unlicense/Phoenix/Sol.Levante.NTSC2HD-PHOENiX/logs/train_200404-232220.log

20-04-04 23:22:20.477 - 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: ../experiments/pretrained_models/RRDB_PSNR_x4.pth
    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
  ]
  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

20-04-04 23:22:20.575 - INFO: Random seed: 977
20-04-04 23:22:20.604 - INFO: Dataset [LRHRDataset - sollevante-train] is created.
20-04-04 23:22:20.604 - INFO: Number of train images: 6,309, iters: 198
20-04-04 23:22:20.604 - INFO: Total epochs needed: 2526 for iters 500,000
20-04-04 23:22:20.604 - INFO: Dataset [LRHRDataset - sollevante-val] is created.
20-04-04 23:22:20.604 - INFO: Number of val images in [sollevante-val]: 4
20-04-04 23:22:20.745 - INFO: Initialization method [kaiming]
20-04-04 23:22:23.042 - INFO: Initialization method [kaiming]
20-04-04 23:22:23.119 - INFO: Loading pretrained model for G [../experiments/pretrained_models/RRDB_PSNR_x4.pth] ...
20-04-04 23:22:24.194 - INFO: Remove HFEN loss.
20-04-04 23:22:24.194 - INFO: Remove TV loss.
20-04-04 23:22:24.194 - INFO: Remove SSIM loss.
20-04-04 23:22:24.195 - INFO: Remove LPIPS loss.
20-04-04 23:22:24.195 - INFO: Remove SPL loss.
20-04-04 23:22:24.203 - INFO: Network G structure: DataParallel - RRDBNet, with parameters: 16,697,987
20-04-04 23:22:24.203 - 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))
  )
)
20-04-04 23:22:24.206 - INFO: Network D structure: DataParallel - Discriminator_VGG_128, with parameters: 14,502,281
20-04-04 23:22:24.206 - 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)
  )
)
20-04-04 23:22:24.206 - INFO: Network F structure: DataParallel - VGGFeatureExtractor, with parameters: 20,024,384
20-04-04 23:22:24.206 - 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))
  )
)
20-04-04 23:22:24.206 - INFO: Model [SRRaGANModel] is created.
20-04-04 23:22:24.206 - INFO: Start training from epoch: 0, iter: 0
20-04-04 23:25:36.321 - INFO: <epoch:  1, iter:     200, lr:1.000e-04> l_g_pix: 1.5067e-04 l_g_fea: 6.4297e-01 l_g_gan: 1.9698e-02 l_d_real: 3.8048e-02 l_d_fake: 4.4457e-02 D_real: -4.7199e+00 D_fake: -8.6182e+00 
20-04-04 23:28:46.420 - INFO: <epoch:  2, iter:     400, lr:1.000e-04> l_g_pix: 1.3205e-04 l_g_fea: 5.9754e-01 l_g_gan: 1.2001e-02 l_d_real: 5.7828e-01 l_d_fake: 8.6757e-01 D_real: -3.6852e+00 D_fake: -5.3624e+00 
20-04-04 23:31:57.108 - INFO: <epoch:  3, iter:     600, lr:1.000e-04> l_g_pix: 1.7049e-04 l_g_fea: 6.4581e-01 l_g_gan: 2.1859e-02 l_d_real: 4.5861e-02 l_d_fake: 4.4064e-02 D_real: -2.7642e+01 D_fake: -3.1969e+01 
20-04-04 23:35:08.808 - INFO: <epoch:  4, iter:     800, lr:1.000e-04> l_g_pix: 1.2745e-04 l_g_fea: 4.9038e-01 l_g_gan: 1.5169e-02 l_d_real: 8.2734e-02 l_d_fake: 6.9391e-02 D_real: -4.2445e+01 D_fake: -4.5403e+01 
20-04-04 23:38:20.465 - INFO: <epoch:  5, iter:   1,000, lr:1.000e-04> l_g_pix: 2.0304e-04 l_g_fea: 6.2301e-01 l_g_gan: 7.7560e-03 l_d_real: 3.7310e-01 l_d_fake: 3.6773e-01 D_real: -2.6110e+01 D_fake: -2.7290e+01 
20-04-04 23:41:32.063 - INFO: <epoch:  6, iter:   1,200, lr:1.000e-04> l_g_pix: 3.3270e-04 l_g_fea: 6.7334e-01 l_g_gan: 5.2605e-02 l_d_real: 5.1194e-04 l_d_fake: 5.7718e-04 D_real: -4.9848e+01 D_fake: -6.0368e+01 
20-04-04 23:44:42.791 - INFO: <epoch:  7, iter:   1,400, lr:1.000e-04> l_g_pix: 9.4193e-05 l_g_fea: 4.4962e-01 l_g_gan: 1.9301e-02 l_d_real: 3.2885e-02 l_d_fake: 3.9705e-02 D_real: -6.5057e+01 D_fake: -6.8881e+01 
20-04-04 23:47:59.742 - INFO: <epoch:  8, iter:   1,600, lr:1.000e-04> l_g_pix: 1.5462e-04 l_g_fea: 4.6072e-01 l_g_gan: 1.6594e-02 l_d_real: 6.2811e-02 l_d_fake: 5.4601e-02 D_real: -3.4867e+01 D_fake: -3.8127e+01 
20-04-04 23:51:11.580 - INFO: <epoch:  9, iter:   1,800, lr:1.000e-04> l_g_pix: 1.1984e-04 l_g_fea: 5.9564e-01 l_g_gan: 1.0626e-02 l_d_real: 1.3767e-01 l_d_fake: 1.3454e-01 D_real: -4.5837e+01 D_fake: -4.7826e+01 
20-04-04 23:54:23.741 - INFO: <epoch: 10, iter:   2,000, lr:1.000e-04> l_g_pix: 9.8765e-05 l_g_fea: 4.4679e-01 l_g_gan: 1.5166e-02 l_d_real: 6.7929e-02 l_d_fake: 6.3209e-02 D_real: -6.8619e+01 D_fake: -7.1586e+01 
20-04-04 23:57:35.874 - INFO: <epoch: 11, iter:   2,200, lr:1.000e-04> l_g_pix: 1.0897e-04 l_g_fea: 4.5104e-01 l_g_gan: 4.7122e-03 l_d_real: 6.0780e-01 l_d_fake: 6.0522e-01 D_real: -7.5787e+01 D_fake: -7.6123e+01 
20-04-05 00:00:48.342 - INFO: <epoch: 12, iter:   2,400, lr:1.000e-04> l_g_pix: 1.4725e-04 l_g_fea: 5.3169e-01 l_g_gan: 3.9355e-02 l_d_real: 6.3501e-02 l_d_fake: 1.2131e-01 D_real: -9.2933e+01 D_fake: -1.0071e+02 
20-04-05 00:03:59.594 - INFO: <epoch: 13, iter:   2,600, lr:1.000e-04> l_g_pix: 1.3640e-04 l_g_fea: 4.6290e-01 l_g_gan: 3.1521e-03 l_d_real: 1.3461e+00 l_d_fake: 1.2871e+00 D_real: -9.8746e+01 D_fake: -9.8060e+01 
20-04-05 00:07:10.849 - INFO: <epoch: 14, iter:   2,800, lr:1.000e-04> l_g_pix: 1.2909e-04 l_g_fea: 5.6723e-01 l_g_gan: 1.8053e-02 l_d_real: 4.0369e-02 l_d_fake: 3.7563e-02 D_real: -1.1559e+02 D_fake: -1.1916e+02 
20-04-05 00:10:22.774 - INFO: <epoch: 15, iter:   3,000, lr:1.000e-04> l_g_pix: 1.3945e-04 l_g_fea: 5.1106e-01 l_g_gan: 1.7204e-02 l_d_real: 3.5538e-02 l_d_fake: 3.5022e-02 D_real: -1.0952e+02 D_fake: -1.1292e+02 
20-04-05 00:13:34.370 - INFO: <epoch: 16, iter:   3,200, lr:1.000e-04> l_g_pix: 1.4709e-04 l_g_fea: 4.9654e-01 l_g_gan: 1.9536e-02 l_d_real: 2.5714e-02 l_d_fake: 2.6977e-02 D_real: -9.8401e+01 D_fake: -1.0228e+02 
20-04-05 00:16:46.174 - INFO: <epoch: 17, iter:   3,400, lr:1.000e-04> l_g_pix: 1.1033e-04 l_g_fea: 4.2519e-01 l_g_gan: 2.7380e-02 l_d_real: 9.6280e-03 l_d_fake: 3.4007e-02 D_real: -1.1473e+02 D_fake: -1.2018e+02 
20-04-05 00:19:57.374 - INFO: <epoch: 18, iter:   3,600, lr:1.000e-04> l_g_pix: 1.3446e-04 l_g_fea: 5.0493e-01 l_g_gan: 1.2163e-02 l_d_real: 9.8201e-02 l_d_fake: 9.9758e-02 D_real: -6.2817e+01 D_fake: -6.5150e+01 
20-04-05 00:23:08.459 - INFO: <epoch: 19, iter:   3,800, lr:1.000e-04> l_g_pix: 1.6831e-04 l_g_fea: 4.4347e-01 l_g_gan: 6.8688e-03 l_d_real: 6.0926e-01 l_d_fake: 6.0525e-01 D_real: -1.0362e+02 D_fake: -1.0439e+02 
20-04-05 00:26:19.569 - INFO: <epoch: 20, iter:   4,000, lr:1.000e-04> l_g_pix: 1.1049e-04 l_g_fea: 3.7011e-01 l_g_gan: 2.6707e-02 l_d_real: 6.7057e-03 l_d_fake: 5.7112e-03 D_real: -8.4363e+01 D_fake: -8.9698e+01 
20-04-05 00:29:46.129 - INFO: <epoch: 21, iter:   4,200, lr:1.000e-04> l_g_pix: 1.3017e-04 l_g_fea: 5.1142e-01 l_g_gan: 8.4404e-03 l_d_real: 3.2139e-01 l_d_fake: 3.1410e-01 D_real: -4.7024e+01 D_fake: -4.8395e+01 
20-04-05 00:33:38.103 - INFO: <epoch: 22, iter:   4,400, lr:1.000e-04> l_g_pix: 3.2017e-04 l_g_fea: 5.2084e-01 l_g_gan: 1.3103e-02 l_d_real: 1.9688e-01 l_d_fake: 1.8874e-01 D_real: -1.0358e+02 D_fake: -1.0601e+02 
20-04-05 00:36:49.272 - INFO: <epoch: 23, iter:   4,600, lr:1.000e-04> l_g_pix: 1.0807e-04 l_g_fea: 5.1753e-01 l_g_gan: 1.3751e-02 l_d_real: 6.8849e-02 l_d_fake: 6.9987e-02 D_real: -7.6408e+01 D_fake: -7.9089e+01 
20-04-05 00:40:00.567 - INFO: <epoch: 24, iter:   4,800, lr:1.000e-04> l_g_pix: 1.0611e-04 l_g_fea: 5.5450e-01 l_g_gan: 8.6396e-03 l_d_real: 2.6326e-01 l_d_fake: 2.6683e-01 D_real: -1.0310e+02 D_fake: -1.0457e+02 
20-04-05 00:43:11.166 - INFO: <epoch: 25, iter:   5,000, lr:1.000e-04> l_g_pix: 2.2638e-04 l_g_fea: 5.5730e-01 l_g_gan: 1.1436e-02 l_d_real: 1.3492e-01 l_d_fake: 1.3454e-01 D_real: -9.1531e+01 D_fake: -9.3683e+01 
20-04-05 00:43:11.609 - INFO: Models and training states saved.
