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103 lines
3.5 KiB
103 lines
3.5 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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import paddle.nn.functional as F |
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from .layers import make_norm, Conv3x3, CBAM |
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from .stanet import Backbone, Decoder |
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class DSAMNet(nn.Layer): |
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""" |
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The DSAMNet implementation based on PaddlePaddle. |
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The original article refers to |
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Q. Shi, et al., "A Deeply Supervised Attention Metric-Based Network and an |
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Open Aerial Image Dataset for Remote Sensing Change Detection" |
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(https://ieeexplore.ieee.org/document/9467555). |
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Note that this implementation differs from the original work in two aspects: |
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1. We do not use multiple dilation rates in layer 4 of the ResNet backbone. |
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2. A classification head is used in place of the original metric learning-based |
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head to stablize the training process. |
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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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ca_ratio (int, optional): Channel reduction ratio for the channel |
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attention module. Default: 8. |
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sa_kernel (int, optional): Size of the convolutional kernel used in the |
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spatial attention module. Default: 7. |
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""" |
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def __init__(self, in_channels, num_classes, ca_ratio=8, sa_kernel=7): |
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super(DSAMNet, self).__init__() |
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WIDTH = 64 |
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self.backbone = Backbone( |
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in_ch=in_channels, arch='resnet18', strides=(1, 1, 2, 2, 1)) |
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self.decoder = Decoder(WIDTH) |
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self.cbam1 = CBAM(64, ratio=ca_ratio, kernel_size=sa_kernel) |
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self.cbam2 = CBAM(64, ratio=ca_ratio, kernel_size=sa_kernel) |
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self.dsl2 = DSLayer(64, num_classes, 32, stride=2, output_padding=1) |
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self.dsl3 = DSLayer(128, num_classes, 32, stride=4, output_padding=3) |
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self.conv_out = nn.Sequential( |
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Conv3x3( |
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WIDTH, WIDTH, norm=True, act=True), |
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Conv3x3(WIDTH, num_classes)) |
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self.init_weight() |
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def forward(self, t1, t2): |
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f1 = self.backbone(t1) |
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f2 = self.backbone(t2) |
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y1 = self.decoder(f1) |
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y2 = self.decoder(f2) |
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y1 = self.cbam1(y1) |
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y2 = self.cbam2(y2) |
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out = paddle.abs(y1 - y2) |
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out = F.interpolate( |
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out, size=paddle.shape(t1)[2:], mode='bilinear', align_corners=True) |
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pred = self.conv_out(out) |
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if not self.training: |
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return [pred] |
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else: |
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ds2 = self.dsl2(paddle.abs(f1[0] - f2[0])) |
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ds3 = self.dsl3(paddle.abs(f1[1] - f2[1])) |
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return [pred, ds2, ds3] |
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def init_weight(self): |
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pass |
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class DSLayer(nn.Sequential): |
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def __init__(self, in_ch, out_ch, itm_ch, **convd_kwargs): |
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super(DSLayer, self).__init__( |
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nn.Conv2DTranspose( |
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in_ch, itm_ch, kernel_size=3, padding=1, **convd_kwargs), |
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make_norm(itm_ch), |
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nn.ReLU(), |
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nn.Dropout2D(p=0.2), |
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nn.Conv2DTranspose( |
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itm_ch, out_ch, kernel_size=3, padding=1))
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