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Running
on
A10G
# Copyright (c) OpenMMLab. All rights reserved. | |
import warnings | |
import torch | |
import torch.nn.functional as F | |
def resize(input, | |
size=None, | |
scale_factor=None, | |
mode='nearest', | |
align_corners=None, | |
warning=True): | |
if warning: | |
if size is not None and align_corners: | |
input_h, input_w = tuple(int(x) for x in input.shape[2:]) | |
output_h, output_w = tuple(int(x) for x in size) | |
if output_h > input_h or output_w > output_h: | |
if ((output_h > 1 and output_w > 1 and input_h > 1 | |
and input_w > 1) and (output_h - 1) % (input_h - 1) | |
and (output_w - 1) % (input_w - 1)): | |
warnings.warn( | |
f'When align_corners={align_corners}, ' | |
'the output would more aligned if ' | |
f'input size {(input_h, input_w)} is `x+1` and ' | |
f'out size {(output_h, output_w)} is `nx+1`') | |
if isinstance(size, torch.Size): | |
size = tuple(int(x) for x in size) | |
return F.interpolate(input, size, scale_factor, mode, align_corners) | |