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75 lines
2.9 KiB
75 lines
2.9 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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class CDNet(nn.Layer): |
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def __init__(self, in_channels=6, num_classes=2): |
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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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class Conv7x7(nn.Layer): |
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def __init__(self, in_ch, out_ch, norm=False, act=False): |
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super(Conv7x7, self).__init__() |
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layers = [ |
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nn.Pad2D(3), nn.Conv2D( |
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in_ch, out_ch, 7, bias_attr=(False if norm else None)) |
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] |
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if norm: |
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layers.append(nn.BatchNorm2D(out_ch)) |
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if act: |
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layers.append(nn.ReLU()) |
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self.layers = nn.Sequential(*layers) |
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def forward(self, x): |
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return self.layers(x) |
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if __name__ == "__main__": |
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t1 = paddle.randn((1, 3, 512, 512), dtype="float32") |
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t2 = paddle.randn((1, 3, 512, 512), dtype="float32") |
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model = CDNet(6, 2) |
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pred = model(t1, t2)[0] |
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print(pred.shape)
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