-
Notifications
You must be signed in to change notification settings - Fork 0
/
dataset.py
executable file
·120 lines (83 loc) · 3.13 KB
/
dataset.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
import torch
import numpy as np
class GraphDataset(torch.utils.data.Dataset):
def __init__(self, input_graph):
super(GraphDataset, self).__init__()
self.graph_list = input_graph
def __getitem__(self, idx):
graph = self.graph_list[idx]
return graph
def __len__(self):
return len(self.graph_list)
@staticmethod
def collate(data_list):
return torch.tensor(data_list)
class SequenceDataset(torch.utils.data.Dataset):
def __init__(self, input_sequence):
super(SequenceDataset, self).__init__()
self.sequence_list = input_sequence
def __getitem__(self, idx):
sequence = self.sequence_list[idx]
#ids = torch.randperm(len(sequence)-2)[:2] + 1
# similar_sequence1 = self.transform(sequence,ids[0])
# similar_sequence2 = self.transform(sequence,ids[1])
return sequence
def update(self, sequence):
self.sequence_list.extend(sequence)
def get_seq(self):
return self.sequence_list
def __len__(self):
return len(self.sequence_list)
def collate(data_list):
return torch.tensor(data_list).long()
class ScoreDataset(torch.utils.data.Dataset):
def __init__(self, input_score):
super(ScoreDataset, self).__init__()
self.scores = torch.FloatTensor(input_score)
self.raw_tsrs = self.scores
self.mean = self.scores.mean()
self.std = torch.std(self.scores)
def __getitem__(self, idx):
return self.scores[idx]
#return self.score_list[idx]
def update(self, scores):
new_scores = torch.FloatTensor(scores)
self.raw_tsrs = torch.cat([self.scores, new_scores], dim=0)
self.scores = self.raw_tsrs
self.mean = self.scores.mean()
self.std = torch.std(self.scores)
def get_tsrs(self):
return self.scores
def __len__(self):
return self.scores.size(0)
def collate(data_list):
return torch.tensor(data_list)
class ZipDataset(torch.utils.data.Dataset):
def __init__(self, *datasets):
self.datasets = datasets
def __len__(self):
return len(self.datasets[0])
def __getitem__(self, idx):
return [dataset[idx] for dataset in self.datasets]
def collate(data_list):
assert(False)
return [dataset.collate(data_list) for dataset, data_list in zip(self.datasets, zip(*data_list))]
class PairDataset(torch.utils.data.Dataset):
def __init__(self, input_score, input_sequence):
super(PairDataset, self).__init__()
self.scores = torch.FloatTensor(input_score)
def __getitem__(self, idx):
return self.scores[idx]
#return self.score_list[idx]
def update(self, scores):
new_scores = torch.FloatTensor(scores)
self.raw_tsrs = torch.cat([self.scores, new_scores], dim=0)
self.scores = self.raw_tsrs
self.mean = self.scores.mean()
self.std = torch.std(self.scores)
def get_tsrs(self):
return self.scores
def __len__(self):
return self.scores.size(0)
def collate(data_list):
return torch.tensor(data_list)