EDIT_DOWN

.gitignore

@@ -0,0 +1,5 @@
+checkpoints/
+*.log
+*.pth
+*.ckpt
+__pycache__/

checkpoints/ROMA_UNSB_001/loss_log.txt

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-================ Training Loss (Sun Feb 23 15:46:44 2025) ================
-================ Training Loss (Sun Feb 23 15:52:29 2025) ================
-================ Training Loss (Sun Feb 23 16:00:07 2025) ================
-================ Training Loss (Sun Feb 23 16:02:40 2025) ================
-================ Training Loss (Sun Feb 23 16:05:19 2025) ================
-================ Training Loss (Sun Feb 23 16:06:44 2025) ================
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-================ Training Loss (Sun Feb 23 16:49:46 2025) ================
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-================ Training Loss (Sun Feb 23 18:06:40 2025) ================
-================ Training Loss (Sun Feb 23 18:11:48 2025) ================
-================ Training Loss (Sun Feb 23 18:13:31 2025) ================
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-================ Training Loss (Sun Feb 23 18:14:29 2025) ================
-================ Training Loss (Sun Feb 23 18:16:27 2025) ================
-================ Training Loss (Sun Feb 23 18:16:44 2025) ================
-================ Training Loss (Sun Feb 23 18:20:39 2025) ================
-================ Training Loss (Sun Feb 23 18:21:44 2025) ================
-================ Training Loss (Sun Feb 23 18:35:27 2025) ================
-================ Training Loss (Sun Feb 23 18:39:21 2025) ================
-================ Training Loss (Sun Feb 23 18:40:15 2025) ================
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-================ Training Loss (Sun Feb 23 18:47:46 2025) ================
-================ Training Loss (Sun Feb 23 18:48:36 2025) ================
-================ Training Loss (Sun Feb 23 18:50:20 2025) ================
-================ Training Loss (Sun Feb 23 18:51:50 2025) ================
-================ Training Loss (Sun Feb 23 18:58:45 2025) ================
-================ Training Loss (Sun Feb 23 18:59:52 2025) ================
-================ Training Loss (Sun Feb 23 19:03:05 2025) ================
-================ Training Loss (Sun Feb 23 19:03:57 2025) ================
-================ Training Loss (Sun Feb 23 21:11:47 2025) ================
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-================ Training Loss (Sun Feb 23 22:29:52 2025) ================
-================ Training Loss (Sun Feb 23 22:30:40 2025) ================
-================ Training Loss (Sun Feb 23 22:33:48 2025) ================
-================ Training Loss (Sun Feb 23 22:39:16 2025) ================
-================ Training Loss (Sun Feb 23 22:39:48 2025) ================
-================ Training Loss (Sun Feb 23 22:41:34 2025) ================
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-================ Training Loss (Sun Feb 23 22:44:17 2025) ================
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-================ Training Loss (Sun Feb 23 22:46:48 2025) ================
-================ Training Loss (Sun Feb 23 22:47:42 2025) ================
-================ Training Loss (Sun Feb 23 22:49:44 2025) ================
-================ Training Loss (Sun Feb 23 22:50:29 2025) ================
-================ Training Loss (Sun Feb 23 22:51:47 2025) ================
-================ Training Loss (Sun Feb 23 22:55:56 2025) ================
-================ Training Loss (Sun Feb 23 22:56:19 2025) ================
-================ Training Loss (Sun Feb 23 22:57:58 2025) ================
-================ Training Loss (Sun Feb 23 22:59:09 2025) ================
-================ Training Loss (Sun Feb 23 23:02:36 2025) ================
-================ Training Loss (Sun Feb 23 23:03:56 2025) ================
-================ Training Loss (Sun Feb 23 23:09:21 2025) ================
-================ Training Loss (Sun Feb 23 23:10:05 2025) ================
-================ Training Loss (Sun Feb 23 23:11:43 2025) ================
-================ Training Loss (Sun Feb 23 23:12:41 2025) ================
-================ Training Loss (Sun Feb 23 23:13:05 2025) ================
-================ Training Loss (Sun Feb 23 23:13:59 2025) ================
-================ Training Loss (Sun Feb 23 23:14:59 2025) ================

