在使用slim之类的tensorflow自带框架的时候一般默认的数据格式就是TFRecords，在训练的时候使用TFRecords中数据的流程如下：使用input pipeline读取tfrecords文件/其他支持的格式，然后随机乱序，生成文件序列，读取并解码数据，输入模型训练。
如果有一串jpg图片地址和相应的标签：images和labels
1. 生成TFrecords
存入TFRecords文件需要数据先存入名为example的protocol buffer，然后将其serialize成为string才能写入。example中包含features，用于描述数据类型：bytes，float，int64。
import tensorflow as tfimport cv2def _int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))def _bytes_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))train_filename = 'train.tfrecords'with tf.python_io.TFRecordWriter(train_filename) as tfrecord_writer: for i in range(len(images)): # read in image data by tf img_data = tf.gfile.FastGFile(images[i], 'rb').read() # image data type is string label = labels[i] # get width and height of image image_shape = cv2.imread(images[i]).shape width = image_shape[1] height = image_shape[0] # create features feature = {'train/image': _bytes_feature(img_data), 'train/label': _int64_feature(label), # label: integer from 0-N 'train/height': _int64_feature(height), 'train/width': _int64_feature(width)} # create example protocol buffer example = tf.train.Example(features=tf.train.Features(feature=feature)) # serialize protocol buffer to string tfrecord_writer.write(example.SerializeToString()) tfrecord_writer.close() 2. 读取TFRecords文件
首先用tf.train.string_input_producer读取tfrecords文件的list建立FIFO序列，可以申明num_epoches和shuffle参数表示需要读取数据的次数以及时候将tfrecords文件读入顺序打乱，然后定义TFRecordReader读取上面的序列返回下一个record，用tf.parse_single_example对读取到TFRecords文件进行解码，根据保存的serialize example和feature字典返回feature所对应的值。此时获得的值都是string，需要进一步解码为所需的数据类型。把图像数据的string reshape成原始图像后可以进行preprocessing操作。此外，还可以通过tf.train.batch或者tf.train.shuffle_batch将图像生成batch序列。
由于tf.train函数会在graph中增加tf.train.QueueRunner类，而这些类有一系列的enqueue选项使一个队列在一个线程里运行。为了填充队列就需要用tf.train.start_queue_runners来为所有graph中的queue runner启动线程，而为了管理这些线程就需要一个tf.train.Coordinator来在合适的时候终止这些线程。
import tensorflow as tfimport matplotlib.pyplot as pltdata_path = 'train.tfrecords'with tf.Session() as sess: # feature key and its data type for data restored in tfrecords file feature = {'train/image': tf.FixedLenFeature([], tf.string), 'train/label': tf.FixedLenFeature([], tf.int64), 'train/height': tf.FixedLenFeature([], tf.int64), 'train/width': tf.FixedLenFeature([], tf.int64)} # define a queue base on input filenames filename_queue = tf.train.string_input_producer([data_path], num_epoches=1) # define a tfrecords file reader reader = tf.TFRecordReader() # read in serialized example data _, serialized_example = reader.read(filename_queue) # decode example by feature features = tf.parse_single_example(serialized_example, features=feature) image = tf.image.decode_jpeg(features['train/image']) image = tf.image.convert_image_dtype(image, dtype=tf.float32) # convert dtype from unit8 to float32 for later resize label = tf.cast(features['train/label'], tf.int64) height = tf.cast(features['train/height'], tf.int32) width = tf.cast(features['train/width'], tf.int32) # restore image to [height, width, 3] image = tf.reshape(image, [height, width, 3]) # resize image = tf.image.resize_images(image, [224, 224]) # create bathch images, labels = tf.train.shuffle_batch([image, label], batch_size=10, capacity=30, num_threads=1, min_after_dequeue=10) # capacity是队列的最大容量，num_threads是dequeue后最小的队列大小，num_threads是进行队列操作的线程数。 # initialize global & local variables init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) sess.run(init_op) # create a coordinate and run queue runner objects coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) for batch_index in range(3): batch_images, batch_labels = sess.run([images, labels]) for i in range(10): plt.imshow(batch_images[i, ...]) plt.show() print "Current image label is: ", batch_lables[i] # close threads coord.request_stop() coord.join(threads) sess.close()