coiltraine.py

import argparse

from coil_core import execute_train, execute_validation, execute_drive, folder_execute
from coilutils.general import create_log_folder, create_exp_path, erase_logs,\
                          erase_wrong_plotting_summaries, erase_validations


if __name__ == '__main__':
    argparser = argparse.ArgumentParser(description=__doc__)

    argparser.add_argument('--single-process', default=None, type=str, help='train / validation / drive')
    argparser.add_argument('--gpus', nargs='+', dest='gpus', type=str, help='gpu ids')
    argparser.add_argument('-f', '--folder', type=str, 'folder where the experiments are placed')
    argparser.add_argument('-e', '--exp', type=str, default='resnet34imnet10S1', help='experiment alias')
    argparser.add_argument('-vd', '--val-datasets', dest='validation_datasets', nargs='+', default=[])
    argparser.add_argument('--no-train', dest='is_training', action='store_false', help='use during validation and evaluation')
    argparser.add_argument('-de', '--drive-envs', dest='driving_environments', nargs='+', default=[], help='check drive/suites')
    argparser.add_argument('-v', '--verbose', action='store_true', dest='debug', help='print debug information')
    argparser.add_argument('-ns', '--no-screen', action='store_true', dest='no_screen', help='Set to carla to run offscreen')
    argparser.add_argument('-ebv', '--erase-bad-validations', action='store_true', dest='erase_bad_validations', help='erase the bad validations (Incomplete)')
    argparser.add_argument('-rv', '--restart-validations', action='store_true', dest='restart_validations', help='Set to carla to run offscreen')
    argparser.add_argument('-gv', '--gpu-value', dest='gpu_value', type=float, default=3.5, help='total gpu cost')
    argparser.add_argument('-nw', '--number-of-workers', dest='number_of_workers', type=int, default=12)
    argparser.add_argument('-dk', '--docker', dest='docker', default='carlasim/carla:0.8.4', type=str, help='Set to run carla using docker')
    argparser.add_argument('-rc', '--record-collisions', action='store_true', dest='record_collisions', help='Set to run carla using docker')

    args = argparser.parse_args()

    # Check if the vector of GPUs passed are valid.
    for gpu in args.gpus:
        try:
            int(gpu)
        except ValueError: 
            raise ValueError("GPU is not a valid int number")

    # Check if the mandatory folder argument is passed
    if args.folder is None:
        raise ValueError("You should set a folder name where the experiments are placed")

    # Check if the driving parameters are passed in a correct way
    if args.driving_environments is not None:
        for de in list(args.driving_environments):
            if len(de.split('_')) < 2:
                raise ValueError("Invalid format for the driving environments should be Suite_Town")

    create_log_folder(args.folder)

    if args.erase_bad_validations:
        erase_wrong_plotting_summaries(args.folder, list(args.validation_datasets))
    if args.restart_validations:
        erase_validations(args.folder, list(args.validation_datasets))

    # The definition of parameters for driving
    drive_params = {
        "suppress_output": True,
        "no_screen": args.no_screen,
        "docker": args.docker,
        "record_collisions": args.record_collisions
    }

    # There are two modes of execution
    if args.single_process is not None:
        ####
        # MODE 1: Execute train, validation and evaluation separately
        ####

        if args.exp is None:
            raise ValueError(" You should set the exp alias when using single process")

        create_exp_path(args.folder, args.exp)
        # args.number_of_workers = 1
        if args.single_process == 'train':
            execute_train(gpu=args.gpus, exp_batch=args.folder, exp_alias=args.exp,
                          suppress_output=False, number_of_workers= args.number_of_workers)

        elif args.single_process == 'validation':
            execute_validation(gpu=args.gpus, exp_batch=args.folder, exp_alias=args.exp,
                               dataset=args.validation_datasets[0], suppress_output=False)

        elif args.single_process == 'drive':
            drive_params['suppress_output'] = False
            execute_drive(args.gpus[0], args.folder, args.exp, list(args.driving_environments)[0], drive_params)

        else:
            raise Exception("Invalid name for single process, chose from (train, validation, test)")

    else:
        ####
        # MODE 2: Folder execution. Execute train/validation/drive for all experiments in a certain folder
        ####
        # We set by default that each gpu has a value of 3.5, allowing a training and a driving/validation
        allocation_parameters = {'gpu_value': args.gpu_value,
                                 'train_cost': 1.5,
                                 'validation_cost': 1.0,
                                 'drive_cost': 1.5}

        params = {
            'folder': args.folder,
            'gpus': list(args.gpus),
            'is_training': args.is_training,
            'validation_datasets': list(args.validation_datasets),
            'driving_environments': list(args.driving_environments),
            'driving_parameters': drive_params,
            'allocation_parameters': allocation_parameters,
            'number_of_workers': args.number_of_workers

        }

        folder_execute(params)
        print("SUCCESSFULLY RAN ALL EXPERIMENTS")