checkpoints/ROMA_UNSB_001/train_opt.txt

@@ -1,87 +0,0 @@
------------------ Options ---------------
-             atten_layers: 5                             
-               batch_size: 1                             
-                    beta1: 0.5                           
-                    beta2: 0.999                         
-          checkpoints_dir: ./checkpoints                 
-           continue_train: False                         
-                crop_size: 256                           
-                 dataroot: /home/openxs/kunyu/datasets/InfraredCity-Lite/Double/Moitor	[default: placeholder]
-             dataset_mode: unaligned_double              	[default: unaligned]
-                direction: AtoB                          
-              display_env: ROMA                          	[default: main]
-             display_freq: 50                            
-               display_id: None                          
-            display_ncols: 4                             
-             display_port: 8097                          
-           display_server: http://localhost              
-          display_winsize: 256                           
-               easy_label: experiment_name               
-                    epoch: latest                        
-              epoch_count: 1                             
-                eta_ratio: 0.1                           
-          evaluation_freq: 5000                          
-        flip_equivariance: False                         
-                 gan_mode: lsgan                         
-                  gpu_ids: 0                             
-                init_gain: 0.02                          
-                init_type: xavier                        
-                 input_nc: 3                             
-                  isTrain: True                          	[default: None]
-             lambda_D_ViT: 1.0                           
-               lambda_GAN: 8.0                           	[default: 1.0]
-               lambda_NCE: 8.0                           	[default: 1.0]
-                lambda_SB: 0.1                           
-               lambda_ctn: 1.0                           
-            lambda_global: 1.0                           
-               lambda_inc: 1.0                           
-                   lmda_1: 0.1                           
-                load_size: 286                           
-                       lr: 1e-05                         	[default: 0.0002]
-           lr_decay_iters: 50                            
-                lr_policy: linear                        
-         max_dataset_size: inf                           
-                    model: roma_unsb                     	[default: cut]
-                 n_epochs: 100                           
-           n_epochs_decay: 100                           
-               n_layers_D: 3                             
-                    n_mlp: 3                             
-                     name: ROMA_UNSB_001                 	[default: experiment_name]
-                    nce_T: 0.07                          
-                  nce_idt: False                         	[default: True]
-nce_includes_all_negatives_from_minibatch: False                         
-               nce_layers: 0,4,8,12,16                   
-                      ndf: 64                            
-                     netD: basic_cond                    
-                     netF: mlp_sample                    
-                  netF_nc: 256                           
-                     netG: resnet_9blocks_cond           
-                      ngf: 64                            
-             no_antialias: False                         
-          no_antialias_up: False                         
-               no_dropout: True                          
-                  no_flip: True                          	[default: False]
-                  no_html: False                         
-                    normD: instance                      
-                    normG: instance                      
-              num_patches: 256                           
-              num_threads: 4                             
-            num_timesteps: 10                            	[default: 5]
-                output_nc: 3                             
-                    phase: train                         
-                pool_size: 0                             
-               preprocess: resize_and_crop               
-          pretrained_name: None                          
-               print_freq: 100                           
-         random_scale_max: 3.0                           
-             save_by_iter: False                         
-          save_epoch_freq: 5                             
-         save_latest_freq: 5000                          
-           serial_batches: False                         
-stylegan2_G_num_downsampling: 1                             
-                   suffix:                               
-                      tau: 0.01                          
-         update_html_freq: 1000                          
-                  use_idt: False                         
-                  verbose: False                         
------------------ End -------------------

models/roma_unsb_model.py

@@ -66,6 +66,10 @@ class ContentAwareOptimization(nn.Module):
         self.lambda_inc = lambda_inc  # 权重增强系数
         self.eta_ratio = eta_ratio    # 选择内容区域的比例
         
+        # 改为类成员变量，确保钩子函数可访问
+        self.gradients_real = []
+        self.gradients_fake = []
+    
     def compute_cosine_similarity(self, gradients):
         """
         计算每个patch梯度与平均梯度的余弦相似度
@@ -79,78 +83,65 @@ class ContentAwareOptimization(nn.Module):
         cosine_sim = F.cosine_similarity(gradients, mean_grad, dim=2)  # [B, N]
         return cosine_sim
 
