forked from Hvass-Labs/TensorFlow-Tutorials
-
Notifications
You must be signed in to change notification settings - Fork 0
/
dataset.py
294 lines (214 loc) · 9.38 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
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
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
########################################################################
#
# Class for creating a data-set consisting of all files in a directory.
#
# Example usage is shown in the file knifey.py and Tutorial #09.
#
# Implemented in Python 3.5
#
########################################################################
#
# This file is part of the TensorFlow Tutorials available at:
#
# https://github.com/Hvass-Labs/TensorFlow-Tutorials
#
# Published under the MIT License. See the file LICENSE for details.
#
# Copyright 2016 by Magnus Erik Hvass Pedersen
#
########################################################################
import numpy as np
import os
from cache import cache
########################################################################
def one_hot_encoded(class_numbers, num_classes=None):
"""
Generate the One-Hot encoded class-labels from an array of integers.
For example, if class_number=2 and num_classes=4 then
the one-hot encoded label is the float array: [0. 0. 1. 0.]
:param class_numbers:
Array of integers with class-numbers.
Assume the integers are from zero to num_classes-1 inclusive.
:param num_classes:
Number of classes. If None then use max(cls)-1.
:return:
2-dim array of shape: [len(cls), num_classes]
"""
# Find the number of classes if None is provided.
if num_classes is None:
num_classes = np.max(class_numbers) - 1
return np.eye(num_classes, dtype=float)[class_numbers]
########################################################################
class DataSet:
def __init__(self, in_dir, exts='.jpg'):
"""
Create a data-set consisting of the filenames in the given directory
and sub-dirs that match the given filename-extensions.
For example, the knifey-spoony data-set (see knifey.py) has the
following dir-structure:
knifey-spoony/forky/
knifey-spoony/knifey/
knifey-spoony/spoony/
knifey-spoony/forky/test/
knifey-spoony/knifey/test/
knifey-spoony/spoony/test/
This means there are 3 classes called: forky, knifey, and spoony.
If we set in_dir = "knifey-spoony/" and create a new DataSet-object
then it will scan through these directories and create a training-set
and test-set for each of these classes.
The training-set will contain a list of all the *.jpg filenames
in the following directories:
knifey-spoony/forky/
knifey-spoony/knifey/
knifey-spoony/spoony/
The test-set will contain a list of all the *.jpg filenames
in the following directories:
knifey-spoony/forky/test/
knifey-spoony/knifey/test/
knifey-spoony/spoony/test/
See the TensorFlow Tutorial #09 for a usage example.
:param in_dir:
Root-dir for the files in the data-set.
This would be 'knifey-spoony/' in the example above.
:param exts:
String or tuple of strings with valid filename-extensions.
Not case-sensitive.
:return:
Object instance.
"""
# Extend the input directory to the full path.
in_dir = os.path.abspath(in_dir)
# Input directory.
self.in_dir = in_dir
# Convert all file-extensions to lower-case.
self.exts = tuple(ext.lower() for ext in exts)
# Names for the classes.
self.class_names = []
# Filenames for all the files in the training-set.
self.filenames = []
# Filenames for all the files in the test-set.
self.filenames_test = []
# Class-number for each file in the training-set.
self.class_numbers = []
# Class-number for each file in the test-set.
self.class_numbers_test = []
# Total number of classes in the data-set.
self.num_classes = 0
# For all files/dirs in the input directory.
for name in os.listdir(in_dir):
# Full path for the file / dir.
current_dir = os.path.join(in_dir, name)
# If it is a directory.
if os.path.isdir(current_dir):
# Add the dir-name to the list of class-names.
self.class_names.append(name)
# Training-set.
# Get all the valid filenames in the dir (not sub-dirs).
filenames = self._get_filenames(current_dir)
# Append them to the list of all filenames for the training-set.
self.filenames.extend(filenames)
# The class-number for this class.
class_number = self.num_classes
# Create an array of class-numbers.
class_numbers = [class_number] * len(filenames)
# Append them to the list of all class-numbers for the training-set.
self.class_numbers.extend(class_numbers)
# Test-set.
# Get all the valid filenames in the sub-dir named 'test'.
filenames_test = self._get_filenames(os.path.join(current_dir, 'test'))
# Append them to the list of all filenames for the test-set.
self.filenames_test.extend(filenames_test)
# Create an array of class-numbers.
class_numbers = [class_number] * len(filenames_test)
# Append them to the list of all class-numbers for the test-set.
self.class_numbers_test.extend(class_numbers)
# Increase the total number of classes in the data-set.
self.num_classes += 1
def _get_filenames(self, dir):
"""
Create and return a list of filenames with matching extensions in the given directory.
:param dir:
Directory to scan for files. Sub-dirs are not scanned.
:return:
List of filenames. Only filenames. Does not include the directory.
"""
# Initialize empty list.
filenames = []
# If the directory exists.
if os.path.exists(dir):
# Get all the filenames with matching extensions.
for filename in os.listdir(dir):
if filename.lower().endswith(self.exts):
filenames.append(filename)
return filenames
def get_paths(self, test=False):
"""
Get the full paths for the files in the data-set.
:param test:
Boolean. Return the paths for the test-set (True) or training-set (False).
:return:
Iterator with strings for the path-names.
"""
if test:
# Use the filenames and class-numbers for the test-set.
filenames = self.filenames_test
class_numbers = self.class_numbers_test
# Sub-dir for test-set.
test_dir = "test/"
else:
# Use the filenames and class-numbers for the training-set.
filenames = self.filenames
class_numbers = self.class_numbers
# Don't use a sub-dir for test-set.
test_dir = ""
for filename, cls in zip(filenames, class_numbers):
# Full path-name for the file.
path = os.path.join(self.in_dir, self.class_names[cls], test_dir, filename)
yield path
def get_training_set(self):
"""
Return the list of paths for the files in the training-set,
and the list of class-numbers as integers,
and the class-numbers as one-hot encoded arrays.
"""
return list(self.get_paths()), \
np.asarray(self.class_numbers), \
one_hot_encoded(class_numbers=self.class_numbers,
num_classes=self.num_classes)
def get_test_set(self):
"""
Return the list of paths for the files in the test-set,
and the list of class-numbers as integers,
and the class-numbers as one-hot encoded arrays.
"""
return list(self.get_paths(test=True)), \
np.asarray(self.class_numbers_test), \
one_hot_encoded(class_numbers=self.class_numbers_test,
num_classes=self.num_classes)
########################################################################
def load_cached(cache_path, in_dir):
"""
Wrapper-function for creating a DataSet-object, which will be
loaded from a cache-file if it already exists, otherwise a new
object will be created and saved to the cache-file.
This is useful if you need to ensure the ordering of the
filenames is consistent every time you load the data-set,
for example if you use the DataSet-object in combination
with Transfer Values saved to another cache-file, see e.g.
Tutorial #09 for an example of this.
:param cache_path:
File-path for the cache-file.
:param in_dir:
Root-dir for the files in the data-set.
This is an argument for the DataSet-init function.
:return:
The DataSet-object.
"""
print("Creating dataset from the files in: " + in_dir)
# If the object-instance for DataSet(in_dir=data_dir) already
# exists in the cache-file then reload it, otherwise create
# an object instance and save it to the cache-file for next time.
dataset = cache(cache_path=cache_path,
fn=DataSet, in_dir=in_dir)
return dataset
########################################################################