data.py
10.2 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
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
import os
import numpy as np
import torch
from torch.utils.data import DataLoader,TensorDataset
from sklearn.model_selection import train_test_split
np.random.seed(42)
def normalize(seq):
'''
normalize to [-1,1]
:param seq:
:return:
'''
return 2*(seq-np.min(seq))/(np.max(seq)-np.min(seq))-1
def load_data(opt):
train_dataset=None
test_dataset=None
val_dataset=None
test_N_dataset=None
test_S_dataset = None
test_V_dataset = None
test_F_dataset = None
test_Q_dataset = None
if opt.dataset=="ecg":
'''N_samples=np.load(os.path.join(opt.dataroot, "N_samples.npy")) #NxCxL
S_samples=np.load(os.path.join(opt.dataroot, "S_samples.npy"))
V_samples = np.load(os.path.join(opt.dataroot, "V_samples.npy"))
F_samples = np.load(os.path.join(opt.dataroot, "F_samples.npy"))
Q_samples = np.load(os.path.join(opt.dataroot, "Q_samples.npy"))
'''
N_samples = np.load(os.path.join(opt.dataroot,'n_spectrogram.npy'))
S_samples = np.load(os.path.join(opt.dataroot,'s_spectrogram.npy'))
V_samples = np.load(os.path.join(opt.dataroot,'v_spectrogram.npy'))
F_samples = np.load(os.path.join(opt.dataroot,'f_spectrogram.npy'))
Q_samples = np.load(os.path.join(opt.dataroot,'q_spectrogram.npy'))
# normalize all
'''for i in range(N_samples.shape[0]):
for j in range(opt.nc):
N_samples[i][j]=normalize(N_samples[i][j][:])
N_samples=N_samples[:,:opt.nc,:]
for i in range(S_samples.shape[0]):
for j in range(opt.nc):
S_samples[i][j] = normalize(S_samples[i][j][:])
S_samples = S_samples[:, :opt.nc, :]
for i in range(V_samples.shape[0]):
for j in range(opt.nc):
V_samples[i][j] = normalize(V_samples[i][j][:])
V_samples = V_samples[:, :opt.nc, :]
for i in range(F_samples.shape[0]):
for j in range(opt.nc):
F_samples[i][j] = normalize(F_samples[i][j][:])
F_samples = F_samples[:, :opt.nc, :]
for i in range(Q_samples.shape[0]):
for j in range(opt.nc):
Q_samples[i][j] = normalize(Q_samples[i][j][:])
Q_samples = Q_samples[:, :opt.nc, :]'''
for i in range(N_samples.shape[0]):
N_samples[i] = normalize(N_samples[i])
for i in range(S_samples.shape[0]):
S_samples[i] = normalize(S_samples[i])
for i in range(V_samples.shape[0]):
V_samples[i] = normalize(V_samples[i])
for i in range(F_samples.shape[0]):
F_samples[i] = normalize(F_samples[i])
for i in range(Q_samples.shape[0]):
Q_samples[i] = normalize(Q_samples[i])
# train / test
test_N,test_N_y, train_N,train_N_y = getFloderK(N_samples,opt.folder,0)
# test_S,test_S_y, train_S,train_S_y = getFloderK(S_samples, opt.folder,1)
# test_V,test_V_y, train_V,train_V_y = getFloderK(V_samples, opt.folder,1)
# test_F,test_F_y, train_F,train_F_y = getFloderK(F_samples, opt.folder,1)
# test_Q,test_Q_y, train_Q,train_Q_y = getFloderK(Q_samples, opt.folder,1)
test_S,test_S_y=S_samples, np.ones((S_samples.shape[0], 1))
test_V, test_V_y = V_samples, np.ones((V_samples.shape[0], 1))
test_F, test_F_y = F_samples, np.ones((F_samples.shape[0], 1))
test_Q, test_Q_y = Q_samples, np.ones((Q_samples.shape[0], 1))
# train / val
train_N, val_N, train_N_y, val_N_y = getPercent(train_N, train_N_y, 0.1, 0)
test_S, val_S, test_S_y, val_S_y = getPercent(test_S, test_S_y, 0.1, 0)
test_V, val_V, test_V_y, val_V_y = getPercent(test_V, test_V_y, 0.1, 0)
test_F, val_F, test_F_y, val_F_y = getPercent(test_F, test_F_y, 0.1, 0)
test_Q, val_Q, test_Q_y, val_Q_y = getPercent(test_Q, test_Q_y, 0.1, 0)
val_data=np.concatenate([val_N,val_S,val_V,val_F,val_Q])
val_y=np.concatenate([val_N_y,val_S_y,val_V_y,val_F_y,val_Q_y])
print("train data size:{}".format(train_N.shape))
print("val data size:{}".format(val_data.shape))
print("test N data size:{}".format(test_N.shape))
print("test S data size:{}".format(test_S.shape))
print("test V data size:{}".format(test_V.shape))
print("test F data size:{}".format(test_F.shape))
