introduce type failures in dispatchers (#6988)

* introduce type failures in dispatchers

* add type checks to all dispatchers

* add missing else

* add test

* fix convert_color_space

test/test_prototype_transforms_functional.py

@@ -422,6 +422,14 @@ class TestDispatchers:
 
         assert dispatcher_params == feature_params
 
+    @pytest.mark.parametrize("info", DISPATCHER_INFOS, ids=lambda info: info.id)
+    def test_unkown_type(self, info):
+        unkown_input = object()
+        (_, *other_args), kwargs = next(iter(info.sample_inputs())).load("cpu")
+
+        with pytest.raises(TypeError, match=re.escape(str(type(unkown_input)))):
+            info.dispatcher(unkown_input, *other_args, **kwargs)
+
 
 @pytest.mark.parametrize(
     ("alias", "target"),

torchvision/prototype/transforms/functional/_augment.py

@@ -51,5 +51,10 @@ def erase(
     elif isinstance(inpt, features.Video):
         output = erase_video(inpt.as_subclass(torch.Tensor), i=i, j=j, h=h, w=w, v=v, inplace=inplace)
         return features.Video.wrap_like(inpt, output)
-    else:  # isinstance(inpt, PIL.Image.Image):
+    elif isinstance(inpt, PIL.Image.Image):
         return erase_image_pil(inpt, i=i, j=j, h=h, w=w, v=v, inplace=inplace)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, an `Image` or `Video` tensor subclass, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )

torchvision/prototype/transforms/functional/_color.py

+import PIL.Image
 import torch
 from torch.nn.functional import conv2d
 from torchvision.prototype import features
@@ -41,8 +42,13 @@ def adjust_brightness(inpt: features.InputTypeJIT, brightness_factor: float) ->
         return adjust_brightness_image_tensor(inpt, brightness_factor=brightness_factor)
     elif isinstance(inpt, features._Feature):
         return inpt.adjust_brightness(brightness_factor=brightness_factor)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return adjust_brightness_image_pil(inpt, brightness_factor=brightness_factor)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def adjust_saturation_image_tensor(image: torch.Tensor, saturation_factor: float) -> torch.Tensor:
@@ -75,8 +81,13 @@ def adjust_saturation(inpt: features.InputTypeJIT, saturation_factor: float) ->
         return adjust_saturation_image_tensor(inpt, saturation_factor=saturation_factor)
     elif isinstance(inpt, features._Feature):
         return inpt.adjust_saturation(saturation_factor=saturation_factor)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return adjust_saturation_image_pil(inpt, saturation_factor=saturation_factor)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def adjust_contrast_image_tensor(image: torch.Tensor, contrast_factor: float) -> torch.Tensor:
@@ -109,8 +120,13 @@ def adjust_contrast(inpt: features.InputTypeJIT, contrast_factor: float) -> feat
         return adjust_contrast_image_tensor(inpt, contrast_factor=contrast_factor)
     elif isinstance(inpt, features._Feature):
         return inpt.adjust_contrast(contrast_factor=contrast_factor)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return adjust_contrast_image_pil(inpt, contrast_factor=contrast_factor)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def adjust_sharpness_image_tensor(image: torch.Tensor, sharpness_factor: float) -> torch.Tensor:
@@ -177,8 +193,13 @@ def adjust_sharpness(inpt: features.InputTypeJIT, sharpness_factor: float) -> fe
         return adjust_sharpness_image_tensor(inpt, sharpness_factor=sharpness_factor)
     elif isinstance(inpt, features._Feature):
         return inpt.adjust_sharpness(sharpness_factor=sharpness_factor)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return adjust_sharpness_image_pil(inpt, sharpness_factor=sharpness_factor)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def _rgb_to_hsv(image: torch.Tensor) -> torch.Tensor:
@@ -284,8 +305,13 @@ def adjust_hue(inpt: features.InputTypeJIT, hue_factor: float) -> features.Input
         return adjust_hue_image_tensor(inpt, hue_factor=hue_factor)
     elif isinstance(inpt, features._Feature):
         return inpt.adjust_hue(hue_factor=hue_factor)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return adjust_hue_image_pil(inpt, hue_factor=hue_factor)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def adjust_gamma_image_tensor(image: torch.Tensor, gamma: float, gain: float = 1.0) -> torch.Tensor:
@@ -319,8 +345,13 @@ def adjust_gamma(inpt: features.InputTypeJIT, gamma: float, gain: float = 1) ->
         return adjust_gamma_image_tensor(inpt, gamma=gamma, gain=gain)
     elif isinstance(inpt, features._Feature):
         return inpt.adjust_gamma(gamma=gamma, gain=gain)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return adjust_gamma_image_pil(inpt, gamma=gamma, gain=gain)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def posterize_image_tensor(image: torch.Tensor, bits: int) -> torch.Tensor:
@@ -348,8 +379,13 @@ def posterize(inpt: features.InputTypeJIT, bits: int) -> features.InputTypeJIT:
         return posterize_image_tensor(inpt, bits=bits)
     elif isinstance(inpt, features._Feature):
         return inpt.posterize(bits=bits)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return posterize_image_pil(inpt, bits=bits)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def solarize_image_tensor(image: torch.Tensor, threshold: float) -> torch.Tensor:
@@ -371,8 +407,13 @@ def solarize(inpt: features.InputTypeJIT, threshold: float) -> features.InputTyp
         return solarize_image_tensor(inpt, threshold=threshold)
     elif isinstance(inpt, features._Feature):
         return inpt.solarize(threshold=threshold)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return solarize_image_pil(inpt, threshold=threshold)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def autocontrast_image_tensor(image: torch.Tensor) -> torch.Tensor:
@@ -416,8 +457,13 @@ def autocontrast(inpt: features.InputTypeJIT) -> features.InputTypeJIT:
         return autocontrast_image_tensor(inpt)
     elif isinstance(inpt, features._Feature):
         return inpt.autocontrast()
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return autocontrast_image_pil(inpt)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def equalize_image_tensor(image: torch.Tensor) -> torch.Tensor:
@@ -501,8 +547,13 @@ def equalize(inpt: features.InputTypeJIT) -> features.InputTypeJIT:
         return equalize_image_tensor(inpt)
     elif isinstance(inpt, features._Feature):
         return inpt.equalize()
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return equalize_image_pil(inpt)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def invert_image_tensor(image: torch.Tensor) -> torch.Tensor:
@@ -527,5 +578,10 @@ def invert(inpt: features.InputTypeJIT) -> features.InputTypeJIT:
         return invert_image_tensor(inpt)
     elif isinstance(inpt, features._Feature):
         return inpt.invert()
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return invert_image_pil(inpt)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )

