我正在尝试使用管道来读取图像到CNN中。我使用了string_input_producer()来获取文件名的队列，但它似乎卡在那里什么也没做。以下是我的代码，请给我一些建议，让它能够正常工作。

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, labeldef 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_batchtrain_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)#训练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)#准确率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()#会话with tf.Session() as sess:    print('已启动')    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('步骤: %d' %step)            sess.run(train_step)    except tf.errors.OutOfRangeError as ex:        pass    coord.request_stop()    coord.join(threads)

回答：

你的代码并不是完全自包含的，因为get_label方法没有定义。

但很可能你的问题出自read_image_file方法中的这些行：

with tf.Session() as sess:    label = getLabel(labels, key.eval())

key.eval部分试图从尚未启动的队列中取出一个元素。在你的输入管道定义之前，你不应该创建任何会话（也不应该尝试对key（和可能的labels）进行评估）。get_label方法应该仅对labels和key执行张量操作，并返回一个label张量。

例如，你可以使用这些tensor字符串操作，这样它们将成为图的一部分。