Adjust adjust_* transforms (#3222)

* adjust_hue

* adjust_*

* colorjitter

torchvision/transforms/functional.py

@@ -709,7 +709,7 @@ def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image to be adjusted.
-        If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
+        If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
         where ... means it can have an arbitrary number of leading dimensions.
         brightness_factor (float):  How much to adjust the brightness. Can be
             any non negative number. 0 gives a black image, 1 gives the
@@ -729,6 +729,8 @@ def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image to be adjusted.
+        If img is torch Tensor, it is expected to be in [..., 3, H, W] format,
+        where ... means it can have an arbitrary number of leading dimensions.
         contrast_factor (float): How much to adjust the contrast. Can be any
             non negative number. 0 gives a solid gray image, 1 gives the
             original image while 2 increases the contrast by a factor of 2.
@@ -747,6 +749,8 @@ def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image to be adjusted.
+        If img is torch Tensor, it is expected to be in [..., 3, H, W] format,
+        where ... means it can have an arbitrary number of leading dimensions.
         saturation_factor (float):  How much to adjust the saturation. 0 will
             give a black and white image, 1 will give the original image while
             2 will enhance the saturation by a factor of 2.
@@ -776,6 +780,9 @@ def adjust_hue(img: Tensor, hue_factor: float) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image to be adjusted.
+        If img is torch Tensor, it is expected to be in [..., 3, H, W] format,
+        where ... means it can have an arbitrary number of leading dimensions.
+        If img is PIL Image mode "1", "L", "I", "F" and modes with transparency (alpha channel) are not supported.
         hue_factor (float):  How much to shift the hue channel. Should be in
             [-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in
             HSV space in positive and negative direction respectively.
@@ -806,8 +813,9 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): PIL Image to be adjusted.
-        If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
+        If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
         where ... means it can have an arbitrary number of leading dimensions.
+        If img is PIL Image, modes with transparency (alpha channel) are not supported.
         gamma (float): Non negative real number, same as :math:`\gamma` in the equation.
             gamma larger than 1 make the shadows darker,
             while gamma smaller than 1 make dark regions lighter.
@@ -1185,7 +1193,7 @@ def invert(img: Tensor) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image to have its colors inverted.
-            If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
+            If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
             where ... means it can have an arbitrary number of leading dimensions.
             If img is PIL Image, it is expected to be in mode "L" or "RGB".
 
@@ -1203,7 +1211,7 @@ def posterize(img: Tensor, bits: int) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image to have its colors posterized.
-            If img is a Tensor, it should be of type torch.uint8 and
+            If img is torch Tensor, it should be of type torch.uint8 and
             it is expected to be in [..., 1 or 3, H, W] format, where ... means
             it can have an arbitrary number of leading dimensions.
             If img is PIL Image, it is expected to be in mode "L" or "RGB".
@@ -1225,7 +1233,7 @@ def solarize(img: Tensor, threshold: float) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image to have its colors inverted.
-            If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
+            If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
             where ... means it can have an arbitrary number of leading dimensions.
             If img is PIL Image, it is expected to be in mode "L" or "RGB".
         threshold (float): All pixels equal or above this value are inverted.
@@ -1243,7 +1251,7 @@ def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image to be adjusted.
-        If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
+        If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
         where ... means it can have an arbitrary number of leading dimensions.
         sharpness_factor (float):  How much to adjust the sharpness. Can be
             any non negative number. 0 gives a blurred image, 1 gives the
@@ -1265,7 +1273,7 @@ def autocontrast(img: Tensor) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image on which autocontrast is applied.
-            If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
+            If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
             where ... means it can have an arbitrary number of leading dimensions.
             If img is PIL Image, it is expected to be in mode "L" or "RGB".
 
@@ -1285,7 +1293,7 @@ def equalize(img: Tensor) -> Tensor:
 
     Args:
         img (PIL Image or Tensor): Image on which equalize is applied.
-            If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
+            If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
             where ... means it can have an arbitrary number of leading dimensions.
             If img is PIL Image, it is expected to be in mode "P", "L" or "RGB".
 

torchvision/transforms/functional_tensor.py

@@ -180,7 +180,9 @@ def adjust_hue(img: Tensor, hue_factor: float) -> Tensor:
 
     _assert_image_tensor(img)
 
-    _assert_channels(img, [3])
+    _assert_channels(img, [1, 3])
+    if _get_image_num_channels(img) == 1:  # Match PIL behaviour
+        return img
 
     orig_dtype = img.dtype
     if img.dtype == torch.uint8:

torchvision/transforms/transforms.py

@@ -1043,7 +1043,8 @@ class LinearTransformation(torch.nn.Module):
 class ColorJitter(torch.nn.Module):
     """Randomly change the brightness, contrast, saturation and hue of an image.
     If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
+    to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+    If img is PIL Image, mode "1", "L", "I", "F" and modes with transparency (alpha channel) are not supported.
 
     Args:
         brightness (float or tuple of float (min, max)): How much to jitter brightness.