better looking

Author: voldemortX

torchvision/transforms/functional.py

@@ -427,21 +427,18 @@ def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "con
         padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
             Default is constant.
 
-            - constant: pads with a constant value, this value is specified with fill
+            - constant: pads with a constant value, this value is specified with fill.
 
-            - edge: pads with the last value at the edge of the image
+            - edge: pads with the last value at the edge of the image.
+              If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
 
-                If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
+            - reflect: pads with reflection of image without repeating the last value on the edge.
+              For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
+              will result in [3, 2, 1, 2, 3, 4, 3, 2]
 
-            - reflect: pads with reflection of image without repeating the last value on the edge
-
-                For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
-                will result in [3, 2, 1, 2, 3, 4, 3, 2]
-
-            - symmetric: pads with reflection of image repeating the last value on the edge
-
-                For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
-                will result in [2, 1, 1, 2, 3, 4, 4, 3]
+            - symmetric: pads with reflection of image repeating the last value on the edge.
+              For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
+              will result in [2, 1, 1, 2, 3, 4, 4, 3]
 
     Returns:
         PIL Image or Tensor: Padded image.

torchvision/transforms/transforms.py

@@ -359,21 +359,18 @@ class Pad(torch.nn.Module):
         padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
             Default is constant.
 
-            - constant: pads with a constant value, this value is specified with fill
+            - constant: pads with a constant value, this value is specified with fill.
 
-            - edge: pads with the last value at the edge of the image
+            - edge: pads with the last value at the edge of the image.
+              If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
 
-                If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
+            - reflect: pads with reflection of image without repeating the last value on the edge.
+              For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
+              will result in [3, 2, 1, 2, 3, 4, 3, 2]
 
-            - reflect: pads with reflection of image without repeating the last value on the edge
-
-                For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
-                will result in [3, 2, 1, 2, 3, 4, 3, 2]
-
-            - symmetric: pads with reflection of image repeating the last value on the edge
-
-                For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
-                will result in [2, 1, 1, 2, 3, 4, 4, 3]
+            - symmetric: pads with reflection of image repeating the last value on the edge.
+              For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
+              will result in [2, 1, 1, 2, 3, 4, 4, 3]
     """
 
     def __init__(self, padding, fill=0, padding_mode="constant"):
@@ -544,21 +541,18 @@ class RandomCrop(torch.nn.Module):
         padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
             Default is constant.
 
-            - constant: pads with a constant value, this value is specified with fill
-
-            - edge: pads with the last value at the edge of the image
-
-                If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
-
-            - reflect: pads with reflection of image without repeating the last value on the edge
+            - constant: pads with a constant value, this value is specified with fill.
 
-                For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
-                will result in [3, 2, 1, 2, 3, 4, 3, 2]
+            - edge: pads with the last value at the edge of the image.
+              If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2
 
-            - symmetric: pads with reflection of image repeating the last value on the edge
+            - reflect: pads with reflection of image without repeating the last value on the edge.
+              For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
+              will result in [3, 2, 1, 2, 3, 4, 3, 2]
 
-                For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
-                will result in [2, 1, 1, 2, 3, 4, 4, 3]
+            - symmetric: pads with reflection of image repeating the last value on the edge.
+              For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
+              will result in [2, 1, 1, 2, 3, 4, 4, 3]
     """
 
     @staticmethod