20-04-05 00:44:24.317 - INFO: # Validation # PSNR: 27.42, SSIM: 0.84759, LPIPS: 0.048405
20-04-05 00:44:24.317 - INFO: <epoch: 25, iter:   5,000> psnr: 27.42, ssim: 0.84759, lpips: 0.048405
20-04-05 00:47:50.180 - INFO: <epoch: 26, iter:   5,200, lr:1.000e-04> l_g_pix: 1.0966e-04 l_g_fea: 4.6482e-01 l_g_gan: 5.3625e-03 l_d_real: 4.9917e-01 l_d_fake: 5.0392e-01 D_real: -3.3431e+01 D_fake: -3.4002e+01 
20-04-05 00:51:24.255 - INFO: <epoch: 27, iter:   5,400, lr:1.000e-04> l_g_pix: 1.3932e-04 l_g_fea: 4.9510e-01 l_g_gan: 2.0830e-02 l_d_real: 2.0122e-02 l_d_fake: 1.9591e-02 D_real: -5.4988e+01 D_fake: -5.9135e+01 
20-04-05 00:55:48.466 - INFO: <epoch: 28, iter:   5,600, lr:1.000e-04> l_g_pix: 1.1144e-04 l_g_fea: 4.7469e-01 l_g_gan: 1.5239e-02 l_d_real: 5.4750e-02 l_d_fake: 5.4829e-02 D_real: -1.0424e+02 D_fake: -1.0724e+02 
20-04-05 00:59:48.414 - INFO: <epoch: 29, iter:   5,800, lr:1.000e-04> l_g_pix: 1.5416e-04 l_g_fea: 6.7273e-01 l_g_gan: 4.3444e-02 l_d_real: 1.1939e-02 l_d_fake: 1.1962e-02 D_real: -1.3331e+02 D_fake: -1.4199e+02 
20-04-05 01:05:26.021 - INFO: <epoch: 30, iter:   6,000, lr:1.000e-04> l_g_pix: 9.1823e-05 l_g_fea: 3.3324e-01 l_g_gan: 4.4898e-03 l_d_real: 5.3428e-01 l_d_fake: 5.3431e-01 D_real: -7.5958e+01 D_fake: -7.6322e+01 
20-04-05 01:08:37.923 - INFO: <epoch: 31, iter:   6,200, lr:1.000e-04> l_g_pix: 1.2368e-04 l_g_fea: 5.2056e-01 l_g_gan: 5.6560e-03 l_d_real: 4.1240e-01 l_d_fake: 4.1315e-01 D_real: -1.6377e+02 D_fake: -1.6449e+02 
20-04-05 01:11:49.748 - INFO: <epoch: 32, iter:   6,400, lr:1.000e-04> l_g_pix: 1.3756e-04 l_g_fea: 4.3860e-01 l_g_gan: 1.8441e-02 l_d_real: 9.2878e-02 l_d_fake: 6.9032e-02 D_real: -1.6591e+02 D_fake: -1.6952e+02 
20-04-05 01:15:01.978 - INFO: <epoch: 33, iter:   6,600, lr:1.000e-04> l_g_pix: 1.2548e-04 l_g_fea: 4.9449e-01 l_g_gan: 9.0458e-03 l_d_real: 3.5493e-01 l_d_fake: 3.6301e-01 D_real: -1.7461e+02 D_fake: -1.7607e+02 
20-04-05 01:18:14.203 - INFO: <epoch: 34, iter:   6,800, lr:1.000e-04> l_g_pix: 1.4897e-04 l_g_fea: 5.0925e-01 l_g_gan: 8.6339e-03 l_d_real: 2.2171e-01 l_d_fake: 2.2235e-01 D_real: -7.7653e+01 D_fake: -7.9158e+01 
20-04-05 01:21:27.143 - INFO: <epoch: 35, iter:   7,000, lr:1.000e-04> l_g_pix: 1.8414e-04 l_g_fea: 5.7507e-01 l_g_gan: 7.8471e-03 l_d_real: 3.4736e-01 l_d_fake: 3.3294e-01 D_real: -7.5933e+01 D_fake: -7.7162e+01 
20-04-05 01:24:39.212 - INFO: <epoch: 36, iter:   7,200, lr:1.000e-04> l_g_pix: 1.4895e-04 l_g_fea: 5.2409e-01 l_g_gan: 3.4292e-03 l_d_real: 7.2798e-01 l_d_fake: 7.2715e-01 D_real: -1.1462e+02 D_fake: -1.1458e+02 
20-04-05 01:27:50.800 - INFO: <epoch: 37, iter:   7,400, lr:1.000e-04> l_g_pix: 1.1425e-04 l_g_fea: 4.4721e-01 l_g_gan: 5.8452e-02 l_d_real: 9.5367e-06 l_d_fake: 1.0468e-05 D_real: -1.6559e+02 D_fake: -1.7728e+02 
20-04-05 01:31:02.576 - INFO: <epoch: 38, iter:   7,600, lr:1.000e-04> l_g_pix: 1.7729e-04 l_g_fea: 4.8372e-01 l_g_gan: 4.5680e-02 l_d_real: 4.8117e-03 l_d_fake: 4.4829e-03 D_real: -1.4455e+02 D_fake: -1.5368e+02 
20-04-05 01:34:14.570 - INFO: <epoch: 39, iter:   7,800, lr:1.000e-04> l_g_pix: 1.5779e-04 l_g_fea: 5.5841e-01 l_g_gan: 1.7132e-02 l_d_real: 7.3069e-02 l_d_fake: 7.4340e-02 D_real: -1.7440e+02 D_fake: -1.7776e+02 
20-04-05 01:37:26.825 - INFO: <epoch: 40, iter:   8,000, lr:1.000e-04> l_g_pix: 2.5441e-04 l_g_fea: 4.4781e-01 l_g_gan: 2.6406e-02 l_d_real: 6.2666e-03 l_d_fake: 6.5308e-03 D_real: -7.9625e+01 D_fake: -8.4899e+01 
20-04-05 01:40:38.020 - INFO: <epoch: 41, iter:   8,200, lr:1.000e-04> l_g_pix: 1.4776e-04 l_g_fea: 4.0763e-01 l_g_gan: 8.9417e-03 l_d_real: 1.9260e-01 l_d_fake: 1.9373e-01 D_real: -1.5198e+02 D_fake: -1.5357e+02 
20-04-05 01:43:50.207 - INFO: <epoch: 42, iter:   8,400, lr:1.000e-04> l_g_pix: 1.5214e-04 l_g_fea: 5.2309e-01 l_g_gan: 2.6347e-03 l_d_real: 9.9587e-01 l_d_fake: 9.9060e-01 D_real: -1.2830e+02 D_fake: -1.2784e+02 
20-04-05 01:47:01.448 - INFO: <epoch: 43, iter:   8,600, lr:1.000e-04> l_g_pix: 1.5511e-04 l_g_fea: 4.6955e-01 l_g_gan: 1.0341e-02 l_d_real: 1.4063e-01 l_d_fake: 1.4082e-01 D_real: -1.5247e+02 D_fake: -1.5440e+02 
20-04-05 01:50:13.446 - INFO: <epoch: 44, iter:   8,800, lr:1.000e-04> l_g_pix: 2.3693e-04 l_g_fea: 4.3622e-01 l_g_gan: 3.9225e-02 l_d_real: 4.3828e-03 l_d_fake: 1.4228e-02 D_real: -3.7370e+01 D_fake: -4.5206e+01 
20-04-05 01:53:25.553 - INFO: <epoch: 45, iter:   9,000, lr:1.000e-04> l_g_pix: 1.2815e-04 l_g_fea: 6.1019e-01 l_g_gan: 3.7381e-03 l_d_real: 6.4664e-01 l_d_fake: 6.4765e-01 D_real: 2.3021e+00 D_fake: 2.2016e+00 
20-04-05 01:56:38.519 - INFO: <epoch: 46, iter:   9,200, lr:1.000e-04> l_g_pix: 1.3876e-04 l_g_fea: 5.6056e-01 l_g_gan: 5.1455e-03 l_d_real: 5.5701e-01 l_d_fake: 5.4845e-01 D_real: 1.8974e+01 D_fake: 1.8498e+01 
20-04-05 01:59:49.959 - INFO: <epoch: 47, iter:   9,400, lr:1.000e-04> l_g_pix: 1.0843e-04 l_g_fea: 4.4856e-01 l_g_gan: 4.4120e-03 l_d_real: 6.2088e-01 l_d_fake: 6.2020e-01 D_real: 1.9923e+01 D_fake: 1.9661e+01 
20-04-05 02:03:03.535 - INFO: <epoch: 48, iter:   9,600, lr:1.000e-04> l_g_pix: 8.4562e-05 l_g_fea: 3.9300e-01 l_g_gan: 3.5520e-03 l_d_real: 7.1460e-01 l_d_fake: 7.1677e-01 D_real: 3.0395e+01 D_fake: 3.0400e+01 
20-04-05 02:06:36.558 - INFO: <epoch: 49, iter:   9,800, lr:1.000e-04> l_g_pix: 9.2877e-05 l_g_fea: 4.3395e-01 l_g_gan: 1.1610e-02 l_d_real: 1.3217e-01 l_d_fake: 1.8780e-01 D_real: 6.1600e+01 D_fake: 5.9437e+01 
20-04-05 02:09:53.824 - INFO: <epoch: 50, iter:  10,000, lr:1.000e-04> l_g_pix: 1.4323e-04 l_g_fea: 4.8017e-01 l_g_gan: 7.5872e-03 l_d_real: 3.5414e-01 l_d_fake: 3.3937e-01 D_real: 5.3492e+01 D_fake: 5.2321e+01 
20-04-05 02:09:54.311 - INFO: Models and training states saved.
20-04-05 02:11:16.150 - INFO: # Validation # PSNR: 30.339, SSIM: 0.85384, LPIPS: 0.052792
20-04-05 02:11:16.150 - INFO: <epoch: 50, iter:  10,000> psnr: 30.339, ssim: 0.85384, lpips: 0.052792
20-04-05 02:17:51.442 - INFO: <epoch: 51, iter:  10,200, lr:1.000e-04> l_g_pix: 1.7941e-04 l_g_fea: 4.9958e-01 l_g_gan: 2.7490e-02 l_d_real: 4.3710e-03 l_d_fake: 5.6518e-03 D_real: 3.0442e+01 D_fake: 2.4949e+01 
20-04-05 02:21:56.655 - INFO: <epoch: 52, iter:  10,400, lr:1.000e-04> l_g_pix: 1.3766e-04 l_g_fea: 4.7753e-01 l_g_gan: 6.3967e-03 l_d_real: 3.3066e-01 l_d_fake: 3.3004e-01 D_real: 2.2847e+01 D_fake: 2.1898e+01 
20-04-05 02:25:48.786 - INFO: <epoch: 53, iter:  10,600, lr:1.000e-04> l_g_pix: 9.2204e-05 l_g_fea: 3.6176e-01 l_g_gan: 7.0867e-03 l_d_real: 3.0492e-01 l_d_fake: 3.0170e-01 D_real: 7.5086e+01 D_fake: 7.3972e+01 
20-04-05 02:31:01.809 - INFO: <epoch: 54, iter:  10,800, lr:1.000e-04> l_g_pix: 1.3665e-04 l_g_fea: 4.3750e-01 l_g_gan: 1.8711e-02 l_d_real: 3.9416e-02 l_d_fake: 3.9316e-02 D_real: 6.3716e+01 D_fake: 6.0013e+01 
20-04-05 02:35:05.038 - INFO: <epoch: 55, iter:  11,000, lr:1.000e-04> l_g_pix: 1.1327e-04 l_g_fea: 4.5747e-01 l_g_gan: 5.7867e-03 l_d_real: 5.3342e-01 l_d_fake: 5.6487e-01 D_real: 5.7523e+01 D_fake: 5.6915e+01 
20-04-05 02:39:03.756 - INFO: <epoch: 56, iter:  11,200, lr:1.000e-04> l_g_pix: 1.1227e-04 l_g_fea: 3.6477e-01 l_g_gan: 8.6198e-03 l_d_real: 2.1249e-01 l_d_fake: 2.1208e-01 D_real: 6.9024e+01 D_fake: 6.7513e+01 
20-04-05 02:48:57.092 - INFO: <epoch: 57, iter:  11,400, lr:1.000e-04> l_g_pix: 1.1359e-04 l_g_fea: 4.5343e-01 l_g_gan: 1.7875e-02 l_d_real: 7.5438e-02 l_d_fake: 5.6726e-02 D_real: 5.7613e+01 D_fake: 5.4104e+01 
20-04-05 03:01:55.310 - INFO: <epoch: 58, iter:  11,600, lr:1.000e-04> l_g_pix: 1.3380e-04 l_g_fea: 4.4611e-01 l_g_gan: 2.6263e-02 l_d_real: 1.1645e-02 l_d_fake: 9.6214e-03 D_real: 9.9127e+01 D_fake: 9.3885e+01 
20-04-05 03:12:30.843 - INFO: <epoch: 59, iter:  11,800, lr:1.000e-04> l_g_pix: 2.0053e-04 l_g_fea: 5.6256e-01 l_g_gan: 2.6236e-02 l_d_real: 1.6998e-02 l_d_fake: 2.2984e-02 D_real: 9.6441e+01 D_fake: 9.1214e+01 
20-04-05 03:16:23.698 - INFO: <epoch: 60, iter:  12,000, lr:1.000e-04> l_g_pix: 2.2350e-04 l_g_fea: 4.5646e-01 l_g_gan: 4.6929e-03 l_d_real: 7.5799e-01 l_d_fake: 7.5156e-01 D_real: 8.8795e+00 D_fake: 8.6957e+00 
20-04-05 03:19:34.255 - INFO: <epoch: 61, iter:  12,200, lr:1.000e-04> l_g_pix: 1.3298e-04 l_g_fea: 4.5436e-01 l_g_gan: 2.3729e-02 l_d_real: 1.2002e-02 l_d_fake: 1.7953e-02 D_real: -2.3014e+01 D_fake: -2.7744e+01 
20-04-05 03:22:44.940 - INFO: <epoch: 62, iter:  12,400, lr:1.000e-04> l_g_pix: 1.6123e-04 l_g_fea: 4.2556e-01 l_g_gan: 4.1653e-03 l_d_real: 7.4728e-01 l_d_fake: 7.5638e-01 D_real: -1.0066e+02 D_fake: -1.0074e+02 
20-04-05 03:25:56.316 - INFO: <epoch: 63, iter:  12,600, lr:1.000e-04> l_g_pix: 1.3938e-04 l_g_fea: 5.1131e-01 l_g_gan: 2.0030e-02 l_d_real: 4.3800e-02 l_d_fake: 3.9567e-02 D_real: -7.9354e+01 D_fake: -8.3318e+01 
20-04-05 03:29:07.238 - INFO: <epoch: 64, iter:  12,800, lr:1.000e-04> l_g_pix: 1.7949e-04 l_g_fea: 5.1459e-01 l_g_gan: 4.2783e-03 l_d_real: 1.0483e+00 l_d_fake: 1.0796e+00 D_real: -5.7608e+01 D_fake: -5.7400e+01 
20-04-05 03:32:18.476 - INFO: <epoch: 65, iter:  13,000, lr:1.000e-04> l_g_pix: 1.3891e-04 l_g_fea: 4.7053e-01 l_g_gan: 1.8471e-02 l_d_real: 6.5805e-02 l_d_fake: 1.1466e-01 D_real: 2.3635e+01 D_fake: 2.0031e+01 
20-04-05 03:35:34.651 - INFO: <epoch: 67, iter:  13,200, lr:1.000e-04> l_g_pix: 1.2747e-04 l_g_fea: 4.0689e-01 l_g_gan: 3.2936e-03 l_d_real: 9.5621e-01 l_d_fake: 9.4614e-01 D_real: 3.4579e+01 D_fake: 3.4872e+01 
20-04-05 03:38:45.147 - INFO: <epoch: 68, iter:  13,400, lr:1.000e-04> l_g_pix: 1.5790e-04 l_g_fea: 4.5461e-01 l_g_gan: 1.3429e-02 l_d_real: 9.1338e-02 l_d_fake: 9.1519e-02 D_real: 7.3964e+01 D_fake: 7.1370e+01 
20-04-05 03:41:56.338 - INFO: <epoch: 69, iter:  13,600, lr:1.000e-04> l_g_pix: 1.0944e-04 l_g_fea: 4.0473e-01 l_g_gan: 2.4673e-02 l_d_real: 2.4503e-02 l_d_fake: 1.7540e-02 D_real: 1.0918e+02 D_fake: 1.0427e+02 
20-04-05 03:45:07.565 - INFO: <epoch: 70, iter:  13,800, lr:1.000e-04> l_g_pix: 1.3727e-04 l_g_fea: 5.5744e-01 l_g_gan: 9.1116e-03 l_d_real: 4.8716e-01 l_d_fake: 3.5481e-01 D_real: 1.9779e+01 D_fake: 1.8378e+01 
20-04-05 03:48:19.163 - INFO: <epoch: 71, iter:  14,000, lr:1.000e-04> l_g_pix: 7.6404e-05 l_g_fea: 4.1030e-01 l_g_gan: 4.6564e-03 l_d_real: 6.4798e-01 l_d_fake: 6.2443e-01 D_real: 7.3285e+01 D_fake: 7.2990e+01 
20-04-05 03:51:31.273 - INFO: <epoch: 72, iter:  14,200, lr:1.000e-04> l_g_pix: 9.6431e-05 l_g_fea: 3.5347e-01 l_g_gan: 6.9238e-03 l_d_real: 3.3095e-01 l_d_fake: 3.3331e-01 D_real: 7.9702e+01 D_fake: 7.8649e+01 
20-04-05 03:54:42.607 - INFO: <epoch: 73, iter:  14,400, lr:1.000e-04> l_g_pix: 1.3377e-04 l_g_fea: 4.5942e-01 l_g_gan: 1.2311e-02 l_d_real: 1.0264e-01 l_d_fake: 1.0283e-01 D_real: 9.6617e+01 D_fake: 9.4257e+01 
20-04-05 03:57:53.409 - INFO: <epoch: 74, iter:  14,600, lr:1.000e-04> l_g_pix: 1.4791e-04 l_g_fea: 5.9720e-01 l_g_gan: 2.5400e-03 l_d_real: 1.2812e+00 l_d_fake: 1.3686e+00 D_real: 9.0988e+01 D_fake: 9.1805e+01 
20-04-05 04:01:04.772 - INFO: <epoch: 75, iter:  14,800, lr:1.000e-04> l_g_pix: 1.5849e-04 l_g_fea: 5.4738e-01 l_g_gan: 2.3305e-02 l_d_real: 1.7131e-02 l_d_fake: 1.5302e-02 D_real: 9.3262e+01 D_fake: 8.8617e+01 
20-04-05 04:04:16.836 - INFO: <epoch: 76, iter:  15,000, lr:1.000e-04> l_g_pix: 1.2297e-04 l_g_fea: 4.0103e-01 l_g_gan: 2.2433e-02 l_d_real: 1.7467e-02 l_d_fake: 2.3739e-02 D_real: 9.8018e+01 D_fake: 9.3552e+01 
20-04-05 04:04:17.301 - INFO: Models and training states saved.
