{
  "run_name": "mapping_dataset.fcn50",

  "data_dir_candidates": [
    "/local/shared/data", // try cluster local node first
    "/data/titane/user/nigirard/data", // Try cluster /data directory
    "~/data", // In home directory (docker)
    "/data" // In landsat's /data volume (docker)
  ],
  "data_root_partial_dirpath": "mapping_challenge_dataset",
  "dataset_params": {
    "small": false
  },
  "num_workers": 10,
  "data_split_params": {
    "seed": 0,  // Change this to change the random splitting of data in train/val/test
    "train_fraction": 0.75,
    "val_fraction": 0.25 // test_fraction is the rest
  },
  "data_aug_params": {
    "enable": true,
    "vflip": true,
    "rotate": true,
    "color_jitter": true,
    "device": "cuda"
  },

  "device": "cuda",  // Only has effects when mode is val or test. When mode is train, always use CUDA
  "use_amp": true,  // Automatic Mixed Precision switch

  "backbone_params": {
    "name": "fcn50",
    "input_features": 3,
    "features": 256,
    "pretrained": false
  },

  "compute_seg": true,
  "compute_crossfield": true,

  "seg_params": {
    "compute_interior": true,
    "compute_edge": true,
    "compute_vertex": false
  },

  "loss_params": {
    "multiloss": {
      "normalization_params": {
        "min_samples": 10,  // Per GPU
        "max_samples": 1000  // Per GPU
      },
      "coefs": {
        "seg_interior": 1,
        "seg_edge": 1,
        "seg_vertex": 0,
        "crossfield_align": 1,
        "crossfield_align90": 0.2,
        "crossfield_smooth": 0.2,
        "seg_interior_crossfield": 0.2,
        "seg_edge_crossfield": 0.2,
        "seg_edge_interior": 0.2
      }
    },
    "seg_loss_params": {  // https://github.com/neptune-ai/open-solution-mapping-challenge/blob/master/neptune.yaml
      "bce_coef": 1.0,
      "dice_coef": 0.2,
      "w0": 50,  // From original U-Net paper: distance weight to increase loss between objects
      "sigma": 10  // From original U-Net paper: distance weight to increase loss between objects
    }
  },

  "batch_size": 16,  // Batch size per GPU. The effective batch size is effective_batch_size=world_size*batch_size
  "base_lr": 1e-4,  // Will be multiplied by the effective_batch_size=world_size*batch_size.
  "max_lr": 1e-2,  // Maximum learning rate
  "warmup_epochs": 1,  // Number of epochs for warmup (learning rate starts at lr*warmup_factor and gradually increases to lr)
  "warmup_factor": 1e-3,
  "weight_decay": 0,
  "dropout_keep_prob": 1.0,  // Not used for now
  "max_epoch": 25,
  "log_steps": 50,
  "checkpoint_epoch": 1,
  "checkpoints_to_keep": 10,  // outputs
  "logs_dirname": "logs",
  "save_input_output": false,
  "log_input_output": false,
  "checkpoints_dirname": "checkpoints",
  "eval_dirname": "eval"
}