-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathdata.py
128 lines (97 loc) · 3.35 KB
/
data.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
import torch
from torch import nn, optim
import torch.utils.data as torch_data
import torch.nn.functional as F
import numpy as np
import random
import sys
from src import batch_seqs
EPS = 1e-7
def encode(data, vocab_x, vocab_y):
encoded = []
for datum in data:
encoded.append(encode_io(datum, vocab_x, vocab_y))
return encoded
def encode_io(datum, vocab_x, vocab_y):
inp, out = datum
return ([vocab_x.sos()] + vocab_x.encode(inp) + [vocab_x.eos()], [vocab_y.sos()] + vocab_y.encode(out) + [vocab_y.eos()])
def encode_io_with_idx(datum, vocab_x, vocab_y, index):
inp, out = datum
return ([vocab_x.sos()] + vocab_x.encode(inp) + [vocab_x.eos()], [vocab_y.sos()] + vocab_y.encode(out) + [vocab_y.eos()], index)
def eval_format(vocab, seq):
if vocab.eos() in seq:
seq = seq[:seq.index(vocab.eos())+1]
if seq[0] == vocab.sos():
seq = seq[1:]
if seq[-1] == vocab.eos():
seq = seq[:-1]
return vocab.decode(seq)
def eval_format_probs(prob_seq, vocab, seq):
prob_seq = prob_seq[:len(seq)+1]
token_prob_lst = []
for tok, prob in zip(seq, prob_seq):
token_prob_lst.append(tok+' '+str(prob))
token_prob_lst.append('eos' + ' ' + str(prob_seq[-1]))
token_with_prob_str = '\t'.join(token_prob_lst)
return token_with_prob_str
def pred_to_ref_format(pred, vocab):
if pred[0] != vocab.sos():
pred = [vocab.sos()] + pred
if pred[-1] != vocab.eos():
pred = pred + [vocab.eos()]
return pred
def preds_to_refs_batch(preds, vocab, return_len=False):
preds = [pred_to_ref_format(p, vocab) for p in preds]
max_len = max([len(p) for p in preds])
all_lens = torch.LongTensor(list(map(len, preds)))
ref_batch = torch.zeros((len(preds),max_len) , dtype=torch.long)
for i, p in enumerate(preds):
ref_batch[i, :len(p)] = torch.LongTensor(p)
ref_batch = ref_batch.transpose(0,1)
if return_len:
return ref_batch, all_lens
else:
return ref_batch
def collate(batch):
batch = sorted(batch,
key=lambda x: len(x[0]),
reverse=True)
if len(batch[0]) == 2:
inp, out = zip(*batch)
index = None
else:
inp, out, index = zip(*batch)
index = torch.LongTensor(list(index))
lens = torch.LongTensor(list(map(len,inp)))
inp = batch_seqs(inp)
out = batch_seqs(out)
return inp, out, lens, index
def collate_with_both_lens(batch):
batch = sorted(batch,
key=lambda x: len(x[0]),
reverse=True)
if len(batch[0]) == 2:
inp, out = zip(*batch)
index = None
else:
inp, out, index = zip(*batch)
index = torch.LongTensor(list(index))
inp_lens = torch.LongTensor(list(map(len,inp)))
out_lens = torch.LongTensor(list(map(len,out)))
inp = batch_seqs(inp)
out = batch_seqs(out)
return inp, out, inp_lens, out_lens, index
def collate_without_sort(batch):
if len(batch[0]) == 2:
inp, out = zip(*batch)
index = None
else:
inp, out, index = zip(*batch)
index = torch.LongTensor(list(index))
inp_lens = torch.LongTensor(list(map(len,inp)))
inp = batch_seqs(inp)
out = batch_seqs(out)
return inp, out, inp_lens, index
def get_lens_of_text(text):
out_lens = torch.LongTensor(list(map(len,text)))
return out_lens