diff --git a/checkpoints/ROMA_UNSB_001/loss_log.txt b/checkpoints/ROMA_UNSB_001/loss_log.txt index d27cb3a..fd8dd2f 100644 --- a/checkpoints/ROMA_UNSB_001/loss_log.txt +++ b/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) ================ diff --git a/checkpoints/ROMA_UNSB_001/train_opt.txt b/checkpoints/ROMA_UNSB_001/train_opt.txt index d7766e4..4d2cd07 100644 --- a/checkpoints/ROMA_UNSB_001/train_opt.txt +++ b/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 diff --git a/models/__pycache__/networks.cpython-39.pyc b/models/__pycache__/networks.cpython-39.pyc index 40fbb5c..91353a9 100644 Binary files a/models/__pycache__/networks.cpython-39.pyc and b/models/__pycache__/networks.cpython-39.pyc differ diff --git a/models/__pycache__/roma_unsb_model.cpython-39.pyc b/models/__pycache__/roma_unsb_model.cpython-39.pyc index bd981a8..ac7f924 100644 Binary files a/models/__pycache__/roma_unsb_model.cpython-39.pyc and b/models/__pycache__/roma_unsb_model.cpython-39.pyc differ diff --git a/models/networks.py b/models/networks.py index 74343e6..7519e80 100644 --- a/models/networks.py +++ b/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, diff --git a/models/roma_unsb_model.py b/models/roma_unsb_model.py index 2c275a7..8dbc273 100644 --- a/models/roma_unsb_model.py +++ b/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: @@ -269,23 +271,25 @@ class RomaUnsbModel(BaseModel): 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) - 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) + if self.isTrain: 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): - """计算判别器的 GAN 损失""" - - fake = self.cat_results(self.fake_B.detach()) - pred_fake = self.netD(fake, self.time) - self.loss_D_fake = self.criterionGAN(pred_fake, False).mean() - - self.pred_real = self.netD(self.real_B0, self.time) - loss_D_real = self.criterionGAN(self.pred_real, True) - 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 + def compute_D_loss(self): #判别器还是没有改 + """Calculate GAN loss for the discriminator""" + + 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 + + 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) diff --git a/options/__pycache__/base_options.cpython-39.pyc b/options/__pycache__/base_options.cpython-39.pyc index 55ab7e1..7658be9 100644 Binary files a/options/__pycache__/base_options.cpython-39.pyc and b/options/__pycache__/base_options.cpython-39.pyc differ diff --git a/options/base_options.py b/options/base_options.py index f9de39b..b20e1b4 100644 --- a/options/base_options.py +++ b/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') diff --git a/scripts/train.sh b/scripts/train.sh index 93a5f96..dea6a51 100755 --- a/scripts/train.sh +++ b/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