-    def generate_weight_map(self, gradients_fake):
+    def generate_weight_map(self, gradients_real, gradients_fake):
         """
         生成内容感知权重图
         Args:
-            gradients_fake: [B, N, D] 生成图像判别器梯度 [2,3,256,256]
+            gradients_real: [B, N, D] 真实图像判别器梯度
+            gradients_fake: [B, N, D] 生成图像判别器梯度
         Returns:
-            weight_fake: [B, N] 生成图像权重图 [2,3,256]
+            weight_real: [B, N] 真实图像权重图
+            weight_fake: [B, N] 生成图像权重图
         """
+        # 计算真实图像块的余弦相似度
+        cosine_real = self.compute_cosine_similarity(gradients_real)  # [B, N]  公式5
         # 计算生成图像块的余弦相似度
         cosine_fake = self.compute_cosine_similarity(gradients_fake)  # [B, N]
         
-        # 选择内容丰富的区域(余弦相似度最低的eta_ratio比例)
-        k = int(self.eta_ratio * cosine_fake.shape[1])
+        # 选择内容丰富的区域（余弦相似度最低的eta_ratio比例）
+        k = int(self.eta_ratio * cosine_real.shape[1])
         
-        # 对生成图像生成权重图(同理)
+        # 对真实图像生成权重图
+        _, real_indices = torch.topk(-cosine_real, k, dim=1)  # 选择最不相似的区域
+        weight_real = torch.ones_like(cosine_real)
+        for b in range(cosine_real.shape[0]):
+            weight_real[b, real_indices[b]] = self.lambda_inc / (1e-6 + torch.abs(cosine_real[b, real_indices[b]])) #公式6
+        
+        # 对生成图像生成权重图（同理）
         _, fake_indices = torch.topk(-cosine_fake, k, dim=1)
         weight_fake = torch.ones_like(cosine_fake)
         for b in range(cosine_fake.shape[0]):
             weight_fake[b, fake_indices[b]] = self.lambda_inc / (1e-6 + torch.abs(cosine_fake[b, fake_indices[b]]))
         
-        return  weight_fake
+        return weight_real, weight_fake
 
     def forward(self, D_real, D_fake, real_scores, fake_scores):
-        """
-        计算内容感知对抗损失
-        Args:
-            D_real: 判别器对真实图像的特征输出 [B, C, H, W]
-            D_fake: 判别器对生成图像的特征输出 [B, C, H, W]
-            real_scores: 真实图像的判别器预测 [B, N] (N=H*W)
-            fake_scores: 生成图像的判别器预测 [B, N]
-        Returns:
-            loss_co_adv: 内容感知对抗损失
-        """
-        B, C, H, W = D_real.shape
-        N = H * W
+        # 清空梯度缓存
+        self.gradients_real.clear()
+        self.gradients_fake.clear()
         
-        # 注册钩子获取梯度
-        gradients_real = []
-        gradients_fake = []
-        
-        def hook_real(grad):
-            gradients_real.append(grad.detach().view(B, N, -1))
-        
-        def hook_fake(grad):
-            gradients_fake.append(grad.detach().view(B, N, -1))
+        # 注册钩子
+        hook_real = lambda grad: self.gradients_real.append(grad.detach())
+        hook_fake = lambda grad: self.gradients_fake.append(grad.detach())
         
         D_real.register_hook(hook_real)
         D_fake.register_hook(hook_fake)
         
-        # 计算原始对抗损失以触发梯度计算
-        loss_real = torch.mean(torch.log(real_scores + 1e-8))
-        loss_fake = torch.mean(torch.log(1 - fake_scores + 1e-8))
-        # 添加与 D_real、D_fake 相关的 dummy 项，确保梯度传递
-        loss_dummy = 1e-8 * (D_real.sum() + D_fake.sum())
-        total_loss = loss_real + loss_fake + loss_dummy
-        total_loss.backward(retain_graph=True)
+        # 触发梯度计算
+        (real_scores.mean() + fake_scores.mean()).backward(retain_graph=True)
         
-        # 获取梯度数据
-        gradients_real = gradients_real[0]  # [B, N, D]
-        gradients_fake = gradients_fake[0]  # [B, N, D]
+        # 获取梯度并调整维度
+        grad_real = self.gradients_real[0]  # [B, N, D]
+        grad_fake = self.gradients_fake[0]
         
         # 生成权重图
-        self.weight_real, self.weight_fake = self.generate_weight_map(gradients_real, gradients_fake)
+        weight_real, weight_fake = self.generate_weight_map(grad_real, grad_fake)
         
