limbus 0.1.0.dev0

High level interface to create Pytorch Graphs.

Limbus: Computer Vision pipelining for PyTorch

(🚨 Warning: Unstable Prototype 🚨)

Similar to the eye corneal limbus - Limbus is a framework to create Computer Vision pipelines within the context of Deep Learning and writen in terms of differentiable tensors message passing on top of Kornia and PyTorch.

Overview

You can create pipelines using limbus.Components as follows:

# define your components
c1 = Constant("c1", 1.)
c2 = Constant("c2", torch.ones(1, 3))
add = Adder("add")
show = Printer("print")

# connect the components
c1.outputs.out >> add.inputs.a
c2.outputs.out >> add.inputs.b
add.outputs.out >> show.inputs.inp

# create the pipeline and add its nodes
pipeline = Pipeline()
pipeline.add_nodes([c1, c2, add, show])

# run your pipeline
pipeline.run(1)

torch.allclose(add.outputs.out.value, torch.ones(1, 3) * 2.)

Example using the stack torch method:

# define your components
c1 = Constant("c1", 0)
t1 = Constant("t1", torch.ones(1, 3))
t2 = Constant("t2", torch.ones(1, 3) * 2)
stack = Stack("stack")
show = Printer("print")

# connect the components
c1.outputs.out >> stack.inputs.dim
t1.outputs.out >> stack.inputs.tensors.select(0)
t2.outputs.out >> stack.inputs.tensors.select(1)
stack.outputs.out >> show.inputs.inp

# create the pipeline and add its nodes
pipeline = Pipeline()
pipeline.add_nodes([c1, t1, t2, stack, show])

# run your pipeline
pipeline.run(1)

torch.allclose(stack.outputs.out.value, torch.tensor([[1., 1., 1.],[2., 2., 2.]]))

Remember that the components can be run without the Pipeline, e.g in the last example you can also run:

asyncio.run(asyncio.gather(c1(), t1(), t2(), stack(), show()))

Basically, Pipeline objects allow you to control the execution flow, e.g. you can stop, pause, resume the execution, determine the number of executions to be run...

A higher level API on top of Pipeline is App allowing to encapsulate some code. E.g.:

class MyApp(App):
    def create_components(self):
        self.c1 = Constant("c1", 0)
        self.t1 = Constant("t1", torch.ones(1, 3))
        self.t2 = Constant("t2", torch.ones(1, 3) * 2)
        self.stack = stack("stack")
        self.show = Printer("print")

    def connect_components(self):
        self.c1.outputs.out >> self.stack.inputs.dim
        self.t1.outputs.out >> self.stack.inputs.tensors.select(0)
        self.t2.outputs.out >> self.stack.inputs.tensors.select(1)
        self.stack.outputs.out >> self.show.inputs.inp

MyApp().run(1)

Installation

from PyPI:

pip install limbus  # limbus alone

optionally some predefined components can be installed.

In the near future with PyPI (not yet available)

pip install limbus[components]  # limbus + some predefined components

currently they can be installed from the repository:

pip install limbus-components@git+https://[e-mail]/kornia/limbus-components.git

from the repository:

pip install limbus@git+https://[e-mail]/kornia/limbus.git  # limbus alone
pip install limbus-components@git+https://[e-mail]/kornia/limbus-components.git  # some predefined components (optional)

for development

you can install the environment with the following commands:

git clone https://github.com/kornia/limbus
cd limbus
source path.bash.inc

In order to regenerate the development environment:

cd limbus
rm -rf .dev_env
source path.bash.inc

Testing

Run pytest and automatically will test: cov, pydocstyle, mypy and flake8