[Feature] Add SNUNet-CD

paddlers/models/cd/models/__init__.py

@@ -17,4 +17,5 @@ from .unet_ef import UNetEarlyFusion
 from .unet_siamconc import UNetSiamConc
 from .unet_siamdiff import UNetSiamDiff
 from .stanet import STANet
-from .bit import BIT
+from .bit import BIT
+from .snunet import SNUNet

paddlers/models/cd/models/layers/__init__.py

@@ -12,4 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .blocks import *
+from .blocks import *
+from .attention import ChannelAttention, SpatialAttention, CBAM

paddlers/models/cd/models/layers/attention.py

@@ -0,0 +1,93 @@
+# 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 .blocks import Conv1x1, BasicConv
+
+
+class ChannelAttention(nn.Layer):
+    """
+    The channel attention module implementation based on PaddlePaddle.
+
+    The original article refers to 
+    Sanghyun Woo, et al., "CBAM: Convolutional Block Attention Module"
+    (https://arxiv.org/abs/1807.06521)
+
+    Args:
+        in_ch (int): The number of channels of the input features.
+        ratio (int, optional): The channel reduction ratio. Default: 8.
+    """
+    def __init__(self, in_ch, ratio=8):
+        super().__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2D(1)
+        self.max_pool = nn.AdaptiveMaxPool2D(1)
+        self.fc1 = Conv1x1(in_ch, in_ch//ratio, bias=False, act=True)
+        self.fc2 = Conv1x1(in_ch//ratio, in_ch, bias=False)
+
+    def forward(self,x):
+        avg_out = self.fc2(self.fc1(self.avg_pool(x)))
+        max_out = self.fc2(self.fc1(self.max_pool(x)))
+        out = avg_out + max_out
+        return F.sigmoid(out)
+
+
+class SpatialAttention(nn.Layer):
+    """
+    The spatial attention module implementation based on PaddlePaddle.
+
+    The original article refers to 
+    Sanghyun Woo, et al., "CBAM: Convolutional Block Attention Module"
+    (https://arxiv.org/abs/1807.06521)
+
+    Args:
+        kernel_size (int, optional): The size of the convolutional kernel. Default: 7.
+    """
+    def __init__(self, kernel_size=7):
+        super().__init__()
+        self.conv = BasicConv(2, 1, kernel_size, bias=False)
+
+    def forward(self, x):
+        avg_out = paddle.mean(x, axis=1, keepdim=True)
+        max_out = paddle.max(x, axis=1, keepdim=True)
+        x = paddle.concat([avg_out, max_out], axis=1)
+        x = self.conv(x)
+        return F.sigmoid(x)
+
+
+class CBAM(nn.Layer):
+    """
+    The CBAM implementation based on PaddlePaddle.
+
+    The original article refers to 
+    Sanghyun Woo, et al., "CBAM: Convolutional Block Attention Module"
+    (https://arxiv.org/abs/1807.06521)
+
+    Args:
+        in_ch (int): The number of channels of the input features.
+        ratio (int, optional): The channel reduction ratio for the channel attention module. Default: 8.
+        kernel_size (int, optional): The size of the convolutional kernel used in the spatial attention module. Default: 7.
+    """
+    def __init__(self, in_ch, ratio=8, kernel_size=7):
+        super().__init__()
+        self.ca = ChannelAttention(in_ch, ratio=ratio)
+        self.sa = SpatialAttention(kernel_size=kernel_size)
+
+    def forward(self, x):
+        y = self.ca(x) * x
+        y = self.sa(y) * y
+        return 

paddlers/models/cd/models/layers/blocks.py

@@ -20,7 +20,8 @@ __all__ = [
     'MaxPool2x2', 'MaxUnPool2x2', 
     'ConvTransposed3x3',
     'Identity',
-    'get_norm_layer', 'get_act_layer'
+    'get_norm_layer', 'get_act_layer', 
+    'make_norm', 'make_act'
 ]
 
