def collate_fn_rnn(batch):
# # PyTorch RNN requires batches to be transposed for speed and integration with CUDA
transpose = lambda b: b.t_().squeeze(0).contiguous()
# Shape tensors in right format
sents_len_batch = [[len(sent) for sent in doc["sents"]] for doc in batch]
max_sent_len = max([max(s) for s in sents_len_batch])
sents_batch, doc_lens_batch = stack_and_pad_tensors([
torch.stack([pad_tensor(sent, max_sent_len) for sent in doc["sents"]])
for doc in batch
])
tags_batch, _ = stack_and_pad_tensors([doc["tags"] for doc in batch])
# Move to device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
sents_batch = sents_batch.to(device)
tags_batch = tags_batch.to(device)
if "encoding" in batch[0].keys(): # add doc encoding if applicable
encoding_batch = torch.stack([doc["encoding"]
for doc in batch]).to(device)
return (sents_batch, tags_batch, encoding_batch)
# return (word_ids_batch, seq_len_batch, label_batch)
return (sents_batch, tags_batch, None)
def parse_dataset(path,
label_to_idx,
word_to_idx,
pos_target=False,
pad_len=None,
encoding='latin-1',
max_len=100):
sentences = []
UNK = 3
PAD = 1
target_index = 1 if pos_target else 3
nr_long = 0
max_sus = 0
with open(path, encoding=encoding) as f:
sample = {'word_ids': [], 'labels': []}
max_len_token = 0
for line in f.read().splitlines():
if line in ['\n', '\r\n', '']: # end of sequence
if len(sample['labels']) > 100:
nr_long += 1
if (len(sample['labels']) > 0) and (len(sample['word_ids']) <
max_len):
max_sus = max(max_sus, len(sample['word_ids']))
sample['labels'] = torch.LongTensor(sample['labels'])
sentences.append(sample)
sample = {'word_ids': [], 'labels': []}
continue
else:
ls = line.split()
max_len_token = max(max_len_token, len(ls[4:]))
word = ls[4:]
label = ls[target_index]
if len(word) > 0:
word_ids = [
word_to_idx[w] if w in word_to_idx.keys() else UNK
for w in word
]
sample['word_ids'].append(
torch.LongTensor(word_ids)) # 3 -> <unk>
sample['labels'].append(label_to_idx[label])
if len(word_ids) > 20:
print(line)
# padd all BPE encodings to max length in dataset
if pad_len is not None:
max_len_token = max(pad_len, max_len_token)
for s in range(len(sentences)):
sen = sentences[s]
for i in range(len(sen['word_ids'])):
sen['word_ids'][i] = pad_tensor(sen['word_ids'][i],
length=max_len_token,
padding_index=PAD)
# stack word ids back together
sen['word_ids'] = torch.stack(sen['word_ids'], dim=0).view(-1)
print('max nr of SUs in sentence: {}'.format(max_sus))
print('Number of long sentences: {}'.format(nr_long))
return Dataset(sentences), max_len_token
def generate_encodings(self, data, labels):
encoder = StaticTokenizerEncoder(data,
tokenize=lambda s: s.split(),
min_occurrences=3)
encoded_data = [encoder.encode(document) for document in data]
encoded_data = [pad_tensor(x, length=10000) for x in encoded_data]
data = {'labels': labels, 'inputs': encoded_data}
return pd.DataFrame(data=data)
def tokenize_pos_tags(X_tags, tag_to_index, max_sen_len=800):
"""
One hot encodes pos tags
:param X_tags:
:param tag_to_index:
:param max_sen_len:
:return: One hot encoded vector
"""
return torch.nn.functional.one_hot(
torch.stack([pad_tensor(torch.LongTensor(lst), max_sen_len) for lst in X_tags]),
num_classes=max(tag_to_index.values()) + 1,
)
def preprocess_request(sentence, start_sign, end_sign, token, max_length):
sentence = " ".join(jieba.cut(sentence))
sentence = preprocess_sentence(start_sign, end_sign, sentence)
inputs = [token.get(i, 3) for i in sentence.split(' ')]
inputs = torch.tensor(inputs)
inputs = [
pad_tensor(tensor=inputs[:max_length],
length=max_length,
padding_index=0)
]
inputs = stack_and_pad_tensors(inputs)[0]
dec_input = torch.unsqueeze(torch.tensor([token[start_sign]]), 0)
return inputs, dec_input
def pad_and_stack_list_of_list(
list_of_list: list, max_sentence_len=800, pad_value=0, tensor_type=torch.FloatTensor
):
"""
:param list_of_list: list of list of sequence
:param max_sentence_len: defaults to 800
:param pad_value: defaults to 0
:param tensor_type: defaults to torch.FloatTensor
:return: stacked tensor
"""
padded = [
pad_tensor(tensor_type(lst), length=max_sentence_len, padding_index=pad_value)
for lst in list_of_list
]
stacked = torch.stack(padded)
return stacked
def collate_fn_transformer(batch): # multigpu implementation
# Shape tensors in right format
sents_len_batch = [[len(sent) for sent in doc["sents"]] for doc in batch]
max_sent_len = max([max(s) for s in sents_len_batch])
sents_batch, doc_lens_batch = stack_and_pad_tensors([
torch.stack([pad_tensor(sent, max_sent_len) for sent in doc["sents"]])
for doc in batch
])
tags_batch, _ = stack_and_pad_tensors([doc["tags"] for doc in batch])
# Move to device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
sents_batch = sents_batch.to(device)
tags_batch = tags_batch.to(device)
if "encoding" in batch[0].keys(): # add doc encoding if applicable
encoding_batch = torch.stack([doc["encoding"]
for doc in batch]).to(device)
return (sents_batch, tags_batch, encoding_batch)
# return (word_ids_batch, seq_len_batch, label_batch)
return (sents_batch, tags_batch, None)
def load_data(dict_fn,
data_fn,
batch_size,
start_sign,
end_sign,
checkpoint_dir,
max_length,
max_train_data_size=0):
"""
数据加载方法，主要将分词好的数据进行整理，过程中保存字典文件，方便后续其他功能
使用，方法返回处理好的dataset，steps_per_epoch，checkpoint_prefix
Args:
dict_fn: 将训练数据的字典保存，用于以后使用，路径
data_fn: 分词好的训练数据路径
batch_size: batch大小
start_sign: 开始标记
end_sign: 结束标记
checkpoint_dir: 检查点保存路径
max_length: 最大句子长度
max_train_data_size: 最大训练数据大小
Returns:
dataset: PyTorch的DataLoader
steps_per_epoch: 每轮的步数
checkpoint_prefix: 保存检查点的前缀
"""
print("训练数据读取中...")
