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#! /usr/bin/python

# -*- coding: utf-8 -*-

"""You will learn.

1. How to convert CIFAR-10 dataset into TFRecord format file.

2. How to read CIFAR-10 from TFRecord format file.

More:

1. tutorial_tfrecord.py

2. tutoral_cifar10_tfrecord.py

"""

import os

import numpy as np

# import matplotlib

# matplotlib.use('GTK')

import tensorflow as tf

import tensorlayer as tl

## 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)

X_train = np.asarray(X_train, dtype=np.float32)

y_train = np.asarray(y_train, dtype=np.int64)

X_test = np.asarray(X_test, dtype=np.float32)

y_test = np.asarray(y_test, dtype=np.int64)

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))

cwd = os.getcwd()

writer = tf.python_io.TFRecordWriter("train.cifar10")

for index, img in enumerate(X_train):

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(y_train[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()

## Read Data by Queue and Thread =======================================

def read_and_decode(filename):

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 # don't need to cast here, as it is float32 already

label = tf.cast(features['label'], tf.int32)

return img, label

img, label = read_and_decode("train.cifar10")

## Use shuffle_batch or batch

# see https://www.tensorflow.org/versions/master/api_docs/python/io_ops.html#shuffle_batch

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)

tl.layers.initialize_global_variables(sess)

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)

tl.vis.save_images(val, [2, 2], '_batch_%d.png' % i)

coord.request_stop()

coord.join(threads)

sess.close()