2025-02-24 nightly release (86f8eb0)

Author: pytorchbot

setup.py

@@ -2,6 +2,7 @@
 import distutils.spawn
 import glob
 import os
+import shlex
 import shutil
 import subprocess
 import sys
@@ -123,7 +124,7 @@ def get_macros_and_flags():
             if NVCC_FLAGS is None:
                 nvcc_flags = []
             else:
-                nvcc_flags = NVCC_FLAGS.split(" ")
+                nvcc_flags = shlex.split(NVCC_FLAGS)
         extra_compile_args["nvcc"] = nvcc_flags
 
     if sys.platform == "win32":

test/datasets_utils.py

@@ -611,6 +611,7 @@ class ImageDatasetTestCase(DatasetTestCase):
     """
 
     FEATURE_TYPES = (PIL.Image.Image, int)
+    SUPPORT_TV_IMAGE_DECODE: bool = False
 
     @contextlib.contextmanager
     def create_dataset(
@@ -632,22 +633,34 @@ def create_dataset(
             # This problem only occurs during testing since some tests, e.g. DatasetTestCase.test_feature_types open an
             # image, but never use the underlying data. During normal operation it is reasonable to assume that the
             # user wants to work with the image he just opened rather than deleting the underlying file.
-            with self._force_load_images():
+            with self._force_load_images(loader=(config or {}).get("loader", None)):
                 yield dataset, info
 
     @contextlib.contextmanager
-    def _force_load_images(self):
-        open = PIL.Image.open
+    def _force_load_images(self, loader: Optional[Callable[[str], Any]] = None):
+        open = loader or PIL.Image.open
 
         def new(fp, *args, **kwargs):
             image = open(fp, *args, **kwargs)
-            if isinstance(fp, (str, pathlib.Path)):
+            if isinstance(fp, (str, pathlib.Path)) and isinstance(image, PIL.Image.Image):
                 image.load()
             return image
 
-        with unittest.mock.patch("PIL.Image.open", new=new):
+        with unittest.mock.patch(open.__module__ + "." + open.__qualname__, new=new):
             yield
 
+    def test_tv_decode_image_support(self):
+        if not self.SUPPORT_TV_IMAGE_DECODE:
+            pytest.skip(f"{self.DATASET_CLASS.__name__} does not support torchvision.io.decode_image.")
+
+        with self.create_dataset(
+            config=dict(
+                loader=torchvision.io.decode_image,
+            )
+        ) as (dataset, _):
+            image = dataset[0][0]
+            assert isinstance(image, torch.Tensor)
+
 
 class VideoDatasetTestCase(DatasetTestCase):
     """Abstract base class for video dataset testcases.

test/test_datasets.py

@@ -405,6 +405,8 @@ class ImageNetTestCase(datasets_utils.ImageDatasetTestCase):
     REQUIRED_PACKAGES = ("scipy",)
     ADDITIONAL_CONFIGS = combinations_grid(split=("train", "val"))
 
+    SUPPORT_TV_IMAGE_DECODE = True
+
     def inject_fake_data(self, tmpdir, config):
         tmpdir = pathlib.Path(tmpdir)
 
@@ -2308,6 +2310,7 @@ def inject_fake_data(self, tmpdir, config):
 class EuroSATTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.EuroSAT
     FEATURE_TYPES = (PIL.Image.Image, int)
+    SUPPORT_TV_IMAGE_DECODE = True
 
     def inject_fake_data(self, tmpdir, config):
         data_folder = os.path.join(tmpdir, "eurosat", "2750")
@@ -2749,6 +2752,8 @@ class Country211TestCase(datasets_utils.ImageDatasetTestCase):
 
     ADDITIONAL_CONFIGS = combinations_grid(split=("train", "valid", "test"))
 
+    SUPPORT_TV_IMAGE_DECODE = True
+
     def inject_fake_data(self, tmpdir: str, config):
         split_folder = pathlib.Path(tmpdir) / "country211" / config["split"]
         split_folder.mkdir(parents=True, exist_ok=True)

torchvision/datasets/coco.py

@@ -10,7 +10,8 @@
 class CocoDetection(VisionDataset):
     """`MS Coco Detection <https://cocodataset.org/#detection-2016>`_ Dataset.
 
-    It requires the `COCO API to be installed <https://github.com/pdollar/coco/tree/master/PythonAPI>`_.
+    It requires `pycocotools <https://github.com/ppwwyyxx/cocoapi>`_ to be installed,
+    which could be installed via ``pip install pycocotools`` or ``conda install conda-forge::pycocotools``.
 
