/
data_generator.py
174 lines (161 loc) · 9.02 KB
/
data_generator.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
""" Code for loading data. """
import numpy as np
import os
import random
import tensorflow as tf
from tensorflow.python.platform import flags
from utils import get_images
FLAGS = flags.FLAGS
class DataGenerator(object):
"""
Data Generator capable of generating batches of sinusoid or Omniglot data.
A "class" is considered a class of omniglot digits or a particular sinusoid function.
"""
def __init__(self, num_samples_per_class, batch_size, config={}):
"""
Args:
num_samples_per_class: num samples to generate per class in one batch
batch_size: size of meta batch size (e.g. number of functions)
"""
self.batch_size = batch_size
self.num_samples_per_class = num_samples_per_class
self.num_classes = 1 # by default 1 (only relevant for classification problems)
if FLAGS.datasource == 'sinusoid':
self.generate = self.generate_sinusoid_batch
self.amp_range = config.get('amp_range', [0.1, 5.0])
self.phase_range = config.get('phase_range', [0, np.pi])
self.input_range = config.get('input_range', [-5.0, 5.0])
self.dim_input = 1
self.dim_output = 1
elif 'omniglot' in FLAGS.datasource:
self.num_classes = config.get('num_classes', FLAGS.num_classes)
self.img_size = config.get('img_size', (28, 28))
self.dim_input = np.prod(self.img_size)
self.dim_output = self.num_classes
# data that is pre-resized using PIL with lanczos filter
data_folder = config.get('data_folder', './data/omniglot_resized')
character_folders = [os.path.join(data_folder, family, character) \
for family in os.listdir(data_folder) \
if os.path.isdir(os.path.join(data_folder, family)) \
for character in os.listdir(os.path.join(data_folder, family))]
random.seed(1)
random.shuffle(character_folders)
num_val = 100
num_train = config.get('num_train', 1200) - num_val
self.metatrain_character_folders = character_folders[:num_train]
if FLAGS.test_set:
self.metaval_character_folders = character_folders[num_train+num_val:]
else:
self.metaval_character_folders = character_folders[num_train:num_train+num_val]
self.rotations = config.get('rotations', [0, 90, 180, 270])
elif FLAGS.datasource == 'miniimagenet':
self.num_classes = config.get('num_classes', FLAGS.num_classes)
self.img_size = config.get('img_size', (84, 84))
self.dim_input = np.prod(self.img_size)*3
self.dim_output = self.num_classes
metatrain_folder = config.get('metatrain_folder', './data/miniImagenet/train')
if FLAGS.test_set:
metaval_folder = config.get('metaval_folder', './data/miniImagenet/test')
else:
metaval_folder = config.get('metaval_folder', './data/miniImagenet/val')
metatrain_folders = [os.path.join(metatrain_folder, label) \
for label in os.listdir(metatrain_folder) \
if os.path.isdir(os.path.join(metatrain_folder, label)) \
]
metaval_folders = [os.path.join(metaval_folder, label) \
for label in os.listdir(metaval_folder) \
if os.path.isdir(os.path.join(metaval_folder, label)) \
]
self.metatrain_character_folders = metatrain_folders
self.metaval_character_folders = metaval_folders
self.rotations = config.get('rotations', [0])
else:
raise ValueError('Unrecognized data source')
def make_data_tensor(self, train=True):
if train:
folders = self.metatrain_character_folders
# number of tasks, not number of meta-iterations. (divide by metabatch size to measure)
num_total_batches = 200000
else:
folders = self.metaval_character_folders
num_total_batches = 600
# make list of files
print('Generating filenames')
all_filenames = []
for _ in range(num_total_batches):
sampled_character_folders = random.sample(folders, self.num_classes)
random.shuffle(sampled_character_folders)
labels_and_images = get_images(sampled_character_folders, range(self.num_classes), nb_samples=self.num_samples_per_class, shuffle=False)
