From 6932bf6e162877a167a79b80e5ab4a0f88d3d1cb Mon Sep 17 00:00:00 2001 From: Bobholamovic Date: Mon, 14 Mar 2022 20:13:50 +0800 Subject: [PATCH] [Feature] Add UNetSiamConc and UNetSiamDiff --- paddlers/models/cd/models/__init__.py | 4 +- paddlers/models/cd/models/unet_siamconc.py | 224 +++++++++++++++++++++ paddlers/models/cd/models/unet_siamdiff.py | 224 +++++++++++++++++++++ paddlers/tasks/changedetector.py | 38 +++- 4 files changed, 488 insertions(+), 2 deletions(-) create mode 100644 paddlers/models/cd/models/unet_siamconc.py create mode 100644 paddlers/models/cd/models/unet_siamdiff.py diff --git a/paddlers/models/cd/models/__init__.py b/paddlers/models/cd/models/__init__.py index 8d4bf82..e4ba507 100644 --- a/paddlers/models/cd/models/__init__.py +++ b/paddlers/models/cd/models/__init__.py @@ -13,4 +13,6 @@ # limitations under the License. from .cdnet import CDNet -from .unet_ef import UNetEarlyFusion \ No newline at end of file +from .unet_ef import UNetEarlyFusion +from .unet_siamconc import UNetSiamConc +from .unet_siamdiff import UNetSiamDiff \ No newline at end of file diff --git a/paddlers/models/cd/models/unet_siamconc.py b/paddlers/models/cd/models/unet_siamconc.py new file mode 100644 index 0000000..afc1644 --- /dev/null +++ b/paddlers/models/cd/models/unet_siamconc.py @@ -0,0 +1,224 @@ +# 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. + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + + +from .layers import Conv3x3, MaxPool2x2, ConvTransposed3x3, Identity +from .param_init import normal_init, constant_init + + +class UNetSiamConc(nn.Layer): + """ + The FC-Siam-conc implementation based on PaddlePaddle. + + The original article refers to + Caye Daudt, R., 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().__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, + paddle.shape(x43_1)[3]-paddle.shape(x4d)[3], + 0, + paddle.shape(x43_1)[2]-paddle.shape(x4d)[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, + paddle.shape(x33_1)[3]-paddle.shape(x3d)[3], + 0, + paddle.shape(x33_1)[2]-paddle.shape(x3d)[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, + paddle.shape(x22_1)[3]-paddle.shape(x2d)[3], + 0, + paddle.shape(x22_1)[2]-paddle.shape(x2d)[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, + paddle.shape(x12_1)[3]-paddle.shape(x1d)[3], + 0, + paddle.shape(x12_1)[2]-paddle.shape(x1d)[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): + for sublayer in self.sublayers(): + if isinstance(sublayer, nn.Conv2D): + normal_init(sublayer.weight, std=0.001) + elif isinstance(sublayer, (nn.BatchNorm, nn.SyncBatchNorm)): + constant_init(sublayer.weight, value=1.0) + constant_init(sublayer.bias, value=0.0) + + def _make_dropout(self): + if self.use_dropout: + return nn.Dropout2D(p=0.2) + else: + return Identity() \ No newline at end of file diff --git a/paddlers/models/cd/models/unet_siamdiff.py b/paddlers/models/cd/models/unet_siamdiff.py new file mode 100644 index 0000000..371f573 --- /dev/null +++ b/paddlers/models/cd/models/unet_siamdiff.py @@ -0,0 +1,224 @@ +# 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. + +import paddle +import paddle.nn as nn +import paddle.nn.functional as F + + +from .layers import Conv3x3, MaxPool2x2, ConvTransposed3x3, Identity +from .param_init import normal_init, constant_init + + +class UNetSiamDiff(nn.Layer): + """ + The FC-Siam-diff implementation based on PaddlePaddle. + + The original article refers to + Caye Daudt, R., 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().__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): + # 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, + paddle.shape(x43_1)[3]-paddle.shape(x4d)[3], + 0, + paddle.shape(x43_1)[2]-paddle.shape(x4d)[2] + ) + x4d = paddle.concat([F.pad(x4d, pad=pad4, mode='replicate'), paddle.abs(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, + paddle.shape(x33_1)[3]-paddle.shape(x3d)[3], + 0, + paddle.shape(x33_1)[2]-paddle.shape(x3d)[2] + ) + x3d = paddle.concat([F.pad(x3d, pad=pad3, mode='replicate'), paddle.abs(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, + paddle.shape(x22_1)[3]-paddle.shape(x2d)[3], + 0, + paddle.shape(x22_1)[2]-paddle.shape(x2d)[2] + ) + x2d = paddle.concat([F.pad(x2d, pad=pad2, mode='replicate'), paddle.abs(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, + paddle.shape(x12_1)[3]-paddle.shape(x1d)[3], + 0, + paddle.shape(x12_1)[2]-paddle.shape(x1d)[2] + ) + x1d = paddle.concat([F.pad(x1d, pad=pad1, mode='replicate'), paddle.abs(x12_1-x12_2)], 1) + x12d = self.do12d(self.conv12d(x1d)) + x11d = self.conv11d(x12d) + + return x11d, + + def init_weight(self): + for sublayer in self.sublayers(): + if isinstance(sublayer, nn.Conv2D): + normal_init(sublayer.weight, std=0.001) + elif isinstance(sublayer, (nn.BatchNorm, nn.SyncBatchNorm)): + constant_init(sublayer.weight, value=1.0) + constant_init(sublayer.bias, value=0.0) + + def _make_dropout(self): + if self.use_dropout: + return nn.Dropout2D(p=0.2) + else: + return Identity() \ No newline at end of file diff --git a/paddlers/tasks/changedetector.py b/paddlers/tasks/changedetector.py index edbe1ce..e01c9f8 100644 --- a/paddlers/tasks/changedetector.py +++ b/paddlers/tasks/changedetector.py @@ -31,7 +31,7 @@ from paddlers.utils.checkpoint import seg_pretrain_weights_dict from paddlers.transforms import ImgDecoder, Resize import paddlers.models.cd as cd -__all__ = ["CDNet", "UNetEarlyFusion"] +__all__ = ["CDNet", "UNetEarlyFusion", "UNetSiamConc", "UNetSiamDiff"] class BaseChangeDetector(BaseModel): @@ -680,4 +680,40 @@ class UNetEarlyFusion(BaseChangeDetector): model_name='UNetEarlyFusion', num_classes=num_classes, use_mixed_loss=use_mixed_loss, + **params) + + +class UNetSiamConc(BaseChangeDetector): + def __init__(self, + num_classes=2, + use_mixed_loss=False, + in_channels=3, + use_dropout=False, + **params): + params.update({ + 'in_channels': in_channels, + 'use_dropout': use_dropout + }) + super(UNetSiamConc, self).__init__( + model_name='UNetSiamConc', + num_classes=num_classes, + use_mixed_loss=use_mixed_loss, + **params) + + +class UNetSiamDiff(BaseChangeDetector): + def __init__(self, + num_classes=2, + use_mixed_loss=False, + in_channels=3, + use_dropout=False, + **params): + params.update({ + 'in_channels': in_channels, + 'use_dropout': use_dropout + }) + super(UNetSiamDiff, self).__init__( + model_name='UNetSiamDiff', + num_classes=num_classes, + use_mixed_loss=use_mixed_loss, **params) \ No newline at end of file