torchvision/prototype/transforms/functional/_geometry.py

@@ -59,8 +59,13 @@ def horizontal_flip(inpt: features.InputTypeJIT) -> features.InputTypeJIT:
         return horizontal_flip_image_tensor(inpt)
     elif isinstance(inpt, features._Feature):
         return inpt.horizontal_flip()
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return horizontal_flip_image_pil(inpt)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def vertical_flip_image_tensor(image: torch.Tensor) -> torch.Tensor:
@@ -100,8 +105,13 @@ def vertical_flip(inpt: features.InputTypeJIT) -> features.InputTypeJIT:
         return vertical_flip_image_tensor(inpt)
     elif isinstance(inpt, features._Feature):
         return inpt.vertical_flip()
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return vertical_flip_image_pil(inpt)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 # We changed the names to align them with the transforms, i.e. `RandomHorizontalFlip`. Still, `hflip` and `vflip` are
@@ -221,10 +231,15 @@ def resize(
         return resize_image_tensor(inpt, size, interpolation=interpolation, max_size=max_size, antialias=antialias)
     elif isinstance(inpt, features._Feature):
         return inpt.resize(size, interpolation=interpolation, max_size=max_size, antialias=antialias)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         if antialias is not None and not antialias:
             warnings.warn("Anti-alias option is always applied for PIL Image input. Argument antialias is ignored.")
         return resize_image_pil(inpt, size, interpolation=interpolation, max_size=max_size)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def _affine_parse_args(
@@ -725,7 +740,7 @@ def affine(
         return inpt.affine(
             angle, translate=translate, scale=scale, shear=shear, interpolation=interpolation, fill=fill, center=center
         )
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return affine_image_pil(
             inpt,
             angle,
@@ -736,6 +751,11 @@ def affine(
             fill=fill,
             center=center,
         )
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def rotate_image_tensor(
@@ -889,8 +909,13 @@ def rotate(
         return rotate_image_tensor(inpt, angle, interpolation=interpolation, expand=expand, fill=fill, center=center)
     elif isinstance(inpt, features._Feature):
         return inpt.rotate(angle, interpolation=interpolation, expand=expand, fill=fill, center=center)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return rotate_image_pil(inpt, angle, interpolation=interpolation, expand=expand, fill=fill, center=center)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def _parse_pad_padding(padding: Union[int, List[int]]) -> List[int]:
@@ -1090,8 +1115,13 @@ def pad(
 
