Improving-Generalization/DataLoader.py at master · sandeshgh/Improving-Generalization · GitHub

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
import torchvision
from PIL import Image
from torch.utils import data as data_utils
import glob
import os
import numpy as np
import torch

#transform = torchvision.transforms.ToTensor()


class SimulatedDataset(data_utils.Dataset):
    """
    Creates a Pytorch Dataset for ImageNet.
    """

    def __init__(self, index, root='Input_data/'):
        self.root = root
        data=torch.load(root + 'Data_{}.pth'.format(index[0]))


        length_index=len(index)
        signals = self.shorten(data['data_tensor'])
        labels = self.shorten(data['target_tensor'])
        tmp_index = self.shorten_v(data['tmp_index'])
        if length_index>1:

            for ind in index[1:]:
                data_i=torch.load(root + 'Data_{}.pth'.format(ind))
                signals=torch.cat((signals,self.shorten(data_i['data_tensor'])),0)
                labels=torch.cat((labels,self.shorten(data_i['target_tensor'])),0)
                tmp_index=np.concatenate((tmp_index, self.shorten_v(data_i['tmp_index'])),axis=0)
        #tmp_index=torch.tensor(tmp_index, dtype=torch.int)

        #data=[signals,labels,tmp_index]
        self.signals = signals
        self.labels = labels
        self.tmp_index=tmp_index
        self.n=signals.shape[0]
        #print('Labels:', labels[0:3,:])
        #print('Finished')

    def shorten(self, T):
        T1=T[1665*40:1665*41,:]
        return T1[list(range(1,1665,3)),:]

    def shorten_v(self, v):
        v1=v[1665*40:1665*41]
        return v1[list(range(1,1665,3))]


    def __len__(self):
        return self.n


    def __getitem__(self, index):

        signals=self.signals[index,:]
        labels=self.labels[index,:]
        tmp_index=self.tmp_index[index]

        return signals, labels, tmp_index


class SimulatedDataEC(data_utils.Dataset):
    """
    Creates a Pytorch Dataset for ImageNet.
    """

    def __init__(self, index, root='EC1862/ECG/'):
        self.root = root
        data=torch.load(root + 'Ecg_{}.pth'.format(index[0]))


        length_index=len(index)
        signal_full=data['Ecg']
        (_, l, t) = signal_full.shape
        signals = self.shorten(signal_full,l,t)
        labels = (data['label'])


        if length_index>1:

            for ind in index[1:]:
                data_i=torch.load(root + 'Ecg_{}.pth'.format(ind))
                signals=torch.cat((signals,self.shorten(data_i['Ecg'],l,t)),0)
                labels=torch.cat((labels,(data_i['label'])),0)


        self.signals = signals
        self.labels = labels

        self.n=signals.shape[0]
        #print('Labels:', labels[0:3,:])
        #print('Finished')

    def shorten(self, T,l,t):
        T1= T[:,list(range(1,l,2)),:]

        return T1[:,:,list(range(1,t,2))]


    def __len__(self):
        return self.n


    def __getitem__(self, index):

        signals=self.signals[index,:,:]
        labels=self.labels[index]


        return signals, labels


class SimulatedDataEC_2factor(data_utils.Dataset):
    """
    Creates a Pytorch Dataset for ImageNet.
    """

    def __init__(self, index_i,index_j, root='EC1862/ECG_2factor/'):
        self.root = root
        data=torch.load(root + 'Ecg_{}_{}.pth'.format(index_i[0],index_j[0]))


        #length_index=len(index)
        signal_full=data['Ecg']
        (_, l, t) = signal_full.shape
        signals = self.shorten(signal_full,l,t)
        labels = (data['label'])

        for ind in index_i:

            for j in index_j:
                if not (ind ==index_i[0] and j==index_j[0]):
                    data_i=torch.load(root + 'Ecg_{}_{}.pth'.format(index_i[0],index_j[0]))
                    signals=torch.cat((signals,self.shorten(data_i['Ecg'],l,t)),0)
                    labels=torch.cat((labels,(data_i['label'])),0)


        self.signals = signals
        self.labels = labels

        self.n=signals.shape[0]
        #print('Labels:', labels[0:3,:])
        #print('Finished')

    def shorten(self, T,l,t):
        T1= T[:,list(range(1,l,2)),:]

        return T1[:,:,list(range(1,t,2))]


    def __len__(self):
        return self.n


    def __getitem__(self, index):

        signals=self.signals[index,:,:]
        labels=self.labels[index]


        return signals, labels[0]