return total_loss / total_len
if torch.cuda.is_available():
if not args.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
eval_batch_size = 32
if args.test:
dictionary = Dictionary(args.data)
test = tokenize(dictionary, args.test)
print("Size, OOV", test.size(0), sum(test == dictionary.word2idx["<unk>"]))
test_data = batchify(test, eval_batch_size, args.cuda)
ntokens = len(dictionary)
else:
corpus = Corpus(args.data)
print("Size, OOV", corpus.test.size(0),
sum(corpus.test == corpus.dictionary.word2idx["<unk>"]))
test_data = batchify(corpus.test, eval_batch_size, args.cuda)
dictionary = corpus.dictionary
# Load the best saved model.
with open(args.checkpoint, 'rb') as f:
print("Loading the model")
if args.cuda:
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)
prev_word = None
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()
eval_batch_size = 1
seq_len = 20
dictionary = dictionary_corpus.Dictionary(args.data)
vocab_size = len(dictionary)
print("Vocab size", vocab_size)
print("TESTING")
# assuming the mask file contains one number per line indicating the index of the target word
index_col = 0
mask = create_target_mask(args.path + ".text", args.path + ".eval", index_col)
mask_data = batchify(torch.LongTensor(mask), eval_batch_size, args.cuda)
test_data = batchify(
dictionary_corpus.tokenize(dictionary, args.path + ".text"),
eval_batch_size, args.cuda)
f_output = open(args.path + ".output_" + args.suffix, 'w')
evaluate(test_data, mask_data)
f_output.close()
with open(args.save, 'rb') as f:
print("Loading the model")
if args.cuda:
model = torch.load(f)
model.cuda()
else:
# to convert model trained on cuda to cpu model
model = torch.load(f, map_location=lambda storage, loc: storage)
model.cpu()
model.eval()
eval_batch_size = 1
ntokens = len(dictionary)
#print("Vocab size", ntokens)
#print("TESTING")
# depends on generation script (constantly modified) - the column where the target word index is written
index_col = 3
mask = create_target_mask(args.test_path + ".text",
args.test_path + ".gold", index_col)
mask_data = batchify(torch.LongTensor(mask), eval_batch_size, False)
test_data = batchify(tokenize(dictionary, args.test_path + ".text"),
eval_batch_size, args.cuda)
f_output = open(args.test_path + ".output_" + args.suffix, 'w')
evaluate_on_mask(test_data, mask_data)
f_output.close()
eprint("Loading model from {}".format(args.checkpoint))
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()
eval_batch_size = 1
seq_len = 20
dictionary = dictionary_corpus.Dictionary(args.data)
vocab_size = len(dictionary)
idx2word = dictionary.idx2word
eprint("GLSTM vocab size", vocab_size)
id_tensor, tokens = dictionary_corpus.tokenize(dictionary, args.path)
test_data = batchify(id_tensor, eval_batch_size, args.cuda)
eprint("Computing surprisal for target words in {}".format(args.path))
print("word totsurp glstmunk")
# GLSTM cannot make predictions for the first token
print(tokens[0]+" inf 1")
evaluate(test_data, tokens[1:])
clean_sentence = sentence.replace('*', '')
target_indices = [
i for i, word in enumerate(split_sentence) if '*' in word
]
mask = np.zeros(np.shape(split_sentence))
mask.put(target_indices, 1)
mask_data = batchify(torch.LongTensor(mask), eval_batch_size,
args.cuda)
current_sentence_file = input_path[0] + '/' + 'current_sentence'
with open(current_sentence_file, 'w') as f:
f.write(clean_sentence)
test_data = batchify(
dictionary_corpus.tokenize(dictionary, current_sentence_file),
eval_batch_size, args.cuda)
outputs = evaluate(test_data, mask_data)
target_words = [
word.replace('*', '') for i, word in enumerate(split_sentence)
if '*' in word
]
try:
target_dict_indices = [
dictionary.word2idx[word] for word in target_words
]
for i in range(len(target_words)):
