without cnt

checkpoints/ROMA_UNSB_001/loss_log.txt

@@ -46,3 +46,25 @@
 ================ 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) ================
+================ Training Loss (Sun Feb 23 22:42:01 2025) ================
+================ Training Loss (Sun Feb 23 22:44:17 2025) ================
+================ Training Loss (Sun Feb 23 22:45:53 2025) ================
+================ 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

@@ -1,5 +1,5 @@
 ----------------- Options ---------------
-             atten_layers: 1,3,5                         
+             atten_layers: 5                             
                batch_size: 1                             
                     beta1: 0.5                           
                     beta2: 0.999                         
@@ -28,10 +28,12 @@
                 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                           
@@ -50,7 +52,7 @@
 nce_includes_all_negatives_from_minibatch: False                         
                nce_layers: 0,4,8,12,16                   
                       ndf: 64                            
-                     netD: basic                         
+                     netD: basic_cond                    
                      netF: mlp_sample                    
                   netF_nc: 256                           
                      netG: resnet_9blocks_cond           
@@ -78,7 +80,7 @@ nce_includes_all_negatives_from_minibatch: False
            serial_batches: False                         
 stylegan2_G_num_downsampling: 1                             
                    suffix:                               
-                      tau: 0.1                           	[default: 0.01]
+                      tau: 0.01                          
          update_html_freq: 1000                          
                   use_idt: False                         
                   verbose: False                         

models/networks.py

@@ -331,6 +331,8 @@ def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal'
         net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
     elif 'stylegan2' in netD:
         net = StyleGAN2Discriminator(input_nc, ndf, n_layers_D, no_antialias=no_antialias, opt=opt)
+    elif netD == 'basic_cond':  # more options
+        net = NLayerDiscriminator_ncsn(input_nc, ndf, n_layers=3, norm_layer=norm_layer, no_antialias=no_antialias)
     else:
         raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
     return init_net(net, init_type, init_gain, gpu_ids,

models/roma_unsb_model.py

@@ -200,6 +200,8 @@ class RomaUnsbModel(BaseModel):
         parser.add_argument('--lambda_NCE', type=float, default=1.0, help='weight for NCE loss: NCE(G(X), X)')
         parser.add_argument('--lambda_SB', type=float, default=0.1, help='weight for SB loss')
         parser.add_argument('--lambda_ctn', type=float, default=1.0, help='weight for content-aware temporal norm')
+        parser.add_argument('--lambda_D_ViT', type=float, default=1.0, help='weight for discriminator')
+        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')
@@ -220,7 +222,7 @@ class RomaUnsbModel(BaseModel):
         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('--atten_layers', type=str, default='1,3,5', help='compute Cross-Similarity on which layers')
+        parser.add_argument('--atten_layers', type=str, default='5', help='compute Cross-Similarity on which layers')
         
         parser.add_argument('--tau', type=float, default=0.01, help='Entropy parameter')
         parser.add_argument('--num_timesteps', type=int, default=5, help='# of discrim filters in the first conv layer')
@@ -258,7 +260,7 @@ class RomaUnsbModel(BaseModel):
             self.visual_names += ['idt_B']
 
         if self.isTrain:
-            self.model_names = ['G', 'D', 'E']
+            self.model_names = ['G', 'D_ViT', 'E']
 
             
         else:
@@ -270,22 +272,24 @@ class RomaUnsbModel(BaseModel):
         
         
         if self.isTrain:
-            self.netD = networks.define_D(opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.normD, opt.init_type, opt.init_gain, opt.no_antialias, self.gpu_ids, opt)
             self.netE = networks.define_D(opt.output_nc*4, opt.ndf, opt.netD, opt.n_layers_D, opt.normD, opt.init_type, opt.init_gain, opt.no_antialias, self.gpu_ids, opt)
             
             self.resize = tfs.Resize(size=(384,384), antialias=True)
             
+            self.netD_ViT = networks.MLPDiscriminator().to(self.device)
+            
             # 加入预训练VIT
             self.netPreViT = timm.create_model("vit_base_patch16_384", pretrained=True).to(self.device)
             
             # 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.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))
             self.optimizer_E = torch.optim.Adam(self.netE.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
             self.optimizers = [self.optimizer_G, self.optimizer_D, self.optimizer_E]
             
