Tensorflow多线程图像加载 [英] Tensorflow multithreading image loading

问题描述

所以我有这个玩具示例代码；

So I have this toy example code;

import glob
from tqdm import tqdm
import tensorflow as tf

imgPaths = glob.glob("/home/msmith/imgs/*/*") # Some images

filenameQ = tf.train.string_input_producer(imgPaths)
reader = tf.WholeFileReader()
key, value = reader.read(filenameQ)

img = tf.image.decode_jpeg(value)
init_op = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init_op)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    for i in tqdm(range(10000)):
        img.eval().mean()

加载图像并打印每个图像的均值.我如何对其进行编辑，以便对图像的加载部分进行多线程处理，这是目前我对tf图像脚本的瓶颈.

which loads images and prints the mean of each one. How to I edit it so it's multithreading the loading part of the images, which is at the moment my bottleneck on my tf image scripts.

推荐答案

编辑(2018/3/5):现在，使用tf.data API可以更轻松地获得相同的结果.

EDIT (2018/3/5): It's now easier to get the same results using the tf.data API.

import glob
from tqdm import tqdm
import tensorflow as tf

imgPaths = glob.glob("/home/msmith/imgs/*/*") # Some images

dataset = (tf.data.Dataset.from_tensor_slices(imgPaths)
           .map(lambda x: tf.reduce_mean(tf.decode_jpeg(tf.read_file(x))),
                num_parallel_calls=16)
           .prefetch(128))

iterator = dataset.make_one_shot_iterator()
next_mean = iterator.get_next()

with tf.Session() as sess:
    for i in tqdm(range(10000)):
        sess.run(next_mean)

---

如sygi在他们的评论中所建议的，

---

As sygi suggests in their comment, a tf.train.QueueRunner can be used to define some ops that run in a separate thread, and (typically) enqueue values into a TensorFlow queue.

import glob
from tqdm import tqdm
import tensorflow as tf

imgPaths = glob.glob("/home/msmith/imgs/*/*") # Some images

filenameQ = tf.train.string_input_producer(imgPaths)

# Define a subgraph that takes a filename, reads the file, decodes it, and                                                                                     
# enqueues it.                                                                                                                                                 
filename = filenameQ.dequeue()
image_bytes = tf.read_file(filename)
decoded_image = tf.image.decode_jpeg(image_bytes)
image_queue = tf.FIFOQueue(128, [tf.uint8], None)
enqueue_op = image_queue.enqueue(decoded_image)

# Create a queue runner that will enqueue decoded images into `image_queue`.                                                                                   
NUM_THREADS = 16
queue_runner = tf.train.QueueRunner(
    image_queue,
    [enqueue_op] * NUM_THREADS,  # Each element will be run from a separate thread.                                                                                       
    image_queue.close(),
    image_queue.close(cancel_pending_enqueues=True))

# Ensure that the queue runner threads are started when we call                                                                                               
# `tf.train.start_queue_runners()` below.                                                                                                                      
tf.train.add_queue_runner(queue_runner)

# Dequeue the next image from the queue, for returning to the client.                                                                                          
img = image_queue.dequeue()

init_op = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init_op)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    for i in tqdm(range(10000)):
        img.eval().mean()

这篇关于Tensorflow多线程图像加载的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！