CAER/
│
├── train.py - main script to start training
├── test.py - evaluation of trained model
│
├── configs/ - holds configuration for training and testing
├── parse_config.py - class to handle config file and cli options
│
├── base/ - abstract base classes
│   ├── base_data_loader.py
│   ├── base_model.py
│   └── base_trainer.py
│
├── data_loader/ - anything about data loading goes here
│   └── data_loaders.py
│
├── data/ - default directory for storing input data
│
├── model/ - models, losses, and metrics
│   ├── model.py
│   ├── metric.py
│   └── loss.py
│
├── saved/
│   ├── models/ - trained models are saved here
│   └── log/ - default logdir for tensorboard and logging output
│
├── trainer/ - trainers
│   └── trainer.py
│
├── logger/ - module for tensorboard visualization and logging
│   ├── visualization.py
│   ├── logger.py
│   └── logger_config.json
│  
└── utils/ - small utility functions
    ├── util.py
    └── ...

{
  // configuration for both training and testing
  "name": "CAERS_Session",             // session name
  "n_gpu": 1,                          // number of GPUs to use for training.
  "arch": {                            // architecture
      "type": "CAERSNet",
      "args": {}
  },
  "loss": "cross_entropy",             // loss
  "metrics": [
    "accuracy"                         // list of metrics to evaluate
  ],   

  // configuration for testing only 
  "test_loader": {
      "type": "CAERSDataLoader",
      "args":{
          "root": "data/CAER-S/test",
          "detect_file": "data/test.txt",
          "train": false,
          "batch_size": 128,
          "shuffle": false,
          "num_workers": 16
      }
  },

  // configuration for training only
  "train_loader": {                    // loader for training 
    "type": "CAERSDataLoader",         // selecting data loader
    "args":{
      "root": "data/CAER-S/train",     // train dataset directory
      "detect_file": "data/train.txt", // face detector results
      "batch_size": 128,               // batch size
      "shuffle": true,                 // shuffle training data 
      "num_workers": 16,               // number of cpu processes to be used for data loading
    }
  },

  "val_loader": {
    "type": "CAERSDataLoader",
    "args":{
        "root": "data/CAER-S/test",
        "detect_file": "data/val.txt",
        "train": false,
        "batch_size": 128,
        "shuffle": false,
        "num_workers": 16
    }
  },

  "optimizer": {
    "type": "SGD",
    "args":{
      "lr": 1e-2,                      // learning rate
      "momentum": 0.9,                 // (optional) weight decay
      "nesterov": true
    }
  },                        
  "trainer": {
    "epochs": 50,                      // number of training epochs
    "save_dir": "saved/",              // checkpoints are saved in save_dir/models/name
    "save_freq": 10,                   // save checkpoints every save_freq epochs
    "verbosity": 2,                    // 0: quiet, 1: per epoch, 2: full
  
    "monitor": "max val_accuracy"      // mode and metric for model performance monitoring. set 'off' to disable.
    "early_stop": 20	                 // number of epochs to wait before early stop. set 0 to disable.
  
    "tensorboard": true,               // enable tensorboard visualization
  }
}

python train.py --config [train config path]

python train.py --resume [your checkpoint path]

python test.py --config [test config path] --resume [your checkpoint path]