2, aux1 = self._forward(x)
         aux_defined = self.training and self.aux_logits
         if torch.jit.is_scripting():
             if not aux_defined:

torchvision/models/maxvit.py

@@ -40,7 +40,7 @@ def _make_block_input_shapes(input_size: Tuple[int, int], n_blocks: int) -> List
 
 
 def _get_relative_position_index(height: int, width: int) -> torch.Tensor:
-    coords = torch.stack(torch.meshgrid([torch.arange(height), torch.arange(width)]))
+    coords = torch.stack(torch.meshgrid([torch.arange(height), torch.arange(width)], indexing="ij"))
     coords_flat = torch.flatten(coords, 1)
     relative_coords = coords_flat[:, :, None] - coords_flat[:, None, :]
     relative_coords = relative_coords.permute(1, 2, 0).contiguous()