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65 lines
2.6 KiB
65 lines
2.6 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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import paddle |
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import paddle.nn as nn |
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from .layers import Conv7x7 |
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class CDNet(nn.Layer): |
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""" |
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The CDNet implementation based on PaddlePaddle. |
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The original article refers to |
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Pablo F. Alcantarilla, et al., "Street-View Change Detection with Deconvolut |
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ional Networks" |
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(https://link.springer.com/article/10.1007/s10514-018-9734-5). |
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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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""" |
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def __init__(self, in_channels, num_classes): |
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super(CDNet, self).__init__() |
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self.conv1 = Conv7x7(in_channels, 64, norm=True, act=True) |
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self.pool1 = nn.MaxPool2D(2, 2, return_mask=True) |
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self.conv2 = Conv7x7(64, 64, norm=True, act=True) |
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self.pool2 = nn.MaxPool2D(2, 2, return_mask=True) |
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self.conv3 = Conv7x7(64, 64, norm=True, act=True) |
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self.pool3 = nn.MaxPool2D(2, 2, return_mask=True) |
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self.conv4 = Conv7x7(64, 64, norm=True, act=True) |
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self.pool4 = nn.MaxPool2D(2, 2, return_mask=True) |
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self.conv5 = Conv7x7(64, 64, norm=True, act=True) |
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self.upool4 = nn.MaxUnPool2D(2, 2) |
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self.conv6 = Conv7x7(64, 64, norm=True, act=True) |
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self.upool3 = nn.MaxUnPool2D(2, 2) |
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self.conv7 = Conv7x7(64, 64, norm=True, act=True) |
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self.upool2 = nn.MaxUnPool2D(2, 2) |
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self.conv8 = Conv7x7(64, 64, norm=True, act=True) |
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self.upool1 = nn.MaxUnPool2D(2, 2) |
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self.conv_out = Conv7x7(64, num_classes, norm=False, act=False) |
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def forward(self, t1, t2): |
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x = paddle.concat([t1, t2], axis=1) |
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x, ind1 = self.pool1(self.conv1(x)) |
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x, ind2 = self.pool2(self.conv2(x)) |
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x, ind3 = self.pool3(self.conv3(x)) |
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x, ind4 = self.pool4(self.conv4(x)) |
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x = self.conv5(self.upool4(x, ind4)) |
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x = self.conv6(self.upool3(x, ind3)) |
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x = self.conv7(self.upool2(x, ind2)) |
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x = self.conv8(self.upool1(x, ind1)) |
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return [self.conv_out(x)]
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