如何使用Tensorflow的tf.cond()与两个不同的数据集迭代器而不迭代两者？

Question

我想用张量"图像"来提供CNN.当占位符is_training为True时,我希望此张量包含来自训练集的图像(具有FIXED大小),否则我希望它包含来自测试集的图像(其未固定大小).

这是必要的,因为在训练中我从训练图像中采取随机固定裁剪,而在测试中我想进行密集评估并将整个图像馈送到网络内部(它完全卷积,因此它将接受它们)

当前的NOT WORKING方法是创建两个不同的迭代器,并尝试在session.run(images,{is_training:True/False})中使用tf.cond选择训练/测试输入.

问题是两个迭代器都被评估了.训练和测试数据集也有不同的大小,所以我不能迭代它们直到最后.有没有办法让这项工作？或者以更聪明的方式重写它？

我已经看到了一些有关这方面的问题/答案,但他们总是使用tf.assign,它采用numpy数组并将其分配给张量.在这种情况下,我不能使用tf.assign,因为我已经有一个来自迭代器的张量.

我拥有的当前代码就是这个代码.它只是检查张量"图像"的形状:

train_filenames, train_labels = list_images(args.train_dir)
val_filenames, val_labels = list_images(args.val_dir)

graph = tf.Graph()
with graph.as_default():

    # Preprocessing (for both training and validation):
    def _parse_function(filename, label):
        image_string = tf.read_file(filename)
        image_decoded = tf.image.decode_jpeg(image_string, channels=3)          
        image = tf.cast(image_decoded, tf.float32)

        return image, label

    # Preprocessing (for training)
    def training_preprocess(image, label):

        # Random flip and crop
        image = tf.image.random_flip_left_right(image)
        image = tf.random_crop(image, [args.crop,args.crop, 3])

        return image, label

    # Preprocessing (for validation)
    def val_preprocess(image, label):

        flipped_image = tf.image.flip_left_right(image)
        batch = tf.stack([image,flipped_image],axis=0)

        return batch, label

    # Training dataset
    train_filenames = tf.constant(train_filenames)
    train_labels = tf.constant(train_labels)
    train_dataset = tf.contrib.data.Dataset.from_tensor_slices((train_filenames, train_labels))
    train_dataset = train_dataset.map(_parse_function,num_threads=args.num_workers, output_buffer_size=args.batch_size)
    train_dataset = train_dataset.map(training_preprocess,num_threads=args.num_workers, output_buffer_size=args.batch_size)
    train_dataset = train_dataset.shuffle(buffer_size=10000) 
    batched_train_dataset = train_dataset.batch(args.batch_size)

    # Validation dataset
    val_filenames = tf.constant(val_filenames)
    val_labels = tf.constant(val_labels)
    val_dataset = tf.contrib.data.Dataset.from_tensor_slices((val_filenames, val_labels))
    val_dataset = val_dataset.map(_parse_function,num_threads=1, output_buffer_size=1)
    val_dataset = val_dataset.map(val_preprocess,num_threads=1, output_buffer_size=1)

    train_iterator = tf.contrib.data.Iterator.from_structure(batched_train_dataset.output_types,batched_train_dataset.output_shapes)
    val_iterator = tf.contrib.data.Iterator.from_structure(val_dataset.output_types,val_dataset.output_shapes)

    train_images, train_labels = train_iterator.get_next()
    val_images, val_labels = val_iterator.get_next()

    train_init_op = train_iterator.make_initializer(batched_train_dataset)
    val_init_op = val_iterator.make_initializer(val_dataset)

    # Indicates whether we are in training or in test mode
    is_training = tf.placeholder(tf.bool)

    def f_true():
        with tf.control_dependencies([tf.identity(train_images)]):
            return tf.identity(train_images)

    def f_false():
        return val_images

    images = tf.cond(is_training,f_true,f_false)

    num_images = images.shape

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

        sess.run(train_init_op)
        #sess.run(val_init_op)

        img = sess.run(images,{is_training:True})
        print(img.shape)

问题是,当我只想使用训练迭代器时,我会注释该行以初始化val_init_op但是存在以下错误:

FailedPreconditionError (see above for traceback): GetNext() failed because the iterator has not been initialized. Ensure that you have run the initializer operation for this iterator before getting the next element.
 [[Node: IteratorGetNext_1 = IteratorGetNext[output_shapes=[[2,?,?,3], []], output_types=[DT_FLOAT, DT_INT32], _device="/job:localhost/replica:0/task:0/cpu:0"](Iterator_1)]]

如果我不评论该行,一切都按预期工作,当is_training为真时,我得到训练图像,当is_training为False时,我得到验证图像.问题是两个迭代器都需要初始化,当我评估其中一个时,另一个也会递增.因为正如我所说,它们的大小不同会导致问题.

我希望有办法解决它!提前致谢

Answer 1

诀窍是调用iterator.get_next() 内部的f_true()和f_false()功能:

def f_true():
    train_images, _ = train_iterator.get_next()
    return train_images

def f_false():
    val_images, _ = val_iterator.get_next()
    return val_images

images = tf.cond(is_training, f_true, f_false)

同样的建议适用于任何具有副作用的TensorFlow操作,例如分配给变量:如果您希望有条件地发生副作用,则必须在传递给的相应分支函数内创建操作tf.cond().