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202 lines
7.0 KiB
202 lines
7.0 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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# Transferred from https://github.com/rcdaudt/fully_convolutional_change_detection/blob/master/siamunet_conc.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 FCSiamConc(nn.Layer): |
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""" |
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The FC-Siam-conc 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 detection" |
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(https://arxiv.org/abs/1810.08462). |
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Args: |
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in_channels (int): The number of bands of the input images. |
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num_classes (int): The number of target classes. |
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use_dropout (bool, optional): A bool value that indicates whether to use dropout layers. When the model is trained |
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on a relatively small dataset, 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(FCSiamConc, 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, 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, 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, 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, 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 = paddle.concat( |
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[F.pad(x4d, pad=pad4, mode='replicate'), 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 = paddle.concat( |
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[F.pad(x3d, pad=pad3, mode='replicate'), 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 = paddle.concat( |
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[F.pad(x2d, pad=pad2, mode='replicate'), 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 = paddle.concat( |
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[F.pad(x1d, pad=pad1, mode='replicate'), 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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