Allow decode_image to support paths (#8624)

docs/source/io.rst

@@ -19,7 +19,6 @@ For encoding, JPEG (cpu and CUDA) and PNG are supported.
  :toctree: generated/
  :template: function.rst
 
- read_image
  decode_image
  encode_jpeg
  decode_jpeg
@@ -38,6 +37,13 @@ For encoding, JPEG (cpu and CUDA) and PNG are supported.
 
  ImageReadMode
 
+Obsolete decoding function:
+
+.. autosummary::
+ :toctree: generated/
+ :template: class.rst
+
+ read_image
 
 
 Video

docs/source/models.rst

@@ -226,10 +226,10 @@ Here is an example of how to use the pre-trained image classification models:
 
 .. code:: python
 
- from torchvision.io import read_image
+ from torchvision.io import decode_image
  from torchvision.models import resnet50, ResNet50_Weights
 
- img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
+ img = decode_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
 
  # Step 1: Initialize model with the best available weights
  weights = ResNet50_Weights.DEFAULT
@@ -283,10 +283,10 @@ Here is an example of how to use the pre-trained quantized image classification
 
 .. code:: python
 
- from torchvision.io import read_image
+ from torchvision.io import decode_image
  from torchvision.models.quantization import resnet50, ResNet50_QuantizedWeights
 
- img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
+ img = decode_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
 
  # Step 1: Initialize model with the best available weights
  weights = ResNet50_QuantizedWeights.DEFAULT
@@ -339,11 +339,11 @@ Here is an example of how to use the pre-trained semantic segmentation models:
 
 .. code:: python
 
- from torchvision.io.image import read_image
+ from torchvision.io.image import decode_image
  from torchvision.models.segmentation import fcn_resnet50, FCN_ResNet50_Weights
  from torchvision.transforms.functional import to_pil_image
 
- img = read_image("gallery/assets/dog1.jpg")
+ img = decode_image("gallery/assets/dog1.jpg")
 
  # Step 1: Initialize model with the best available weights
  weights = FCN_ResNet50_Weights.DEFAULT
@@ -411,12 +411,12 @@ Here is an example of how to use the pre-trained object detection models:
 .. code:: python
 
 
- from torchvision.io.image import read_image
+ from torchvision.io.image import decode_image
  from torchvision.models.detection import fasterrcnn_resnet50_fpn_v2, FasterRCNN_ResNet50_FPN_V2_Weights
  from torchvision.utils import draw_bounding_boxes
  from torchvision.transforms.functional import to_pil_image
 
- img = read_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
+ img = decode_image("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
 
  # Step 1: Initialize model with the best available weights
  weights = FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT

gallery/others/plot_repurposing_annotations.py

@@ -66,12 +66,12 @@ def show(imgs):
 # We will take images and masks from the `PenFudan Dataset <https://www.cis.upenn.edu/~jshi/ped_html/>`_.
 
 
-from torchvision.io import read_image
+from torchvision.io import decode_image
 
 img_path = os.path.join(ASSETS_DIRECTORY, "FudanPed00054.png")
 mask_path = os.path.join(ASSETS_DIRECTORY, "FudanPed00054_mask.png")
-img = read_image(img_path)
-mask = read_image(mask_path)
+img = decode_image(img_path)
+mask = decode_image(mask_path)
 
 
 # %%
@@ -181,8 +181,8 @@ def __getitem__(self, idx):
  img_path = os.path.join(self.root, "PNGImages", self.imgs[idx])
  mask_path = os.path.join(self.root, "PedMasks", self.masks[idx])
 
- img = read_image(img_path)
- mask = read_image(mask_path)
+ img = decode_image(img_path)
+ mask = decode_image(mask_path)
 
  img = F.convert_image_dtype(img, dtype=torch.float)
  mask = F.convert_image_dtype(mask, dtype=torch.float)

gallery/others/plot_scripted_tensor_transforms.py

@@ -21,7 +21,7 @@
 import torch.nn as nn
 
 import torchvision.transforms as v1
-from torchvision.io import read_image
+from torchvision.io import decode_image
 
 plt.rcParams["savefig.bbox"] = 'tight'
 torch.manual_seed(1)
@@ -39,8 +39,8 @@
 # :class:`torch.nn.Sequential` instead of
 # :class:`~torchvision.transforms.v2.Compose`:
 
