process_data.py
5.64 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
#!/usr/bin/env python
# Copyright 2017 Johns Hopkins University (author: Hossein Hadian)
# Apache 2.0
""" This script prepares the training and test data for CIFAR-10 or CIFAR-100.
"""
from __future__ import division
import argparse
import os
import sys
parser = argparse.ArgumentParser(description="""Converts train/test data of
CIFAR-10 or CIFAR-100 to
Kaldi feature format""")
parser.add_argument('database',
default='data/dl/cifar-10-batches-bin',
help='path to downloaded cifar data (binary version)')
parser.add_argument('dir', help='output dir')
parser.add_argument('--cifar-version', default='CIFAR-10', choices=['CIFAR-10', 'CIFAR-100'])
parser.add_argument('--dataset', default='train', choices=['train', 'test'])
parser.add_argument('--out-ark', default='-', help='where to write output feature data')
args = parser.parse_args()
# CIFAR image dimensions:
C = 3 # num_channels
H = 32 # num_rows
W = 32 # num_cols
def load_cifar10_data_batch(datafile):
num_images_in_batch = 10000
data = []
labels = []
with open(datafile, 'rb') as fh:
for i in range(num_images_in_batch):
label = ord(fh.read(1))
bin_img = fh.read(C * H * W)
img = [[[ord(byte)/255.0 for byte in bin_img[channel*H*W+row*W:channel*H*W+(row+1)*W]]
for row in range(H)] for channel in range(C)]
labels += [label]
data += [img]
return data, labels
def load_cifar100_data_batch(datafile, num_images_in_batch):
data = []
fine_labels = []
coarse_labels = []
with open(datafile, 'rb') as fh:
for i in range(num_images_in_batch):
coarse_label = ord(fh.read(1))
fine_label = ord(fh.read(1))
bin_img = fh.read(C * H * W)
img = [[[ord(byte)/255.0 for byte in bin_img[channel*H*W+row*W:channel*H*W+(row+1)*W]]
for row in range(H)] for channel in range(C)]
fine_labels += [fine_label]
coarse_labels += [coarse_label]
data += [img]
return data, fine_labels, coarse_labels
def image_to_feat_matrix(img):
mat = [0]*H # 32 * 96
for i in range(W):
mat[i] = [0]*C*H
for ch in range(C):
for j in range(H):
mat[i][j*C+ch] = img[ch][j][i]
return mat
def write_kaldi_matrix(file_handle, matrix, key):
# matrix is a list of lists
file_handle.write(key + " [ ")
num_rows = len(matrix)
if num_rows == 0:
raise Exception("Matrix is empty")
num_cols = len(matrix[0])
for row_index in range(len(matrix)):
if num_cols != len(matrix[row_index]):
raise Exception("All the rows of a matrix are expected to "
"have the same length")
file_handle.write(" ".join([str(x) for x in matrix[row_index]]))
if row_index != num_rows - 1:
file_handle.write("\n")
file_handle.write(" ]\n")
def zeropad(x, length):
s = str(x)
while len(s) < length:
s = '0' + s
return s
### main ###
cifar10 = (args.cifar_version.lower() == 'cifar-10')
if args.out_ark == '-':
out_fh = sys.stdout # output file handle to write the feats to
else:
out_fh = open(args.out_ark, 'wb')
if cifar10:
img_id = 1 # similar to utt_id
labels_file = os.path.join(args.dir, 'labels.txt')
labels_fh = open(labels_file, 'wb')
if args.dataset == 'train':
for i in range(1, 6):
fpath = os.path.join(args.database, 'data_batch_' + str(i) + '.bin')
data, labels = load_cifar10_data_batch(fpath)
for i in range(len(data)):
key = zeropad(img_id, 5)
labels_fh.write(key + ' ' + str(labels[i]) + '\n')
feat_mat = image_to_feat_matrix(data[i])
write_kaldi_matrix(out_fh, feat_mat, key)
img_id += 1
else:
fpath = os.path.join(args.database, 'test_batch.bin')
data, labels = load_cifar10_data_batch(fpath)
for i in range(len(data)):
key = zeropad(img_id, 5)
labels_fh.write(key + ' ' + str(labels[i]) + '\n')
feat_mat = image_to_feat_matrix(data[i])
write_kaldi_matrix(out_fh, feat_mat, key)
img_id += 1
labels_fh.close()
else:
img_id = 1 # similar to utt_id
fine_labels_file = os.path.join(args.dir, 'labels.txt')
# coarse_labels_file = os.path.join(args.dir, 'coarse_labels.txt')
fine_labels_fh = open(fine_labels_file, 'wb')
# coarse_labels_fh = open(coarse_labels_file, 'wb')
if args.dataset == 'train':
fpath = os.path.join(args.database, 'train.bin')
data, fine_labels, coarse_labels = load_cifar100_data_batch(fpath, 50000)
for i in range(len(data)):
key = zeropad(img_id, 5)
fine_labels_fh.write(key + ' ' + str(fine_labels[i]) + '\n')
# coarse_labels_fh.write(key + ' ' + str(coarse_labels[i]) + '\n')
feat_mat = image_to_feat_matrix(data[i])
write_kaldi_matrix(out_fh, feat_mat, key)
img_id += 1
else:
fpath = os.path.join(args.database, 'test.bin')
data, fine_labels, coarse_labels = load_cifar100_data_batch(fpath, 10000)
for i in range(len(data)):
key = zeropad(img_id, 5)
fine_labels_fh.write(key + ' ' + str(fine_labels[i]) + '\n')
# coarse_labels_fh.write(key + ' ' + str(coarse_labels[i]) + '\n')
feat_mat = image_to_feat_matrix(data[i])
write_kaldi_matrix(out_fh, feat_mat, key)
img_id += 1
fine_labels_fh.close()
# coarse_labels_fh.close()
out_fh.close()