(input_lang,
target_lang), diag_weight = read_tokenized_data(data_fn, start_sign,
end_sign,
max_train_data_size)
diag_weight = torch.tensor(diag_weight, dtype=torch.float32)
# 合并input，target用于生成统一的字典
lang = np.hstack((input_lang, target_lang))
print("读取完成，正在格式化训练数据...")
tokenizer = StaticTokenizerEncoder(sample=lang,
tokenize=lambda x: x.split())
# 将文本序列转换文token id之后，并进行填充
input_data = [
pad_tensor(tensor=tokenizer.encode(example)[:max_length],
length=max_length,
padding_index=0) for example in input_lang
]
target_data = [
pad_tensor(tensor=tokenizer.encode(example)[:max_length],
length=max_length,
padding_index=0) for example in target_lang
]
input_tensor = stack_and_pad_tensors(input_data)[0]
target_tensor = stack_and_pad_tensors(target_data)[0]
print("格式化完成，正在整理训练数据并保存字典")
word_index = {}
vocab_list = tokenizer.vocab
for i in range(tokenizer.vocab_size):
word_index[vocab_list[i]] = i
word_index[i] = vocab_list[i]
with open(dict_fn, 'w', encoding='utf-8') as file:
file.write(json.dumps(word_index, indent=4, ensure_ascii=False))
print("数据字典保存完成！")
dataset = PairDataset(input_tensor, target_tensor, diag_weight)
loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
steps_per_epoch = len(input_tensor) // batch_size
return loader, steps_per_epoch
def test_pad_tensor():
padded = pad_tensor(torch.LongTensor([1, 2, 3]), 5, DEFAULT_PADDING_INDEX)
assert padded.tolist() == [
1, 2, 3, DEFAULT_PADDING_INDEX, DEFAULT_PADDING_INDEX
]
def test_pad_tensor_multiple_dim_float_tensor():
padded = pad_tensor(torch.FloatTensor(778, 80), 804, DEFAULT_PADDING_INDEX)
assert padded.size() == (804, 80)
assert padded[-1].sum().item() == pytest.approx(0)
assert padded.type() == 'torch.FloatTensor'
def test_pad_tensor_multiple_dim():
padded = pad_tensor(torch.LongTensor(1, 2, 3), 5, DEFAULT_PADDING_INDEX)
assert padded.size() == (5, 2, 3)
assert padded[1].sum().item() == pytest.approx(0)
max_sentence_len=max_sentence_len,
pad_value=-1,
tensor_type=torch.FloatTensor,
)
ascii = pad_and_stack_list_of_list(
ascii,
max_sentence_len=max_sentence_len,
pad_value=-1,
tensor_type=torch.FloatTensor,
)
x_enriched_features = torch.stack(
(alnum, numeric, alpha, digit, lower, title, ascii), dim=2)
x_encoded = [x_encoder.encode(text) for text in X_text_list]
x_padded = [pad_tensor(tensor, max_sentence_len) for tensor in x_encoded]
x_padded = torch.LongTensor(torch.stack(x_padded))
x_char_padded = [[
pad_tensor(x_char_encoder.encode(char[:max_word_length]), max_word_length)
for char in word
] for word in X_text_list_as_is]
x_char_padded = [
pad_tensor(torch.stack(lst), max_sentence_len) for lst in x_char_padded
]
x_char_padded = torch.stack(x_char_padded).type(torch.LongTensor)
x_postag_padded = tokenize_pos_tags(X_tags,
tag_to_index=tag_to_index,
max_sen_len=max_sentence_len)
reviews = []
labels = []
# 丢弃过长的数据
for i in tqdm(range(len(encoded_texts))):
if len(encoded_texts[i]) < max_pad_length:
reviews.append(encoded_texts[i])
labels.append(1 if labels_as_list[i] == "positive" else 0)
assert len(reviews) == len(
labels), "The labels and feature lists should have the same time"
# 扩充单个sequence到最大序列长度
padded_dataset = []
for i in tqdm(range(len(reviews))):
padded_dataset.append(pad_tensor(reviews[i], int(max_pad_length)))
# preparing the final dataset:
X = torch.stack(padded_dataset)
y = torch.tensor(labels)
(y == 1).float().mean()
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.25,
random_state=42)
X_train, y_train = torch.tensor(X_train), torch.tensor(y_train)
X_test, y_test = torch.tensor(X_test), torch.tensor(y_test)
