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81 lines
2.7 KiB
81 lines
2.7 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from math import exp
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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def gaussian(window_size, sigma):
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gauss = paddle.to_tensor([
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exp(-(x - window_size // 2)**2 / float(2 * sigma**2))
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for x in range(window_size)
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])
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return gauss / gauss.sum()
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def create_window(window_size, channel):
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_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
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_2D_window = paddle.matmul(_1D_window, paddle.transpose(
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_1D_window, [1, 0])).unsqueeze([0, 1])
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window = paddle.expand(_2D_window, [channel, 1, window_size, window_size])
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return window
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def _ssim(img1, img2, window, window_size, channel, size_average=True):
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mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
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mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
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mu1_sq = mu1.pow(2)
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mu2_sq = mu2.pow(2)
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mu1_mu2 = mu1 * mu2
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sigma1_sq = F.conv2d(
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img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
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sigma2_sq = F.conv2d(
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img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
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sigma12 = F.conv2d(
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img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
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C1 = 0.01**2
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C2 = 0.03**2
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ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) \
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/ ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
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if size_average:
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return ssim_map.mean()
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else:
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return ssim_map.mean(1).mean(1).mean(1)
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class SSIM(nn.Layer):
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def __init__(self, window_size=11, size_average=True):
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super().__init__()
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self.window_size = window_size
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self.size_average = size_average
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self.channel = 1
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self.window = create_window(window_size, self.channel)
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def forward(self, img1, img2):
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return _ssim(img1, img2, self.window, self.window_size, self.channel,
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self.size_average)
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def ssim(img1, img2, window_size=11, size_average=True):
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(_, channel, _, _) = img1.shape
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window = create_window(window_size, channel)
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return _ssim(img1, img2, window, window_size, channel, size_average)
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