These repositories are small self-contained tools written in pure PyTorch, that I have found useful in many projects.
They are (relatively) stable, as backward-compatible as possible with respect to PyTorch versions, and can be used as core dependencies to higher level projects.
| Package | Description | Readiness |
|---|---|---|
torch-bounds |
Boundary conditions (circulant, mirror, reflect) and real transforms (DCT, DST) | 🟢 |
torch-interpol |
High-order spline interpolation | 🟢 |
torch-distmap |
Euclidean distance transform | 🟢 |
torch-relay |
Backward-compatible PyTorch functions (work-in-progress) | 🔴 |
torch-diffeo |
Scaling-and-squaring and Geodesic Shooting layers in PyTorch (work-in-progress) | 🟠 |
jitfields |
Fast functions for dense scalar and vector fields, implemented using just-in-time compilation | 🟠 |
Note
The last package, jitfields, reimplements many of the utilities from the other core
packages, but does it directly in CUDA/C++.
The CUDA/C++ sources are compiled
just-in-time using cupy
and cppyy.
These packages underpin my research in medical image computing.
In general, my aim is to write a set of mid-level packages that specialize in various tasks (data augmentation, network architectures, modality-specific tasks, etc.).
| Package | Description | Readiness |
|---|---|---|
cornucopia |
An abundance of augmentation layers | 🟢 |
nitorch |
An (overweight and poorly maintained) package for everything neuroimaging | 🟠 |
synthsurf |
Surface-based image synthesis and PyTorch utilities for triangular surfaces | 🟠 |
braindataprep |
Download, bidsify and preprocess public datasets (work-in-progress) | 🔴 |
| Package | Description | Readiness |
|---|---|---|
variational_staple |
STAPLE and variants | 🟢 |
optimal_affine |
Build optimal "subject to mean space" affines from "subject to subject" pairwise affines | 🟢 |
metrics |
A bunch of metrics | 🔴 |
| Package | Description | Readiness |
|---|---|---|
spm_mni_align |
SPM toolbox to align an image to SPM's template space | 🟠 |
multi-bias |
Fit a multi-view bias field | 🟢 |
super-resolution |
MTV-based denoising/super-resolution | 🟢 |
cmaps |
(Some) Matplotlib colormaps in Matlab | 🟢 |
| Package | Description | Readiness |
|---|---|---|
tfaffine |
Affine matrices encoded in their Lie algebra, in tensorflow | 🟢 |
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent