Question

我在Tensorflow中没有太多经验。我正在尝试使用预训练的ResNet152模型来获取最后一层的激活作为输出。我用于输入的图像存储在我的硬盘中。因此，我需要加载图像，对其进行预处理，然后从预训练的模型中获取输出。我找到了使用图片网址的示例，但是当我尝试使用图片路径时，却无法使用它。这是我到目前为止（目前仅一张图像）：

with tf.Graph().as_default():

    filename_queue = tf.train.string_input_producer(['./testimg/A_008.jpg'])
    reader = tf.WholeFileReader()
    key, value = reader.read(filename_queue)
    image = tf.image.decode_jpeg(value, channels=3)
    preprocessing = preprocessing_factory.get_preprocessing('resnet_v2_152', is_training=False)
    processed_image = preprocessing(image, 299,299)
    processed_images  = tf.expand_dims(processed_image, 0)

    with slim.arg_scope(resnet_v2.resnet_arg_scope()):
        logits, end_points = resnet_v2.resnet_v2_152(processed_images, is_training=False)

    checkpoints_dir='./models/resnet_v2_152' 
    init_fn = slim.assign_from_checkpoint_fn(
        os.path.join(checkpoints_dir, 'resnet_v2_152.ckpt'),
        slim.get_variables_to_restore())

    with tf.Session() as sess:
        init_fn(sess)        
        np_image, fv = sess.run([image, logits])

我正在Jupyter笔记本中执行此操作。当我执行代码时，没有收到错误消息，它会一直运行，直到重新启动内核为止。

任何想法我做错了什么？我要如何处理多张图片？

Answer 1

我找到了解决方案，将tf.WholeFileReader()替换为tf.read_file()：

graph = tf.Graph()

with graph.as_default():
    image_path = image = tf.placeholder(tf.string)
    image = tf.image.decode_jpeg(tf.read_file(image_path), channels=3)
    preprocessing = preprocessing_factory.get_preprocessing('resnet_v2_152', is_training=False)
    processed_image = preprocessing(image, image_size, image_size)
    processed_images  = tf.expand_dims(processed_image, 0)

    with slim.arg_scope(resnet_v2.resnet_arg_scope()):
        logits, end_points = resnet_v2.resnet_v2_152(processed_images, is_training=False)

    checkpoints_dir='./models/resnet_v2_152' 
    init_fn = slim.assign_from_checkpoint_fn(
        os.path.join(checkpoints_dir, 'resnet_v2_152.ckpt'),
        slim.get_variables_to_restore())


images = ['./testimg/A_008.jpg', './testimg/logo.jpg']

with tf.Session(graph=graph) as sess:
    init_fn(sess)  

    for img in images:
        fv = sess.run(logits, feed_dict={image_path: img})
        print(fv)

读取并预处理张量流预训练模型的图像

1 个答案: