python - Tensorflow "map operation" for tensor?

Question

I am adapting the cifar10 convolution example to my problem. I'd like to change the data input from a design that reads images one-at-a-time from a file to a design that operates on an already-in-memory set of images. The original inputs() function looks like this:

read_input = cifar10_input.read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
# Crop the central [height, width] of the image.
resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,
                                                     width, height)

In the original version, read_input is a tensor containing one image.

I keep all my images in RAM, so instead of using filename_queue, I have one huge images_tensor = tf.constant(images), where images_tensor.shape is (something, 32, 32, 3).

My question is very-very basic: what is the best way to apply some function (tf.image.resize_image_with_crop_or_pad in my case) to all elements of images_tensor?

Iterating is problematic in tensorflow, with limited slices(TensorFlow - numpy-like tensor indexing). Is there a solution to achieving this using just one command?

Answer 1

There are a few answers - none quite as elegant as a map function. Which is best depends a bit on your desire for memory efficiency.

(a) You can use enqueue_many to throw them into a tf.FIFOQueue and then dequeue and tf.image.resize_image_with_crop_or_pad an image at a time, and concat it all back into one big smoosh. This is probably slow. Requires N calls to run for N images.

(b) You could use a single placeholder feed and run to resize and crop them on their way in from your original datasource. This is possibly the best option from a memory perspective, because you never have to store the unresized data in memory.

(c) You could use the tf.control_flow_ops.While op to iterate through the full batch and build up the result in a tf.Variable. Particularly if you take advantage of the parallel execution permitted by while, this is likely to be the fastest approach.

I'd probably go for option (c) unless you want to minimize memory use, in which case filtering it on the way in (option b) would be a better choice.

Answer 2

As of version 0.8 there is map_fn. From the documentation:

map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)

map on the list of tensors unpacked from elems on dimension 0.

This map operator repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems. dtype is the data type of the return value of fn. Users must provide dtype if it is different from the data type of elems.

Suppose that elems is unpacked into values, a list of tensors. The shape of the result tensor is [len(values)] + fn(values[0]).shape.

Args:

fn: The callable to be performed.

elems: A tensor to be unpacked to apply fn.

dtype: (optional) The output type of fn.

parallel_iterations: (optional) The number of iterations allowed to run in parallel. back_prop: (optional) True enables back propagation. swap_memory: (optional) True enables GPU-CPU memory swapping. name: (optional) Name prefix for the returned tensors.

Returns:

A tensor that packs the results of applying fn to the list of tensors unpacked from elems, from first to last.

Raises:

TypeError: if fn is not callable.

Example:

  elems = [1, 2, 3, 4, 5, 6]
  squares = map_fn(lambda x: x * x, elems)
  # squares == [1, 4, 9, 16, 25, 36]
  ```

Answer 3

Tensorflow provides a couple of higher-order functions and one of them is tf.map_fn. The usage is very easy: you define your mappping and apply it to the tensor:

variable = tf.Variable(...)
mapping = lambda x: f(x)
res = tf.map_fn(mapping, variable)