train-classif.py [166:224]:
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        for k, v in scores.items():
            logger.info('%s -> %.6f' % (k, v))
        logger.info("__log__:%s" % json.dumps(scores))
        exit()

    # training
    for epoch in range(trainer.epoch, params.epochs):

        # update epoch / sampler / learning rate
        trainer.epoch = epoch
        logger.info("============ Starting epoch %i ... ============" % trainer.epoch)
        if params.multi_gpu:
            train_sampler.set_epoch(epoch)

        # update learning rate
        trainer.update_learning_rate()

        # train
        for i, (images, targets) in enumerate(train_data_loader):
            trainer.classif_step(images, targets)
            trainer.iter()

        logger.info("============ End of epoch %i ============" % trainer.epoch)

        # evaluate classification accuracy
        scores = evaluator.run_all_evals(trainer, evals=['classif'], data_loader=valid_data_loader)

        for name, val in trainer.get_scores().items():
            scores[name] = val

        # print / JSON log
        for k, v in scores.items():
            logger.info('%s -> %.6f' % (k, v))
        if params.is_master:
            logger.info("__log__:%s" % json.dumps(scores))

        # end of epoch
        trainer.save_best_model(scores)
        trainer.save_periodic()
        trainer.end_epoch(scores)


if __name__ == '__main__':

    # generate parser / parse parameters
    parser = get_parser()
    params = parser.parse_args()

    # debug mode
    if params.debug is True:
        params.exp_name = 'debug'
        params.debug_slurm = True
        params.debug_train = True

    # check parameters
    check_model_params(params)

    # run experiment
    main(params)
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train-logistic.py [177:235]:
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        for k, v in scores.items():
            logger.info('%s -> %.6f' % (k, v))
        logger.info("__log__:%s" % json.dumps(scores))
        exit()

    # training
    for epoch in range(trainer.epoch, params.epochs):

        # update epoch / sampler / learning rate
        trainer.epoch = epoch
        logger.info("============ Starting epoch %i ... ============" % trainer.epoch)
        if params.multi_gpu:
            train_sampler.set_epoch(epoch)

        # update learning rate
        trainer.update_learning_rate()

        # train
        for i, (images, targets) in enumerate(train_data_loader):
            trainer.classif_step(images, targets)
            trainer.iter()

        logger.info("============ End of epoch %i ============" % trainer.epoch)

        # evaluate classification accuracy
        scores = evaluator.run_all_evals(trainer, evals=['classif'], data_loader=valid_data_loader)

        for name, val in trainer.get_scores().items():
            scores[name] = val

        # print / JSON log
        for k, v in scores.items():
            logger.info('%s -> %.6f' % (k, v))
        if params.is_master:
            logger.info("__log__:%s" % json.dumps(scores))

        # end of epoch
        trainer.save_best_model(scores)
        trainer.save_periodic()
        trainer.end_epoch(scores)


if __name__ == '__main__':

    # generate parser / parse parameters
    parser = get_parser()
    params = parser.parse_args()

    # debug mode
    if params.debug is True:
        params.exp_name = 'debug'
        params.debug_slurm = True
        params.debug_train = True

    # check parameters
    check_model_params(params)

    # run experiment
    main(params)
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