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20-04-05 17:42:40.469 - INFO: Set [resume_state] to ../experiments/sollevante/training_state/60000.state
20-04-05 17:42:40.469 - INFO: Resuming training from epoch: 304, iter: 60000.
20-04-05 17:42:40.469 - WARNING: pretrain_model path will be ignored when resuming training.
20-04-05 17:42:40.469 - INFO: Set [pretrain_model_G] to /home/owner/github/BasicSR/experiments/sollevante/models/60000_G.pth
20-04-05 17:42:40.469 - INFO: Set [pretrain_model_D] to /home/owner/github/BasicSR/experiments/sollevante/models/60000_D.pth
20-04-05 17:42:40.470 - INFO: name: sollevante
use_tb_logger: True
model: srragan
scale: 4
gpu_ids: [0]
datasets:[
train:[
name: sollevante-train
mode: LRHR
dataroot_HR: ['/mnt/8tb-hdd-1/datasets/sollevante/hr/train']
dataroot_LR: ['/mnt/8tb-hdd-1/datasets/sollevante/lr/train']
subset_file: None
use_shuffle: True
znorm: False
n_workers: 8
batch_size: 32
HR_size: 128
lr_downscale: True
lr_downscale_types: [1, 2, 777]
use_flip: True
use_rot: True
hr_rrot: False
lr_blur: False
lr_blur_types: ['gaussian', 'clean', 'clean', 'clean']
lr_noise: False
lr_noise_types: ['gaussian', 'clean', 'clean', 'clean', 'clean']
lr_noise2: False
lr_noise_types2: ['dither', 'dither', 'clean', 'clean']
hr_noise: False
hr_noise_types: ['gaussian', 'clean', 'clean', 'clean', 'clean']
phase: train
scale: 4
data_type: img
]
val:[
name: sollevante-val
mode: LRHR
dataroot_HR: ['/mnt/8tb-hdd-1/datasets/sollevante/hr/val']
dataroot_LR: ['/mnt/8tb-hdd-1/datasets/sollevante/lr/val']
znorm: False
lr_downscale: False
lr_downscale_types: [0, 1]
phase: val
scale: 4
data_type: img
]
]
path:[
root: /home/owner/github/BasicSR
pretrain_model_G: /home/owner/github/BasicSR/experiments/sollevante/models/60000_G.pth
resume_state: ../experiments/sollevante/training_state/60000.state
experiments_root: /home/owner/github/BasicSR/experiments/sollevante
models: /home/owner/github/BasicSR/experiments/sollevante/models
training_state: /home/owner/github/BasicSR/experiments/sollevante/training_state
log: /home/owner/github/BasicSR/experiments/sollevante
val_images: /home/owner/github/BasicSR/experiments/sollevante/val_images
pretrain_model_D: /home/owner/github/BasicSR/experiments/sollevante/models/60000_D.pth
]
network_G:[
which_model_G: RRDB_net
norm_type: None
mode: CNA
nf: 64
nb: 23
in_nc: 3
out_nc: 3
gc: 32
group: 1
convtype: Conv2D
net_act: leakyrelu
scale: 4
]
network_D:[
which_model_D: discriminator_vgg_128
norm_type: batch
act_type: leakyrelu
mode: CNA
nf: 64
in_nc: 3
]
train:[
lr_G: 0.0001
weight_decay_G: 0
beta1_G: 0.9
lr_D: 0.0001
weight_decay_D: 0
beta1_D: 0.9
lr_scheme: MultiStepLR
lr_steps: [50000, 100000, 200000, 300000]
lr_gamma: 0.5
pixel_criterion: l1
pixel_weight: 0.01
feature_criterion: l1
feature_weight: 1
gan_type: vanilla
gan_weight: 0.005
niter: 500000.0
val_freq: 5000.0
]
logger:[
print_freq: 200
save_checkpoint_freq: 5000.0
]
is_train: True
20-04-05 17:42:40.575 - INFO: Random seed: 6670
20-04-05 17:42:40.610 - INFO: Dataset [LRHRDataset - sollevante-train] is created.
20-04-05 17:42:40.610 - INFO: Number of train images: 6,309, iters: 198
20-04-05 17:42:40.610 - INFO: Total epochs needed: 2526 for iters 500,000
20-04-05 17:42:40.610 - INFO: Dataset [LRHRDataset - sollevante-val] is created.
20-04-05 17:42:40.610 - INFO: Number of val images in [sollevante-val]: 4
20-04-05 17:42:40.751 - INFO: Initialization method [kaiming]
20-04-05 17:42:40.958 - INFO: Initialization method [kaiming]
20-04-05 17:42:41.037 - INFO: Loading pretrained model for G [/home/owner/github/BasicSR/experiments/sollevante/models/60000_G.pth] ...
20-04-05 17:42:41.148 - INFO: Loading pretrained model for D [/home/owner/github/BasicSR/experiments/sollevante/models/60000_D.pth] ...
20-04-05 17:42:42.224 - INFO: Remove HFEN loss.
20-04-05 17:42:42.224 - INFO: Remove TV loss.
20-04-05 17:42:42.224 - INFO: Remove SSIM loss.
20-04-05 17:42:42.224 - INFO: Remove LPIPS loss.
20-04-05 17:42:42.224 - INFO: Remove SPL loss.
20-04-05 17:42:42.232 - INFO: Network G structure: DataParallel - RRDBNet, with parameters: 16,697,987
20-04-05 17:42:42.232 - INFO: RRDBNet(
(model): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Identity +
|Sequential(
| (0): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (1): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (2): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (3): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (4): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (5): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (6): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (7): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (8): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (9): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (10): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (11): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (12): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (13): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (14): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (15): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (16): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (17): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (18): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (19): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (20): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (21): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (22): RRDB(
| (RDB1): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB2): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| (RDB3): ResidualDenseBlock_5C(
| (conv1): Sequential(
| (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv2): Sequential(
| (0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv3): Sequential(
| (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv4): Sequential(
| (0): Conv2d(160, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| (1): LeakyReLU(negative_slope=0.2, inplace=True)
| )
| (conv5): Sequential(
| (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
| )
| )
| )
| (23): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|)
(2): Upsample(scale_factor=2.0, mode=nearest)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): LeakyReLU(negative_slope=0.2, inplace=True)
(5): Upsample(scale_factor=2.0, mode=nearest)
(6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): LeakyReLU(negative_slope=0.2, inplace=True)
(8): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): LeakyReLU(negative_slope=0.2, inplace=True)
(10): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
20-04-05 17:42:42.236 - INFO: Network D structure: DataParallel - Discriminator_VGG_128, with parameters: 14,502,281
20-04-05 17:42:42.236 - INFO: Discriminator_VGG_128(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.2, inplace=True)
(2): Conv2d(64, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): LeakyReLU(negative_slope=0.2, inplace=True)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): LeakyReLU(negative_slope=0.2, inplace=True)
(8): Conv2d(128, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): LeakyReLU(negative_slope=0.2, inplace=True)
(11): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(13): LeakyReLU(negative_slope=0.2, inplace=True)
(14): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(16): LeakyReLU(negative_slope=0.2, inplace=True)
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(19): LeakyReLU(negative_slope=0.2, inplace=True)
(20): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(21): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(22): LeakyReLU(negative_slope=0.2, inplace=True)
(23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(25): LeakyReLU(negative_slope=0.2, inplace=True)
(26): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(27): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(28): LeakyReLU(negative_slope=0.2, inplace=True)
)
(classifier): Sequential(
(0): Linear(in_features=8192, out_features=100, bias=True)
(1): LeakyReLU(negative_slope=0.2, inplace=True)
(2): Linear(in_features=100, out_features=1, bias=True)
)
)
20-04-05 17:42:42.236 - INFO: Network F structure: DataParallel - VGGFeatureExtractor, with parameters: 20,024,384
20-04-05 17:42:42.236 - INFO: VGGFeatureExtractor(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace=True)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace=True)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace=True)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace=True)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace=True)
(16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(17): ReLU(inplace=True)
(18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace=True)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace=True)
(23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): ReLU(inplace=True)
(25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(26): ReLU(inplace=True)
(27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace=True)
(30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(31): ReLU(inplace=True)
(32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(33): ReLU(inplace=True)
(34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
20-04-05 17:42:42.236 - INFO: Model [SRRaGANModel] is created.
20-04-05 17:42:42.302 - INFO: Start training from epoch: 304, iter: 60000
20-04-05 17:45:57.654 - INFO: <epoch:305, iter: 60,200, lr:5.000e-05> l_g_pix: 1.4920e-04 l_g_fea: 5.0552e-01 l_g_gan: 7.6096e-03 l_d_real: 2.8285e-01 l_d_fake: 2.9871e-01 D_real: 3.7648e+01 D_fake: 3.6417e+01
20-04-05 17:49:07.082 - INFO: <epoch:306, iter: 60,400, lr:5.000e-05> l_g_pix: 1.6805e-04 l_g_fea: 5.6645e-01 l_g_gan: 1.9379e-02 l_d_real: 3.4413e-02 l_d_fake: 3.3444e-02 D_real: 6.7796e+01 D_fake: 6.3954e+01
20-04-05 17:52:17.385 - INFO: <epoch:307, iter: 60,600, lr:5.000e-05> l_g_pix: 1.1755e-04 l_g_fea: 4.1561e-01 l_g_gan: 2.9043e-02 l_d_real: 4.4754e-03 l_d_fake: 5.4263e-03 D_real: 6.4989e+01 D_fake: 5.9185e+01
20-04-05 17:55:28.231 - INFO: <epoch:308, iter: 60,800, lr:5.000e-05> l_g_pix: 9.4591e-05 l_g_fea: 4.0670e-01 l_g_gan: 6.0991e-03 l_d_real: 4.0453e-01 l_d_fake: 3.9927e-01 D_real: 8.5834e+01 D_fake: 8.5016e+01
20-04-05 17:58:38.863 - INFO: <epoch:309, iter: 61,000, lr:5.000e-05> l_g_pix: 1.5079e-04 l_g_fea: 5.0973e-01 l_g_gan: 3.2770e-02 l_d_real: 1.8009e-03 l_d_fake: 1.9827e-03 D_real: 1.2741e+02 D_fake: 1.2086e+02
20-04-05 18:01:49.487 - INFO: <epoch:310, iter: 61,200, lr:5.000e-05> l_g_pix: 8.4722e-05 l_g_fea: 4.6990e-01 l_g_gan: 1.7428e-02 l_d_real: 3.4085e-02 l_d_fake: 3.5274e-02 D_real: 6.7751e+01 D_fake: 6.4300e+01
20-04-05 18:05:00.374 - INFO: <epoch:311, iter: 61,400, lr:5.000e-05> l_g_pix: 1.1428e-04 l_g_fea: 4.0989e-01 l_g_gan: 4.3307e-03 l_d_real: 6.1917e-01 l_d_fake: 6.2407e-01 D_real: 8.3871e+01 D_fake: 8.3627e+01
20-04-05 18:08:10.828 - INFO: <epoch:312, iter: 61,600, lr:5.000e-05> l_g_pix: 1.6183e-04 l_g_fea: 4.9662e-01 l_g_gan: 1.1615e-02 l_d_real: 1.1200e-01 l_d_fake: 1.1834e-01 D_real: 7.9509e+01 D_fake: 7.7302e+01
20-04-05 18:11:21.336 - INFO: <epoch:313, iter: 61,800, lr:5.000e-05> l_g_pix: 1.0846e-04 l_g_fea: 4.2820e-01 l_g_gan: 7.1860e-03 l_d_real: 2.9912e-01 l_d_fake: 3.1064e-01 D_real: 4.3538e+01 D_fake: 4.2406e+01
20-04-05 18:14:33.280 - INFO: <epoch:314, iter: 62,000, lr:5.000e-05> l_g_pix: 1.3505e-04 l_g_fea: 4.4922e-01 l_g_gan: 9.2906e-03 l_d_real: 2.0661e-01 l_d_fake: 2.2380e-01 D_real: 4.4204e+01 D_fake: 4.2561e+01
20-04-05 18:17:44.886 - INFO: <epoch:315, iter: 62,200, lr:5.000e-05> l_g_pix: 1.5551e-04 l_g_fea: 5.1826e-01 l_g_gan: 6.8055e-03 l_d_real: 4.6039e-01 l_d_fake: 4.4047e-01 D_real: 5.8844e+01 D_fake: 5.7933e+01
20-04-05 18:20:55.932 - INFO: <epoch:316, iter: 62,400, lr:5.000e-05> l_g_pix: 1.1975e-04 l_g_fea: 4.4144e-01 l_g_gan: 6.7295e-03 l_d_real: 3.1694e-01 l_d_fake: 3.1806e-01 D_real: 5.4820e+01 D_fake: 5.3791e+01
20-04-05 18:24:07.726 - INFO: <epoch:317, iter: 62,600, lr:5.000e-05> l_g_pix: 1.4878e-04 l_g_fea: 5.4802e-01 l_g_gan: 1.5005e-03 l_d_real: 1.6188e+00 l_d_fake: 1.5636e+00 D_real: 7.4169e+01 D_fake: 7.5460e+01
20-04-05 18:27:19.823 - INFO: <epoch:318, iter: 62,800, lr:5.000e-05> l_g_pix: 1.2529e-04 l_g_fea: 4.5490e-01 l_g_gan: 5.6609e-03 l_d_real: 5.1210e-01 l_d_fake: 5.2366e-01 D_real: 9.9402e+01 D_fake: 9.8788e+01
20-04-05 18:30:31.157 - INFO: <epoch:319, iter: 63,000, lr:5.000e-05> l_g_pix: 1.1334e-04 l_g_fea: 4.7915e-01 l_g_gan: 9.1592e-03 l_d_real: 2.3335e-01 l_d_fake: 2.0819e-01 D_real: 4.5922e+01 D_fake: 4.4311e+01
20-04-05 18:33:42.300 - INFO: <epoch:320, iter: 63,200, lr:5.000e-05> l_g_pix: 1.4822e-04 l_g_fea: 3.9742e-01 l_g_gan: 1.5309e-03 l_d_real: 1.6062e+00 l_d_fake: 1.5861e+00 D_real: 5.3385e+01 D_fake: 5.4675e+01
20-04-05 18:36:53.561 - INFO: <epoch:321, iter: 63,400, lr:5.000e-05> l_g_pix: 1.0364e-04 l_g_fea: 4.1811e-01 l_g_gan: 1.7089e-02 l_d_real: 3.8306e-02 l_d_fake: 3.8939e-02 D_real: 5.9679e+01 D_fake: 5.6300e+01
20-04-05 18:40:04.713 - INFO: <epoch:322, iter: 63,600, lr:5.000e-05> l_g_pix: 1.1940e-04 l_g_fea: 4.0567e-01 l_g_gan: 1.1291e-02 l_d_real: 2.1058e-01 l_d_fake: 1.5440e-01 D_real: 1.0717e+02 D_fake: 1.0510e+02
20-04-05 18:43:16.060 - INFO: <epoch:323, iter: 63,800, lr:5.000e-05> l_g_pix: 7.7891e-05 l_g_fea: 3.2243e-01 l_g_gan: 9.6772e-03 l_d_real: 1.8601e-01 l_d_fake: 1.9287e-01 D_real: 5.9616e+01 D_fake: 5.7870e+01
20-04-05 18:46:27.462 - INFO: <epoch:324, iter: 64,000, lr:5.000e-05> l_g_pix: 1.6022e-04 l_g_fea: 4.5294e-01 l_g_gan: 5.1016e-02 l_d_real: 4.4829e-05 l_d_fake: 5.2743e-05 D_real: 1.2257e+02 D_fake: 1.1237e+02
20-04-05 18:49:38.469 - INFO: <epoch:325, iter: 64,200, lr:5.000e-05> l_g_pix: 8.7632e-05 l_g_fea: 4.5462e-01 l_g_gan: 1.5239e-02 l_d_real: 8.0071e-02 l_d_fake: 6.9572e-02 D_real: 3.0361e+01 D_fake: 2.7388e+01
20-04-05 18:52:49.615 - INFO: <epoch:326, iter: 64,400, lr:5.000e-05> l_g_pix: 1.3296e-04 l_g_fea: 3.9011e-01 l_g_gan: 1.2972e-02 l_d_real: 1.3981e-01 l_d_fake: 1.0698e-01 D_real: 4.6151e+01 D_fake: 4.3680e+01
20-04-05 18:56:00.516 - INFO: <epoch:327, iter: 64,600, lr:5.000e-05> l_g_pix: 1.0356e-04 l_g_fea: 4.4320e-01 l_g_gan: 1.4147e-02 l_d_real: 8.5226e-02 l_d_fake: 8.9239e-02 D_real: 6.1111e+01 D_fake: 5.8369e+01
20-04-05 18:59:10.984 - INFO: <epoch:328, iter: 64,800, lr:5.000e-05> l_g_pix: 1.3901e-04 l_g_fea: 5.7633e-01 l_g_gan: 5.8241e-03 l_d_real: 4.2737e-01 l_d_fake: 4.4253e-01 D_real: 8.6762e+01 D_fake: 8.6032e+01
20-04-05 19:02:21.827 - INFO: <epoch:329, iter: 65,000, lr:5.000e-05> l_g_pix: 1.2603e-04 l_g_fea: 5.0492e-01 l_g_gan: 9.1765e-03 l_d_real: 2.2353e-01 l_d_fake: 2.2360e-01 D_real: 9.4107e+01 D_fake: 9.2496e+01
20-04-05 19:02:22.260 - INFO: Models and training states saved.
