def get_scores(config, task, model_path, word_dict_path, label_dict_path, input_path, \
lower_case=True, allow_new_words=True, replace_vocab=True):
with Timer('Data loading'):
print ('Task: {}'.format(task))
print ('Allow new words in test data: {}. Lower case words: {}'.format(allow_new_words, lower_case))
# Load word and tag dictionary
word_dict = Dictionary(unknown_token=UNKNOWN_TOKEN)
label_dict = Dictionary()
word_dict.load(word_dict_path)
label_dict.load(label_dict_path)
data = TaggerData(config, [], [], word_dict, label_dict, None, None)
# Load test data.
if task == 'srl':
test_sentences, emb_inits, emb_shapes = reader.get_srl_test_data(
input_path,
config,
data.word_dict,
data.label_dict,
lower_case,
allow_new_words)
else:
test_sentences, emb_inits, emb_shapes = reader.get_postag_test_data(
input_path,
config,
data.word_dict,
data.label_dict,
lower_case,
allow_new_words)
print ('Read {} sentences.'.format(len(test_sentences)))
# Add pre-trained embeddings for new words in the test data.
#if allow_new_words:
data.embedding_shapes = emb_shapes
data.embeddings = emb_inits
# Batching.
test_data = data.get_test_data(test_sentences, batch_size=config.dev_batch_size)
with Timer('Model building and loading'):
model = BiLSTMTaggerModel(data, config=config, fast_predict=True)
model.load(model_path, word_dict, replace_vocab)
dist_function = model.get_distribution_function()
with Timer('Running model'):
scores = None
for i, batched_tensor in enumerate(test_data):
x, _, num_tokens, weights = batched_tensor
p, sc = dist_function(x, weights)
scores = numpy.concatenate((scores, sc), axis=0) if i > 0 else sc
return scores, data, test_sentences, test_data
def get_scores(config, task, model_path, word_dict_path, label_dict_path, syntactic_dict_path, input_path):
with Timer('Data loading'):
print ('Task: {}'.format(task))
allow_new_words = True
print ('Allow new words in test data: {}'.format(allow_new_words))
# Load word and tag dictionary
word_dict = Dictionary(padding_token=PADDING_TOKEN, unknown_token=UNKNOWN_TOKEN) # word tokens to Dict
label_dict, syntactic_dict = Dictionary(), Dictionary()
word_dict.load(word_dict_path)
label_dict.load(label_dict_path)
syntactic_dict.load(syntactic_dict_path)
data = TaggerData(config, [], [], word_dict, label_dict, None, None)
data.syntactic_dict = syntactic_dict
# Load test data.
if task == 'srl':
test_sentences, emb_inits, emb_shapes = reader.get_srl_test_data(
input_path,
config,
data.word_dict,
data.label_dict,
allow_new_words)
else:
test_sentences, emb_inits, emb_shapes = reader.get_postag_test_data(
input_path,
config,
data.word_dict,
data.label_dict,
allow_new_words)
print ('Read {} sentences.'.format(len(test_sentences)))
# Add pre-trained embeddings for new words in the test data.
# if allow_new_words:
data.embedding_shapes = emb_shapes
data.embeddings = emb_inits
# Batching.
test_data = data.get_test_data(test_sentences, batch_size=config.dev_batch_size)
with Timer('Syntactic Information Extracting'): # extract the syntactic information from file
test_dep_trees = SyntacticCONLL()
test_dep_trees.read_from_file(args.input_dep_trees)
# generate the syntactic label dict in training corpus
data.syntactic_dict.accept_new = False
test_dep_trees.get_syntactic_label_dict(data.syntactic_dict)
with Timer('Model building and loading'):
model = BiLSTMTaggerModel(data, config=config, gpu_id=args.gpu)
model.load(model_path)
for param in model.parameters():
print param.size()
if args.gpu:
print("Initialize the model with GPU!")
model = model.cuda()
with Timer('Running model'):
scores = []
model.eval()
for i, batched_tensor in enumerate(test_data):
x, y, lengths, weights = batched_tensor
word_inputs_seqs, predicate_inputs_seqs, syn_label_inputs_seqs, pes, answers, input_lengths, masks, padding_answers = \
batch_data_variable(test_dep_trees, None, x, y, lengths, weights)
if args.gpu:
word_inputs_seqs, predicate_inputs_seqs, syn_label_inputs_seqs, input_lengths, masks, \
padding_answers = \
word_inputs_seqs.cuda(), predicate_inputs_seqs.cuda(), syn_label_inputs_seqs.cuda(), \
input_lengths.cuda(), masks.cuda(), padding_answers.cuda()
sc = model.forward(word_inputs_seqs, predicate_inputs_seqs, syn_label_inputs_seqs, pes, input_lengths)
sc = sc.data.cpu().numpy() if args.gpu else sc.data.numpy()
sc = [sc[j] for j in range(sc.shape[0])]
scores.extend(sc)
return scores, data, test_sentences, test_data
def get_scores(config, task, model_path, word_dict_path, label_dict_path,
tpf_dict_path, input_path):
with Timer('Data loading'):
print('Task: {}'.format(task))
allow_new_words = True
print('Allow new words in test data: {}'.format(allow_new_words))
# Load word and tag dictionary
word_dict = Dictionary(
padding_token=PADDING_TOKEN,
unknown_token=UNKNOWN_TOKEN) # word tokens to Dict
label_dict = Dictionary()
tpf_dict = Dictionary()
word_dict.load(word_dict_path)
label_dict.load(label_dict_path)
tpf_dict.load(tpf_dict_path)
data = TaggerData(config, [], [], word_dict, label_dict, None, None)
