如何将 TFRecords 转换为 numpy 数组? [英] How can I convert TFRecords into numpy arrays?

问题描述

主要思想是将 TFRecords 转换为 numpy 数组.假设 TFRecord 存储图像.具体:

1. 读取 TFRecord 文件并将每个图像转换为一个 numpy 数组.
2. 将图片写入1.jpg、2.jpg等格式
3. 同时将文件名和标签写入文本文件中，如下所示:<块引用>

   1.jpg 22.jpg 43.jpg 5

我目前使用以下代码:

将tensorflow导入为tf导入操作系统def read_and_decode(filename_queue):阅读器 = tf.TFRecordReader()_, serialized_example = reader.read(filename_queue)特征 = tf.parse_single_example(serialized_example,# 未指定默认值，因为两个键都是必需的.特征={'image_raw': tf.FixedLenFeature([], tf.string),'标签':tf.FixedLenFeature([], tf.int64),'高度':tf.FixedLenFeature([], tf.int64),'宽度':tf.FixedLenFeature([], tf.int64),'深度':tf.FixedLenFeature([], tf.int64)})图像 = tf.decode_raw(features['image_raw'], tf.uint8)标签 = tf.cast(功能['标签'], tf.int32)高度 = tf.cast(特征['高度'], tf.int32)宽度 = tf.cast(功能['宽度'], tf.int32)深度 = tf.cast(特征['深度']，tf.int32)返回图像、标签、高度、宽度、深度使用 tf.Session() 作为 sess:filename_queue = tf.train.string_input_producer(["../data/svhn/svhn_train.tfrecords"])图像、标签、高度、宽度、深度 = read_and_decode(filename_queue)image = tf.reshape(image, tf.pack([height, width, 3]))image.set_shape([32,32,3])init_op = tf.initialize_all_variables()sess.run(init_op)打印 (image.eval())

我只是在阅读，试图为初学者获得至少一张图片.当我运行这个代码时，代码就卡住了.

解决方案

糟糕，我犯了一个愚蠢的错误.我使用了 string_input_producer 但忘记运行 queue_runners.

使用 tf.Session() 作为 sess:filename_queue = tf.train.string_input_producer(["../data/svhn/svhn_train.tfrecords"])图像、标签、高度、宽度、深度 = read_and_decode(filename_queue)image = tf.reshape(image, tf.pack([height, width, 3]))image.set_shape([32,32,3])init_op = tf.initialize_all_variables()sess.run(init_op)坐标 = tf.train.Coordinator()线程 = tf.train.start_queue_runners(coord=coord)对于我在范围内(1000):例如，l = sess.run([图像，标签])打印(例如，l)coord.request_stop()coord.join(线程)

The main idea is to convert TFRecords into numpy arrays. Assume that the TFRecord stores images. Specifically:

1. Read a TFRecord File and convert each image into a numpy array.
2. Write the image into 1.jpg, 2.jpg, etc.
3. At the same time, write the file name and label to the text file like this:

   1.jpg 2
   2.jpg 4
   3.jpg 5
   

I currently use the following code:

import tensorflow as tf
import os

def read_and_decode(filename_queue):
  reader = tf.TFRecordReader()
  _, serialized_example = reader.read(filename_queue)
  features = tf.parse_single_example(
      serialized_example,
      # Defaults are not specified since both keys are required.
      features={
          'image_raw': tf.FixedLenFeature([], tf.string),
          'label': tf.FixedLenFeature([], tf.int64),
          'height': tf.FixedLenFeature([], tf.int64),
          'width': tf.FixedLenFeature([], tf.int64),
          'depth': tf.FixedLenFeature([], tf.int64)
      })
  image = tf.decode_raw(features['image_raw'], tf.uint8)
  label = tf.cast(features['label'], tf.int32)
  height = tf.cast(features['height'], tf.int32)
  width = tf.cast(features['width'], tf.int32)
  depth = tf.cast(features['depth'], tf.int32)
  return image, label, height, width, depth

with tf.Session() as sess:
  filename_queue = tf.train.string_input_producer(["../data/svhn/svhn_train.tfrecords"])
  image, label, height, width, depth = read_and_decode(filename_queue)
  image = tf.reshape(image, tf.pack([height, width, 3]))
  image.set_shape([32,32,3])
  init_op = tf.initialize_all_variables()
  sess.run(init_op)
  print (image.eval())

I'm just reading trying to get at least one image for starters. The code just gets stuck when I run this.

解决方案

Oops, it was a silly mistake on my part. I used a string_input_producer but forgot to run the queue_runners.

with tf.Session() as sess:
  filename_queue = tf.train.string_input_producer(["../data/svhn/svhn_train.tfrecords"])
  image, label, height, width, depth = read_and_decode(filename_queue)
  image = tf.reshape(image, tf.pack([height, width, 3]))
  image.set_shape([32,32,3])
  init_op = tf.initialize_all_variables()
  sess.run(init_op)
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(coord=coord)
  for i in range(1000):
    example, l = sess.run([image, label])
    print (example,l)
  coord.request_stop()
  coord.join(threads)

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