20-04-05 19:03:31.587 - INFO: # Validation # PSNR: 31.38, SSIM: 0.84938, LPIPS: 0.044759
20-04-05 19:03:31.587 - INFO: <epoch:329, iter: 65,000> psnr: 31.38, ssim: 0.84938, lpips: 0.044759
20-04-05 19:06:44.936 - INFO: <epoch:330, iter: 65,200, lr:5.000e-05> l_g_pix: 1.2679e-04 l_g_fea: 4.0677e-01 l_g_gan: 8.4452e-03 l_d_real: 2.6940e-01 l_d_fake: 2.7573e-01 D_real: 7.8757e+01 D_fake: 7.7341e+01
20-04-05 19:10:02.737 - INFO: <epoch:331, iter: 65,400, lr:5.000e-05> l_g_pix: 9.7548e-05 l_g_fea: 3.8229e-01 l_g_gan: 1.1409e-02 l_d_real: 1.2468e-01 l_d_fake: 1.2742e-01 D_real: 4.4124e+01 D_fake: 4.1968e+01
20-04-05 19:13:15.259 - INFO: <epoch:332, iter: 65,600, lr:5.000e-05> l_g_pix: 1.2933e-04 l_g_fea: 4.1560e-01 l_g_gan: 2.1634e-02 l_d_real: 1.7301e-02 l_d_fake: 1.6573e-02 D_real: 1.1185e+02 D_fake: 1.0754e+02
20-04-05 19:16:26.840 - INFO: <epoch:333, iter: 65,800, lr:5.000e-05> l_g_pix: 8.1568e-05 l_g_fea: 3.9845e-01 l_g_gan: 1.4427e-02 l_d_real: 6.5083e-02 l_d_fake: 6.7502e-02 D_real: 3.1947e+01 D_fake: 2.9128e+01
20-04-05 19:19:38.743 - INFO: <epoch:334, iter: 66,000, lr:5.000e-05> l_g_pix: 9.8380e-05 l_g_fea: 3.5542e-01 l_g_gan: 1.4267e-02 l_d_real: 1.0810e-01 l_d_fake: 8.3081e-02 D_real: 4.5101e+01 D_fake: 4.2343e+01
20-04-05 19:22:50.521 - INFO: <epoch:335, iter: 66,200, lr:5.000e-05> l_g_pix: 1.2862e-04 l_g_fea: 4.6451e-01 l_g_gan: 6.5539e-03 l_d_real: 3.4308e-01 l_d_fake: 3.5218e-01 D_real: 2.0185e+01 D_fake: 1.9222e+01
20-04-05 19:26:02.542 - INFO: <epoch:336, iter: 66,400, lr:5.000e-05> l_g_pix: 1.3824e-04 l_g_fea: 5.6690e-01 l_g_gan: 8.7386e-04 l_d_real: 2.0395e+00 l_d_fake: 2.0461e+00 D_real: 8.2711e+01 D_fake: 8.4579e+01
20-04-05 19:29:14.322 - INFO: <epoch:337, iter: 66,600, lr:5.000e-05> l_g_pix: 9.5914e-05 l_g_fea: 3.7431e-01 l_g_gan: 2.8820e-02 l_d_real: 5.6822e-03 l_d_fake: 4.3164e-03 D_real: 6.0798e+01 D_fake: 5.5039e+01
20-04-05 19:32:25.288 - INFO: <epoch:338, iter: 66,800, lr:5.000e-05> l_g_pix: 1.2153e-04 l_g_fea: 5.0836e-01 l_g_gan: 3.2546e-02 l_d_real: 2.7078e-03 l_d_fake: 4.6793e-03 D_real: 8.3276e+01 D_fake: 7.6771e+01
20-04-05 19:35:38.536 - INFO: <epoch:339, iter: 67,000, lr:5.000e-05> l_g_pix: 1.1148e-04 l_g_fea: 4.7803e-01 l_g_gan: 1.0869e-02 l_d_real: 1.7475e-01 l_d_fake: 1.8454e-01 D_real: 8.4181e+01 D_fake: 8.2187e+01
20-04-05 19:38:51.421 - INFO: <epoch:340, iter: 67,200, lr:5.000e-05> l_g_pix: 1.1626e-04 l_g_fea: 5.3357e-01 l_g_gan: 1.6603e-03 l_d_real: 1.4774e+00 l_d_fake: 1.5017e+00 D_real: 3.7716e+01 D_fake: 3.8873e+01
20-04-05 19:42:02.651 - INFO: <epoch:341, iter: 67,400, lr:5.000e-05> l_g_pix: 9.6205e-05 l_g_fea: 4.0898e-01 l_g_gan: 6.6202e-03 l_d_real: 5.2520e-01 l_d_fake: 5.4577e-01 D_real: 1.1379e+02 D_fake: 1.1300e+02
20-04-05 19:45:16.119 - INFO: <epoch:342, iter: 67,600, lr:5.000e-05> l_g_pix: 1.5530e-04 l_g_fea: 5.2739e-01 l_g_gan: 7.4224e-03 l_d_real: 4.4305e-01 l_d_fake: 4.2896e-01 D_real: 6.0377e+01 D_fake: 5.9328e+01
20-04-05 19:48:27.990 - INFO: <epoch:343, iter: 67,800, lr:5.000e-05> l_g_pix: 1.6948e-04 l_g_fea: 4.6831e-01 l_g_gan: 4.1166e-03 l_d_real: 6.3772e-01 l_d_fake: 6.3360e-01 D_real: 5.6253e+01 D_fake: 5.6066e+01
20-04-05 19:51:40.126 - INFO: <epoch:344, iter: 68,000, lr:5.000e-05> l_g_pix: 1.0173e-04 l_g_fea: 4.7201e-01 l_g_gan: 7.2020e-03 l_d_real: 3.3367e-01 l_d_fake: 3.2499e-01 D_real: 4.2726e+01 D_fake: 4.1615e+01
20-04-05 19:54:52.210 - INFO: <epoch:345, iter: 68,200, lr:5.000e-05> l_g_pix: 8.5836e-05 l_g_fea: 4.3348e-01 l_g_gan: 1.4224e-02 l_d_real: 9.0617e-02 l_d_fake: 7.9907e-02 D_real: 3.2482e+01 D_fake: 2.9722e+01
20-04-05 19:58:04.392 - INFO: <epoch:346, iter: 68,400, lr:5.000e-05> l_g_pix: 1.6081e-04 l_g_fea: 5.6199e-01 l_g_gan: 4.4119e-02 l_d_real: 4.4575e-04 l_d_fake: 8.4644e-04 D_real: 6.6746e+01 D_fake: 5.7923e+01
20-04-05 20:01:17.776 - INFO: <epoch:347, iter: 68,600, lr:5.000e-05> l_g_pix: 1.1866e-04 l_g_fea: 5.3544e-01 l_g_gan: 2.6007e-02 l_d_real: 9.8808e-03 l_d_fake: 8.0435e-03 D_real: 7.9045e+01 D_fake: 7.3852e+01
20-04-05 20:04:30.145 - INFO: <epoch:348, iter: 68,800, lr:5.000e-05> l_g_pix: 1.1879e-04 l_g_fea: 4.2946e-01 l_g_gan: 1.0199e-02 l_d_real: 1.6068e-01 l_d_fake: 1.8729e-01 D_real: 5.6965e+01 D_fake: 5.5099e+01
20-04-05 20:07:41.983 - INFO: <epoch:349, iter: 69,000, lr:5.000e-05> l_g_pix: 1.5510e-04 l_g_fea: 4.9406e-01 l_g_gan: 3.9216e-03 l_d_real: 6.9541e-01 l_d_fake: 6.9207e-01 D_real: 5.3763e+01 D_fake: 5.3673e+01
20-04-05 20:10:54.342 - INFO: <epoch:350, iter: 69,200, lr:5.000e-05> l_g_pix: 7.6413e-05 l_g_fea: 2.8270e-01 l_g_gan: 1.9241e-03 l_d_real: 1.3537e+00 l_d_fake: 1.3252e+00 D_real: 2.0718e+01 D_fake: 2.1672e+01
20-04-05 20:14:06.435 - INFO: <epoch:351, iter: 69,400, lr:5.000e-05> l_g_pix: 1.1433e-04 l_g_fea: 5.5174e-01 l_g_gan: 1.2397e-02 l_d_real: 1.0288e-01 l_d_fake: 1.1626e-01 D_real: 3.7419e+01 D_fake: 3.5049e+01
20-04-05 20:17:18.776 - INFO: <epoch:352, iter: 69,600, lr:5.000e-05> l_g_pix: 1.1581e-04 l_g_fea: 4.1335e-01 l_g_gan: 1.4133e-02 l_d_real: 7.5386e-02 l_d_fake: 7.2928e-02 D_real: 7.7864e+01 D_fake: 7.5111e+01
20-04-05 20:20:29.913 - INFO: <epoch:353, iter: 69,800, lr:5.000e-05> l_g_pix: 8.6314e-05 l_g_fea: 3.6203e-01 l_g_gan: 5.5070e-03 l_d_real: 4.8780e-01 l_d_fake: 5.2471e-01 D_real: 3.7671e+01 D_fake: 3.7076e+01
20-04-05 20:23:41.989 - INFO: <epoch:354, iter: 70,000, lr:5.000e-05> l_g_pix: 1.0402e-04 l_g_fea: 3.7619e-01 l_g_gan: 5.3810e-04 l_d_real: 2.3886e+00 l_d_fake: 2.3888e+00 D_real: 7.1647e+01 D_fake: 7.3928e+01
20-04-05 20:23:42.449 - INFO: Models and training states saved.
20-04-05 20:24:55.071 - INFO: # Validation # PSNR: 31.291, SSIM: 0.84065, LPIPS: 0.045839
20-04-05 20:24:55.071 - INFO: <epoch:354, iter: 70,000> psnr: 31.291, ssim: 0.84065, lpips: 0.045839
20-04-05 20:29:00.878 - INFO: <epoch:355, iter: 70,200, lr:5.000e-05> l_g_pix: 9.5370e-05 l_g_fea: 4.3588e-01 l_g_gan: 7.3815e-03 l_d_real: 2.9088e-01 l_d_fake: 2.9675e-01 D_real: 5.4494e+01 D_fake: 5.3312e+01
20-04-05 20:32:12.492 - INFO: <epoch:356, iter: 70,400, lr:5.000e-05> l_g_pix: 1.0381e-04 l_g_fea: 3.7815e-01 l_g_gan: 2.8280e-02 l_d_real: 5.0693e-03 l_d_fake: 5.9760e-03 D_real: 6.5944e+01 D_fake: 6.0293e+01
20-04-05 20:35:24.641 - INFO: <epoch:357, iter: 70,600, lr:5.000e-05> l_g_pix: 9.9022e-05 l_g_fea: 4.2100e-01 l_g_gan: 1.5534e-02 l_d_real: 5.3473e-02 l_d_fake: 6.0102e-02 D_real: 1.0090e+02 D_fake: 9.7850e+01
20-04-05 20:38:36.219 - INFO: <epoch:358, iter: 70,800, lr:5.000e-05> l_g_pix: 1.1036e-04 l_g_fea: 5.2361e-01 l_g_gan: 1.8340e-02 l_d_real: 2.9711e-02 l_d_fake: 2.8735e-02 D_real: 3.4190e+01 D_fake: 3.0551e+01
20-04-05 20:41:48.442 - INFO: <epoch:359, iter: 71,000, lr:5.000e-05> l_g_pix: 1.2033e-04 l_g_fea: 4.9372e-01 l_g_gan: 1.2979e-02 l_d_real: 9.8545e-02 l_d_fake: 8.8529e-02 D_real: 3.3505e+01 D_fake: 3.1003e+01
20-04-05 20:45:00.236 - INFO: <epoch:360, iter: 71,200, lr:5.000e-05> l_g_pix: 8.8315e-05 l_g_fea: 3.9716e-01 l_g_gan: 1.6271e-02 l_d_real: 4.3926e-02 l_d_fake: 4.5248e-02 D_real: 5.8603e+01 D_fake: 5.5394e+01
20-04-05 20:48:12.266 - INFO: <epoch:361, iter: 71,400, lr:5.000e-05> l_g_pix: 1.0622e-04 l_g_fea: 4.0389e-01 l_g_gan: 1.4858e-02 l_d_real: 7.0176e-02 l_d_fake: 6.0527e-02 D_real: 7.4795e+01 D_fake: 7.1889e+01
20-04-05 20:51:24.343 - INFO: <epoch:362, iter: 71,600, lr:5.000e-05> l_g_pix: 1.2625e-04 l_g_fea: 4.6043e-01 l_g_gan: 7.5876e-03 l_d_real: 7.6533e-01 l_d_fake: 8.3687e-01 D_real: 1.7348e+01 D_fake: 1.6632e+01
20-04-05 20:54:36.882 - INFO: <epoch:363, iter: 71,800, lr:5.000e-05> l_g_pix: 1.5823e-04 l_g_fea: 5.6615e-01 l_g_gan: 1.5077e-02 l_d_real: 7.5428e-02 l_d_fake: 7.5071e-02 D_real: 6.3702e+01 D_fake: 6.0762e+01
20-04-05 20:57:49.432 - INFO: <epoch:364, iter: 72,000, lr:5.000e-05> l_g_pix: 1.3097e-04 l_g_fea: 6.0031e-01 l_g_gan: 1.4324e-03 l_d_real: 1.5862e+00 l_d_fake: 1.5801e+00 D_real: 3.9687e+01 D_fake: 4.0984e+01
20-04-05 21:01:01.624 - INFO: <epoch:365, iter: 72,200, lr:5.000e-05> l_g_pix: 1.3976e-04 l_g_fea: 4.8912e-01 l_g_gan: 1.9403e-02 l_d_real: 4.3156e-02 l_d_fake: 4.2251e-02 D_real: 9.2164e+01 D_fake: 8.8326e+01
20-04-05 21:04:14.282 - INFO: <epoch:366, iter: 72,400, lr:5.000e-05> l_g_pix: 9.5218e-05 l_g_fea: 4.6867e-01 l_g_gan: 6.8859e-03 l_d_real: 3.7782e-01 l_d_fake: 3.6981e-01 D_real: 2.7151e+01 D_fake: 2.6148e+01
20-04-05 21:07:27.742 - INFO: <epoch:367, iter: 72,600, lr:5.000e-05> l_g_pix: 8.3319e-05 l_g_fea: 3.7536e-01 l_g_gan: 6.7135e-03 l_d_real: 3.4942e-01 l_d_fake: 3.4626e-01 D_real: 6.3551e+01 D_fake: 6.2556e+01
20-04-05 21:10:40.332 - INFO: <epoch:368, iter: 72,800, lr:5.000e-05> l_g_pix: 9.6919e-05 l_g_fea: 4.5542e-01 l_g_gan: 1.4087e-02 l_d_real: 1.0676e-01 l_d_fake: 9.2252e-02 D_real: 4.7909e+01 D_fake: 4.5191e+01
20-04-05 21:13:52.780 - INFO: <epoch:369, iter: 73,000, lr:5.000e-05> l_g_pix: 1.2455e-04 l_g_fea: 5.5134e-01 l_g_gan: 1.0733e-02 l_d_real: 1.4025e-01 l_d_fake: 1.4419e-01 D_real: 6.0995e+00 D_fake: 4.0951e+00
20-04-05 21:17:09.888 - INFO: <epoch:371, iter: 73,200, lr:5.000e-05> l_g_pix: 1.6963e-04 l_g_fea: 5.5535e-01 l_g_gan: 1.0827e-02 l_d_real: 1.5321e-01 l_d_fake: 1.6310e-01 D_real: 2.3837e+01 D_fake: 2.1830e+01
20-04-05 21:20:22.780 - INFO: <epoch:372, iter: 73,400, lr:5.000e-05> l_g_pix: 1.2665e-04 l_g_fea: 4.3369e-01 l_g_gan: 9.6351e-03 l_d_real: 2.0997e-01 l_d_fake: 2.2676e-01 D_real: 5.6327e+01 D_fake: 5.4618e+01
20-04-05 21:23:35.614 - INFO: <epoch:373, iter: 73,600, lr:5.000e-05> l_g_pix: 9.6544e-05 l_g_fea: 3.9204e-01 l_g_gan: 7.3944e-03 l_d_real: 2.9451e-01 l_d_fake: 2.9414e-01 D_real: 1.7993e+01 D_fake: 1.6809e+01
20-04-05 21:26:48.441 - INFO: <epoch:374, iter: 73,800, lr:5.000e-05> l_g_pix: 1.3089e-04 l_g_fea: 4.9459e-01 l_g_gan: 1.6885e-02 l_d_real: 4.2808e-02 l_d_fake: 4.4288e-02 D_real: 1.2786e+02 D_fake: 1.2453e+02
20-04-05 21:29:59.814 - INFO: <epoch:375, iter: 74,000, lr:5.000e-05> l_g_pix: 1.7063e-04 l_g_fea: 4.9078e-01 l_g_gan: 1.1379e-02 l_d_real: 1.9227e-01 l_d_fake: 1.5310e-01 D_real: 7.5311e+01 D_fake: 7.3208e+01
20-04-05 21:33:12.843 - INFO: <epoch:376, iter: 74,200, lr:5.000e-05> l_g_pix: 9.6206e-05 l_g_fea: 3.1680e-01 l_g_gan: 1.1940e-02 l_d_real: 1.4100e-01 l_d_fake: 1.9718e-01 D_real: 5.2857e+01 D_fake: 5.0638e+01
20-04-05 21:36:25.038 - INFO: <epoch:377, iter: 74,400, lr:5.000e-05> l_g_pix: 9.8604e-05 l_g_fea: 4.3983e-01 l_g_gan: 1.0722e-02 l_d_real: 1.4707e-01 l_d_fake: 1.4378e-01 D_real: 8.0962e+01 D_fake: 7.8963e+01
20-04-05 21:39:37.541 - INFO: <epoch:378, iter: 74,600, lr:5.000e-05> l_g_pix: 1.3580e-04 l_g_fea: 4.9499e-01 l_g_gan: 3.3195e-03 l_d_real: 8.1459e-01 l_d_fake: 8.1129e-01 D_real: 5.1169e+01 D_fake: 5.1318e+01
20-04-05 21:42:50.650 - INFO: <epoch:379, iter: 74,800, lr:5.000e-05> l_g_pix: 1.1214e-04 l_g_fea: 3.6522e-01 l_g_gan: 2.6863e-02 l_d_real: 7.8313e-03 l_d_fake: 5.4607e-03 D_real: 6.1333e+01 D_fake: 5.5967e+01
20-04-05 21:46:03.824 - INFO: <epoch:380, iter: 75,000, lr:5.000e-05> l_g_pix: 8.8217e-05 l_g_fea: 3.8177e-01 l_g_gan: 6.7913e-03 l_d_real: 3.4151e-01 l_d_fake: 3.5130e-01 D_real: 4.9898e+01 D_fake: 4.8886e+01
20-04-05 21:46:04.319 - INFO: Models and training states saved.
20-04-05 21:47:14.153 - INFO: # Validation # PSNR: 30.979, SSIM: 0.82168, LPIPS: 0.028558
20-04-05 21:47:14.153 - INFO: <epoch:380, iter: 75,000> psnr: 30.979, ssim: 0.82168, lpips: 0.028558
20-04-05 21:53:54.664 - INFO: <epoch:381, iter: 75,200, lr:5.000e-05> l_g_pix: 9.3217e-05 l_g_fea: 4.4205e-01 l_g_gan: 4.1458e-03 l_d_real: 6.6498e-01 l_d_fake: 6.7775e-01 D_real: 4.8118e+01 D_fake: 4.7960e+01
20-04-05 21:57:05.472 - INFO: <epoch:382, iter: 75,400, lr:5.000e-05> l_g_pix: 1.2870e-04 l_g_fea: 5.5896e-01 l_g_gan: 1.2245e-02 l_d_real: 1.0344e-01 l_d_fake: 1.1300e-01 D_real: 5.7093e+01 D_fake: 5.4752e+01
20-04-05 22:00:16.870 - INFO: <epoch:383, iter: 75,600, lr:5.000e-05> l_g_pix: 1.2876e-04 l_g_fea: 4.7773e-01 l_g_gan: 8.0843e-03 l_d_real: 3.0072e-01 l_d_fake: 3.0604e-01 D_real: 4.4770e+01 D_fake: 4.3456e+01
20-04-05 22:03:29.395 - INFO: <epoch:384, iter: 75,800, lr:5.000e-05> l_g_pix: 9.5181e-05 l_g_fea: 4.0154e-01 l_g_gan: 1.4429e-02 l_d_real: 6.9895e-02 l_d_fake: 7.3274e-02 D_real: 2.6577e+01 D_fake: 2.3763e+01
20-04-05 22:06:41.622 - INFO: <epoch:385, iter: 76,000, lr:5.000e-05> l_g_pix: 9.6997e-05 l_g_fea: 4.8712e-01 l_g_gan: 2.2464e-02 l_d_real: 1.9162e-02 l_d_fake: 1.5439e-02 D_real: 1.8989e+01 D_fake: 1.4514e+01
20-04-05 22:09:53.793 - INFO: <epoch:386, iter: 76,200, lr:5.000e-05> l_g_pix: 7.4495e-05 l_g_fea: 3.3814e-01 l_g_gan: 2.3388e-02 l_d_real: 1.2139e-02 l_d_fake: 1.3385e-02 D_real: 9.5921e+01 D_fake: 9.1256e+01
20-04-05 22:13:05.559 - INFO: <epoch:387, iter: 76,400, lr:5.000e-05> l_g_pix: 1.0060e-04 l_g_fea: 4.3364e-01 l_g_gan: 7.8762e-03 l_d_real: 2.6207e-01 l_d_fake: 2.6200e-01 D_real: 4.8582e+01 D_fake: 4.7269e+01
20-04-05 22:16:17.879 - INFO: <epoch:388, iter: 76,600, lr:5.000e-05> l_g_pix: 9.2321e-05 l_g_fea: 3.9353e-01 l_g_gan: 1.0818e-02 l_d_real: 1.5028e-01 l_d_fake: 1.4293e-01 D_real: 3.5311e+01 D_fake: 3.3294e+01
20-04-05 22:19:30.326 - INFO: <epoch:389, iter: 76,800, lr:5.000e-05> l_g_pix: 1.3964e-04 l_g_fea: 3.6664e-01 l_g_gan: 1.7483e-02 l_d_real: 4.0597e-02 l_d_fake: 3.9690e-02 D_real: 6.6623e+01 D_fake: 6.3166e+01
20-04-05 22:22:42.831 - INFO: <epoch:390, iter: 77,000, lr:5.000e-05> l_g_pix: 9.1248e-05 l_g_fea: 2.9119e-01 l_g_gan: 1.4564e-02 l_d_real: 6.6352e-02 l_d_fake: 6.2918e-02 D_real: 3.3810e+01 D_fake: 3.0961e+01
20-04-05 22:25:55.773 - INFO: <epoch:391, iter: 77,200, lr:5.000e-05> l_g_pix: 1.0822e-04 l_g_fea: 4.0713e-01 l_g_gan: 1.3928e-02 l_d_real: 7.1199e-02 l_d_fake: 7.5041e-02 D_real: 6.1490e+01 D_fake: 5.8777e+01
20-04-05 22:29:08.020 - INFO: <epoch:392, iter: 77,400, lr:5.000e-05> l_g_pix: 1.0444e-04 l_g_fea: 4.9434e-01 l_g_gan: 1.0374e-02 l_d_real: 1.7279e-01 l_d_fake: 1.7715e-01 D_real: 4.9704e+01 D_fake: 4.7804e+01
20-04-05 22:32:20.477 - INFO: <epoch:393, iter: 77,600, lr:5.000e-05> l_g_pix: 1.3019e-04 l_g_fea: 5.1000e-01 l_g_gan: 9.0384e-03 l_d_real: 2.0335e-01 l_d_fake: 2.0602e-01 D_real: 3.1310e+01 D_fake: 2.9707e+01
20-04-05 22:35:32.506 - INFO: <epoch:394, iter: 77,800, lr:5.000e-05> l_g_pix: 1.1788e-04 l_g_fea: 4.2488e-01 l_g_gan: 6.8180e-03 l_d_real: 3.3118e-01 l_d_fake: 3.3849e-01 D_real: 1.6725e+01 D_fake: 1.5696e+01
20-04-05 22:38:44.763 - INFO: <epoch:395, iter: 78,000, lr:5.000e-05> l_g_pix: 8.7950e-05 l_g_fea: 3.6701e-01 l_g_gan: 7.4189e-03 l_d_real: 3.1479e-01 l_d_fake: 3.0519e-01 D_real: 3.6408e+01 D_fake: 3.5234e+01
20-04-05 22:41:55.885 - INFO: <epoch:396, iter: 78,200, lr:5.000e-05> l_g_pix: 7.5351e-05 l_g_fea: 3.3496e-01 l_g_gan: 2.8108e-02 l_d_real: 4.2673e-03 l_d_fake: 5.1055e-03 D_real: 7.1527e+01 D_fake: 6.5910e+01
20-04-05 22:45:08.888 - INFO: <epoch:397, iter: 78,400, lr:5.000e-05> l_g_pix: 1.7411e-04 l_g_fea: 4.9089e-01 l_g_gan: 1.7442e-03 l_d_real: 1.3501e+00 l_d_fake: 1.3442e+00 D_real: 6.7799e+01 D_fake: 6.8797e+01
20-04-05 22:48:21.416 - INFO: <epoch:398, iter: 78,600, lr:5.000e-05> l_g_pix: 1.2379e-04 l_g_fea: 5.2514e-01 l_g_gan: 2.5428e-03 l_d_real: 9.8736e-01 l_d_fake: 9.8824e-01 D_real: 3.6404e+01 D_fake: 3.6884e+01
20-04-05 22:51:34.442 - INFO: <epoch:399, iter: 78,800, lr:5.000e-05> l_g_pix: 1.3326e-04 l_g_fea: 5.2242e-01 l_g_gan: 2.8718e-03 l_d_real: 9.4646e-01 l_d_fake: 9.6100e-01 D_real: 5.7983e+01 D_fake: 5.8362e+01
20-04-05 22:54:47.853 - INFO: <epoch:400, iter: 79,000, lr:5.000e-05> l_g_pix: 1.1698e-04 l_g_fea: 3.9541e-01 l_g_gan: 6.8159e-03 l_d_real: 3.2789e-01 l_d_fake: 3.2515e-01 D_real: 4.3526e+01 D_fake: 4.2489e+01
20-04-05 22:58:00.245 - INFO: <epoch:401, iter: 79,200, lr:5.000e-05> l_g_pix: 1.3551e-04 l_g_fea: 5.2136e-01 l_g_gan: 7.1226e-03 l_d_real: 3.4789e-01 l_d_fake: 3.2462e-01 D_real: 5.0333e+01 D_fake: 4.9245e+01
20-04-05 23:01:12.676 - INFO: <epoch:402, iter: 79,400, lr:5.000e-05> l_g_pix: 1.0618e-04 l_g_fea: 4.2613e-01 l_g_gan: 6.9920e-03 l_d_real: 3.6974e-01 l_d_fake: 4.0990e-01 D_real: 5.9799e+01 D_fake: 5.8791e+01
20-04-05 23:04:24.949 - INFO: <epoch:403, iter: 79,600, lr:5.000e-05> l_g_pix: 1.2520e-04 l_g_fea: 4.5725e-01 l_g_gan: 9.3443e-03 l_d_real: 3.1935e-01 l_d_fake: 5.8851e-01 D_real: 8.9153e+00 D_fake: 7.5003e+00
20-04-05 23:07:37.342 - INFO: <epoch:404, iter: 79,800, lr:5.000e-05> l_g_pix: 9.3940e-05 l_g_fea: 4.2892e-01 l_g_gan: 1.6186e-02 l_d_real: 5.3361e-02 l_d_fake: 6.4054e-02 D_real: 9.7775e+01 D_fake: 9.4596e+01
20-04-05 23:10:49.473 - INFO: <epoch:405, iter: 80,000, lr:5.000e-05> l_g_pix: 1.2051e-04 l_g_fea: 4.1721e-01 l_g_gan: 4.2240e-03 l_d_real: 8.1323e-01 l_d_fake: 8.0429e-01 D_real: -3.3365e+00 D_fake: -3.3725e+00
20-04-05 23:10:49.942 - INFO: Models and training states saved.
