Upload dataset.py
Browse files- Others/dataset.py +90 -0
Others/dataset.py
ADDED
@@ -0,0 +1,90 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
from torch.utils.data import Dataset
|
4 |
+
|
5 |
+
class BilingualDataset(Dataset):
|
6 |
+
|
7 |
+
def __init__(self, ds, tokenizer_src, tokenizer_tgt, src_lang, tgt_lang, seq_len):
|
8 |
+
super().__init__()
|
9 |
+
self.seq_len = seq_len
|
10 |
+
|
11 |
+
self.ds = ds
|
12 |
+
self.tokenizer_src = tokenizer_src
|
13 |
+
self.tokenizer_tgt = tokenizer_tgt
|
14 |
+
self.src_lang = src_lang
|
15 |
+
self.tgt_lang = tgt_lang
|
16 |
+
|
17 |
+
self.sos_token = torch.tensor([tokenizer_tgt.token_to_id("[SOS]")], dtype=torch.int64)
|
18 |
+
self.eos_token = torch.tensor([tokenizer_tgt.token_to_id("[EOS]")], dtype=torch.int64)
|
19 |
+
self.pad_token = torch.tensor([tokenizer_tgt.token_to_id("[PAD]")], dtype=torch.int64)
|
20 |
+
|
21 |
+
def __len__(self):
|
22 |
+
return len(self.ds)
|
23 |
+
|
24 |
+
def __getitem__(self, idx):
|
25 |
+
src_target_pair = self.ds[idx]
|
26 |
+
src_text = src_target_pair['translation'][self.src_lang]
|
27 |
+
tgt_text = src_target_pair['translation'][self.tgt_lang]
|
28 |
+
|
29 |
+
# Transform the text into tokens
|
30 |
+
enc_input_tokens = self.tokenizer_src.encode(src_text).ids
|
31 |
+
dec_input_tokens = self.tokenizer_tgt.encode(tgt_text).ids
|
32 |
+
|
33 |
+
# Add sos, eos and padding to each sentence
|
34 |
+
enc_num_padding_tokens = self.seq_len - len(enc_input_tokens) - 2 # We will add <s> and </s>
|
35 |
+
# We will only add <s>, and </s> only on the label
|
36 |
+
dec_num_padding_tokens = self.seq_len - len(dec_input_tokens) - 1
|
37 |
+
|
38 |
+
# Make sure the number of padding tokens is not negative. If it is, the sentence is too long
|
39 |
+
if enc_num_padding_tokens < 0 or dec_num_padding_tokens < 0:
|
40 |
+
raise ValueError("Sentence is too long")
|
41 |
+
|
42 |
+
# Add <s> and </s> token
|
43 |
+
encoder_input = torch.cat(
|
44 |
+
[
|
45 |
+
self.sos_token,
|
46 |
+
torch.tensor(enc_input_tokens, dtype=torch.int64),
|
47 |
+
self.eos_token,
|
48 |
+
torch.tensor([self.pad_token] * enc_num_padding_tokens, dtype=torch.int64),
|
49 |
+
],
|
50 |
+
dim=0,
|
51 |
+
)
|
52 |
+
|
53 |
+
# Add only <s> token
|
54 |
+
decoder_input = torch.cat(
|
55 |
+
[
|
56 |
+
self.sos_token,
|
57 |
+
torch.tensor(dec_input_tokens, dtype=torch.int64),
|
58 |
+
torch.tensor([self.pad_token] * dec_num_padding_tokens, dtype=torch.int64),
|
59 |
+
],
|
60 |
+
dim=0,
|
61 |
+
)
|
62 |
+
|
63 |
+
# Add only </s> token
|
64 |
+
label = torch.cat(
|
65 |
+
[
|
66 |
+
torch.tensor(dec_input_tokens, dtype=torch.int64),
|
67 |
+
self.eos_token,
|
68 |
+
torch.tensor([self.pad_token] * dec_num_padding_tokens, dtype=torch.int64),
|
69 |
+
],
|
70 |
+
dim=0,
|
71 |
+
)
|
72 |
+
|
73 |
+
# Double check the size of the tensors to make sure they are all seq_len long
|
74 |
+
assert encoder_input.size(0) == self.seq_len
|
75 |
+
assert decoder_input.size(0) == self.seq_len
|
76 |
+
assert label.size(0) == self.seq_len
|
77 |
+
|
78 |
+
return {
|
79 |
+
"encoder_input": encoder_input, # (seq_len)
|
80 |
+
"decoder_input": decoder_input, # (seq_len)
|
81 |
+
"encoder_mask": (encoder_input != self.pad_token).unsqueeze(0).unsqueeze(0).int(), # (1, 1, seq_len)
|
82 |
+
"decoder_mask": (decoder_input != self.pad_token).unsqueeze(0).int() & causal_mask(decoder_input.size(0)), # (1, seq_len) & (1, seq_len, seq_len),
|
83 |
+
"label": label, # (seq_len)
|
84 |
+
"src_text": src_text,
|
85 |
+
"tgt_text": tgt_text,
|
86 |
+
}
|
87 |
+
|
88 |
+
def causal_mask(size):
|
89 |
+
mask = torch.triu(torch.ones((1, size, size)), diagonal=1).type(torch.int)
|
90 |
+
return mask == 0
|