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Gray Zone Assessment

Get started

  1. Clone github repository
git clone https://github.com/andreanne-lemay/gray_zone_assessment.git
  1. Build docker image
docker build -t gray_zone docker/
  1. Run docker container
docker run -it -v tunnel/to/local/folder:/tunnel --gpus 0 gray_zone:latest bash
  1. Run the following command at the root of the repository to install the modules
cd path/to/gray_zone_assessment
pip install -e .
  1. Train model
python run_model.py -o <outpath/path> -p <resources/training_configs/config.json> -d <image/data/path> -c <path/csv/file.csv>

For more information on the different flags: python run_model.py --help

Configuration file (flag -p or --param-path)

The configuration file is a json file containing the main training parameters.
Some json file examples are located in gray_zone/resources/training_configs/

Required configuration parameters

Parameter Description
architecture Architecture id contained in Densenet or Resnet family. Choice between: 'densenet121', 'densenet169', 'densenet201', 'densenet264', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2', 'wide_resnet101_2'
model_type Choice between "classification", "ordinal", "regression".
loss Loss function id. Choice between 'ce' (Cross entropy), 'mse' (Mean square error), 'l1' (L1), 'bce' (Binary cross entropy), 'coral' (Ordinal loss), 'qwk' (Quadratic weighted kappa).
batch_size Batch size (int).
lr Learning rate (float).
n_epochs Number of training epochs (int).
device Device id (e.g., 'cuda:0', 'cpu') (str).
val_metric Choice between "auc" (average ROC AUC over all classes), "val_loss" (minimum validation loss), "kappa" (linear Cohen's kappa), default "accuracy".
dropout_rate Dropout rate (Necessary for Monte Carlo model's). A dropout rate of 0 will disable dropout. (float).
is_weighted_loss Indicates if the loss is weighted by the number of cases by class (bool).
is_weighted_sampling Indicates if the sampling is weighted by the number of cases by class (bool).
seed Random seed (int).
train_frac Fraction of cases used for training if splitting not already done in csv file, or else the parameter is ignored (float).
test_frac Fraction of cases used for testing if splitting not already done in csv file, or else the parameter is ignored (float).
train_transforms / val_transforms monai training / validation transforms with parameters. Validation transforms are also used during testing (see https://docs.monai.io/en/latest/transforms.html for transform list)

csv file (flag -c or --csv-path)

The provided csv file contains the filename of the images used for training, GT labels (int from 0-n_class), patient ID (str) and split column (containing 'train', 'val' or 'test') (optional).

Example of csv file with the default column names. If the column names are different from the default values, the flags --label-colname, --image-colname, --patient-colname, and --split-colname can be used to indicate the custom column names. There can be more columns in the csv file. All this metadata will be included in predictions.csv and split_df.csv.

image label patient dataset
patient1_000.png 0 patient1 train
patient1_001.png 0 patient1 train
patient2_000.png 2 patient2 val
patient2_001.png 2 patient2 val
patient2_002.png 2 patient2 val
patient3_000.png 1 patient3 test
patient3_001.png 1 patient3 test

Output directory (flag -o or --output-path)


└── output directory                # Output directory specified with `-o`  
    ├──   checkpoints               # All models (one .pth per epoch)  
    |     ├──  checkpoint0.pth   
    |     ├──  ...  
    |     └──  checkpointn.pth   
    ├──   best_metric_model.pth     # Best model based on validation metric  
    ├──   params.json               # Parameters used for training (configuration file)  
    ├──   predictions.csv           # Test results  
    ├──   split_df.csv              # csv file containing image filenames, labels, split and patient id  
    └──   train_record.json         # Record of CLI used to train and other info for reproducibility