You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
173 lines
6.2 KiB
173 lines
6.2 KiB
# 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/unet.py . |
|
|
|
import paddle |
|
import paddle.nn as nn |
|
import paddle.nn.functional as F |
|
|
|
from .layers import Conv3x3, MaxPool2x2, ConvTransposed3x3, Identity |
|
|
|
|
|
class FCEarlyFusion(nn.Layer): |
|
""" |
|
The FC-EF 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): Number of bands of the input images. |
|
num_classes (int): 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(FCEarlyFusion, 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, 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, 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, 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, norm=True, act=True) |
|
self.do12d = self._make_dropout() |
|
self.conv11d = Conv3x3(C1, num_classes) |
|
|
|
self.init_weight() |
|
|
|
def forward(self, t1, t2): |
|
x = paddle.concat([t1, t2], axis=1) |
|
|
|
# Stage 1 |
|
x11 = self.do11(self.conv11(x)) |
|
x12 = self.do12(self.conv12(x11)) |
|
x1p = self.pool1(x12) |
|
|
|
# Stage 2 |
|
x21 = self.do21(self.conv21(x1p)) |
|
x22 = self.do22(self.conv22(x21)) |
|
x2p = self.pool2(x22) |
|
|
|
# Stage 3 |
|
x31 = self.do31(self.conv31(x2p)) |
|
x32 = self.do32(self.conv32(x31)) |
|
x33 = self.do33(self.conv33(x32)) |
|
x3p = self.pool3(x33) |
|
|
|
# Stage 4 |
|
x41 = self.do41(self.conv41(x3p)) |
|
x42 = self.do42(self.conv42(x41)) |
|
x43 = self.do43(self.conv43(x42)) |
|
x4p = self.pool4(x43) |
|
|
|
# Stage 4d |
|
x4d = self.upconv4(x4p) |
|
pad4 = (0, x43.shape[3] - x4d.shape[3], 0, x43.shape[2] - x4d.shape[2]) |
|
x4d = paddle.concat([F.pad(x4d, pad=pad4, mode='replicate'), x43], 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.shape[3] - x3d.shape[3], 0, x33.shape[2] - x3d.shape[2]) |
|
x3d = paddle.concat([F.pad(x3d, pad=pad3, mode='replicate'), x33], 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.shape[3] - x2d.shape[3], 0, x22.shape[2] - x2d.shape[2]) |
|
x2d = paddle.concat([F.pad(x2d, pad=pad2, mode='replicate'), x22], 1) |
|
x22d = self.do22d(self.conv22d(x2d)) |
|
x21d = self.do21d(self.conv21d(x22d)) |
|
|
|
# Stage 1d |
|
x1d = self.upconv1(x21d) |
|
pad1 = (0, x12.shape[3] - x1d.shape[3], 0, x12.shape[2] - x1d.shape[2]) |
|
x1d = paddle.concat([F.pad(x1d, pad=pad1, mode='replicate'), x12], 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()
|
|
|