data.tpf2_dict = tpf_dict
# Load test data.
if task == 'srl':
test_sentences, emb_inits, emb_shapes = reader.get_srl_test_data(
input_path, config, data.word_dict, data.label_dict,
allow_new_words)
else:
test_sentences, emb_inits, emb_shapes = reader.get_postag_test_data(
input_path, config, data.word_dict, data.label_dict,
allow_new_words)
print('Read {} sentences.'.format(len(test_sentences)))
# Add pre-trained embeddings for new words in the test data.
# if allow_new_words:
data.embedding_shapes = emb_shapes
data.embeddings = emb_inits
# Batching.
test_data = data.get_test_data(test_sentences,
batch_size=config.dev_batch_size)
with Timer("Get test sentences dict"):
test_sentences_w_id = []
for sen in get_srl_sentences(args.input):
test_sentences_w_id.append(' '.join(sen[1]))
test_sentences_ids = [int(sen[0][0]) for sen in test_sentences]
temp = {}
assert len(test_sentences_w_id) == len(test_sentences_ids)
for idx, sen in zip(test_sentences_ids, test_sentences_w_id):
temp[idx] = sen
test_sentences_w_id = temp
with Timer("Loading ELMO"):
test_elmo_hdf5 = hdf5_reader()
test_elmo_hdf5.read_from_file(args.input_elmo, test_sentences_w_id)
with Timer('Syntactic Information Extracting'
): # extract the syntactic information from file
test_dep_trees = SyntacticCONLL()
test_dep_trees.read_from_file(args.input_dep_trees)
with Timer("TPF2 generating..."):
# generate the tree-based position features according the Dependency Tree.
data.tpf2_dict.accept_new = False
test_tpf2 = test_dep_trees.get_tpf2_dict(data.test_tensors,
data.tpf2_dict)
print("Extract {} test TPF2 features".format(len(test_tpf2)))
assert len(test_tpf2) == len(data.test_tensors)
with Timer('Model building and loading'):
model = BiLSTMTaggerModel(data, config=config, gpu_id=args.gpu)
model.load(model_path)
for param in model.parameters():
print(param.size())
if args.gpu:
print("Initialize the model with GPU!")
model = model.cuda()
with Timer('Running model'):
scores = []
model.eval()
for i, batched_tensor in enumerate(test_data):
x, y, lengths, weights = batched_tensor
word_inputs_seqs, predicate_inputs_seqs, tpf_ids, sentences_ids, answers, input_lengths, masks, padding_answers = \
batch_data_variable(test_tpf2, x, y, lengths, weights)
elmo_representations = test_elmo_hdf5.forward(
sentences_ids,
word_inputs_seqs.size()[-1], [len(ans) for ans in answers])
if args.gpu:
word_inputs_seqs, predicate_inputs_seqs, tpf_ids, input_lengths, masks, padding_answers = \
word_inputs_seqs.cuda(), predicate_inputs_seqs.cuda(), tpf_ids.cuda(), input_lengths.cuda(), masks.cuda(), padding_answers.cuda()
elmo_representations = elmo_representations.cuda()
sc = model.forward(word_inputs_seqs, predicate_inputs_seqs,
tpf_ids, elmo_representations, input_lengths)
sc = sc.data.cpu().numpy() if args.gpu else sc.data.numpy()
sc = [sc[j] for j in range(sc.shape[0])]
scores.extend(sc)
return scores, data, test_sentences, test_data
# Load test data.
if task == 'srl':
test_sentences, emb, emb_shapes = reader.get_srl_test_data(
args.input, config, data.word_dict, data.head_dict,
data.char_dict, data.label_dict, allow_new_words)
eval_data = load_eval_data(args.input)
print('Read {} sentences.'.format(len(test_sentences)))
# Add pre-trained embeddings for new words in the test data.
# if allow_new_words:
data.word_embeddings = emb[0]
data.head_embeddings = emb[1]
data.word_embedding_shapes = emb_shapes[0]
data.head_embedding_shapes = emb_shapes[1]
# Batching.
test_data = data.get_test_data(test_sentences,
batch_size=config.dev_batch_size)
with Timer('Model building and loading'):
model = BiLSTMTaggerModel(data, config=config, gpu_id=args.gpu)
model.load(model_path)
for param in model.parameters():
print param.size()
if args.gpu:
print("Initialize the model with GPU!")
model = model.cuda()
with Timer('Running model'):
dev_loss = 0.0
srl_predictions = []
# with torch.no_grad(): # Eval don't need the grad