20-04-05 23:11:59.810 - INFO: # Validation # PSNR: 31.559, SSIM: 0.84308, LPIPS: 0.040322
20-04-05 23:11:59.810 - INFO: <epoch:405, iter: 80,000> psnr: 31.559, ssim: 0.84308, lpips: 0.040322
20-04-05 23:18:47.713 - INFO: <epoch:406, iter: 80,200, lr:5.000e-05> l_g_pix: 9.8384e-05 l_g_fea: 3.4914e-01 l_g_gan: 1.0330e-02 l_d_real: 1.8243e-01 l_d_fake: 1.6470e-01 D_real: 3.3628e+01 D_fake: 3.1736e+01
20-04-05 23:22:24.990 - INFO: <epoch:407, iter: 80,400, lr:5.000e-05> l_g_pix: 1.1291e-04 l_g_fea: 4.8725e-01 l_g_gan: 1.4767e-02 l_d_real: 8.2060e-02 l_d_fake: 7.8888e-02 D_real: 7.2990e+01 D_fake: 7.0118e+01
20-04-05 23:25:37.332 - INFO: <epoch:408, iter: 80,600, lr:5.000e-05> l_g_pix: 1.1011e-04 l_g_fea: 4.3980e-01 l_g_gan: 9.9973e-03 l_d_real: 1.8823e-01 l_d_fake: 2.4990e-01 D_real: 2.4405e+01 D_fake: 2.2625e+01
20-04-05 23:28:49.977 - INFO: <epoch:409, iter: 80,800, lr:5.000e-05> l_g_pix: 1.0988e-04 l_g_fea: 5.1937e-01 l_g_gan: 9.7106e-04 l_d_real: 1.9450e+00 l_d_fake: 1.9441e+00 D_real: 4.4205e+01 D_fake: 4.5955e+01
20-04-05 23:32:02.200 - INFO: <epoch:410, iter: 81,000, lr:5.000e-05> l_g_pix: 1.0441e-04 l_g_fea: 3.6311e-01 l_g_gan: 1.7176e-02 l_d_real: 4.5731e-02 l_d_fake: 3.9288e-02 D_real: 3.2719e+01 D_fake: 2.9326e+01
20-04-05 23:35:14.538 - INFO: <epoch:411, iter: 81,200, lr:5.000e-05> l_g_pix: 1.3902e-04 l_g_fea: 5.1188e-01 l_g_gan: 8.6744e-03 l_d_real: 2.1395e-01 l_d_fake: 2.1352e-01 D_real: 2.7230e+01 D_fake: 2.5709e+01
20-04-05 23:38:26.531 - INFO: <epoch:412, iter: 81,400, lr:5.000e-05> l_g_pix: 7.8460e-05 l_g_fea: 2.8924e-01 l_g_gan: 7.2637e-03 l_d_real: 2.9830e-01 l_d_fake: 2.9506e-01 D_real: 1.9553e+01 D_fake: 1.8397e+01
20-04-05 23:41:39.136 - INFO: <epoch:413, iter: 81,600, lr:5.000e-05> l_g_pix: 9.6056e-05 l_g_fea: 4.4655e-01 l_g_gan: 5.4771e-03 l_d_real: 7.0615e-01 l_d_fake: 7.7776e-01 D_real: -1.2094e+01 D_fake: -1.2447e+01
20-04-05 23:44:51.551 - INFO: <epoch:414, iter: 81,800, lr:5.000e-05> l_g_pix: 1.1434e-04 l_g_fea: 4.2101e-01 l_g_gan: 1.6224e-02 l_d_real: 4.5773e-02 l_d_fake: 4.4168e-02 D_real: 3.3941e+01 D_fake: 3.0741e+01
20-04-05 23:48:03.795 - INFO: <epoch:415, iter: 82,000, lr:5.000e-05> l_g_pix: 1.0595e-04 l_g_fea: 4.6536e-01 l_g_gan: 1.1489e-03 l_d_real: 1.6964e+00 l_d_fake: 1.6996e+00 D_real: 2.5212e+01 D_fake: 2.6681e+01
20-04-05 23:51:16.590 - INFO: <epoch:416, iter: 82,200, lr:5.000e-05> l_g_pix: 9.4009e-05 l_g_fea: 4.1350e-01 l_g_gan: 1.0615e-02 l_d_real: 1.5380e-01 l_d_fake: 1.5422e-01 D_real: 3.1077e+01 D_fake: 2.9108e+01
20-04-05 23:54:29.009 - INFO: <epoch:417, iter: 82,400, lr:5.000e-05> l_g_pix: 1.1125e-04 l_g_fea: 4.5941e-01 l_g_gan: 5.4528e-03 l_d_real: 4.8284e-01 l_d_fake: 4.8366e-01 D_real: 3.7201e+01 D_fake: 3.6594e+01
20-04-05 23:57:41.317 - INFO: <epoch:418, iter: 82,600, lr:5.000e-05> l_g_pix: 1.5666e-04 l_g_fea: 5.8516e-01 l_g_gan: 6.7357e-03 l_d_real: 3.5516e-01 l_d_fake: 3.6780e-01 D_real: 6.9311e+01 D_fake: 6.8325e+01
20-04-06 00:00:54.045 - INFO: <epoch:419, iter: 82,800, lr:5.000e-05> l_g_pix: 1.8380e-04 l_g_fea: 6.5873e-01 l_g_gan: 1.4441e-02 l_d_real: 1.2408e-01 l_d_fake: 1.0976e-01 D_real: 3.1807e-01 D_fake: -2.4532e+00
20-04-06 00:04:05.688 - INFO: <epoch:420, iter: 83,000, lr:5.000e-05> l_g_pix: 1.0745e-04 l_g_fea: 4.0509e-01 l_g_gan: 2.3569e-02 l_d_real: 1.2215e-02 l_d_fake: 1.2777e-02 D_real: 5.0978e+01 D_fake: 4.6277e+01
20-04-06 00:07:17.550 - INFO: <epoch:421, iter: 83,200, lr:5.000e-05> l_g_pix: 8.8643e-05 l_g_fea: 3.4665e-01 l_g_gan: 4.8465e-03 l_d_real: 7.1731e-01 l_d_fake: 7.2761e-01 D_real: 1.9869e+01 D_fake: 1.9622e+01
20-04-06 00:10:30.210 - INFO: <epoch:422, iter: 83,400, lr:5.000e-05> l_g_pix: 1.1436e-04 l_g_fea: 5.1089e-01 l_g_gan: 1.3508e-02 l_d_real: 8.4249e-02 l_d_fake: 8.6175e-02 D_real: 4.1300e+01 D_fake: 3.8684e+01
20-04-06 00:13:42.617 - INFO: <epoch:423, iter: 83,600, lr:5.000e-05> l_g_pix: 1.0718e-04 l_g_fea: 3.9077e-01 l_g_gan: 1.2474e-02 l_d_real: 9.3050e-02 l_d_fake: 9.2880e-02 D_real: 2.9952e+01 D_fake: 2.7550e+01
20-04-06 00:16:55.329 - INFO: <epoch:424, iter: 83,800, lr:5.000e-05> l_g_pix: 7.7241e-05 l_g_fea: 4.5382e-01 l_g_gan: 5.3542e-03 l_d_real: 4.6873e-01 l_d_fake: 4.7492e-01 D_real: 2.2029e+00 D_fake: 1.6039e+00
20-04-06 00:20:07.739 - INFO: <epoch:425, iter: 84,000, lr:5.000e-05> l_g_pix: 1.1062e-04 l_g_fea: 4.0775e-01 l_g_gan: 1.2056e-02 l_d_real: 1.0277e-01 l_d_fake: 1.0619e-01 D_real: 5.8601e+01 D_fake: 5.6294e+01
20-04-06 00:23:20.278 - INFO: <epoch:426, iter: 84,200, lr:5.000e-05> l_g_pix: 1.0991e-04 l_g_fea: 3.7112e-01 l_g_gan: 1.0160e-03 l_d_real: 1.7798e+00 l_d_fake: 1.7739e+00 D_real: 4.2777e+01 D_fake: 4.4350e+01
20-04-06 00:26:33.239 - INFO: <epoch:427, iter: 84,400, lr:5.000e-05> l_g_pix: 8.6244e-05 l_g_fea: 3.2850e-01 l_g_gan: 9.8954e-04 l_d_real: 1.8134e+00 l_d_fake: 1.8132e+00 D_real: 5.6951e+01 D_fake: 5.8566e+01
20-04-06 00:29:45.496 - INFO: <epoch:428, iter: 84,600, lr:5.000e-05> l_g_pix: 1.1353e-04 l_g_fea: 5.0718e-01 l_g_gan: 2.8619e-03 l_d_real: 9.1002e-01 l_d_fake: 9.0016e-01 D_real: 1.7211e+01 D_fake: 1.7544e+01
20-04-06 00:32:57.438 - INFO: <epoch:429, iter: 84,800, lr:5.000e-05> l_g_pix: 9.9039e-05 l_g_fea: 3.8957e-01 l_g_gan: 7.3871e-03 l_d_real: 2.7872e-01 l_d_fake: 2.9031e-01 D_real: 3.0986e+01 D_fake: 2.9793e+01
20-04-06 00:36:09.329 - INFO: <epoch:430, iter: 85,000, lr:5.000e-05> l_g_pix: 1.6535e-04 l_g_fea: 5.0089e-01 l_g_gan: 1.2805e-02 l_d_real: 1.0737e-01 l_d_fake: 1.1583e-01 D_real: 2.4644e+01 D_fake: 2.2195e+01
20-04-06 00:36:09.764 - INFO: Models and training states saved.
20-04-06 00:37:19.174 - INFO: # Validation # PSNR: 29.643, SSIM: 0.84546, LPIPS: 0.035241
20-04-06 00:37:19.174 - INFO: <epoch:430, iter: 85,000> psnr: 29.643, ssim: 0.84546, lpips: 0.035241
20-04-06 00:41:06.035 - INFO: <epoch:431, iter: 85,200, lr:5.000e-05> l_g_pix: 1.1386e-04 l_g_fea: 4.9373e-01 l_g_gan: 1.6335e-02 l_d_real: 4.4351e-02 l_d_fake: 4.6560e-02 D_real: 3.0712e+01 D_fake: 2.7490e+01
20-04-06 00:44:17.686 - INFO: <epoch:432, iter: 85,400, lr:5.000e-05> l_g_pix: 1.2712e-04 l_g_fea: 4.6155e-01 l_g_gan: 7.4378e-03 l_d_real: 2.9927e-01 l_d_fake: 3.0194e-01 D_real: 1.7470e+00 D_fake: 5.6009e-01
20-04-06 00:47:30.457 - INFO: <epoch:433, iter: 85,600, lr:5.000e-05> l_g_pix: 1.0759e-04 l_g_fea: 4.2845e-01 l_g_gan: 7.1883e-03 l_d_real: 3.5129e-01 l_d_fake: 3.4076e-01 D_real: -2.1652e+01 D_fake: -2.2743e+01
20-04-06 00:50:42.715 - INFO: <epoch:434, iter: 85,800, lr:5.000e-05> l_g_pix: 1.2562e-04 l_g_fea: 4.8756e-01 l_g_gan: 2.4806e-02 l_d_real: 1.3751e-02 l_d_fake: 1.9699e-02 D_real: 1.6143e+01 D_fake: 1.1199e+01
20-04-06 00:53:54.853 - INFO: <epoch:435, iter: 86,000, lr:5.000e-05> l_g_pix: 1.2144e-04 l_g_fea: 4.2776e-01 l_g_gan: 1.1224e-02 l_d_real: 1.5119e-01 l_d_fake: 1.3876e-01 D_real: -2.4084e+01 D_fake: -2.6184e+01
20-04-06 00:57:06.486 - INFO: <epoch:436, iter: 86,200, lr:5.000e-05> l_g_pix: 8.8914e-05 l_g_fea: 3.7579e-01 l_g_gan: 3.6256e-03 l_d_real: 8.7458e-01 l_d_fake: 8.7757e-01 D_real: 4.4518e+00 D_fake: 4.6028e+00
20-04-06 01:00:23.048 - INFO: <epoch:438, iter: 86,400, lr:5.000e-05> l_g_pix: 9.9648e-05 l_g_fea: 3.5803e-01 l_g_gan: 9.4881e-03 l_d_real: 1.8533e-01 l_d_fake: 1.7920e-01 D_real: 4.2840e+01 D_fake: 4.1125e+01
20-04-06 01:03:34.943 - INFO: <epoch:439, iter: 86,600, lr:5.000e-05> l_g_pix: 1.7324e-04 l_g_fea: 5.5893e-01 l_g_gan: 5.1962e-03 l_d_real: 4.6525e-01 l_d_fake: 4.6673e-01 D_real: 5.6944e+01 D_fake: 5.6370e+01
20-04-06 01:06:47.048 - INFO: <epoch:440, iter: 86,800, lr:5.000e-05> l_g_pix: 1.0078e-04 l_g_fea: 4.3225e-01 l_g_gan: 1.0837e-02 l_d_real: 1.4636e-01 l_d_fake: 1.5258e-01 D_real: -4.3515e+00 D_fake: -6.3694e+00
20-04-06 01:09:59.479 - INFO: <epoch:441, iter: 87,000, lr:5.000e-05> l_g_pix: 1.3777e-04 l_g_fea: 4.8961e-01 l_g_gan: 2.7388e-02 l_d_real: 9.0700e-03 l_d_fake: 1.0262e-02 D_real: 4.8248e+01 D_fake: 4.2780e+01
20-04-06 01:13:11.102 - INFO: <epoch:442, iter: 87,200, lr:5.000e-05> l_g_pix: 1.2734e-04 l_g_fea: 5.3293e-01 l_g_gan: 9.0652e-03 l_d_real: 1.9488e-01 l_d_fake: 1.9548e-01 D_real: 2.6142e+01 D_fake: 2.4525e+01
20-04-06 01:16:22.753 - INFO: <epoch:443, iter: 87,400, lr:5.000e-05> l_g_pix: 7.9968e-05 l_g_fea: 4.5668e-01 l_g_gan: 2.9772e-03 l_d_real: 9.1149e-01 l_d_fake: 9.5246e-01 D_real: 5.4100e+01 D_fake: 5.4436e+01
20-04-06 01:19:34.859 - INFO: <epoch:444, iter: 87,600, lr:5.000e-05> l_g_pix: 9.8112e-05 l_g_fea: 4.4162e-01 l_g_gan: 4.3795e-03 l_d_real: 6.5634e-01 l_d_fake: 6.8056e-01 D_real: 4.3614e+01 D_fake: 4.3407e+01
20-04-06 01:22:47.243 - INFO: <epoch:445, iter: 87,800, lr:5.000e-05> l_g_pix: 1.3582e-04 l_g_fea: 4.5780e-01 l_g_gan: 4.3444e-03 l_d_real: 6.3333e-01 l_d_fake: 6.3169e-01 D_real: 3.5961e+01 D_fake: 3.5724e+01
20-04-06 01:25:59.862 - INFO: <epoch:446, iter: 88,000, lr:5.000e-05> l_g_pix: 7.8986e-05 l_g_fea: 4.4501e-01 l_g_gan: 6.3692e-03 l_d_real: 3.8373e-01 l_d_fake: 3.8431e-01 D_real: 3.0052e+01 D_fake: 2.9163e+01
20-04-06 01:29:12.900 - INFO: <epoch:447, iter: 88,200, lr:5.000e-05> l_g_pix: 8.5203e-05 l_g_fea: 3.8499e-01 l_g_gan: 2.7484e-03 l_d_real: 9.0821e-01 l_d_fake: 9.2303e-01 D_real: 4.6232e+01 D_fake: 4.6598e+01
20-04-06 01:32:24.847 - INFO: <epoch:448, iter: 88,400, lr:5.000e-05> l_g_pix: 1.2273e-04 l_g_fea: 4.8240e-01 l_g_gan: 3.7916e-02 l_d_real: 5.9814e-04 l_d_fake: 6.0615e-04 D_real: 3.4573e+01 D_fake: 2.6991e+01
20-04-06 01:35:35.864 - INFO: <epoch:449, iter: 88,600, lr:5.000e-05> l_g_pix: 1.5716e-04 l_g_fea: 6.0280e-01 l_g_gan: 1.1701e-03 l_d_real: 1.6788e+00 l_d_fake: 1.6746e+00 D_real: 4.1419e+01 D_fake: 4.2862e+01
20-04-06 01:38:47.846 - INFO: <epoch:450, iter: 88,800, lr:5.000e-05> l_g_pix: 1.0387e-04 l_g_fea: 3.9857e-01 l_g_gan: 6.9512e-03 l_d_real: 3.2113e-01 l_d_fake: 3.3056e-01 D_real: 3.0295e+01 D_fake: 2.9231e+01
20-04-06 01:41:59.465 - INFO: <epoch:451, iter: 89,000, lr:5.000e-05> l_g_pix: 1.2964e-04 l_g_fea: 3.3772e-01 l_g_gan: 2.0766e-02 l_d_real: 2.5451e-02 l_d_fake: 2.8335e-02 D_real: 1.0715e+01 D_fake: 6.5891e+00
20-04-06 01:45:11.568 - INFO: <epoch:452, iter: 89,200, lr:5.000e-05> l_g_pix: 1.0055e-04 l_g_fea: 4.2707e-01 l_g_gan: 4.4700e-03 l_d_real: 6.1413e-01 l_d_fake: 6.1768e-01 D_real: -1.3231e+00 D_fake: -1.6012e+00
20-04-06 01:48:22.835 - INFO: <epoch:453, iter: 89,400, lr:5.000e-05> l_g_pix: 1.4415e-04 l_g_fea: 5.0291e-01 l_g_gan: 8.7691e-03 l_d_real: 3.5115e-01 l_d_fake: 3.2385e-01 D_real: 6.5989e+01 D_fake: 6.4573e+01
20-04-06 01:51:35.392 - INFO: <epoch:454, iter: 89,600, lr:5.000e-05> l_g_pix: 8.8652e-05 l_g_fea: 4.3721e-01 l_g_gan: 1.3974e-02 l_d_real: 8.8141e-02 l_d_fake: 1.2844e-01 D_real: 4.5992e+01 D_fake: 4.3305e+01
20-04-06 01:54:46.657 - INFO: <epoch:455, iter: 89,800, lr:5.000e-05> l_g_pix: 9.6692e-05 l_g_fea: 3.9790e-01 l_g_gan: 4.7966e-03 l_d_real: 5.1960e-01 l_d_fake: 5.3964e-01 D_real: 3.4289e+01 D_fake: 3.3859e+01
20-04-06 01:57:58.338 - INFO: <epoch:456, iter: 90,000, lr:5.000e-05> l_g_pix: 6.5640e-05 l_g_fea: 3.1604e-01 l_g_gan: 4.0505e-03 l_d_real: 6.1952e-01 l_d_fake: 6.1909e-01 D_real: 3.6603e+01 D_fake: 3.6412e+01
20-04-06 01:57:58.791 - INFO: Models and training states saved.
