nb_candidates=opt_.hits_at_nb_cands)
valid_data = env.build_valid_dataloader(True)
test_data = env.build_valid_dataloader(True, test=True)
with torch.no_grad():
logging.info("Validating on the valid set -shuffle")
validate(0,
net,
valid_data,
is_test=False,
nb_candidates=opt_.hits_at_nb_cands)
logging.info("Validating on the hidden test set -shuffle")
validate(0,
net,
test_data,
is_test=True,
nb_candidates=opt_.hits_at_nb_cands)
else:
train_model(opt_)
if __name__ == "__main__":
opt = get_opt()
# Set random state
torch.manual_seed(opt.random_seed)
opt.cuda = opt.cuda and torch.cuda.is_available()
if opt.cuda:
torch.cuda.manual_seed(opt.random_seed)
# Set logging
logger = get_logger(opt)
main(opt)
"--task",
type=str,
choices=["dailydialog", "empchat", "reddit"],
default="empchat",
help="Dataset for context/target-response pairs",
)
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
torch.cuda.set_device(args.gpu)
logger.info(f"CUDA enabled (GPU {args.gpu:d})")
else:
logger.info("Running on CPU only.")
if args.fasttext is not None:
args.max_cand_length += args.fasttext
net, net_dictionary = load_model(args.model, get_opt(existing_opt=args))
if "bert_tokenizer" in net_dictionary:
if args.task == "dailydialog":
raise NotImplementedError(
"BERT model currently incompatible with DailyDialog!")
if args.bleu_dict is not None:
_, bleu_dictionary = load_model(args.bleu_dict, get_opt(existing_opt=args))
else:
bleu_dictionary = net_dictionary
paramnum = 0
trainable = 0
for parameter in net.parameters():
if parameter.requires_grad:
trainable += parameter.numel()
paramnum += parameter.numel()
print(paramnum, trainable)