Merge remote-tracking branch 'origin/gan_lab' into gan_lab

codes/models/archs/SPSR_arch.py

@@ -156,6 +156,35 @@ class SPSRNet(nn.Module):
 
         self.get_g_nopadding = ImageGradientNoPadding()
 
+    def bl1(self, x):
+        block_list = self.model[1].sub
+        for i in range(5):
+            x = block_list[i](x)
+        return x
+
+    def bl2(self, x):
+        block_list = self.model[1].sub
+        for i in range(5):
+            x = block_list[i+5](x)
+        return x
+
+    def bl3(self, x):
+        block_list = self.model[1].sub
+        for i in range(5):
+            x = block_list[i+10](x)
+        return x
+
+    def bl4(self, x):
+        block_list = self.model[1].sub
+        for i in range(5):
+            x = block_list[i+15](x)
+        return x
+
+    def bl5(self, x):
+        block_list = self.model[1].sub
+        x = block_list[20:](x)
+        return x
+
     def forward(self, x, ref=None):
         b,f,h,w = x.shape
         if ref is None:
@@ -170,26 +199,22 @@ class SPSRNet(nn.Module):
         x, block_list = self.model[1](x)
 
         x_ori = x
-        for i in range(5):
-            x = block_list[i](x)
+        x = checkpoint(self.bl1, x)
         x_fea1 = x
 
-        for i in range(5):
-            x = block_list[i + 5](x)
+        x = checkpoint(self.bl2, x)
         x_fea2 = x
 
-        for i in range(5):
-            x = block_list[i + 10](x)
+        x = checkpoint(self.bl3, x)
         x_fea3 = x
 
-        for i in range(5):
-            x = block_list[i + 15](x)
+        x = checkpoint(self.bl4, x)
         x_fea4 = x
 
-        x = block_list[20:](x)
+        x = checkpoint(self.bl5, x)
         # short cut
         x = x_ori + x
-        x = self.model[2:](x)
+        x = checkpoint(self.model[2:], x)
         x = self.HR_conv1_new(x)
 
         x_b_fea = self.b_fea_conv(x_grad)
@@ -219,16 +244,16 @@ class SPSRNet(nn.Module):
 
         # short cut
         x_cat_4 = x_cat_4 + x_b_fea
-        x_branch = self.b_module(x_cat_4)
+        x_branch = checkpoint(self.b_module, x_cat_4)
 
-        x_out_branch = self.conv_w(x_branch)
+        x_out_branch = checkpoint(self.conv_w, x_branch)
         ########
         x_branch_d = x_branch
         x_f_cat = torch.cat([x_branch_d, x], dim=1)
-        x_f_cat = self.f_block(x_f_cat)
+        x_f_cat = checkpoint(self.f_block, x_f_cat)
         x_out = self.f_concat(x_f_cat)
-        x_out = self.f_HR_conv0(x_out)
-        x_out = self.f_HR_conv1(x_out)
+        x_out = checkpoint(self.f_HR_conv0, x_out)
+        x_out = checkpoint(self.f_HR_conv1, x_out)
 
         #########
         return x_out_branch, x_out, x_grad