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#! /usr/bin/python
# -*- coding: utf-8 -*-
"""Reimplementation of the TensorFlow official CIFAR-10 CNN tutorials.
- 1. This model has 1,068,298 paramters, after few hours of training with GPU,
accurcy of 86% 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
from tensorlayer.layers import *
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.
try: # TF 0.12+
img = tf.image.per_image_standardization(img)
except Exception: # earlier TF versions
img = tf.image.per_image_whitening(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.
try: # TF 0.12+
img = tf.image.per_image_standardization(img)
except Exception: # earlier TF versions
img = tf.image.per_image_whitening(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")
## Example to visualize data
# img, label = read_and_decode("train.cifar10", None)
# img_batch, label_batch = tf.train.shuffle_batch([img, label],
# batch_size=4,
# capacity=50000,
# min_after_dequeue=10000,
# num_threads=1)
# print("img_batch : %s" % img_batch._shape)
# print("label_batch : %s" % label_batch._shape)
#
# init = tf.initialize_all_variables()
# with tf.Session() as sess:
# sess.run(init)
# coord = tf.train.Coordinator()
# threads = tf.train.start_queue_runners(sess=sess, coord=coord)
#
# for i in range(3): # number of mini-batch (step)
# print("Step %d" % i)
# val, l = sess.run([img_batch, label_batch])
# # exit()
# print(val.shape, l)
# tl.visualize.images2d(val, second=1, saveable=False, name='batch'+str(i), dtype=np.uint8, fig_idx=2020121)
#
# coord.request_stop()
# coord.join(threads)
# sess.close()
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)
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
) # set the number of threads here
# 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 """
W_init = tf.truncated_normal_initializer(stddev=5e-2)
W_init2 = tf.truncated_normal_initializer(stddev=0.04)
b_init2 = tf.constant_initializer(value=0.1)
with tf.variable_scope("model", reuse=reuse):
net = InputLayer(x_crop, name='input')
net = Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', W_init=W_init, name='cnn1')
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
net = LocalResponseNormLayer(net, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')
net = Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', W_init=W_init, name='cnn2')
net = LocalResponseNormLayer(net, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
net = FlattenLayer(net, name='flatten')
net = DenseLayer(net, 384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')
net = DenseLayer(net, 192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')
net = DenseLayer(net, n_units=10, act=tf.identity, W_init=W_init2, 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
def model_batch_norm(x_crop, y_, reuse, is_train):
""" Batch normalization should be placed before rectifier. """
W_init = tf.truncated_normal_initializer(stddev=5e-2)
W_init2 = tf.truncated_normal_initializer(stddev=0.04)
b_init2 = tf.constant_initializer(value=0.1)
with tf.variable_scope("model", reuse=reuse):
net = InputLayer(x_crop, name='input')
net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME', W_init=W_init, b_init=None, name='cnn1')
net = BatchNormLayer(net, is_train, act=tf.nn.relu, name='batch1')
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME', W_init=W_init, b_init=None, name='cnn2')
net = BatchNormLayer(net, is_train, act=tf.nn.relu, name='batch2')
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
net = FlattenLayer(net, name='flatten')
net = DenseLayer(net, 384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')
net = DenseLayer(net, 192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')
net = DenseLayer(net, n_units=10, act=tf.identity, W_init=W_init2, 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.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
## using local response normalization
network, cost, acc, = model(x_train_batch, y_train_batch, False)
_, cost_test, acc_test = model(x_test_batch, y_test_batch, True)
## you may want to try batch normalization
# network, cost, acc, = model_batch_norm(x_train_batch, y_train_batch, None, is_train=True)
# _, cost_test, acc_test = model_batch_norm(x_test_batch, y_test_batch, True, is_train=False)
## 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()
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