20-04-05 04:05:27.848 - INFO: # Validation # PSNR: 30.61, SSIM: 0.85395, LPIPS: 0.061615
20-04-05 04:05:27.848 - INFO: <epoch: 76, iter:  15,000> psnr: 30.61, ssim: 0.85395, lpips: 0.061615
20-04-05 04:11:27.360 - INFO: <epoch: 77, iter:  15,200, lr:1.000e-04> l_g_pix: 1.3583e-04 l_g_fea: 5.1930e-01 l_g_gan: 1.3816e-02 l_d_real: 2.2067e-01 l_d_fake: 2.0291e-01 D_real: 5.0017e+01 D_fake: 4.7465e+01 
20-04-05 04:14:37.306 - INFO: <epoch: 78, iter:  15,400, lr:1.000e-04> l_g_pix: 1.2748e-04 l_g_fea: 4.1374e-01 l_g_gan: 7.9685e-03 l_d_real: 3.0331e-01 l_d_fake: 3.2215e-01 D_real: 6.8373e+01 D_fake: 6.7092e+01 
20-04-05 04:17:48.261 - INFO: <epoch: 79, iter:  15,600, lr:1.000e-04> l_g_pix: 1.1728e-04 l_g_fea: 4.0533e-01 l_g_gan: 1.8300e-02 l_d_real: 1.2574e-01 l_d_fake: 9.4504e-02 D_real: 9.4030e+01 D_fake: 9.0481e+01 
20-04-05 04:20:59.884 - INFO: <epoch: 80, iter:  15,800, lr:1.000e-04> l_g_pix: 1.1409e-04 l_g_fea: 4.3466e-01 l_g_gan: 7.3402e-03 l_d_real: 3.5332e-01 l_d_fake: 3.2967e-01 D_real: 4.3820e+01 D_fake: 4.2693e+01 
20-04-05 04:24:11.905 - INFO: <epoch: 81, iter:  16,000, lr:1.000e-04> l_g_pix: 1.2790e-04 l_g_fea: 5.1740e-01 l_g_gan: 1.8539e-02 l_d_real: 3.1445e-02 l_d_fake: 2.9690e-02 D_real: 5.0848e+00 D_fake: 1.4075e+00 
20-04-05 04:33:59.589 - INFO: <epoch: 82, iter:  16,200, lr:1.000e-04> l_g_pix: 9.9768e-05 l_g_fea: 3.4239e-01 l_g_gan: 2.0533e-02 l_d_real: 3.5131e-02 l_d_fake: 6.4917e-02 D_real: 6.5871e+01 D_fake: 6.1814e+01 
20-04-05 04:45:17.359 - INFO: <epoch: 83, iter:  16,400, lr:1.000e-04> l_g_pix: 1.0807e-04 l_g_fea: 5.1946e-01 l_g_gan: 2.0200e-02 l_d_real: 2.5034e-02 l_d_fake: 2.2227e-02 D_real: 5.8098e+01 D_fake: 5.4082e+01 
20-04-05 04:54:56.843 - INFO: <epoch: 84, iter:  16,600, lr:1.000e-04> l_g_pix: 1.3698e-04 l_g_fea: 4.1263e-01 l_g_gan: 1.6835e-03 l_d_real: 1.4335e+00 l_d_fake: 1.4538e+00 D_real: 4.4212e+00 D_fake: 5.5282e+00 
20-04-05 04:58:09.043 - INFO: <epoch: 85, iter:  16,800, lr:1.000e-04> l_g_pix: 1.1719e-04 l_g_fea: 5.2378e-01 l_g_gan: 1.7436e-02 l_d_real: 1.3145e-01 l_d_fake: 1.6941e-01 D_real: 5.6944e+01 D_fake: 5.3607e+01 
20-04-05 05:01:22.444 - INFO: <epoch: 86, iter:  17,000, lr:1.000e-04> l_g_pix: 1.3756e-04 l_g_fea: 4.9501e-01 l_g_gan: 1.0530e-02 l_d_real: 1.6118e-01 l_d_fake: 1.9111e-01 D_real: 2.8000e+01 D_fake: 2.6070e+01 
20-04-05 05:04:33.685 - INFO: <epoch: 87, iter:  17,200, lr:1.000e-04> l_g_pix: 1.0870e-04 l_g_fea: 4.1902e-01 l_g_gan: 8.4421e-03 l_d_real: 2.4047e-01 l_d_fake: 2.4000e-01 D_real: 5.4945e+01 D_fake: 5.3497e+01 
20-04-05 05:07:48.283 - INFO: <epoch: 88, iter:  17,400, lr:1.000e-04> l_g_pix: 1.4376e-04 l_g_fea: 5.6535e-01 l_g_gan: 3.4688e-03 l_d_real: 7.6382e-01 l_d_fake: 7.6312e-01 D_real: 4.5692e+01 D_fake: 4.5761e+01 
20-04-05 05:11:02.728 - INFO: <epoch: 89, iter:  17,600, lr:1.000e-04> l_g_pix: 1.2240e-04 l_g_fea: 5.1111e-01 l_g_gan: 4.6723e-03 l_d_real: 6.2242e-01 l_d_fake: 6.1814e-01 D_real: 6.8959e+01 D_fake: 6.8645e+01 
20-04-05 05:14:16.232 - INFO: <epoch: 90, iter:  17,800, lr:1.000e-04> l_g_pix: 9.1249e-05 l_g_fea: 4.2670e-01 l_g_gan: 5.0727e-03 l_d_real: 4.7512e-01 l_d_fake: 4.7528e-01 D_real: 6.4244e+01 D_fake: 6.3705e+01 
20-04-05 05:17:29.314 - INFO: <epoch: 91, iter:  18,000, lr:1.000e-04> l_g_pix: 1.7362e-04 l_g_fea: 5.9170e-01 l_g_gan: 3.4652e-02 l_d_real: 5.0704e-01 l_d_fake: 1.1724e-01 D_real: 8.4865e+01 D_fake: 7.8247e+01 
20-04-05 05:20:43.713 - INFO: <epoch: 92, iter:  18,200, lr:1.000e-04> l_g_pix: 1.0576e-04 l_g_fea: 5.2478e-01 l_g_gan: 1.3772e-02 l_d_real: 1.0935e-01 l_d_fake: 1.4057e-01 D_real: 4.6748e+01 D_fake: 4.4119e+01 
20-04-05 05:23:55.545 - INFO: <epoch: 93, iter:  18,400, lr:1.000e-04> l_g_pix: 1.3819e-04 l_g_fea: 4.8284e-01 l_g_gan: 3.7804e-02 l_d_real: 1.3070e-03 l_d_fake: 4.1660e-03 D_real: 5.3804e+01 D_fake: 4.6246e+01 
20-04-05 05:27:07.647 - INFO: <epoch: 94, iter:  18,600, lr:1.000e-04> l_g_pix: 1.5724e-04 l_g_fea: 4.5573e-01 l_g_gan: 2.7477e-02 l_d_real: 3.7869e-02 l_d_fake: 6.0123e-03 D_real: 1.9302e+01 D_fake: 1.3828e+01 
20-04-05 05:30:18.289 - INFO: <epoch: 95, iter:  18,800, lr:1.000e-04> l_g_pix: 1.5861e-04 l_g_fea: 5.0817e-01 l_g_gan: 1.8617e-02 l_d_real: 6.0843e-02 l_d_fake: 1.6890e-01 D_real: 3.4733e+01 D_fake: 3.1124e+01 
20-04-05 05:33:29.634 - INFO: <epoch: 96, iter:  19,000, lr:1.000e-04> l_g_pix: 1.1666e-04 l_g_fea: 4.9935e-01 l_g_gan: 4.4200e-02 l_d_real: 2.5816e-03 l_d_fake: 4.0628e-03 D_real: 1.0297e+02 D_fake: 9.4138e+01 
20-04-05 05:36:41.415 - INFO: <epoch: 97, iter:  19,200, lr:1.000e-04> l_g_pix: 1.4681e-04 l_g_fea: 5.6348e-01 l_g_gan: 3.1034e-03 l_d_real: 9.3165e-01 l_d_fake: 9.2749e-01 D_real: 5.4189e+01 D_fake: 5.4498e+01 
20-04-05 05:39:53.493 - INFO: <epoch: 98, iter:  19,400, lr:1.000e-04> l_g_pix: 1.0987e-04 l_g_fea: 4.4675e-01 l_g_gan: 7.2085e-03 l_d_real: 3.0261e-01 l_d_fake: 2.9654e-01 D_real: -1.8303e+00 D_fake: -2.9725e+00 
20-04-05 05:43:05.010 - INFO: <epoch: 99, iter:  19,600, lr:1.000e-04> l_g_pix: 1.3648e-04 l_g_fea: 4.8161e-01 l_g_gan: 8.3090e-03 l_d_real: 2.7419e-01 l_d_fake: 2.6723e-01 D_real: 2.0894e+01 D_fake: 1.9503e+01 
20-04-05 05:46:16.741 - INFO: <epoch:100, iter:  19,800, lr:1.000e-04> l_g_pix: 1.3937e-04 l_g_fea: 6.0278e-01 l_g_gan: 1.9874e-02 l_d_real: 5.9099e-02 l_d_fake: 1.0371e-01 D_real: 2.4686e+01 D_fake: 2.0792e+01 
20-04-05 05:49:28.416 - INFO: <epoch:101, iter:  20,000, lr:1.000e-04> l_g_pix: 1.4293e-04 l_g_fea: 4.9796e-01 l_g_gan: 1.0649e-02 l_d_real: 1.5496e-01 l_d_fake: 1.5358e-01 D_real: 9.2601e+01 D_fake: 9.0626e+01 
20-04-05 05:49:28.833 - INFO: Models and training states saved.
20-04-05 05:51:04.404 - INFO: # Validation # PSNR: 30.407, SSIM: 0.85197, LPIPS: 0.058558
20-04-05 05:51:04.404 - INFO: <epoch:101, iter:  20,000> psnr: 30.407, ssim: 0.85197, lpips: 0.058558
20-04-05 05:54:16.670 - INFO: <epoch:102, iter:  20,200, lr:1.000e-04> l_g_pix: 1.0958e-04 l_g_fea: 3.9336e-01 l_g_gan: 3.8286e-03 l_d_real: 7.6754e-01 l_d_fake: 7.9088e-01 D_real: 2.3147e+01 D_fake: 2.3160e+01 
20-04-05 05:57:27.556 - INFO: <epoch:103, iter:  20,400, lr:1.000e-04> l_g_pix: 1.7309e-04 l_g_fea: 4.8402e-01 l_g_gan: 4.0063e-03 l_d_real: 6.5520e-01 l_d_fake: 6.5449e-01 D_real: 8.0578e+01 D_fake: 8.0431e+01 
20-04-05 06:00:38.754 - INFO: <epoch:104, iter:  20,600, lr:1.000e-04> l_g_pix: 1.4503e-04 l_g_fea: 5.0857e-01 l_g_gan: 9.7135e-03 l_d_real: 4.2142e-01 l_d_fake: 4.0996e-01 D_real: 6.4081e+01 D_fake: 6.2554e+01 
20-04-05 06:03:49.514 - INFO: <epoch:105, iter:  20,800, lr:1.000e-04> l_g_pix: 1.1103e-04 l_g_fea: 4.0605e-01 l_g_gan: 1.8401e-02 l_d_real: 3.8314e-02 l_d_fake: 3.9341e-02 D_real: 2.7303e+01 D_fake: 2.3662e+01 
20-04-05 06:07:01.168 - INFO: <epoch:106, iter:  21,000, lr:1.000e-04> l_g_pix: 1.6396e-04 l_g_fea: 4.9360e-01 l_g_gan: 1.6741e-02 l_d_real: 6.2579e-02 l_d_fake: 5.5661e-02 D_real: 2.8263e+01 D_fake: 2.4974e+01 
20-04-05 06:10:12.911 - INFO: <epoch:107, iter:  21,200, lr:1.000e-04> l_g_pix: 1.2544e-04 l_g_fea: 4.8418e-01 l_g_gan: 3.7015e-02 l_d_real: 8.4140e-04 l_d_fake: 7.4298e-04 D_real: 6.1078e+01 D_fake: 5.3676e+01 
20-04-05 06:13:25.083 - INFO: <epoch:108, iter:  21,400, lr:1.000e-04> l_g_pix: 1.1937e-04 l_g_fea: 4.7374e-01 l_g_gan: 1.2838e-02 l_d_real: 1.1787e-01 l_d_fake: 1.0661e-01 D_real: 5.4067e+01 D_fake: 5.1612e+01 
20-04-05 06:16:37.085 - INFO: <epoch:109, iter:  21,600, lr:1.000e-04> l_g_pix: 1.2669e-04 l_g_fea: 6.0884e-01 l_g_gan: 1.3762e-02 l_d_real: 1.4883e-01 l_d_fake: 1.5857e-01 D_real: 7.1430e+01 D_fake: 6.8832e+01 
20-04-05 06:19:48.764 - INFO: <epoch:110, iter:  21,800, lr:1.000e-04> l_g_pix: 1.2569e-04 l_g_fea: 4.3566e-01 l_g_gan: 2.0002e-02 l_d_real: 3.4747e-02 l_d_fake: 3.1841e-02 D_real: 5.5475e+01 D_fake: 5.1508e+01 
20-04-05 06:23:00.310 - INFO: <epoch:111, iter:  22,000, lr:1.000e-04> l_g_pix: 1.4690e-04 l_g_fea: 4.9539e-01 l_g_gan: 1.4959e-02 l_d_real: 2.3469e-01 l_d_fake: 1.2446e-01 D_real: 7.7275e+01 D_fake: 7.4462e+01 
20-04-05 06:26:12.404 - INFO: <epoch:112, iter:  22,200, lr:1.000e-04> l_g_pix: 7.8230e-05 l_g_fea: 3.8007e-01 l_g_gan: 6.7577e-03 l_d_real: 5.4912e-01 l_d_fake: 5.6914e-01 D_real: 1.1008e+02 D_fake: 1.0929e+02 
20-04-05 06:29:24.252 - INFO: <epoch:113, iter:  22,400, lr:1.000e-04> l_g_pix: 1.4401e-04 l_g_fea: 4.9048e-01 l_g_gan: 2.5974e-03 l_d_real: 1.6910e+00 l_d_fake: 1.7574e+00 D_real: 5.6348e+01 D_fake: 5.7552e+01 
