如何處理輸入管道中的標籤？

Question

我確實有一個圖像的名稱和標籤作爲列表，我想獲得一批64個圖像/標籤。我可以以正確的方式獲取圖像，但對於標籤，其尺寸是（64,8126）。每列有64次相同的元素。行由8126個原始標籤值組成，不會混亂。

我瞭解每個圖像tf.train.shuffle_batch考慮8126元素標籤矢量的問題。但是，我將如何通過每個圖像只有一個元素？

def _get_images(shuffle=True): 

"""Gets the images and labels as a batch""" 

    #get image and label list 
    _img_names,_img_class = _get_list() #list of image names and labels 

    filename_queue = tf.train.string_input_producer(_img_names) 

    #reader 
    image_reader = tf.WholeFileReader() 
    _, image_file = image_reader.read(filename_queue) 

    #decode jpeg 
    image_original = tf.image.decode_jpeg(image_file) 
    label_original = tf.convert_to_tensor(_img_class,dtype=tf.int32) 
    #print label_original 

    #image preprocessing 
    image = tf.image.resize_images(image_original, [224,224]) 
    float_image = tf.cast(image,dtype=tf.float32) 
    float_image = tf.image.per_image_standardization(image) 
    #set the shape 
    float_image.set_shape((224, 224, 3)) 
    #label_original.set_shape([8126]) #<<<<<=========== causes (64,8126) dimension label without shuffle 

    #parameters for shuffle 
    batch_size = 64 
    num_preprocess_threads = 16 
    num_examples_per_epoch = 8000 
    min_fraction_of_examples_in_queue = 0.4 
    min_queue_examples = int(num_examples_per_epoch * 
         min_fraction_of_examples_in_queue) 

    if shuffle: 
     images_batch, label_batch = tf.train.shuffle_batch(
      [float_image,label_original], 
      batch_size=batch_size, 
      num_threads=num_preprocess_threads, 
      capacity=min_queue_examples + 3 * batch_size, 
      min_after_dequeue=min_queue_examples) 
    else: 
     images_batch, label_original = tf.train.batch(
      [float_image,_img_class], 
      batch_size=batch_size, 
      num_threads=num_preprocess_threads, 
      capacity=min_queue_examples + 3 * batch_size) 

    return images_batch,label_batch 

Answer 1

您可以使用tf.train.slice_input_producer

# here _img_class should be a list 
labels_queue = tf.train.slice_input_producer([_img_class]) 
... 
images_batch, label_batch = tf.train.shuffle_batch(
     [float_image,labels_queue], 
     batch_size=batch_size, 
     num_threads=num_preprocess_threads, 
     capacity=min_queue_examples + 3 * batch_size, 
     min_after_dequeue=min_queue_examples)