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