 

paddlers/models/cd/models/snunet.py

@@ -0,0 +1,154 @@
+# 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 Conv1x1, MaxPool2x2, make_norm, ChannelAttention
+from .param_init import KaimingInitMixin
+
+
+class SNUNet(nn.Layer, KaimingInitMixin):
+    """
+    The SNUNet implementation based on PaddlePaddle.
+
+    The original article refers to
+    S. Fang, et al., "SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images"
+    (https://ieeexplore.ieee.org/document/9355573)
+
+    Note that bilinear interpolation is adopted as the upsampling method, which is different from the paper.
+
+    Args:
+        in_channels (int): The number of bands of the input images.
+        num_classes (int): The number of target classes.
+        width (int, optional): The output channels of the first convolutional layer. Default: 32.
+    """
+    def __init__(self, in_channels, num_classes, width=32):
+        super().__init__()
+
+        filters = (width, width*2, width*4, width*8, width*16)
+
+        self.conv0_0 = ConvBlockNested(in_channels, filters[0], filters[0])
+        self.conv1_0 = ConvBlockNested(filters[0], filters[1], filters[1])
+        self.conv2_0 = ConvBlockNested(filters[1], filters[2], filters[2])
+        self.conv3_0 = ConvBlockNested(filters[2], filters[3], filters[3])
+        self.conv4_0 = ConvBlockNested(filters[3], filters[4], filters[4])
+        self.down1 = MaxPool2x2()
+        self.down2 = MaxPool2x2()
+        self.down3 = MaxPool2x2()
+        self.down4 = MaxPool2x2()
+        self.up1_0 = Up(filters[1])
+        self.up2_0 = Up(filters[2])
+        self.up3_0 = Up(filters[3])
+        self.up4_0 = Up(filters[4])
+
+        self.conv0_1 = ConvBlockNested(filters[0]*2+filters[1], filters[0], filters[0])
+        self.conv1_1 = ConvBlockNested(filters[1]*2+filters[2], filters[1], filters[1])
+        self.conv2_1 = ConvBlockNested(filters[2]*2+filters[3], filters[2], filters[2])
+        self.conv3_1 = ConvBlockNested(filters[3]*2+filters[4], filters[3], filters[3])
+        self.up1_1 = Up(filters[1])
+        self.up2_1 = Up(filters[2])
+        self.up3_1 = Up(filters[3])
+
+        self.conv0_2 = ConvBlockNested(filters[0]*3+filters[1], filters[0], filters[0])
+        self.conv1_2 = ConvBlockNested(filters[1]*3+filters[2], filters[1], filters[1])
+        self.conv2_2 = ConvBlockNested(filters[2]*3+filters[3], filters[2], filters[2])
+        self.up1_2 = Up(filters[1])
+        self.up2_2 = Up(filters[2])
+
+        self.conv0_3 = ConvBlockNested(filters[0]*4+filters[1], filters[0], filters[0])
+        self.conv1_3 = ConvBlockNested(filters[1]*4+filters[2], filters[1], filters[1])
+        self.up1_3 = Up(filters[1])
+
+        self.conv0_4 = ConvBlockNested(filters[0]*5+filters[1], filters[0], filters[0])
+
+        self.ca_intra = ChannelAttention(filters[0], ratio=4)
+        self.ca_inter = ChannelAttention(filters[0]*4, ratio=16)
+
+        self.conv_out = Conv1x1(filters[0]*4, num_classes)
+
+        self.init_weight()
+
+    def forward(self, t1, t2):
+        x0_0_t1 = self.conv0_0(t1)
+        x1_0_t1 = self.conv1_0(self.down1(x0_0_t1))
+        x2_0_t1 = self.conv2_0(self.down2(x1_0_t1))
+        x3_0_t1 = self.conv3_0(self.down3(x2_0_t1))
+
+        x0_0_t2 = self.conv0_0(t2)
+        x1_0_t2 = self.conv1_0(self.down1(x0_0_t2))
