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98 lines
3.2 KiB
98 lines
3.2 KiB
# Copyright (c) 2022 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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import paddle |
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import paddle.nn as nn |
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import paddle.nn.functional as F |
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from .blocks import Conv1x1, BasicConv |
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class ChannelAttention(nn.Layer): |
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""" |
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The channel attention module implementation based on PaddlePaddle. |
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The original article refers to |
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Sanghyun Woo, et al., "CBAM: Convolutional Block Attention Module" |
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(https://arxiv.org/abs/1807.06521). |
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Args: |
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in_ch (int): Number of channels of the input features. |
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ratio (int, optional): Channel reduction ratio. Default: 8. |
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""" |
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def __init__(self, in_ch, ratio=8): |
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super(ChannelAttention, self).__init__() |
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self.avg_pool = nn.AdaptiveAvgPool2D(1) |
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self.max_pool = nn.AdaptiveMaxPool2D(1) |
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self.fc1 = Conv1x1(in_ch, in_ch // ratio, bias=False, act=True) |
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self.fc2 = Conv1x1(in_ch // ratio, in_ch, bias=False) |
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def forward(self, x): |
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avg_out = self.fc2(self.fc1(self.avg_pool(x))) |
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max_out = self.fc2(self.fc1(self.max_pool(x))) |
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out = avg_out + max_out |
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return F.sigmoid(out) |
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class SpatialAttention(nn.Layer): |
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""" |
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The spatial attention module implementation based on PaddlePaddle. |
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The original article refers to |
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Sanghyun Woo, et al., "CBAM: Convolutional Block Attention Module" |
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(https://arxiv.org/abs/1807.06521). |
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Args: |
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kernel_size (int, optional): Size of the convolutional kernel. |
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Default: 7. |
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""" |
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def __init__(self, kernel_size=7): |
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super(SpatialAttention, self).__init__() |
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self.conv = BasicConv(2, 1, kernel_size, bias=False) |
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def forward(self, x): |
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avg_out = paddle.mean(x, axis=1, keepdim=True) |
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max_out = paddle.max(x, axis=1, keepdim=True) |
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x = paddle.concat([avg_out, max_out], axis=1) |
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x = self.conv(x) |
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return F.sigmoid(x) |
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class CBAM(nn.Layer): |
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""" |
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The CBAM implementation based on PaddlePaddle. |
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The original article refers to |
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Sanghyun Woo, et al., "CBAM: Convolutional Block Attention Module" |
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(https://arxiv.org/abs/1807.06521). |
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Args: |
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in_ch (int): Number of channels of the input features. |
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ratio (int, optional): Channel reduction ratio for the channel |
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attention module. Default: 8. |
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kernel_size (int, optional): Size of the convolutional kernel used in |
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the spatial attention module. Default: 7. |
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""" |
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def __init__(self, in_ch, ratio=8, kernel_size=7): |
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super(CBAM, self).__init__() |
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self.ca = ChannelAttention(in_ch, ratio=ratio) |
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self.sa = SpatialAttention(kernel_size=kernel_size) |
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def forward(self, x): |
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y = self.ca(x) * x |
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y = self.sa(y) * y |
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return y
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