-dog1 = read_image(str(ASSETS_PATH / 'dog1.jpg'))
-dog2 = read_image(str(ASSETS_PATH / 'dog2.jpg'))
+dog1 = decode_image(str(ASSETS_PATH / 'dog1.jpg'))
+dog2 = decode_image(str(ASSETS_PATH / 'dog2.jpg'))
 
 transforms = torch.nn.Sequential(
  v1.RandomCrop(224),

gallery/others/plot_visualization_utils.py

@@ -42,11 +42,11 @@ def show(imgs):
 # image of dtype ``uint8`` as input.
 
 from torchvision.utils import make_grid
-from torchvision.io import read_image
+from torchvision.io import decode_image
 from pathlib import Path
 
-dog1_int = read_image(str(Path('../assets') / 'dog1.jpg'))
-dog2_int = read_image(str(Path('../assets') / 'dog2.jpg'))
+dog1_int = decode_image(str(Path('../assets') / 'dog1.jpg'))
+dog2_int = decode_image(str(Path('../assets') / 'dog2.jpg'))
 dog_list = [dog1_int, dog2_int]
 
 grid = make_grid(dog_list)
@@ -362,9 +362,9 @@ def show(imgs):
 #
 
 from torchvision.models.detection import keypointrcnn_resnet50_fpn, KeypointRCNN_ResNet50_FPN_Weights
-from torchvision.io import read_image
+from torchvision.io import decode_image
 
-person_int = read_image(str(Path("../assets") / "person1.jpg"))
+person_int = decode_image(str(Path("../assets") / "person1.jpg"))
 
 weights = KeypointRCNN_ResNet50_FPN_Weights.DEFAULT
 transforms = weights.transforms()

gallery/transforms/plot_transforms_getting_started.py

@@ -21,14 +21,14 @@
 plt.rcParams["savefig.bbox"] = 'tight'
 
 from torchvision.transforms import v2
-from torchvision.io import read_image
+from torchvision.io import decode_image
 
 torch.manual_seed(1)
 
 # If you're trying to run that on Colab, you can download the assets and the
 # helpers from https://github.com/pytorch/vision/tree/main/gallery/
 from helpers import plot
-img = read_image(str(Path('../assets') / 'astronaut.jpg'))
+img = decode_image(str(Path('../assets') / 'astronaut.jpg'))
 print(f"{type(img) = }, {img.dtype = }, {img.shape = }")
 
 # %%

test/smoke_test.py

@@ -6,7 +6,7 @@
 
 import torch
 import torchvision
-from torchvision.io import decode_jpeg, decode_webp, read_file, read_image
+from torchvision.io import decode_image, decode_jpeg, decode_webp, read_file
 from torchvision.models import resnet50, ResNet50_Weights
 
 
@@ -21,13 +21,13 @@ def smoke_test_torchvision() -> None:
 
 
 def smoke_test_torchvision_read_decode() -> None:
- img_jpg = read_image(str(SCRIPT_DIR / "assets" / "encode_jpeg" / "grace_hopper_517x606.jpg"))
+ img_jpg = decode_image(str(SCRIPT_DIR / "assets" / "encode_jpeg" / "grace_hopper_517x606.jpg"))
  if img_jpg.shape != (3, 606, 517):
  raise RuntimeError(f"Unexpected shape of img_jpg: {img_jpg.shape}")
- img_png = read_image(str(SCRIPT_DIR / "assets" / "interlaced_png" / "wizard_low.png"))
+ img_png = decode_image(str(SCRIPT_DIR / "assets" / "interlaced_png" / "wizard_low.png"))
  if img_png.shape != (4, 471, 354):
  raise RuntimeError(f"Unexpected shape of img_png: {img_png.shape}")
- img_webp = read_image(str(SCRIPT_DIR / "assets/fakedata/logos/rgb_pytorch.webp"))
+ img_webp = decode_image(str(SCRIPT_DIR / "assets/fakedata/logos/rgb_pytorch.webp"))
  if img_webp.shape != (3, 100, 100):
  raise RuntimeError(f"Unexpected shape of img_webp: {img_webp.shape}")
 
@@ -54,7 +54,7 @@ def smoke_test_compile() -> None:
 
 
 def smoke_test_torchvision_resnet50_classify(device: str = "cpu") -> None:
- img = read_image(str(SCRIPT_DIR / ".." / "gallery" / "assets" / "dog2.jpg")).to(device)
+ img = decode_image(str(SCRIPT_DIR / ".." / "gallery" / "assets" / "dog2.jpg")).to(device)
 
