train.py

import argparse
import collections
import random

import numpy as np
import torch
import torch.nn as nn
from torchinfo import summary

import data_loader.data_loaders as module_data
import model as module_arch
import model.loss as module_loss
import model.metric as module_metric
from parse_config import ConfigParser
from trainer import Trainer
from utils import get_logger, prepare_device


def main(config):
    logger = get_logger(name=__name__, log_dir=config.log_dir,
                        verbosity=config['trainer']['verbosity'])
    torch.backends.cudnn.benchmark = True
    if config['seed'] is not None:
        torch.manual_seed(config['seed'])
        torch.backends.cudnn.deterministic = True
        np.random.seed(config['seed'])
        random.seed(config['seed'])
        logger.warning('You seeded the training. '
                       'This turns on the CUDNN deterministic setting, '
                       'which can slow down your training '
                       'You may see unexpected behavior when restarting '
                       'from checkpoints.')
    # setup data_loader instances
    data_loader_obj = config.init_obj('data_loader', module_data)
    data_loader = data_loader_obj.get_train_loader()
    valid_data_loader = data_loader_obj.get_valid_loader()

    # build model architecture, then print to console
    model = config.init_obj('arch', module_arch)
    # prepare for (multi-device) GPU training
    device, device_ids = prepare_device(config['n_gpu'])
    model = model.to(device)
    if len(device_ids) > 1:
        model = torch.nn.DataParallel(model, device_ids=device_ids)
    trainable_params = filter(lambda p: p.requires_grad, model.parameters())
    logger.info(summary(model, input_size=[
                config['data_loader']['args']['batch_size']]+config['input_size'], verbose=0))
    logger.info('Trainable parameters: {}'.format(
        sum([p.numel() for p in trainable_params])))

    # 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.
    optimizer = config.init_obj('optimizer', torch.optim, model.parameters())
    lr_scheduler = config.init_obj(
        'lr_scheduler', torch.optim.lr_scheduler, optimizer)

    trainer = Trainer(model, criterion, metrics, optimizer,
                      config=config,
                      device=device,
                      data_loader=data_loader,
                      valid_data_loader=valid_data_loader,
                      lr_scheduler=lr_scheduler)

    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 help')
    options = [
        CustomArgs(['--lr', '--learning_rate'], type=float,
                   target='optimizer;args;lr', help=""),
        CustomArgs(['--bs', '--batch_size'], type=int,
                   target='data_loader;args;batch_size', help="")
    ]
    config = ConfigParser.from_args(args, options)
    main(config)