# 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. # Transferred from https://github.com/rcdaudt/fully_convolutional_change_detection/blob/master/siamunet_diff.py . import paddle import paddle.nn as nn import paddle.nn.functional as F from .layers import Conv3x3, MaxPool2x2, ConvTransposed3x3, Identity class FCSiamDiff(nn.Layer): """ The FC-Siam-diff implementation based on PaddlePaddle. The original article refers to Rodrigo Caye Daudt, et al. "Fully convolutional siamese networks for change detection" (https://arxiv.org/abs/1810.08462). Args: in_channels (int): The number of bands of the input images. num_classes (int): The number of target classes. use_dropout (bool, optional): A bool value that indicates whether to use dropout layers. When the model is trained on a relatively small dataset, the dropout layers help prevent overfitting. Default: False. """ def __init__(self, in_channels, num_classes, use_dropout=False): super(FCSiamDiff, self).__init__() C1, C2, C3, C4, C5 = 16, 32, 64, 128, 256 self.use_dropout = use_dropout self.conv11 = Conv3x3(in_channels, C1, norm=True, act=True) self.do11 = self._make_dropout() self.conv12 = Conv3x3(C1, C1, norm=True, act=True) self.do12 = self._make_dropout() self.pool1 = MaxPool2x2() self.conv21 = Conv3x3(C1, C2, norm=True, act=True) self.do21 = self._make_dropout() self.conv22 = Conv3x3(C2, C2, norm=True, act=True) self.do22 = self._make_dropout() self.pool2 = MaxPool2x2() self.conv31 = Conv3x3(C2, C3, norm=True, act=True) self.do31 = self._make_dropout() self.conv32 = Conv3x3(C3, C3, norm=True, act=True) self.do32 = self._make_dropout() self.conv33 = Conv3x3(C3, C3, norm=True, act=True) self.do33 = self._make_dropout() self.pool3 = MaxPool2x2() self.conv41 = Conv3x3(C3, C4, norm=True, act=True) self.do41 = self._make_dropout() self.conv42 = Conv3x3(C4, C4, norm=True, act=True) self.do42 = self._make_dropout() self.conv43 = Conv3x3(C4, C4, norm=True, act=True) self.do43 = self._make_dropout() self.pool4 = MaxPool2x2() self.upconv4 = ConvTransposed3x3(C4, C4, output_padding=1) self.conv43d = Conv3x3(C5, C4, norm=True, act=True) self.do43d = self._make_dropout() self.conv42d = Conv3x3(C4, C4, norm=True, act=True) self.do42d = self._make_dropout() self.conv41d = Conv3x3(C4, C3, norm=True, act=True) self.do41d = self._make_dropout() self.upconv3 = ConvTransposed3x3(C3, C3, output_padding=1) self.conv33d = Conv3x3(C4, C3, norm=True, act=True) self.do33d = self._make_dropout() self.conv32d = Conv3x3(C3, C3, norm=True, act=True) self.do32d = self._make_dropout() self.conv31d = Conv3x3(C3, C2, norm=True, act=True) self.do31d = self._make_dropout() self.upconv2 = ConvTransposed3x3(C2, C2, output_padding=1) self.conv22d = Conv3x3(C3, C2, norm=True, act=True) self.do22d = self._make_dropout() self.conv21d = Conv3x3(C2, C1, norm=True, act=True) self.do21d = self._make_dropout() self.upconv1 = ConvTransposed3x3(C1, C1, output_padding=1) self.conv12d = Conv3x3(C2, C1, norm=True, act=True) self.do12d = self._make_dropout() self.conv11d = Conv3x3(C1, num_classes) self.init_weight() def forward(self, t1, t2): # Encode t1 # Stage 1 x11 = self.do11(self.conv11(t1)) x12_1 = self.do12(self.conv12(x11)) x1p = self.pool1(x12_1) # Stage 2 x21 = self.do21(self.conv21(x1p)) x22_1 = self.do22(self.conv22(x21)) x2p = self.pool2(x22_1) # Stage 3 x31 = self.do31(self.conv31(x2p)) x32 = self.do32(self.conv32(x31)) x33_1 = self.do33(self.conv33(x32)) x3p = self.pool3(x33_1) # Stage 4 x41 = self.do41(self.conv41(x3p)) x42 = self.do42(self.conv42(x41)) x43_1 = self.do43(self.conv43(x42)) x4p = self.pool4(x43_1) # Encode t2 # Stage 1 x11 = self.do11(self.conv11(t2)) x12_2 = self.do12(self.conv12(x11)) x1p = self.pool1(x12_2) # Stage 2 x21 = self.do21(self.conv21(x1p)) x22_2 = self.do22(self.conv22(x21)) x2p = self.pool2(x22_2) # Stage 3 x31 = self.do31(self.conv31(x2p)) x32 = self.do32(self.conv32(x31)) x33_2 = self.do33(self.conv33(x32)) x3p = self.pool3(x33_2) # Stage 4 x41 = self.do41(self.conv41(x3p)) x42 = self.do42(self.conv42(x41)) x43_2 = self.do43(self.conv43(x42)) x4p = self.pool4(x43_2) # Decode # Stage 4d x4d = self.upconv4(x4p) pad4 = (0, x43_1.shape[3] - x4d.shape[3], 0, x43_1.shape[2] - x4d.shape[2]) x4d = F.pad(x4d, pad=pad4, mode='replicate') x4d = paddle.concat([x4d, paddle.abs(x43_1 - x43_2)], 1) x43d = self.do43d(self.conv43d(x4d)) x42d = self.do42d(self.conv42d(x43d)) x41d = self.do41d(self.conv41d(x42d)) # Stage 3d x3d = self.upconv3(x41d) pad3 = (0, x33_1.shape[3] - x3d.shape[3], 0, x33_1.shape[2] - x3d.shape[2]) x3d = F.pad(x3d, pad=pad3, mode='replicate') x3d = paddle.concat([x3d, paddle.abs(x33_1 - x33_2)], 1) x33d = self.do33d(self.conv33d(x3d)) x32d = self.do32d(self.conv32d(x33d)) x31d = self.do31d(self.conv31d(x32d)) # Stage 2d x2d = self.upconv2(x31d) pad2 = (0, x22_1.shape[3] - x2d.shape[3], 0, x22_1.shape[2] - x2d.shape[2]) x2d = F.pad(x2d, pad=pad2, mode='replicate') x2d = paddle.concat([x2d, paddle.abs(x22_1 - x22_2)], 1) x22d = self.do22d(self.conv22d(x2d)) x21d = self.do21d(self.conv21d(x22d)) # Stage 1d x1d = self.upconv1(x21d) pad1 = (0, x12_1.shape[3] - x1d.shape[3], 0, x12_1.shape[2] - x1d.shape[2]) x1d = F.pad(x1d, pad=pad1, mode='replicate') x1d = paddle.concat([x1d, paddle.abs(x12_1 - x12_2)], 1) x12d = self.do12d(self.conv12d(x1d)) x11d = self.conv11d(x12d) return [x11d] def init_weight(self): pass def _make_dropout(self): if self.use_dropout: return nn.Dropout2D(p=0.2) else: return Identity()