fifo_queue中OutOfRangeError的原因

2024年5月6日 互联网

OutOfRangeError

错误信息为：

padding_fifo_queue is closed and has insufficient elements (required 1 current size 0)[[Node: get_batch/batch = QueueDequeueManyV2[component_types=[DT_STRING, DT_FLOAT, DT_INT32, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](get_batch/batch/padding_fiflo_queue, get_batch/batch/n)]]

错误原因

这个错误耗费了我整整两天的时间，主要原因在于tensorflow中报错是insufficient elements，让我认为时queue设置参数有误导致的问题，但其实不然。真正原因是输入数据的问题。

对于检测应用，输入的图像尺寸与对应xml文件中记录的图像尺寸(h, w)不一致，从而引起了该错误。

该原因从属于输入reshape时shape不一致这一范畴。

而该错误（图像保存后的尺寸与xml记录尺寸不一致）的原因在于使用plt.save保存图像时，图像尺寸有时会发生一个像素单位的偏差（真的坑，万万没想到）。

其他可能原因：data preprocessing

Problem solved : it was an error that randomly appears during the preprocessing. When my images_queue tries to enqueue a new image and raise this exception, nothing is printed but the queue closes. Then later when I try to access the queue, it raises the OutOfRangeError.

来源于该网址

其他可能原因：文件末尾的空行

I had the exactly same issue today and later I found it was the input data file I downloaded from “famous data set” (such as https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data) that caused the error: It has some empty lines at the end of the file. Remove the empty lines, the error was gone!

来源于该网址

其他可能原因：输入reshape时shape不一致

You’re likely processing the parsed TFRecord example wrong. E.g. trying to reshape a tensor to an incompatible size. You can debug using a tf_record_iterator to confirm the data you’re reading is stored the way you think it is:

import tensorflow as tf
import numpy as nptfrecords_filename = '/path/to/some.tfrecord'
record_iterator = tf.python_io.tf_record_iterator(path=tfrecords_filename)for string_record in record_iterator:# Parse the next exampleexample = tf.train.Example()example.ParseFromString(string_record)# Get the features you stored (change to match your tfrecord writing code)height = int(example.features.feature['height'].int64_list.value[0])width = int(example.features.feature['width'].int64_list.value[0])img_string = (example.features.feature['image_raw'].bytes_list.value[0])# Convert to a numpy array (change dtype to the datatype you stored)img_1d = np.fromstring(img_string, dtype=np.float32)# Print the image shape; does it match your expectations?print(img_1d.shape)

Phew, that was a rough bug, but your answer solved it. I had a tf.py_func node that returned the wrong type, but TF only shows a misleading error message about insufficient elements. – Lenar Hoyt Jun 15 ‘17 at 18:46

来源于网址

其他可能原因：未初始化local variables

I had a similar problem. Digging around the web, it turned out that if you use some num_epochs argument, you have to initialize all the local variables, so your code should end up looking like:

with tf.Session() as sess:sess.run(tf.local_variables_initializer())sess.run(tf.global_variables_initializer())coord = tf.train.Coordinator()threads = tf.train.start_queue_runners(coord=coord)# do your stuff herecoord.request_stop()coord.join(threads)

Queue详解

  1. 十图详解tensorflow数据读取机制（附代码）

  2. tensorflow队列操作详解

  3. 理解TensorFlow的Queue

最新文章