1-JZTXT

23-03-18 16:30:58.219 - INFO:   name: LOLv1_model
  use_tb_logger: True
  model: video_base4_m
  distortion: sr
  scale: 1
  gpu_ids: cuda:0
  datasets:[
    train:[
      name: indoor
      mode: video_samesize_lol
      interval_list: [1]
      random_reverse: False
      border_mode: False
      dataroot_GT: /media/mmsys/6f1091c9-4ed8-4a10-a03d-2acef144d2e1/SXY/Data/LOL/LOL-v1/our485/high
      dataroot_LQ: /media/mmsys/6f1091c9-4ed8-4a10-a03d-2acef144d2e1/SXY/Data/LOL/LOL-v1/our485/low
      train_size: [600, 400]
      cache_keys: None
      cache_data: True
      padding: new_info
      N_frames: 5
      use_shuffle: True
      n_workers: 3
      batch_size: 32
      GT_size: 128
      LQ_size: 128
      use_flip: True
      use_rot: True
      color: RGB
      phase: train
      scale: 1
      data_type: img
    ]
    val:[
      name: REDS4
      mode: video_samesize_lol
      dataroot_GT: path_to_datasets/LOL-v1/our485/high
      dataroot_LQ: path_to_datasets/LOL-v1/our485/low
      cache_data: True
      N_frames: 5
      padding: new_info
      train_size: [600, 400]
      phase: val
      scale: 1
      data_type: img
    ]
  ]
  network_G:[
    which_model_G: low_light_transformer
    nf: 64
    nframes: 5
    groups: 8
    front_RBs: 1
    back_RBs: 1
    predeblur: True
    HR_in: True
    w_TSA: True
    scale: 1
  ]
  path:[
    root: ./
    strict_load: False
    experiments_root: ./experiments/LOLv1_model
    models: ./experiments/LOLv1_model/models
    training_state: ./experiments/LOLv1_model/training_state
    log: ./experiments/LOLv1_model
    val_images: ./experiments/LOLv1_model/val_images
  ]
  train:[
    lr_G: 0.0004
    lr_scheme: MultiStepLR
    beta1: 0.9
    beta2: 0.99
    niter: 600000
    ft_tsa_only: 0
    warmup_iter: -1
    lr_steps: [50000, 100000, 200000, 300000]
    lr_gamma: 0.5
    eta_min: 1e-07
    pixel_criterion: cb
    pixel_weight: 1.0
    val_freq: 5000.0
    manual.resize960x512_seed: 0
  ]
  logger:[
    print_freq: 100
    save_checkpoint_freq: 5000.0
  ]
  is_train: True
  dist: False

23-03-18 16:30:58.338 - INFO: Random seed: 3882
23-03-18 16:30:58.358 - INFO: Dataset [VideoSameSizeDataset - indoor] is created.
23-03-18 16:30:58.359 - INFO: Number of train images: 485, iters: 16
23-03-18 16:30:58.359 - INFO: Total epochs needed: 37500 for iters 600,000
23-03-18 16:30:58.359 - INFO: Dataset [VideoSameSizeDataset - REDS4] is created.
23-03-18 16:30:58.359 - INFO: Number of val images in [REDS4]: 0
23-03-18 16:31:00.388 - INFO: Network G structure: DataParallel - low_light_transformer, with parameters: 39,124,099
23-03-18 16:31:00.388 - INFO: low_light_transformer(
  (conv_first_1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv_first_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
  (conv_first_3): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
  (feature_extraction): Sequential(
    (0): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
  )
  (recon_trunk): Sequential(
    (0): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
  )
  (upconv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (upconv2): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (pixel_shuffle): PixelShuffle(upscale_factor=2)
  (HRconv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv_last): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (lrelu): LeakyReLU(negative_slope=0.1, inplace=True)
  (transformer): Encoder_patch66(
    (dropout): Dropout(p=0.0, inplace=False)
    (layer_stack): ModuleList(
      (0): EncoderLayer3(
        (slf_attn): MultiHeadAttention4(
          (w_qs): Linear(in_features=1024, out_features=512, bias=False)
          (w_ks): Linear(in_features=1024, out_features=512, bias=False)
          (w_vs): Linear(in_features=1024, out_features=512, bias=False)
          (fc): Linear(in_features=512, out_features=1024, bias=False)
          (attention): ScaledDotProductAttention(
            (dropout): Dropout(p=0.0, inplace=False)
          )
          (dropout): Dropout(p=0.0, inplace=False)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
        )
        (pos_ffn): PositionwiseFeedForward4(
          (w_1): Linear(in_features=1024, out_features=2048, bias=True)
          (w_2): Linear(in_features=2048, out_features=1024, bias=True)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
      (1): EncoderLayer3(
        (slf_attn): MultiHeadAttention4(
          (w_qs): Linear(in_features=1024, out_features=512, bias=False)
