修改后的最新

models/roma_unsb_model.py

@@ -118,8 +118,7 @@ class ContentAwareOptimization(nn.Module):
     def forward(self, D_real, D_fake, real_scores, fake_scores):
         # 清空梯度缓存
         self.gradients_real.clear()
-        self.gradients_fake.clear()
-        
+        self.gradients_fake.clear()       
         # 注册钩子
         hook_real = lambda grad: self.gradients_real.append(grad.detach())
         hook_fake = lambda grad: self.gradients_fake.append(grad.detach())
@@ -138,10 +137,10 @@ class ContentAwareOptimization(nn.Module):
         weight_real, weight_fake = self.generate_weight_map(grad_real, grad_fake)
         
         # 计算加权损失
-        loss_co_real = (weight_real * torch.log(real_scores + 1e-8)).mean()
-        loss_co_fake = (weight_fake * torch.log(1 - fake_scores + 1e-8)).mean()
+        loss_co_real = (weight_real * real_scores).mean()
+        loss_co_fake = (weight_fake * fake_scores).mean() 
         
-        return -(loss_co_real + loss_co_fake), weight_real, weight_fake
+        return (loss_co_real + loss_co_fake), weight_real, weight_fake
 
 class ContentAwareTemporalNorm(nn.Module):
     def __init__(self, gamma_stride=0.1, kernel_size=21, sigma=5.0):
@@ -252,7 +251,7 @@ class RomaUnsbModel(BaseModel):
         BaseModel.__init__(self, opt)
         
         # 指定需要打印的训练损失
-        self.loss_names = ['G_GAN', 'D_real_ViT', 'D_fake_ViT', 'G', 'SB', 'global', 'ctn']
+        self.loss_names = ['G_GAN', 'D_real_ViT', 'D_fake_ViT', 'G', 'SB', 'global', 'ctn',]
         self.visual_names = ['real_A0', 'real_A_noisy', 'fake_B0', 'real_B0']
         self.atten_layers = [int(i) for i in self.opt.atten_layers.split(',')]
         
@@ -368,40 +367,6 @@ class RomaUnsbModel(BaseModel):
         self.image_paths = input['A_paths' if AtoB else 'B_paths']
         
         
-    def tokens_concat(self, origin_tokens, adjacent_size):
-        adj_size = adjacent_size
-        B, token_num, C = origin_tokens.shape[0], origin_tokens.shape[1], origin_tokens.shape[2]
-        S = int(math.sqrt(token_num))
-        if S * S != token_num:
-            print('Error! Not a square!')
-        token_map = origin_tokens.clone().reshape(B,S,S,C)
-        cut_patch_list = []
-        for i in range(0, S, adj_size):
-            for j in range(0, S, adj_size):
-                i_left = i
-                i_right = i + adj_size + 1 if i + adj_size <= S else S + 1
-                j_left = j
-                j_right = j + adj_size if j + adj_size <= S else S + 1
-
-                cut_patch = token_map[:, i_left:i_right, j_left: j_right, :]
-                cut_patch= cut_patch.reshape(B,-1,C)
-                cut_patch = torch.mean(cut_patch, dim=1, keepdim=True)
-                cut_patch_list.append(cut_patch)
-
-
-        result = torch.cat(cut_patch_list,dim=1)
-        return result
-        
-    def cat_results(self, origin_tokens, adj_size_list):
-        res_list = [origin_tokens]
-        for ad_s in adj_size_list:
-            cat_result = self.tokens_concat(origin_tokens, ad_s)
-            res_list.append(cat_result)
-        
-        result = torch.cat(res_list, dim=1)
-
-        return result
-    
     def forward(self):
         """Run forward pass; called by both functions <optimize_parameters> and <test>."""
         
@@ -462,8 +427,8 @@ class RomaUnsbModel(BaseModel):
                 self.real = torch.flip(self.real, [3])
                 self.realt = torch.flip(self.realt, [3])
         
-        self.fake_B0 = self.netG(self.real_A0, self.time, self.z_in)
-        self.fake_B1 = self.netG(self.real_A1, self.time, self.z_in2)
+        self.fake_B0 = self.netG(self.real_A_noisy, self.time, self.z_in)
+        self.fake_B1 = self.netG(self.real_A_noisy2, self.time, self.z_in2)
 
         if self.opt.phase == 'train':
             real_A0 = self.real_A0
@@ -507,8 +472,8 @@ class RomaUnsbModel(BaseModel):
         self.loss_D_real_ViT = self.criterionGAN(pred_real0_ViT, True)
 
         self.losscao, self.weight_real, self.weight_fake = self.cao(pred_real0_ViT, pre_fake0_ViT, self.loss_D_real_ViT, self.loss_D_fake_ViT)
-        
-        return self.losscao* lambda_D_ViT
+        self.loss_D_ViT = self.losscao* lambda_D_ViT
+        return self.loss_D_ViT
 
     def compute_E_loss(self):
         """计算判别器 E 的损失"""

scripts/train.sh

@@ -7,7 +7,7 @@
 
 python train.py \
     --dataroot /home/openxs/kunyu/datasets/InfraredCity-Lite/Double/Moitor \
-    --name UNIV_2 \
+    --name UNIV_1 \
     --dataset_mode unaligned_double \
     --no_flip \
     --display_env UNIV \
@@ -17,13 +17,14 @@ python train.py \
     --lambda_ctn 1.0 \
     --lambda_inc 1.0 \
     --lr 0.00001 \
-    --gpu_id 1 \
+    --gpu_id 0 \
+    --lambda_D_ViT 1 \
     --nce_idt False \
     --netF mlp_sample \
     --flip_equivariance True \
     --eta_ratio 0.4 \
     --tau 0.01 \
-    --num_timesteps 5 \
+    --num_timesteps 4 \
     --input_nc 3 \
     --n_epochs 400 \
     --n_epochs_decay 200 \