TorchSUL
This package is created for a better experience with Pytorch.
Quick start
References
Why making this
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I dont want to write in_channels when building models. It's wired that I should care about input when I only want to write forward flows.
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It inlines with my TF wrap-ups so I can easily move my old code and model structures to the current package.
Installation
You need to install pytorch>=1.10, and python>=3.7
Good, then just
pip install --upgrade torchsul
Patch Notes
2023-xx-xx: Upgrade to 0.3.0
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Refactor the codes. Now the structure becomes clearer and more extendable
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Almost supports type hinting
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(Base) build_forward is removed
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(Tools) Add more tools
Projects
You can find some examples in the "example" folder. It's almost like a trash bin, and some of the functions & modules may be no longer supported in the current version.
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ArcFace (Deng, Jiankang, et al. "Arcface: Additive angular margin loss for deep face recognition." arXiv preprint arXiv:1801.07698 (2018))
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HR Net (Sun, Ke, et al. "Deep High-Resolution Representation Learning for Human Pose Estimation." arXiv preprint arXiv:1902.09212 (2019))
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AutoDeepLab (Liu, Chenxi, et al. "Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019)
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Knowledge distillation (Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. "Distilling the knowledge in a neural network." arXiv preprint arXiv:1503.02531 (2015))
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3DCNN (Ji, Shuiwang, et al. "3D convolutional neural networks for human action recognition." IEEE transactions on pattern analysis and machine intelligence 35.1 (2012): 221-231)
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Temporal Convolutional Network (Not the same) (Pavllo, Dario, et al. "3D human pose estimation in video with temporal convolutions and semi-supervised training." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019)
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RetinaFace for face detection (Deng, Jiankang, et al. "Retinaface: Single-stage dense face localisation in the wild." arXiv preprint arXiv:1905.00641 (2019))
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Fractal Net (Larsson, Gustav, Michael Maire, and Gregory Shakhnarovich. "Fractalnet: Ultra-deep neural networks without residuals." arXiv preprint arXiv:1605.07648 (2016))
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Polarized Self Attention (Liu, Huajun, et al. "Polarized self-attention: Towards high-quality pixel-wise regression." arXiv preprint arXiv:2107.00782 (2021))
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Some 2D/3D pose estimation works
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Some network structures
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Some cuda kernels
1 maintainer
Release files for TorchSUL 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| TorchSUL-0.3.1.tar.gz | 26.2 kB | Details |
Release files / TorchSUL-0.3.1.tar.gz
| Download URL | TorchSUL-0.3.1.tar.gz |
|---|---|
| Size | 26.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
4d16732c9dd3efe52ec3cac99ea12af6ec5288c08b20faadaf89996fc12cb07c
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twine/5.0.0 CPython/3.9.18
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