TensorFlow image reading queue empty - machine-learning

Question

I'm trying to use the pipeline for reading images to the CNN. I used string_input_producer() to obtain the queue of file names, but it seems to hang there without doing anything. Below is my code, please give me some advise of how to make it work.
def read_image_file(filename_queue, labels):
reader = tf.WholeFileReader()
key, value = reader.read(filename_queue)
image = tf.image.decode_png(value, channels=3)
image = tf.cast(image, tf.float32)
resized_image = tf.image.resize_images(image, [224, 112])
with tf.Session() as sess:
label = getLabel(labels, key.eval())
return resized_image, label
def input_pipeline(filename_queue, queue_names, batch_size, num_epochs, labels):
image, label = read_image_file(filename_queue, labels)
min_after_dequeue = 10 * batch_size
capacity = 20 * batch_size
image_batch, label_batch = tf.train.shuffle_batch(
[image, label], batch_size=batch_size, num_threads=1, capacity=capacity,
min_after_dequeue=min_after_dequeue)
return image_batch, label_batch
train_queue = tf.train.string_input_producer(trainnames, shuffle=True, num_epochs=epochs)
train_batch, train_label = input_pipeline(train_queue, trainnames, batch_size, epochs, labels)
prediction = AlexNet(x)
#Training
with tf.name_scope("cost_function") as scope:
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=train_label, logits=prediction(train_batch)))
tf.summary.scalar("cost_function", cost)
train_step = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(cost)
#Accuracy
with tf.name_scope("accuracy") as scope:
correct_prediction = tf.equal(tf.argmax(prediction,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
merged = tf.summary.merge_all()
#Session
with tf.Session() as sess:
print('started')
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord, start=True)
sess.run(threads)
try:
for step in range(steps_per_epch * epochs):
print('step: %d' %step)
sess.run(train_step)
except tf.errors.OutOfRangeError as ex:
pass
coord.request_stop()
coord.join(threads)

Answer 1

Your code is not completely self-contained as the get_label method is not defined.
But it is very likely that the issue you have comes from these lines in the read_image_file method:
with tf.Session() as sess:
label = getLabel(labels, key.eval())
The key.eval part tries to dequeue an element of a queue which has not started yet.
You shouldn't create any session before your input pipeline is defined (nor try to eval key (and possibly labels)). The get_label method should only perform tensor operations on labels and key and return a label tensor..
For example, you can use these tensor string operations so they will be part of the graph.