proactive_train.py

import argparse
import collections
import torch
import numpy as np
import data_loader.data_loaders as module_data
import model.loss as module_loss
import model.metric as module_metric
import model.model as module_arch
from parse_config import ConfigParser
from trainer import ProactiveTrainer, SparseTrainer
from utils import str2bool

# fix random seeds for reproducibility
SEED = 123
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(SEED)


def main(config):
    logger = config.get_logger('train')

    # setup data_loader instances
    data_loader = config.init_obj('data_loader', module_data)
    valid_data_loader = config.init_obj('valid_data_loader', module_data)

    # build model architecture, then print to console
    model = config.init_obj('arch', module_arch)
    logger.info(model)

    # get function handles of loss and metrics
    criterion = getattr(module_loss, config['loss'])
    metrics = [getattr(module_metric, met) for met in config['metrics']]

    # build optimizer, learning rate scheduler. delete every lines containing lr_scheduler for disabling scheduler
    trainable_params = filter(lambda p: p.requires_grad, model.parameters())
    optimizer = config.init_obj('optimizer', torch.optim, trainable_params)
    print("Config sparse_train", config["sparse_train"])
    if config["sparse_train"]:
        logger.debug("Starting sparse proactive trainer...")
        trainer = SparseTrainer(model, criterion, metrics, optimizer,
                                config=config,
                                data_loader=data_loader, valid_data_loader=valid_data_loader)
    else:
        logger.debug("Starting simple proactive trainer...")
        trainer = ProactiveTrainer(model, criterion, metrics, optimizer,
                                   config=config,
                                   data_loader=data_loader)

    trainer.train()


if __name__ == '__main__':
    args = argparse.ArgumentParser(description='PyTorch Template')
    args.add_argument('-c', '--config', default=None, type=str,
                      help='config file path (default: None)')
    args.add_argument('-r', '--resume', default=None, type=str,
                      help='path to latest checkpoint (default: None)')
    args.add_argument('-d', '--device', default=None, type=str,
                      help='indices of GPUs to enable (default: all)')

    # custom cli options to modify configuration from default values given in json file.
    CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')
    options = [
        CustomArgs(['--opt', '--optimizer'], type=str, target='optimizer'),
        CustomArgs(['--lr', '--learning_rate'], type=float, target='optimizer;args;lr'),
        CustomArgs(['--include_new'], type=str2bool, target='data_loader;args;always_include_new'),
        CustomArgs(['--keep_probs'], type=str2bool, target='data_loader;args;keep_probs'),
        CustomArgs(['--bs', '--batch_size'], type=int, target='data_loader;args;batch_size'),
        CustomArgs(['--ts', '--trigger_size'], type=int, target='data_loader;args;trigger_size'),
        CustomArgs(['--ws', '--window_size'], type=int, target='data_loader;args;window_size'),
        CustomArgs(['--n', '--name'], type=str, target='name'),
        CustomArgs(['--dr', '--deployment_ratio'], type=float, target='deployment_ratio'),
        CustomArgs(['--st', '--sparse_train'], type=str2bool, target='sparse_train'),
        CustomArgs(['--o', '--online_training'], type=str2bool, target='online_training'),
        CustomArgs(['--tu', '--train_until'], type=int, target='trainer;epochs'),
        CustomArgs(['--ct', '--compress_type'], type=str, target='compress_type'),
        CustomArgs(['--cmem', '--compress_memory'], type=str2bool, target='compress_memory'),
        CustomArgs(['--model_name'], type=str, target='arch;args;model_name'),
        CustomArgs(['--history_end'], type=int, target='data_loader;args;history_end'),

    ]
    config = ConfigParser.from_args(args, options)
    main(config)