     elif isinstance(inpt, features._Feature):
         return inpt.pad(padding, fill=fill, padding_mode=padding_mode)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return pad_image_pil(inpt, padding, fill=fill, padding_mode=padding_mode)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def crop_image_tensor(image: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor:
@@ -1159,8 +1189,13 @@ def crop(inpt: features.InputTypeJIT, top: int, left: int, height: int, width: i
         return crop_image_tensor(inpt, top, left, height, width)
     elif isinstance(inpt, features._Feature):
         return inpt.crop(top, left, height, width)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return crop_image_pil(inpt, top, left, height, width)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
@@ -1411,10 +1446,15 @@ def perspective(
         return inpt.perspective(
             startpoints, endpoints, interpolation=interpolation, fill=fill, coefficients=coefficients
         )
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return perspective_image_pil(
             inpt, startpoints, endpoints, interpolation=interpolation, fill=fill, coefficients=coefficients
         )
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def elastic_image_tensor(
@@ -1560,8 +1600,13 @@ def elastic(
         return elastic_image_tensor(inpt, displacement, interpolation=interpolation, fill=fill)
     elif isinstance(inpt, features._Feature):
         return inpt.elastic(displacement, interpolation=interpolation, fill=fill)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return elastic_image_pil(inpt, displacement, interpolation=interpolation, fill=fill)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 elastic_transform = elastic
@@ -1665,8 +1710,13 @@ def center_crop(inpt: features.InputTypeJIT, output_size: List[int]) -> features
         return center_crop_image_tensor(inpt, output_size)
     elif isinstance(inpt, features._Feature):
         return inpt.center_crop(output_size)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return center_crop_image_pil(inpt, output_size)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def resized_crop_image_tensor(
@@ -1753,8 +1803,13 @@ def resized_crop(
         )
     elif isinstance(inpt, features._Feature):
         return inpt.resized_crop(top, left, height, width, antialias=antialias, size=size, interpolation=interpolation)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return resized_crop_image_pil(inpt, top, left, height, width, size=size, interpolation=interpolation)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def _parse_five_crop_size(size: List[int]) -> List[int]:
@@ -1831,8 +1886,13 @@ def five_crop(
     elif isinstance(inpt, features.Video):
         output = five_crop_video(inpt.as_subclass(torch.Tensor), size)
         return tuple(features.Video.wrap_like(inpt, item) for item in output)  # type: ignore[return-value]
-    else:  # isinstance(inpt, PIL.Image.Image):
+    elif isinstance(inpt, PIL.Image.Image):
         return five_crop_image_pil(inpt, size)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, an `Image` or `Video` tensor subclass, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def ten_crop_image_tensor(image: torch.Tensor, size: List[int], vertical_flip: bool = False) -> List[torch.Tensor]:
@@ -1879,5 +1939,10 @@ def ten_crop(
     elif isinstance(inpt, features.Video):
         output = ten_crop_video(inpt.as_subclass(torch.Tensor), size, vertical_flip=vertical_flip)
         return [features.Video.wrap_like(inpt, item) for item in output]
-    else:  # isinstance(inpt, PIL.Image.Image):
+    elif isinstance(inpt, PIL.Image.Image):
         return ten_crop_image_pil(inpt, size, vertical_flip=vertical_flip)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, an `Image` or `Video` tensor subclass, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )

torchvision/prototype/transforms/functional/_meta.py

@@ -23,17 +23,22 @@ def get_dimensions_image_tensor(image: torch.Tensor) -> List[int]:
 get_dimensions_image_pil = _FP.get_dimensions
 
