[Feature] Add UNetSiamConc and UNetSiamDiff

paddlers/models/cd/models/__init__.py

@@ -13,4 +13,6 @@
 # limitations under the License.
 
 from .cdnet import CDNet
-from .unet_ef import UNetEarlyFusion
+from .unet_ef import UNetEarlyFusion
+from .unet_siamconc import UNetSiamConc
+from .unet_siamdiff import UNetSiamDiff

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()

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()

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)