From 4c2ad9f16b99643b3dc7a27d8717963b3a0bf5a2 Mon Sep 17 00:00:00 2001
From: Huyf9 <568558927@qq.com>
Date: Wed, 23 Aug 2023 15:39:50 +0800
Subject: [PATCH] DANet

---
 model/attention/DANet.py | 61 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 model/attention/DANet.py

diff --git a/model/attention/DANet.py b/model/attention/DANet.py
new file mode 100644
index 0000000..7b4f765
--- /dev/null
+++ b/model/attention/DANet.py
@@ -0,0 +1,61 @@
+"""
+MindSpore implementation of 'DANet'
+Refer to "Dual Attention Network for Scene Segmentation"
+"""
+# pylint: disable=E0401
+import mindspore as ms
+from mindspore import nn
+from SelfAttention import SelfAttention
+from SimplifiedSelfAttention import SimplifiedScaledDotProductAttention
+
+
+class PositionAttentionCell(nn.Cell):
+    """ Position Attention """
+    def __init__(self, d_model=512, kernel_size=3):
+        super().__init__()
+        self.cnn = nn.Conv2d(d_model, d_model, kernel_size=kernel_size, pad_mode='pad', padding=(kernel_size - 1) // 2)
+        self.pa = SelfAttention(d_model, d_k=d_model, d_v=d_model, h=1)
+
+    def construct(self, x):
+        B, C, _, _ = x.shape
+        y = self.cnn(x)
+        y = y.view(B, C, -1).permute(0, 2, 1)
+        y = self.pa(y)
+        return y
+
+
+class ChannelAttentionCell(nn.Cell):
+    """ Channel Attention """
+    def __init__(self, d_model=512, kernel_size=3, H=7, W=7):
+        super().__init__()
+        self.cnn = nn.Conv2d(d_model, d_model, kernel_size=kernel_size, pad_mode='pad', padding=(kernel_size - 1) // 2)
+        self.pa = SimplifiedScaledDotProductAttention(H * W, h=1)
+
+    def construct(self, x):
+        B, C, _, _ = x.shape
+        y = self.cnn(x)
+        y = y.view(B, C, -1)
+        y = self.pa(y)
+        return y
+
+
+class DACell(nn.Cell):
+    """ DANet """
+    def __init__(self, d_model=512, kernel_size=3, H=7, W=7):
+        super().__init__()
+        self.position_attention = PositionAttentionCell(d_model=d_model, kernel_size=kernel_size)
+        self.channel_attention = ChannelAttentionCell(d_model=d_model, kernel_size=kernel_size, H=H, W=W)
+
+    def construct(self, x):
+        B, C, H, W = x.shape
+        p_out = self.position_attention(x)
+        c_out = self.channel_attention(x)
+        p_out = p_out.permute(0, 2, 1).view(B, C, H, W)
+        c_out = c_out.view(B, C, H, W)
+        return p_out + c_out
+
+
+if __name__ == '__main__':
+    dummy_input = ms.ops.randn((50, 512, 7, 7))
+    model = DACell(d_model=512, kernel_size=3, H=7, W=7)
+    print(model(dummy_input).shape)