github地址：https://github.com/tensorflow/models.git
本文分析tutorial/image/cifar10教程项目的cifar10_input.py代码。
给外部调用的方法是：
distorted_inputs()和inputs()
cifar10.py文件调用了此文件中定义的方法。
"""Routine for decoding the CIFAR-10 binary file format.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf # 定义图片的像素，原生图片32 x 32 # Process images of this size. Note that this differs from the original CIFAR # image size of 32 x 32. If one alters this number, then the entire model # architecture will change and any model would need to be retrained. # IMAGE_SIZE = 24 IMAGE_SIZE = 32 # Global constants describing the CIFAR-10 data set. # 分类数量 NUM_CLASSES = 10 # 训练集大小 NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000 # 评价集大小 NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000 # 从CIFAR10数据文件中读取样例 # filename_queue一个队列的文件名 def read_cifar10(filename_queue): class CIFAR10Record(object): pass result = CIFAR10Record() # Dimensions of the images in the CIFAR-10 dataset. # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the # input format. # 分类结果的长度，CIFAR-100长度为2 label_bytes = 1 # 2 for CIFAR-100 result.height = 32 result.width = 32 # 3位表示rgb颜色（0-255,0-255,0-255） result.depth = 3 image_bytes = result.height * result.width * result.depth # Every record consists of a label followed by the image, with a # fixed number of bytes for each. # 单个记录的总长度=分类结果长度+图片长度 record_bytes = label_bytes + image_bytes # Read a record, getting filenames from the filename_queue. No # header or footer in the CIFAR-10 format, so we leave header_bytes # and footer_bytes at their default of 0. # 读取 reader = tf.FixedLengthRecordReader(record_bytes=record_bytes) result.key, value = reader.read(filename_queue) # Convert from a string to a vector of uint8 that is record_bytes long. record_bytes = tf.decode_raw(value, tf.uint8) # 第一位代表lable-图片的正确分类结果，从uint8转换为int32类型 # The first bytes represent the label, which we convert from uint8->int32. result.label = tf.cast( tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32) # 分类结果之后的数据代表图片，我们重新调整大小 # The remaining bytes after the label represent the image, which we reshape # from [depth * height * width] to [depth, height, width]. depth_major = tf.reshape( tf.strided_slice(record_bytes, [label_bytes], [label_bytes + image_bytes]), [result.depth, result.height, result.width]) # 格式转换，从[颜色,高度,宽度]--》[高度,宽度,颜色] # Convert from [depth, height, width] to [height, width, depth]. result.uint8image = tf.transpose(depth_major, [1, 2, 0]) return result # 构建一个排列后的一组图片和分类 def _generate_image_and_label_batch(image, label, min_queue_examples, batch_size, shuffle): # Create a queue that shuffles the examples, and then # read 'batch_size' images + labels from the example queue. # 线程数 num_preprocess_threads = 8 if shuffle: images, label_batch = tf.train.shuffle_batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size, min_after_dequeue=min_queue_examples) else: images, label_batch = tf.train.batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size) # Display the training images in the visualizer. tf.summary.image('images', images) return images, tf.reshape(label_batch, [batch_size]) # 为CIFAR评价构建输入 # data_dir路径 # batch_size一个组的大小 def distorted_inputs(data_dir, batch_size): filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)] for f in filenames: if not tf.gfile.Exists(f): raise ValueError('Failed to find file: ' + f) # Create a queue that produces the filenames to read. filename_queue = tf.train.string_input_producer(filenames) # Read examples from files in the filename queue. read_input = read_cifar10(filename_queue) reshaped_image = tf.cast(read_input.uint8image, tf.float32) height = IMAGE_SIZE width = IMAGE_SIZE # Image processing for training the network. Note the many random # distortions applied to the image. # 随机裁剪图片 # Randomly crop a [height, width] section of the image. distorted_image = tf.random_crop(reshaped_image, [height, width, 3]) # 随机旋转图片 # Randomly flip the image horizontally. distorted_image = tf.image.random_flip_left_right(distorted_image) # Because these operations are not commutative, consider randomizing # the order their operation. # 亮度变换 distorted_image = tf.image.random_brightness(distorted_image, max_delta=63) # 对比度变换 distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8) # Subtract off the mean and divide by the variance of the pixels. # Linearly scales image to have zero mean and unit norm # 标准化 float_image = tf.image.per_image_standardization(distorted_image) # Set the shapes of tensors. # 设置张量的型 float_image.set_shape([height, width, 3]) read_input.label.set_shape([1]) # Ensure that the random shuffling has good mixing properties. # 确保洗牌的随机性 min_fraction_of_examples_in_queue = 0.4 min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue) print('Filling queue with %d CIFAR images before starting to train. ' 'This will take a few minutes.' % min_queue_examples) # Generate a batch of images and labels by building up a queue of examples. return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=True) # 为CIFAR评价构建输入 # eval_data使用训练还是评价数据集 # data_dir路径 # batch_size一个组的大小 def inputs(eval_data, data_dir, batch_size): if not eval_data: filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)] num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN else: filenames = [os.path.join(data_dir, 'test_batch.bin')] num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL for f in filenames: if not tf.gfile.Exists(f): raise ValueError('Failed to find file: ' + f) # Create a queue that produces the filenames to read. # 文件名队列 filename_queue = tf.train.string_input_producer(filenames) # Read examples from files in the filename queue. # 从文件中读取解析出的图片队列 read_input = read_cifar10(filename_queue) # 转换为float reshaped_image = tf.cast(read_input.uint8image, tf.float32) height = IMAGE_SIZE width = IMAGE_SIZE # Image processing for evaluation. # Crop the central [height, width] of the image. # 剪切图片的中心 resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, height, width) # Subtract off the mean and divide by the variance of the pixels. # 标准化图片 float_image = tf.image.per_image_standardization(resized_image) # Set the shapes of tensors. # 设置张量的型 float_image.set_shape([height, width, 3]) read_input.label.set_shape([1]) # Ensure that the random shuffling has good mixing properties. # 确保洗牌的随机性 min_fraction_of_examples_in_queue = 0.4 min_queue_examples = int(num_examples_per_epoch * min_fraction_of_examples_in_queue) # Generate a batch of images and labels by building up a queue of examples. return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=False)