20-04-05 06:32:36.452 - INFO: <epoch:114, iter:  22,600, lr:1.000e-04> l_g_pix: 1.8464e-04 l_g_fea: 5.4651e-01 l_g_gan: 8.8356e-03 l_d_real: 2.2623e-01 l_d_fake: 2.3082e-01 D_real: 2.1756e+01 D_fake: 2.0217e+01 
20-04-05 06:35:47.571 - INFO: <epoch:115, iter:  22,800, lr:1.000e-04> l_g_pix: 1.2793e-04 l_g_fea: 4.1144e-01 l_g_gan: 1.6394e-02 l_d_real: 8.9987e-02 l_d_fake: 7.5205e-02 D_real: 9.0303e+01 D_fake: 8.7107e+01 
20-04-05 06:38:58.493 - INFO: <epoch:116, iter:  23,000, lr:1.000e-04> l_g_pix: 1.2763e-04 l_g_fea: 5.7044e-01 l_g_gan: 4.1850e-03 l_d_real: 6.4589e-01 l_d_fake: 6.3781e-01 D_real: 3.8112e+01 D_fake: 3.7917e+01 
20-04-05 06:42:10.659 - INFO: <epoch:117, iter:  23,200, lr:1.000e-04> l_g_pix: 9.0617e-05 l_g_fea: 4.1188e-01 l_g_gan: 4.0201e-03 l_d_real: 6.2857e-01 l_d_fake: 6.2756e-01 D_real: 1.6890e+01 D_fake: 1.6714e+01 
20-04-05 06:45:22.115 - INFO: <epoch:118, iter:  23,400, lr:1.000e-04> l_g_pix: 1.5156e-04 l_g_fea: 5.1461e-01 l_g_gan: 1.4194e-02 l_d_real: 1.1538e-01 l_d_fake: 7.7912e-02 D_real: 3.7848e+01 D_fake: 3.5105e+01 
20-04-05 06:48:33.986 - INFO: <epoch:119, iter:  23,600, lr:1.000e-04> l_g_pix: 1.4405e-04 l_g_fea: 5.1331e-01 l_g_gan: 1.8223e-02 l_d_real: 3.5910e-02 l_d_fake: 3.6962e-02 D_real: 6.8930e+01 D_fake: 6.5322e+01 
20-04-05 06:51:45.287 - INFO: <epoch:120, iter:  23,800, lr:1.000e-04> l_g_pix: 1.1710e-04 l_g_fea: 5.2701e-01 l_g_gan: 4.7542e-03 l_d_real: 5.8428e-01 l_d_fake: 5.9706e-01 D_real: 6.8899e+01 D_fake: 6.8539e+01 
20-04-05 06:54:57.216 - INFO: <epoch:121, iter:  24,000, lr:1.000e-04> l_g_pix: 1.4912e-04 l_g_fea: 4.9467e-01 l_g_gan: 1.1506e-02 l_d_real: 1.4055e-01 l_d_fake: 1.2771e-01 D_real: 4.4317e+01 D_fake: 4.2150e+01 
20-04-05 06:58:08.117 - INFO: <epoch:122, iter:  24,200, lr:1.000e-04> l_g_pix: 9.6493e-05 l_g_fea: 3.3197e-01 l_g_gan: 2.4284e-02 l_d_real: 3.8989e-02 l_d_fake: 8.1843e-02 D_real: 6.5290e+01 D_fake: 6.0493e+01 
20-04-05 07:01:19.487 - INFO: <epoch:123, iter:  24,400, lr:1.000e-04> l_g_pix: 1.1749e-04 l_g_fea: 4.6268e-01 l_g_gan: 1.1278e-02 l_d_real: 1.8045e-01 l_d_fake: 2.3088e-01 D_real: 5.0656e+01 D_fake: 4.8606e+01 
20-04-05 07:04:31.669 - INFO: <epoch:124, iter:  24,600, lr:1.000e-04> l_g_pix: 1.2043e-04 l_g_fea: 4.5734e-01 l_g_gan: 8.3267e-04 l_d_real: 1.9893e+00 l_d_fake: 1.9930e+00 D_real: 3.8933e+01 D_fake: 4.0758e+01 
20-04-05 07:07:42.789 - INFO: <epoch:125, iter:  24,800, lr:1.000e-04> l_g_pix: 1.4967e-04 l_g_fea: 4.2834e-01 l_g_gan: 5.1946e-03 l_d_real: 8.0666e-01 l_d_fake: 9.5851e-01 D_real: 6.2169e+01 D_fake: 6.2012e+01 
20-04-05 07:10:54.156 - INFO: <epoch:126, iter:  25,000, lr:1.000e-04> l_g_pix: 1.6882e-04 l_g_fea: 5.7499e-01 l_g_gan: 2.7984e-02 l_d_real: 1.7409e-02 l_d_fake: 9.1500e-03 D_real: 8.8061e+01 D_fake: 8.2478e+01 
20-04-05 07:10:54.549 - INFO: Models and training states saved.
20-04-05 07:12:27.560 - INFO: # Validation # PSNR: 30.135, SSIM: 0.85044, LPIPS: 0.055354
20-04-05 07:12:27.561 - INFO: <epoch:126, iter:  25,000> psnr: 30.135, ssim: 0.85044, lpips: 0.055354
20-04-05 07:15:41.926 - INFO: <epoch:127, iter:  25,200, lr:1.000e-04> l_g_pix: 1.2637e-04 l_g_fea: 5.3184e-01 l_g_gan: 1.4906e-02 l_d_real: 1.0687e-01 l_d_fake: 6.0371e-02 D_real: 4.5664e+01 D_fake: 4.2767e+01 
20-04-05 07:18:53.115 - INFO: <epoch:128, iter:  25,400, lr:1.000e-04> l_g_pix: 1.4789e-04 l_g_fea: 4.2606e-01 l_g_gan: 1.2633e-02 l_d_real: 1.5789e-01 l_d_fake: 1.0593e-01 D_real: 5.4078e+01 D_fake: 5.1683e+01 
20-04-05 07:22:04.351 - INFO: <epoch:129, iter:  25,600, lr:1.000e-04> l_g_pix: 1.1780e-04 l_g_fea: 4.7561e-01 l_g_gan: 1.2241e-02 l_d_real: 2.4355e-01 l_d_fake: 2.2020e-01 D_real: 5.4741e+01 D_fake: 5.2525e+01 
20-04-05 07:25:15.977 - INFO: <epoch:130, iter:  25,800, lr:1.000e-04> l_g_pix: 1.1938e-04 l_g_fea: 5.7092e-01 l_g_gan: 1.6247e-02 l_d_real: 1.1971e-01 l_d_fake: 6.1033e-02 D_real: 5.4382e+01 D_fake: 5.1223e+01 
20-04-05 07:28:27.846 - INFO: <epoch:131, iter:  26,000, lr:1.000e-04> l_g_pix: 1.4718e-04 l_g_fea: 4.5887e-01 l_g_gan: 4.3524e-03 l_d_real: 5.7462e-01 l_d_fake: 5.9247e-01 D_real: 4.1259e+01 D_fake: 4.0972e+01 
20-04-05 07:31:39.215 - INFO: <epoch:132, iter:  26,200, lr:1.000e-04> l_g_pix: 1.2696e-04 l_g_fea: 5.1286e-01 l_g_gan: 2.4338e-02 l_d_real: 9.2545e-03 l_d_fake: 1.0094e-02 D_real: 6.5586e+01 D_fake: 6.0728e+01 
20-04-05 07:34:55.558 - INFO: <epoch:134, iter:  26,400, lr:1.000e-04> l_g_pix: 9.1099e-05 l_g_fea: 4.1520e-01 l_g_gan: 7.8680e-03 l_d_real: 3.7033e-01 l_d_fake: 3.0792e-01 D_real: 2.9776e+01 D_fake: 2.8542e+01 
20-04-05 07:38:07.110 - INFO: <epoch:135, iter:  26,600, lr:1.000e-04> l_g_pix: 1.1177e-04 l_g_fea: 3.8471e-01 l_g_gan: 3.6912e-03 l_d_real: 6.9456e-01 l_d_fake: 7.0313e-01 D_real: 1.7224e+01 D_fake: 1.7184e+01 
20-04-05 07:41:19.187 - INFO: <epoch:136, iter:  26,800, lr:1.000e-04> l_g_pix: 1.0549e-04 l_g_fea: 3.9845e-01 l_g_gan: 1.2481e-02 l_d_real: 9.9072e-02 l_d_fake: 9.6814e-02 D_real: 7.0486e+01 D_fake: 6.8087e+01 
20-04-05 07:44:31.318 - INFO: <epoch:137, iter:  27,000, lr:1.000e-04> l_g_pix: 1.6603e-04 l_g_fea: 4.8068e-01 l_g_gan: 2.9199e-02 l_d_real: 3.8177e-03 l_d_fake: 3.8939e-03 D_real: 4.7203e+01 D_fake: 4.1367e+01 
20-04-05 07:47:43.499 - INFO: <epoch:138, iter:  27,200, lr:1.000e-04> l_g_pix: 1.7065e-04 l_g_fea: 5.0862e-01 l_g_gan: 1.1093e-02 l_d_real: 1.8974e-01 l_d_fake: 1.8688e-01 D_real: 4.8533e+01 D_fake: 4.6502e+01 
20-04-05 07:50:55.263 - INFO: <epoch:139, iter:  27,400, lr:1.000e-04> l_g_pix: 1.4867e-04 l_g_fea: 5.8563e-01 l_g_gan: 1.0227e-02 l_d_real: 1.7248e-01 l_d_fake: 1.7383e-01 D_real: 4.7300e+01 D_fake: 4.5427e+01 
20-04-05 07:54:07.257 - INFO: <epoch:140, iter:  27,600, lr:1.000e-04> l_g_pix: 1.2237e-04 l_g_fea: 4.1491e-01 l_g_gan: 1.4563e-02 l_d_real: 1.2463e-01 l_d_fake: 7.4181e-02 D_real: 7.7877e+01 D_fake: 7.5064e+01 
20-04-05 07:57:18.575 - INFO: <epoch:141, iter:  27,800, lr:1.000e-04> l_g_pix: 1.3995e-04 l_g_fea: 5.8000e-01 l_g_gan: 5.2662e-04 l_d_real: 3.4660e+00 l_d_fake: 3.4916e+00 D_real: 6.4069e+01 D_fake: 6.7442e+01 
20-04-05 08:00:30.776 - INFO: <epoch:142, iter:  28,000, lr:1.000e-04> l_g_pix: 1.1989e-04 l_g_fea: 4.7103e-01 l_g_gan: 1.9605e-02 l_d_real: 3.2018e-02 l_d_fake: 3.0359e-02 D_real: 7.0819e+01 D_fake: 6.6929e+01 
20-04-05 08:03:42.451 - INFO: <epoch:143, iter:  28,200, lr:1.000e-04> l_g_pix: 1.0620e-04 l_g_fea: 3.5113e-01 l_g_gan: 5.1410e-03 l_d_real: 4.8605e-01 l_d_fake: 5.3837e-01 D_real: 5.0564e+01 D_fake: 5.0048e+01 
20-04-05 08:06:53.539 - INFO: <epoch:144, iter:  28,400, lr:1.000e-04> l_g_pix: 1.2032e-04 l_g_fea: 4.7931e-01 l_g_gan: 5.5927e-03 l_d_real: 4.3916e-01 l_d_fake: 4.4392e-01 D_real: 4.0472e+01 D_fake: 3.9795e+01 
20-04-05 08:10:05.039 - INFO: <epoch:145, iter:  28,600, lr:1.000e-04> l_g_pix: 1.1307e-04 l_g_fea: 4.9898e-01 l_g_gan: 1.0075e-02 l_d_real: 3.2152e-01 l_d_fake: 3.4645e-01 D_real: 5.3302e+01 D_fake: 5.1621e+01 
20-04-05 08:13:16.496 - INFO: <epoch:146, iter:  28,800, lr:1.000e-04> l_g_pix: 1.3711e-04 l_g_fea: 4.7454e-01 l_g_gan: 6.4921e-03 l_d_real: 5.9481e-01 l_d_fake: 5.3367e-01 D_real: 7.2585e+01 D_fake: 7.1850e+01 
20-04-05 08:16:27.634 - INFO: <epoch:147, iter:  29,000, lr:1.000e-04> l_g_pix: 2.2056e-04 l_g_fea: 5.5211e-01 l_g_gan: 2.6899e-03 l_d_real: 1.4119e+00 l_d_fake: 1.4012e+00 D_real: 3.7467e+01 D_fake: 3.8335e+01 
20-04-05 08:19:38.996 - INFO: <epoch:148, iter:  29,200, lr:1.000e-04> l_g_pix: 1.3965e-04 l_g_fea: 4.1996e-01 l_g_gan: 1.0396e-02 l_d_real: 3.2889e-01 l_d_fake: 1.9519e-01 D_real: 6.3811e+01 D_fake: 6.1994e+01 
20-04-05 08:22:50.843 - INFO: <epoch:149, iter:  29,400, lr:1.000e-04> l_g_pix: 1.5939e-04 l_g_fea: 5.7214e-01 l_g_gan: 7.4309e-03 l_d_real: 6.2670e-01 l_d_fake: 6.6653e-01 D_real: 5.1849e+01 D_fake: 5.1009e+01 
20-04-05 08:26:02.934 - INFO: <epoch:150, iter:  29,600, lr:1.000e-04> l_g_pix: 1.3045e-04 l_g_fea: 4.5165e-01 l_g_gan: 1.5465e-02 l_d_real: 7.4974e-02 l_d_fake: 8.5239e-02 D_real: 8.0918e+01 D_fake: 7.7905e+01 
20-04-05 08:29:14.918 - INFO: <epoch:151, iter:  29,800, lr:1.000e-04> l_g_pix: 1.1350e-04 l_g_fea: 4.6056e-01 l_g_gan: 4.7539e-03 l_d_real: 5.1460e-01 l_d_fake: 5.1514e-01 D_real: 7.8635e+01 D_fake: 7.8199e+01 
20-04-05 08:32:27.124 - INFO: <epoch:152, iter:  30,000, lr:1.000e-04> l_g_pix: 1.4496e-04 l_g_fea: 5.2086e-01 l_g_gan: 4.9454e-03 l_d_real: 5.6185e-01 l_d_fake: 5.7023e-01 D_real: 6.4399e+01 D_fake: 6.3976e+01 
20-04-05 08:32:27.557 - INFO: Models and training states saved.
