在Numpy中预处理Tensorflow张量

Question

I have set up a CNN in Tensorflow where I read my data with a TFRecordReader. It works well but I would like to do some more preprocessing and data augmentation than offered by the tf.image functions. I would specifically like to do some randomized scaling.

Is it possible to process a Tensorflow tensor in Numpy? Or do I need to drop the TFRecordReader and rather do all my preprocessing in Numpy and feed data using the feed_dict? I suspect that the feed_dict method is slow when training on images, but I might be wrong?

Answer 1

If you could create a custom I/O pipeline that fetches intermediate results back from TensorFlow using one or more threads, applies arbitrary Python logic, and then feeds them into a queue for subsequent processing. The resulting program would be somewhat more complicated, but I suggest you look at the threading and queues HOWTO for information on how to get started.

---

There is an experimental feature that might make this easier, if you install from source .

If you have already built a preprocessing pipeline using TensorFlow ops, the easiest way to add some custom Python code is to use the tf.py_func() operator, which takes a list of Tensor objects, and a Python function that maps one or more NumPy arrays to one or more NumPy arrays.

For example, let's say you have a pipeline like this:

reader = tf.TFRecordReader(...)
image_t = tf.image.decode_png(tf.parse_single_example(reader.read(), ...))

...you could use tf.py_func() to apply some custom NumPy processing as follows:

from scipy import ndimage
def preprocess(array):
  # `array` is a NumPy array containing.
  return ndimage.rotate(array, 45)

image_t = tf.py_func(preprocess, [image_t], [tf.float32])