Part-1

Welcome to einops

  1. We don’t write
    y = x.transpose(0,2,3,1)
  2. We write comprehensible code
    y = einops.rearrange(x, "b c h w -> b h w c")
  3. einops supports widely used tensor packages viz. numpy, pytorch, tensorflow, chainer, gluon and extends them.

What’s in this tutorial?

  1. Fundamentals: reordering, composition, and decomposition of tensors.
  2. Operations: rearrange, reduce, repeat
  3. How much can you do with a single operation?

Preparations

import numpy
from utils import display_np_arrays_as_images
display_np_arrays_as_images()

Load a batch of images

## there are 6 images of shape 96x96
## with 3 color channels packed as tensors
images = np.load("./resources/test_images.npy", allow_pickle=False)
print(images.shape)  ## (6, 96, 96, 3)

Composition of axes

from einops import rearrange

## composing the first two axes together
## i.e. 6 images of shape (96, 96, 3) are rearranged
## into a single image of shape (6*96, 96, 3)
rearrange(images, "b h w c -> (b h) w c").shape  ## (576, 96, 3)

Decomposition of axes

## decomposition is the reverse of composition
rearrange(images, "b h w c -> b h w c").shape  ## (6, 96, 96, 3)

Order of axes matters

## moving color channel to the 2nd position
rearrange(images, "b h w c -> b c h w").shape  ## (6, 3, 96, 96)