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178 lines
6.7 KiB
178 lines
6.7 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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# Refer to https://github.com/likyoo/Siam-NestedUNet . |
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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 .layers import Conv1x1, MaxPool2x2, make_norm, ChannelAttention |
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from .param_init import KaimingInitMixin |
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class SNUNet(nn.Layer, KaimingInitMixin): |
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""" |
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The SNUNet implementation based on PaddlePaddle. |
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The original article refers to |
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S. Fang, et al., "SNUNet-CD: A Densely Connected Siamese Network for Change |
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Detection of VHR Images" |
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(https://ieeexplore.ieee.org/document/9355573). |
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Note that bilinear interpolation is adopted as the upsampling method, which is |
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different from the paper. |
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Args: |
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in_channels (int): Number of bands of the input images. |
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num_classes (int): Number of target classes. |
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width (int, optional): Output channels of the first convolutional layer. |
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Default: 32. |
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""" |
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def __init__(self, in_channels, num_classes, width=32): |
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super(SNUNet, self).__init__() |
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filters = (width, width * 2, width * 4, width * 8, width * 16) |
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self.conv0_0 = ConvBlockNested(in_channels, filters[0], filters[0]) |
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self.conv1_0 = ConvBlockNested(filters[0], filters[1], filters[1]) |
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self.conv2_0 = ConvBlockNested(filters[1], filters[2], filters[2]) |
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self.conv3_0 = ConvBlockNested(filters[2], filters[3], filters[3]) |
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self.conv4_0 = ConvBlockNested(filters[3], filters[4], filters[4]) |
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self.down1 = MaxPool2x2() |
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self.down2 = MaxPool2x2() |
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self.down3 = MaxPool2x2() |
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self.down4 = MaxPool2x2() |
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self.up1_0 = Up(filters[1]) |
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self.up2_0 = Up(filters[2]) |
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self.up3_0 = Up(filters[3]) |
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self.up4_0 = Up(filters[4]) |
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self.conv0_1 = ConvBlockNested(filters[0] * 2 + filters[1], filters[0], |
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filters[0]) |
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self.conv1_1 = ConvBlockNested(filters[1] * 2 + filters[2], filters[1], |
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filters[1]) |
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self.conv2_1 = ConvBlockNested(filters[2] * 2 + filters[3], filters[2], |
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filters[2]) |
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self.conv3_1 = ConvBlockNested(filters[3] * 2 + filters[4], filters[3], |
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filters[3]) |
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self.up1_1 = Up(filters[1]) |
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self.up2_1 = Up(filters[2]) |
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self.up3_1 = Up(filters[3]) |
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self.conv0_2 = ConvBlockNested(filters[0] * 3 + filters[1], filters[0], |
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filters[0]) |
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self.conv1_2 = ConvBlockNested(filters[1] * 3 + filters[2], filters[1], |
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filters[1]) |
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self.conv2_2 = ConvBlockNested(filters[2] * 3 + filters[3], filters[2], |
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filters[2]) |
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self.up1_2 = Up(filters[1]) |
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self.up2_2 = Up(filters[2]) |
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self.conv0_3 = ConvBlockNested(filters[0] * 4 + filters[1], filters[0], |
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filters[0]) |
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self.conv1_3 = ConvBlockNested(filters[1] * 4 + filters[2], filters[1], |
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filters[1]) |
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self.up1_3 = Up(filters[1]) |
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self.conv0_4 = ConvBlockNested(filters[0] * 5 + filters[1], filters[0], |
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filters[0]) |
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self.ca_intra = ChannelAttention(filters[0], ratio=4) |