20-04-06 01:59:03.204 - INFO: # Validation # PSNR: 32.176, SSIM: 0.84419, LPIPS: 0.038451
20-04-06 01:59:03.204 - INFO: <epoch:456, iter: 90,000> psnr: 32.176, ssim: 0.84419, lpips: 0.038451
20-04-06 02:04:58.944 - INFO: <epoch:457, iter: 90,200, lr:5.000e-05> l_g_pix: 1.1739e-04 l_g_fea: 4.8583e-01 l_g_gan: 2.0991e-02 l_d_real: 1.7785e-02 l_d_fake: 1.6376e-02 D_real: 4.4579e+01 D_fake: 4.0397e+01
20-04-06 02:08:47.913 - INFO: <epoch:458, iter: 90,400, lr:5.000e-05> l_g_pix: 1.1844e-04 l_g_fea: 4.9864e-01 l_g_gan: 2.3122e-03 l_d_real: 1.1367e+00 l_d_fake: 1.1577e+00 D_real: 3.7923e+01 D_fake: 3.8608e+01
20-04-06 02:11:59.981 - INFO: <epoch:459, iter: 90,600, lr:5.000e-05> l_g_pix: 1.0121e-04 l_g_fea: 4.1909e-01 l_g_gan: 1.8768e-02 l_d_real: 3.5465e-02 l_d_fake: 3.2248e-02 D_real: 3.9029e+01 D_fake: 3.5310e+01
20-04-06 02:15:11.884 - INFO: <epoch:460, iter: 90,800, lr:5.000e-05> l_g_pix: 1.2624e-04 l_g_fea: 5.1890e-01 l_g_gan: 1.3796e-02 l_d_real: 9.4842e-02 l_d_fake: 9.5618e-02 D_real: 4.2604e+01 D_fake: 3.9940e+01
20-04-06 02:18:23.955 - INFO: <epoch:461, iter: 91,000, lr:5.000e-05> l_g_pix: 1.1444e-04 l_g_fea: 3.4098e-01 l_g_gan: 1.6963e-02 l_d_real: 4.1051e-02 l_d_fake: 5.4013e-02 D_real: 8.9236e+00 D_fake: 5.5786e+00
20-04-06 02:21:36.453 - INFO: <epoch:462, iter: 91,200, lr:5.000e-05> l_g_pix: 1.2952e-04 l_g_fea: 5.4322e-01 l_g_gan: 1.0504e-02 l_d_real: 1.5285e-01 l_d_fake: 1.6997e-01 D_real: 2.0077e+01 D_fake: 1.8138e+01
20-04-06 02:24:48.364 - INFO: <epoch:463, iter: 91,400, lr:5.000e-05> l_g_pix: 1.0118e-04 l_g_fea: 3.8221e-01 l_g_gan: 1.9936e-02 l_d_real: 4.7341e-02 l_d_fake: 2.8363e-02 D_real: 4.3663e+01 D_fake: 3.9713e+01
20-04-06 02:27:59.654 - INFO: <epoch:464, iter: 91,600, lr:5.000e-05> l_g_pix: 1.2597e-04 l_g_fea: 4.9777e-01 l_g_gan: 4.3751e-03 l_d_real: 6.8182e-01 l_d_fake: 7.7565e-01 D_real: 2.5382e+01 D_fake: 2.5236e+01
20-04-06 02:31:11.483 - INFO: <epoch:465, iter: 91,800, lr:5.000e-05> l_g_pix: 1.3466e-04 l_g_fea: 4.4500e-01 l_g_gan: 3.6830e-03 l_d_real: 7.6647e-01 l_d_fake: 7.5616e-01 D_real: 4.8572e+01 D_fake: 4.8597e+01
20-04-06 02:34:23.835 - INFO: <epoch:466, iter: 92,000, lr:5.000e-05> l_g_pix: 1.4962e-04 l_g_fea: 4.8468e-01 l_g_gan: 6.0937e-03 l_d_real: 4.7114e-01 l_d_fake: 5.0201e-01 D_real: 2.6641e+01 D_fake: 2.5909e+01
20-04-06 02:37:35.923 - INFO: <epoch:467, iter: 92,200, lr:5.000e-05> l_g_pix: 1.0037e-04 l_g_fea: 4.5968e-01 l_g_gan: 4.7774e-03 l_d_real: 5.3208e-01 l_d_fake: 5.3632e-01 D_real: 4.8159e+01 D_fake: 4.7738e+01
20-04-06 02:40:48.240 - INFO: <epoch:468, iter: 92,400, lr:5.000e-05> l_g_pix: 1.2146e-04 l_g_fea: 5.0217e-01 l_g_gan: 9.9679e-03 l_d_real: 1.7586e-01 l_d_fake: 1.7309e-01 D_real: 3.9535e+01 D_fake: 3.7715e+01
20-04-06 02:44:00.115 - INFO: <epoch:469, iter: 92,600, lr:5.000e-05> l_g_pix: 1.1169e-04 l_g_fea: 3.5474e-01 l_g_gan: 9.4726e-03 l_d_real: 1.8541e-01 l_d_fake: 1.8207e-01 D_real: 5.5657e+01 D_fake: 5.3947e+01
20-04-06 02:47:12.589 - INFO: <epoch:470, iter: 92,800, lr:5.000e-05> l_g_pix: 1.2497e-04 l_g_fea: 5.2358e-01 l_g_gan: 2.2497e-02 l_d_real: 1.6347e-02 l_d_fake: 1.8973e-02 D_real: 8.2277e+00 D_fake: 3.7461e+00
20-04-06 02:50:23.844 - INFO: <epoch:471, iter: 93,000, lr:5.000e-05> l_g_pix: 1.0264e-04 l_g_fea: 5.1522e-01 l_g_gan: 1.4285e-02 l_d_real: 7.2759e-02 l_d_fake: 7.9220e-02 D_real: 1.5408e+01 D_fake: 1.2627e+01
20-04-06 02:53:35.877 - INFO: <epoch:472, iter: 93,200, lr:5.000e-05> l_g_pix: 9.2471e-05 l_g_fea: 3.9078e-01 l_g_gan: 3.1647e-03 l_d_real: 8.9784e-01 l_d_fake: 8.7691e-01 D_real: 5.4975e+01 D_fake: 5.5229e+01
20-04-06 02:56:47.584 - INFO: <epoch:473, iter: 93,400, lr:5.000e-05> l_g_pix: 9.2238e-05 l_g_fea: 4.3554e-01 l_g_gan: 1.0559e-02 l_d_real: 1.6258e-01 l_d_fake: 1.5527e-01 D_real: 1.9399e+01 D_fake: 1.7446e+01
20-04-06 02:59:58.972 - INFO: <epoch:474, iter: 93,600, lr:5.000e-05> l_g_pix: 7.3131e-05 l_g_fea: 3.4428e-01 l_g_gan: 7.2536e-03 l_d_real: 4.1318e-01 l_d_fake: 4.1901e-01 D_real: 3.4831e+01 D_fake: 3.3796e+01
20-04-06 03:03:25.298 - INFO: <epoch:475, iter: 93,800, lr:5.000e-05> l_g_pix: 1.4645e-04 l_g_fea: 5.1059e-01 l_g_gan: 6.5493e-03 l_d_real: 4.5537e-01 l_d_fake: 4.2630e-01 D_real: 4.2085e+01 D_fake: 4.1216e+01
20-04-06 03:07:57.062 - INFO: <epoch:476, iter: 94,000, lr:5.000e-05> l_g_pix: 9.0970e-05 l_g_fea: 4.1753e-01 l_g_gan: 1.6571e-02 l_d_real: 4.2596e-02 l_d_fake: 4.6833e-02 D_real: 9.2459e+00 D_fake: 5.9764e+00
20-04-06 03:13:34.439 - INFO: <epoch:477, iter: 94,200, lr:5.000e-05> l_g_pix: 1.0019e-04 l_g_fea: 4.2287e-01 l_g_gan: 2.6844e-03 l_d_real: 1.0041e+00 l_d_fake: 1.0267e+00 D_real: 5.1169e+01 D_fake: 5.1647e+01
20-04-06 03:19:39.847 - INFO: <epoch:478, iter: 94,400, lr:5.000e-05> l_g_pix: 9.0567e-05 l_g_fea: 4.3550e-01 l_g_gan: 2.4206e-02 l_d_real: 1.0886e-02 l_d_fake: 1.3279e-02 D_real: 2.8640e+01 D_fake: 2.3811e+01
20-04-06 03:23:58.686 - INFO: <epoch:479, iter: 94,600, lr:5.000e-05> l_g_pix: 1.0523e-04 l_g_fea: 4.0073e-01 l_g_gan: 7.4437e-03 l_d_real: 3.0329e-01 l_d_fake: 2.9665e-01 D_real: 4.1972e+01 D_fake: 4.0783e+01
20-04-06 03:27:10.308 - INFO: <epoch:480, iter: 94,800, lr:5.000e-05> l_g_pix: 1.2408e-04 l_g_fea: 5.4961e-01 l_g_gan: 6.3868e-03 l_d_real: 3.7881e-01 l_d_fake: 3.7875e-01 D_real: 3.4108e+01 D_fake: 3.3209e+01
20-04-06 03:30:21.683 - INFO: <epoch:481, iter: 95,000, lr:5.000e-05> l_g_pix: 1.3878e-04 l_g_fea: 4.4428e-01 l_g_gan: 1.6042e-02 l_d_real: 6.4661e-02 l_d_fake: 4.8524e-02 D_real: 3.1825e+01 D_fake: 2.8673e+01
20-04-06 03:30:22.170 - INFO: Models and training states saved.
20-04-06 03:31:31.236 - INFO: # Validation # PSNR: 30.396, SSIM: 0.82259, LPIPS: 0.038421
20-04-06 03:31:31.236 - INFO: <epoch:481, iter: 95,000> psnr: 30.396, ssim: 0.82259, lpips: 0.038421
20-04-06 03:36:05.301 - INFO: <epoch:482, iter: 95,200, lr:5.000e-05> l_g_pix: 8.8346e-05 l_g_fea: 3.5561e-01 l_g_gan: 1.5083e-03 l_d_real: 1.5135e+00 l_d_fake: 1.4998e+00 D_real: -8.2981e+00 D_fake: -7.0931e+00
20-04-06 03:39:23.136 - INFO: <epoch:483, iter: 95,400, lr:5.000e-05> l_g_pix: 1.4885e-04 l_g_fea: 5.5511e-01 l_g_gan: 1.6925e-02 l_d_real: 5.2614e-02 l_d_fake: 5.1241e-02 D_real: 2.6600e+01 D_fake: 2.3267e+01
20-04-06 03:42:34.376 - INFO: <epoch:484, iter: 95,600, lr:5.000e-05> l_g_pix: 8.4864e-05 l_g_fea: 4.0817e-01 l_g_gan: 1.9932e-03 l_d_real: 1.3953e+00 l_d_fake: 1.3978e+00 D_real: 5.0806e+01 D_fake: 5.1804e+01
20-04-06 03:45:47.874 - INFO: <epoch:485, iter: 95,800, lr:5.000e-05> l_g_pix: 8.7329e-05 l_g_fea: 4.2512e-01 l_g_gan: 3.9272e-03 l_d_real: 6.5115e-01 l_d_fake: 6.6277e-01 D_real: 3.7299e+01 D_fake: 3.7171e+01
20-04-06 03:49:01.418 - INFO: <epoch:486, iter: 96,000, lr:5.000e-05> l_g_pix: 1.2706e-04 l_g_fea: 4.8114e-01 l_g_gan: 5.5671e-03 l_d_real: 4.3374e-01 l_d_fake: 4.2823e-01 D_real: 5.5731e+01 D_fake: 5.5048e+01
20-04-06 03:52:13.015 - INFO: <epoch:487, iter: 96,200, lr:5.000e-05> l_g_pix: 1.4184e-04 l_g_fea: 4.3594e-01 l_g_gan: 6.0627e-03 l_d_real: 4.1441e-01 l_d_fake: 3.8460e-01 D_real: 5.7580e+01 D_fake: 5.6767e+01
20-04-06 03:55:24.442 - INFO: <epoch:488, iter: 96,400, lr:5.000e-05> l_g_pix: 1.0513e-04 l_g_fea: 4.4013e-01 l_g_gan: 1.1637e-02 l_d_real: 1.1238e-01 l_d_fake: 1.2031e-01 D_real: 6.0722e+01 D_fake: 5.8511e+01
20-04-06 03:58:36.447 - INFO: <epoch:489, iter: 96,600, lr:5.000e-05> l_g_pix: 1.0875e-04 l_g_fea: 4.2176e-01 l_g_gan: 1.0770e-02 l_d_real: 1.5515e-01 l_d_fake: 1.5209e-01 D_real: 3.3508e+01 D_fake: 3.1508e+01
20-04-06 04:01:47.974 - INFO: <epoch:490, iter: 96,800, lr:5.000e-05> l_g_pix: 8.9749e-05 l_g_fea: 3.5896e-01 l_g_gan: 4.6517e-03 l_d_real: 7.1133e-01 l_d_fake: 7.1810e-01 D_real: 5.5711e+01 D_fake: 5.5495e+01
20-04-06 04:05:00.258 - INFO: <epoch:491, iter: 97,000, lr:5.000e-05> l_g_pix: 8.6013e-05 l_g_fea: 3.8641e-01 l_g_gan: 1.0682e-02 l_d_real: 1.4032e-01 l_d_fake: 1.3767e-01 D_real: 5.8659e+01 D_fake: 5.6661e+01
20-04-06 04:08:12.173 - INFO: <epoch:492, iter: 97,200, lr:5.000e-05> l_g_pix: 1.0515e-04 l_g_fea: 4.2071e-01 l_g_gan: 1.8014e-02 l_d_real: 2.9755e-02 l_d_fake: 3.0810e-02 D_real: 1.3498e+01 D_fake: 9.9252e+00
20-04-06 04:11:24.352 - INFO: <epoch:493, iter: 97,400, lr:5.000e-05> l_g_pix: 8.8061e-05 l_g_fea: 4.6056e-01 l_g_gan: 5.3565e-03 l_d_real: 4.9079e-01 l_d_fake: 4.9962e-01 D_real: -1.1138e+00 D_fake: -1.6899e+00
20-04-06 04:16:11.259 - INFO: <epoch:494, iter: 97,600, lr:5.000e-05> l_g_pix: 1.4577e-04 l_g_fea: 6.0538e-01 l_g_gan: 9.2151e-03 l_d_real: 2.2918e-01 l_d_fake: 1.9961e-01 D_real: 8.1279e+00 D_fake: 6.4993e+00
20-04-06 04:20:03.873 - INFO: <epoch:495, iter: 97,800, lr:5.000e-05> l_g_pix: 1.2638e-04 l_g_fea: 4.1229e-01 l_g_gan: 2.5150e-03 l_d_real: 1.0700e+00 l_d_fake: 1.0670e+00 D_real: 5.2484e+01 D_fake: 5.3050e+01
20-04-06 04:23:30.736 - INFO: <epoch:496, iter: 98,000, lr:5.000e-05> l_g_pix: 1.0326e-04 l_g_fea: 4.7335e-01 l_g_gan: 2.5998e-02 l_d_real: 7.5082e-03 l_d_fake: 7.2395e-03 D_real: 2.9640e+01 D_fake: 2.4448e+01
20-04-06 04:26:42.667 - INFO: <epoch:497, iter: 98,200, lr:5.000e-05> l_g_pix: 1.1792e-04 l_g_fea: 4.3279e-01 l_g_gan: 9.7408e-03 l_d_real: 1.9285e-01 l_d_fake: 1.8775e-01 D_real: 4.8848e+01 D_fake: 4.7091e+01
20-04-06 04:29:53.790 - INFO: <epoch:498, iter: 98,400, lr:5.000e-05> l_g_pix: 1.1929e-04 l_g_fea: 4.1205e-01 l_g_gan: 1.0157e-03 l_d_real: 1.9522e+00 l_d_fake: 1.9463e+00 D_real: 3.4794e+01 D_fake: 3.6540e+01
20-04-06 04:33:06.016 - INFO: <epoch:499, iter: 98,600, lr:5.000e-05> l_g_pix: 1.3240e-04 l_g_fea: 5.8050e-01 l_g_gan: 4.1107e-03 l_d_real: 6.4417e-01 l_d_fake: 6.4095e-01 D_real: 1.5026e+01 D_fake: 1.4846e+01
20-04-06 04:36:18.772 - INFO: <epoch:500, iter: 98,800, lr:5.000e-05> l_g_pix: 7.9056e-05 l_g_fea: 3.9049e-01 l_g_gan: 8.5170e-03 l_d_real: 2.5917e-01 l_d_fake: 2.8167e-01 D_real: 3.3663e+01 D_fake: 3.2230e+01
20-04-06 04:39:31.230 - INFO: <epoch:501, iter: 99,000, lr:5.000e-05> l_g_pix: 1.3381e-04 l_g_fea: 4.3554e-01 l_g_gan: 6.2928e-03 l_d_real: 4.1367e-01 l_d_fake: 3.6945e-01 D_real: 1.7393e+01 D_fake: 1.6526e+01
20-04-06 04:42:42.426 - INFO: <epoch:502, iter: 99,200, lr:5.000e-05> l_g_pix: 1.3350e-04 l_g_fea: 4.0969e-01 l_g_gan: 1.3840e-02 l_d_real: 8.1178e-02 l_d_fake: 8.1088e-02 D_real: 3.8754e+01 D_fake: 3.6067e+01
20-04-06 04:45:55.006 - INFO: <epoch:503, iter: 99,400, lr:5.000e-05> l_g_pix: 1.1234e-04 l_g_fea: 4.6113e-01 l_g_gan: 7.5684e-03 l_d_real: 2.8089e-01 l_d_fake: 2.7728e-01 D_real: -1.9405e+01 D_fake: -2.0640e+01
20-04-06 04:49:12.406 - INFO: <epoch:505, iter: 99,600, lr:5.000e-05> l_g_pix: 1.4335e-04 l_g_fea: 5.0515e-01 l_g_gan: 1.3314e-02 l_d_real: 8.3879e-02 l_d_fake: 8.7967e-02 D_real: -1.9946e+01 D_fake: -2.2523e+01
20-04-06 04:52:24.453 - INFO: <epoch:506, iter: 99,800, lr:5.000e-05> l_g_pix: 1.2481e-04 l_g_fea: 4.1851e-01 l_g_gan: 5.4125e-03 l_d_real: 5.1077e-01 l_d_fake: 5.0094e-01 D_real: -5.0380e+00 D_fake: -5.6147e+00
20-04-06 04:55:37.162 - INFO: <epoch:507, iter: 100,000, lr:5.000e-05> l_g_pix: 1.0093e-04 l_g_fea: 4.1397e-01 l_g_gan: 1.1084e-02 l_d_real: 1.6110e-01 l_d_fake: 1.7905e-01 D_real: -3.9600e+00 D_fake: -6.0068e+00
20-04-06 04:55:37.605 - INFO: Models and training states saved.