@@ -320,10 +324,10 @@ class RomaUnsbModel(BaseModel):
         
         self.netG.train()
         self.netE.train()
-        self.netD.train()
+        self.netD_ViT.train()
         
         # update D
-        self.set_requires_grad(self.netD, True)
+        self.set_requires_grad(self.netD_ViT, True)
         self.optimizer_D.zero_grad()
         self.loss_D = self.compute_D_loss()
         self.loss_D.backward()
@@ -337,7 +341,7 @@ class RomaUnsbModel(BaseModel):
         self.optimizer_E.step()
         
         # update G
-        self.set_requires_grad(self.netD, False)
+        self.set_requires_grad(self.netD_ViT, False)
         self.set_requires_grad(self.netE, False)
         
         self.optimizer_G.zero_grad()
@@ -443,7 +447,7 @@ 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=[2 * bs, 4 * self.opt.ngf]).to(self.real_A0.device)
+        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)
         # 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
@@ -455,9 +459,10 @@ 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}')
 
         if self.opt.phase == 'train':
             real_A0 = self.real_A0
@@ -507,23 +512,35 @@ class RomaUnsbModel(BaseModel):
             #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):
+    def compute_D_loss(self):      #判别器还是没有改
-        """计算判别器的 GAN 损失"""
+        """Calculate GAN loss for the discriminator"""
 
-        fake = self.cat_results(self.fake_B.detach())
+        lambda_D_ViT = self.opt.lambda_D_ViT
-        pred_fake = self.netD(fake, self.time)
+        fake_B0_tokens = self.mutil_fake_B0_tokens[0].detach()
-        self.loss_D_fake = self.criterionGAN(pred_fake, False).mean()
+        fake_B1_tokens = self.mutil_fake_B1_tokens[0].detach()
 
-        self.pred_real = self.netD(self.real_B0, self.time)
+        real_B0_tokens = self.mutil_real_B0_tokens[0]
-        loss_D_real = self.criterionGAN(self.pred_real, True)
+        real_B1_tokens = self.mutil_real_B1_tokens[0]
-        self.loss_D_real = loss_D_real.mean()
 
-        self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
+
-        return self.loss_D
+        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
+
+        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_ViT = (self.loss_D_fake_ViT + self.loss_D_real_ViT) * 0.5
+
+
+        return self.loss_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()
@@ -534,14 +551,8 @@ class RomaUnsbModel(BaseModel):
     def compute_G_loss(self):
         """计算生成器的 GAN 损失"""
         
-        bs =  self.real_A0.size(0)
-        tau = self.opt.tau
-        
-        fake = self.fake_B0
-        std = torch.rand(size=[1]).item() * self.opt.std
-        
         if self.opt.lambda_GAN > 0.0:
-            pred_fake = self.netD(fake, self.time)
+            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
         else:
             self.loss_G_GAN = 0.0
@@ -555,7 +566,7 @@ class RomaUnsbModel(BaseModel):
             # 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)
             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_B) ** 2)
+            self.loss_SB += self.opt.tau * 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)

options/base_options.py

@@ -35,7 +35,7 @@ class BaseOptions():
         parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
         parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
         parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')
-        parser.add_argument('--netD', type=str, default='basic', choices=['basic', 'n_layers', 'pixel', 'patch', 'tilestylegan2', 'stylegan2'], help='specify discriminator architecture. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
+        parser.add_argument('--netD', type=str, default='basic_cond', choices=['basic_cond', 'basic', 'n_layers', 'pixel', 'patch', 'tilestylegan2', 'stylegan2'], help='specify discriminator architecture. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
         parser.add_argument('--netG', type=str, default='resnet_9blocks_cond', choices=['resnet_9blocks','resnet_9blocks_mask', 'resnet_6blocks', 'unet_256', 'unet_128', 'stylegan2', 'smallstylegan2', 'resnet_cat', 'resnet_9blocks_cond'], help='specify generator architecture')
         parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
         parser.add_argument('--normG', type=str, default='instance', choices=['instance', 'batch', 'none'], help='instance normalization or batch normalization for G')

scripts/train.sh

@@ -28,6 +28,6 @@ python train.py \
     --num_patches 256 \
     --flip_equivariance False \
     --eta_ratio 0.1 \
-    --tau 0.1 \
+    --tau 0.01 \
     --num_timesteps 10 \
     --input_nc 3