  # Step 1: Initialize model with the best available weights
  weights = ResNet50_Weights.DEFAULT

test/test_image.py

@@ -1044,5 +1044,26 @@ def test_decode_heic(decode_fun, scripted):
  img += 123 # make sure image buffer wasn't freed by underlying decoding lib
 
 
+@pytest.mark.parametrize("input_type", ("Path", "str", "tensor"))
+@pytest.mark.parametrize("scripted", (False, True))
+def test_decode_image_path(input_type, scripted):
+ # Check that decode_image can support not just tensors as input
+ path = next(get_images(IMAGE_ROOT, ".jpg"))
+ if input_type == "Path":
+ input = Path(path)
+ elif input_type == "str":
+ input = path
+ elif input_type == "tensor":
+ input = read_file(path)
+ else:
+ raise ValueError("Oops")
+
+ if scripted and input_type == "Path":
+ pytest.xfail(reason="Can't pass a Path when scripting")
+
+ decode_fun = torch.jit.script(decode_image) if scripted else decode_image
+ decode_fun(input)
+
+
 if __name__ == "__main__":
  pytest.main([__file__])

torchvision/io/image.py

@@ -277,13 +277,13 @@ def write_jpeg(input: torch.Tensor, filename: str, quality: int = 75):
 
 
 def decode_image(
- input: torch.Tensor,
+ input: Union[torch.Tensor, str],
  mode: ImageReadMode = ImageReadMode.UNCHANGED,
  apply_exif_orientation: bool = False,
 ) -> torch.Tensor:
- """
- Detect whether an image is a JPEG, PNG, WEBP, or GIF and performs the
- appropriate operation to decode the image into a Tensor.
+ """Decode an image into a tensor.
+
+ Currently supported image formats are jpeg, png, gif and webp.
 
  The values of the output tensor are in uint8 in [0, 255] for most cases.
 
@@ -295,8 +295,9 @@ def decode_image(
  tensor.
 
  Args:
- input (Tensor): a one dimensional uint8 tensor containing the raw bytes of the
- image.
+ input (Tensor or str or ``pathlib.Path``): The image to decode. If a
+ tensor is passed, it must be one dimensional uint8 tensor containing
+ the raw bytes of the image. Otherwise, this must be a path to the image file.
  mode (ImageReadMode): the read mode used for optionally converting the image.
  Default: ``ImageReadMode.UNCHANGED``.
  See ``ImageReadMode`` class for more information on various
@@ -309,6 +310,8 @@ def decode_image(
  """
  if not torch.jit.is_scripting() and not torch.jit.is_tracing():
  _log_api_usage_once(decode_image)
+ if not isinstance(input, torch.Tensor):
+ input = read_file(str(input))
  output = torch.ops.image.decode_image(input, mode.value, apply_exif_orientation)
  return output
 
@@ -318,30 +321,7 @@ def read_image(
  mode: ImageReadMode = ImageReadMode.UNCHANGED,
  apply_exif_orientation: bool = False,
 ) -> torch.Tensor:
- """
- Reads a JPEG, PNG, WEBP, or GIF image into a Tensor.
-
- The values of the output tensor are in uint8 in [0, 255] for most cases.
-
- If the image is a 16-bit png, then the output tensor is uint16 in [0, 65535]
- (supported from torchvision ``0.21``. Since uint16 support is limited in
- pytorch, we recommend calling
- :func:`torchvision.transforms.v2.functional.to_dtype()` with ``scale=True``
- after this function to convert the decoded image into a uint8 or float
- tensor.
-
- Args:
- path (str or ``pathlib.Path``): path of the image.
- mode (ImageReadMode): the read mode used for optionally converting the image.
- Default: ``ImageReadMode.UNCHANGED``.
- See ``ImageReadMode`` class for more information on various
- available modes. Only applies to JPEG and PNG images.
- apply_exif_orientation (bool): apply EXIF orientation transformation to the output tensor.
- Only applies to JPEG and PNG images. Default: False.
-
- Returns:
- output (Tensor[image_channels, image_height, image_width])
- """
+ """[OBSOLETE] Use :func:`~torchvision.io.decode_image` instead."""
  if not torch.jit.is_scripting() and not torch.jit.is_tracing():
  _log_api_usage_once(read_image)
  data = read_file(path)