- Clone github repository
git clone https://github.com/andreanne-lemay/gray_zone_assessment.git- Build docker image
docker build -t gray_zone docker/- Run docker container
docker run -it -v tunnel/to/local/folder:/tunnel --gpus 0 gray_zone:latest bash- Run the following command at the root of the repository to install the modules
cd path/to/gray_zone_assessmentpip install -e .- 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
The configuration file is a json file containing the main training parameters.
Some json file examples are located in gray_zone/resources/training_configs/
| 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) |
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 # 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