     Args:
         root (str or ``pathlib.Path``): Root directory where images are downloaded to.
@@ -65,7 +66,8 @@ def __len__(self) -> int:
 class CocoCaptions(CocoDetection):
     """`MS Coco Captions <https://cocodataset.org/#captions-2015>`_ Dataset.
 
-    It requires the `COCO API to be installed <https://github.com/pdollar/coco/tree/master/PythonAPI>`_.
+    It requires `pycocotools <https://github.com/ppwwyyxx/cocoapi>`_ to be installed,
+    which could be installed via ``pip install pycocotools`` or ``conda install conda-forge::pycocotools``.
 
     Args:
         root (str or ``pathlib.Path``): Root directory where images are downloaded to.

torchvision/datasets/country211.py

@@ -1,7 +1,7 @@
 from pathlib import Path
-from typing import Callable, Optional, Union
+from typing import Any, Callable, Optional, Union
 
-from .folder import ImageFolder
+from .folder import default_loader, ImageFolder
 from .utils import download_and_extract_archive, verify_str_arg
 
 
@@ -21,6 +21,9 @@ class Country211(ImageFolder):
         target_transform (callable, optional): A function/transform that takes in the target and transforms it.
         download (bool, optional): If True, downloads the dataset from the internet and puts it into
             ``root/country211/``. If dataset is already downloaded, it is not downloaded again.
+        loader (callable, optional): A function to load an image given its path.
+            By default, it uses PIL as its image loader, but users could also pass in
+            ``torchvision.io.decode_image`` for decoding image data into tensors directly.
     """
 
     _URL = "https://openaipublic.azureedge.net/clip/data/country211.tgz"
@@ -33,6 +36,7 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         download: bool = False,
+        loader: Callable[[str], Any] = default_loader,
     ) -> None:
         self._split = verify_str_arg(split, "split", ("train", "valid", "test"))
 
@@ -46,7 +50,12 @@ def __init__(
         if not self._check_exists():
             raise RuntimeError("Dataset not found. You can use download=True to download it")
 
-        super().__init__(str(self._base_folder / self._split), transform=transform, target_transform=target_transform)
+        super().__init__(
+            str(self._base_folder / self._split),
+            transform=transform,
+            target_transform=target_transform,
+            loader=loader,
+        )
         self.root = str(root)
 
     def _check_exists(self) -> bool:

torchvision/datasets/eurosat.py

@@ -1,8 +1,8 @@
 import os
 from pathlib import Path
-from typing import Callable, Optional, Union
+from typing import Any, Callable, Optional, Union
 
-from .folder import ImageFolder
+from .folder import default_loader, ImageFolder
 from .utils import download_and_extract_archive
 
 
@@ -21,6 +21,9 @@ class EuroSAT(ImageFolder):
         download (bool, optional): If True, downloads the dataset from the internet and
             puts it in root directory. If dataset is already downloaded, it is not
             downloaded again. Default is False.
+        loader (callable, optional): A function to load an image given its path.
+            By default, it uses PIL as its image loader, but users could also pass in
+            ``torchvision.io.decode_image`` for decoding image data into tensors directly.
     """
 
     def __init__(
@@ -29,6 +32,7 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         download: bool = False,
+        loader: Callable[[str], Any] = default_loader,
     ) -> None:
         self.root = os.path.expanduser(root)
         self._base_folder = os.path.join(self.root, "eurosat")
@@ -40,7 +44,12 @@ def __init__(
         if not self._check_exists():
             raise RuntimeError("Dataset not found. You can use download=True to download it")
 
-        super().__init__(self._data_folder, transform=transform, target_transform=target_transform)
+        super().__init__(
+            self._data_folder,
+            transform=transform,
+            target_transform=target_transform,
+            loader=loader,
+        )
         self.root = os.path.expanduser(root)
 
     def __len__(self) -> int:

torchvision/datasets/imagenet.py

@@ -36,6 +36,8 @@ class ImageNet(ImageFolder):
         target_transform (callable, optional): A function/transform that takes in the
             target and transforms it.
         loader (callable, optional): A function to load an image given its path.
+            By default, it uses PIL as its image loader, but users could also pass in
+            ``torchvision.io.decode_image`` for decoding image data into tensors directly.
 
      Attributes:
         classes (list): List of the class name tuples.