-        # 应用权重到对抗损失
-        loss_co_real = torch.mean(self.weight_real * torch.log(real_scores + 1e-8))
-        loss_co_fake = torch.mean(self.weight_fake * torch.log(1 - fake_scores + 1e-8))
+        # 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()
         
-        # 计算并返回最终内容感知对抗损失
-        loss_co_adv = -(loss_co_real + loss_co_fake)
-        
-        return loss_co_adv
+        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):
@@ -158,6 +149,33 @@ class ContentAwareTemporalNorm(nn.Module):
         self.gamma_stride = gamma_stride  # 控制整体运动幅度
         self.smoother = GaussianBlur(kernel_size, sigma=sigma)  # 高斯平滑层
 
+    def upsample_weight_map(self, weight_patch, target_size=(256, 256)):
+        """
+        将patch级别的权重图上采样到目标分辨率
+        Args:
+            weight_patch: [B, 1, 24, 24] 来自ViT的patch权重图
+            target_size: 目标分辨率 (H, W)
+        Returns:
+            weight_full: [B, 1, 256, 256] 上采样后的全分辨率权重图
+        """
+        # 使用双线性插值上采样
+        B = weight_patch.shape[0]
+        weight_patch = weight_patch.view(B, 1, 24, 24)
+        
+        weight_full = F.interpolate(
+            weight_patch,
+            size=target_size,
+            mode='bilinear',
+            align_corners=False
+        )
+        
+        # 对每个16x16的patch内部保持权重一致（可选）
+        # 通过平均池化再扩展，消除插值引入的渐变
+        weight_full = F.avg_pool2d(weight_full, kernel_size=16, stride=16)
+        weight_full = F.interpolate(weight_full, scale_factor=16, mode='nearest')
+        
+        return weight_full
+    
     def forward(self, weight_map):
         """
         生成内容感知光流
@@ -166,15 +184,16 @@ class ContentAwareTemporalNorm(nn.Module):
         Returns:
             F_content: [B, 2, H, W] 生成的光流场(x/y方向位移)
         """
-        print(weight_map.shape)
-        B, _, H, W = weight_map.shape
+        # 上采样权重图到全分辨率
+        weight_full = self.upsample_weight_map(weight_map)  # [B,1,384,384]     
         
         # 1. 归一化权重图
         # 保持区域相对强度，同时限制数值范围
-        weight_norm = F.normalize(weight_map, p=1, dim=(2,3))  # L1归一化 [B,1,H,W]
+        weight_norm = F.normalize(weight_full, p=1, dim=(2,3))  # L1归一化 [B,1,H,W]
         
-        # 2. 生成高斯噪声(与光流场同尺寸)
-        z = torch.randn(B, 2, H, W, device=weight_map.device)  # [B,2,H,W]
+        # 2. 生成高斯噪声
+        B, _, H, W = weight_norm.shape
+        z = torch.randn(B, 2, H, W, device=weight_norm.device)  # [B,2,H,W]
         
         # 3. 合成基础光流
         # 将权重图扩展为2通道(x/y方向共享权重)
@@ -204,23 +223,19 @@ class RomaUnsbModel(BaseModel):
         parser.add_argument('--lambda_global', type=float, default=1.0, help='weight for Global Structural Consistency')
         
         parser.add_argument('--nce_idt', type=util.str2bool, nargs='?', const=True, default=False, help='use NCE loss for identity mapping: NCE(G(Y), Y))')
-        parser.add_argument('--nce_layers', type=str, default='0,4,8,12,16', help='compute NCE loss on which layers')
         parser.add_argument('--nce_includes_all_negatives_from_minibatch',
                             type=util.str2bool, nargs='?', const=True, default=False,
                             help='(used for single image translation) If True, include the negatives from the other samples of the minibatch when computing the contrastive loss. Please see models/patchnce.py for more details.')
+        parser.add_argument('--nce_layers', type=str, default='0,4,8,12,16', help='compute NCE loss on which layers')
         
         parser.add_argument('--netF', type=str, default='mlp_sample', choices=['sample', 'reshape', 'mlp_sample'], help='how to downsample the feature map')
-        parser.add_argument('--netF_nc', type=int, default=256)
-        parser.add_argument('--nce_T', type=float, default=0.07, help='temperature for NCE loss')
         
