update van

code/channel_spatial_attentions/scnet.py

@@ -1,7 +1,6 @@
 # Improving convolutional networks with self-calibrated convolutions (CVPR 2020)
 import jittor as jt
 from jittor import nn
-from matplotlib.pyplot import grid
 
 
 class SCConv(nn.Module):

code/spatial_attentions/lka.py

@@ -0,0 +1,50 @@
+# Visual Attention Network
+import jittor as jt
+import jittor.nn as nn
+
+
+class AttentionModule(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.conv0 = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
+        self.conv_spatial = nn.Conv2d(
+            dim, dim, 7, stride=1, padding=9, groups=dim, dilation=3)
+        self.conv1 = nn.Conv2d(dim, dim, 1)
+
+    def execute(self, x):
+        u = x.clone()
+        attn = self.conv0(x)
+        attn = self.conv_spatial(attn)
+        attn = self.conv1(attn)
+
+        return u * attn
+
+
+class SpatialAttention(nn.Module):
+    def __init__(self, d_model):
+        super().__init__()
+
+        self.proj_1 = nn.Conv2d(d_model, d_model, 1)
+        self.activation = nn.GELU()
+        self.spatial_gating_unit = AttentionModule(d_model)
+        self.proj_2 = nn.Conv2d(d_model, d_model, 1)
+
+    def execute(self, x):
+        shorcut = x.clone()
+        x = self.proj_1(x)
+        x = self.activation(x)
+        x = self.spatial_gating_unit(x)
+        x = self.proj_2(x)
+        x = x + shorcut
+        return x
+
+
+def main():
+    attention_block = SpatialAttention(64)
+    input = jt.rand([4, 64, 32, 32])
+    output = attention_block(input)
+    print(input.size(), output.size())
+
+
+if __name__ == '__main__':
+    main()