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self.ca_inter = ChannelAttention(filters[0] * 4, ratio=16) |
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self.conv_out = Conv1x1(filters[0] * 4, num_classes) |
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self.init_weight() |
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def forward(self, t1, t2): |
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x0_0_t1 = self.conv0_0(t1) |
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x1_0_t1 = self.conv1_0(self.down1(x0_0_t1)) |
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x2_0_t1 = self.conv2_0(self.down2(x1_0_t1)) |
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x3_0_t1 = self.conv3_0(self.down3(x2_0_t1)) |
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x0_0_t2 = self.conv0_0(t2) |
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x1_0_t2 = self.conv1_0(self.down1(x0_0_t2)) |
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x2_0_t2 = self.conv2_0(self.down2(x1_0_t2)) |
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x3_0_t2 = self.conv3_0(self.down3(x2_0_t2)) |
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x4_0_t2 = self.conv4_0(self.down4(x3_0_t2)) |
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x0_1 = self.conv0_1( |
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paddle.concat([x0_0_t1, x0_0_t2, self.up1_0(x1_0_t2)], 1)) |
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x1_1 = self.conv1_1( |
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paddle.concat([x1_0_t1, x1_0_t2, self.up2_0(x2_0_t2)], 1)) |
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x0_2 = self.conv0_2( |
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paddle.concat([x0_0_t1, x0_0_t2, x0_1, self.up1_1(x1_1)], 1)) |
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x2_1 = self.conv2_1( |
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paddle.concat([x2_0_t1, x2_0_t2, self.up3_0(x3_0_t2)], 1)) |
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x1_2 = self.conv1_2( |
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paddle.concat([x1_0_t1, x1_0_t2, x1_1, self.up2_1(x2_1)], 1)) |
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x0_3 = self.conv0_3( |
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paddle.concat([x0_0_t1, x0_0_t2, x0_1, x0_2, self.up1_2(x1_2)], 1)) |
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x3_1 = self.conv3_1( |
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paddle.concat([x3_0_t1, x3_0_t2, self.up4_0(x4_0_t2)], 1)) |
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x2_2 = self.conv2_2( |
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paddle.concat([x2_0_t1, x2_0_t2, x2_1, self.up3_1(x3_1)], 1)) |
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x1_3 = self.conv1_3( |
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paddle.concat([x1_0_t1, x1_0_t2, x1_1, x1_2, self.up2_2(x2_2)], 1)) |
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x0_4 = self.conv0_4( |
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paddle.concat( |
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[x0_0_t1, x0_0_t2, x0_1, x0_2, x0_3, self.up1_3(x1_3)], 1)) |
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out = paddle.concat([x0_1, x0_2, x0_3, x0_4], 1) |
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intra = x0_1 + x0_2 + x0_3 + x0_4 |
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m_intra = self.ca_intra(intra) |
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out = self.ca_inter(out) * (out + paddle.tile(m_intra, (1, 4, 1, 1))) |
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pred = self.conv_out(out) |
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return [pred] |
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class ConvBlockNested(nn.Layer): |
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def __init__(self, in_ch, out_ch, mid_ch): |
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super(ConvBlockNested, self).__init__() |
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self.act = nn.ReLU() |
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self.conv1 = nn.Conv2D(in_ch, mid_ch, kernel_size=3, padding=1) |
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self.bn1 = make_norm(mid_ch) |
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self.conv2 = nn.Conv2D(mid_ch, out_ch, kernel_size=3, padding=1) |
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self.bn2 = make_norm(out_ch) |
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def forward(self, x): |
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x = self.conv1(x) |
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identity = x |
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x = self.bn1(x) |
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x = self.act(x) |
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x = self.conv2(x) |
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x = self.bn2(x) |
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output = self.act(x + identity) |
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return output |
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class Up(nn.Layer): |
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def __init__(self, in_ch, use_conv=False): |
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super(Up, self).__init__() |
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if use_conv: |
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self.up = nn.Conv2DTranspose(in_ch, in_ch, 2, stride=2) |
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else: |
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self.up = nn.Upsample( |
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scale_factor=2, mode='bilinear', align_corners=True) |
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def forward(self, x): |
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x = self.up(x) |
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return x
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