+        x2_0_t2 = self.conv2_0(self.down2(x1_0_t2))
+        x3_0_t2 = self.conv3_0(self.down3(x2_0_t2))
+        x4_0_t2 = self.conv4_0(self.down4(x3_0_t2))
+
+        x0_1 = self.conv0_1(paddle.concat([x0_0_t1, x0_0_t2, self.up1_0(x1_0_t2)], 1))
+        x1_1 = self.conv1_1(paddle.concat([x1_0_t1, x1_0_t2, self.up2_0(x2_0_t2)], 1))
+        x0_2 = self.conv0_2(paddle.concat([x0_0_t1, x0_0_t2, x0_1, self.up1_1(x1_1)], 1))
+
+        x2_1 = self.conv2_1(paddle.concat([x2_0_t1, x2_0_t2, self.up3_0(x3_0_t2)], 1))
+        x1_2 = self.conv1_2(paddle.concat([x1_0_t1, x1_0_t2, x1_1, self.up2_1(x2_1)], 1))
+        x0_3 = self.conv0_3(paddle.concat([x0_0_t1, x0_0_t2, x0_1, x0_2, self.up1_2(x1_2)], 1))
+
+        x3_1 = self.conv3_1(paddle.concat([x3_0_t1, x3_0_t2, self.up4_0(x4_0_t2)], 1))
+        x2_2 = self.conv2_2(paddle.concat([x2_0_t1, x2_0_t2, x2_1, self.up3_1(x3_1)], 1))
+        x1_3 = self.conv1_3(paddle.concat([x1_0_t1, x1_0_t2, x1_1, x1_2, self.up2_2(x2_2)], 1))
+        x0_4 = self.conv0_4(paddle.concat([x0_0_t1, x0_0_t2, x0_1, x0_2, x0_3, self.up1_3(x1_3)], 1))
+
+        out = paddle.concat([x0_1, x0_2, x0_3, x0_4], 1)
+
+        intra = paddle.sum(paddle.stack([x0_1, x0_2, x0_3, x0_4]), axis=0)
+        m_intra = self.ca_intra(intra)
+        out = self.ca_inter(out) * (out + paddle.tile(m_intra, (1,4,1,1)))
+
+        pred = self.conv_out(out)
+        return pred,
+
+
+class ConvBlockNested(nn.Layer):
+    def __init__(self, in_ch, out_ch, mid_ch):
+        super().__init__()
+        self.act = nn.ReLU()
+        self.conv1 = nn.Conv2D(in_ch, mid_ch, kernel_size=3, padding=1)
+        self.bn1 = make_norm(mid_ch)
+        self.conv2 = nn.Conv2D(mid_ch, out_ch, kernel_size=3, padding=1)
+        self.bn2 = make_norm(out_ch)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        identity = x
+        x = self.bn1(x)
+        x = self.act(x)
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+        output = self.act(x + identity)
+        return output
+
+
+class Up(nn.Layer):
+    def __init__(self, in_ch, use_conv=False):
+        super().__init__()
+        if use_conv:
+            self.up = nn.Conv2DTranspose(in_ch, in_ch, 2, stride=2)
+        else:
+            self.up = nn.Upsample(scale_factor=2,
+                                  mode='bilinear',
+                                  align_corners=True)
+
+    def forward(self, x):
+        x = self.up(x)
+        return x

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", "UNetSiamConc", "UNetSiamDiff", "STANet", "BIT"]
+__all__ = ["CDNet", "UNetEarlyFusion", "UNetSiamConc", "UNetSiamDiff", "STANet", "BIT", "SNUNet"]
 
 
 class BaseChangeDetector(BaseModel):
@@ -774,4 +774,22 @@ class BIT(BaseChangeDetector):
             model_name='BIT',
             num_classes=num_classes,
             use_mixed_loss=use_mixed_loss,
+            **params)
+
+
+class SNUNet(BaseChangeDetector):
+    def __init__(self,
+                 num_classes=2,
+                 use_mixed_loss=False,
+                 in_channels=3,
+                 width=32,
+                 **params):
+        params.update({
+            'in_channels': in_channels,
+            'width': width
+        })
+        super(SNUNet, self).__init__(
+            model_name='SNUNet',
+            num_classes=num_classes,
+            use_mixed_loss=use_mixed_loss,
             **params)