20-04-06 04:56:47.526 - INFO: # Validation # PSNR: 30.517, SSIM: 0.83536, LPIPS: 0.031511
20-04-06 04:56:47.526 - INFO: <epoch:507, iter: 100,000> psnr: 30.517, ssim: 0.83536, lpips: 0.031511
20-04-06 05:03:26.474 - INFO: <epoch:508, iter: 100,200, lr:2.500e-05> l_g_pix: 1.4561e-04 l_g_fea: 5.4536e-01 l_g_gan: 7.0627e-04 l_d_real: 2.4138e+00 l_d_fake: 2.4358e+00 D_real: -2.0503e+01 D_fake: -1.8219e+01
20-04-06 05:06:36.573 - INFO: <epoch:509, iter: 100,400, lr:2.500e-05> l_g_pix: 7.2745e-05 l_g_fea: 3.0477e-01 l_g_gan: 3.8145e-03 l_d_real: 6.7592e-01 l_d_fake: 6.5342e-01 D_real: -8.3757e+00 D_fake: -8.4739e+00
20-04-06 05:09:48.464 - INFO: <epoch:510, iter: 100,600, lr:2.500e-05> l_g_pix: 9.6193e-05 l_g_fea: 4.4296e-01 l_g_gan: 4.8851e-03 l_d_real: 5.0272e-01 l_d_fake: 5.1233e-01 D_real: 1.5889e+01 D_fake: 1.5420e+01
20-04-06 05:13:00.890 - INFO: <epoch:511, iter: 100,800, lr:2.500e-05> l_g_pix: 1.1357e-04 l_g_fea: 4.6988e-01 l_g_gan: 9.5421e-03 l_d_real: 1.8093e-01 l_d_fake: 1.9458e-01 D_real: 2.4479e+01 D_fake: 2.2758e+01
20-04-06 05:16:12.611 - INFO: <epoch:512, iter: 101,000, lr:2.500e-05> l_g_pix: 1.0529e-04 l_g_fea: 4.0790e-01 l_g_gan: 4.5302e-04 l_d_real: 2.8204e+00 l_d_fake: 2.8137e+00 D_real: 1.3182e+01 D_fake: 1.5909e+01
20-04-06 05:19:24.564 - INFO: <epoch:513, iter: 101,200, lr:2.500e-05> l_g_pix: 1.4201e-04 l_g_fea: 5.5789e-01 l_g_gan: 6.2380e-03 l_d_real: 4.1396e-01 l_d_fake: 3.9773e-01 D_real: 1.5253e+01 D_fake: 1.4411e+01
20-04-06 05:22:36.075 - INFO: <epoch:514, iter: 101,400, lr:2.500e-05> l_g_pix: 1.5445e-04 l_g_fea: 5.1221e-01 l_g_gan: 6.9409e-03 l_d_real: 3.5281e-01 l_d_fake: 3.5565e-01 D_real: 1.1870e+01 D_fake: 1.0836e+01
20-04-06 05:25:48.611 - INFO: <epoch:515, iter: 101,600, lr:2.500e-05> l_g_pix: 1.2366e-04 l_g_fea: 4.0495e-01 l_g_gan: 1.2285e-02 l_d_real: 1.0406e-01 l_d_fake: 1.1736e-01 D_real: -1.7702e+00 D_fake: -4.1165e+00
20-04-06 05:29:01.298 - INFO: <epoch:516, iter: 101,800, lr:2.500e-05> l_g_pix: 8.8971e-05 l_g_fea: 3.7868e-01 l_g_gan: 5.2998e-03 l_d_real: 4.7913e-01 l_d_fake: 4.8068e-01 D_real: 2.9292e+01 D_fake: 2.8712e+01
20-04-06 05:32:12.855 - INFO: <epoch:517, iter: 102,000, lr:2.500e-05> l_g_pix: 9.5434e-05 l_g_fea: 4.4377e-01 l_g_gan: 8.7782e-03 l_d_real: 2.1272e-01 l_d_fake: 2.0748e-01 D_real: 1.2941e+01 D_fake: 1.1395e+01
20-04-06 05:35:25.217 - INFO: <epoch:518, iter: 102,200, lr:2.500e-05> l_g_pix: 1.4169e-04 l_g_fea: 4.7368e-01 l_g_gan: 2.6879e-03 l_d_real: 9.9595e-01 l_d_fake: 9.9327e-01 D_real: 1.5297e+01 D_fake: 1.5754e+01
20-04-06 05:38:37.303 - INFO: <epoch:519, iter: 102,400, lr:2.500e-05> l_g_pix: 1.0028e-04 l_g_fea: 4.4888e-01 l_g_gan: 7.4861e-03 l_d_real: 2.7712e-01 l_d_fake: 2.6611e-01 D_real: 1.4589e+01 D_fake: 1.3363e+01
20-04-06 05:41:50.007 - INFO: <epoch:520, iter: 102,600, lr:2.500e-05> l_g_pix: 8.9829e-05 l_g_fea: 4.5226e-01 l_g_gan: 1.6496e-02 l_d_real: 4.4963e-02 l_d_fake: 4.6066e-02 D_real: 4.5693e+01 D_fake: 4.2439e+01
20-04-06 05:45:03.201 - INFO: <epoch:521, iter: 102,800, lr:2.500e-05> l_g_pix: 8.3811e-05 l_g_fea: 4.2284e-01 l_g_gan: 1.6310e-02 l_d_real: 4.6374e-02 l_d_fake: 4.6451e-02 D_real: 2.9355e+01 D_fake: 2.6139e+01
20-04-06 05:48:15.864 - INFO: <epoch:522, iter: 103,000, lr:2.500e-05> l_g_pix: 9.6930e-05 l_g_fea: 4.2381e-01 l_g_gan: 1.6647e-02 l_d_real: 5.0424e-02 l_d_fake: 4.6462e-02 D_real: 2.7910e+01 D_fake: 2.4629e+01
20-04-06 05:51:27.621 - INFO: <epoch:523, iter: 103,200, lr:2.500e-05> l_g_pix: 7.6525e-05 l_g_fea: 2.9039e-01 l_g_gan: 5.7853e-03 l_d_real: 4.0733e-01 l_d_fake: 4.1919e-01 D_real: 1.6764e+01 D_fake: 1.6021e+01
20-04-06 05:54:38.961 - INFO: <epoch:524, iter: 103,400, lr:2.500e-05> l_g_pix: 9.2490e-05 l_g_fea: 3.4541e-01 l_g_gan: 3.8431e-03 l_d_real: 6.8317e-01 l_d_fake: 6.8526e-01 D_real: 1.0873e+01 D_fake: 1.0788e+01
20-04-06 05:57:50.805 - INFO: <epoch:525, iter: 103,600, lr:2.500e-05> l_g_pix: 1.4730e-04 l_g_fea: 5.0318e-01 l_g_gan: 4.4943e-03 l_d_real: 6.2784e-01 l_d_fake: 6.1158e-01 D_real: 1.6614e+01 D_fake: 1.6335e+01
20-04-06 06:01:03.068 - INFO: <epoch:526, iter: 103,800, lr:2.500e-05> l_g_pix: 1.2416e-04 l_g_fea: 4.5257e-01 l_g_gan: 6.0955e-03 l_d_real: 3.8555e-01 l_d_fake: 3.8084e-01 D_real: 2.4207e+01 D_fake: 2.3371e+01
20-04-06 06:04:15.399 - INFO: <epoch:527, iter: 104,000, lr:2.500e-05> l_g_pix: 1.0383e-04 l_g_fea: 3.8371e-01 l_g_gan: 1.2697e-02 l_d_real: 1.2277e-01 l_d_fake: 1.0538e-01 D_real: 2.7472e+01 D_fake: 2.5047e+01
20-04-06 06:07:26.866 - INFO: <epoch:528, iter: 104,200, lr:2.500e-05> l_g_pix: 9.2955e-05 l_g_fea: 3.9286e-01 l_g_gan: 3.4319e-03 l_d_real: 7.7001e-01 l_d_fake: 7.6670e-01 D_real: 2.3040e+01 D_fake: 2.3122e+01
20-04-06 06:10:39.566 - INFO: <epoch:529, iter: 104,400, lr:2.500e-05> l_g_pix: 1.0417e-04 l_g_fea: 4.7024e-01 l_g_gan: 5.6240e-03 l_d_real: 4.2277e-01 l_d_fake: 4.2550e-01 D_real: 1.5711e+01 D_fake: 1.5011e+01
20-04-06 06:13:51.791 - INFO: <epoch:530, iter: 104,600, lr:2.500e-05> l_g_pix: 8.6189e-05 l_g_fea: 3.5388e-01 l_g_gan: 3.3943e-03 l_d_real: 7.7724e-01 l_d_fake: 7.8090e-01 D_real: 1.5143e+01 D_fake: 1.5243e+01
20-04-06 06:17:04.228 - INFO: <epoch:531, iter: 104,800, lr:2.500e-05> l_g_pix: 1.0039e-04 l_g_fea: 4.1626e-01 l_g_gan: 5.6891e-03 l_d_real: 4.4151e-01 l_d_fake: 4.4655e-01 D_real: 4.3445e+01 D_fake: 4.2751e+01
20-04-06 06:20:16.729 - INFO: <epoch:532, iter: 105,000, lr:2.500e-05> l_g_pix: 8.6857e-05 l_g_fea: 3.9110e-01 l_g_gan: 5.7319e-03 l_d_real: 4.3557e-01 l_d_fake: 4.5832e-01 D_real: 3.9677e+01 D_fake: 3.8978e+01
20-04-06 06:20:17.146 - INFO: Models and training states saved.
20-04-06 06:21:25.733 - INFO: # Validation # PSNR: 31.392, SSIM: 0.83527, LPIPS: 0.028824
20-04-06 06:21:25.733 - INFO: <epoch:532, iter: 105,000> psnr: 31.392, ssim: 0.83527, lpips: 0.028824
20-04-06 06:25:00.092 - INFO: <epoch:533, iter: 105,200, lr:2.500e-05> l_g_pix: 1.2757e-04 l_g_fea: 4.9484e-01 l_g_gan: 5.9982e-03 l_d_real: 4.2208e-01 l_d_fake: 4.0888e-01 D_real: 2.7208e+01 D_fake: 2.6423e+01
20-04-06 06:28:12.390 - INFO: <epoch:534, iter: 105,400, lr:2.500e-05> l_g_pix: 1.0454e-04 l_g_fea: 4.2063e-01 l_g_gan: 1.0728e-02 l_d_real: 1.4481e-01 l_d_fake: 1.5471e-01 D_real: 4.9131e+01 D_fake: 4.7135e+01
20-04-06 06:31:24.697 - INFO: <epoch:535, iter: 105,600, lr:2.500e-05> l_g_pix: 1.0692e-04 l_g_fea: 5.6727e-01 l_g_gan: 3.1203e-03 l_d_real: 8.5589e-01 l_d_fake: 8.4544e-01 D_real: 1.3814e+01 D_fake: 1.4040e+01
20-04-06 06:35:04.378 - INFO: <epoch:536, iter: 105,800, lr:2.500e-05> l_g_pix: 8.3986e-05 l_g_fea: 3.8580e-01 l_g_gan: 8.6291e-03 l_d_real: 2.3757e-01 l_d_fake: 2.2447e-01 D_real: 2.5277e+01 D_fake: 2.3782e+01
20-04-06 06:38:42.139 - INFO: <epoch:537, iter: 106,000, lr:2.500e-05> l_g_pix: 1.0076e-04 l_g_fea: 4.2652e-01 l_g_gan: 8.5985e-03 l_d_real: 2.1760e-01 l_d_fake: 2.2926e-01 D_real: 1.2205e+01 D_fake: 1.0709e+01
20-04-06 06:43:38.751 - INFO: <epoch:538, iter: 106,200, lr:2.500e-05> l_g_pix: 9.7204e-05 l_g_fea: 4.4199e-01 l_g_gan: 6.3485e-04 l_d_real: 2.2411e+00 l_d_fake: 2.2566e+00 D_real: 1.8887e+00 D_fake: 4.0106e+00
20-04-06 06:46:49.973 - INFO: <epoch:539, iter: 106,400, lr:2.500e-05> l_g_pix: 1.1822e-04 l_g_fea: 4.1328e-01 l_g_gan: 1.1952e-02 l_d_real: 1.1472e-01 l_d_fake: 1.1434e-01 D_real: 4.3230e+01 D_fake: 4.0954e+01
20-04-06 06:50:01.442 - INFO: <epoch:540, iter: 106,600, lr:2.500e-05> l_g_pix: 1.2280e-04 l_g_fea: 4.2950e-01 l_g_gan: 1.9667e-02 l_d_real: 2.5298e-02 l_d_fake: 2.3837e-02 D_real: 5.1814e+01 D_fake: 4.7905e+01
20-04-06 06:53:13.292 - INFO: <epoch:541, iter: 106,800, lr:2.500e-05> l_g_pix: 1.2847e-04 l_g_fea: 5.1155e-01 l_g_gan: 1.9616e-02 l_d_real: 3.3107e-02 l_d_fake: 2.7090e-02 D_real: 4.6853e+01 D_fake: 4.2960e+01
20-04-06 06:56:26.272 - INFO: <epoch:542, iter: 107,000, lr:2.500e-05> l_g_pix: 8.1787e-05 l_g_fea: 3.9661e-01 l_g_gan: 1.0075e-02 l_d_real: 1.6032e-01 l_d_fake: 1.5622e-01 D_real: 1.4727e+00 D_fake: -3.8410e-01
20-04-06 06:59:37.097 - INFO: <epoch:543, iter: 107,200, lr:2.500e-05> l_g_pix: 1.3658e-04 l_g_fea: 4.9832e-01 l_g_gan: 1.7240e-03 l_d_real: 1.4176e+00 l_d_fake: 1.4360e+00 D_real: 2.5655e+01 D_fake: 2.6737e+01
20-04-06 07:02:48.396 - INFO: <epoch:544, iter: 107,400, lr:2.500e-05> l_g_pix: 9.1057e-05 l_g_fea: 3.9749e-01 l_g_gan: 5.6685e-03 l_d_real: 4.0842e-01 l_d_fake: 4.1196e-01 D_real: 2.8780e+01 D_fake: 2.8057e+01
20-04-06 07:05:59.734 - INFO: <epoch:545, iter: 107,600, lr:2.500e-05> l_g_pix: 1.3336e-04 l_g_fea: 5.7318e-01 l_g_gan: 2.7659e-04 l_d_real: 3.1200e+00 l_d_fake: 3.1221e+00 D_real: 2.0704e+01 D_fake: 2.3770e+01
20-04-06 07:09:10.399 - INFO: <epoch:546, iter: 107,800, lr:2.500e-05> l_g_pix: 1.1988e-04 l_g_fea: 4.5667e-01 l_g_gan: 1.2991e-03 l_d_real: 1.5854e+00 l_d_fake: 1.5818e+00 D_real: 2.7697e+01 D_fake: 2.9021e+01
20-04-06 07:12:21.565 - INFO: <epoch:547, iter: 108,000, lr:2.500e-05> l_g_pix: 1.2443e-04 l_g_fea: 4.2555e-01 l_g_gan: 7.7617e-03 l_d_real: 2.8579e-01 l_d_fake: 2.7119e-01 D_real: 1.6120e+01 D_fake: 1.4846e+01
20-04-06 07:15:33.352 - INFO: <epoch:548, iter: 108,200, lr:2.500e-05> l_g_pix: 1.2046e-04 l_g_fea: 5.3548e-01 l_g_gan: 8.8053e-03 l_d_real: 2.2429e-01 l_d_fake: 2.2579e-01 D_real: 8.8101e+00 D_fake: 7.2740e+00
20-04-06 07:18:44.159 - INFO: <epoch:549, iter: 108,400, lr:2.500e-05> l_g_pix: 1.1456e-04 l_g_fea: 5.1919e-01 l_g_gan: 1.3132e-02 l_d_real: 1.2180e-01 l_d_fake: 1.0157e-01 D_real: 3.1355e+01 D_fake: 2.8840e+01
20-04-06 07:21:54.782 - INFO: <epoch:550, iter: 108,600, lr:2.500e-05> l_g_pix: 1.1393e-04 l_g_fea: 4.7306e-01 l_g_gan: 1.4332e-02 l_d_real: 7.9408e-02 l_d_fake: 8.0915e-02 D_real: 8.9415e+00 D_fake: 6.1552e+00
20-04-06 07:25:06.712 - INFO: <epoch:551, iter: 108,800, lr:2.500e-05> l_g_pix: 1.0207e-04 l_g_fea: 4.0161e-01 l_g_gan: 1.3886e-02 l_d_real: 7.5554e-02 l_d_fake: 7.5496e-02 D_real: 1.7716e+01 D_fake: 1.5015e+01
20-04-06 07:28:18.078 - INFO: <epoch:552, iter: 109,000, lr:2.500e-05> l_g_pix: 1.1573e-04 l_g_fea: 5.8640e-01 l_g_gan: 1.9046e-02 l_d_real: 2.9301e-02 l_d_fake: 2.6735e-02 D_real: 2.3718e+01 D_fake: 1.9937e+01
20-04-06 07:31:28.858 - INFO: <epoch:553, iter: 109,200, lr:2.500e-05> l_g_pix: 1.0509e-04 l_g_fea: 4.1561e-01 l_g_gan: 1.1626e-02 l_d_real: 1.1705e-01 l_d_fake: 1.2561e-01 D_real: -1.2163e-01 D_fake: -2.3256e+00
20-04-06 07:34:40.630 - INFO: <epoch:554, iter: 109,400, lr:2.500e-05> l_g_pix: 9.4396e-05 l_g_fea: 4.8507e-01 l_g_gan: 3.4386e-03 l_d_real: 7.7721e-01 l_d_fake: 7.7388e-01 D_real: 1.8334e+01 D_fake: 1.8422e+01
20-04-06 07:37:52.196 - INFO: <epoch:555, iter: 109,600, lr:2.500e-05> l_g_pix: 8.5954e-05 l_g_fea: 3.7069e-01 l_g_gan: 7.8876e-03 l_d_real: 2.7759e-01 l_d_fake: 2.6011e-01 D_real: 3.0742e+01 D_fake: 2.9434e+01
20-04-06 07:41:03.782 - INFO: <epoch:556, iter: 109,800, lr:2.500e-05> l_g_pix: 8.1595e-05 l_g_fea: 4.4059e-01 l_g_gan: 1.0646e-02 l_d_real: 1.5457e-01 l_d_fake: 1.6037e-01 D_real: 9.6381e+00 D_fake: 7.6665e+00
20-04-06 07:44:14.796 - INFO: <epoch:557, iter: 110,000, lr:2.500e-05> l_g_pix: 1.0638e-04 l_g_fea: 4.8980e-01 l_g_gan: 1.8298e-02 l_d_real: 3.3713e-02 l_d_fake: 3.3030e-02 D_real: 3.5359e+01 D_fake: 3.1733e+01
20-04-06 07:44:15.195 - INFO: Models and training states saved.
