Metadata-Version: 2.1
Name: einops
Version: 0.1.0
Summary: A new flavour of deep learning operations
Home-page: https://github.com/arogozhnikov/einops
Author: Alex Rogozhnikov
License: UNKNOWN
Keywords: deep learning,neural networks,tensor manipulation,scientific computations
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3 
Description-Content-Type: text/markdown

<div align="center">
  <img src="http://arogozhnikov.github.io/images/einops/einops_logo_350x350.png" alt="einops package logo" />
  <br><br>
</div>

# einops

A new flavour of deep learning ops for numpy, pytorch, tensorflow, chainer, gluon, and [others](#supported-frameworks).

`einops` introduces a new way to manipulate tensors, 
providing safer, more readable and semantically richer code.

<a href='http://arogozhnikov.github.io/images/einops/einops_video.mp4' >
<div align="center">
  <br><br>
  <img src="http://arogozhnikov.github.io/images/einops/einops_video.gif" alt="einops package logo" />
  <br><br>
</div>
</a>

[This video in better quality.](http://arogozhnikov.github.io/images/einops/einops_video.mp4)

- [Tutorials](#Documentation--tutorials) 
- [API micro-reference](#API)
- [Installation](#Installation)
- [Naming](#Naming-and-terminology)
- [Why using einops](#Why-using-einops-notation)
- [Contributing](#Contributing)
- [github repository](https://github.com/arogozhnikov/einops)


## Documentation / Tutorials

Tutorials are the most convenient way to see `einops` in action

- part1: [einops fundamentals](https://github.com/arogozhnikov/einops/blob/master/docs/1-einops-basics.ipynb) 
- part2: [einops for deep learning](https://github.com/arogozhnikov/einops/blob/master/docs/2-einops-for-deep-learning.ipynb)
- part3: TBD 

(Tutorials are working as a documentation too.)

## Installation

`einops` has no mandatory dependencies.

To obtain the latest github version 
```bash
pip install https://github.com/arogozhnikov/einops/archive/master.zip
```

pypi release will follow soon.


<!--
Plain and simple:
```bash
pip install einops
```
-->


## API 

Micro-reference on public API.

`einops` API is very minimalistic and powerful.

Two operations provided (see the guide to `einops` fundamentals)
```python
from einops import rearrange, reduce

# rearrange elements according to pattern
output_tensor = rearrange(input_tensor, pattern, **axes_lengths)

# rearrange elements according to pattern
output_tensor = reduce(input_tensor, pattern, reduction, **axes_lengths)
```

Two auxiliary functions
```python
from einops import asnumpy, parse_shape

# einops.asnumpy converts tensors of imperative frameworks to numpy
numpy_tensor = asnumpy(input_tensor)

# einops.parse_shape returns a shape in the form of a dictionary, axis name mapped to its length 
parse_shape(input_tensor, pattern)
```

And two layers (separate version for each framework) with the same API.

```python
from einops.layers.chainer import Rearrange, Reduce
from einops.layers.gluon import Rearrange, Reduce
from einops.layers.keras import Rearrange, Reduce
from einops.layers.torch import Rearrange, Reduce
```

`Einops` layers are behaving in the same way as operations, and have same parameters 
(for the exception of first argument, which should be passed during call)

```python
layer = Rearrange(pattern, **axes_lengths)
# applying to tensor
x = layer(x)

layer = Reduce(pattern, reduction, **axes_lengths)
# applying to tensor
x = layer(x)
```

Usually it is more convenient to use layers, not operations, to build models
```python
# example given for pytorch, but code in other frameworks is almost identical  
from torch.nn import Sequential, Conv2d, MaxPool2d, Linear, ReLU
from einops.layers.torch import Reduce

model = Sequential(
    Conv2d(3, 6, kernel_size=5),
    MaxPool2d(kernel_size=2),
    Conv2d(6, 16, kernel_size=5),
    Reduce('b c (h h2) (w w2) -> b (c h w)', 'max', h2=2, w2=2), # combined pooling and flattening
    Linear(16*5*5, 120), 
    ReLU(),
    Linear(120, 10), 
)
```

Layers are available for `chainer`, `gluon`, `keras` and `torch`. 

