Bilinear upsample in tensorflow?

Question

I want to do a simple bilinear resize (not necessarily by an integer factor) in TensorFlow. For example, starting from a (32,3,64,64) tensor, I would like a (32,3,96,96) tensor, where each 64x64 has been rescaled by a factor of 1.5 using bilinear interpolation. What is the best way to do that?

I would like this to support arbitrary factors > 1, not just 1.5 specifically.

Note: the operation on each 64x64 would be the same as what skimage.transform.rescale (scale=1.5, order=1) does.

Answer 1

tf.image.resize_images should do what you need. It accepts both 3d (single image) and 4d (batch of images) tensors, with arbitrary depth (number of channels). So this should hopefully work:

# it's height, width in TF - not width, height
new_height = int(round(old_height * scale))
new_width = int(round(old_width * scale))
resized = tf.image.resize_images(input_tensor, [new_height, new_width])

Bilinear interpolation is the default so you don't need to specify it. You could also use resize_bilinear directly.