Pytorch MaskRCNN

This is a Pytorch/Fastai implementation of MaskRCNN based on previous versions by Matterport and MultiModal Learning (see acknowledgements at bottom of page). This was mainly a personal learning exercise but the simplified structure may help others wanting to understand maskrcnn.

Includes:

Overview diagram that shows the key components of maskRCNN
Code cleaned up and structured as per the diagram to make it easy to understand and experiment with each part.
Works with pytorch v1 and fastai v1
Training and prediction working on nuke data from 2018 kaggle bowl; training and prediction on synthetic shapes data; and prediction on images using coco categories.

Todo:

More tests. How can it best be tested?
Could the experimental test framework be useful for continuous testing? Is there a good framework already written?
How to expand testing to keras. i.e. how to adapt matterport for eager mode?

Structure

Note that training and prediction follow slightly different paths.

The core code reflects the diagram:

datagen - anchors, dataset, head_targets, rpn_targets
model - maskrcnn (whole thing), resnet (backbone), resnetFPN (feature pyramid), rpn
filter - proposals, roialign, detections
loss - loss functions for rpn and head

Utilities

lib - c extensions for nms and roialign
utils - box_utils, image_utils, visualize, batch (function decorator to process batches)
callbacks - to tailor fastai for maskrcnn
ipstartup - startup script for notebooks
config - configuration constants

Samples

Applications typically with dataset, config, learner, notebooks

Test (experimental)

baseline - classes to help compare a new versus baseline version
test_*.py
- limited number of pytest functions versus multimodal
- tried versus matterport but original needs adapting to work with eager mode
tests.ipynb - notebook to run pytests

Installation

Clone this repository.

 git clone https://github.com/simonm3/maskr.git

Download pretrained coco weights from Google Drive.
Build the nms and roialign binaries:
```
 cd maskr/maskr/lib
 ./make.sh
```
Install the python package and dependencies in edit mode
```
 cd maskr
 pip install -e .
```