python-3.x - 从python迭代器填充队列

Question

我想创建一个从迭代器填充的队列。然而，在以下 MWE 中，总是将相同的值排入队列:

import tensorflow as tf
import numpy as np

# data
imgs = [np.random.randn(i,i) for i in [2,3,4,5]]

# iterate through data infinitly
def data_iterator():
    while True:
        for img in imgs:
            yield img

it = data_iterator()

# create queue for data
q = tf.FIFOQueue(capacity=5, dtypes=[tf.float64])

# feed next element from iterator
enqueue_op = q.enqueue(list(next(it)))

# setup queue runner
numberOfThreads = 1 
qr = tf.train.QueueRunner(q, [enqueue_op] * numberOfThreads)
tf.train.add_queue_runner(qr) 

# dequeue
dequeue_op  = q.dequeue() 
dequeue_op = tf.Print(dequeue_op, data=[dequeue_op], message="dequeue()")

# We start the session as usual ...
with tf.Session() as sess:
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    for i in range(10):
        data = sess.run(dequeue_op)
        print(data)
.
    coord.request_stop()
    coord.join(threads)

我是否必须使用 feed_dict ?如果是，我必须如何将它与 QueueRunner 结合使用？

Answer 1

运行时

enqueue_op = q.enqueue(list(next(it)))

tensorflow 将只执行一次 list(next(it)) 。此后，它将保存第一个列表并将其添加到 q 每次运行 enqueue_op .为避免这种情况，您必须使用占位符。馈送占位符与 tf.train.QueueRunner 不兼容.而是使用这个:

import tensorflow as tf
import numpy as np
import threading

# data
imgs = [np.random.randn(i,i) for i in [2,3,4,5]]

# iterate through data infinitly
def data_iterator():
    while True:
        for img in imgs:
            yield img

it = data_iterator()

# create queue for data
q = tf.FIFOQueue(capacity=5, dtypes=[tf.float64])

# feed next element from iterator

img_p = tf.placeholder(tf.float64, [None, None])
enqueue_op = q.enqueue(img_p)

dequeue_op  = q.dequeue()


with tf.Session() as sess:
    coord = tf.train.Coordinator()

    def enqueue_thread():
        with coord.stop_on_exception():
            while not coord.should_stop():
                sess.run(enqueue_op, feed_dict={img_p: list(next(it))})

    numberOfThreads = 1
    for i in range(numberOfThreads):
      threading.Thread(target=enqueue_thread).start()



    for i in range(3):
        data = sess.run(dequeue_op)
        print(data)