20-04-05 08:33:39.115 - INFO: # Validation # PSNR: 30.717, SSIM: 0.85481, LPIPS: 0.05554
20-04-05 08:33:39.115 - INFO: <epoch:152, iter:  30,000> psnr: 30.717, ssim: 0.85481, lpips: 0.05554
20-04-05 08:36:51.595 - INFO: <epoch:153, iter:  30,200, lr:1.000e-04> l_g_pix: 1.4376e-04 l_g_fea: 4.2486e-01 l_g_gan: 2.5196e-03 l_d_real: 9.9106e-01 l_d_fake: 9.9352e-01 D_real: 6.0036e+01 D_fake: 6.0525e+01 
20-04-05 08:40:02.816 - INFO: <epoch:154, iter:  30,400, lr:1.000e-04> l_g_pix: 8.6438e-05 l_g_fea: 2.6899e-01 l_g_gan: 3.2716e-02 l_d_real: 3.3901e-03 l_d_fake: 2.5666e-03 D_real: 5.9409e+01 D_fake: 5.2869e+01 
20-04-05 08:43:14.971 - INFO: <epoch:155, iter:  30,600, lr:1.000e-04> l_g_pix: 1.2108e-04 l_g_fea: 3.3922e-01 l_g_gan: 1.7665e-02 l_d_real: 1.5468e-01 l_d_fake: 1.2169e-01 D_real: 8.0675e+01 D_fake: 7.7280e+01 
20-04-05 08:46:27.249 - INFO: <epoch:156, iter:  30,800, lr:1.000e-04> l_g_pix: 1.3569e-04 l_g_fea: 3.9088e-01 l_g_gan: 3.5655e-02 l_d_real: 2.1689e-03 l_d_fake: 1.8019e-03 D_real: 8.0665e+01 D_fake: 7.3536e+01 
20-04-05 08:49:39.328 - INFO: <epoch:157, iter:  31,000, lr:1.000e-04> l_g_pix: 1.0648e-04 l_g_fea: 3.4060e-01 l_g_gan: 7.9899e-03 l_d_real: 2.5093e-01 l_d_fake: 2.4899e-01 D_real: 5.5580e+01 D_fake: 5.4232e+01 
20-04-05 08:52:51.857 - INFO: <epoch:158, iter:  31,200, lr:1.000e-04> l_g_pix: 1.1870e-04 l_g_fea: 3.9497e-01 l_g_gan: 7.5498e-03 l_d_real: 4.2139e-01 l_d_fake: 4.3234e-01 D_real: 8.1514e+01 D_fake: 8.0431e+01 
20-04-05 08:56:03.936 - INFO: <epoch:159, iter:  31,400, lr:1.000e-04> l_g_pix: 1.3726e-04 l_g_fea: 4.0466e-01 l_g_gan: 6.4928e-03 l_d_real: 3.3781e-01 l_d_fake: 3.3534e-01 D_real: 9.3257e+01 D_fake: 9.2295e+01 
20-04-05 08:59:15.980 - INFO: <epoch:160, iter:  31,600, lr:1.000e-04> l_g_pix: 1.0524e-04 l_g_fea: 3.2938e-01 l_g_gan: 8.0565e-04 l_d_real: 1.9445e+00 l_d_fake: 1.9449e+00 D_real: 6.9997e+01 D_fake: 7.1780e+01 
20-04-05 09:02:27.215 - INFO: <epoch:161, iter:  31,800, lr:1.000e-04> l_g_pix: 1.1276e-04 l_g_fea: 5.1044e-01 l_g_gan: 8.7795e-03 l_d_real: 1.9827e-01 l_d_fake: 1.9602e-01 D_real: 6.3299e+01 D_fake: 6.1740e+01 
20-04-05 09:05:38.983 - INFO: <epoch:162, iter:  32,000, lr:1.000e-04> l_g_pix: 1.1400e-04 l_g_fea: 3.4924e-01 l_g_gan: 9.9808e-03 l_d_real: 2.2341e-01 l_d_fake: 2.6532e-01 D_real: 5.4170e+01 D_fake: 5.2419e+01 
20-04-05 09:08:50.870 - INFO: <epoch:163, iter:  32,200, lr:1.000e-04> l_g_pix: 1.0875e-04 l_g_fea: 4.1015e-01 l_g_gan: 1.2722e-02 l_d_real: 1.0425e-01 l_d_fake: 1.2473e-01 D_real: 6.2342e+01 D_fake: 5.9913e+01 
20-04-05 09:12:02.403 - INFO: <epoch:164, iter:  32,400, lr:1.000e-04> l_g_pix: 1.2195e-04 l_g_fea: 4.6183e-01 l_g_gan: 1.0584e-02 l_d_real: 1.3476e-01 l_d_fake: 1.3280e-01 D_real: 7.0893e+01 D_fake: 6.8910e+01 
20-04-05 09:15:14.762 - INFO: <epoch:165, iter:  32,600, lr:1.000e-04> l_g_pix: 1.2659e-04 l_g_fea: 3.5268e-01 l_g_gan: 3.8816e-03 l_d_real: 6.8008e-01 l_d_fake: 6.9281e-01 D_real: 5.5789e+01 D_fake: 5.5699e+01 
20-04-05 09:18:26.257 - INFO: <epoch:166, iter:  32,800, lr:1.000e-04> l_g_pix: 1.2666e-04 l_g_fea: 4.8591e-01 l_g_gan: 5.4530e-03 l_d_real: 4.1985e-01 l_d_fake: 4.2227e-01 D_real: 3.1203e+01 D_fake: 3.0533e+01 
20-04-05 09:21:37.339 - INFO: <epoch:167, iter:  33,000, lr:1.000e-04> l_g_pix: 1.6201e-04 l_g_fea: 5.3655e-01 l_g_gan: 1.4327e-02 l_d_real: 6.9200e-02 l_d_fake: 7.5050e-02 D_real: 7.0279e+01 D_fake: 6.7485e+01 
20-04-05 09:24:49.401 - INFO: <epoch:168, iter:  33,200, lr:1.000e-04> l_g_pix: 1.0844e-04 l_g_fea: 6.0343e-01 l_g_gan: 1.2842e-02 l_d_real: 1.5294e-01 l_d_fake: 1.2766e-01 D_real: 1.9659e+01 D_fake: 1.7230e+01 
20-04-05 09:28:00.573 - INFO: <epoch:169, iter:  33,400, lr:1.000e-04> l_g_pix: 9.9356e-05 l_g_fea: 4.7016e-01 l_g_gan: 1.1635e-03 l_d_real: 1.9724e+00 l_d_fake: 1.9333e+00 D_real: 4.2763e+01 D_fake: 4.4483e+01 
20-04-05 09:31:12.204 - INFO: <epoch:170, iter:  33,600, lr:1.000e-04> l_g_pix: 1.4659e-04 l_g_fea: 5.1246e-01 l_g_gan: 8.3122e-03 l_d_real: 2.6946e-01 l_d_fake: 2.5783e-01 D_real: 6.3433e+01 D_fake: 6.2035e+01 
20-04-05 09:34:24.323 - INFO: <epoch:171, iter:  33,800, lr:1.000e-04> l_g_pix: 1.2380e-04 l_g_fea: 4.4718e-01 l_g_gan: 4.5682e-03 l_d_real: 6.0467e-01 l_d_fake: 6.1360e-01 D_real: 5.3626e+01 D_fake: 5.3321e+01 
20-04-05 09:37:36.159 - INFO: <epoch:172, iter:  34,000, lr:1.000e-04> l_g_pix: 1.3176e-04 l_g_fea: 4.6164e-01 l_g_gan: 1.2918e-02 l_d_real: 1.4976e-01 l_d_fake: 2.1101e-01 D_real: 7.4712e+01 D_fake: 7.2308e+01 
20-04-05 09:40:48.561 - INFO: <epoch:173, iter:  34,200, lr:1.000e-04> l_g_pix: 1.4205e-04 l_g_fea: 5.5461e-01 l_g_gan: 6.3791e-03 l_d_real: 4.7843e-01 l_d_fake: 4.2995e-01 D_real: 6.8169e+01 D_fake: 6.7348e+01 
20-04-05 09:44:00.779 - INFO: <epoch:174, iter:  34,400, lr:1.000e-04> l_g_pix: 1.2141e-04 l_g_fea: 5.7466e-01 l_g_gan: 1.2793e-02 l_d_real: 1.1792e-01 l_d_fake: 1.1857e-01 D_real: 1.0832e+02 D_fake: 1.0588e+02 
20-04-05 09:47:12.504 - INFO: <epoch:175, iter:  34,600, lr:1.000e-04> l_g_pix: 1.3233e-04 l_g_fea: 5.1577e-01 l_g_gan: 1.1738e-02 l_d_real: 3.8836e-01 l_d_fake: 3.0614e-01 D_real: 9.9001e+01 D_fake: 9.7001e+01 
20-04-05 09:50:23.647 - INFO: <epoch:176, iter:  34,800, lr:1.000e-04> l_g_pix: 1.7730e-04 l_g_fea: 6.6304e-01 l_g_gan: 2.9471e-02 l_d_real: 4.0039e-03 l_d_fake: 4.3401e-03 D_real: 1.1075e+02 D_fake: 1.0486e+02 
20-04-05 09:53:34.793 - INFO: <epoch:177, iter:  35,000, lr:1.000e-04> l_g_pix: 9.6854e-05 l_g_fea: 4.7499e-01 l_g_gan: 2.3830e-03 l_d_real: 1.0128e+00 l_d_fake: 1.0137e+00 D_real: 7.8301e+01 D_fake: 7.8838e+01 
20-04-05 09:53:35.189 - INFO: Models and training states saved.
20-04-05 09:54:46.002 - INFO: # Validation # PSNR: 30.577, SSIM: 0.85183, LPIPS: 0.050674
20-04-05 09:54:46.002 - INFO: <epoch:177, iter:  35,000> psnr: 30.577, ssim: 0.85183, lpips: 0.050674
20-04-05 09:57:57.514 - INFO: <epoch:178, iter:  35,200, lr:1.000e-04> l_g_pix: 9.8456e-05 l_g_fea: 4.8019e-01 l_g_gan: 4.1191e-03 l_d_real: 6.2325e-01 l_d_fake: 6.2441e-01 D_real: 5.4023e+01 D_fake: 5.3823e+01 
20-04-05 10:01:08.584 - INFO: <epoch:179, iter:  35,400, lr:1.000e-04> l_g_pix: 1.0728e-04 l_g_fea: 4.1800e-01 l_g_gan: 9.8201e-03 l_d_real: 2.0282e-01 l_d_fake: 1.7608e-01 D_real: 3.6988e+01 D_fake: 3.5213e+01 
20-04-05 10:04:21.128 - INFO: <epoch:180, iter:  35,600, lr:1.000e-04> l_g_pix: 1.0787e-04 l_g_fea: 4.6435e-01 l_g_gan: 1.5690e-02 l_d_real: 8.6865e-02 l_d_fake: 9.4246e-02 D_real: 8.7636e+01 D_fake: 8.4589e+01 
20-04-05 10:07:32.433 - INFO: <epoch:181, iter:  35,800, lr:1.000e-04> l_g_pix: 1.2923e-04 l_g_fea: 4.9223e-01 l_g_gan: 4.4044e-03 l_d_real: 1.1477e+00 l_d_fake: 1.2117e+00 D_real: 7.9436e+01 D_fake: 7.9735e+01 
20-04-05 10:10:44.078 - INFO: <epoch:182, iter:  36,000, lr:1.000e-04> l_g_pix: 9.5829e-05 l_g_fea: 4.5462e-01 l_g_gan: 2.7355e-03 l_d_real: 1.2473e+00 l_d_fake: 1.4374e+00 D_real: 6.0516e+01 D_fake: 6.1312e+01 
20-04-05 10:13:56.499 - INFO: <epoch:183, iter:  36,200, lr:1.000e-04> l_g_pix: 1.3795e-04 l_g_fea: 5.3337e-01 l_g_gan: 1.3765e-02 l_d_real: 7.5717e-02 l_d_fake: 8.5031e-02 D_real: 4.8269e+01 D_fake: 4.5597e+01 
20-04-05 10:17:08.352 - INFO: <epoch:184, iter:  36,400, lr:1.000e-04> l_g_pix: 1.4676e-04 l_g_fea: 4.5075e-01 l_g_gan: 5.1468e-03 l_d_real: 6.2754e-01 l_d_fake: 6.1904e-01 D_real: 2.3598e+01 D_fake: 2.3192e+01 
20-04-05 10:20:19.516 - INFO: <epoch:185, iter:  36,600, lr:1.000e-04> l_g_pix: 1.1506e-04 l_g_fea: 4.1257e-01 l_g_gan: 9.9647e-03 l_d_real: 1.7865e-01 l_d_fake: 2.3750e-01 D_real: 4.8991e+01 D_fake: 4.7206e+01 
20-04-05 10:23:30.864 - INFO: <epoch:186, iter:  36,800, lr:1.000e-04> l_g_pix: 1.0347e-04 l_g_fea: 5.1085e-01 l_g_gan: 1.1653e-02 l_d_real: 2.5783e-01 l_d_fake: 2.3967e-01 D_real: 6.7391e+01 D_fake: 6.5309e+01 
20-04-05 10:26:42.733 - INFO: <epoch:187, iter:  37,000, lr:1.000e-04> l_g_pix: 1.0697e-04 l_g_fea: 3.6257e-01 l_g_gan: 2.3314e-02 l_d_real: 1.4515e-02 l_d_fake: 1.5938e-02 D_real: 9.0584e+01 D_fake: 8.5937e+01 
20-04-05 10:29:53.773 - INFO: <epoch:188, iter:  37,200, lr:1.000e-04> l_g_pix: 1.2249e-04 l_g_fea: 4.0753e-01 l_g_gan: 1.4857e-02 l_d_real: 5.4728e-02 l_d_fake: 5.6605e-02 D_real: 6.7660e+01 D_fake: 6.4744e+01 
20-04-05 10:33:04.844 - INFO: <epoch:189, iter:  37,400, lr:1.000e-04> l_g_pix: 1.3173e-04 l_g_fea: 5.0859e-01 l_g_gan: 5.3465e-03 l_d_real: 4.5925e-01 l_d_fake: 4.5628e-01 D_real: 6.5083e+01 D_fake: 6.4472e+01 
20-04-05 10:36:16.050 - INFO: <epoch:190, iter:  37,600, lr:1.000e-04> l_g_pix: 1.2474e-04 l_g_fea: 4.4627e-01 l_g_gan: 5.1293e-03 l_d_real: 6.9053e-01 l_d_fake: 7.3176e-01 D_real: 5.4902e+01 D_fake: 5.4587e+01 
20-04-05 10:39:28.662 - INFO: <epoch:191, iter:  37,800, lr:1.000e-04> l_g_pix: 1.3017e-04 l_g_fea: 5.4499e-01 l_g_gan: 1.9675e-03 l_d_real: 1.4710e+00 l_d_fake: 1.5024e+00 D_real: 8.7725e+01 D_fake: 8.8818e+01 
20-04-05 10:42:39.510 - INFO: <epoch:192, iter:  38,000, lr:1.000e-04> l_g_pix: 1.0794e-04 l_g_fea: 4.3410e-01 l_g_gan: 1.3189e-02 l_d_real: 7.6867e-01 l_d_fake: 9.5025e-01 D_real: 6.2138e+01 D_fake: 6.0360e+01 
20-04-05 10:45:50.686 - INFO: <epoch:193, iter:  38,200, lr:1.000e-04> l_g_pix: 1.1763e-04 l_g_fea: 4.2042e-01 l_g_gan: 1.8959e-02 l_d_real: 3.0261e-02 l_d_fake: 2.6668e-02 D_real: 7.5912e+01 D_fake: 7.2149e+01 
20-04-05 10:49:02.619 - INFO: <epoch:194, iter:  38,400, lr:1.000e-04> l_g_pix: 9.2962e-05 l_g_fea: 3.9887e-01 l_g_gan: 1.1322e-02 l_d_real: 1.3542e-01 l_d_fake: 1.3840e-01 D_real: 8.7431e+01 D_fake: 8.5303e+01 
20-04-05 10:52:14.382 - INFO: <epoch:195, iter:  38,600, lr:1.000e-04> l_g_pix: 9.9223e-05 l_g_fea: 2.7460e-01 l_g_gan: 2.8228e-03 l_d_real: 1.1592e+00 l_d_fake: 1.1515e+00 D_real: 4.4257e+01 D_fake: 4.4847e+01 
20-04-05 10:55:26.468 - INFO: <epoch:196, iter:  38,800, lr:1.000e-04> l_g_pix: 1.2450e-04 l_g_fea: 4.8342e-01 l_g_gan: 7.5819e-03 l_d_real: 2.7833e-01 l_d_fake: 2.8124e-01 D_real: 6.9135e+01 D_fake: 6.7899e+01 
20-04-05 10:58:39.037 - INFO: <epoch:197, iter:  39,000, lr:1.000e-04> l_g_pix: 1.1320e-04 l_g_fea: 4.2505e-01 l_g_gan: 7.6306e-03 l_d_real: 2.7868e-01 l_d_fake: 2.6623e-01 D_real: 9.0365e+01 D_fake: 8.9111e+01 
20-04-05 11:01:51.079 - INFO: <epoch:198, iter:  39,200, lr:1.000e-04> l_g_pix: 1.1035e-04 l_g_fea: 4.7605e-01 l_g_gan: 6.2083e-03 l_d_real: 5.4853e-01 l_d_fake: 4.8943e-01 D_real: 6.9697e+01 D_fake: 6.8974e+01 
20-04-05 11:05:03.051 - INFO: <epoch:199, iter:  39,400, lr:1.000e-04> l_g_pix: 1.0961e-04 l_g_fea: 5.0820e-01 l_g_gan: 2.0449e-02 l_d_real: 1.8899e-02 l_d_fake: 1.9221e-02 D_real: 7.2712e+01 D_fake: 6.8641e+01 
20-04-05 11:08:19.843 - INFO: <epoch:201, iter:  39,600, lr:1.000e-04> l_g_pix: 1.1181e-04 l_g_fea: 4.2330e-01 l_g_gan: 3.6981e-03 l_d_real: 7.0289e-01 l_d_fake: 7.0575e-01 D_real: 3.1671e+01 D_fake: 3.1636e+01 
20-04-05 11:11:31.518 - INFO: <epoch:202, iter:  39,800, lr:1.000e-04> l_g_pix: 1.5140e-04 l_g_fea: 5.7993e-01 l_g_gan: 5.7281e-03 l_d_real: 4.9356e-01 l_d_fake: 4.8556e-01 D_real: 5.0609e+01 D_fake: 4.9953e+01 
20-04-05 11:14:43.134 - INFO: <epoch:203, iter:  40,000, lr:1.000e-04> l_g_pix: 1.0920e-04 l_g_fea: 4.4715e-01 l_g_gan: 5.6100e-03 l_d_real: 5.3742e-01 l_d_fake: 5.1876e-01 D_real: 5.4734e+01 D_fake: 5.4140e+01 
20-04-05 11:14:43.580 - INFO: Models and training states saved.
