r"""
.. todo::
doc
"""
import numpy as np
import torch
from torch import nn as nn
from ..core.vocabulary import Vocabulary
__all__ = [
'get_embeddings',
'get_sinusoid_encoding_table'
]
def _construct_char_vocab_from_vocab(vocab: Vocabulary, min_freq: int = 1, include_word_start_end=True):
r"""
给定一个word的vocabulary生成character的vocabulary.
:param vocab: 从vocab
:param min_freq:
:param include_word_start_end: 是否需要包含特殊的<bow>和<eos>
:return:
"""
char_vocab = Vocabulary(min_freq=min_freq)
for word, index in vocab:
if not vocab._is_word_no_create_entry(word):
char_vocab.add_word_lst(list(word))
if include_word_start_end:
char_vocab.add_word_lst(['<bow>', '<eow>'])
return char_vocab
[文档]def get_embeddings(init_embed, padding_idx=None):
r"""
根据输入的init_embed返回Embedding对象。如果输入是tuple, 则随机初始化一个nn.Embedding; 如果输入是numpy.ndarray, 则按照ndarray
的值将nn.Embedding初始化; 如果输入是torch.Tensor, 则按该值初始化nn.Embedding; 如果输入是fastNLP中的embedding将不做处理
返回原对象。
:param init_embed: 可以是 tuple:(num_embedings, embedding_dim), 即embedding的大小和每个词的维度;也可以传入
nn.Embedding 对象, 此时就以传入的对象作为embedding; 传入np.ndarray也行，将使用传入的ndarray作为作为Embedding初始化;
传入torch.Tensor, 将使用传入的值作为Embedding初始化。
:param padding_idx: 当传入tuple时，padding_idx有效
:return nn.Embedding: embeddings
"""
if isinstance(init_embed, tuple):
res = nn.Embedding(
num_embeddings=init_embed[0], embedding_dim=init_embed[1], padding_idx=padding_idx)
nn.init.uniform_(res.weight.data, a=-np.sqrt(3 / res.weight.data.size(1)),
b=np.sqrt(3 / res.weight.data.size(1)))
elif isinstance(init_embed, nn.Module):
res = init_embed
elif isinstance(init_embed, torch.Tensor):
res = nn.Embedding.from_pretrained(init_embed, freeze=False)
elif isinstance(init_embed, np.ndarray):
init_embed = torch.tensor(init_embed, dtype=torch.float32)
res = nn.Embedding.from_pretrained(init_embed, freeze=False)
else:
raise TypeError(
'invalid init_embed type: {}'.format((type(init_embed))))
return res
def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
"""
sinusoid的embedding，其中position的表示中，偶数维(0,2,4,...)是sin, 奇数(1,3,5...)是cos
:param int n_position: 一共多少个position
:param int d_hid: 多少维度，需要为偶数
:param padding_idx:
:return: torch.FloatTensor, shape为n_position x d_hid
"""
def cal_angle(position, hid_idx):
return position / np.power(10000, 2 * (hid_idx // 2) / d_hid)
def get_posi_angle_vec(position):
return [cal_angle(position, hid_j) for hid_j in range(d_hid)]
sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
if padding_idx is not None:
# zero vector for padding dimension
sinusoid_table[padding_idx] = 0.
return torch.FloatTensor(sinusoid_table)
def _check_vocab_has_same_index(vocab, other_vocab):
"""
检查两个vocabulary是否含有相同的word idx
:param Vocabulary vocab:
:param Vocabulary other_vocab:
:return:
"""
if other_vocab != vocab:
for word, word_ix in vocab:
other_word_idx = other_vocab.to_index(word)
assert other_word_idx == word_ix, f"Word {word} has different index in vocabs, {word_ix} Vs. {other_word_idx}."