update gc block

attention/GCBlock/gcnet.py

@@ -0,0 +1,94 @@
+import torch
+from torch import nn
+
+
+
+
+class ContextBlock(nn.Module):
+    def __init__(self,inplanes,ratio,pooling_type='att',
+                 fusion_types=('channel_add', )):
+        super(ContextBlock, self).__init__()
+        valid_fusion_types = ['channel_add', 'channel_mul']
+
+        assert pooling_type in ['avg', 'att']
+        assert isinstance(fusion_types, (list, tuple))
+        assert all([f in valid_fusion_types for f in fusion_types])
+        assert len(fusion_types) > 0, 'at least one fusion should be used'
+
+        self.inplanes = inplanes
+        self.ratio = ratio
+        self.planes = int(inplanes * ratio)
+        self.pooling_type = pooling_type
+        self.fusion_types = fusion_types
+
+        if pooling_type == 'att':
+            self.conv_mask = nn.Conv2d(inplanes, 1, kernel_size=1)
+            self.softmax = nn.Softmax(dim=2)
+        else:
+            self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        if 'channel_add' in fusion_types:
+            self.channel_add_conv = nn.Sequential(
+                nn.Conv2d(self.inplanes, self.planes, kernel_size=1),
+                nn.LayerNorm([self.planes, 1, 1]),
+                nn.ReLU(inplace=True),  # yapf: disable
+                nn.Conv2d(self.planes, self.inplanes, kernel_size=1))
+        else:
+            self.channel_add_conv = None
+        if 'channel_mul' in fusion_types:
+            self.channel_mul_conv = nn.Sequential(
+                nn.Conv2d(self.inplanes, self.planes, kernel_size=1),
+                nn.LayerNorm([self.planes, 1, 1]),
+                nn.ReLU(inplace=True),  # yapf: disable
+                nn.Conv2d(self.planes, self.inplanes, kernel_size=1))
+        else:
+            self.channel_mul_conv = None
+
+
+    def spatial_pool(self, x):
+        batch, channel, height, width = x.size()
+        if self.pooling_type == 'att':
+            input_x = x
+            # [N, C, H * W]
+            input_x = input_x.view(batch, channel, height * width)
+            # [N, 1, C, H * W]
+            input_x = input_x.unsqueeze(1)
+            # [N, 1, H, W]
+            context_mask = self.conv_mask(x)
+            # [N, 1, H * W]
+            context_mask = context_mask.view(batch, 1, height * width)
+            # [N, 1, H * W]
+            context_mask = self.softmax(context_mask)
+            # [N, 1, H * W, 1]
+            context_mask = context_mask.unsqueeze(-1)
+            # [N, 1, C, 1]
+            context = torch.matmul(input_x, context_mask)
+            # [N, C, 1, 1]
+            context = context.view(batch, channel, 1, 1)
+        else:
+            # [N, C, 1, 1]
+            context = self.avg_pool(x)
+        return context
+
+    def forward(self, x):
+        # [N, C, 1, 1]
+        context = self.spatial_pool(x)
+        out = x
+        if self.channel_mul_conv is not None:
+            # [N, C, 1, 1]
+            channel_mul_term = torch.sigmoid(self.channel_mul_conv(context))
+            out = out * channel_mul_term
+        if self.channel_add_conv is not None:
+            # [N, C, 1, 1]
+            channel_add_term = self.channel_add_conv(context)
+            out = out + channel_add_term
+        return out
+
+if __name__ == "__main__":
+    in_tensor = torch.ones((12, 64, 128, 128))
+
+    cb = ContextBlock(inplanes=64, ratio=1./16.,pooling_type='att')
+
+    out_tensor = cb(in_tensor)
+
+    print(in_tensor.shape)
+    print(out_tensor.shape)