 
-def get_dimensions(image: Union[features.ImageTypeJIT, features.VideoTypeJIT]) -> List[int]:
-    if isinstance(image, torch.Tensor) and (
-        torch.jit.is_scripting() or not isinstance(image, (features.Image, features.Video))
+def get_dimensions(inpt: Union[features.ImageTypeJIT, features.VideoTypeJIT]) -> List[int]:
+    if isinstance(inpt, torch.Tensor) and (
+        torch.jit.is_scripting() or not isinstance(inpt, (features.Image, features.Video))
     ):
-        return get_dimensions_image_tensor(image)
-    elif isinstance(image, (features.Image, features.Video)):
-        channels = image.num_channels
-        height, width = image.spatial_size
+        return get_dimensions_image_tensor(inpt)
+    elif isinstance(inpt, (features.Image, features.Video)):
+        channels = inpt.num_channels
+        height, width = inpt.spatial_size
         return [channels, height, width]
+    elif isinstance(inpt, PIL.Image.Image):
+        return get_dimensions_image_pil(inpt)
     else:
-        return get_dimensions_image_pil(image)
+        raise TypeError(
+            f"Input can either be a plain tensor, an `Image` or `Video` tensor subclass, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def get_num_channels_image_tensor(image: torch.Tensor) -> int:
@@ -54,15 +59,20 @@ def get_num_channels_video(video: torch.Tensor) -> int:
     return get_num_channels_image_tensor(video)
 
 
-def get_num_channels(image: Union[features.ImageTypeJIT, features.VideoTypeJIT]) -> int:
-    if isinstance(image, torch.Tensor) and (
-        torch.jit.is_scripting() or not isinstance(image, (features.Image, features.Video))
+def get_num_channels(inpt: Union[features.ImageTypeJIT, features.VideoTypeJIT]) -> int:
+    if isinstance(inpt, torch.Tensor) and (
+        torch.jit.is_scripting() or not isinstance(inpt, (features.Image, features.Video))
     ):
-        return get_num_channels_image_tensor(image)
-    elif isinstance(image, (features.Image, features.Video)):
-        return image.num_channels
+        return get_num_channels_image_tensor(inpt)
+    elif isinstance(inpt, (features.Image, features.Video)):
+        return inpt.num_channels
+    elif isinstance(inpt, PIL.Image.Image):
+        return get_num_channels_image_pil(inpt)
     else:
-        return get_num_channels_image_pil(image)
+        raise TypeError(
+            f"Input can either be a plain tensor, an `Image` or `Video` tensor subclass, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 # We changed the names to ensure it can be used not only for images but also videos. Thus, we just alias it without
@@ -103,8 +113,13 @@ def get_spatial_size(inpt: features.InputTypeJIT) -> List[int]:
         return get_spatial_size_image_tensor(inpt)
     elif isinstance(inpt, (features.Image, features.Video, features.BoundingBox, features.Mask)):
         return list(inpt.spatial_size)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return get_spatial_size_image_pil(inpt)  # type: ignore[no-any-return]
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def get_num_frames_video(video: torch.Tensor) -> int:
@@ -117,7 +132,9 @@ def get_num_frames(inpt: features.VideoTypeJIT) -> int:
     elif isinstance(inpt, features.Video):
         return inpt.num_frames
     else:
-        raise TypeError(f"The video should be a Tensor. Got {type(inpt)}")
+        raise TypeError(
+            f"Input can either be a plain tensor or a `Video` tensor subclass, but got {type(inpt)} instead."
+        )
 
 
 def _xywh_to_xyxy(xywh: torch.Tensor, inplace: bool) -> torch.Tensor:
@@ -315,8 +332,13 @@ def convert_color_space(
             inpt.as_subclass(torch.Tensor), old_color_space=inpt.color_space, new_color_space=color_space
         )
         return features.Video.wrap_like(inpt, output, color_space=color_space)
+    elif isinstance(inpt, PIL.Image.Image):
+        return convert_color_space_image_pil(inpt, color_space=color_space)
     else:
-        return convert_color_space_image_pil(inpt, color_space)
+        raise TypeError(
+            f"Input can either be a plain tensor, an `Image` or `Video` tensor subclass, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
 