-        parser.add_argument('--lmda_1', type=float, default=0.1)
-        parser.add_argument('--num_patches', type=int, default=256, help='number of patches per layer')
         parser.add_argument('--flip_equivariance',
                             type=util.str2bool, nargs='?', const=True, default=False,
                             help="Enforce flip-equivariance as additional regularization. It's used by FastCUT, but not CUT")
         
         parser.add_argument('--lambda_inc', type=float, default=1.0, help='incremental weight for content-aware optimization')
-        parser.add_argument('--eta_ratio', type=float, default=0.1, help='ratio of content-rich regions')
+        parser.add_argument('--eta_ratio', type=float, default=0.4, help='ratio of content-rich regions')
 
         parser.add_argument('--atten_layers', type=str, default='5', help='compute Cross-Similarity on which layers')
         
@@ -243,9 +258,8 @@ class RomaUnsbModel(BaseModel):
         BaseModel.__init__(self, opt)
         
         # 指定需要打印的训练损失
-        self.loss_names = ['G_GAN_1', 'D_real_1', 'D_fake_1', 'G_1', 'NCE_1', 'SB_1', 
-                           'G_2']
-        self.visual_names = ['real_A', 'real_A_noisy', 'fake_B', 'real_B']
+        self.loss_names = ['G_GAN', 'D_real_ViT', 'D_fake_ViT', 'G', 'SB']
+        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(',')]
         
         if self.opt.phase == 'test':
@@ -262,11 +276,9 @@ class RomaUnsbModel(BaseModel):
         if self.isTrain:
             self.model_names = ['G', 'D_ViT', 'E']
             
-            
         else:
             self.model_names = ['G']
         
-        print(f'input_nc = {self.opt.input_nc}')
         # 创建网络
         self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.normG, not opt.no_dropout, opt.init_type, opt.init_gain, opt.no_antialias, opt.no_antialias_up, self.gpu_ids, opt)
         
@@ -284,9 +296,6 @@ class RomaUnsbModel(BaseModel):
             # 定义损失函数
             self.criterionL1 = torch.nn.L1Loss().to(self.device)
             self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
-            self.criterionNCE = []
-            for nce_layer in self.nce_layers:
-                self.criterionNCE.append(PatchNCELoss(opt).to(self.device))
             self.criterionIdt = torch.nn.L1Loss().to(self.device)
             self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
             self.optimizer_D = torch.optim.Adam(self.netD_ViT.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
@@ -447,8 +456,8 @@ class RomaUnsbModel(BaseModel):
 
         # ============ 第三步：拼接输入并执行网络推理 =============
         bs = self.real_A0.size(0)
-        z_in = torch.randn(size=[bs, 4 * self.opt.ngf]).to(self.real_A0.device)
-        z_in2 = torch.randn(size=[bs, 4 * self.opt.ngf]).to(self.real_A1.device)
+        self.z_in = torch.randn(size=[bs, 4 * self.opt.ngf]).to(self.real_A0.device)
+        self.z_in2 = torch.randn(size=[bs, 4 * self.opt.ngf]).to(self.real_A1.device)
         # 将 real_A, real_B 拼接 (如 nce_idt=True)，并同样处理 real_A_noisy 与 XtB
         self.real = self.real_A0
         self.realt = self.real_A_noisy
@@ -459,10 +468,8 @@ class RomaUnsbModel(BaseModel):
                 self.real = torch.flip(self.real, [3])
                 self.realt = torch.flip(self.realt, [3])
         
-        print(f'fake_B0: {self.real_A0.shape}, fake_B1: {self.real_A1.shape}')
-        self.fake_B0 = self.netG(self.real_A0, self.time, z_in)
-        self.fake_B1 = self.netG(self.real_A1, self.time, z_in2)
-        print(f'fake_B0: {self.fake_B0.shape}, fake_B1: {self.fake_B1.shape}')
+        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)
 
         if self.opt.phase == 'train':
             real_A0 = self.real_A0
@@ -488,28 +495,6 @@ class RomaUnsbModel(BaseModel):
             # [[1,576,768],[1,576,768],[1,576,768]]
             #  [3,576,768]
             