20-04-06 07:45:29.297 - INFO: # Validation # PSNR: 31.356, SSIM: 0.83125, LPIPS: 0.035853
20-04-06 07:45:29.298 - INFO: <epoch:557, iter: 110,000> psnr: 31.356, ssim: 0.83125, lpips: 0.035853
20-04-06 07:48:39.122 - INFO: <epoch:558, iter: 110,200, lr:2.500e-05> l_g_pix: 9.2829e-05 l_g_fea: 5.1631e-01 l_g_gan: 1.3940e-02 l_d_real: 7.5914e-02 l_d_fake: 7.7449e-02 D_real: 4.0829e+01 D_fake: 3.8118e+01
20-04-06 07:51:50.538 - INFO: <epoch:559, iter: 110,400, lr:2.500e-05> l_g_pix: 7.8808e-05 l_g_fea: 3.1064e-01 l_g_gan: 7.5406e-03 l_d_real: 2.9176e-01 l_d_fake: 2.7908e-01 D_real: 4.1326e+01 D_fake: 4.0103e+01
20-04-06 07:55:02.974 - INFO: <epoch:560, iter: 110,600, lr:2.500e-05> l_g_pix: 9.3559e-05 l_g_fea: 4.3955e-01 l_g_gan: 9.5684e-04 l_d_real: 1.8555e+00 l_d_fake: 1.8552e+00 D_real: -2.5214e+00 D_fake: -8.5739e-01
20-04-06 07:58:13.773 - INFO: <epoch:561, iter: 110,800, lr:2.500e-05> l_g_pix: 8.3338e-05 l_g_fea: 4.1960e-01 l_g_gan: 2.5987e-02 l_d_real: 6.4031e-03 l_d_fake: 6.0567e-03 D_real: 2.4108e+01 D_fake: 1.8917e+01
20-04-06 08:01:24.169 - INFO: <epoch:562, iter: 111,000, lr:2.500e-05> l_g_pix: 8.1903e-05 l_g_fea: 4.0371e-01 l_g_gan: 1.4549e-02 l_d_real: 7.0510e-02 l_d_fake: 8.4321e-02 D_real: -4.7622e-01 D_fake: -3.3086e+00
20-04-06 08:04:35.779 - INFO: <epoch:563, iter: 111,200, lr:2.500e-05> l_g_pix: 1.1624e-04 l_g_fea: 4.8450e-01 l_g_gan: 1.8059e-02 l_d_real: 3.4753e-02 l_d_fake: 3.3665e-02 D_real: 2.3959e+01 D_fake: 2.0382e+01
20-04-06 08:07:46.648 - INFO: <epoch:564, iter: 111,400, lr:2.500e-05> l_g_pix: 1.2599e-04 l_g_fea: 5.5397e-01 l_g_gan: 2.7725e-03 l_d_real: 9.2762e-01 l_d_fake: 9.2589e-01 D_real: 2.6396e+01 D_fake: 2.6768e+01
20-04-06 08:10:58.567 - INFO: <epoch:565, iter: 111,600, lr:2.500e-05> l_g_pix: 1.0836e-04 l_g_fea: 5.1347e-01 l_g_gan: 3.6201e-03 l_d_real: 8.2593e-01 l_d_fake: 8.1079e-01 D_real: 6.1138e+01 D_fake: 6.1232e+01
20-04-06 08:14:09.855 - INFO: <epoch:566, iter: 111,800, lr:2.500e-05> l_g_pix: 1.0913e-04 l_g_fea: 5.1750e-01 l_g_gan: 7.2442e-04 l_d_real: 2.1435e+00 l_d_fake: 2.1392e+00 D_real: 1.7070e+01 D_fake: 1.9066e+01
20-04-06 08:17:20.877 - INFO: <epoch:567, iter: 112,000, lr:2.500e-05> l_g_pix: 9.1084e-05 l_g_fea: 3.9187e-01 l_g_gan: 1.7427e-02 l_d_real: 4.3922e-02 l_d_fake: 4.0305e-02 D_real: 2.2346e+01 D_fake: 1.8902e+01
20-04-06 08:20:31.365 - INFO: <epoch:568, iter: 112,200, lr:2.500e-05> l_g_pix: 1.0560e-04 l_g_fea: 4.6573e-01 l_g_gan: 1.0729e-02 l_d_real: 1.3892e-01 l_d_fake: 1.4481e-01 D_real: 1.1758e+01 D_fake: 9.7545e+00
20-04-06 08:23:42.949 - INFO: <epoch:569, iter: 112,400, lr:2.500e-05> l_g_pix: 1.0386e-04 l_g_fea: 3.8621e-01 l_g_gan: 8.6442e-03 l_d_real: 2.3316e-01 l_d_fake: 2.4245e-01 D_real: 2.8927e+01 D_fake: 2.7436e+01
20-04-06 08:26:58.302 - INFO: <epoch:571, iter: 112,600, lr:2.500e-05> l_g_pix: 8.8541e-05 l_g_fea: 3.2197e-01 l_g_gan: 1.2027e-02 l_d_real: 1.0746e-01 l_d_fake: 1.4098e-01 D_real: 2.4517e+01 D_fake: 2.2235e+01
20-04-06 08:30:10.497 - INFO: <epoch:572, iter: 112,800, lr:2.500e-05> l_g_pix: 1.1803e-04 l_g_fea: 4.3168e-01 l_g_gan: 1.3667e-02 l_d_real: 9.0712e-02 l_d_fake: 1.0506e-01 D_real: 3.2371e+01 D_fake: 2.9735e+01
20-04-06 08:33:21.757 - INFO: <epoch:573, iter: 113,000, lr:2.500e-05> l_g_pix: 1.0558e-04 l_g_fea: 4.7039e-01 l_g_gan: 1.1775e-02 l_d_real: 1.2243e-01 l_d_fake: 1.3746e-01 D_real: -7.5471e+00 D_fake: -9.7721e+00
20-04-06 08:36:32.591 - INFO: <epoch:574, iter: 113,200, lr:2.500e-05> l_g_pix: 9.6281e-05 l_g_fea: 4.3446e-01 l_g_gan: 2.0212e-02 l_d_real: 2.1460e-02 l_d_fake: 2.1971e-02 D_real: 4.2795e+01 D_fake: 3.8775e+01
20-04-06 08:39:43.548 - INFO: <epoch:575, iter: 113,400, lr:2.500e-05> l_g_pix: 6.3095e-05 l_g_fea: 3.1499e-01 l_g_gan: 1.2283e-03 l_d_real: 1.7573e+00 l_d_fake: 1.7606e+00 D_real: 1.3726e+01 D_fake: 1.5239e+01
20-04-06 08:42:54.364 - INFO: <epoch:576, iter: 113,600, lr:2.500e-05> l_g_pix: 9.5715e-05 l_g_fea: 3.7889e-01 l_g_gan: 9.4221e-03 l_d_real: 2.4671e-01 l_d_fake: 2.0817e-01 D_real: 1.1186e+01 D_fake: 9.5287e+00
20-04-06 08:46:06.157 - INFO: <epoch:577, iter: 113,800, lr:2.500e-05> l_g_pix: 1.0027e-04 l_g_fea: 4.3287e-01 l_g_gan: 1.0301e-02 l_d_real: 1.8227e-01 l_d_fake: 1.6766e-01 D_real: 1.6822e+01 D_fake: 1.4936e+01
20-04-06 08:49:17.346 - INFO: <epoch:578, iter: 114,000, lr:2.500e-05> l_g_pix: 1.3150e-04 l_g_fea: 5.5201e-01 l_g_gan: 1.0848e-02 l_d_real: 1.3669e-01 l_d_fake: 1.5054e-01 D_real: 1.2798e+01 D_fake: 1.0772e+01
20-04-06 08:52:28.643 - INFO: <epoch:579, iter: 114,200, lr:2.500e-05> l_g_pix: 7.7726e-05 l_g_fea: 3.7931e-01 l_g_gan: 2.0225e-03 l_d_real: 1.1777e+00 l_d_fake: 1.1734e+00 D_real: 2.6571e+01 D_fake: 2.7342e+01
20-04-06 08:55:40.078 - INFO: <epoch:580, iter: 114,400, lr:2.500e-05> l_g_pix: 6.8930e-05 l_g_fea: 3.7147e-01 l_g_gan: 1.0095e-02 l_d_real: 1.6007e-01 l_d_fake: 1.6471e-01 D_real: 1.2755e+01 D_fake: 1.0898e+01
20-04-06 08:58:51.571 - INFO: <epoch:581, iter: 114,600, lr:2.500e-05> l_g_pix: 1.1753e-04 l_g_fea: 4.3158e-01 l_g_gan: 1.1205e-02 l_d_real: 1.3761e-01 l_d_fake: 1.4060e-01 D_real: 4.9059e+01 D_fake: 4.6957e+01
20-04-06 09:02:02.835 - INFO: <epoch:582, iter: 114,800, lr:2.500e-05> l_g_pix: 1.1561e-04 l_g_fea: 3.9897e-01 l_g_gan: 1.2508e-02 l_d_real: 9.5629e-02 l_d_fake: 1.1251e-01 D_real: 2.9876e+01 D_fake: 2.7478e+01
20-04-06 09:05:14.330 - INFO: <epoch:583, iter: 115,000, lr:2.500e-05> l_g_pix: 7.7388e-05 l_g_fea: 3.7800e-01 l_g_gan: 4.1196e-03 l_d_real: 6.7609e-01 l_d_fake: 6.8914e-01 D_real: 3.0264e+01 D_fake: 3.0123e+01
20-04-06 09:05:14.732 - INFO: Models and training states saved.
20-04-06 09:06:33.446 - INFO: # Validation # PSNR: 31.994, SSIM: 0.84802, LPIPS: 0.030219
20-04-06 09:06:33.446 - INFO: <epoch:583, iter: 115,000> psnr: 31.994, ssim: 0.84802, lpips: 0.030219
20-04-06 09:09:42.415 - INFO: <epoch:584, iter: 115,200, lr:2.500e-05> l_g_pix: 1.0546e-04 l_g_fea: 4.4633e-01 l_g_gan: 9.9688e-03 l_d_real: 1.7043e-01 l_d_fake: 1.9994e-01 D_real: 2.0707e+01 D_fake: 1.8898e+01
20-04-06 09:12:53.724 - INFO: <epoch:585, iter: 115,400, lr:2.500e-05> l_g_pix: 1.1362e-04 l_g_fea: 4.3792e-01 l_g_gan: 1.9019e-02 l_d_real: 3.7735e-02 l_d_fake: 3.1654e-02 D_real: 2.8150e+01 D_fake: 2.4381e+01
20-04-06 09:16:05.279 - INFO: <epoch:586, iter: 115,600, lr:2.500e-05> l_g_pix: 7.8403e-05 l_g_fea: 3.6015e-01 l_g_gan: 1.8857e-02 l_d_real: 2.7436e-02 l_d_fake: 3.4048e-02 D_real: 1.8093e+01 D_fake: 1.4352e+01
20-04-06 09:19:16.983 - INFO: <epoch:587, iter: 115,800, lr:2.500e-05> l_g_pix: 9.0140e-05 l_g_fea: 4.2409e-01 l_g_gan: 7.2196e-03 l_d_real: 3.4175e-01 l_d_fake: 3.4633e-01 D_real: 9.4572e+00 D_fake: 8.3574e+00
20-04-06 09:22:28.663 - INFO: <epoch:588, iter: 116,000, lr:2.500e-05> l_g_pix: 9.5864e-05 l_g_fea: 3.9039e-01 l_g_gan: 6.1802e-03 l_d_real: 4.0731e-01 l_d_fake: 4.0928e-01 D_real: 2.2355e+01 D_fake: 2.1527e+01
20-04-06 09:25:39.892 - INFO: <epoch:589, iter: 116,200, lr:2.500e-05> l_g_pix: 1.0479e-04 l_g_fea: 5.4421e-01 l_g_gan: 4.0972e-03 l_d_real: 7.1415e-01 l_d_fake: 7.2286e-01 D_real: 3.3114e+01 D_fake: 3.3013e+01
20-04-06 09:28:51.152 - INFO: <epoch:590, iter: 116,400, lr:2.500e-05> l_g_pix: 1.1184e-04 l_g_fea: 5.1051e-01 l_g_gan: 1.0700e-02 l_d_real: 1.8579e-01 l_d_fake: 1.6162e-01 D_real: 3.8261e+01 D_fake: 3.6295e+01
20-04-06 09:32:02.624 - INFO: <epoch:591, iter: 116,600, lr:2.500e-05> l_g_pix: 8.9542e-05 l_g_fea: 4.1720e-01 l_g_gan: 1.3443e-02 l_d_real: 8.9327e-02 l_d_fake: 9.7671e-02 D_real: 4.6070e+01 D_fake: 4.3475e+01
20-04-06 09:35:14.481 - INFO: <epoch:592, iter: 116,800, lr:2.500e-05> l_g_pix: 8.9703e-05 l_g_fea: 4.2571e-01 l_g_gan: 1.6746e-02 l_d_real: 5.1579e-02 l_d_fake: 5.0479e-02 D_real: 5.8110e+01 D_fake: 5.4812e+01
20-04-06 09:38:25.505 - INFO: <epoch:593, iter: 117,000, lr:2.500e-05> l_g_pix: 8.2469e-05 l_g_fea: 3.8933e-01 l_g_gan: 2.8648e-03 l_d_real: 9.9518e-01 l_d_fake: 1.0003e+00 D_real: 4.2949e+01 D_fake: 4.3374e+01
20-04-06 09:41:37.072 - INFO: <epoch:594, iter: 117,200, lr:2.500e-05> l_g_pix: 8.6484e-05 l_g_fea: 3.9118e-01 l_g_gan: 1.9862e-03 l_d_real: 1.2175e+00 l_d_fake: 1.2120e+00 D_real: 3.1609e+01 D_fake: 3.2427e+01
20-04-06 09:44:48.596 - INFO: <epoch:595, iter: 117,400, lr:2.500e-05> l_g_pix: 8.9355e-05 l_g_fea: 4.2820e-01 l_g_gan: 5.9841e-03 l_d_real: 4.2058e-01 l_d_fake: 4.1879e-01 D_real: 2.5445e+01 D_fake: 2.4668e+01
20-04-06 09:48:00.234 - INFO: <epoch:596, iter: 117,600, lr:2.500e-05> l_g_pix: 1.1506e-04 l_g_fea: 4.7360e-01 l_g_gan: 1.3855e-02 l_d_real: 8.0994e-02 l_d_fake: 8.1147e-02 D_real: 2.0294e+01 D_fake: 1.7604e+01
20-04-06 09:51:11.027 - INFO: <epoch:597, iter: 117,800, lr:2.500e-05> l_g_pix: 8.5247e-05 l_g_fea: 4.2878e-01 l_g_gan: 2.5385e-03 l_d_real: 1.0342e+00 l_d_fake: 1.0341e+00 D_real: 3.0085e+01 D_fake: 3.0612e+01
20-04-06 09:54:22.024 - INFO: <epoch:598, iter: 118,000, lr:2.500e-05> l_g_pix: 1.6431e-04 l_g_fea: 4.7683e-01 l_g_gan: 7.4342e-03 l_d_real: 3.1959e-01 l_d_fake: 3.2111e-01 D_real: 3.1799e+01 D_fake: 3.0632e+01
20-04-06 09:57:33.554 - INFO: <epoch:599, iter: 118,200, lr:2.500e-05> l_g_pix: 1.0876e-04 l_g_fea: 4.6061e-01 l_g_gan: 1.5693e-02 l_d_real: 5.9267e-02 l_d_fake: 5.8275e-02 D_real: 3.0985e+01 D_fake: 2.7905e+01
20-04-06 10:00:45.060 - INFO: <epoch:600, iter: 118,400, lr:2.500e-05> l_g_pix: 8.3412e-05 l_g_fea: 3.6161e-01 l_g_gan: 8.8589e-03 l_d_real: 2.2719e-01 l_d_fake: 2.2268e-01 D_real: 2.5600e+01 D_fake: 2.4053e+01
20-04-06 10:03:56.244 - INFO: <epoch:601, iter: 118,600, lr:2.500e-05> l_g_pix: 1.0076e-04 l_g_fea: 4.3553e-01 l_g_gan: 1.2706e-02 l_d_real: 1.1740e-01 l_d_fake: 1.2300e-01 D_real: 4.3518e+01 D_fake: 4.1097e+01
20-04-06 10:07:07.590 - INFO: <epoch:602, iter: 118,800, lr:2.500e-05> l_g_pix: 8.6453e-05 l_g_fea: 4.3018e-01 l_g_gan: 9.0292e-03 l_d_real: 2.3087e-01 l_d_fake: 2.3602e-01 D_real: 3.7796e+01 D_fake: 3.6223e+01
20-04-06 10:10:19.238 - INFO: <epoch:603, iter: 119,000, lr:2.500e-05> l_g_pix: 7.4910e-05 l_g_fea: 3.3838e-01 l_g_gan: 1.7563e-02 l_d_real: 3.6953e-02 l_d_fake: 3.7314e-02 D_real: 3.4081e+01 D_fake: 3.0605e+01
20-04-06 10:13:30.701 - INFO: <epoch:604, iter: 119,200, lr:2.500e-05> l_g_pix: 6.7668e-05 l_g_fea: 3.6987e-01 l_g_gan: 1.0938e-02 l_d_real: 1.9860e-01 l_d_fake: 2.0364e-01 D_real: 2.3851e+01 D_fake: 2.1864e+01
20-04-06 10:16:41.323 - INFO: <epoch:605, iter: 119,400, lr:2.500e-05> l_g_pix: 1.0985e-04 l_g_fea: 3.8444e-01 l_g_gan: 1.3590e-02 l_d_real: 9.2960e-02 l_d_fake: 8.1866e-02 D_real: 2.9279e+01 D_fake: 2.6648e+01
20-04-06 10:19:52.845 - INFO: <epoch:606, iter: 119,600, lr:2.500e-05> l_g_pix: 1.0480e-04 l_g_fea: 4.5387e-01 l_g_gan: 1.4067e-02 l_d_real: 9.5195e-02 l_d_fake: 8.2214e-02 D_real: 1.8967e+01 D_fake: 1.6243e+01
20-04-06 10:23:03.795 - INFO: <epoch:607, iter: 119,800, lr:2.500e-05> l_g_pix: 7.1391e-05 l_g_fea: 3.1303e-01 l_g_gan: 4.7387e-03 l_d_real: 5.4724e-01 l_d_fake: 5.3156e-01 D_real: 1.7274e+01 D_fake: 1.6866e+01
20-04-06 10:26:15.673 - INFO: <epoch:608, iter: 120,000, lr:2.500e-05> l_g_pix: 1.1363e-04 l_g_fea: 4.5317e-01 l_g_gan: 1.9420e-02 l_d_real: 2.5527e-02 l_d_fake: 2.3685e-02 D_real: 3.0129e+01 D_fake: 2.6269e+01
20-04-06 10:26:16.090 - INFO: Models and training states saved.