# make sure the above isn't randomized order
labels = [li[0] for li in labels_and_images]
filenames = [li[1] for li in labels_and_images]
all_filenames.extend(filenames)
# make queue for tensorflow to read from
filename_queue = tf.train.string_input_producer(tf.convert_to_tensor(all_filenames), shuffle=False)
print('Generating image processing ops')
image_reader = tf.WholeFileReader()
_, image_file = image_reader.read(filename_queue)
if FLAGS.datasource == 'miniimagenet':
image = tf.image.decode_jpeg(image_file, channels=3)
image.set_shape((self.img_size[0],self.img_size[1],3))
image = tf.reshape(image, [self.dim_input])
image = tf.cast(image, tf.float32) / 255.0
else:
image = tf.image.decode_png(image_file)
image.set_shape((self.img_size[0],self.img_size[1],1))
image = tf.reshape(image, [self.dim_input])
image = tf.cast(image, tf.float32) / 255.0
image = 1.0 - image # invert
num_preprocess_threads = 1 # TODO - enable this to be set to >1
min_queue_examples = 256
examples_per_batch = self.num_classes * self.num_samples_per_class
batch_image_size = self.batch_size * examples_per_batch
print('Batching images')
images = tf.train.batch(
[image],
batch_size = batch_image_size,
num_threads=num_preprocess_threads,
capacity=min_queue_examples + 3 * batch_image_size,
)
all_image_batches, all_label_batches = [], []
print('Manipulating image data to be right shape')
for i in range(self.batch_size):
image_batch = images[i*examples_per_batch:(i+1)*examples_per_batch]
if FLAGS.datasource == 'omniglot':
# omniglot augments the dataset by rotating digits to create new classes
# get rotation per class (e.g. 0,1,2,0,0 if there are 5 classes)
rotations = tf.multinomial(tf.log([[1., 1.,1.,1.]]), self.num_classes)
label_batch = tf.convert_to_tensor(labels)
new_list, new_label_list = [], []
for k in range(self.num_samples_per_class):
class_idxs = tf.range(0, self.num_classes)
class_idxs = tf.random_shuffle(class_idxs)
true_idxs = class_idxs*self.num_samples_per_class + k
new_list.append(tf.gather(image_batch,true_idxs))
if FLAGS.datasource == 'omniglot': # and FLAGS.train:
new_list[-1] = tf.stack([tf.reshape(tf.image.rot90(
tf.reshape(new_list[-1][ind], [self.img_size[0],self.img_size[1],1]),
k=tf.cast(rotations[0,class_idxs[ind]], tf.int32)), (self.dim_input,))
for ind in range(self.num_classes)])
new_label_list.append(tf.gather(label_batch, true_idxs))
new_list = tf.concat(new_list, 0) # has shape [self.num_classes*self.num_samples_per_class, self.dim_input]
new_label_list = tf.concat(new_label_list, 0)
all_image_batches.append(new_list)
all_label_batches.append(new_label_list)
all_image_batches = tf.stack(all_image_batches)
all_label_batches = tf.stack(all_label_batches)
all_label_batches = tf.one_hot(all_label_batches, self.num_classes)
return all_image_batches, all_label_batches
def generate_sinusoid_batch(self, train=True, input_idx=None):
# Note train arg is not used (but it is used for omniglot method.
# input_idx is used during qualitative testing --the number of examples used for the grad update
amp = np.random.uniform(self.amp_range[0], self.amp_range[1], [self.batch_size])
phase = np.random.uniform(self.phase_range[0], self.phase_range[1], [self.batch_size])
outputs = np.zeros([self.batch_size, self.num_samples_per_class, self.dim_output])
init_inputs = np.zeros([self.batch_size, self.num_samples_per_class, self.dim_input])
for func in range(self.batch_size):
init_inputs[func] = np.random.uniform(self.input_range[0], self.input_range[1], [self.num_samples_per_class, 1])
if input_idx is not None:
init_inputs[:,input_idx:,0] = np.linspace(self.input_range[0], self.input_range[1], num=self.num_samples_per_class-input_idx, retstep=False)
outputs[func] = amp[func] * np.sin(init_inputs[func]-phase[func])
return init_inputs, outputs, amp, phase