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from __future__ import print_function
import torch.backends.cudnn as cudnn
import torch
import torchvision.transforms as transforms
import argparse
import os
import random
import sys
import pprint
import datetime
import dateutil
import dateutil.tz
dir_path = (os.path.abspath(os.path.join(os.path.realpath(__file__), './.')))
sys.path.append(dir_path)
from miscc.datasets import TextDataset
from miscc.config import cfg, cfg_from_file
from miscc.utils import mkdir_p
from trainer import GANTrainer
def parse_args():
parser = argparse.ArgumentParser(description='Train a GAN network')
parser.add_argument('--cfg', dest='cfg_file',
help='optional config file',
default='birds_stage1.yml', type=str)
parser.add_argument('--gpu', dest='gpu_id', type=str, default='0')
parser.add_argument('--data_dir', dest='data_dir', type=str, default='')
parser.add_argument('--manualSeed', type=int, help='manual seed')
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.gpu_id != -1:
cfg.GPU_ID = args.gpu_id
if args.data_dir != '':
cfg.DATA_DIR = args.data_dir
print('Using config:')
pprint.pprint(cfg)
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
if cfg.CUDA:
torch.cuda.manual_seed_all(args.manualSeed)
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
output_dir = '../output/%s_%s_%s' % \
(cfg.DATASET_NAME, cfg.CONFIG_NAME, timestamp)
num_gpu = len(cfg.GPU_ID.split(','))
if cfg.TRAIN.FLAG:
dataset = TextDataset(cfg.DATA_DIR, 'train',
rirsize=cfg.RIRSIZE)
assert dataset
#commented for temporary
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=cfg.TRAIN.BATCH_SIZE * num_gpu,
drop_last=True, shuffle=True, num_workers=int(cfg.WORKERS))
algo = GANTrainer(output_dir)
algo.train(dataloader, cfg.STAGE)
else:
file_path = cfg.EVAL_DIR
algo = GANTrainer(output_dir)
algo.sample(file_path, cfg.STAGE)
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