python 3.x - Tensorflow OutOfRangeError on tf.train.shuffle_batch won't get fixed -


i'm stuck on error , can't figure out how fix it. have read many similar questions on stack overflow none of suggested fixes helped me on problem.

i outofrangeerror on randomshufflequeue when try reconstruct image data saved .tfrecord file.

my reconstruction-code looks this:

image_width = 640 image_height = 480  tfrecords_filename = 'records/train_record_000.tfrecord'   def read_and_decode(filename_queue):     reader = tf.tfrecordreader()      _, serialized_example = reader.read(filename_queue)      features = tf.parse_single_example(         serialized_example,         features={             'height': tf.fixedlenfeature([], tf.int64),             'width': tf.fixedlenfeature([], tf.int64),             'image_raw': tf.fixedlenfeature([], tf.string),             'bbox-label_text': tf.fixedlenfeature([], tf.string)         })      image = tf.decode_raw(features['image_raw'], tf.uint8)     annotation = tf.decode_raw(features['bbox-label_text'], tf.uint8)     height = tf.cast(features['height'], tf.int32)     width = tf.cast(features['width'], tf.int32)      image_shape = tf.stack([height, width, 3])     annotation_shape = tf.stack([height, width, 1])      image = tf.reshape(image, image_shape)     annotation = tf.reshape(annotation, annotation_shape)        image_size_const = tf.constant((image_height, image_width, 3),                                      dtype=tf.int32)     annotation_size_const = tf.constant((image_height, image_width,                                           1), dtype=tf.int32)      resized_image = tf.image.resize_image_with_crop_or_pad(image=image,                                                        target_height=image_height,                                                        target_width=image_width)      resized_annotation = tf.image.resize_image_with_crop_or_pad(image=annotation,                                                             target_height=image_height,                                                             target_width=image_width)      images, annotations = tf.train.shuffle_batch([resized_image, resized_annotation],                                              batch_size=4,                                              capacity=200,                                              num_threads=2,                                              min_after_dequeue=4) return images, annotations

and run code:

if __name__ == '__main__':     filename_queue = tf.train.string_input_producer([tfrecords_filename])      image, annotation = read_and_decode(filename_queue)      init_op = tf.group(tf.global_variables_initializer(),                    tf.local_variables_initializer())      tf.session() sess:         sess.run(init_op)          coord = tf.train.coordinator()         threads = tf.train.start_queue_runners(sess=sess, coord=coord)          in range(124):             print('iteration {}/124'.format(i))             img, anno = sess.run([image, annotation])             print(img[0, :, :, :].shape)             print('current batch')          coord.request_stop()         coord.join(threads)

i trying on small dataset because want , running, therefore have 124 examples in .tfrecord file.

the error message follows:

2017-07-28 11:22:22.244499: w  tensorflow/core/kernels/queue_base.cc:294] _0_input_producer: skipping  cancelled enqueue attempt queue not closed traceback (most recent call last):   file "/usr/lib/python3.6/site- packages/tensorflow/python/client/session.py", line 1139, in _do_call     return fn(*args)   file "/usr/lib/python3.6/site- packages/tensorflow/python/client/session.py", line 1121, in _run_fn      status, run_metadata)   file "/usr/lib/python3.6/contextlib.py", line 88, in __exit__     next(self.gen)   file "/usr/lib/python3.6/site- packages/tensorflow/python/framework/errors_impl.py", line 466, in  raise_exception_on_not_ok_status     pywrap_tensorflow.tf_getcode(status)) tensorflow.python.framework.errors_impl.outofrangeerror:   randomshufflequeue '_1_shuffle_batch/random_shuffle_queue' closed  , has insufficient elements (requested 4, current size 0)      [[node: shuffle_batch = queuedequeuemanyv2[component_types= [dt_uint8, dt_uint8], timeout_ms=-1,  _device="/job:localhost/replica:0/task:0/cpu:0"]  (shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]

this happens on first iteration.


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