print("test Q data size:{}".format(test_Q.shape))
if not opt.istest and opt.n_aug>0:
train_N,train_N_y=data_aug(train_N,train_N_y,times=opt.n_aug)
print("after aug, train data size:{}".format(train_N.shape))
train_dataset = TensorDataset(torch.Tensor(train_N),torch.Tensor(train_N_y))
val_dataset= TensorDataset(torch.Tensor(val_data), torch.Tensor(val_y))
test_N_dataset = TensorDataset(torch.Tensor(test_N), torch.Tensor(test_N_y))
test_S_dataset = TensorDataset(torch.Tensor(test_S), torch.Tensor(test_S_y))
test_V_dataset = TensorDataset(torch.Tensor(test_V), torch.Tensor(test_V_y))
test_F_dataset = TensorDataset(torch.Tensor(test_F), torch.Tensor(test_F_y))
test_Q_dataset = TensorDataset(torch.Tensor(test_Q), torch.Tensor(test_Q_y))
# assert (train_dataset is not None and test_dataset is not None and val_dataset is not None)
dataloader = {"train": DataLoader(
dataset=train_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=True),
"val": DataLoader(
dataset=val_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_N":DataLoader(
dataset=test_N_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_S": DataLoader(
dataset=test_S_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_V": DataLoader(
dataset=test_V_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_F": DataLoader(
dataset=test_F_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
"test_Q": DataLoader(
dataset=test_Q_dataset, # torch TensorDataset format
batch_size=opt.batchsize, # mini batch size
shuffle=True,
num_workers=int(opt.workers),
drop_last=False),
}
return dataloader
def getFloderK(data,folder,label):
normal_cnt = data.shape[0]
folder_num = int(normal_cnt / 5)
folder_idx = folder * folder_num
folder_data = data[folder_idx:folder_idx + folder_num]
remain_data = np.concatenate([data[:folder_idx], data[folder_idx + folder_num:]])
if label==0:
folder_data_y = np.zeros((folder_data.shape[0], 1))
remain_data_y=np.zeros((remain_data.shape[0], 1))
elif label==1:
folder_data_y = np.ones((folder_data.shape[0], 1))
remain_data_y = np.ones((remain_data.shape[0], 1))
else:
raise Exception("label should be 0 or 1, get:{}".format(label))
return folder_data,folder_data_y,remain_data,remain_data_y
def getPercent(data_x,data_y,percent,seed):
train_x, test_x, train_y, test_y = train_test_split(data_x, data_y,test_size=percent,random_state=seed)
return train_x, test_x, train_y, test_y
def get_full_data(dataloader):
full_data_x=[]
full_data_y=[]
for batch_data in dataloader:
batch_x,batch_y=batch_data[0],batch_data[1]
batch_x=batch_x.numpy()
batch_y=batch_y.numpy()
# print(batch_x.shape)
# assert False
for i in range(batch_x.shape[0]):
full_data_x.append(batch_x[i,0,:])
full_data_y.append(batch_y[i])
full_data_x=np.array(full_data_x)
full_data_y=np.array(full_data_y)
assert full_data_x.shape[0]==full_data_y.shape[0]
print("full data size:{}".format(full_data_x.shape))
return full_data_x,full_data_y
def data_aug(train_x,train_y,times=2):
res_train_x=[]
res_train_y=[]
for idx in range(train_x.shape[0]):
x=train_x[idx]
y=train_y[idx]
res_train_x.append(x)
res_train_y.append(y)
for i in range(times):
x_aug=aug_ts(x)
res_train_x.append(x_aug)
res_train_y.append(y)
res_train_x=np.array(res_train_x)
res_train_y=np.array(res_train_y)
return res_train_x,res_train_y
def aug_ts(x):
left_ticks_index = np.arange(0, 140)
right_ticks_index = np.arange(140, 319)
np.random.shuffle(left_ticks_index)
np.random.shuffle(right_ticks_index)
left_up_ticks = left_ticks_index[:7]
right_up_ticks = right_ticks_index[:7]
left_down_ticks = left_ticks_index[7:14]
right_down_ticks = right_ticks_index[7:14]
x_1 = np.zeros_like(x)
j = 0
for i in range(x.shape[1]):
if i in left_down_ticks or i in right_down_ticks:
continue
elif i in left_up_ticks or i in right_up_ticks:
x_1[:, j] =x[:,i]
j += 1
x_1[:, j] = (x[:, i] + x[:, i + 1]) / 2
j += 1
else:
x_1[:, j] = x[:, i]
j += 1
return x_1