diff --git a/checkpoints/ROMA_UNSB_001/loss_log.txt b/checkpoints/ROMA_UNSB_001/loss_log.txt index fd8dd2f..c7fad7f 100644 --- a/checkpoints/ROMA_UNSB_001/loss_log.txt +++ b/checkpoints/ROMA_UNSB_001/loss_log.txt @@ -68,3 +68,4 @@ ================ 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) ================ +================ Training Loss (Mon Feb 24 22:59:41 2025) ================ diff --git a/checkpoints/ROMA_UNSB_001/train_opt.txt b/checkpoints/ROMA_UNSB_001/train_opt.txt index 4d2cd07..8f10014 100644 --- a/checkpoints/ROMA_UNSB_001/train_opt.txt +++ b/checkpoints/ROMA_UNSB_001/train_opt.txt @@ -19,9 +19,9 @@ easy_label: experiment_name epoch: latest epoch_count: 1 - eta_ratio: 0.1 + eta_ratio: 0.4 evaluation_freq: 5000 - flip_equivariance: False + flip_equivariance: True [default: False] gan_mode: lsgan gpu_ids: 0 init_gain: 0.02 @@ -31,11 +31,10 @@ lambda_D_ViT: 1.0 lambda_GAN: 8.0 [default: 1.0] lambda_NCE: 8.0 [default: 1.0] - lambda_SB: 0.1 + lambda_SB: 1.0 [default: 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 @@ -47,14 +46,12 @@ 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 @@ -64,9 +61,8 @@ nce_includes_all_negatives_from_minibatch: False no_html: False normD: instance normG: instance - num_patches: 256 num_threads: 4 - num_timesteps: 10 [default: 5] + num_timesteps: 4 [default: 5] output_nc: 3 phase: train pool_size: 0 diff --git a/models/__pycache__/roma_unsb_model.cpython-39.pyc b/models/__pycache__/roma_unsb_model.cpython-39.pyc index ac7f924..15c9868 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/roma_unsb_model.py b/models/roma_unsb_model.py index 8dbc273..497ec9c 100644 --- a/models/roma_unsb_model.py +++ b/models/roma_unsb_model.py @@ -166,7 +166,7 @@ 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) + #print(weight_map.shape) B, _, H, W = weight_map.shape # 1. 归一化权重图 @@ -204,23 +204,19 @@ 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') @@ -261,12 +257,10 @@ 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 +278,6 @@ 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)) @@ -459,10 +450,8 @@ 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}') if self.opt.phase == 'train': real_A0 = self.real_A0 @@ -540,7 +529,6 @@ class RomaUnsbModel(BaseModel): 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() diff --git a/scripts/train.sh b/scripts/train.sh index dea6a51..77b67e2 100755 --- a/scripts/train.sh +++ b/scripts/train.sh @@ -14,20 +14,15 @@ python train.py \ --model roma_unsb \ --lambda_GAN 8.0 \ --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