The goal of this workflow is to assing a label to the input image.
.csvfile with the assigned class to each image.
In the figure below a few examples of this workflow’s input are depicted:
Each of these examples are of a different class and were obtained from MedMNIST v2 ([YSW+21]), concretely from DermaMNIST dataset which is a large collection of multi-source dermatoscopic images of common pigmented skin lesions.
Each image label is obtained from the directory name in which that image resides. That is why is so important to follow the directory tree as described below. If you have a .csv file with each image label, as is provided by MedMNIST v2, you can use our script from_class_csv_to_folders.py to create the directory tree as below:
dataset/ ├── train │ ├── 0 │ │ ├── train0_0.png │ │ ├── train1013_0.png │ │ ├── . . . │ │ └── train932_0.png │ ├── 1 │ │ ├── train104_1.png │ │ ├── train1049_1.png │ │ ├── . . . │ │ └── train964_1.png | . . . │ └── 6 │ ├── train1105_6.png │ ├── train1148_6.png │ ├── . . . │ └── train98_6.png └── test ├── 0 │ ├── test1008_0.png │ ├── test1084_0.png │ ├── . . . │ └── test914_0.png ├── 1 │ ├── test10_1.png │ ├── test1034_1.png │ ├── . . . │ └── test984_1.png . . . └── 6 ├── test1021_6.png ├── test1069_6.png ├── . . . └── test806_6.png
Here each directory is a number but it can be any string. Notice that they will be considered the class names. Regarding the test, if you have no classes it doesn’t matter if the images are separated in several folders or are all in one folder. But, if
DATA.TEST.LOAD_GT is enabled, each folder in test path (i.e.
DATA.TEST.PATH) will be considered as a class (as done for training and validation).
Find in templates/classification folder of BiaPy a few YAML configuration templates for this workflow.
Special workflow configuration¶
Here some special configuration options that can be selected in this workflow are described:
Metrics: during the inference phase the performance of the test data is measured using different metrics if test masks were provided (i.e. ground truth) and, consequently,
DATA.TEST.LOAD_GTis enabled. In the case of classification the accuracy, precision, recall, and F1 are calculated. Apart from that, the confusion matrix is also printed.
Jupyter notebooks: run via Google Colab
Command line: Open a terminal as described in Installation. For instance, using classification.yaml template file, the code can be run as follows:
# Configuration file job_cfg_file=/home/user/classification.yaml # Where the experiment output directory should be created result_dir=/home/user/exp_results # Just a name for the job job_name=classification # Number that should be increased when one need to run the same job multiple times (reproducibility) job_counter=1 # Number of the GPU to run the job in (according to 'nvidia-smi' command) gpu_number=0 # Move where BiaPy installation resides cd BiaPy # Load the environment conda activate BiaPy_env python -u main.py \ --config $job_cfg_file \ --result_dir $result_dir \ --name $job_name \ --run_id $job_counter \ --gpu $gpu_number
Docker: Open a terminal as described in Installation. For instance, using classification.yaml template file, the code can be run as follows:
# Configuration file job_cfg_file=/home/user/classification.yaml # Path to the data directory data_dir=/home/user/data # Where the experiment output directory should be created result_dir=/home/user/exp_results # Just a name for the job job_name=classification # Number that should be increased when one need to run the same job multiple times (reproducibility) job_counter=1 # Number of the GPU to run the job in (according to 'nvidia-smi' command) gpu_number=0 docker run --rm \ --gpus "device=$gpu_number" \ --mount type=bind,source=$job_cfg_file,target=$job_cfg_file \ --mount type=bind,source=$result_dir,target=$result_dir \ --mount type=bind,source=$data_dir,target=$data_dir \ danifranco/biapy \ -cfg $job_cfg_file \ -rdir $result_dir \ -name $job_name \ -rid $job_counter \ -gpu $gpu_number
data_dir must contain the path
DATA.*.PATH so the container can find it. For instance, if you want to only train in this example
DATA.TRAIN.PATH could be
The main output of this workflow will be a file named
predictions.csv that will contain the predicted image class:
All files are placed in
results folder under
--result_dir directory with the
--name given. Following the example, you should see that the directory
/home/user/exp_results/classification has been created. If the same experiment is run 5 times, varying
--run_id argument only, you should find the following directory tree:
classification/ ├── config_files/ │ └── classification.yaml ├── checkpoints │ └── model_weights_classification_1.h5 └── results ├── classification_1 ├── . . . └── classification_5 ├── predictions.csv ├── aug │ └── .tif files └── charts ├── classification_1_accuracy.png ├── classification_1_loss.png └── model_plot_classification_1.png
config_files: directory where the .yaml filed used in the experiment is stored.
classification.yaml: YAML configuration file used (it will be overwrited every time the code is run).
checkpoints: directory where model’s weights are stored.
model_weights_classification_1.h5: model’s weights file.
results: directory where all the generated checks and results will be stored. There, one folder per each run are going to be placed.
classification_1: run 1 experiment folder.
predictions.csv: list of assigned class per test image.
aug: image augmentation samples.
classification_1_accuracy.png: accuracy over epochs plot (when training is done).
classification_1_loss.png: Loss over epochs plot (when training is done).
model_plot_classification_1.png: plot of the model.
Here, for visualization purposes, only
classification_1 has been described but
classification_5 directories will follow the same structure.