def run(args, myargs):
my_config = getattr(myargs.config, args.command)
config = AugmentConfig()
for k, v in args.items():
assert not hasattr(config, k)
setattr(config, k, v)
for k, v in my_config.items():
if not hasattr(config, k):
print('* config does not have %s'%k)
setattr(config, k, v)
device = torch.device("cuda")
writer = myargs.writer
writer.add_text('all_config', config.as_markdown(), 0)
logger = myargs.logger
config.print_params(logger.info_msg)
config.genotype = gt.from_str(config.genotype)
config.data_path = os.path.expanduser(config.data_path)
config.plot_path = os.path.join(args.outdir, 'plot')
config.path = args.outdir
main(config=config, logger=logger, device=device,
myargs=myargs)
from config import AugmentConfig
import utils
from models.augment_cnn import AugmentCNN
from utils import *
config = AugmentConfig()
device = torch.device("cuda")
# tensorboard
writer = SummaryWriter(log_dir=os.path.join(config.path, "tb"))
writer.add_text('config', config.as_markdown(), 0)
logger = utils.get_logger(
os.path.join(config.path, "{}.log".format(config.name)))
config.print_params(logger.info)
def main():
logger.info("Logger is set - training start")
logger.info("Torch version is: {}".format(torch.__version__))
logger.info("Torch_vision version is: {}".format(torchvision.__version__))
# set default gpu device id
torch.cuda.set_device(config.gpus[0])
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)