NicolasHug
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Contribution activity
August 2022
Created 31 commits in 4 repositories
Created 1 repository
- NicolasHug/data Python
Created a pull request in pytorch/data that received 7 comments
Add benchmark from torchvision training references
Towards #416 This is a modified and simplified version of the torchvision classification training reference that provides: Distributed Learning (D…
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Opened 21 other pull requests in 3 repositories
NicolasHug/Surprise
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11
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- benchmark on 1m
- http -> https
- Prepare release 1.1.2
- Reduce python anotations in cython files (except co-clustering)
- Replace np.int with np.int_
- Try removing numpy dep message
- Update min versions of dependencies and drop 3.7
- Fix link to scikits doc
- Fix docs
- NOMRG, Big revert
- Add benchmark CI job
- Apply black, usort and flake8, and create linting job
- Remove travis stuff
- Create GitHub Action CI test
- Remove six, Remove __future__, update cython compiler directives, set python_requires=">=3.7"
pytorch/vision
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open
3
merged
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pytorch/data
1
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Reviewed 26 pull requests in 3 repositories
pytorch/vision
20 pull requests
- Try to fix mypy issue with calculate_md5
- remove unused import
- Fix minor typos in Docstrings
- expand has_any and has_all to also accept check callables
- Make test precision stricter for Classification
- [proto] Fix for handling numpy arrays by Transform
- Fix anaconda upload for nightly
- [proto] Clean-up Label.to_categories
- disable download test for Places365 and SBU
- add headings to weights table.
- Expose on Hub the public methods of the registration API
- Added SceneFLow variant datasets
- Added ETH3D stereo dataset
- Add Sintel Stereo dataset
- Added InStereo2k dataset
- Add FallingThings dataset
- Fix broken linter on main branch
- Add CarlaStereo, Kitti2012Stereo, and Kitti2015Stereo datasets for Stereo(#6269)
- Remove duplicate doc args.
- Add registration mechanism for models
NicolasHug/Surprise
5 pull requests
pytorch/data
1 pull request
Created an issue in pytorch/data that received 8 comments
Recommended way to shuffle intra and inter archives?
Say I have a bunch of archives containing samples. In my case each archive is a pickle file containing a list of samples, but it could be a tar or …
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comments
Opened 6 other issues in 3 repositories
pytorch/vision
4
open
- Evaluate models accuracy when using inference transforms on Tensors (instead of PIL images)
- decode_jpeg() creates tensors with different stride from PIL.Image.open()
- Inference pre-processing transforms when not using pre-trained weights?
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Label.from_categoryis O(num_classes) complexity, for every single sample