20-04-06 10:27:12.593 - INFO: # Validation # PSNR: 31.726, SSIM: 0.83427, LPIPS: 0.032943
20-04-06 10:27:12.593 - INFO: <epoch:608, iter: 120,000> psnr: 31.726, ssim: 0.83427, lpips: 0.032943
20-04-06 10:30:23.393 - INFO: <epoch:609, iter: 120,200, lr:2.500e-05> l_g_pix: 7.1495e-05 l_g_fea: 3.7857e-01 l_g_gan: 4.5789e-03 l_d_real: 6.2286e-01 l_d_fake: 6.2885e-01 D_real: 3.7916e+01 D_fake: 3.7626e+01
20-04-06 10:33:34.133 - INFO: <epoch:610, iter: 120,400, lr:2.500e-05> l_g_pix: 1.0613e-04 l_g_fea: 3.8669e-01 l_g_gan: 7.0605e-03 l_d_real: 3.3101e-01 l_d_fake: 3.2127e-01 D_real: 4.2378e+01 D_fake: 4.1292e+01
20-04-06 10:36:44.812 - INFO: <epoch:611, iter: 120,600, lr:2.500e-05> l_g_pix: 1.0356e-04 l_g_fea: 4.6590e-01 l_g_gan: 7.9770e-03 l_d_real: 2.5926e-01 l_d_fake: 2.6789e-01 D_real: 2.1641e+01 D_fake: 2.0309e+01
20-04-06 10:39:56.070 - INFO: <epoch:612, iter: 120,800, lr:2.500e-05> l_g_pix: 1.1834e-04 l_g_fea: 4.9650e-01 l_g_gan: 9.4563e-03 l_d_real: 1.8304e-01 l_d_fake: 1.8610e-01 D_real: 3.1458e+01 D_fake: 2.9751e+01
20-04-06 10:43:06.657 - INFO: <epoch:613, iter: 121,000, lr:2.500e-05> l_g_pix: 8.0799e-05 l_g_fea: 4.6696e-01 l_g_gan: 4.2258e-03 l_d_real: 7.0269e-01 l_d_fake: 7.0912e-01 D_real: 3.2188e+01 D_fake: 3.2048e+01
20-04-06 10:46:17.760 - INFO: <epoch:614, iter: 121,200, lr:2.500e-05> l_g_pix: 8.8504e-05 l_g_fea: 3.1335e-01 l_g_gan: 1.4024e-02 l_d_real: 7.9919e-02 l_d_fake: 6.8957e-02 D_real: 3.5531e+01 D_fake: 3.2801e+01
20-04-06 10:49:29.410 - INFO: <epoch:615, iter: 121,400, lr:2.500e-05> l_g_pix: 7.7756e-05 l_g_fea: 2.9750e-01 l_g_gan: 4.4251e-03 l_d_real: 5.7268e-01 l_d_fake: 5.6762e-01 D_real: 3.6033e+01 D_fake: 3.5718e+01
20-04-06 10:52:41.285 - INFO: <epoch:616, iter: 121,600, lr:2.500e-05> l_g_pix: 1.0665e-04 l_g_fea: 4.7121e-01 l_g_gan: 4.1280e-03 l_d_real: 6.3676e-01 l_d_fake: 6.5889e-01 D_real: 1.6677e+01 D_fake: 1.6499e+01
20-04-06 10:55:53.039 - INFO: <epoch:617, iter: 121,800, lr:2.500e-05> l_g_pix: 9.5287e-05 l_g_fea: 4.5800e-01 l_g_gan: 2.2904e-02 l_d_real: 1.3005e-02 l_d_fake: 1.2487e-02 D_real: 2.4135e+01 D_fake: 1.9567e+01
20-04-06 10:59:04.004 - INFO: <epoch:618, iter: 122,000, lr:2.500e-05> l_g_pix: 1.1223e-04 l_g_fea: 4.2345e-01 l_g_gan: 1.2238e-02 l_d_real: 1.1465e-01 l_d_fake: 1.1369e-01 D_real: 3.7900e+01 D_fake: 3.5567e+01
20-04-06 11:02:14.778 - INFO: <epoch:619, iter: 122,200, lr:2.500e-05> l_g_pix: 9.4731e-05 l_g_fea: 4.1149e-01 l_g_gan: 9.4901e-03 l_d_real: 1.8962e-01 l_d_fake: 1.8348e-01 D_real: 1.2987e+01 D_fake: 1.1276e+01
20-04-06 11:05:26.243 - INFO: <epoch:620, iter: 122,400, lr:2.500e-05> l_g_pix: 1.1472e-04 l_g_fea: 5.2018e-01 l_g_gan: 1.1246e-02 l_d_real: 1.4516e-01 l_d_fake: 1.4282e-01 D_real: 4.3827e+00 D_fake: 2.2775e+00
20-04-06 11:08:37.688 - INFO: <epoch:621, iter: 122,600, lr:2.500e-05> l_g_pix: 1.1014e-04 l_g_fea: 4.9125e-01 l_g_gan: 1.6529e-02 l_d_real: 4.4263e-02 l_d_fake: 4.0972e-02 D_real: 2.8848e+01 D_fake: 2.5585e+01
20-04-06 11:11:49.518 - INFO: <epoch:622, iter: 122,800, lr:2.500e-05> l_g_pix: 1.0358e-04 l_g_fea: 4.8929e-01 l_g_gan: 8.7315e-03 l_d_real: 2.2180e-01 l_d_fake: 2.2302e-01 D_real: 2.1491e+01 D_fake: 1.9967e+01
20-04-06 11:15:00.291 - INFO: <epoch:623, iter: 123,000, lr:2.500e-05> l_g_pix: 1.2175e-04 l_g_fea: 4.9323e-01 l_g_gan: 7.3315e-03 l_d_real: 3.0889e-01 l_d_fake: 2.8703e-01 D_real: 1.1450e+01 D_fake: 1.0281e+01
20-04-06 11:18:11.158 - INFO: <epoch:624, iter: 123,200, lr:2.500e-05> l_g_pix: 7.3431e-05 l_g_fea: 3.4514e-01 l_g_gan: 2.3753e-03 l_d_real: 1.1543e+00 l_d_fake: 1.1414e+00 D_real: 9.4575e+00 D_fake: 1.0130e+01
20-04-06 11:21:23.014 - INFO: <epoch:625, iter: 123,400, lr:2.500e-05> l_g_pix: 9.7817e-05 l_g_fea: 4.3958e-01 l_g_gan: 1.9754e-03 l_d_real: 1.3112e+00 l_d_fake: 1.3191e+00 D_real: 2.3011e+01 D_fake: 2.3931e+01
20-04-06 11:24:33.595 - INFO: <epoch:626, iter: 123,600, lr:2.500e-05> l_g_pix: 1.0572e-04 l_g_fea: 3.9858e-01 l_g_gan: 3.2187e-02 l_d_real: 1.9973e-03 l_d_fake: 1.8102e-03 D_real: 1.1178e+01 D_fake: 4.7429e+00
20-04-06 11:27:44.087 - INFO: <epoch:627, iter: 123,800, lr:2.500e-05> l_g_pix: 8.0538e-05 l_g_fea: 3.6282e-01 l_g_gan: 6.0997e-03 l_d_real: 4.1530e-01 l_d_fake: 4.1475e-01 D_real: 5.3200e+00 D_fake: 4.5151e+00
20-04-06 11:30:55.511 - INFO: <epoch:628, iter: 124,000, lr:2.500e-05> l_g_pix: 9.2693e-05 l_g_fea: 3.8207e-01 l_g_gan: 2.0498e-03 l_d_real: 1.1768e+00 l_d_fake: 1.1682e+00 D_real: 1.6914e+01 D_fake: 1.7677e+01
20-04-06 11:34:06.002 - INFO: <epoch:629, iter: 124,200, lr:2.500e-05> l_g_pix: 6.4122e-05 l_g_fea: 3.0431e-01 l_g_gan: 5.7154e-03 l_d_real: 4.7566e-01 l_d_fake: 4.6392e-01 D_real: 3.8149e+01 D_fake: 3.7476e+01
20-04-06 11:37:17.133 - INFO: <epoch:630, iter: 124,400, lr:2.500e-05> l_g_pix: 1.1216e-04 l_g_fea: 5.0492e-01 l_g_gan: 1.1889e-02 l_d_real: 1.2724e-01 l_d_fake: 1.3241e-01 D_real: 3.3497e+01 D_fake: 3.1249e+01
20-04-06 11:40:28.703 - INFO: <epoch:631, iter: 124,600, lr:2.500e-05> l_g_pix: 6.6295e-05 l_g_fea: 3.2187e-01 l_g_gan: 3.2481e-03 l_d_real: 8.9072e-01 l_d_fake: 9.1616e-01 D_real: 1.3941e+01 D_fake: 1.4195e+01
20-04-06 11:43:40.387 - INFO: <epoch:632, iter: 124,800, lr:2.500e-05> l_g_pix: 8.3004e-05 l_g_fea: 4.3210e-01 l_g_gan: 1.1915e-02 l_d_real: 1.1344e-01 l_d_fake: 1.0926e-01 D_real: 3.7243e+01 D_fake: 3.4971e+01
20-04-06 11:46:51.590 - INFO: <epoch:633, iter: 125,000, lr:2.500e-05> l_g_pix: 1.1851e-04 l_g_fea: 5.1376e-01 l_g_gan: 2.2758e-02 l_d_real: 1.3948e-02 l_d_fake: 1.2778e-02 D_real: 2.1972e+01 D_fake: 1.7434e+01
20-04-06 11:46:51.952 - INFO: Models and training states saved.
20-04-06 11:47:45.813 - INFO: # Validation # PSNR: 31.123, SSIM: 0.83208, LPIPS: 0.028689
20-04-06 11:47:45.813 - INFO: <epoch:633, iter: 125,000> psnr: 31.123, ssim: 0.83208, lpips: 0.028689
20-04-06 11:51:20.886 - INFO: <epoch:634, iter: 125,200, lr:2.500e-05> l_g_pix: 1.2894e-04 l_g_fea: 5.4934e-01 l_g_gan: 5.6035e-03 l_d_real: 4.8337e-01 l_d_fake: 4.6683e-01 D_real: 2.3317e+01 D_fake: 2.2671e+01
20-04-06 11:54:31.341 - INFO: <epoch:635, iter: 125,400, lr:2.500e-05> l_g_pix: 1.1323e-04 l_g_fea: 5.1023e-01 l_g_gan: 1.0102e-02 l_d_real: 1.7818e-01 l_d_fake: 1.6242e-01 D_real: 1.4487e+01 D_fake: 1.2637e+01
20-04-06 11:57:42.876 - INFO: <epoch:636, iter: 125,600, lr:2.500e-05> l_g_pix: 6.9303e-05 l_g_fea: 3.8679e-01 l_g_gan: 6.3632e-03 l_d_real: 3.8814e-01 l_d_fake: 3.9548e-01 D_real: 3.5217e+01 D_fake: 3.4336e+01
20-04-06 12:00:59.921 - INFO: <epoch:638, iter: 125,800, lr:2.500e-05> l_g_pix: 1.0054e-04 l_g_fea: 4.6007e-01 l_g_gan: 8.4623e-03 l_d_real: 2.7376e-01 l_d_fake: 2.9194e-01 D_real: 2.3871e+01 D_fake: 2.2462e+01
20-04-06 12:04:11.393 - INFO: <epoch:639, iter: 126,000, lr:2.500e-05> l_g_pix: 8.7393e-05 l_g_fea: 4.6662e-01 l_g_gan: 1.1545e-02 l_d_real: 1.1323e-01 l_d_fake: 1.1290e-01 D_real: 1.7621e+01 D_fake: 1.5425e+01
20-04-06 12:07:22.160 - INFO: <epoch:640, iter: 126,200, lr:2.500e-05> l_g_pix: 1.0193e-04 l_g_fea: 5.3591e-01 l_g_gan: 4.7114e-03 l_d_real: 6.1085e-01 l_d_fake: 6.0445e-01 D_real: 1.5271e+01 D_fake: 1.4936e+01
20-04-06 12:10:33.175 - INFO: <epoch:641, iter: 126,400, lr:2.500e-05> l_g_pix: 1.3114e-04 l_g_fea: 5.3958e-01 l_g_gan: 1.9288e-03 l_d_real: 1.2293e+00 l_d_fake: 1.2503e+00 D_real: 4.6527e+00 D_fake: 5.5067e+00
20-04-06 12:13:44.809 - INFO: <epoch:642, iter: 126,600, lr:2.500e-05> l_g_pix: 1.1421e-04 l_g_fea: 5.5475e-01 l_g_gan: 3.1915e-03 l_d_real: 9.9018e-01 l_d_fake: 1.0062e+00 D_real: 2.9472e+01 D_fake: 2.9832e+01
20-04-06 12:16:56.038 - INFO: <epoch:643, iter: 126,800, lr:2.500e-05> l_g_pix: 1.0718e-04 l_g_fea: 4.4356e-01 l_g_gan: 1.2000e-02 l_d_real: 1.1939e-01 l_d_fake: 1.2414e-01 D_real: 4.3800e+01 D_fake: 4.1522e+01
20-04-06 12:20:06.838 - INFO: <epoch:644, iter: 127,000, lr:2.500e-05> l_g_pix: 1.1157e-04 l_g_fea: 5.3697e-01 l_g_gan: 1.4065e-02 l_d_real: 7.6083e-02 l_d_fake: 6.8837e-02 D_real: 4.1369e+01 D_fake: 3.8628e+01
20-04-06 12:23:18.636 - INFO: <epoch:645, iter: 127,200, lr:2.500e-05> l_g_pix: 9.6301e-05 l_g_fea: 3.6809e-01 l_g_gan: 5.2123e-03 l_d_real: 4.9710e-01 l_d_fake: 4.8207e-01 D_real: 7.1697e+00 D_fake: 6.6168e+00
20-04-06 12:26:29.857 - INFO: <epoch:646, iter: 127,400, lr:2.500e-05> l_g_pix: 9.3769e-05 l_g_fea: 4.9057e-01 l_g_gan: 6.4315e-03 l_d_real: 3.8172e-01 l_d_fake: 3.6702e-01 D_real: 2.2110e+01 D_fake: 2.1198e+01
20-04-06 12:29:41.272 - INFO: <epoch:647, iter: 127,600, lr:2.500e-05> l_g_pix: 9.1961e-05 l_g_fea: 4.3102e-01 l_g_gan: 1.2757e-02 l_d_real: 1.0122e-01 l_d_fake: 1.2196e-01 D_real: 2.2654e+01 D_fake: 2.0214e+01
20-04-06 12:32:53.173 - INFO: <epoch:648, iter: 127,800, lr:2.500e-05> l_g_pix: 1.1889e-04 l_g_fea: 4.8484e-01 l_g_gan: 1.3470e-02 l_d_real: 8.6042e-02 l_d_fake: 9.5389e-02 D_real: 2.8007e+01 D_fake: 2.5404e+01
20-04-06 12:36:04.392 - INFO: <epoch:649, iter: 128,000, lr:2.500e-05> l_g_pix: 9.9932e-05 l_g_fea: 4.2116e-01 l_g_gan: 1.3091e-02 l_d_real: 8.7690e-02 l_d_fake: 8.7748e-02 D_real: 1.7591e+01 D_fake: 1.5061e+01
20-04-06 12:39:15.591 - INFO: <epoch:650, iter: 128,200, lr:2.500e-05> l_g_pix: 8.5304e-05 l_g_fea: 4.1024e-01 l_g_gan: 2.4097e-03 l_d_real: 1.0843e+00 l_d_fake: 1.0874e+00 D_real: 1.6115e+00 D_fake: 2.2153e+00
20-04-06 12:42:26.758 - INFO: <epoch:651, iter: 128,400, lr:2.500e-05> l_g_pix: 1.1269e-04 l_g_fea: 5.0345e-01 l_g_gan: 1.7210e-02 l_d_real: 4.6374e-02 l_d_fake: 4.8863e-02 D_real: 1.5373e+01 D_fake: 1.1979e+01
20-04-06 12:45:38.147 - INFO: <epoch:652, iter: 128,600, lr:2.500e-05> l_g_pix: 9.8923e-05 l_g_fea: 4.2048e-01 l_g_gan: 2.3434e-02 l_d_real: 1.0745e-02 l_d_fake: 2.4112e-02 D_real: 8.6600e+00 D_fake: 3.9906e+00
20-04-06 12:48:49.643 - INFO: <epoch:653, iter: 128,800, lr:2.500e-05> l_g_pix: 1.1506e-04 l_g_fea: 5.3736e-01 l_g_gan: 1.8107e-02 l_d_real: 3.6725e-02 l_d_fake: 3.6878e-02 D_real: 1.5618e+01 D_fake: 1.2033e+01
20-04-06 12:52:00.393 - INFO: <epoch:654, iter: 129,000, lr:2.500e-05> l_g_pix: 9.3395e-05 l_g_fea: 5.1834e-01 l_g_gan: 1.0651e-02 l_d_real: 1.4980e-01 l_d_fake: 1.6109e-01 D_real: 1.5577e+01 D_fake: 1.3602e+01
20-04-06 12:55:11.788 - INFO: <epoch:655, iter: 129,200, lr:2.500e-05> l_g_pix: 9.6027e-05 l_g_fea: 4.7472e-01 l_g_gan: 2.1072e-03 l_d_real: 1.1901e+00 l_d_fake: 1.1941e+00 D_real: 3.2453e+01 D_fake: 3.3224e+01
20-04-06 12:58:22.668 - INFO: <epoch:656, iter: 129,400, lr:2.500e-05> l_g_pix: 9.0795e-05 l_g_fea: 3.9093e-01 l_g_gan: 6.1297e-03 l_d_real: 4.7879e-01 l_d_fake: 4.6738e-01 D_real: 2.7665e+01 D_fake: 2.6912e+01
20-04-06 13:01:34.521 - INFO: <epoch:657, iter: 129,600, lr:2.500e-05> l_g_pix: 1.6147e-04 l_g_fea: 4.4696e-01 l_g_gan: 7.5213e-03 l_d_real: 3.2639e-01 l_d_fake: 3.0046e-01 D_real: 3.0446e+01 D_fake: 2.9255e+01
20-04-06 13:04:45.192 - INFO: <epoch:658, iter: 129,800, lr:2.500e-05> l_g_pix: 1.0528e-04 l_g_fea: 4.6647e-01 l_g_gan: 7.6747e-03 l_d_real: 3.0763e-01 l_d_fake: 2.7951e-01 D_real: 3.8059e+01 D_fake: 3.6817e+01
20-04-06 13:07:56.764 - INFO: <epoch:659, iter: 130,000, lr:2.500e-05> l_g_pix: 1.2149e-04 l_g_fea: 4.4718e-01 l_g_gan: 3.7719e-03 l_d_real: 7.9434e-01 l_d_fake: 7.8833e-01 D_real: 3.2556e+01 D_fake: 3.2593e+01
20-04-06 13:07:57.182 - INFO: Models and training states saved.
20-04-06 13:09:05.087 - INFO: # Validation # PSNR: 31.207, SSIM: 0.83788, LPIPS: 0.027606
20-04-06 13:09:05.087 - INFO: <epoch:659, iter: 130,000> psnr: 31.207, ssim: 0.83788, lpips: 0.027606
20-04-06 13:12:23.516 - INFO: <epoch:660, iter: 130,200, lr:2.500e-05> l_g_pix: 9.1232e-05 l_g_fea: 4.4070e-01 l_g_gan: 4.0021e-03 l_d_real: 6.8014e-01 l_d_fake: 6.5354e-01 D_real: 2.1411e+01 D_fake: 2.1278e+01
20-04-06 13:15:34.703 - INFO: <epoch:661, iter: 130,400, lr:2.500e-05> l_g_pix: 5.5016e-05 l_g_fea: 2.9631e-01 l_g_gan: 9.5159e-03 l_d_real: 2.2418e-01 l_d_fake: 2.1520e-01 D_real: 1.2764e+01 D_fake: 1.1081e+01
20-04-06 13:18:46.248 - INFO: <epoch:662, iter: 130,600, lr:2.500e-05> l_g_pix: 1.0848e-04 l_g_fea: 4.0882e-01 l_g_gan: 8.6591e-03 l_d_real: 2.4418e-01 l_d_fake: 2.3149e-01 D_real: 1.8606e+01 D_fake: 1.7112e+01
20-04-06 13:21:57.407 - INFO: <epoch:663, iter: 130,800, lr:2.500e-05> l_g_pix: 1.4305e-04 l_g_fea: 5.6180e-01 l_g_gan: 8.6899e-03 l_d_real: 2.5500e-01 l_d_fake: 2.7774e-01 D_real: 4.3175e+01 D_fake: 4.1704e+01
20-04-06 13:25:08.771 - INFO: <epoch:664, iter: 131,000, lr:2.500e-05> l_g_pix: 7.1893e-05 l_g_fea: 3.3800e-01 l_g_gan: 3.3708e-03 l_d_real: 8.2053e-01 l_d_fake: 7.9802e-01 D_real: 5.0472e+01 D_fake: 5.0607e+01
20-04-06 13:28:19.426 - INFO: <epoch:665, iter: 131,200, lr:2.500e-05> l_g_pix: 9.1326e-05 l_g_fea: 4.8116e-01 l_g_gan: 2.2505e-02 l_d_real: 1.5941e-02 l_d_fake: 1.2901e-02 D_real: 3.9790e+01 D_fake: 3.5304e+01
20-04-06 13:31:30.093 - INFO: <epoch:666, iter: 131,400, lr:2.500e-05> l_g_pix: 1.1944e-04 l_g_fea: 4.7611e-01 l_g_gan: 5.1861e-03 l_d_real: 5.3954e-01 l_d_fake: 5.1574e-01 D_real: 4.4010e+01 D_fake: 4.3500e+01
20-04-06 13:34:40.966 - INFO: <epoch:667, iter: 131,600, lr:2.500e-05> l_g_pix: 1.0410e-04 l_g_fea: 4.4384e-01 l_g_gan: 5.1048e-03 l_d_real: 5.1724e-01 l_d_fake: 5.2704e-01 D_real: 3.3738e+01 D_fake: 3.3239e+01
20-04-06 13:37:51.732 - INFO: <epoch:668, iter: 131,800, lr:2.500e-05> l_g_pix: 1.3090e-04 l_g_fea: 5.1187e-01 l_g_gan: 3.7367e-02 l_d_real: 6.8114e-04 l_d_fake: 7.3195e-04 D_real: 1.7575e+01 D_fake: 1.0102e+01
20-04-06 13:41:02.379 - INFO: <epoch:669, iter: 132,000, lr:2.500e-05> l_g_pix: 1.1807e-04 l_g_fea: 5.2690e-01 l_g_gan: 3.1629e-03 l_d_real: 8.8403e-01 l_d_fake: 9.0251e-01 D_real: 2.6501e+01 D_fake: 2.6762e+01
20-04-06 13:44:13.151 - INFO: <epoch:670, iter: 132,200, lr:2.500e-05> l_g_pix: 7.5353e-05 l_g_fea: 3.8541e-01 l_g_gan: 5.2401e-03 l_d_real: 5.6913e-01 l_d_fake: 5.0334e-01 D_real: 2.0007e+01 D_fake: 1.9495e+01
20-04-06 13:47:24.746 - INFO: <epoch:671, iter: 132,400, lr:2.500e-05> l_g_pix: 1.0125e-04 l_g_fea: 3.0223e-01 l_g_gan: 6.5987e-03 l_d_real: 3.8785e-01 l_d_fake: 3.8249e-01 D_real: 1.4575e+01 D_fake: 1.3640e+01
20-04-06 13:50:35.519 - INFO: <epoch:672, iter: 132,600, lr:2.500e-05> l_g_pix: 1.3099e-04 l_g_fea: 5.5153e-01 l_g_gan: 1.8544e-02 l_d_real: 2.8867e-02 l_d_fake: 2.9937e-02 D_real: 3.0742e+01 D_fake: 2.7063e+01
20-04-06 13:53:46.229 - INFO: <epoch:673, iter: 132,800, lr:2.500e-05> l_g_pix: 9.3837e-05 l_g_fea: 4.6359e-01 l_g_gan: 1.2579e-02 l_d_real: 1.0139e-01 l_d_fake: 1.0096e-01 D_real: 1.2651e+01 D_fake: 1.0236e+01
20-04-06 13:56:57.574 - INFO: <epoch:674, iter: 133,000, lr:2.500e-05> l_g_pix: 6.7706e-05 l_g_fea: 3.5821e-01 l_g_gan: 1.1428e-02 l_d_real: 1.4037e-01 l_d_fake: 1.3345e-01 D_real: 3.3611e+01 D_fake: 3.1462e+01
20-04-06 14:00:08.099 - INFO: <epoch:675, iter: 133,200, lr:2.500e-05> l_g_pix: 1.2084e-04 l_g_fea: 5.9770e-01 l_g_gan: 3.9245e-03 l_d_real: 7.3377e-01 l_d_fake: 7.1983e-01 D_real: 2.0415e+01 D_fake: 2.0357e+01
20-04-06 14:03:18.852 - INFO: <epoch:676, iter: 133,400, lr:2.500e-05> l_g_pix: 1.5306e-04 l_g_fea: 5.3528e-01 l_g_gan: 4.9736e-03 l_d_real: 5.3470e-01 l_d_fake: 5.5654e-01 D_real: 2.7017e+01 D_fake: 2.6568e+01
20-04-06 14:06:29.698 - INFO: <epoch:677, iter: 133,600, lr:2.500e-05> l_g_pix: 1.1965e-04 l_g_fea: 4.7301e-01 l_g_gan: 1.4679e-02 l_d_real: 7.4257e-02 l_d_fake: 7.3971e-02 D_real: 1.6939e+01 D_fake: 1.4077e+01
20-04-06 14:09:41.447 - INFO: <epoch:678, iter: 133,800, lr:2.500e-05> l_g_pix: 1.6054e-04 l_g_fea: 5.5903e-01 l_g_gan: 1.7962e-02 l_d_real: 3.6017e-02 l_d_fake: 3.5597e-02 D_real: 2.9070e+01 D_fake: 2.5514e+01
20-04-06 14:12:53.054 - INFO: <epoch:679, iter: 134,000, lr:2.500e-05> l_g_pix: 1.0286e-04 l_g_fea: 4.2154e-01 l_g_gan: 1.2280e-02 l_d_real: 1.1847e-01 l_d_fake: 1.1357e-01 D_real: 1.4609e+01 D_fake: 1.2269e+01
20-04-06 14:16:03.719 - INFO: <epoch:680, iter: 134,200, lr:2.500e-05> l_g_pix: 1.2299e-04 l_g_fea: 5.3577e-01 l_g_gan: 1.0064e-02 l_d_real: 1.8082e-01 l_d_fake: 1.7196e-01 D_real: 2.3881e+01 D_fake: 2.2045e+01
20-04-06 14:19:15.204 - INFO: <epoch:681, iter: 134,400, lr:2.500e-05> l_g_pix: 7.3022e-05 l_g_fea: 3.7583e-01 l_g_gan: 7.6022e-03 l_d_real: 3.0968e-01 l_d_fake: 3.2644e-01 D_real: 4.0629e+01 D_fake: 3.9427e+01
20-04-06 14:22:25.905 - INFO: <epoch:682, iter: 134,600, lr:2.500e-05> l_g_pix: 1.2130e-04 l_g_fea: 4.6707e-01 l_g_gan: 1.9051e-03 l_d_real: 1.3030e+00 l_d_fake: 1.3099e+00 D_real: 4.0848e+01 D_fake: 4.1773e+01
20-04-06 14:25:37.380 - INFO: <epoch:683, iter: 134,800, lr:2.500e-05> l_g_pix: 1.1336e-04 l_g_fea: 3.6914e-01 l_g_gan: 7.7980e-03 l_d_real: 2.6991e-01 l_d_fake: 2.6860e-01 D_real: 2.4040e+01 D_fake: 2.2750e+01
20-04-06 14:28:48.231 - INFO: <epoch:684, iter: 135,000, lr:2.500e-05> l_g_pix: 6.8967e-05 l_g_fea: 3.6108e-01 l_g_gan: 9.7178e-03 l_d_real: 2.7104e-01 l_d_fake: 2.2636e-01 D_real: 5.3503e+01 D_fake: 5.1808e+01
20-04-06 14:28:48.643 - INFO: Models and training states saved.
