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Simple but useful layers for Pytorch

Project description


This package is created for a better experience with Pytorch.

Quick start

Quick start



Model Flags

Why making this

  • 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.

  • It inlines with my TF wrap-ups so I can easily move my old code and model structures to the current package.


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

  1. Refactor the codes. Now the structure becomes clearer and more extendable

  2. Almost supports type hinting

  3. (Base) build_forward is removed

  4. (Tools) Add more tools


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.

  • ArcFace (Deng, Jiankang, et al. "Arcface: Additive angular margin loss for deep face recognition." arXiv preprint arXiv:1801.07698 (2018))

  • HR Net (Sun, Ke, et al. "Deep High-Resolution Representation Learning for Human Pose Estimation." arXiv preprint arXiv:1902.09212 (2019))

  • 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)

  • Knowledge distillation (Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. "Distilling the knowledge in a neural network." arXiv preprint arXiv:1503.02531 (2015))

  • 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)

  • 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)

  • RetinaFace for face detection (Deng, Jiankang, et al. "Retinaface: Single-stage dense face localisation in the wild." arXiv preprint arXiv:1905.00641 (2019))

  • Fractal Net (Larsson, Gustav, Michael Maire, and Gregory Shakhnarovich. "Fractalnet: Ultra-deep neural networks without residuals." arXiv preprint arXiv:1605.07648 (2016))

  • Polarized Self Attention (Liu, Huajun, et al. "Polarized self-attention: Towards high-quality pixel-wise regression." arXiv preprint arXiv:2107.00782 (2021))

  • Some 2D/3D pose estimation works

  • Some network structures

  • Some cuda kernels

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