Allow decoding functions to accept the mode parameter as a string (#8627

)

test/test_image.py

@@ -1065,5 +1065,16 @@ def test_decode_image_path(input_type, scripted):
     decode_fun(input)
 
 
+def test_mode_str():
+    # Make sure decode_image supports string modes. We just test decode_image,
+    # not all of the decoding functions, but they should all support that too.
+    # Torchscript fails when passing strings, which is expected.
+    path = next(get_images(IMAGE_ROOT, ".png"))
+    assert decode_image(path, mode="RGB").shape[0] == 3
+    assert decode_image(path, mode="rGb").shape[0] == 3
+    assert decode_image(path, mode="GRAY").shape[0] == 1
+    assert decode_image(path, mode="RGBA").shape[0] == 4
+
+
 if __name__ == "__main__":
     pytest.main([__file__])

torchvision/io/image.py

@@ -40,6 +40,7 @@ class ImageReadMode(Enum):
     GRAY_ALPHA = 2
     RGB = 3
     RGB_ALPHA = 4
+    RGBA = RGB_ALPHA  # Alias for convenience
 
 
 def read_file(path: str) -> torch.Tensor:
@@ -92,7 +93,7 @@ def decode_png(
     Args:
         input (Tensor[1]): a one dimensional uint8 tensor containing
             the raw bytes of the PNG image.
-        mode (ImageReadMode): the read mode used for optionally
+        mode (str or ImageReadMode): the read mode used for optionally
             converting the image. Default: ``ImageReadMode.UNCHANGED``.
             See `ImageReadMode` class for more information on various
             available modes.
@@ -104,6 +105,8 @@ def decode_png(
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(decode_png)
+    if isinstance(mode, str):
+        mode = ImageReadMode[mode.upper()]
     output = torch.ops.image.decode_png(input, mode.value, apply_exif_orientation)
     return output
 
@@ -168,7 +171,7 @@ def decode_jpeg(
         input (Tensor[1] or list[Tensor[1]]): a (list of) one dimensional uint8 tensor(s) containing
             the raw bytes of the JPEG image. The tensor(s) must be on CPU,
             regardless of the ``device`` parameter.
-        mode (ImageReadMode): the read mode used for optionally
+        mode (str or ImageReadMode): the read mode used for optionally
             converting the image(s). The supported modes are: ``ImageReadMode.UNCHANGED``,
             ``ImageReadMode.GRAY`` and ``ImageReadMode.RGB``
             Default: ``ImageReadMode.UNCHANGED``.
@@ -198,6 +201,8 @@ def decode_jpeg(
         _log_api_usage_once(decode_jpeg)
     if isinstance(device, str):
         device = torch.device(device)
+    if isinstance(mode, str):
+        mode = ImageReadMode[mode.upper()]
 
     if isinstance(input, list):
         if len(input) == 0:
@@ -298,7 +303,7 @@ def decode_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.
+        mode (str or 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.
@@ -312,6 +317,8 @@ def decode_image(
         _log_api_usage_once(decode_image)
     if not isinstance(input, torch.Tensor):
         input = read_file(str(input))
+    if isinstance(mode, str):
+        mode = ImageReadMode[mode.upper()]
     output = torch.ops.image.decode_image(input, mode.value, apply_exif_orientation)
     return output
 
@@ -360,7 +367,7 @@ def decode_webp(
     Args:
         input (Tensor[1]): a one dimensional contiguous uint8 tensor containing
             the raw bytes of the WEBP image.
-        mode (ImageReadMode): The read mode used for optionally
+        mode (str or ImageReadMode): The read mode used for optionally
             converting the image color space. Default: ``ImageReadMode.UNCHANGED``.
             Other supported values are ``ImageReadMode.RGB`` and ``ImageReadMode.RGB_ALPHA``.
 
@@ -369,6 +376,8 @@ def decode_webp(
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(decode_webp)
+    if isinstance(mode, str):
+        mode = ImageReadMode[mode.upper()]
     return torch.ops.image.decode_webp(input, mode.value)
 
 
@@ -389,7 +398,7 @@ def _decode_avif(
     Args:
         input (Tensor[1]): a one dimensional contiguous uint8 tensor containing
             the raw bytes of the AVIF image.
-        mode (ImageReadMode): The read mode used for optionally
+        mode (str or ImageReadMode): The read mode used for optionally
             converting the image color space. Default: ``ImageReadMode.UNCHANGED``.
             Other supported values are ``ImageReadMode.RGB`` and ``ImageReadMode.RGB_ALPHA``.
 
@@ -398,6 +407,8 @@ def _decode_avif(
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(_decode_avif)
+    if isinstance(mode, str):
+        mode = ImageReadMode[mode.upper()]
     return torch.ops.image.decode_avif(input, mode.value)
 
 
@@ -415,7 +426,7 @@ def _decode_heic(input: torch.Tensor, mode: ImageReadMode = ImageReadMode.UNCHAN
     Args:
         input (Tensor[1]): a one dimensional contiguous uint8 tensor containing
             the raw bytes of the HEIC image.
-        mode (ImageReadMode): The read mode used for optionally
+        mode (str or ImageReadMode): The read mode used for optionally
             converting the image color space. Default: ``ImageReadMode.UNCHANGED``.
             Other supported values are ``ImageReadMode.RGB`` and ``ImageReadMode.RGB_ALPHA``.
 
@@ -424,4 +435,6 @@ def _decode_heic(input: torch.Tensor, mode: ImageReadMode = ImageReadMode.UNCHAN
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(_decode_heic)
+    if isinstance(mode, str):
+        mode = ImageReadMode[mode.upper()]
     return torch.ops.image.decode_heic(input, mode.value)