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reid/models/models_utils/rga_modules.py

@@ -26,34 +26,6 @@ class RGA_Module(nn.Module):
 
 		self.inter_channel = in_channel // cha_ratio
 		self.inter_spatial = in_spatial // spa_ratio
-
-		# Embedding functions for modeling relations
-		if self.use_spatial:
-			self.theta_spatial = nn.Sequential(
-				nn.Conv2d(in_channels=self.in_channel, out_channels=self.inter_channel,
-								kernel_size=1, stride=1, padding=0, bias=False),
-				nn.BatchNorm2d(self.inter_channel),
-				nn.ReLU()
-			)
-			self.phi_spatial = nn.Sequential(
-				nn.Conv2d(in_channels=self.in_channel, out_channels=self.inter_channel,
-							kernel_size=1, stride=1, padding=0, bias=False),
-				nn.BatchNorm2d(self.inter_channel),
-				nn.ReLU()
-			)
-		if self.use_channel:
-			self.theta_channel = nn.Sequential(
-				nn.Conv2d(in_channels=self.in_spatial, out_channels=self.inter_spatial,
-								kernel_size=1, stride=1, padding=0, bias=False),
-				nn.BatchNorm2d(self.inter_spatial),
-				nn.ReLU()
-			)
-			self.phi_channel = nn.Sequential(
-				nn.Conv2d(in_channels=self.in_spatial, out_channels=self.inter_spatial,
-							kernel_size=1, stride=1, padding=0, bias=False),
-				nn.BatchNorm2d(self.inter_spatial),
-				nn.ReLU()
-			)
 		
 		# Embedding functions for original features
 		if self.use_spatial:
@@ -110,6 +82,34 @@ class RGA_Module(nn.Module):
 						kernel_size=1, stride=1, padding=0, bias=False),
 				nn.BatchNorm2d(1)
 			)
+
+		# Embedding functions for modeling relations
+		if self.use_spatial:
+			self.theta_spatial = nn.Sequential(
+				nn.Conv2d(in_channels=self.in_channel, out_channels=self.inter_channel,
+								kernel_size=1, stride=1, padding=0, bias=False),
+				nn.BatchNorm2d(self.inter_channel),
+				nn.ReLU()
+			)
+			self.phi_spatial = nn.Sequential(
+				nn.Conv2d(in_channels=self.in_channel, out_channels=self.inter_channel,
+							kernel_size=1, stride=1, padding=0, bias=False),
+				nn.BatchNorm2d(self.inter_channel),
+				nn.ReLU()
+			)
+		if self.use_channel:
+			self.theta_channel = nn.Sequential(
+				nn.Conv2d(in_channels=self.in_spatial, out_channels=self.inter_spatial,
+								kernel_size=1, stride=1, padding=0, bias=False),
+				nn.BatchNorm2d(self.inter_spatial),
+				nn.ReLU()
+			)
+			self.phi_channel = nn.Sequential(
+				nn.Conv2d(in_channels=self.in_spatial, out_channels=self.inter_spatial,
+							kernel_size=1, stride=1, padding=0, bias=False),
+				nn.BatchNorm2d(self.inter_spatial),
+				nn.ReLU()
+			)
 				
 	def forward(self, x):
 		b, c, h, w = x.size()
@@ -156,5 +156,4 @@ class RGA_Module(nn.Module):
 			W_yc = self.W_channel(yc).transpose(1, 2)
 			out = F.sigmoid(W_yc) * x
 
-			return out
-
+			return out