20-04-06 14:29:53.025 - INFO: # Validation # PSNR: 31.706, SSIM: 0.83449, LPIPS: 0.025505
20-04-06 14:29:53.025 - INFO: <epoch:684, iter: 135,000> psnr: 31.706, ssim: 0.83449, lpips: 0.025505
20-04-06 14:33:03.528 - INFO: <epoch:685, iter: 135,200, lr:2.500e-05> l_g_pix: 1.0373e-04 l_g_fea: 5.0598e-01 l_g_gan: 1.4297e-02 l_d_real: 7.6654e-02 l_d_fake: 8.2509e-02 D_real: 2.0292e+01 D_fake: 1.7513e+01
20-04-06 14:36:15.181 - INFO: <epoch:686, iter: 135,400, lr:2.500e-05> l_g_pix: 6.7405e-05 l_g_fea: 4.1274e-01 l_g_gan: 2.0404e-02 l_d_real: 3.0635e-02 l_d_fake: 3.5680e-02 D_real: 5.8653e-01 D_fake: -3.4611e+00
20-04-06 14:39:26.831 - INFO: <epoch:687, iter: 135,600, lr:2.500e-05> l_g_pix: 1.1168e-04 l_g_fea: 3.7627e-01 l_g_gan: 1.0481e-02 l_d_real: 1.7070e-01 l_d_fake: 1.7337e-01 D_real: 1.4379e+01 D_fake: 1.2455e+01
20-04-06 14:42:38.606 - INFO: <epoch:688, iter: 135,800, lr:2.500e-05> l_g_pix: 1.3547e-04 l_g_fea: 6.1468e-01 l_g_gan: 8.1550e-03 l_d_real: 2.7376e-01 l_d_fake: 2.5797e-01 D_real: 2.4811e+01 D_fake: 2.3446e+01
20-04-06 14:45:50.338 - INFO: <epoch:689, iter: 136,000, lr:2.500e-05> l_g_pix: 1.1382e-04 l_g_fea: 4.0912e-01 l_g_gan: 1.5218e-02 l_d_real: 5.9770e-02 l_d_fake: 5.3919e-02 D_real: 2.0859e+01 D_fake: 1.7872e+01
20-04-06 14:49:01.597 - INFO: <epoch:690, iter: 136,200, lr:2.500e-05> l_g_pix: 8.3937e-05 l_g_fea: 3.5795e-01 l_g_gan: 4.6785e-03 l_d_real: 6.4958e-01 l_d_fake: 6.4684e-01 D_real: 2.0523e+01 D_fake: 2.0235e+01
20-04-06 14:52:12.983 - INFO: <epoch:691, iter: 136,400, lr:2.500e-05> l_g_pix: 8.3621e-05 l_g_fea: 3.9898e-01 l_g_gan: 7.5898e-03 l_d_real: 3.0848e-01 l_d_fake: 2.9906e-01 D_real: 1.0550e+01 D_fake: 9.3354e+00
20-04-06 14:55:24.153 - INFO: <epoch:692, iter: 136,600, lr:2.500e-05> l_g_pix: 1.2267e-04 l_g_fea: 4.7159e-01 l_g_gan: 5.3505e-03 l_d_real: 4.6450e-01 l_d_fake: 4.6556e-01 D_real: 9.8943e+00 D_fake: 9.2892e+00
20-04-06 14:58:36.394 - INFO: <epoch:693, iter: 136,800, lr:2.500e-05> l_g_pix: 9.8557e-05 l_g_fea: 4.8110e-01 l_g_gan: 5.3267e-03 l_d_real: 4.9283e-01 l_d_fake: 5.2159e-01 D_real: 2.2952e+01 D_fake: 2.2394e+01
20-04-06 15:01:48.239 - INFO: <epoch:694, iter: 137,000, lr:2.500e-05> l_g_pix: 7.8668e-05 l_g_fea: 3.9753e-01 l_g_gan: 3.6440e-03 l_d_real: 7.5553e-01 l_d_fake: 7.7161e-01 D_real: 1.7783e+01 D_fake: 1.7817e+01
20-04-06 15:04:59.913 - INFO: <epoch:695, iter: 137,200, lr:2.500e-05> l_g_pix: 9.3558e-05 l_g_fea: 3.7920e-01 l_g_gan: 5.2237e-03 l_d_real: 4.9958e-01 l_d_fake: 4.9418e-01 D_real: 3.5543e+01 D_fake: 3.4995e+01
20-04-06 15:08:11.347 - INFO: <epoch:696, iter: 137,400, lr:2.500e-05> l_g_pix: 6.8956e-05 l_g_fea: 2.5792e-01 l_g_gan: 8.6777e-03 l_d_real: 2.4250e-01 l_d_fake: 2.3671e-01 D_real: 3.9303e+01 D_fake: 3.7807e+01
20-04-06 15:11:23.008 - INFO: <epoch:697, iter: 137,600, lr:2.500e-05> l_g_pix: 5.2583e-05 l_g_fea: 2.5966e-01 l_g_gan: 7.3603e-03 l_d_real: 3.0241e-01 l_d_fake: 3.2575e-01 D_real: 1.9818e+01 D_fake: 1.8660e+01
20-04-06 15:14:33.625 - INFO: <epoch:698, iter: 137,800, lr:2.500e-05> l_g_pix: 7.4977e-05 l_g_fea: 4.4169e-01 l_g_gan: 3.3990e-03 l_d_real: 7.6731e-01 l_d_fake: 7.6930e-01 D_real: 5.2390e+00 D_fake: 5.3276e+00
20-04-06 15:17:44.623 - INFO: <epoch:699, iter: 138,000, lr:2.500e-05> l_g_pix: 1.0210e-04 l_g_fea: 4.2852e-01 l_g_gan: 1.8442e-02 l_d_real: 3.1694e-02 l_d_fake: 3.2187e-02 D_real: 2.2463e+01 D_fake: 1.8806e+01
20-04-06 15:20:55.133 - INFO: <epoch:700, iter: 138,200, lr:2.500e-05> l_g_pix: 6.9520e-05 l_g_fea: 3.4909e-01 l_g_gan: 9.4475e-03 l_d_real: 2.0449e-01 l_d_fake: 1.8077e-01 D_real: 1.7212e+01 D_fake: 1.5515e+01
20-04-06 15:24:06.473 - INFO: <epoch:701, iter: 138,400, lr:2.500e-05> l_g_pix: 1.5006e-04 l_g_fea: 5.4142e-01 l_g_gan: 7.9806e-03 l_d_real: 2.7480e-01 l_d_fake: 2.6515e-01 D_real: 1.6051e+01 D_fake: 1.4725e+01
20-04-06 15:27:17.938 - INFO: <epoch:702, iter: 138,600, lr:2.500e-05> l_g_pix: 1.3855e-04 l_g_fea: 4.3020e-01 l_g_gan: 5.7193e-03 l_d_real: 4.5845e-01 l_d_fake: 4.7838e-01 D_real: 5.2814e+01 D_fake: 5.2139e+01
20-04-06 15:30:29.251 - INFO: <epoch:703, iter: 138,800, lr:2.500e-05> l_g_pix: 9.7705e-05 l_g_fea: 3.6811e-01 l_g_gan: 6.3529e-03 l_d_real: 4.0990e-01 l_d_fake: 4.1346e-01 D_real: 2.7883e+01 D_fake: 2.7024e+01
20-04-06 15:33:45.188 - INFO: <epoch:705, iter: 139,000, lr:2.500e-05> l_g_pix: 9.7513e-05 l_g_fea: 4.1620e-01 l_g_gan: 7.7831e-03 l_d_real: 2.6423e-01 l_d_fake: 2.6221e-01 D_real: 1.4204e+01 D_fake: 1.2911e+01
20-04-06 15:36:56.841 - INFO: <epoch:706, iter: 139,200, lr:2.500e-05> l_g_pix: 7.1032e-05 l_g_fea: 3.0730e-01 l_g_gan: 3.3367e-03 l_d_real: 7.8105e-01 l_d_fake: 7.9056e-01 D_real: 1.3606e+01 D_fake: 1.3724e+01
20-04-06 15:40:08.639 - INFO: <epoch:707, iter: 139,400, lr:2.500e-05> l_g_pix: 1.2608e-04 l_g_fea: 5.0282e-01 l_g_gan: 1.0692e-02 l_d_real: 1.6212e-01 l_d_fake: 1.7743e-01 D_real: 1.8149e+01 D_fake: 1.6180e+01
20-04-06 15:43:20.157 - INFO: <epoch:708, iter: 139,600, lr:2.500e-05> l_g_pix: 8.8116e-05 l_g_fea: 4.2543e-01 l_g_gan: 1.6191e-02 l_d_real: 4.3115e-02 l_d_fake: 4.4798e-02 D_real: 4.4760e+01 D_fake: 4.1566e+01
20-04-06 15:46:31.641 - INFO: <epoch:709, iter: 139,800, lr:2.500e-05> l_g_pix: 1.0235e-04 l_g_fea: 4.7989e-01 l_g_gan: 7.5192e-03 l_d_real: 3.3420e-01 l_d_fake: 3.5759e-01 D_real: 4.4383e+01 D_fake: 4.3225e+01
20-04-06 15:49:42.114 - INFO: <epoch:710, iter: 140,000, lr:2.500e-05> l_g_pix: 1.2283e-04 l_g_fea: 5.1671e-01 l_g_gan: 1.1348e-02 l_d_real: 1.5953e-01 l_d_fake: 1.4616e-01 D_real: 3.2810e+01 D_fake: 3.0693e+01
20-04-06 15:49:42.563 - INFO: Models and training states saved.
20-04-06 15:50:45.860 - INFO: # Validation # PSNR: 31.45, SSIM: 0.83828, LPIPS: 0.029365
20-04-06 15:50:45.861 - INFO: <epoch:710, iter: 140,000> psnr: 31.45, ssim: 0.83828, lpips: 0.029365
20-04-06 15:54:03.333 - INFO: <epoch:711, iter: 140,200, lr:2.500e-05> l_g_pix: 1.2933e-04 l_g_fea: 5.8280e-01 l_g_gan: 1.6778e-02 l_d_real: 4.8190e-02 l_d_fake: 5.6292e-02 D_real: 2.4977e+01 D_fake: 2.1673e+01
20-04-06 15:57:14.055 - INFO: <epoch:712, iter: 140,400, lr:2.500e-05> l_g_pix: 1.2731e-04 l_g_fea: 4.5871e-01 l_g_gan: 2.7229e-02 l_d_real: 5.3350e-03 l_d_fake: 5.2207e-03 D_real: 2.1871e+01 D_fake: 1.6430e+01
20-04-06 16:00:25.287 - INFO: <epoch:713, iter: 140,600, lr:2.500e-05> l_g_pix: 9.5928e-05 l_g_fea: 4.6648e-01 l_g_gan: 1.4689e-02 l_d_real: 6.1880e-02 l_d_fake: 6.1285e-02 D_real: 1.1548e+01 D_fake: 8.6713e+00
20-04-06 16:03:36.014 - INFO: <epoch:714, iter: 140,800, lr:2.500e-05> l_g_pix: 1.0577e-04 l_g_fea: 5.1435e-01 l_g_gan: 3.0361e-03 l_d_real: 8.8146e-01 l_d_fake: 8.8346e-01 D_real: 2.0496e+01 D_fake: 2.0771e+01
20-04-06 16:06:47.631 - INFO: <epoch:715, iter: 141,000, lr:2.500e-05> l_g_pix: 7.7997e-05 l_g_fea: 4.1161e-01 l_g_gan: 5.1601e-03 l_d_real: 4.9109e-01 l_d_fake: 4.8135e-01 D_real: 9.7939e+00 D_fake: 9.2481e+00
20-04-06 16:09:59.138 - INFO: <epoch:716, iter: 141,200, lr:2.500e-05> l_g_pix: 1.1763e-04 l_g_fea: 5.2808e-01 l_g_gan: 5.0622e-03 l_d_real: 5.5914e-01 l_d_fake: 5.6187e-01 D_real: 4.0211e+01 D_fake: 3.9760e+01
20-04-06 16:13:10.567 - INFO: <epoch:717, iter: 141,400, lr:2.500e-05> l_g_pix: 1.0544e-04 l_g_fea: 4.4679e-01 l_g_gan: 6.1630e-03 l_d_real: 3.9911e-01 l_d_fake: 4.0398e-01 D_real: 2.7699e+01 D_fake: 2.6868e+01
20-04-06 16:16:21.411 - INFO: <epoch:718, iter: 141,600, lr:2.500e-05> l_g_pix: 9.1172e-05 l_g_fea: 4.5991e-01 l_g_gan: 1.9437e-02 l_d_real: 2.7855e-02 l_d_fake: 2.7090e-02 D_real: 2.6939e+01 D_fake: 2.3079e+01
20-04-06 16:19:32.641 - INFO: <epoch:719, iter: 141,800, lr:2.500e-05> l_g_pix: 9.3523e-05 l_g_fea: 4.3544e-01 l_g_gan: 1.4888e-02 l_d_real: 6.4138e-02 l_d_fake: 6.7888e-02 D_real: 3.7673e+01 D_fake: 3.4761e+01
20-04-06 16:22:43.888 - INFO: <epoch:720, iter: 142,000, lr:2.500e-05> l_g_pix: 1.2289e-04 l_g_fea: 4.7794e-01 l_g_gan: 5.2887e-03 l_d_real: 5.0012e-01 l_d_fake: 5.2489e-01 D_real: 3.3783e+01 D_fake: 3.3238e+01
20-04-06 16:25:54.978 - INFO: <epoch:721, iter: 142,200, lr:2.500e-05> l_g_pix: 9.6641e-05 l_g_fea: 4.0895e-01 l_g_gan: 9.8488e-03 l_d_real: 1.8868e-01 l_d_fake: 1.7409e-01 D_real: 2.4928e+01 D_fake: 2.3139e+01
20-04-06 16:29:06.517 - INFO: <epoch:722, iter: 142,400, lr:2.500e-05> l_g_pix: 8.4108e-05 l_g_fea: 4.1906e-01 l_g_gan: 9.2835e-03 l_d_real: 1.9868e-01 l_d_fake: 2.0777e-01 D_real: 1.6477e+01 D_fake: 1.4824e+01
20-04-06 16:32:18.100 - INFO: <epoch:723, iter: 142,600, lr:2.500e-05> l_g_pix: 9.6105e-05 l_g_fea: 4.6228e-01 l_g_gan: 9.3641e-03 l_d_real: 2.1405e-01 l_d_fake: 2.2183e-01 D_real: 3.6389e+01 D_fake: 3.4734e+01
20-04-06 16:35:29.206 - INFO: <epoch:724, iter: 142,800, lr:2.500e-05> l_g_pix: 9.3495e-05 l_g_fea: 3.1829e-01 l_g_gan: 5.7959e-03 l_d_real: 4.6643e-01 l_d_fake: 4.7404e-01 D_real: 3.3988e+01 D_fake: 3.3299e+01
20-04-06 16:38:40.259 - INFO: <epoch:725, iter: 143,000, lr:2.500e-05> l_g_pix: 9.6606e-05 l_g_fea: 4.9823e-01 l_g_gan: 7.8683e-03 l_d_real: 2.5705e-01 l_d_fake: 2.6043e-01 D_real: 2.5038e+01 D_fake: 2.3723e+01
20-04-06 16:41:50.778 - INFO: <epoch:726, iter: 143,200, lr:2.500e-05> l_g_pix: 7.9119e-05 l_g_fea: 4.0651e-01 l_g_gan: 1.4070e-02 l_d_real: 6.8983e-02 l_d_fake: 6.7446e-02 D_real: 1.5900e+01 D_fake: 1.3154e+01
20-04-06 16:45:01.879 - INFO: <epoch:727, iter: 143,400, lr:2.500e-05> l_g_pix: 9.4727e-05 l_g_fea: 3.8087e-01 l_g_gan: 2.5126e-02 l_d_real: 7.5641e-03 l_d_fake: 9.2403e-03 D_real: 2.2746e+01 D_fake: 1.7730e+01
20-04-06 16:48:12.609 - INFO: <epoch:728, iter: 143,600, lr:2.500e-05> l_g_pix: 8.2986e-05 l_g_fea: 4.3709e-01 l_g_gan: 4.0552e-03 l_d_real: 6.7004e-01 l_d_fake: 6.6506e-01 D_real: 3.8273e+01 D_fake: 3.8129e+01
20-04-06 16:51:23.485 - INFO: <epoch:729, iter: 143,800, lr:2.500e-05> l_g_pix: 8.4433e-05 l_g_fea: 3.4283e-01 l_g_gan: 8.0619e-03 l_d_real: 2.6168e-01 l_d_fake: 2.4676e-01 D_real: 3.2419e+01 D_fake: 3.1061e+01
20-04-06 16:54:34.915 - INFO: <epoch:730, iter: 144,000, lr:2.500e-05> l_g_pix: 8.4115e-05 l_g_fea: 4.3772e-01 l_g_gan: 6.7672e-03 l_d_real: 3.6982e-01 l_d_fake: 3.4955e-01 D_real: 1.4876e+01 D_fake: 1.3882e+01