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''' | |
* Copyright (c) 2023 Salesforce, Inc. | |
* All rights reserved. | |
* SPDX-License-Identifier: Apache License 2.0 | |
* For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/ | |
* By Can Qin | |
* Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet | |
* Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala | |
* Modified from MMCV repo: From https://github.com/open-mmlab/mmcv | |
* Copyright (c) OpenMMLab. All rights reserved. | |
''' | |
import warnings | |
import torch.nn as nn | |
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`') | |
return F.interpolate(input, size, scale_factor, mode, align_corners) | |
class Upsample(nn.Module): | |
def __init__(self, | |
size=None, | |
scale_factor=None, | |
mode='nearest', | |
align_corners=None): | |
super(Upsample, self).__init__() | |
self.size = size | |
if isinstance(scale_factor, tuple): | |
self.scale_factor = tuple(float(factor) for factor in scale_factor) | |
else: | |
self.scale_factor = float(scale_factor) if scale_factor else None | |
self.mode = mode | |
self.align_corners = align_corners | |
def forward(self, x): | |
if not self.size: | |
size = [int(t * self.scale_factor) for t in x.shape[-2:]] | |
else: | |
size = self.size | |
return resize(x, size, None, self.mode, self.align_corners) | |