if opt.layers != -1:
opt.enc_layers = opt.layers
opt.dec_layers = opt.layers
opt.brnn = (opt.encoder_type == "brnn")
opt.pre_word_vecs = os.path.join(opt.embd, 'embedding')
print(vars(opt))
json.dump(opt.__dict__, open(os.path.join(
opt.save_path, 'opt.json'), 'w'), sort_keys=True, indent=2)
#if torch.cuda.is_available():
torch.cuda.set_device(0)
device = torch.device("cuda")
set_seed(opt.seed)
# Set up the logging server.
# logger = Logger(os.path.join(opt.save_path, 'tb'))
def report_func(epoch, batch, num_batches,
start_time, lr, report_stats):
"""
This is the user-defined batch-level traing progress
report function.
Args:
epoch(int): current epoch count.
batch(int): current batch count.
num_batches(int): total number of batches.
# arg_parser.add_argument('-tgt_vocab', help="Path to an existing target vocabulary")
# arg_parser.add_argument('-report_every', type=int, default=100000, help="Report status every this many sentences")
# ---
options.set_common_options(arg_parser)
options.set_preprocess_options(arg_parser)
args = arg_parser.parse_args()
args.train_anno = os.path.join(args.root_dir, args.dataset, 'train.json')
args.valid_anno = os.path.join(args.root_dir, args.dataset, 'dev.json')
args.test_anno = os.path.join(args.root_dir, args.dataset, 'test.json')
args.save_data = os.path.join(args.root_dir, args.dataset)
if args.cuda and args.seed is not None:
set_seed(args.seed)
def main():
fields = table.IO.TableDataset.get_fields()
logger.info(" * building training")
train = table.IO.TableDataset(args.train_anno, fields, args.permute_order,
args, True)
if os.path.isfile(args.valid_anno):
logger.info(" * building valid")
valid = table.IO.TableDataset(args.valid_anno,
fields,
permute_order=0,
args=args,
