Allow decode_image to support paths

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

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)