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add padding-mode choice to RandomCrop #512

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merged 1 commit into from
May 24, 2018
Merged

add padding-mode choice to RandomCrop #512

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11 changes: 7 additions & 4 deletions torchvision/transforms/transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -368,16 +368,19 @@ class RandomCrop(object):
of the image. Default is 0, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively.
padding_mode (str): Type of padding. Should be: constant, edge, reflect
or symmetric. Default is constant.
pad_if_needed (boolean): It will pad the image if smaller than the
desired size to avoid raising an exception.
"""

def __init__(self, size, padding=0, pad_if_needed=False):
def __init__(self, size, padding=0, padding_mode='constant', pad_if_needed=False):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
self.padding = padding
self.padding_mode = padding_mode
self.pad_if_needed = pad_if_needed

@staticmethod
Expand Down Expand Up @@ -409,14 +412,14 @@ def __call__(self, img):
PIL Image: Cropped image.
"""
if self.padding > 0:
img = F.pad(img, self.padding)
img = F.pad(img, self.padding, padding_mode=self.padding_mode)

# pad the width if needed
if self.pad_if_needed and img.size[0] < self.size[1]:
img = F.pad(img, (int((1 + self.size[1] - img.size[0]) / 2), 0))
img = F.pad(img, (int((1 + self.size[1] - img.size[0]) / 2), 0), padding_mode=self.padding_mode)
# pad the height if needed
if self.pad_if_needed and img.size[1] < self.size[0]:
img = F.pad(img, (0, int((1 + self.size[0] - img.size[1]) / 2)))
img = F.pad(img, (0, int((1 + self.size[0] - img.size[1]) / 2)), padding_mode=self.padding_mode)

i, j, h, w = self.get_params(img, self.size)

Expand Down