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OpenDAS
vision
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993325dd
"vscode:/vscode.git/clone" did not exist on "85494e88189aa9aedf98f22ff6d61da39ebd2800"
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Commit
993325dd
authored
Jun 30, 2021
by
Nicolas Hug
Committed by
GitHub
Jun 30, 2021
Browse files
Minor additions to Resize docs (#4138)
parent
a83b9a17
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-6
torchvision/transforms/functional.py
torchvision/transforms/functional.py
+5
-3
torchvision/transforms/transforms.py
torchvision/transforms/transforms.py
+5
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torchvision/transforms/functional.py
View file @
993325dd
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@@ -346,7 +346,8 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = Inte
The output image might be different depending on its type: when downsampling, the interpolation of PIL images
and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
types.
types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors
closer.
Args:
img (PIL Image or Tensor): Image to be resized.
...
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@@ -372,8 +373,9 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = Inte
if ``size`` is an int (or a sequence of length 1 in torchscript
mode).
antialias (bool, optional): antialias flag. If ``img`` is PIL Image, the flag is ignored and anti-alias
is always used. If ``img`` is Tensor, the flag is False by default and can be set True for
``InterpolationMode.BILINEAR`` only mode.
is always used. If ``img`` is Tensor, the flag is False by default and can be set to True for
``InterpolationMode.BILINEAR`` only mode. This can help making the output for PIL images and tensors
closer.
.. warning::
There is no autodiff support for ``antialias=True`` option with input ``img`` as Tensor.
...
...
torchvision/transforms/transforms.py
View file @
993325dd
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@@ -233,7 +233,8 @@ class Resize(torch.nn.Module):
The output image might be different depending on its type: when downsampling, the interpolation of PIL images
and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
types.
types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors
closer.
Args:
size (sequence or int): Desired output size. If size is a sequence like
...
...
@@ -258,8 +259,9 @@ class Resize(torch.nn.Module):
if ``size`` is an int (or a sequence of length 1 in torchscript
mode).
antialias (bool, optional): antialias flag. If ``img`` is PIL Image, the flag is ignored and anti-alias
is always used. If ``img`` is Tensor, the flag is False by default and can be set True for
``InterpolationMode.BILINEAR`` only mode.
is always used. If ``img`` is Tensor, the flag is False by default and can be set to True for
``InterpolationMode.BILINEAR`` only mode. This can help making the output for PIL images and tensors
closer.
.. warning::
There is no autodiff support for ``antialias=True`` option with input ``img`` as Tensor.
...
...
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