model/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 Trainer
from utils import prepare_device


# 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 = data_loader.split_validation()

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

 # 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)

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