torch.cuda.manual_seed(args.seed)
with open(args.checkpoint, 'rb') as f:
if args.cuda:
model = torch.load(f)
else:
# to convert model trained on cuda to cpu model
model = torch.load(f, map_location=lambda storage, loc: storage)
model.eval()
if args.cuda:
model.cuda()
else:
model.cpu()
dictionary = dictionary_corpus.Dictionary(args.data)
vocab_size = len(dictionary)
prefix = dictionary_corpus.tokenize(dictionary, args.prefixfile)
def _get_predictions_inner(sentences, model, dictionary, seed, device="cpu"):
"""
Returns torch tensors. See `get_predictions` for Numpy returns.
"""
ntokens = dictionary.__len__()
with torch.no_grad():
for i, sentence in enumerate(sentences):
torch.manual_seed(seed)
hidden = model.init_hidden(1)
input = torch.randint(ntokens, (1, 1), dtype=torch.long).to(device)
print("final average: " + str(np.mean(ratings)))
with open(args.results, 'w') as f:
f.write(recall_str)
with open(args.model, 'rb') as f:
if args.cuda:
model = torch.load(f)
else:
import warnings
warnings.filterwarnings("ignore")
model = torch.load(f, map_location = lambda storage, loc: storage)
if args.cuda:
model.cuda()
model.eval()
total_loss = 0
eval_batch_size = 1
hidden = model.init_hidden(eval_batch_size)
dictionary = dictionary_corpus.Dictionary("CHILDES")
with torch.no_grad():
if args.interactive:
interactive(word = "", hidden = hidden, dictionary = dictionary, model = model)
elif args.eval != "none":
auto_eval(hidden = hidden, dictionary = dictionary, model = model)
elif args.gen:
generate(hidden = hidden, dictionary = dictionary, model = model)
else:
parser.print_help()