20-04-05 11:15:49.377 - INFO: # Validation # PSNR: 31.386, SSIM: 0.84495, LPIPS: 0.048181
20-04-05 11:15:49.377 - INFO: <epoch:203, iter:  40,000> psnr: 31.386, ssim: 0.84495, lpips: 0.048181
20-04-05 11:19:58.474 - INFO: <epoch:204, iter:  40,200, lr:1.000e-04> l_g_pix: 1.2685e-04 l_g_fea: 4.9225e-01 l_g_gan: 1.0660e-02 l_d_real: 1.5421e-01 l_d_fake: 1.4337e-01 D_real: 4.0630e+01 D_fake: 3.8647e+01 
20-04-05 11:23:08.562 - INFO: <epoch:205, iter:  40,400, lr:1.000e-04> l_g_pix: 9.8769e-05 l_g_fea: 3.9846e-01 l_g_gan: 4.7403e-03 l_d_real: 5.6028e-01 l_d_fake: 5.7880e-01 D_real: 2.7592e+01 D_fake: 2.7214e+01 
20-04-05 11:26:20.820 - INFO: <epoch:206, iter:  40,600, lr:1.000e-04> l_g_pix: 1.2723e-04 l_g_fea: 4.4484e-01 l_g_gan: 1.7311e-02 l_d_real: 5.7254e-02 l_d_fake: 9.1726e-02 D_real: 3.9868e+01 D_fake: 3.6480e+01 
20-04-05 11:29:31.804 - INFO: <epoch:207, iter:  40,800, lr:1.000e-04> l_g_pix: 1.8544e-04 l_g_fea: 5.9183e-01 l_g_gan: 5.3518e-03 l_d_real: 5.9455e-01 l_d_fake: 5.9451e-01 D_real: 6.7670e+01 D_fake: 6.7194e+01 
20-04-05 11:32:43.301 - INFO: <epoch:208, iter:  41,000, lr:1.000e-04> l_g_pix: 1.1849e-04 l_g_fea: 5.1815e-01 l_g_gan: 9.5052e-03 l_d_real: 2.4826e-01 l_d_fake: 2.1484e-01 D_real: 5.4876e+01 D_fake: 5.3206e+01 
20-04-05 11:35:55.604 - INFO: <epoch:209, iter:  41,200, lr:1.000e-04> l_g_pix: 1.1663e-04 l_g_fea: 4.8193e-01 l_g_gan: 1.5101e-02 l_d_real: 8.5425e-02 l_d_fake: 7.6627e-02 D_real: 7.6111e+01 D_fake: 7.3172e+01 
20-04-05 11:39:07.009 - INFO: <epoch:210, iter:  41,400, lr:1.000e-04> l_g_pix: 9.6313e-05 l_g_fea: 4.0155e-01 l_g_gan: 4.7107e-03 l_d_real: 6.4113e-01 l_d_fake: 6.3433e-01 D_real: 2.3952e+01 D_fake: 2.3647e+01 
20-04-05 11:42:18.945 - INFO: <epoch:211, iter:  41,600, lr:1.000e-04> l_g_pix: 9.3100e-05 l_g_fea: 4.3510e-01 l_g_gan: 7.0084e-03 l_d_real: 3.6094e-01 l_d_fake: 3.3821e-01 D_real: 6.8729e+01 D_fake: 6.7677e+01 
20-04-05 11:45:30.797 - INFO: <epoch:212, iter:  41,800, lr:1.000e-04> l_g_pix: 1.2167e-04 l_g_fea: 5.5781e-01 l_g_gan: 1.1436e-02 l_d_real: 2.2173e-01 l_d_fake: 2.3078e-01 D_real: 4.3251e+01 D_fake: 4.1190e+01 
20-04-05 11:48:41.967 - INFO: <epoch:213, iter:  42,000, lr:1.000e-04> l_g_pix: 1.2311e-04 l_g_fea: 5.3283e-01 l_g_gan: 9.6682e-04 l_d_real: 2.0295e+00 l_d_fake: 2.0212e+00 D_real: 8.4029e+01 D_fake: 8.5861e+01 
20-04-05 11:51:53.250 - INFO: <epoch:214, iter:  42,200, lr:1.000e-04> l_g_pix: 1.3401e-04 l_g_fea: 5.6085e-01 l_g_gan: 7.4936e-03 l_d_real: 3.4073e-01 l_d_fake: 3.4341e-01 D_real: 5.9674e+01 D_fake: 5.8518e+01 
20-04-05 11:55:04.684 - INFO: <epoch:215, iter:  42,400, lr:1.000e-04> l_g_pix: 1.2045e-04 l_g_fea: 5.5020e-01 l_g_gan: 7.2099e-03 l_d_real: 3.3244e-01 l_d_fake: 3.3791e-01 D_real: 7.3253e+01 D_fake: 7.2146e+01 
20-04-05 11:58:16.114 - INFO: <epoch:216, iter:  42,600, lr:1.000e-04> l_g_pix: 1.2557e-04 l_g_fea: 4.4908e-01 l_g_gan: 9.3083e-03 l_d_real: 2.6408e-01 l_d_fake: 2.5708e-01 D_real: 6.5425e+01 D_fake: 6.3824e+01 
20-04-05 12:01:27.747 - INFO: <epoch:217, iter:  42,800, lr:1.000e-04> l_g_pix: 1.3485e-04 l_g_fea: 4.4919e-01 l_g_gan: 5.5729e-03 l_d_real: 4.3454e-01 l_d_fake: 4.4091e-01 D_real: 5.6159e+01 D_fake: 5.5482e+01 
20-04-05 12:04:39.544 - INFO: <epoch:218, iter:  43,000, lr:1.000e-04> l_g_pix: 1.2239e-04 l_g_fea: 5.3135e-01 l_g_gan: 2.4437e-02 l_d_real: 9.9498e-03 l_d_fake: 9.4393e-03 D_real: 8.5759e+01 D_fake: 8.0881e+01 
20-04-05 12:07:51.514 - INFO: <epoch:219, iter:  43,200, lr:1.000e-04> l_g_pix: 1.1897e-04 l_g_fea: 3.9450e-01 l_g_gan: 1.9478e-02 l_d_real: 1.3407e-01 l_d_fake: 8.4675e-02 D_real: 4.1335e+01 D_fake: 3.7548e+01 
20-04-05 12:11:03.532 - INFO: <epoch:220, iter:  43,400, lr:1.000e-04> l_g_pix: 1.4031e-04 l_g_fea: 4.7463e-01 l_g_gan: 1.4016e-02 l_d_real: 9.5800e-02 l_d_fake: 1.2025e-01 D_real: 6.2894e+01 D_fake: 6.0199e+01 
20-04-05 12:14:15.004 - INFO: <epoch:221, iter:  43,600, lr:1.000e-04> l_g_pix: 9.7273e-05 l_g_fea: 4.3236e-01 l_g_gan: 9.3772e-03 l_d_real: 1.9371e-01 l_d_fake: 1.8689e-01 D_real: 4.3269e+01 D_fake: 4.1584e+01 
20-04-05 12:17:26.348 - INFO: <epoch:222, iter:  43,800, lr:1.000e-04> l_g_pix: 1.2578e-04 l_g_fea: 4.6159e-01 l_g_gan: 1.4232e-02 l_d_real: 7.3860e-02 l_d_fake: 8.1009e-02 D_real: 8.4708e+01 D_fake: 8.1939e+01 
20-04-05 12:20:37.887 - INFO: <epoch:223, iter:  44,000, lr:1.000e-04> l_g_pix: 1.2442e-04 l_g_fea: 4.3466e-01 l_g_gan: 1.5521e-02 l_d_real: 6.1097e-02 l_d_fake: 8.1845e-02 D_real: 6.3488e+01 D_fake: 6.0455e+01 
20-04-05 12:23:49.364 - INFO: <epoch:224, iter:  44,200, lr:1.000e-04> l_g_pix: 1.3918e-04 l_g_fea: 4.8088e-01 l_g_gan: 1.5691e-02 l_d_real: 7.4093e-02 l_d_fake: 8.0851e-02 D_real: 7.3121e+01 D_fake: 7.0060e+01 
20-04-05 12:27:00.961 - INFO: <epoch:225, iter:  44,400, lr:1.000e-04> l_g_pix: 1.4664e-04 l_g_fea: 4.5672e-01 l_g_gan: 2.8078e-03 l_d_real: 1.4198e+00 l_d_fake: 1.4211e+00 D_real: 7.8120e+01 D_fake: 7.8979e+01 
20-04-05 12:30:12.547 - INFO: <epoch:226, iter:  44,600, lr:1.000e-04> l_g_pix: 1.0412e-04 l_g_fea: 4.1781e-01 l_g_gan: 2.2775e-02 l_d_real: 1.2583e-02 l_d_fake: 1.4088e-02 D_real: 1.0495e+02 D_fake: 1.0041e+02 
20-04-05 12:33:24.645 - INFO: <epoch:227, iter:  44,800, lr:1.000e-04> l_g_pix: 1.0843e-04 l_g_fea: 4.4278e-01 l_g_gan: 1.9263e-02 l_d_real: 6.0319e-02 l_d_fake: 3.3949e-02 D_real: 1.0287e+02 D_fake: 9.9060e+01 
20-04-05 12:36:35.865 - INFO: <epoch:228, iter:  45,000, lr:1.000e-04> l_g_pix: 9.6675e-05 l_g_fea: 3.4590e-01 l_g_gan: 1.0385e-02 l_d_real: 1.6154e-01 l_d_fake: 1.6665e-01 D_real: 1.1961e+02 D_fake: 1.1770e+02 
20-04-05 12:36:36.258 - INFO: Models and training states saved.
