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bishe
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@ -68,3 +68,4 @@
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================ Training Loss (Sun Feb 23 23:13:05 2025) ================
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================ Training Loss (Sun Feb 23 23:13:59 2025) ================
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================ Training Loss (Sun Feb 23 23:14:59 2025) ================
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================ Training Loss (Mon Feb 24 22:59:41 2025) ================
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@ -19,9 +19,9 @@
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easy_label: experiment_name
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epoch: latest
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epoch_count: 1
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eta_ratio: 0.1
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eta_ratio: 0.4
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evaluation_freq: 5000
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flip_equivariance: False
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flip_equivariance: True [default: False]
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gan_mode: lsgan
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gpu_ids: 0
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init_gain: 0.02
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@ -31,11 +31,10 @@
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lambda_D_ViT: 1.0
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lambda_GAN: 8.0 [default: 1.0]
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lambda_NCE: 8.0 [default: 1.0]
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lambda_SB: 0.1
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lambda_SB: 1.0 [default: 0.1]
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lambda_ctn: 1.0
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lambda_global: 1.0
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lambda_inc: 1.0
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lmda_1: 0.1
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load_size: 286
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lr: 1e-05 [default: 0.0002]
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lr_decay_iters: 50
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@ -47,14 +46,12 @@
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n_layers_D: 3
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n_mlp: 3
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name: ROMA_UNSB_001 [default: experiment_name]
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nce_T: 0.07
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nce_idt: False [default: True]
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nce_includes_all_negatives_from_minibatch: False
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nce_layers: 0,4,8,12,16
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ndf: 64
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netD: basic_cond
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netF: mlp_sample
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netF_nc: 256
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netG: resnet_9blocks_cond
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ngf: 64
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no_antialias: False
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@ -64,9 +61,8 @@ nce_includes_all_negatives_from_minibatch: False
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no_html: False
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normD: instance
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normG: instance
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num_patches: 256
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num_threads: 4
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num_timesteps: 10 [default: 5]
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num_timesteps: 4 [default: 5]
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output_nc: 3
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phase: train
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pool_size: 0
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Binary file not shown.
@ -166,7 +166,7 @@ class ContentAwareTemporalNorm(nn.Module):
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Returns:
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F_content: [B, 2, H, W] 生成的光流场(x/y方向位移)
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"""
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print(weight_map.shape)
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#print(weight_map.shape)
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B, _, H, W = weight_map.shape
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# 1. 归一化权重图
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@ -204,23 +204,19 @@ class RomaUnsbModel(BaseModel):
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parser.add_argument('--lambda_global', type=float, default=1.0, help='weight for Global Structural Consistency')
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parser.add_argument('--nce_idt', type=util.str2bool, nargs='?', const=True, default=False, help='use NCE loss for identity mapping: NCE(G(Y), Y))')
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parser.add_argument('--nce_layers', type=str, default='0,4,8,12,16', help='compute NCE loss on which layers')
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parser.add_argument('--nce_includes_all_negatives_from_minibatch',
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type=util.str2bool, nargs='?', const=True, default=False,
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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.')
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parser.add_argument('--nce_layers', type=str, default='0,4,8,12,16', help='compute NCE loss on which layers')
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parser.add_argument('--netF', type=str, default='mlp_sample', choices=['sample', 'reshape', 'mlp_sample'], help='how to downsample the feature map')
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parser.add_argument('--netF_nc', type=int, default=256)
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parser.add_argument('--nce_T', type=float, default=0.07, help='temperature for NCE loss')
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parser.add_argument('--lmda_1', type=float, default=0.1)
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parser.add_argument('--num_patches', type=int, default=256, help='number of patches per layer')
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parser.add_argument('--flip_equivariance',
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type=util.str2bool, nargs='?', const=True, default=False,
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help="Enforce flip-equivariance as additional regularization. It's used by FastCUT, but not CUT")
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parser.add_argument('--lambda_inc', type=float, default=1.0, help='incremental weight for content-aware optimization')
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parser.add_argument('--eta_ratio', type=float, default=0.1, help='ratio of content-rich regions')
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parser.add_argument('--eta_ratio', type=float, default=0.4, help='ratio of content-rich regions')
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parser.add_argument('--atten_layers', type=str, default='5', help='compute Cross-Similarity on which layers')
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@ -261,12 +257,10 @@ class RomaUnsbModel(BaseModel):
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if self.isTrain:
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self.model_names = ['G', 'D_ViT', 'E']
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else:
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self.model_names = ['G']
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print(f'input_nc = {self.opt.input_nc}')
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# 创建网络
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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)
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@ -284,9 +278,6 @@ class RomaUnsbModel(BaseModel):
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# 定义损失函数
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self.criterionL1 = torch.nn.L1Loss().to(self.device)
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self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
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self.criterionNCE = []
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for nce_layer in self.nce_layers:
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self.criterionNCE.append(PatchNCELoss(opt).to(self.device))
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self.criterionIdt = torch.nn.L1Loss().to(self.device)
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self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
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self.optimizer_D = torch.optim.Adam(self.netD_ViT.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
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@ -459,10 +450,8 @@ class RomaUnsbModel(BaseModel):
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self.real = torch.flip(self.real, [3])
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self.realt = torch.flip(self.realt, [3])
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print(f'fake_B0: {self.real_A0.shape}, fake_B1: {self.real_A1.shape}')
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self.fake_B0 = self.netG(self.real_A0, self.time, z_in)
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self.fake_B1 = self.netG(self.real_A1, self.time, z_in2)
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print(f'fake_B0: {self.fake_B0.shape}, fake_B1: {self.fake_B1.shape}')
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if self.opt.phase == 'train':
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real_A0 = self.real_A0
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@ -540,7 +529,6 @@ class RomaUnsbModel(BaseModel):
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def compute_E_loss(self):
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"""计算判别器 E 的损失"""
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print(f'resl_A_noisy: {self.real_A_noisy.shape} \n fake_B0: {self.fake_B0.shape}')
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XtXt_1 = torch.cat([self.real_A_noisy, self.fake_B0.detach()], dim=1)
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XtXt_2 = torch.cat([self.real_A_noisy2, self.fake_B1.detach()], dim=1)
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temp = torch.logsumexp(self.netE(XtXt_1, self.time, XtXt_2).reshape(-1), dim=0).mean()
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@ -14,20 +14,15 @@ python train.py \
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--model roma_unsb \
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--lambda_GAN 8.0 \
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--lambda_NCE 8.0 \
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--lambda_SB 0.1 \
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--lambda_SB 1.0 \
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--lambda_ctn 1.0 \
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--lambda_inc 1.0 \
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--lr 0.00001 \
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--gpu_id 0 \
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--nce_idt False \
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--nce_layers 0,4,8,12,16 \
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--netF mlp_sample \
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--netF_nc 256 \
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--nce_T 0.07 \
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--lmda_1 0.1 \
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--num_patches 256 \
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--flip_equivariance False \
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--eta_ratio 0.1 \
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--flip_equivariance True \
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--eta_ratio 0.4 \
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--tau 0.01 \
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--num_timesteps 10 \
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--num_timesteps 4 \
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--input_nc 3
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