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（具有FIXED大小）的图像，否则我希望它包含来自测试集（不是固定大小）的图像。

这是必要的，因为在训练中，我从训练图像中随机取出固定的作物，而在测试中，我想执行密集评估并馈送网络内的整个图像（它是完全卷积的，因此它会接受它们）

当前不工作的方法是创建两个不同的迭代器，并尝试在session.run（images，{is_training：True/False}）处用tf.cond选择训练/测试输入。

问题是迭代器被评估。训练和测试数据集也有不同的大小，所以我不能将它们迭代到最后。有没有办法做到这一点？或者以更聪明的方式重写？

我已经看到了一些关于这个问题的答案，但他们总是使用tf.assign，它需要一个numpy数组并将其赋值给张量。在这种情况下，我不能使用tf.assign，因为我已经有了来自迭代器的张量。

我现在的代码是这一个。它只是简单地检查了张量的形状“图像”：

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) 

的问题是，当我想只使用训练迭代器，我的评论行初始化val_init_op，但有以下错误：

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)]] 

如果我不评论那条线，一切正常，当is_training为true时，我得到训练图像，当is_training为False时，我得到验证图像。问题是这两个迭代器都需要初始化，当我评估其中一个迭代器时，另一个迭代器也会增加。正如我所说，他们是不同的大小，这会导致一个问题。

我希望有办法解决它！在此先感谢

Answer 1

诀窍是调用iterator.get_next()内的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) 

的建议同样适用于具有副作用，如赋值给一个变量任何TensorFlow OP：如果您希望有条件地发生副作用，必须在传递到tf.cond()的相应分支函数内创建操作。