plums 0.5.2

Description:

plums 0.5.2

PLaygroundML Unified Microlib Set : The Playground ML python toolbox package


The Plums library set aims to defined a common set of packages to be used by people involved in thePlaygroundML team.
Those packages puropose is to set a unique baseline to help make the code base more unified and avoid countless reimplementation of the same tools which in turns make people waste time and make the code base herd to understand,
debug and reuse.
Installation is simple with PyPI repository:
pip install plums

[TODO]
More information on installation can be found in the Getting Started section of the documentation.
Documentation and tests specific dependencies can be installed with the docs and tests extra keywords respectively.
Packages
Commons
The Plums commons package aims to offer a set of lightweight highly
reusable classes and utilities for all other packages.
To import do:
import plums.commons

Plot
The Plums plot package aims to offer a set of lightweight highly
reusable classes and utilities for visualizing detection and segmentation
results.
To import do:
import plums.plot

Model
The Plums model package aims to offer a framework-agnostic model
format specification (the Plums Model Format) along with its
python representation and helper implementation to ease integration into
producer and consumer codebases.
To import do:
import plums.model

Dataflow
I/O operations and efficient dataset iteration and indexing
handling.
To import do:
import plums.dataflow


Objectives
Dataflow
Dataflow elements to speedup future developements: e.g. Dataset classes, Sampler and/or Dataloader (?) and handle augmentation (imgaug, albumentation, pure numpy ?)

Python representation of data elements (e.g. Annotation, Feature, Image, Datapoint...)
Dataset classes for playground datasets
TranformedDataset-like classes to manipulate, combine and transform (e.g. augmentations) datasets in an online fashion
Sampler and BatchSampler classes to port PyTorch functionalities into Keras and build upon them

Data-preparation

Loop through datasets in a multi-threaded or multi-processed fashion
Some convenient data-preparation functions such as image transformations or annotations refinement
Some convenient statistic and analysis tools

Visualisation

Plot annotation on image
Handle single image and image grids
Plot differentials and/or superposition of annotations on same images
Handle multiple color code mode, e.g.:

By label
By confidence
By size
By type (in differential plotting)
And possibly combination of examples above (e.g. Color by label and shades by confidence)



Model Format (UMF like ?)

Python representation of a model and its format components
IO functionalities, e.g.:

Save a model (as collect disparate resources into a coherent model directory w/ metadata)
Load a model (as create a Python model representation w/ metadata from a model directory)
Verify an existing model directory
Copy and/or prune a model