SPSR Log

/home/mmsys/anaconda3/envs/HWMNet/bin/python3.8 /media/mmsys/6f1091c9-4ed8-4a10-a03d-2acef144d2e1/SXY/otherCode/SPSR-master/train.py
export CUDA_VISIBLE_DEVICES=1
Path already exists. Rename it to [Exp/experiments/SPSR_archived_230605-154256]
23-06-05 15:42:56.395 - INFO:   name: SPSR
  use_tb_logger: True
  model: spsr
  scale: 4
  gpu_ids: [1]
  datasets:[
    train:[
      name: DIV2K
      mode: LRHR
      train_dataroot: /media/mmsys/6f1091c9-4ed8-4a10-a03d-2acef144d2e1/SXY/Data/LOL/LOL-v1/train
      dataroot_HR: /media/mmsys/6f1091c9-4ed8-4a10-a03d-2acef144d2e1/SXY/Data/suntest/train/high
      dataroot_LR: /media/mmsys/6f1091c9-4ed8-4a10-a03d-2acef144d2e1/SXY/Data/suntest/train/low
      subset_file: None
      use_shuffle: True
      n_workers: 0
      batch_size: 1
      HR_size: 128
      use_flip: True
      use_rot: True
      phase: train
      scale: 4
      data_type: img
    ]
    val:[
      name: v1
      mode: LRHR
      val_dataroot: /media/mmsys/6f1091c9-4ed8-4a10-a03d-2acef144d2e1/SXY/Data/LOL/LOL-v1/test
      dataroot_HR: /media/mmsys/6f1091c9-4ed8-4a10-a03d-2acef144d2e1/SXY/Data/LOL/LOL-v1/test/high
      dataroot_LR: /media/mmsys/6f1091c9-4ed8-4a10-a03d-2acef144d2e1/SXY/Data/LOL/LOL-v1/test/low
      phase: val
      scale: 4
      data_type: img
    ]
  ]
  path:[
    root: Exp
    pretrain_model_G: None
    experiments_root: Exp/experiments/SPSR
    models: Exp/experiments/SPSR/models
    training_state: Exp/experiments/SPSR/training_state
    log: Exp/experiments/SPSR
    val_images: Exp/experiments/SPSR/val_images
  ]
  network_G:[
    which_model_G: spsr_net
    norm_type: None
    mode: CNA
    nf: 64
    nb: 23
    in_nc: 3
    out_nc: 3
    gc: 32
    group: 1
    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
    lr_G_grad: 0.0001
    weight_decay_G: 0
    weight_decay_G_grad: 0
    beta1_G: 0.9
    beta1_G_grad: 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
    gradient_pixel_weight: 0.01
    gradient_gan_weight: 0.005
    pixel_branch_criterion: l1
    pixel_branch_weight: 0.5
    Branch_pretrain: 1
    Branch_init_iters: 5000
    manual_seed: 9
    niter: 242500
    val_freq: 2
  ]
  logger:[
    print_freq: 100
    save_checkpoint_freq: 5000.0
  ]
  is_train: True

23-06-05 15:42:56.410 - INFO: Random seed: 9
23-06-05 15:42:56.412 - INFO: Dataset [LRHRDataset - DIV2K] is created.
23-06-05 15:42:56.412 - INFO: Number of train images: 485, iters: 485
23-06-05 15:42:56.412 - INFO: Total epochs needed: 500 for iters 242,500
23-06-05 15:42:56.412 - INFO: Dataset [LRHRDataset - v1] is created.
23-06-05 15:42:56.412 - INFO: Number of val images in [v1]: 15
23-06-05 15:42:56.581 - INFO: Initialization method [kaiming]
23-06-05 15:43:03.863 - INFO: Initialization method [kaiming]
23-06-05 15:43:04.367 - INFO: Initialization method [kaiming]
23-06-05 15:43:05.949 - WARNING: Params [module.get_g_nopadding.weight_h] will not optimize.
23-06-05 15:43:05.949 - WARNING: Params [module.get_g_nopadding.weight_v] will not optimize.
23-06-05 15:43:05.950 - INFO: Model [SPSRModel] is created.
23-06-05 15:43:05.950 - INFO: Start training from epoch: 0, iter: 0
/home/mmsys/anaconda3/envs/HWMNet/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:416: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.
  warnings.warn("To get the last learning rate computed by the scheduler, "
23-06-05 15:47:56.184 - INFO: <epoch:  0, lr:1.000e-04>
23-06-05 15:48:18.096 - INFO: # epoch:  0 Validation # avg_PSNR: 3.1868e+00
23-06-05 15:53:04.440 - INFO: <epoch:  1, lr:1.000e-04>
23-06-05 15:57:53.871 - INFO: <epoch:  2, lr:1.000e-04>
23-06-05 15:58:14.686 - INFO: # epoch:  2 Validation # avg_PSNR: 3.1868e+00
23-06-05 16:03:04.067 - INFO: <epoch:  3, lr:1.000e-04>
23-06-05 16:07:51.074 - INFO: <epoch:  4, lr:1.000e-04>
23-06-05 16:08:12.135 - INFO: # epoch:  4 Validation # avg_PSNR: 3.1868e+00