-            ## 生成图像的梯度
-            #fake_gradient = torch.autograd.grad(self.mutil_fake_B0_tokens.sum(), self.mutil_fake_B0_tokens, create_graph=True)[0]
-            #
-            ## 梯度图
-            #self.weight_fake = self.cao.generate_weight_map(fake_gradient)
-            #
-            ## 生成图像的CTN光流图
-            #self.f_content = self.ctn(self.weight_fake)
-            #
-            ## 变换后的图片
-            #self.warped_real_A_noisy2 = warp(self.real_A_noisy, self.f_content)
-            #self.warped_fake_B0       = warp(self.fake_B0,self.f_content)
-            #
-            ## 经过第二次生成器
-            #self.warped_fake_B0_2 = self.netG(self.warped_real_A_noisy2, self.time, z_in)   
-            
-            #warped_fake_B0_2=self.warped_fake_B0_2
-            #warped_fake_B0=self.warped_fake_B0
-            #self.warped_fake_B0_2_resize = self.resize(warped_fake_B0_2)
-            #self.warped_fake_B0_resize = self.resize(warped_fake_B0)
-            #self.mutil_warped_fake_B0_tokens = self.netPreViT(self.warped_fake_B0_resize, self.atten_layers, get_tokens=True)
-            #self.mutil_fake_B0_2_tokens = self.netPreViT(self.warped_fake_B0_2_resize, self.atten_layers, get_tokens=True)
 
             
     def compute_D_loss(self):      #判别器还是没有改
@@ -517,30 +502,23 @@ class RomaUnsbModel(BaseModel):
 
         lambda_D_ViT = self.opt.lambda_D_ViT
         fake_B0_tokens = self.mutil_fake_B0_tokens[0].detach()
-        fake_B1_tokens = self.mutil_fake_B1_tokens[0].detach()
+       
 
         real_B0_tokens = self.mutil_real_B0_tokens[0]
-        real_B1_tokens = self.mutil_real_B1_tokens[0]
-
 
         pre_fake0_ViT = self.netD_ViT(fake_B0_tokens)
-        pre_fake1_ViT = self.netD_ViT(fake_B1_tokens)
-
-        self.loss_D_fake_ViT = (self.criterionGAN(pre_fake0_ViT, False).mean() + self.criterionGAN(pre_fake1_ViT, False).mean()) * 0.5 * lambda_D_ViT
+        self.loss_D_fake_ViT = self.criterionGAN(pre_fake0_ViT, False)
 
         pred_real0_ViT = self.netD_ViT(real_B0_tokens)       
-        pred_real1_ViT = self.netD_ViT(real_B1_tokens)
-        self.loss_D_real_ViT = (self.criterionGAN(pred_real0_ViT, True).mean() + self.criterionGAN(pred_real1_ViT, True).mean()) * 0.5 * lambda_D_ViT
+        self.loss_D_real_ViT = self.criterionGAN(pred_real0_ViT, True)
 
-        self.loss_D_ViT = (self.loss_D_fake_ViT + self.loss_D_real_ViT) * 0.5
+        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.loss_D_ViT
+        return self.losscao* lambda_D_ViT
 
     def compute_E_loss(self):
         """计算判别器 E 的损失"""
         
-        print(f'resl_A_noisy: {self.real_A_noisy.shape} \n fake_B0: {self.fake_B0.shape}')
         XtXt_1 = torch.cat([self.real_A_noisy, self.fake_B0.detach()], dim=1)
         XtXt_2 = torch.cat([self.real_A_noisy2, self.fake_B1.detach()], dim=1)
         temp = torch.logsumexp(self.netE(XtXt_1, self.time, XtXt_2).reshape(-1), dim=0).mean()
@@ -550,12 +528,28 @@ class RomaUnsbModel(BaseModel):
         
     def compute_G_loss(self):
         """计算生成器的 GAN 损失"""
+        if self.opt.lambda_ctn > 0.0:
+            # 生成图像的CTN光流图
+            self.f_content = self.ctn(self.weight_fake)
+            
+            # 变换后的图片
+            self.warped_real_A_noisy2 = warp(self.real_A_noisy, self.f_content)
+            self.warped_fake_B0       = warp(self.fake_B0,self.f_content)
+            
+            # 经过第二次生成器
+            self.warped_fake_B0_2 = self.netG(self.warped_real_A_noisy2, self.time, self.z_in)   
+            
+            warped_fake_B0_2=self.warped_fake_B0_2
+            warped_fake_B0=self.warped_fake_B0
+            # 计算L2损失
+            self.ctn_loss = F.mse_loss(warped_fake_B0_2, warped_fake_B0)    
 
