cruigo93/human_activity_training

Training and Inference pipeline for Human Activity prediction

Project based on Pytorch-Lightning

Training

Experiments are written in Yaml format and stored in experiments folder

EXPERIMENT_NAME: "baseline" - You need to specify experiment name, this name will be used to store logs

DATA: ... - This block is used to specify what samples to train on and where the data stored. Moreover, BATCH_SIZE and validation size are needed to be specified

OPTIMIZER: ... - Optimizer configuration: from what package import, what optimizer to use and arguments to this optimizer

To run training process You need to run python main.py --experiment_cfg experiments/train.yaml

MODEL: ... - What model to use

SCHEDULER: ... - Parameters of scheduler to use.

CRITERION: ... - What loss to use to optimize

AUGMENTATION: ... - The training augmentations. The augmentation need to be written in package specified in PY

EARLY_STOPPING: ... - Parameters of early stopping process

CHECKPOINT: ... - Parameters of checkpointing process

EPOCHS: ... - The number of epochs

GPUS: ... - GPUS to train on

To start training process with baseline experiment config run :

python main.py --experiment_cfg experiments/baseline.yaml

###Convert checkpoint of Pytorch-Lightning

To convert checkpoint of Pytorch-Lightning run:

python convert_checkpoint.py --checkpoint path_to_checkpoint --config_file path_to_config

###Inference

Inference config has the following structure:

MODEL: ... - What model to use

TEST: ... - This block is used to specify what samples to test on and where the data stored

To run inference python inference.py --config_file test.yaml