adascan.py 文件源码

def make_input(model_options):
    '''
    Prepare the input placeholders and queues
    '''
    model_vars = {}
    if model_options['mode'] == 'train':
        images = tf.placeholder("float",[None,224,224,model_options['num_channels']])
        model_vars['images'] = images

        labels = tf.placeholder("uint8",[1])
        model_vars['labels'] = labels

        q = tf.RandomShuffleQueue(200, model_options['min_to_keep'], [tf.float32, tf.uint8],
                                  shapes=[[model_options['example_size'],224,224,\
                                  model_options['num_channels']],1])
        model_vars['queue'] = q
        enqueue_op = q.enqueue([images, labels])
        model_vars['enqueue_op'] = enqueue_op

    elif model_options['mode'] == 'test':
        num_crops = 10 if model_options['flip'] else 5;
        images = tf.placeholder("float",[num_crops,model_options['example_size']\
                                         ,224,224,model_options['num_channels']])
        labels = tf.placeholder("uint8",[num_crops,1])
        names = tf.placeholder("string",[num_crops,1])
        model_vars['images'] = images
        model_vars['labels'] = labels
        model_vars['names'] = names

        q = tf.FIFOQueue(200, [tf.float32, tf.uint8, "string"],
                              shapes=[[model_options['example_size'],224,224,\
                              model_options['num_channels']],[1],[1]])

        model_vars['queue'] = q
        enqueue_op = q.enqueue_many([images, labels, names])
        model_vars['enqueue_op'] = enqueue_op

    elif model_options['mode'] == 'save':
    images = tf.placeholder("float",[None,224,224,model_options['num_channels']],
                                name = 'images')
        model_vars['images'] = images

    return model_vars