Skip to content
Navigation Menu
{{ message }}
forked from tensorlayer/TensorLayer
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtutorial_binarynet_cifar10_tfrecord.py
More file actions
272 lines (225 loc) · 11 KB
/
Copy pathtutorial_binarynet_cifar10_tfrecord.py
File metadata and controls
272 lines (225 loc) · 11 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
#! /usr/bin/python
# -*- coding: utf-8 -*-
"""
- 1. This model has 1,068,298 paramters and Dorefa compression strategy(weight:1 bit, active: 1 bit),
after 500 epoches' training with GPU,accurcy of 41.1% was found.
- 2. For simplified CNN layers see "Convolutional layer (Simplified)"
in read the docs website.
- 3. Data augmentation without TFRecord see `tutorial_image_preprocess.py` !!
Links
-------
.. https://www.tensorflow.org/versions/r0.9/tutorials/deep_cnn/index.html
.. https://github.com/tensorflow/tensorflow/tree/r0.9/tensorflow/models/image/cifar10
Note
------
The optimizers between official code and this code are different.
Description
-----------
The images are processed as follows:
.. They are cropped to 24 x 24 pixels, centrally for evaluation or randomly for training.
.. They are approximately whitened to make the model insensitive to dynamic range.
For training, we additionally apply a series of random distortions to
artificially increase the data set size:
.. Randomly flip the image from left to right.
.. Randomly distort the image brightness.
.. Randomly distort the image contrast.
Speed Up
--------
Reading images from disk and distorting them can use a non-trivial amount
of processing time. To prevent these operations from slowing down training,
we run them inside 16 separate threads which continuously fill a TensorFlow queue.
"""
import os
import time
import tensorflow as tf
import tensorlayer as tl
tf.logging.set_verbosity(tf.logging.DEBUG)
tl.logging.set_verbosity(tl.logging.DEBUG)
model_file_name = "./model_cifar10_tfrecord.ckpt"
resume = False # load model, resume from previous checkpoint?
## Download data, and convert to TFRecord format, see ```tutorial_tfrecord.py```
X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
print('X_train.shape', X_train.shape) # (50000, 32, 32, 3)
print('y_train.shape', y_train.shape) # (50000,)
print('X_test.shape', X_test.shape) # (10000, 32, 32, 3)
print('y_test.shape', y_test.shape) # (10000,)
print('X %s y %s' % (X_test.dtype, y_test.dtype))
def data_to_tfrecord(images, labels, filename):
""" Save data into TFRecord """
if os.path.isfile(filename):
print("%s exists" % filename)
return
print("Converting data into %s ..." % filename)
# cwd = os.getcwd()
writer = tf.python_io.TFRecordWriter(filename)
for index, img in enumerate(images):
img_raw = img.tobytes()
## Visualize a image
# tl.visualize.frame(np.asarray(img, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
label = int(labels[index])
# print(label)
## Convert the bytes back to image as follow:
# image = Image.frombytes('RGB', (32, 32), img_raw)
# image = np.fromstring(img_raw, np.float32)
# image = image.reshape([32, 32, 3])
# tl.visualize.frame(np.asarray(image, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
example = tf.train.Example(
features=tf.train.Features(
feature={
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
}
)
)
writer.write(example.SerializeToString()) # Serialize To String
writer.close()
def read_and_decode(filename, is_train=None):
""" Return tensor to read from TFRecord """
filename_queue = tf.train.string_input_producer([filename])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example, features={
'label': tf.FixedLenFeature([], tf.int64),
'img_raw': tf.FixedLenFeature([], tf.string),
}
)
# You can do more image distortion here for training data
img = tf.decode_raw(features['img_raw'], tf.float32)
img = tf.reshape(img, [32, 32, 3])
# img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
if is_train ==True:
# 1. Randomly crop a [height, width] section of the image.
img = tf.random_crop(img, [24, 24, 3])
# 2. Randomly flip the image horizontally.
img = tf.image.random_flip_left_right(img)
# 3. Randomly change brightness.
img = tf.image.random_brightness(img, max_delta=63)
# 4. Randomly change contrast.
img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
# 5. Subtract off the mean and divide by the variance of the pixels.
img = tf.image.per_image_standardization(img)
elif is_train == False:
# 1. Crop the central [height, width] of the image.
img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
# 2. Subtract off the mean and divide by the variance of the pixels.
img = tf.image.per_image_standardization(img)
elif is_train == None:
img = img
label = tf.cast(features['label'], tf.int32)
return img, label
## Save data into TFRecord files
data_to_tfrecord(images=X_train, labels=y_train, filename="train.cifar10")
data_to_tfrecord(images=X_test, labels=y_test, filename="test.cifar10")
batch_size = 128
model_file_name = "./model_cifar10_advanced.ckpt"
resume = False # load model, resume from previous checkpoint?
