def main(argv):
print("Torch GPU set:", argv.gpu_id)
torch.manual_seed(argv.seed)
print("Torch Seed was:", argv.seed)
utils.mkdir(os.path.join(argv.res_root, argv.model_folder))
loader = DataLoader(argv)
loader.read_data()
argv.desired_len = loader.desired_len
# assert loader.max_idx == argv.max_idx
is_nn = (argv.model != 'nb')
model = create_model(argv, loader)
# load chkpt
checkpoint = None
if argv.mode == 'test' or argv.use_checkpoint:
chkpt_load_path = None
for file in os.listdir(os.path.join(argv.res_root, argv.model_folder)):
if f"{argv.checkpoint_name}_epoch{argv.checkpoint_ver}" in file:
chkpt_load_path = os.path.join(argv.res_root,
argv.model_folder, file)
break
if chkpt_load_path is None:
raise Exception("Can't find checkpoint")
print(f"\tLoading {chkpt_load_path}")
checkpoint = torch.load(chkpt_load_path)
# old argv content that still want to keep
checkpoint['argv'].output = argv.output
checkpoint['argv'].mode = argv.mode
checkpoint['argv'].use_checkpoint = argv.use_checkpoint
assert checkpoint['epoch'] == argv.checkpoint_ver
checkpoint['argv'].checkpoint_ver = argv.checkpoint_ver
argv = checkpoint['argv']
epoch = checkpoint['epoch'] + 1
model = create_model(argv, loader)
loader.desired_len = argv.desired_len
loader.batch_size = argv.batch_size
if USE_CUDA and is_nn:
torch.cuda.set_device(argv.gpu_id)
model = model.cuda()
print(f"\n{argv.mode} {type(model).__name__} {'#'*50}")
if argv.mode == 'test':
model.load_state_dict(checkpoint['model_state_dict'])
test_model(model, loader, is_nn)
else:
train_model(model, loader, checkpoint, is_nn)
print()
from argparse import Namespace
data_path = "./src_zzc/data/Training.txt"
dest = "./src_zzc/data_ensemble"
argv = Namespace()
argv.data_path = data_path
argv.batch_size = 50
argv.fold = 5
argv.mode = 'train'
argv.desired_len_percent = 0.5
argv.num_label = 20
argv.seed = 1587064891
loader = DataLoader(argv)
loader.read_data()
train = open(os.path.join(dest, 'Training.txt'), 'w')
train_label = open(os.path.join(dest, 'Training_Label.txt'), 'w')
for gt, msg in loader.data:
train_label.write(f"{gt}\n")
train.write(",".join([str(word) for word in msg]))
train.write("\n")
train.close()
train_label.close()
train = open(os.path.join(dest, 'Training_val.txt'), 'w')
train_label = open(os.path.join(dest, 'Training_val_Label.txt'), 'w')
for gt, msg in loader.data_val:
train_label.write(f"{gt}\n")