dataset.py
4.16 KB
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
128
129
130
131
132
133
import torch
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import Sampler
import const
# 0, batch * 1, batch * 2 ...
class BatchIntervalSampler(Sampler):
def __init__(self, data_length, batch_size):
# data length 가 batch size 로 나뉘게 만듦
if data_length % batch_size != 0:
data_length = data_length - (data_length % batch_size)
self.indices =[]
# print(data_length)
batch_group_interval = int(data_length / batch_size)
for group_idx in range(batch_group_interval):
for local_idx in range(batch_size):
self.indices.append(group_idx + local_idx * batch_group_interval)
# print('sampler init', self.indices)
def __iter__(self):
return iter(self.indices)
def __len__(self):
return len(self.indices)
def record_net_data_stats(label_temp, data_idx_map):
net_class_count = {}
net_data_count= {}
for net_i, dataidx in data_idx_map.items():
unq, unq_cnt = np.unique(label_temp[dataidx], return_counts=True)
tmp = {unq[i]: unq_cnt[i] for i in range(len(unq))}
net_class_count[net_i] = tmp
net_data_count[net_i] = len(dataidx)
print('Data statistics: %s' % str(net_class_count))
return net_class_count, net_data_count
def GetCanDataset(total_edge, fold_num, packet_num, csv_path, txt_path):
csv = pd.read_csv(csv_path)
txt = open(txt_path, "r")
lines = txt.read().splitlines()
idx = 0
datum = []
label_temp = []
# [cur_idx ~ cur_idx + packet_num)
while idx + packet_num - 1 < len(csv) // 2:
line = lines[idx + packet_num - 1]
if not line:
break
if line.split(' ')[1] == 'R':
datum.append((idx, 1))
label_temp.append(1)
else:
datum.append((idx, 0))
label_temp.append(0)
idx += 1
if (idx % 1000000 == 0):
print(idx)
fold_length = int(len(label_temp) / 5)
train_datum = []
train_label_temp = []
for i in range(5):
if i != fold_num:
train_datum += datum[i*fold_length:(i+1)*fold_length]
train_label_temp += label_temp[i*fold_length:(i+1)*fold_length]
else:
test_datum = datum[i*fold_length:(i+1)*fold_length]
N = len(train_label_temp)
train_label_temp = np.array(train_label_temp)
proportions = np.random.dirichlet(np.repeat(1, total_edge))
proportions = np.cumsum(proportions)
idx_batch = [[] for _ in range(total_edge)]
data_idx_map = {}
prev = 0.0
for j in range(total_edge):
idx_batch[j] = [idx for idx in range(int(prev * N), int(proportions[j] * N))]
prev = proportions[j]
data_idx_map[j] = idx_batch[j]
_, net_data_count = record_net_data_stats(train_label_temp, data_idx_map)
return CanDataset(csv, train_datum, packet_num), data_idx_map, net_data_count, CanDataset(csv, test_datum, packet_num, False)
class CanDataset(Dataset):
def __init__(self, csv, datum, packet_num, is_train=True):
self.csv = csv
self.datum = datum
self.packet_num = packet_num
if is_train:
self.idx_map = []
else:
self.idx_map = [idx for idx in range(len(self.datum))]
def __len__(self):
return len(self.idx_map) - self.packet_num + 1
def set_idx_map(self, data_idx_map):
self.idx_map = data_idx_map
def __getitem__(self, idx):
# [cur_idx ~ cur_idx + packet_num)
start_i = self.datum[self.idx_map[idx]][0]
is_regular = self.datum[self.idx_map[idx + self.packet_num - 1]][1]
packet = np.zeros((const.CAN_DATA_LEN * self.packet_num))
for next_i in range(self.packet_num):
packet = np.zeros((const.CAN_DATA_LEN * self.packet_num))
data_len = self.csv.iloc[start_i + next_i, 1]
for j in range(data_len):
data_value = int(self.csv.iloc[start_i + next_i, 2 + j], 16) / 255.0
packet[j + const.CAN_DATA_LEN * next_i] = data_value
return torch.from_numpy(packet).float(), is_regular
if __name__ == "__main__":
pass