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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)
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