20-04-05 12:37:34.815 - INFO: # Validation # PSNR: 31.466, SSIM: 0.85016, LPIPS: 0.050883
20-04-05 12:37:34.815 - INFO: <epoch:228, iter:  45,000> psnr: 31.466, ssim: 0.85016, lpips: 0.050883
20-04-05 12:43:17.289 - INFO: <epoch:229, iter:  45,200, lr:1.000e-04> l_g_pix: 1.3000e-04 l_g_fea: 5.3198e-01 l_g_gan: 1.3474e-02 l_d_real: 1.2824e-01 l_d_fake: 1.2663e-01 D_real: 6.3261e+01 D_fake: 6.0694e+01 
20-04-05 12:46:44.929 - INFO: <epoch:230, iter:  45,400, lr:1.000e-04> l_g_pix: 1.0389e-04 l_g_fea: 4.3914e-01 l_g_gan: 1.4676e-02 l_d_real: 1.4948e-01 l_d_fake: 1.0621e-01 D_real: 8.2722e+01 D_fake: 7.9915e+01 
20-04-05 12:49:57.427 - INFO: <epoch:231, iter:  45,600, lr:1.000e-04> l_g_pix: 1.5557e-04 l_g_fea: 5.4565e-01 l_g_gan: 1.7367e-02 l_d_real: 4.7826e-02 l_d_fake: 6.0750e-02 D_real: 8.3076e+01 D_fake: 7.9657e+01 
20-04-05 12:53:09.625 - INFO: <epoch:232, iter:  45,800, lr:1.000e-04> l_g_pix: 1.4517e-04 l_g_fea: 5.3818e-01 l_g_gan: 4.9011e-03 l_d_real: 6.2846e-01 l_d_fake: 6.2732e-01 D_real: 6.8253e+01 D_fake: 6.7901e+01 
20-04-05 12:56:21.122 - INFO: <epoch:233, iter:  46,000, lr:1.000e-04> l_g_pix: 1.3177e-04 l_g_fea: 4.9817e-01 l_g_gan: 1.1564e-02 l_d_real: 1.9203e-01 l_d_fake: 2.0073e-01 D_real: 9.2693e+01 D_fake: 9.0577e+01 
20-04-05 12:59:33.611 - INFO: <epoch:234, iter:  46,200, lr:1.000e-04> l_g_pix: 1.2966e-04 l_g_fea: 4.9001e-01 l_g_gan: 4.8176e-03 l_d_real: 7.3743e-01 l_d_fake: 7.8316e-01 D_real: 1.0054e+02 D_fake: 1.0034e+02 
20-04-05 13:02:45.492 - INFO: <epoch:235, iter:  46,400, lr:1.000e-04> l_g_pix: 1.2050e-04 l_g_fea: 4.2132e-01 l_g_gan: 3.5966e-02 l_d_real: 3.8254e-03 l_d_fake: 4.3320e-03 D_real: 9.8377e+01 D_fake: 9.1188e+01 
20-04-05 13:05:57.952 - INFO: <epoch:236, iter:  46,600, lr:1.000e-04> l_g_pix: 9.1987e-05 l_g_fea: 3.4127e-01 l_g_gan: 1.3460e-02 l_d_real: 1.3700e-01 l_d_fake: 1.2156e-01 D_real: 4.8566e+01 D_fake: 4.6003e+01 
20-04-05 13:09:09.976 - INFO: <epoch:237, iter:  46,800, lr:1.000e-04> l_g_pix: 1.4014e-04 l_g_fea: 5.1057e-01 l_g_gan: 3.6845e-03 l_d_real: 7.4430e-01 l_d_fake: 7.3791e-01 D_real: 4.0676e+01 D_fake: 4.0680e+01 
20-04-05 13:12:21.561 - INFO: <epoch:238, iter:  47,000, lr:1.000e-04> l_g_pix: 1.3447e-04 l_g_fea: 4.4234e-01 l_g_gan: 4.9983e-03 l_d_real: 5.9292e-01 l_d_fake: 6.1781e-01 D_real: 4.3928e+01 D_fake: 4.3534e+01 
20-04-05 13:15:33.187 - INFO: <epoch:239, iter:  47,200, lr:1.000e-04> l_g_pix: 9.9045e-05 l_g_fea: 4.2540e-01 l_g_gan: 2.3111e-02 l_d_real: 1.5040e-02 l_d_fake: 1.1374e-02 D_real: 5.3800e+01 D_fake: 4.9191e+01 
20-04-05 13:18:45.951 - INFO: <epoch:240, iter:  47,400, lr:1.000e-04> l_g_pix: 1.0035e-04 l_g_fea: 4.2187e-01 l_g_gan: 1.2921e-02 l_d_real: 1.4007e-01 l_d_fake: 9.7249e-02 D_real: 7.4705e+01 D_fake: 7.2239e+01 
20-04-05 13:21:58.819 - INFO: <epoch:241, iter:  47,600, lr:1.000e-04> l_g_pix: 1.5834e-04 l_g_fea: 5.0104e-01 l_g_gan: 5.1375e-02 l_d_real: 9.6299e-05 l_d_fake: 1.4611e-04 D_real: 1.0447e+02 D_fake: 9.4195e+01 
20-04-05 13:25:10.834 - INFO: <epoch:242, iter:  47,800, lr:1.000e-04> l_g_pix: 1.4859e-04 l_g_fea: 4.8303e-01 l_g_gan: 7.5481e-03 l_d_real: 3.6714e-01 l_d_fake: 4.9977e-01 D_real: 5.3552e+01 D_fake: 5.2476e+01 
20-04-05 13:28:23.161 - INFO: <epoch:243, iter:  48,000, lr:1.000e-04> l_g_pix: 1.3741e-04 l_g_fea: 4.6177e-01 l_g_gan: 4.5623e-03 l_d_real: 8.6170e-01 l_d_fake: 8.5958e-01 D_real: 6.4294e+01 D_fake: 6.4242e+01 
20-04-05 13:31:35.569 - INFO: <epoch:244, iter:  48,200, lr:1.000e-04> l_g_pix: 1.3584e-04 l_g_fea: 5.1463e-01 l_g_gan: 8.8688e-03 l_d_real: 2.2351e-01 l_d_fake: 2.2397e-01 D_real: 7.2369e+01 D_fake: 7.0819e+01 
20-04-05 13:34:48.170 - INFO: <epoch:245, iter:  48,400, lr:1.000e-04> l_g_pix: 1.3966e-04 l_g_fea: 4.6427e-01 l_g_gan: 1.8264e-02 l_d_real: 5.9810e-02 l_d_fake: 6.9173e-02 D_real: 6.9300e+01 D_fake: 6.5712e+01 
20-04-05 13:38:00.776 - INFO: <epoch:246, iter:  48,600, lr:1.000e-04> l_g_pix: 1.1005e-04 l_g_fea: 4.3754e-01 l_g_gan: 1.1053e-02 l_d_real: 1.7744e-01 l_d_fake: 1.7531e-01 D_real: 4.9496e+01 D_fake: 4.7462e+01 
20-04-05 13:41:13.094 - INFO: <epoch:247, iter:  48,800, lr:1.000e-04> l_g_pix: 1.4019e-04 l_g_fea: 5.9753e-01 l_g_gan: 7.8108e-03 l_d_real: 2.5404e-01 l_d_fake: 2.5375e-01 D_real: 1.7001e+01 D_fake: 1.5692e+01 
20-04-05 13:44:24.794 - INFO: <epoch:248, iter:  49,000, lr:1.000e-04> l_g_pix: 1.0313e-04 l_g_fea: 4.3558e-01 l_g_gan: 4.8063e-03 l_d_real: 6.8547e-01 l_d_fake: 7.1282e-01 D_real: 3.5931e+01 D_fake: 3.5669e+01 
20-04-05 13:47:37.299 - INFO: <epoch:249, iter:  49,200, lr:1.000e-04> l_g_pix: 1.1753e-04 l_g_fea: 4.7352e-01 l_g_gan: 6.5445e-03 l_d_real: 3.8192e-01 l_d_fake: 3.7198e-01 D_real: 3.7250e+01 D_fake: 3.6318e+01 
20-04-05 13:50:48.613 - INFO: <epoch:250, iter:  49,400, lr:1.000e-04> l_g_pix: 1.3436e-04 l_g_fea: 5.9035e-01 l_g_gan: 3.1264e-02 l_d_real: 1.5278e-02 l_d_fake: 3.2013e-02 D_real: 4.9505e+01 D_fake: 4.3276e+01 
20-04-05 13:54:00.615 - INFO: <epoch:251, iter:  49,600, lr:1.000e-04> l_g_pix: 1.2626e-04 l_g_fea: 4.8451e-01 l_g_gan: 2.4567e-03 l_d_real: 1.0625e+00 l_d_fake: 1.0776e+00 D_real: 4.2441e+01 D_fake: 4.3020e+01 
20-04-05 13:57:13.422 - INFO: <epoch:252, iter:  49,800, lr:1.000e-04> l_g_pix: 1.3890e-04 l_g_fea: 4.2702e-01 l_g_gan: 1.4979e-02 l_d_real: 6.5642e-02 l_d_fake: 6.5447e-02 D_real: 6.4440e+01 D_fake: 6.1510e+01 
20-04-05 14:00:26.277 - INFO: <epoch:253, iter:  50,000, lr:1.000e-04> l_g_pix: 1.1430e-04 l_g_fea: 4.3665e-01 l_g_gan: 1.4105e-02 l_d_real: 1.0411e-01 l_d_fake: 9.5090e-02 D_real: 2.7839e+01 D_fake: 2.5118e+01 
20-04-05 14:00:26.763 - INFO: Models and training states saved.
20-04-05 14:01:42.540 - INFO: # Validation # PSNR: 30.316, SSIM: 0.8508, LPIPS: 0.052897
20-04-05 14:01:42.540 - INFO: <epoch:253, iter:  50,000> psnr: 30.316, ssim: 0.8508, lpips: 0.052897
20-04-05 14:07:38.809 - INFO: <epoch:254, iter:  50,200, lr:5.000e-05> l_g_pix: 1.0541e-04 l_g_fea: 4.8841e-01 l_g_gan: 3.8553e-03 l_d_real: 7.8187e-01 l_d_fake: 7.4747e-01 D_real: 7.0532e+01 D_fake: 7.0525e+01 
20-04-05 14:11:20.000 - INFO: <epoch:255, iter:  50,400, lr:5.000e-05> l_g_pix: 1.3231e-04 l_g_fea: 3.9216e-01 l_g_gan: 1.2962e-02 l_d_real: 8.9933e-02 l_d_fake: 8.4489e-02 D_real: 6.7862e+01 D_fake: 6.5357e+01 
20-04-05 14:14:31.583 - INFO: <epoch:256, iter:  50,600, lr:5.000e-05> l_g_pix: 1.3497e-04 l_g_fea: 4.3413e-01 l_g_gan: 5.7357e-03 l_d_real: 6.4638e-01 l_d_fake: 6.1206e-01 D_real: 4.7457e+01 D_fake: 4.6940e+01 
20-04-05 14:17:43.930 - INFO: <epoch:257, iter:  50,800, lr:5.000e-05> l_g_pix: 1.4755e-04 l_g_fea: 5.6002e-01 l_g_gan: 8.9712e-03 l_d_real: 2.5857e-01 l_d_fake: 2.6373e-01 D_real: 9.3510e+01 D_fake: 9.1977e+01 
20-04-05 14:20:54.963 - INFO: <epoch:258, iter:  51,000, lr:5.000e-05> l_g_pix: 1.4607e-04 l_g_fea: 4.5742e-01 l_g_gan: 3.9921e-03 l_d_real: 8.9427e-01 l_d_fake: 9.0302e-01 D_real: 6.1857e+01 D_fake: 6.1957e+01 
20-04-05 14:24:06.833 - INFO: <epoch:259, iter:  51,200, lr:5.000e-05> l_g_pix: 1.0374e-04 l_g_fea: 4.2556e-01 l_g_gan: 1.5732e-02 l_d_real: 7.3586e-02 l_d_fake: 8.7054e-02 D_real: 6.6666e+01 D_fake: 6.3600e+01 
20-04-05 14:27:18.681 - INFO: <epoch:260, iter:  51,400, lr:5.000e-05> l_g_pix: 1.3353e-04 l_g_fea: 5.1132e-01 l_g_gan: 1.4761e-02 l_d_real: 6.4334e-02 l_d_fake: 6.6131e-02 D_real: 5.1262e+01 D_fake: 4.8375e+01 
20-04-05 14:30:30.243 - INFO: <epoch:261, iter:  51,600, lr:5.000e-05> l_g_pix: 1.1471e-04 l_g_fea: 4.4886e-01 l_g_gan: 1.1140e-02 l_d_real: 1.4963e-01 l_d_fake: 1.3941e-01 D_real: 7.2024e+01 D_fake: 6.9940e+01 
20-04-05 14:33:42.018 - INFO: <epoch:262, iter:  51,800, lr:5.000e-05> l_g_pix: 9.8479e-05 l_g_fea: 3.9131e-01 l_g_gan: 1.6249e-02 l_d_real: 4.9388e-02 l_d_fake: 4.9177e-02 D_real: 5.7685e+01 D_fake: 5.4485e+01 
20-04-05 14:36:53.527 - INFO: <epoch:263, iter:  52,000, lr:5.000e-05> l_g_pix: 9.4024e-05 l_g_fea: 4.6835e-01 l_g_gan: 1.1410e-02 l_d_real: 1.9629e-01 l_d_fake: 1.5929e-01 D_real: 7.8160e+01 D_fake: 7.6055e+01 
20-04-05 14:40:04.854 - INFO: <epoch:264, iter:  52,200, lr:5.000e-05> l_g_pix: 1.3210e-04 l_g_fea: 3.9711e-01 l_g_gan: 3.3032e-03 l_d_real: 8.0078e-01 l_d_fake: 8.2963e-01 D_real: 7.3069e+01 D_fake: 7.3224e+01 
20-04-05 14:43:16.610 - INFO: <epoch:265, iter:  52,400, lr:5.000e-05> l_g_pix: 1.0067e-04 l_g_fea: 4.1428e-01 l_g_gan: 6.3532e-04 l_d_real: 2.4209e+00 l_d_fake: 2.3886e+00 D_real: 9.5939e+01 D_fake: 9.8217e+01 
20-04-05 14:46:33.826 - INFO: <epoch:267, iter:  52,600, lr:5.000e-05> l_g_pix: 1.3802e-04 l_g_fea: 4.7975e-01 l_g_gan: 3.4209e-03 l_d_real: 9.6210e-01 l_d_fake: 9.4127e-01 D_real: 8.4198e+01 D_fake: 8.4466e+01 
20-04-05 14:49:46.347 - INFO: <epoch:268, iter:  52,800, lr:5.000e-05> l_g_pix: 1.0945e-04 l_g_fea: 5.0123e-01 l_g_gan: 1.3668e-02 l_d_real: 8.1190e-02 l_d_fake: 1.4790e-01 D_real: 6.0155e+01 D_fake: 5.7536e+01 
20-04-05 14:53:32.564 - INFO: <epoch:269, iter:  53,000, lr:5.000e-05> l_g_pix: 8.6250e-05 l_g_fea: 3.0655e-01 l_g_gan: 3.4893e-03 l_d_real: 7.5025e-01 l_d_fake: 7.5038e-01 D_real: 6.2103e+01 D_fake: 6.2155e+01 
20-04-05 14:56:44.456 - INFO: <epoch:270, iter:  53,200, lr:5.000e-05> l_g_pix: 9.7275e-05 l_g_fea: 4.0998e-01 l_g_gan: 4.1642e-03 l_d_real: 6.1501e-01 l_d_fake: 6.2078e-01 D_real: 3.4784e+01 D_fake: 3.4569e+01 
20-04-05 14:59:55.252 - INFO: <epoch:271, iter:  53,400, lr:5.000e-05> l_g_pix: 1.1653e-04 l_g_fea: 4.6983e-01 l_g_gan: 1.1624e-02 l_d_real: 1.4622e-01 l_d_fake: 1.2401e-01 D_real: 8.6083e+01 D_fake: 8.3893e+01 
20-04-05 15:03:06.384 - INFO: <epoch:272, iter:  53,600, lr:5.000e-05> l_g_pix: 7.8515e-05 l_g_fea: 3.6646e-01 l_g_gan: 4.7525e-03 l_d_real: 6.5848e-01 l_d_fake: 6.3286e-01 D_real: 4.1929e+01 D_fake: 4.1625e+01 
20-04-05 15:06:18.219 - INFO: <epoch:273, iter:  53,800, lr:5.000e-05> l_g_pix: 1.4032e-04 l_g_fea: 3.7418e-01 l_g_gan: 2.9195e-03 l_d_real: 1.0034e+00 l_d_fake: 1.0078e+00 D_real: 7.2040e+01 D_fake: 7.2462e+01 
20-04-05 15:09:31.489 - INFO: <epoch:274, iter:  54,000, lr:5.000e-05> l_g_pix: 1.2584e-04 l_g_fea: 4.3494e-01 l_g_gan: 2.0058e-02 l_d_real: 2.6288e-02 l_d_fake: 2.1702e-02 D_real: 6.9792e+01 D_fake: 6.5804e+01 
20-04-05 15:12:43.247 - INFO: <epoch:275, iter:  54,200, lr:5.000e-05> l_g_pix: 1.0957e-04 l_g_fea: 4.3571e-01 l_g_gan: 1.0838e-02 l_d_real: 1.4627e-01 l_d_fake: 1.4880e-01 D_real: 6.4256e+01 D_fake: 6.2236e+01 
20-04-05 15:15:54.985 - INFO: <epoch:276, iter:  54,400, lr:5.000e-05> l_g_pix: 1.2849e-04 l_g_fea: 5.3642e-01 l_g_gan: 6.3404e-03 l_d_real: 3.8022e-01 l_d_fake: 3.8098e-01 D_real: 6.1165e+01 D_fake: 6.0278e+01 
20-04-05 15:19:06.501 - INFO: <epoch:277, iter:  54,600, lr:5.000e-05> l_g_pix: 8.6026e-05 l_g_fea: 3.8743e-01 l_g_gan: 9.9829e-03 l_d_real: 1.9987e-01 l_d_fake: 2.8134e-01 D_real: 2.5884e+01 D_fake: 2.4128e+01 
20-04-05 15:22:17.218 - INFO: <epoch:278, iter:  54,800, lr:5.000e-05> l_g_pix: 1.0490e-04 l_g_fea: 5.2962e-01 l_g_gan: 1.0537e-02 l_d_real: 1.7210e-01 l_d_fake: 1.6688e-01 D_real: 4.6848e+01 D_fake: 4.4910e+01 
20-04-05 15:25:29.281 - INFO: <epoch:279, iter:  55,000, lr:5.000e-05> l_g_pix: 1.2915e-04 l_g_fea: 5.3088e-01 l_g_gan: 1.9501e-02 l_d_real: 2.5430e-02 l_d_fake: 2.5326e-02 D_real: 3.3376e+01 D_fake: 2.9501e+01 
20-04-05 15:25:29.679 - INFO: Models and training states saved.
