model-optimization
Torch-Pruning
| model-optimization | Torch-Pruning | |
|---|---|---|
| 1 | 2 | |
| 1,470 | 2,324 | |
| 0.8% | - | |
| 6.8 | 9.4 | |
| 11 days ago | 7 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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model-optimization
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Need Help With Pruning Model Weights in Tensorflow 2
I have been following the example shown here, and so far I've had mixed results and wanted to ask for some help because the resources I've found online have not been able to answer some of my questions (perhaps because some of these are obvious and I am just being dumb).
Torch-Pruning
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[P] Help: I want to compress EfficientnetV2 using pruning.
I also tried structured pruning from https://github.com/VainF/Torch-Pruning, as they report EfficientNetV2 to be "prunable", but got much worse results. However, the advantage of this approach is that it keeps the model dense, and you can get a real speed-up with common GPUs, while unstructured pruning sparsifies the model and you need hardware that can exploit such sparsity.
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[D] What is your go-to implementation for structured pruning?
I'm currently using this repo and I find it very intuitive: https://github.com/VainF/Torch-Pruning. Not sure if it fits your needs, but check it out :)
What are some alternatives?
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
SadTalker - [CVPR 2023] SadTalker：Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation
qkeras - QKeras: a quantization deep learning library for Tensorflow Keras
efficient-gnns - Code and resources on scalable and efficient Graph Neural Networks
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
only_train_once - OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM
3d-model-convert-to-gltf - Convert 3d model (STL/IGES/STEP/OBJ/FBX) to gltf and compression
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
UPop - [ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.
larq - An Open-Source Library for Training Binarized Neural Networks
Painter - Painter & SegGPT Series: Vision Foundation Models from BAAI