 
 def _num_value_bits(dtype: torch.dtype) -> int:
@@ -402,6 +424,11 @@ def convert_dtype(
     elif isinstance(inpt, features.Image):
         output = convert_dtype_image_tensor(inpt.as_subclass(torch.Tensor), dtype)
         return features.Image.wrap_like(inpt, output)
-    else:  # isinstance(inpt, features.Video):
+    elif isinstance(inpt, features.Video):
         output = convert_dtype_video(inpt.as_subclass(torch.Tensor), dtype)
         return features.Video.wrap_like(inpt, output)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor or an `Image` or `Video` tensor subclass, "
+            f"but got {type(inpt)} instead."
+        )

torchvision/prototype/transforms/functional/_misc.py

@@ -53,13 +53,14 @@ def normalize(
     std: List[float],
     inplace: bool = False,
 ) -> torch.Tensor:
-    if torch.jit.is_scripting():
-        correct_type = isinstance(inpt, torch.Tensor)
-    else:
-        correct_type = features.is_simple_tensor(inpt) or isinstance(inpt, (features.Image, features.Video))
+    if not torch.jit.is_scripting():
+        if features.is_simple_tensor(inpt) or isinstance(inpt, (features.Image, features.Video)):
             inpt = inpt.as_subclass(torch.Tensor)
-    if not correct_type:
-        raise TypeError(f"img should be Tensor Image. Got {type(inpt)}")
+        else:
+            raise TypeError(
+                f"Input can either be a plain tensor or an `Image` or `Video` tensor subclass, "
+                f"but got {type(inpt)} instead."
+            )
 
     # Image or Video type should not be retained after normalization due to unknown data range
     # Thus we return Tensor for input Image
@@ -168,5 +169,10 @@ def gaussian_blur(
         return gaussian_blur_image_tensor(inpt, kernel_size=kernel_size, sigma=sigma)
     elif isinstance(inpt, features._Feature):
         return inpt.gaussian_blur(kernel_size=kernel_size, sigma=sigma)
-    else:
+    elif isinstance(inpt, PIL.Image.Image):
         return gaussian_blur_image_pil(inpt, kernel_size=kernel_size, sigma=sigma)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, one of the tensor subclasses TorchVision provides, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )

torchvision/prototype/transforms/functional/_temporal.py

@@ -15,10 +15,14 @@ def uniform_temporal_subsample(
 ) -> features.VideoTypeJIT:
     if isinstance(inpt, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(inpt, features.Video)):
         return uniform_temporal_subsample_video(inpt, num_samples, temporal_dim=temporal_dim)
-    else:  # isinstance(inpt, features.Video)
+    elif isinstance(inpt, features.Video):
         if temporal_dim != -4 and inpt.ndim - 4 != temporal_dim:
             raise ValueError("Video inputs must have temporal_dim equivalent to -4")
         output = uniform_temporal_subsample_video(
             inpt.as_subclass(torch.Tensor), num_samples, temporal_dim=temporal_dim
         )
         return features.Video.wrap_like(inpt, output)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor or a `Video` tensor subclass, but got {type(inpt)} instead."
+        )

torchvision/prototype/transforms/functional/_type_conversion.py

@@ -8,13 +8,15 @@ from torchvision.transforms import functional as _F
 
 
 @torch.jit.unused
-def to_image_tensor(image: Union[torch.Tensor, PIL.Image.Image, np.ndarray]) -> features.Image:
-    if isinstance(image, np.ndarray):
-        output = torch.from_numpy(image).permute((2, 0, 1)).contiguous()
-    elif isinstance(image, PIL.Image.Image):
-        output = pil_to_tensor(image)
-    else:  # isinstance(inpt, torch.Tensor):
-        output = image
+def to_image_tensor(inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray]) -> features.Image:
+    if isinstance(inpt, np.ndarray):
+        output = torch.from_numpy(inpt).permute((2, 0, 1)).contiguous()
+    elif isinstance(inpt, PIL.Image.Image):
+        output = pil_to_tensor(inpt)
+    elif isinstance(inpt, torch.Tensor):
+        output = inpt
+    else:
+        raise TypeError(f"Input can either be a numpy array or a PIL image, but got {type(inpt)} instead.")
     return features.Image(output)