diff --git a/codes/models/SRGAN_model.py b/codes/models/SRGAN_model.py
index 39a01e5b..75e6061e 100644
--- a/codes/models/SRGAN_model.py
+++ b/codes/models/SRGAN_model.py
@@ -30,10 +30,6 @@ class SRGANModel(BaseModel):
         train_opt = opt['train']
         self.spsr_enabled = 'spsr' in opt['model']
 
-        # Only pixgan and gan are currently supported in spsr_mode
-        if self.spsr_enabled:
-            assert train_opt['gan_type'] == 'pixgan' or train_opt['gan_type'] == 'gan'
-
         # define networks and load pretrained models
         self.netG = networks.define_G(opt).to(self.device)
         if self.is_train:
@@ -488,7 +484,7 @@ class SRGANModel(BaseModel):
                         l_g_gan_grad = self.l_gan_grad_w * self.cri_grad_gan(pred_g_fake_grad, True)
                     elif self.opt['train']['gan_type'] == 'ragan':
                         pred_g_real_grad = self.netD(self.get_grad_nopadding(var_ref)).detach()
-                        l_g_gan = self.l_gan_w * (
+                        l_g_gan_grad = self.l_gan_w * (
                             self.cri_gan(pred_g_real_grad - torch.mean(pred_g_fake_grad), False) +
                             self.cri_gan(pred_g_fake_grad - torch.mean(pred_g_real_grad), True)) / 2
                     l_g_total += l_g_gan_grad
@@ -629,7 +625,9 @@ class SRGANModel(BaseModel):
                     pred_d_fake = self.netD(fake_H).detach()
                     pred_d_real = self.netD(var_ref)
                     l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True)
+                    l_d_real_log = l_d_real
                     l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real), False)
+                    l_d_fake_log = l_d_fake
                     l_d_total = (l_d_real + l_d_fake) / 2
                     l_d_total /= self.mega_batch_factor
                     with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled:
@@ -661,8 +659,8 @@ class SRGANModel(BaseModel):
                         l_d_real_grad = self.cri_grad_gan(pred_d_real_grad, real)
                         l_d_fake_grad = self.cri_grad_gan(pred_d_fake_grad, fake)
                     elif self.opt['train']['gan_type'] == 'ragan':
-                        pred_g_fake_grad = self.netD_grad(self.fake_H_grad)
-                        pred_d_real_grad = self.netD_grad(self.var_ref_grad).detach()
+                        pred_g_fake_grad = self.netD_grad(fake_H_grad)
+                        pred_d_real_grad = self.netD_grad(var_ref_grad).detach()
                         l_d_real_grad = self.cri_grad_gan(pred_d_real_grad - torch.mean(pred_g_fake_grad), True)
                         l_d_fake_grad = self.cri_grad_gan(pred_g_fake_grad - torch.mean(pred_d_real_grad), False)
 
diff --git a/codes/models/archs/SPSR_arch.py b/codes/models/archs/SPSR_arch.py
index b4f5da7d..577a17dc 100644
--- a/codes/models/archs/SPSR_arch.py
+++ b/codes/models/archs/SPSR_arch.py
@@ -388,19 +388,12 @@ class SPSRNetSimplifiedNoSkip(nn.Module):
         x_ori = x
         for i in range(5):
             x = self.model_shortcut_blk[i](x)
-        x_fea1 = x
-
         for i in range(5):
             x = self.model_shortcut_blk[i + 5](x)
-        x_fea2 = x
-
         for i in range(5):
             x = self.model_shortcut_blk[i + 10](x)
-        x_fea3 = x
-
         for i in range(5):
             x = self.model_shortcut_blk[i + 15](x)
-        x_fea4 = x
 
         x = self.model_shortcut_blk[20:](x)
         x = self.feature_lr_conv(x)
@@ -430,7 +423,6 @@ class SPSRNetSimplifiedNoSkip(nn.Module):
         x_out = self._branch_pretrain_concat(x__branch_pretrain_cat)
         x_out = self._branch_pretrain_HR_conv0(x_out)
         x_out = self._branch_pretrain_HR_conv1(x_out)
-
         #########
         return x_out_branch, x_out, x_grad