          (w_ks): Linear(in_features=1024, out_features=512, bias=False)
          (w_vs): Linear(in_features=1024, out_features=512, bias=False)
          (fc): Linear(in_features=512, out_features=1024, bias=False)
          (attention): ScaledDotProductAttention(
            (dropout): Dropout(p=0.0, inplace=False)
          )
          (dropout): Dropout(p=0.0, inplace=False)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
        )
        (pos_ffn): PositionwiseFeedForward4(
          (w_1): Linear(in_features=1024, out_features=2048, bias=True)
          (w_2): Linear(in_features=2048, out_features=1024, bias=True)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
      (2): EncoderLayer3(
        (slf_attn): MultiHeadAttention4(
          (w_qs): Linear(in_features=1024, out_features=512, bias=False)
          (w_ks): Linear(in_features=1024, out_features=512, bias=False)
          (w_vs): Linear(in_features=1024, out_features=512, bias=False)
          (fc): Linear(in_features=512, out_features=1024, bias=False)
          (attention): ScaledDotProductAttention(
            (dropout): Dropout(p=0.0, inplace=False)
          )
          (dropout): Dropout(p=0.0, inplace=False)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
        )
        (pos_ffn): PositionwiseFeedForward4(
          (w_1): Linear(in_features=1024, out_features=2048, bias=True)
          (w_2): Linear(in_features=2048, out_features=1024, bias=True)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
      (3): EncoderLayer3(
        (slf_attn): MultiHeadAttention4(
          (w_qs): Linear(in_features=1024, out_features=512, bias=False)
          (w_ks): Linear(in_features=1024, out_features=512, bias=False)
          (w_vs): Linear(in_features=1024, out_features=512, bias=False)
          (fc): Linear(in_features=512, out_features=1024, bias=False)
          (attention): ScaledDotProductAttention(
            (dropout): Dropout(p=0.0, inplace=False)
          )
          (dropout): Dropout(p=0.0, inplace=False)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
        )
        (pos_ffn): PositionwiseFeedForward4(
          (w_1): Linear(in_features=1024, out_features=2048, bias=True)
          (w_2): Linear(in_features=2048, out_features=1024, bias=True)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
      (4): EncoderLayer3(
        (slf_attn): MultiHeadAttention4(
          (w_qs): Linear(in_features=1024, out_features=512, bias=False)
          (w_ks): Linear(in_features=1024, out_features=512, bias=False)
          (w_vs): Linear(in_features=1024, out_features=512, bias=False)
          (fc): Linear(in_features=512, out_features=1024, bias=False)
          (attention): ScaledDotProductAttention(
            (dropout): Dropout(p=0.0, inplace=False)
          )
          (dropout): Dropout(p=0.0, inplace=False)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
        )
        (pos_ffn): PositionwiseFeedForward4(
          (w_1): Linear(in_features=1024, out_features=2048, bias=True)
          (w_2): Linear(in_features=2048, out_features=1024, bias=True)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
      (5): EncoderLayer3(
        (slf_attn): MultiHeadAttention4(
          (w_qs): Linear(in_features=1024, out_features=512, bias=False)
          (w_ks): Linear(in_features=1024, out_features=512, bias=False)
          (w_vs): Linear(in_features=1024, out_features=512, bias=False)
          (fc): Linear(in_features=512, out_features=1024, bias=False)
          (attention): ScaledDotProductAttention(
            (dropout): Dropout(p=0.0, inplace=False)
          )
          (dropout): Dropout(p=0.0, inplace=False)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
        )
        (pos_ffn): PositionwiseFeedForward4(
          (w_1): Linear(in_features=1024, out_features=2048, bias=True)
          (w_2): Linear(in_features=2048, out_features=1024, bias=True)
          (layer_norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
    )
  )
  (recon_trunk_light): Sequential(
    (0): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (1): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (2): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (3): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (4): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (5): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
  )
)
23-03-18 16:31:00.815 - INFO: Model [VideoBaseModel] is created.
23-03-18 16:31:00.816 - INFO: Start training from epoch: 0, iter: 0