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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Transferred from https://github.com/rcdaudt/fully_convolutional_change_detection/blob/master/siamunet_conc.py .
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from .layers import Conv3x3, MaxPool2x2, ConvTransposed3x3, Identity
class FCSiamConc(nn.Layer):
"""
The FC-Siam-conc implementation based on PaddlePaddle.
The original article refers to
Rodrigo Caye Daudt, et al. "Fully convolutional siamese networks for change detection"
(https://arxiv.org/abs/1810.08462).
Args:
in_channels (int): The number of bands of the input images.
num_classes (int): The number of target classes.
use_dropout (bool, optional): A bool value that indicates whether to use dropout layers. When the model is trained
on a relatively small dataset, the dropout layers help prevent overfitting. Default: False.
"""
def __init__(self, in_channels, num_classes, use_dropout=False):
super(FCSiamConc, self).__init__()
C1, C2, C3, C4, C5 = 16, 32, 64, 128, 256
self.use_dropout = use_dropout
self.conv11 = Conv3x3(in_channels, C1, norm=True, act=True)
self.do11 = self._make_dropout()
self.conv12 = Conv3x3(C1, C1, norm=True, act=True)
self.do12 = self._make_dropout()
self.pool1 = MaxPool2x2()
self.conv21 = Conv3x3(C1, C2, norm=True, act=True)
self.do21 = self._make_dropout()
self.conv22 = Conv3x3(C2, C2, norm=True, act=True)
self.do22 = self._make_dropout()
self.pool2 = MaxPool2x2()
self.conv31 = Conv3x3(C2, C3, norm=True, act=True)
self.do31 = self._make_dropout()
self.conv32 = Conv3x3(C3, C3, norm=True, act=True)
self.do32 = self._make_dropout()
self.conv33 = Conv3x3(C3, C3, norm=True, act=True)
self.do33 = self._make_dropout()
self.pool3 = MaxPool2x2()
self.conv41 = Conv3x3(C3, C4, norm=True, act=True)
self.do41 = self._make_dropout()
self.conv42 = Conv3x3(C4, C4, norm=True, act=True)
self.do42 = self._make_dropout()
self.conv43 = Conv3x3(C4, C4, norm=True, act=True)
self.do43 = self._make_dropout()
self.pool4 = MaxPool2x2()
self.upconv4 = ConvTransposed3x3(C4, C4, output_padding=1)
self.conv43d = Conv3x3(C5 + C4, C4, norm=True, act=True)
self.do43d = self._make_dropout()
self.conv42d = Conv3x3(C4, C4, norm=True, act=True)
self.do42d = self._make_dropout()
self.conv41d = Conv3x3(C4, C3, norm=True, act=True)
self.do41d = self._make_dropout()
self.upconv3 = ConvTransposed3x3(C3, C3, output_padding=1)
self.conv33d = Conv3x3(C4 + C3, C3, norm=True, act=True)
self.do33d = self._make_dropout()
self.conv32d = Conv3x3(C3, C3, norm=True, act=True)
self.do32d = self._make_dropout()
self.conv31d = Conv3x3(C3, C2, norm=True, act=True)
self.do31d = self._make_dropout()
self.upconv2 = ConvTransposed3x3(C2, C2, output_padding=1)
self.conv22d = Conv3x3(C3 + C2, C2, norm=True, act=True)
self.do22d = self._make_dropout()
self.conv21d = Conv3x3(C2, C1, norm=True, act=True)
self.do21d = self._make_dropout()
self.upconv1 = ConvTransposed3x3(C1, C1, output_padding=1)
self.conv12d = Conv3x3(C2 + C1, C1, norm=True, act=True)
self.do12d = self._make_dropout()
self.conv11d = Conv3x3(C1, num_classes)
self.init_weight()
def forward(self, t1, t2):
# Encode t1
# Stage 1
x11 = self.do11(self.conv11(t1))
x12_1 = self.do12(self.conv12(x11))
x1p = self.pool1(x12_1)
# Stage 2
x21 = self.do21(self.conv21(x1p))
x22_1 = self.do22(self.conv22(x21))
x2p = self.pool2(x22_1)
# Stage 3
x31 = self.do31(self.conv31(x2p))
x32 = self.do32(self.conv32(x31))
x33_1 = self.do33(self.conv33(x32))
x3p = self.pool3(x33_1)
# Stage 4
x41 = self.do41(self.conv41(x3p))
x42 = self.do42(self.conv42(x41))
x43_1 = self.do43(self.conv43(x42))
x4p = self.pool4(x43_1)
# Encode t2
# Stage 1
x11 = self.do11(self.conv11(t2))
x12_2 = self.do12(self.conv12(x11))
x1p = self.pool1(x12_2)
# Stage 2
x21 = self.do21(self.conv21(x1p))
x22_2 = self.do22(self.conv22(x21))
x2p = self.pool2(x22_2)
# Stage 3
x31 = self.do31(self.conv31(x2p))
x32 = self.do32(self.conv32(x31))
x33_2 = self.do33(self.conv33(x32))
x3p = self.pool3(x33_2)
# Stage 4
x41 = self.do41(self.conv41(x3p))
x42 = self.do42(self.conv42(x41))
x43_2 = self.do43(self.conv43(x42))
x4p = self.pool4(x43_2)
# Decode
# Stage 4d
x4d = self.upconv4(x4p)
pad4 = (0, x43_1.shape[3] - x4d.shape[3], 0,
x43_1.shape[2] - x4d.shape[2])
x4d = paddle.concat(
[F.pad(x4d, pad=pad4, mode='replicate'), x43_1, x43_2], 1)
x43d = self.do43d(self.conv43d(x4d))
x42d = self.do42d(self.conv42d(x43d))
x41d = self.do41d(self.conv41d(x42d))
# Stage 3d
x3d = self.upconv3(x41d)
pad3 = (0, x33_1.shape[3] - x3d.shape[3], 0,
x33_1.shape[2] - x3d.shape[2])
x3d = paddle.concat(
[F.pad(x3d, pad=pad3, mode='replicate'), x33_1, x33_2], 1)
x33d = self.do33d(self.conv33d(x3d))
x32d = self.do32d(self.conv32d(x33d))
x31d = self.do31d(self.conv31d(x32d))
# Stage 2d
x2d = self.upconv2(x31d)
pad2 = (0, x22_1.shape[3] - x2d.shape[3], 0,
x22_1.shape[2] - x2d.shape[2])
x2d = paddle.concat(
[F.pad(x2d, pad=pad2, mode='replicate'), x22_1, x22_2], 1)
x22d = self.do22d(self.conv22d(x2d))
x21d = self.do21d(self.conv21d(x22d))
# Stage 1d
x1d = self.upconv1(x21d)
pad1 = (0, x12_1.shape[3] - x1d.shape[3], 0,
x12_1.shape[2] - x1d.shape[2])
x1d = paddle.concat(
[F.pad(x1d, pad=pad1, mode='replicate'), x12_1, x12_2], 1)
x12d = self.do12d(self.conv12d(x1d))
x11d = self.conv11d(x12d)
return [x11d]
def init_weight(self):
pass
def _make_dropout(self):
if self.use_dropout:
return nn.Dropout2D(p=0.2)
else:
return Identity()