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train.py
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train.py
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import torch
import argparse
import data.dataset as dataset_module
import data.dataloader as dataloader_module
import model as model_module
from model import *
from trainer import *
from parse_config import ConfigParser
from utils import weights_init
def main(config):
logger = config.get_logger(name='train')
# fix random seeds for reproducibility
seed = config.set_seed()
logger.info('seed: {}'.format(seed))
train_dataset = config.init_obj('train_dataset', dataset_module, **{'seed': seed})
valid_dataset = config.init_obj('valid_dataset', dataset_module, **{'seed': seed})
data_loader = config.init_obj('train_data_loader', dataloader_module, **{'dataset': train_dataset})
valid_data_loader = config.init_obj('valid_data_loader', dataloader_module, **{'dataset': valid_dataset})
model = config.init_obj('arch', model_module)
weights_init(model, seed=seed)
logger.debug(model)
trainable_params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = config.init_obj('optimizer', torch.optim, trainable_params)
if 'lr_scheduler' in config.config:
lr_scheduler = config.init_obj('lr_scheduler', torch.optim.lr_scheduler, optimizer)
else:
lr_scheduler = None
Trainer = eval(config['trainer']['trainer_type'])
trainer = Trainer(model, optimizer,
config=config,
data_loader=data_loader,
valid_data_loader=valid_data_loader,
lr_scheduler=lr_scheduler)
trainer.train()
if __name__ == '__main__':
args = argparse.ArgumentParser()
args.add_argument('-c', '--config', default=None, type=str, required=True,
help='path to config file (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)')
args.add_argument('-s', '--seed', default=1234, type=str)
config = ConfigParser.from_args(args)
main(config)