         if self.opt.lambda_GAN > 0.0:
             pred_fake = self.netD_ViT(self.mutil_fake_B0_tokens[0])
-            self.loss_G_GAN = self.criterionGAN(pred_fake, True).mean() * self.opt.lambda_GAN
+            self.loss_G_GAN = self.criterionGAN(pred_fake, True).mean() 
         else:
             self.loss_G_GAN = 0.0
+            
         self.loss_SB = 0
         if self.opt.lambda_SB > 0.0:
             XtXt_1 = torch.cat([self.real_A_noisy, self.fake_B0], dim=1)
@@ -564,9 +558,9 @@ class RomaUnsbModel(BaseModel):
             bs = self.opt.batch_size
 
             # eq.9
-            ET_XY    = self.netE(XtXt_1, self.time, XtXt_1).mean() - torch.logsumexp(self.netE(XtXt_1, self.time, XtXt_2).reshape(-1), dim=0)
+            ET_XY    = self.netE(XtXt_1, self.time, XtXt_1).mean() - self.netE(XtXt_1, self.time, XtXt_2).mean()
             self.loss_SB = -(self.opt.num_timesteps - self.time[0]) / self.opt.num_timesteps * self.opt.tau * ET_XY
-            self.loss_SB += self.opt.tau * torch.mean((self.real_A_noisy - self.fake_B0) ** 2)
+            self.loss_SB += torch.mean((self.real_A_noisy - self.fake_B0) ** 2)
         
         if self.opt.lambda_global > 0.0:
             loss_global = self.calculate_similarity(self.real_A0, self.fake_B0) + self.calculate_similarity(self.real_A1, self.fake_B1)
@@ -574,12 +568,10 @@ class RomaUnsbModel(BaseModel):
         else:
             loss_global = 0.0
         
-        self.l2_loss = 0.0
-        #if self.opt.lambda_ctn > 0.0:
-        #    wapped_fake_B = warp(self.fake_B, self.f_content)  # use updated self.f_content
-        #    self.l2_loss = F.mse_loss(self.fake_B_2, wapped_fake_B)  # complete the loss calculation
-        
-        self.loss_G = self.loss_G_GAN + self.opt.lambda_SB * self.loss_SB + self.opt.lambda_ctn * self.l2_loss + loss_global * self.opt.lambda_global
+        self.loss_G = self.opt.lambda_GAN * self.loss_G_GAN + \
+                      self.opt.lambda_SB * self.loss_SB + \
+                      self.opt.lambda_ctn * self.ctn_loss + \
+                      loss_global * self.opt.lambda_global
         return self.loss_G
     
     def calculate_attention_loss(self):

scripts/train.sh

@@ -7,27 +7,22 @@
 
 python train.py \
     --dataroot /home/openxs/kunyu/datasets/InfraredCity-Lite/Double/Moitor \
-    --name ROMA_UNSB_001 \
+    --name ROMA_UNSB_003 \
     --dataset_mode unaligned_double \
     --no_flip \
     --display_env ROMA \
     --model roma_unsb \
-    --lambda_GAN 8.0 \
+    --lambda_GAN 1.0 \
     --lambda_NCE 8.0 \
-    --lambda_SB 0.1 \
+    --lambda_SB 1.0 \
     --lambda_ctn 1.0 \
     --lambda_inc 1.0 \
     --lr 0.00001 \
     --gpu_id 0 \
     --nce_idt False \
-    --nce_layers 0,4,8,12,16 \
     --netF mlp_sample \
-    --netF_nc 256 \
-    --nce_T 0.07 \
-    --lmda_1 0.1 \
-    --num_patches 256 \
-    --flip_equivariance False \
-    --eta_ratio 0.1 \
+    --flip_equivariance True \
+    --eta_ratio 0.4 \
     --tau 0.01 \
-    --num_timesteps 10 \
+    --num_timesteps 4 \
     --input_nc 3

train.py

@@ -44,6 +44,7 @@ if __name__ == '__main__':
                 model.setup(opt)               # regular setup: load and print networks; create schedulers
                 model.parallelize()
             model.set_input(data)  # unpack data from dataset and apply preprocessing
+            #print('Call opt paras')
             model.optimize_parameters()   # calculate loss functions, get gradients, update network weights
             if len(opt.gpu_ids) > 0:
                 torch.cuda.synchronize()