train_ccsr_stage2.yaml

data:
  target: dataset.data_module.BIRDataModule
  params:
    # Path to training set configuration file.
    train_config: configs/dataset/general_deg_stablesr_realesrgan_train.yaml
    # Path to validation set configuration file.
    val_config: configs/dataset/general_deg_stablesr_realesrgan_val.yaml

model:
  # You can set learning rate in the following configuration file.
  config: configs/model/ccsr_stage2.yaml
  # Path to the checkpoints or weights you want to resume. At the begining, 
  # this should be set to the weights obtained from ccsr_stage1
  resume: /ccsr_stage1.ckpt


lightning:
  seed: 231
  
  trainer:
    accelerator: ddp
    precision: 32
    # Indices of GPUs used for training.
    gpus: [0,1,2,3,]
    # Path to save logs and checkpoints.
    default_root_dir: experiments/test_ccsr_stage2
    # Max number of training steps (batches).
    max_steps: 60
    # Validation frequency in terms of training steps.
    val_check_interval: 390
    log_every_n_steps: 50
    # 50
    # Accumulate gradients from multiple batches so as to increase batch size.
    accumulate_grad_batches: 8
  
  callbacks:
    - target: model.callbacks.ImageLogger
      params:
        # Log frequency of image logger.
        log_every_n_steps: 150000
        max_images_each_step: 4
        log_images_kwargs: ~

    - target: model.callbacks.ModelCheckpoint
      params:
        # Frequency of saving checkpoints.
        every_n_train_steps: 20
        save_top_k: -1
        filename: "{step}"