# How to config 

The config file includes data path, optimizer, scheduler, etc, ...

In each configure file: 

- stages/data_params/root: To the folder where stores image data.

- image_size: determine the size of image 

Note:  

You do not need to change: `train_csv` and `valid_csv` because they are overrided by running bash file bellow. 

# Preprocessing 

The following data is used for different models.

* 3 windows (3w) data:

    ```bash

    python src/preprocessing.py extract-images --inputdir <kaggle_input_dir> --outputdir <output_folder>

    ```

* 3 windows (3w) with crop data:

    ```bash

    python src/preprocessing_3w.py extract-images --inputdir <kaggle_input_dir> --outputdir <output_folder>

    ```

* 3d data:

    ```bash

    python src/preprocessing2.py

    ```

# How to run  

* Start docker: 

    ```bash

    make run

    make exec 

    cd /kaggle-rsna/

    ```

* Train `resnet18, resnet34, resnet50, alexnet` with `3 windows (3w)` setting:

    ```bash

    bash bin/train_bac_3w.sh 

    ``` 

    Note: normalize=True

* Train `resnet50` with `3d` setting:

    ```bash

    bash bin/train_bac_3d.sh 

    ``` 

    Note: normalize=False

* Train `densenet169` with `3 windows and crop` setting:

    ```bash

    bash bin/train_toan.sh 

    bash bin/train_toan_resume.sh

    ``` 

    Note: normalize=True

where: 

- CUDA_VISIBLE_DEVICES: GPUs number required to train. 

- LOGDIR: Output folder which stores the checkpoints, logs, etc. 

- model_name: the name of model to be trained. The script supports the name of model in [here](https://github.com/creafz/pytorch-cnn-finetune)

- It is better to create a `wandb` account, it will help you track your log, backup the code, store the checkpoints on the

could in real-time. If you dont want to use `wandb`, please set: `WANDB=0`

Output:  

The best checkpoint is saved at: `${LOGDIR}/${log_name}/checkpoints/best.pth`. 

# How to test  

```bash

python src/inference.py

```

Check function `predict_test_tta_ckp` for more information, you may want to change the path, the name of model and the output path.

For `3d` setting, `normalization=False`, otherwise `normalization=True` 

# Ensemble KFOLD 

In `src/ensemble.py`, you should change the prediction path of each fold of model and the name of output ensemble. 

```bash

python src/ensemble.py

```