def main(_):
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu_index
solver = Solver(FLAGS)
if FLAGS.is_train:
solver.train()
else:
solver.test()
def main():
# make a string that describes the current running setup
num = 0
run_setup_str = f"{args.source}2{args.target}_k_{args.num_k}_kq_{args.num_kq}_lamb_{args.lamb_marg_loss}"
while os.path.exists(f"record/{run_setup_str}_run_{num}.txt"):
num += 1
run_setup_str = f"{run_setup_str}_run_{num}"
# eg, svhn2mnist_k_4_kq_4_lamb_10.0_run_5
# set file names for records (storing training stats)
record_train = f"record/{run_setup_str}.txt"
record_test = f"record/{run_setup_str}_test.txt"
if not os.path.exists('record'):
os.mkdir('record') # create a folder for records if not exist
# set the checkpoint dir name (storing model params)
checkpoint_dir = f'checkpoint/{run_setup_str}'
if not os.path.exists('checkpoint'):
os.mkdir('checkpoint') # create a folder if not exist
if not os.path.exists(checkpoint_dir):
os.mkdir(checkpoint_dir) # create a folder if not exist
####
# create a solver: load data, create models (or load existing models),
# and create optimizers
solver = Solver(args,
source=args.source,
target=args.target,
nsamps_q=args.nsamps_q,
lamb_marg_loss=args.lamb_marg_loss,
learning_rate=args.lr,
batch_size=args.batch_size,
optimizer=args.optimizer,
num_k=args.num_k,
num_kq=args.num_kq,
all_use=args.all_use,
checkpoint_dir=checkpoint_dir,
save_epoch=args.save_epoch)
# run it (test or training)
if args.eval_only:
solver.test(0)
else: # training
count = 0
for t in range(args.max_epoch):
num = solver.train(t, record_file=record_train)
count += num
if t % 1 == 0: # run it on test data every epoch (and save models)
solver.test(t,
record_file=record_test,
save_model=args.save_model)
if count >= 20000 * 10:
break
def main(config):
os.environ["CUDA_VISIBLE_DEVICES"] = str(config["gpu_no"])
save_path = Path(config["training"]["save_path"])
save_path.mkdir(parents=True, exist_ok=True)
mode = config["mode"]
solver = Solver(config)
if mode == "train":
solver.train()
if mode == "test":
solver.test()
def main(config):
os.environ["CUDA_VISIBLE_DEVICES"] = str(config["gpu_no"])
mode = config["mode"]
save_path = Path(config["training"]["save_path"]) / config["version"]
save_path.mkdir(parents=True, exist_ok=True)
config["save_path"] = save_path
datasets = DataLoader(mode, **config["dataset"])
solver = Solver(config, datasets)
if mode == "train":
solver.train()
if mode == "test":
solver.test()
