composable-mapping
PyTorch utility library for handling geometric deformations
Science Score: 57.0%
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Low similarity (11.1%) to scientific vocabulary
Repository
PyTorch utility library for handling geometric deformations
Basic Info
- Host: GitHub
- Owner: honkamj
- License: mit
- Language: Python
- Default Branch: main
- Size: 12.2 MB
Statistics
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 3
Metadata Files
README.md
Composable mapping
Composable mapping is a PyTorch utility library developed for handling coordinate mappings between images (2D or 3D), develped as part of SITReg, a deep learning intra-modality image registration arhitecture fulfilling strict symmetry properties.
Developed originally for medical imaging, this library provides a set of classes and functions for handling spatial coordinate transformations.
The most powerful feature of this library is the ability to easily compose transformations lazily and resample them to different coordinate systems as well as sampler classes for sampling volumes defined on regular grids such that the optimal method (either slicing operation, convolution, or torch.grid_sample) is used based on the sampling locations.
The main idea was to develop a library that allows handling of the coordinate mappings as if they were mathematical functions, without losing much performance compared to more manual implementation.
Installation
Install using pip by running the command
pip install composable-mapping
Requirements
Python 3.8+PyTorch 2.0+nibabelmatplotlib(optional)ninja(optional)
Documentation
For a quick start tutorial, see quick_start.ipynb. For API reference, go to https://honkamj.github.io/composable-mapping/.
SITReg
For SITReg implementation, see repository SITReg.
Publication
If you use composable mapping, please cite (see bibtex):
- SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration
Joel Honkamaa, Pekka Marttinen
The Journal of Machine Learning for Biomedical Imaging (MELBA) (10.59275/j.melba.2024-276b)
License
Composable mapping is released under the MIT license.
Owner
- Name: Joel Honkamaa
- Login: honkamj
- Kind: user
- Location: Finland
- Company: Aalto University
- Repositories: 1
- Profile: https://github.com/honkamj
AI researcher at Aalto University, Finland
Citation (citations.bib)
@article{melba:2024:026:honkamaa,
title = "SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration",
author = "Honkamaa, Joel and Marttinen, Pekka",
journal = "Machine Learning for Biomedical Imaging",
volume = "2",
issue = "November 2024 issue",
year = "2024",
pages = "2148--2194",
issn = "2766-905X",
doi = "https://doi.org/10.59275/j.melba.2024-276b",
url = "https://melba-journal.org/2024:026"
}
GitHub Events
Total
- Release event: 5
- Watch event: 5
- Public event: 1
- Push event: 111
- Pull request event: 12
- Create event: 6
Last Year
- Release event: 5
- Watch event: 5
- Public event: 1
- Push event: 111
- Pull request event: 12
- Create event: 6
Committers
Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Joel Honkamaa | j****a@a****i | 175 |
| Honkamaa Joel | h****2@c****i | 3 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 0
- Total pull requests: 9
- Average time to close issues: N/A
- Average time to close pull requests: less than a minute
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 9
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 9
- Average time to close issues: N/A
- Average time to close pull requests: less than a minute
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 9
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
- honkamj (13)
Top Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 56 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 4
- Total maintainers: 1
pypi.org: composable-mapping
Composable mapping is a PyTorch utility library for handling geometric deformations
- Homepage: https://github.com/honkamj/composable-mapping
- Documentation: https://composable-mapping.readthedocs.io/
- License: MIT License Copyright (c) 2023 Joel Honkamaa Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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Latest release: 0.1.4
published about 1 year ago
Rankings
Maintainers (1)
Dependencies
- deformation-inversion-layer >=1.1.2
- deformation_inversion_layer >=1.1.2
- matplotlib *
- torch >=2.0