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bishe 2025-02-24 23:00:25 +08:00
parent 67151c73f7
commit 4af0d7463d
5 changed files with 12 additions and 32 deletions
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1
checkpoints/ROMA_UNSB_001/loss_log.txt
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@ -68,3 +68,4 @@
================ Training Loss (Sun Feb 23 23:13:05 2025) ================ ================ Training Loss (Sun Feb 23 23:13:05 2025) ================
================ Training Loss (Sun Feb 23 23:13:59 2025) ================ ================ Training Loss (Sun Feb 23 23:13:59 2025) ================
================ Training Loss (Sun Feb 23 23:14:59 2025) ================ ================ Training Loss (Sun Feb 23 23:14:59 2025) ================
================ Training Loss (Mon Feb 24 22:59:41 2025) ================

12
checkpoints/ROMA_UNSB_001/train_opt.txt
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@ -19,9 +19,9 @@
easy_label: experiment_name easy_label: experiment_name
epoch: latest epoch: latest
epoch_count: 1 epoch_count: 1
eta_ratio: 0.1 eta_ratio: 0.4
evaluation_freq: 5000 evaluation_freq: 5000
flip_equivariance: False flip_equivariance: True [default: False]
gan_mode: lsgan gan_mode: lsgan
gpu_ids: 0 gpu_ids: 0
init_gain: 0.02 init_gain: 0.02
@ -31,11 +31,10 @@
lambda_D_ViT: 1.0 lambda_D_ViT: 1.0
lambda_GAN: 8.0 [default: 1.0] lambda_GAN: 8.0 [default: 1.0]
lambda_NCE: 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_ctn: 1.0
lambda_global: 1.0 lambda_global: 1.0
lambda_inc: 1.0 lambda_inc: 1.0
lmda_1: 0.1
load_size: 286 load_size: 286
lr: 1e-05 [default: 0.0002] lr: 1e-05 [default: 0.0002]
lr_decay_iters: 50 lr_decay_iters: 50
@ -47,14 +46,12 @@
n_layers_D: 3 n_layers_D: 3
n_mlp: 3 n_mlp: 3
name: ROMA_UNSB_001 [default: experiment_name] name: ROMA_UNSB_001 [default: experiment_name]
nce_T: 0.07
nce_idt: False [default: True] nce_idt: False [default: True]
nce_includes_all_negatives_from_minibatch: False nce_includes_all_negatives_from_minibatch: False
nce_layers: 0,4,8,12,16 nce_layers: 0,4,8,12,16
ndf: 64 ndf: 64
netD: basic_cond netD: basic_cond
netF: mlp_sample netF: mlp_sample
netF_nc: 256
netG: resnet_9blocks_cond netG: resnet_9blocks_cond
ngf: 64 ngf: 64
no_antialias: False no_antialias: False
@ -64,9 +61,8 @@ nce_includes_all_negatives_from_minibatch: False
no_html: False no_html: False
normD: instance normD: instance
normG: instance normG: instance
num_patches: 256
num_threads: 4 num_threads: 4
num_timesteps: 10 [default: 5] num_timesteps: 4 [default: 5]
output_nc: 3 output_nc: 3
phase: train phase: train
pool_size: 0 pool_size: 0

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models/__pycache__/roma_unsb_model.cpython-39.pyc
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18
models/roma_unsb_model.py
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@ -166,7 +166,7 @@ class ContentAwareTemporalNorm(nn.Module):
Returns: Returns:
F_content: [B, 2, H, W] 生成的光流场(x/y方向位移) 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 B, _, H, W = weight_map.shape
# 1. 归一化权重图 # 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('--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_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', parser.add_argument('--nce_includes_all_negatives_from_minibatch',
type=util.str2bool, nargs='?', const=True, default=False, 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.') 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', 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', parser.add_argument('--flip_equivariance',
type=util.str2bool, nargs='?', const=True, default=False, type=util.str2bool, nargs='?', const=True, default=False,
help="Enforce flip-equivariance as additional regularization. It's used by FastCUT, but not CUT") 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('--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') 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: if self.isTrain:
self.model_names = ['G', 'D_ViT', 'E'] self.model_names = ['G', 'D_ViT', 'E']
else: else:
self.model_names = ['G'] self.model_names = ['G']
print(f'input_nc = {self.opt.input_nc}')
# 创建网络 # 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) 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 # 定义损失函数
self.criterionL1 = torch.nn.L1Loss().to(self.device) self.criterionL1 = torch.nn.L1Loss().to(self.device)
self.criterionGAN = networks.GANLoss(opt.gan_mode).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.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_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)) 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.real = torch.flip(self.real, [3])
self.realt = torch.flip(self.realt, [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_B0 = self.netG(self.real_A0, self.time, z_in)
self.fake_B1 = self.netG(self.real_A1, self.time, z_in2) 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': if self.opt.phase == 'train':
real_A0 = self.real_A0 real_A0 = self.real_A0
@ -540,7 +529,6 @@ class RomaUnsbModel(BaseModel):
def compute_E_loss(self): def compute_E_loss(self):
"""计算判别器 E 的损失""" """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_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) 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() temp = torch.logsumexp(self.netE(XtXt_1, self.time, XtXt_2).reshape(-1), dim=0).mean()

13
scripts/train.sh
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@ -14,20 +14,15 @@ python train.py \
--model roma_unsb \ --model roma_unsb \
--lambda_GAN 8.0 \ --lambda_GAN 8.0 \
--lambda_NCE 8.0 \ --lambda_NCE 8.0 \
--lambda_SB 0.1 \ --lambda_SB 1.0 \
--lambda_ctn 1.0 \ --lambda_ctn 1.0 \
--lambda_inc 1.0 \ --lambda_inc 1.0 \
--lr 0.00001 \ --lr 0.00001 \
--gpu_id 0 \ --gpu_id 0 \
--nce_idt False \ --nce_idt False \
--nce_layers 0,4,8,12,16 \
--netF mlp_sample \ --netF mlp_sample \
--netF_nc 256 \ --flip_equivariance True \
--nce_T 0.07 \ --eta_ratio 0.4 \
--lmda_1 0.1 \
--num_patches 256 \
--flip_equivariance False \
--eta_ratio 0.1 \
--tau 0.01 \ --tau 0.01 \
--num_timesteps 10 \ --num_timesteps 4 \
--input_nc 3 --input_nc 3