problem
Traceback (most recent call last):
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _do_call
return fn(*args)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.OutOfRangeError: RandomShuffleQueue '_0_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested , current size )
[[{{node shuffle_batch}}]] During handling of the above exception, another exception occurred: Traceback (most recent call last):
File "test_TFRecord.py", line , in <module>
print(sess.run(label_batch))
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in run
run_metadata_ptr)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _run
feed_dict_tensor, options, run_metadata)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _do_run
run_metadata)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/client/session.py", line , in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.OutOfRangeError: RandomShuffleQueue '_0_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested , current size )
[[node shuffle_batch (defined at test_TFRecord.py:) ]] Original stack trace for 'shuffle_batch':
File "test_TFRecord.py", line , in <module>
num_threads=)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line , in new_func
return func(*args, **kwargs)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/training/input.py", line , in shuffle_batch
name=name)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/training/input.py", line , in _shuffle_batch
dequeued = queue.dequeue_many(batch_size, name=name)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/ops/data_flow_ops.py", line , in dequeue_many
self._queue_ref, n=n, component_types=self._dtypes, name=name)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line , in queue_dequeue_many_v2
timeout_ms=timeout_ms, name=name)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line , in _apply_op_helper
op_def=op_def)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line , in new_func
return func(*args, **kwargs)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line , in create_op
op_def=op_def)
File "/home/xxx/anaconda3/envs/mtcnn_tfgpu/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line , in __init__
self._traceback = tf_stack.extract_stack()
code
# -*- coding: utf-8 -*-
"""
@author: friedhelm """
import tensorflow as tf
img_size = 12
filename_queue = tf.train.string_input_producer(["/home/xxx/workspace/test_code/github_test/MTCNN-tensorflow/version_1_0/DATA/12/neg_12_train.tfrecords"],shuffle=True,num_epochs=10) reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue) #返回文件名和文件 features = tf.parse_single_example(serialized_example,
features={
'img':tf.FixedLenFeature([],tf.string),
'label':tf.FixedLenFeature([],tf.int64),
'roi':tf.FixedLenFeature([4],tf.float32),
'landmark':tf.FixedLenFeature([10],tf.float32),
})
img=tf.decode_raw(features['img'],tf.uint8)
label=tf.cast(features['label'],tf.int64)
roi=tf.cast(features['roi'],tf.float32)
landmark=tf.cast(features['landmark'],tf.float32)
# img = tf.reshape(img, [48,48,3])
img = tf.reshape(img, [img_size,img_size,3])
# img=img_preprocess(img)
min_after_dequeue = 1000 #
batch_size = 10 #
capacity = min_after_dequeue + 10 * batch_size
image_batch, label_batch, roi_batch, landmark_batch = tf.train.shuffle_batch([img,label,roi,landmark],
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue,
num_threads=7) i=0
with tf.Session() as sess:
sess.run((tf.global_variables_initializer(),
tf.local_variables_initializer()))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
while(1):
i=i+1
if(i%9==1):
print(sess.run(label_batch))
why?
解决方法：详见here;
完