20-04-05 15:26:48.263 - INFO: # Validation # PSNR: 31.382, SSIM: 0.82734, LPIPS: 0.032429
20-04-05 15:26:48.264 - INFO: <epoch:279, iter:  55,000> psnr: 31.382, ssim: 0.82734, lpips: 0.032429
20-04-05 15:32:42.602 - INFO: <epoch:280, iter:  55,200, lr:5.000e-05> l_g_pix: 1.1128e-04 l_g_fea: 4.8174e-01 l_g_gan: 5.0208e-03 l_d_real: 5.0152e-01 l_d_fake: 5.1880e-01 D_real: 3.1138e+01 D_fake: 3.0644e+01 
20-04-05 15:35:54.582 - INFO: <epoch:281, iter:  55,400, lr:5.000e-05> l_g_pix: 1.3757e-04 l_g_fea: 5.4563e-01 l_g_gan: 4.3003e-03 l_d_real: 5.8152e-01 l_d_fake: 5.9057e-01 D_real: 5.9566e+01 D_fake: 5.9292e+01 
20-04-05 15:39:05.711 - INFO: <epoch:282, iter:  55,600, lr:5.000e-05> l_g_pix: 8.7105e-05 l_g_fea: 3.4698e-01 l_g_gan: 4.5586e-03 l_d_real: 5.8355e-01 l_d_fake: 5.8575e-01 D_real: 2.2125e+01 D_fake: 2.1798e+01 
20-04-05 15:42:17.203 - INFO: <epoch:283, iter:  55,800, lr:5.000e-05> l_g_pix: 1.4104e-04 l_g_fea: 5.8447e-01 l_g_gan: 1.6265e-02 l_d_real: 6.1276e-02 l_d_fake: 6.2833e-02 D_real: 4.5254e+01 D_fake: 4.2063e+01 
20-04-05 15:45:29.388 - INFO: <epoch:284, iter:  56,000, lr:5.000e-05> l_g_pix: 1.5401e-04 l_g_fea: 5.0801e-01 l_g_gan: 3.1034e-02 l_d_real: 3.0145e-03 l_d_fake: 3.4144e-03 D_real: 6.1716e+01 D_fake: 5.5512e+01 
20-04-05 15:48:40.093 - INFO: <epoch:285, iter:  56,200, lr:5.000e-05> l_g_pix: 1.0174e-04 l_g_fea: 4.3288e-01 l_g_gan: 1.6312e-02 l_d_real: 5.5113e-02 l_d_fake: 4.7276e-02 D_real: 5.4393e+01 D_fake: 5.1182e+01 
20-04-05 15:51:50.611 - INFO: <epoch:286, iter:  56,400, lr:5.000e-05> l_g_pix: 1.0422e-04 l_g_fea: 4.7680e-01 l_g_gan: 1.5302e-02 l_d_real: 8.2894e-02 l_d_fake: 1.5972e-01 D_real: 5.9667e+01 D_fake: 5.6728e+01 
20-04-05 15:55:01.907 - INFO: <epoch:287, iter:  56,600, lr:5.000e-05> l_g_pix: 1.5576e-04 l_g_fea: 5.2135e-01 l_g_gan: 6.3733e-03 l_d_real: 4.5480e-01 l_d_fake: 4.5120e-01 D_real: 4.1522e+01 D_fake: 4.0700e+01 
20-04-05 15:58:12.732 - INFO: <epoch:288, iter:  56,800, lr:5.000e-05> l_g_pix: 1.2263e-04 l_g_fea: 4.1814e-01 l_g_gan: 2.6476e-03 l_d_real: 1.0304e+00 l_d_fake: 1.0619e+00 D_real: 4.9366e+01 D_fake: 4.9883e+01 
20-04-05 16:01:24.932 - INFO: <epoch:289, iter:  57,000, lr:5.000e-05> l_g_pix: 1.3971e-04 l_g_fea: 5.0068e-01 l_g_gan: 6.5914e-03 l_d_real: 3.8445e-01 l_d_fake: 3.6521e-01 D_real: 4.8299e+01 D_fake: 4.7355e+01 
20-04-05 16:04:57.928 - INFO: <epoch:290, iter:  57,200, lr:5.000e-05> l_g_pix: 1.1106e-04 l_g_fea: 5.0837e-01 l_g_gan: 5.1867e-03 l_d_real: 4.8532e-01 l_d_fake: 4.7171e-01 D_real: 4.4106e+01 D_fake: 4.3548e+01 
20-04-05 16:08:30.707 - INFO: <epoch:291, iter:  57,400, lr:5.000e-05> l_g_pix: 1.2993e-04 l_g_fea: 4.2947e-01 l_g_gan: 4.6909e-03 l_d_real: 5.8873e-01 l_d_fake: 5.7259e-01 D_real: 4.5536e+01 D_fake: 4.5179e+01 
20-04-05 16:11:41.800 - INFO: <epoch:292, iter:  57,600, lr:5.000e-05> l_g_pix: 1.1714e-04 l_g_fea: 4.4436e-01 l_g_gan: 8.8554e-03 l_d_real: 3.3023e-01 l_d_fake: 3.4354e-01 D_real: 8.5204e+01 D_fake: 8.3770e+01 
20-04-05 16:14:52.793 - INFO: <epoch:293, iter:  57,800, lr:5.000e-05> l_g_pix: 1.4480e-04 l_g_fea: 4.4475e-01 l_g_gan: 1.5762e-02 l_d_real: 6.4952e-02 l_d_fake: 5.7475e-02 D_real: 4.2276e+01 D_fake: 3.9185e+01 
20-04-05 16:18:03.582 - INFO: <epoch:294, iter:  58,000, lr:5.000e-05> l_g_pix: 1.0882e-04 l_g_fea: 4.5977e-01 l_g_gan: 1.0504e-02 l_d_real: 1.5472e-01 l_d_fake: 1.6191e-01 D_real: 5.8284e+01 D_fake: 5.6342e+01 
20-04-05 16:21:14.271 - INFO: <epoch:295, iter:  58,200, lr:5.000e-05> l_g_pix: 1.1743e-04 l_g_fea: 4.6778e-01 l_g_gan: 7.1944e-03 l_d_real: 3.3570e-01 l_d_fake: 3.3325e-01 D_real: 4.4325e+01 D_fake: 4.3221e+01 
20-04-05 16:24:25.707 - INFO: <epoch:296, iter:  58,400, lr:5.000e-05> l_g_pix: 1.6785e-04 l_g_fea: 4.8632e-01 l_g_gan: 1.5184e-02 l_d_real: 7.5438e-02 l_d_fake: 6.1936e-02 D_real: 4.9655e+01 D_fake: 4.6686e+01 
20-04-05 16:27:37.412 - INFO: <epoch:297, iter:  58,600, lr:5.000e-05> l_g_pix: 8.2516e-05 l_g_fea: 3.3530e-01 l_g_gan: 5.4087e-03 l_d_real: 4.8487e-01 l_d_fake: 4.8179e-01 D_real: 5.1157e+01 D_fake: 5.0559e+01 
20-04-05 16:30:48.191 - INFO: <epoch:298, iter:  58,800, lr:5.000e-05> l_g_pix: 1.0427e-04 l_g_fea: 3.5497e-01 l_g_gan: 5.8458e-03 l_d_real: 4.0956e-01 l_d_fake: 4.1980e-01 D_real: 3.8498e+01 D_fake: 3.7743e+01 
20-04-05 16:33:58.444 - INFO: <epoch:299, iter:  59,000, lr:5.000e-05> l_g_pix: 1.5773e-04 l_g_fea: 5.6541e-01 l_g_gan: 5.6619e-03 l_d_real: 4.9724e-01 l_d_fake: 4.8426e-01 D_real: 3.4545e+01 D_fake: 3.3903e+01 
20-04-05 16:37:09.572 - INFO: <epoch:300, iter:  59,200, lr:5.000e-05> l_g_pix: 1.1590e-04 l_g_fea: 4.1810e-01 l_g_gan: 1.0976e-02 l_d_real: 1.3999e-01 l_d_fake: 1.4347e-01 D_real: 6.3443e+01 D_fake: 6.1390e+01 
20-04-05 16:40:20.508 - INFO: <epoch:301, iter:  59,400, lr:5.000e-05> l_g_pix: 1.0254e-04 l_g_fea: 4.6844e-01 l_g_gan: 8.7588e-03 l_d_real: 2.3743e-01 l_d_fake: 2.6892e-01 D_real: 3.9619e+01 D_fake: 3.8121e+01 
20-04-05 16:43:31.689 - INFO: <epoch:302, iter:  59,600, lr:5.000e-05> l_g_pix: 1.0216e-04 l_g_fea: 4.7529e-01 l_g_gan: 2.3725e-03 l_d_real: 1.3975e+00 l_d_fake: 1.3073e+00 D_real: 7.5470e+01 D_fake: 7.6348e+01 
20-04-05 16:46:42.217 - INFO: <epoch:303, iter:  59,800, lr:5.000e-05> l_g_pix: 1.4056e-04 l_g_fea: 5.3361e-01 l_g_gan: 1.8762e-02 l_d_real: 3.3013e-02 l_d_fake: 2.6903e-02 D_real: 8.3300e+01 D_fake: 7.9577e+01 
20-04-05 16:49:54.165 - INFO: <epoch:304, iter:  60,000, lr:5.000e-05> l_g_pix: 1.3508e-04 l_g_fea: 5.1079e-01 l_g_gan: 3.3361e-03 l_d_real: 8.1715e-01 l_d_fake: 8.1811e-01 D_real: 5.2654e+01 D_fake: 5.2805e+01 
20-04-05 16:49:54.619 - INFO: Models and training states saved.
20-04-05 16:51:02.491 - INFO: # Validation # PSNR: 31.384, SSIM: 0.82025, LPIPS: 0.035657
20-04-05 16:51:02.492 - INFO: <epoch:304, iter:  60,000> psnr: 31.384, ssim: 0.82025, lpips: 0.035657
20-04-05 16:56:44.900 - INFO: <epoch:305, iter:  60,200, lr:5.000e-05> l_g_pix: 9.2096e-05 l_g_fea: 5.0447e-01 l_g_gan: 7.9625e-03 l_d_real: 2.8922e-01 l_d_fake: 2.6318e-01 D_real: 3.3142e+01 D_fake: 3.1826e+01 
20-04-05 17:01:07.042 - INFO: <epoch:306, iter:  60,400, lr:5.000e-05> l_g_pix: 1.1561e-04 l_g_fea: 5.1223e-01 l_g_gan: 3.1698e-03 l_d_real: 9.8922e-01 l_d_fake: 9.5779e-01 D_real: 2.4575e+01 D_fake: 2.4915e+01 
20-04-05 17:05:06.567 - INFO: <epoch:307, iter:  60,600, lr:5.000e-05> l_g_pix: 1.4152e-04 l_g_fea: 4.6966e-01 l_g_gan: 4.4700e-03 l_d_real: 5.7995e-01 l_d_fake: 5.8305e-01 D_real: 2.0291e+01 D_fake: 1.9979e+01 
20-04-05 17:09:33.832 - INFO: <epoch:308, iter:  60,800, lr:5.000e-05> l_g_pix: 1.1410e-04 l_g_fea: 4.3763e-01 l_g_gan: 6.8445e-03 l_d_real: 3.5447e-01 l_d_fake: 3.6832e-01 D_real: 6.1742e+01 D_fake: 6.0735e+01 
20-04-05 17:14:15.063 - INFO: <epoch:309, iter:  61,000, lr:5.000e-05> l_g_pix: 1.0632e-04 l_g_fea: 4.6603e-01 l_g_gan: 8.4151e-03 l_d_real: 2.2829e-01 l_d_fake: 2.2429e-01 D_real: 4.7006e+01 D_fake: 4.5549e+01 
20-04-05 17:18:04.708 - INFO: <epoch:310, iter:  61,200, lr:5.000e-05> l_g_pix: 1.2656e-04 l_g_fea: 3.5015e-01 l_g_gan: 7.0637e-03 l_d_real: 4.3192e-01 l_d_fake: 5.9200e-01 D_real: 5.6372e+01 D_fake: 5.5471e+01 
20-04-05 17:21:42.904 - INFO: <epoch:311, iter:  61,400, lr:5.000e-05> l_g_pix: 1.0609e-04 l_g_fea: 4.7831e-01 l_g_gan: 1.6646e-02 l_d_real: 5.2935e-02 l_d_fake: 5.8295e-02 D_real: 5.6937e+01 D_fake: 5.3663e+01 
20-04-05 17:25:15.619 - INFO: <epoch:312, iter:  61,600, lr:5.000e-05> l_g_pix: 1.0065e-04 l_g_fea: 3.6989e-01 l_g_gan: 1.6839e-02 l_d_real: 3.8265e-02 l_d_fake: 3.9768e-02 D_real: 5.6737e+01 D_fake: 5.3408e+01 
20-04-05 17:28:39.520 - INFO: <epoch:313, iter:  61,800, lr:5.000e-05> l_g_pix: 1.1300e-04 l_g_fea: 5.3760e-01 l_g_gan: 2.3766e-02 l_d_real: 1.1344e-02 l_d_fake: 9.5102e-03 D_real: 8.0107e+01 D_fake: 7.5364e+01 
20-04-05 17:32:06.526 - INFO: <epoch:314, iter:  62,000, lr:5.000e-05> l_g_pix: 1.0744e-04 l_g_fea: 4.3478e-01 l_g_gan: 2.4481e-02 l_d_real: 1.1800e-02 l_d_fake: 1.4403e-02 D_real: 6.6973e+01 D_fake: 6.2090e+01