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666DZY666 / micronet

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micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

pytorch pruning convolutional-networks quantization xnor-net tensorrt model-compression bnn neuromorphic-computing group-convolution onnx network-in-network tensorrt-int8-python dorefa twn network-slimming integer-arithmetic-only quantization-aware-training post-training-quantization batch-normalization-fuse
  • Updated Oct 6, 2021
  • Python
sparseml

neuralmagic / sparseml

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Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

nlp sparsity tensorflow keras pytorch deep-learning-algorithms image-classification deep-learning-library pruning object-detection automl computer-vision-algorithms onnx deep-learning-models sparsification pruning-algorithms smaller-models model-sparsification sparsification-recipes recipe-driven-approaches
  • Updated Nov 11, 2021
  • Python

quic / aimet

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Open

Update TF Bias Correction to use new Quantsim implementation

quic-ssiddego
quic-ssiddego commented Jul 16, 2021
  • Deprecate the usage of old quantsim implementation
  • Update bias correction implementation to use new quantsim api

If you are interested in working on this issue - please indicate via a comment on this issue. It should be possible for us to pair you up with an existing contributor to help you get started.

From a complexity perspective, this ticket is at an easy level.

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good first issue Easy
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SforAiDl / KD_Lib

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mehtadushy / SelecSLS-Pytorch

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Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".

sparsity deep-learning efficient cnn pytorch imagenet pruning siggraph pytorch-implementation cvpr2019 efficient-architectures
  • Updated Jul 23, 2020
  • Python

intel / neural-compressor

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Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool), targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.

sparsity pruning quantization knowledge-distillation auto-tuning low-precision quantization-aware-training post-training-quantization
  • Updated Nov 5, 2021
  • Python

tasket / wyng-backup

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Create automated tests

tasket
tasket commented Jan 4, 2019

A good testing regime could include the following:

  • Perform an initial backup
  • Verify volume
  • Perform an incremental backup
  • Verify volume
  • Prune first backup
  • Verify volume
  • Diff volume (cross-check with source vol data)

Changing program_name in the code could allow the test data set to be created under a different base folder name without interfering with real-life back

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help wanted good first issue testing

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