with tf.device('/cpu:0'):
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# prepare data in cpu
x_train_, y_train_ = read_and_decode("train.cifar10", True)
x_test_, y_test_ = read_and_decode("test.cifar10", False)
# set the number of threads here
x_train_batch, y_train_batch = tf.train.shuffle_batch(
[x_train_, y_train_], batch_size=batch_size, capacity=2000, min_after_dequeue=1000, num_threads=32
)
# for testing, uses batch instead of shuffle_batch
x_test_batch, y_test_batch = tf.train.batch(
[x_test_, y_test_], batch_size=batch_size, capacity=50000, num_threads=32
)
def model(x_crop, y_, reuse):
""" For more simplified CNN APIs, check tensorlayer.org """
with tf.variable_scope("model", reuse=reuse):
net = tl.layers.InputLayer(x_crop, name='input')
net = tl.layers.Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='cnn1')
net = tl.layers.SignLayer(net)
net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
net = tl.layers.LocalResponseNormLayer(net, 4, 1.0, 0.001 / 9.0, 0.75, name='norm1')
net = tl.layers.BinaryConv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', name='cnn2')
net = tl.layers.LocalResponseNormLayer(net, 4, 1.0, 0.001 / 9.0, 0.75, name='norm2')
net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
net = tl.layers.FlattenLayer(net, name='flatten')
net = tl.layers.SignLayer(net)
net = tl.layers.BinaryDenseLayer(net, 384, act=tf.nn.relu, name='d1relu')
net = tl.layers.SignLayer(net)
net = tl.layers.BinaryDenseLayer(net, 192, act=tf.nn.relu, name='d2relu')
net = tl.layers.DenseLayer(net, 10, act=None, name='output')
y = net.outputs
ce = tl.cost.cross_entropy(y, y_, name='cost')
# L2 for the MLP, without this, the accuracy will be reduced by 15%.
L2 = 0
for p in tl.layers.get_variables_with_name('relu/W', True, True):
L2 += tf.contrib.layers.l2_regularizer(0.004)(p)
cost = ce + L2
# correct_prediction = tf.equal(tf.argmax(tf.nn.softmax(y), 1), y_)
correct_prediction = tf.equal(tf.cast(tf.argmax(y, 1), tf.int32), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return net, cost, acc
## You can also use placeholder to feed_dict in data after using
## val, l = sess.run([x_train_batch, y_train_batch]) to get the data
# x_crop = tf.placeholder(tf.float32, shape=[batch_size, 24, 24, 3])
# y_ = tf.placeholder(tf.int32, shape=[batch_size,])
# cost, acc, network = model(x_crop, y_, None)
with tf.device('/gpu:0'): # <-- remove it if you don't have GPU
network, cost, acc, = model(x_train_batch, y_train_batch, False)
_, cost_test, acc_test = model(x_test_batch, y_test_batch, True)
## train
n_epoch = 50000
learning_rate = 0.0001
print_freq = 1
n_step_epoch = int(len(y_train) / batch_size)
n_step = n_epoch * n_step_epoch
with tf.device('/gpu:0'): # <-- remove it if you don't have GPU
train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)
tl.layers.initialize_global_variables(sess)
if resume:
print("Load existing model " + "!" * 10)
saver = tf.train.Saver()
saver.restore(sess, model_file_name)
network.print_params(False)
network.print_layers()
print(' learning_rate: %f' % learning_rate)
print(' batch_size: %d' % batch_size)
print(' n_epoch: %d, step in an epoch: %d, total n_step: %d' % (n_epoch, n_step_epoch, n_step))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
step = 0
for epoch in range(n_epoch):
start_time = time.time()
train_loss, train_acc, n_batch = 0, 0, 0
for s in range(n_step_epoch):
## You can also use placeholder to feed_dict in data after using
# val, l = sess.run([x_train_batch, y_train_batch])
# tl.visualize.images2d(val, second=3, saveable=False, name='batch', dtype=np.uint8, fig_idx=2020121)
# err, ac, _ = sess.run([cost, acc, train_op], feed_dict={x_crop: val, y_: l})
err, ac, _ = sess.run([cost, acc, train_op])
step += 1
train_loss += err
train_acc += ac
n_batch += 1
if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
print(
"Epoch %d : Step %d-%d of %d took %fs" %
(epoch, step, step + n_step_epoch, n_step, time.time() - start_time)
)
print(" train loss: %f" % (train_loss / n_batch))
print(" train acc: %f" % (train_acc / n_batch))
test_loss, test_acc, n_batch = 0, 0, 0
for _ in range(int(len(y_test) / batch_size)):
err, ac = sess.run([cost_test, acc_test])
test_loss += err
test_acc += ac
n_batch += 1
print(" test loss: %f" % (test_loss / n_batch))
print(" test acc: %f" % (test_acc / n_batch))
if (epoch + 1) % (print_freq * 50) == 0:
print("Save model " + "!" * 10)
saver = tf.train.Saver()
save_path = saver.save(sess, model_file_name)
# you can also save model into npz
tl.files.save_npz(network.all_params, name='model.npz', sess=sess)
# and restore it as follow:
# tl.files.load_and_assign_npz(sess=sess, name='model.npz', network=network)
coord.request_stop()
coord.join(threads)
sess.close()
You can’t perform that action at this time.