# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Refer to https://github.com/likyoo/Siam-NestedUNet . import paddle import paddle.nn as nn import paddle.nn.functional as F from .layers import Conv1x1, MaxPool2x2, make_norm, ChannelAttention from .param_init import KaimingInitMixin class SNUNet(nn.Layer, KaimingInitMixin): """ The SNUNet implementation based on PaddlePaddle. The original article refers to S. Fang, et al., "SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images" (https://ieeexplore.ieee.org/document/9355573). Note that bilinear interpolation is adopted as the upsampling method, which is different from the paper. Args: in_channels (int): The number of bands of the input images. num_classes (int): The number of target classes. width (int, optional): The output channels of the first convolutional layer. Default: 32. """ def __init__(self, in_channels, num_classes, width=32): super(SNUNet, self).__init__() filters = (width, width * 2, width * 4, width * 8, width * 16) self.conv0_0 = ConvBlockNested(in_channels, filters[0], filters[0]) self.conv1_0 = ConvBlockNested(filters[0], filters[1], filters[1]) self.conv2_0 = ConvBlockNested(filters[1], filters[2], filters[2]) self.conv3_0 = ConvBlockNested(filters[2], filters[3], filters[3]) self.conv4_0 = ConvBlockNested(filters[3], filters[4], filters[4]) self.down1 = MaxPool2x2() self.down2 = MaxPool2x2() self.down3 = MaxPool2x2() self.down4 = MaxPool2x2() self.up1_0 = Up(filters[1]) self.up2_0 = Up(filters[2]) self.up3_0 = Up(filters[3]) self.up4_0 = Up(filters[4]) self.conv0_1 = ConvBlockNested(filters[0] * 2 + filters[1], filters[0], filters[0]) self.conv1_1 = ConvBlockNested(filters[1] * 2 + filters[2], filters[1], filters[1]) self.conv2_1 = ConvBlockNested(filters[2] * 2 + filters[3], filters[2], filters[2]) self.conv3_1 = ConvBlockNested(filters[3] * 2 + filters[4], filters[3], filters[3]) self.up1_1 = Up(filters[1]) self.up2_1 = Up(filters[2]) self.up3_1 = Up(filters[3]) self.conv0_2 = ConvBlockNested(filters[0] * 3 + filters[1], filters[0], filters[0]) self.conv1_2 = ConvBlockNested(filters[1] * 3 + filters[2], filters[1], filters[1]) self.conv2_2 = ConvBlockNested(filters[2] * 3 + filters[3], filters[2], filters[2]) self.up1_2 = Up(filters[1]) self.up2_2 = Up(filters[2]) self.conv0_3 = ConvBlockNested(filters[0] * 4 + filters[1], filters[0], filters[0]) self.conv1_3 = ConvBlockNested(filters[1] * 4 + filters[2], filters[1], filters[1]) self.up1_3 = Up(filters[1]) self.conv0_4 = ConvBlockNested(filters[0] * 5 + filters[1], filters[0], filters[0]) self.ca_intra = ChannelAttention(filters[0], ratio=4) self.ca_inter = ChannelAttention(filters[0] * 4, ratio=16) self.conv_out = Conv1x1(filters[0] * 4, num_classes) self.init_weight() def forward(self, t1, t2): x0_0_t1 = self.conv0_0(t1) x1_0_t1 = self.conv1_0(self.down1(x0_0_t1)) x2_0_t1 = self.conv2_0(self.down2(x1_0_t1)) x3_0_t1 = self.conv3_0(self.down3(x2_0_t1)) x0_0_t2 = self.conv0_0(t2) x1_0_t2 = self.conv1_0(self.down1(x0_0_t2)) x2_0_t2 = self.conv2_0(self.down2(x1_0_t2)) x3_0_t2 = self.conv3_0(self.down3(x2_0_t2)) x4_0_t2 = self.conv4_0(self.down4(x3_0_t2)) x0_1 = self.conv0_1( paddle.concat([x0_0_t1, x0_0_t2, self.up1_0(x1_0_t2)], 1)) x1_1 = self.conv1_1( paddle.concat([x1_0_t1, x1_0_t2, self.up2_0(x2_0_t2)], 1)) x0_2 = self.conv0_2( paddle.concat([x0_0_t1, x0_0_t2, x0_1, self.up1_1(x1_1)], 1)) x2_1 = self.conv2_1( paddle.concat([x2_0_t1, x2_0_t2, self.up3_0(x3_0_t2)], 1)) x1_2 = self.conv1_2( paddle.concat([x1_0_t1, x1_0_t2, x1_1, self.up2_1(x2_1)], 1)) x0_3 = self.conv0_3( paddle.concat([x0_0_t1, x0_0_t2, x0_1, x0_2, self.up1_2(x1_2)], 1)) x3_1 = self.conv3_1( paddle.concat([x3_0_t1, x3_0_t2, self.up4_0(x4_0_t2)], 1)) x2_2 = self.conv2_2( paddle.concat([x2_0_t1, x2_0_t2, x2_1, self.up3_1(x3_1)], 1)) x1_3 = self.conv1_3( paddle.concat([x1_0_t1, x1_0_t2, x1_1, x1_2, self.up2_2(x2_2)], 1)) x0_4 = self.conv0_4( paddle.concat( [x0_0_t1, x0_0_t2, x0_1, x0_2, x0_3, self.up1_3(x1_3)], 1)) out = paddle.concat([x0_1, x0_2, x0_3, x0_4], 1) intra = x0_1 + x0_2 + x0_3 + x0_4 m_intra = self.ca_intra(intra) out = self.ca_inter(out) * (out + paddle.tile(m_intra, (1, 4, 1, 1))) pred = self.conv_out(out) return [pred] class ConvBlockNested(nn.Layer): def __init__(self, in_ch, out_ch, mid_ch): super(ConvBlockNested, self).__init__() self.act = nn.ReLU() self.conv1 = nn.Conv2D(in_ch, mid_ch, kernel_size=3, padding=1) self.bn1 = make_norm(mid_ch) self.conv2 = nn.Conv2D(mid_ch, out_ch, kernel_size=3, padding=1) self.bn2 = make_norm(out_ch) def forward(self, x): x = self.conv1(x) identity = x x = self.bn1(x) x = self.act(x) x = self.conv2(x) x = self.bn2(x) output = self.act(x + identity) return output class Up(nn.Layer): def __init__(self, in_ch, use_conv=False): super(Up, self).__init__() if use_conv: self.up = nn.Conv2DTranspose(in_ch, in_ch, 2, stride=2) else: self.up = nn.Upsample( scale_factor=2, mode='bilinear', align_corners=True) def forward(self, x): x = self.up(x) return x