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fix vocab

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This commit is contained in:
yunfan 2018-09-19 14:49:10 +08:00
parent 9c7f3cf261
commit 819c8f05be
7 changed files with 23 additions and 18 deletions
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10
fastNLP/core/predictor.py
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@ -27,8 +27,8 @@ class Predictor(object):
self.batch_output = []
self.pickle_path = pickle_path
self._task = task # one of ("seq_label", "text_classify")
self.index2label = load_pickle(self.pickle_path, "id2class.pkl")
self.word2index = load_pickle(self.pickle_path, "word2id.pkl")
self.label_vocab = load_pickle(self.pickle_path, "class2id.pkl")
self.word_vocab = load_pickle(self.pickle_path, "word2id.pkl")
def predict(self, network, data):
"""Perform inference using the trained model.
@ -82,7 +82,7 @@ class Predictor(object):
:return data_set: a DataSet instance.
"""
assert isinstance(data, list)
return create_dataset_from_lists(data, self.word2index, has_target=False)
return create_dataset_from_lists(data, self.word_vocab, has_target=False)
def prepare_output(self, data):
"""Transform list of batch outputs into strings."""
@ -97,14 +97,14 @@ class Predictor(object):
results = []
for batch in batch_outputs:
for example in np.array(batch):
results.append([self.index2label[int(x)] for x in example])
results.append([self.label_vocab.to_word(int(x)) for x in example])
return results
def _text_classify_prepare_output(self, batch_outputs):
results = []
for batch_out in batch_outputs:
idx = np.argmax(batch_out.detach().numpy(), axis=-1)
results.extend([self.index2label[i] for i in idx])
results.extend([self.label_vocab.to_word(i) for i in idx])
return results

10
fastNLP/fastnlp.py
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@ -69,7 +69,7 @@ class FastNLP(object):
:param model_dir: this directory should contain the following files:
1. a pre-trained model
2. a config file
3. "id2class.pkl"
3. "class2id.pkl"
4. "word2id.pkl"
"""
self.model_dir = model_dir
@ -99,10 +99,10 @@ class FastNLP(object):
print("Restore model hyper-parameters {}".format(str(model_args.data)))
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(self.model_dir, "word2id.pkl")
model_args["vocab_size"] = len(word2index)
index2label = load_pickle(self.model_dir, "id2class.pkl")
model_args["num_classes"] = len(index2label)
word_vocab = load_pickle(self.model_dir, "word2id.pkl")
model_args["vocab_size"] = len(word_vocab)
label_vocab = load_pickle(self.model_dir, "class2id.pkl")
model_args["num_classes"] = len(label_vocab)
# Construct the model
model = model_class(model_args)

4
reproduction/chinese_word_segment/run.py
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@ -32,7 +32,7 @@ def infer():
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "id2class.pkl")
index2label = load_pickle(pickle_path, "class2id.pkl")
test_args["num_classes"] = len(index2label)
@ -105,7 +105,7 @@ def test():
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "id2class.pkl")
index2label = load_pickle(pickle_path, "class2id.pkl")
test_args["num_classes"] = len(index2label)
# load dev data

4
reproduction/pos_tag_model/train_pos_tag.py
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@ -33,7 +33,7 @@ def infer():
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "id2class.pkl")
index2label = load_pickle(pickle_path, "class2id.pkl")
test_args["num_classes"] = len(index2label)
# Define the same model
@ -105,7 +105,7 @@ def test():
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "id2class.pkl")
index2label = load_pickle(pickle_path, "class2id.pkl")
test_args["num_classes"] = len(index2label)
# load dev data

9
test/core/test_predictor.py
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@ -4,6 +4,7 @@ import unittest
from fastNLP.core.predictor import Predictor
from fastNLP.core.preprocess import save_pickle
from fastNLP.models.sequence_modeling import SeqLabeling
from fastNLP.core.vocabulary import Vocabulary
class TestPredictor(unittest.TestCase):
@ -23,10 +24,14 @@ class TestPredictor(unittest.TestCase):
['a', 'b', 'c', 'd', '$'],
['!', 'b', 'c', 'd', 'e']
]
vocab = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, '!': 5, '@': 6, '#': 7, '$': 8, '?': 9}
vocab = Vocabulary()
vocab.word2idx = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, '!': 5, '@': 6, '#': 7, '$': 8, '?': 9}
class_vocab = Vocabulary()
class_vocab.word2idx = {"0":0, "1":1, "2":2, "3":3, "4":4}
os.system("mkdir save")
save_pickle({0: "0", 1: "1", 2: "2", 3: "3", 4: "4"}, "./save/", "id2class.pkl")
save_pickle(class_vocab, "./save/", "class2id.pkl")
save_pickle(vocab, "./save/", "word2id.pkl")
model = SeqLabeling(model_args)

2
test/model/seq_labeling.py
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@ -38,7 +38,7 @@ def infer():
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "id2class.pkl")
index2label = load_pickle(pickle_path, "class2id.pkl")
test_args["num_classes"] = len(index2label)
# Define the same model

2
test/model/test_cws.py
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@ -27,7 +27,7 @@ def infer():
# fetch dictionary size and number of labels from pickle files
word2index = load_pickle(pickle_path, "word2id.pkl")
test_args["vocab_size"] = len(word2index)
index2label = load_pickle(pickle_path, "id2class.pkl")
index2label = load_pickle(pickle_path, "class2id.pkl")
test_args["num_classes"] = len(index2label)
# Define the same model