## Naming and terminology

`einops` stays for Einstein-Inspired Notation for operations 
(though "Einstein operations" sounds simpler and more attractive).

Notation was loosely inspired by Einstein summation (in particular by `numpy.einsum` operation).

- Terms `tensor` and `ndarray` are equivalently used and refer to multidimensional array 
- Terms `axis` and `dimension` are also equivalent


## Why using `einops` notation


### Semantic information:

```python
y = x.view(x.shape[0], -1)
y = rearrange(x, 'b c h w -> b (c h w)')
```
while these two lines are doing the same job in some context,
second one provides information about input and output.
In other words, `einops` focuses on interface: *what is input and output*, not *how* output is computed.

The next operation looks similar to previous two:
```python
y = rearrange(x, 'time c h w -> time (c h w)')
```
It gives reader a hint: 
this is not an independent batch of images we are processing, 
but rather a sequence (video). 

Semantic information makes code easier to read and maintain. 

### More checks

Back to the same example:
```python
y = x.view(x.shape[0], -1) # x: (batch, 256, 19, 19)
y = rearrange(x, 'b c h w -> b (c h w)')
```
second line checks that there are four dimensions in input, 
but you can also specify particular dimensions. 
That's opposed to just writing comments about shapes since 
[comments don't work](https://medium.freecodecamp.org/code-comments-the-good-the-bad-and-the-ugly-be9cc65fbf83)
as we know   
```python
y = x.view(x.shape[0], -1) # x: (batch, 256, 19, 19)
y = rearrange(x, 'b c h w -> b (c h w)', c=256, h=19, w=19)
```

### Result is strictly determined

Below we have at least two ways to define depth-to-space operation
```python
# depth to space
rearrange(x, 'b c (h h2) (w w2) -> b (c h2 w2) h w', h2=2, w2=2)
rearrange(x, 'b c (h h2) (w w2) -> b (h2 w2 c) h w', h2=2, w2=2)
```
there are at least four more ways to do it. Which one is used by the framework?

These details are ignored, since usually it makes no difference, 
but it can make a big difference (e.g. if you use grouped convolutions on the next stage), 
and you'd like to specify this in your code.

<!-- TODO add same with 1d elements -->

### Uniformity

```python
reduce(x, 'b c (x dx) -> b c x', 'max', dx=2)
reduce(x, 'b c (x dx) (y dx) -> b c x y', 'max', dx=2, dy=3)
reduce(x, 'b c (x dx) (y dx) (z dz)-> b c x y z', 'max', dx=2, dy=3, dz=4)
```
These examples demonstrated that we don't use separate operations for 1d/2d/3d pooling, 
those all are defined in a uniform way. 

Space-to-depth and depth-to space are defined in many frameworks. But how about width-to-height?
```python
rearrange(x, 'b c h (w w2) -> b c (h w2) w', w2=2)
```

### Framework independent behavior

Even simple functions may be understood differently within different frameworks

```python
y = x.flatten() # or flatten(x)
```

Suppose `x` shape was `(3, 4, 5)`, then `y` has shape ...
- numpy, cupy, chainer: `(60,)`
- keras, tensorflow.layers, mxnet and gluon: `(3, 20)`
- pytorch: no such function


## Supported frameworks

Einops works with ...

- [numpy](http://www.numpy.org/)
- [pytorch](https://pytorch.org/)
- [tensorflow eager](https://www.tensorflow.org/guide/eager)
- [cupy](https://cupy.chainer.org/)
- [chainer](https://chainer.org/)
- [gluon](https://mxnet.apache.org/)
- [tensorflow](https://www.tensorflow.org/)
- [mxnet](https://gluon.mxnet.io/) (experimental)
- and [keras](https://keras.io/) (experimental)

## Contributing 

Best ways to contribute are

- spread the word about `einops`
- **prepare a guide/post/tutorial** for your favorite deep learning framework
- translating examples in languages other than English is also a good idea 
- use `einops` notation in your papers to strictly define an operation you're using

## Supported python versions

`einops` works with python 3.5 or later. 

There is nothing specific to python 3 in the code, 
we simply [need to move further](http://github.com/arogozhnikov/python3_with_pleasure) 
and I decided not to support python 2.


