зеркало из https://github.com/microsoft/nni.git
23 строки
1.4 KiB
ReStructuredText
23 строки
1.4 KiB
ReStructuredText
Quantizer in NNI
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NNI implements the main part of the quantizaiton algorithm as quantizer. All quantizers are implemented as close as possible to what is described in the paper (if it has).
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The following table provides a brief introduction to the quantizers implemented in nni, click the link in table to view a more detailed introduction and use cases.
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.. list-table::
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:header-rows: 1
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:widths: auto
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* - Name
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- Brief Introduction of Algorithm
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* - :ref:`NewQATQuantizer`
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- Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. `Reference Paper <http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf>`__
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* - :ref:`NewDorefaQuantizer`
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- DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. `Reference Paper <https://arxiv.org/abs/1606.06160>`__
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* - :ref:`NewBNNQuantizer`
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- Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. `Reference Paper <https://arxiv.org/abs/1602.02830>`__
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* - :ref:`NewLsqQuantizer`
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- Learned step size quantization. `Reference Paper <https://arxiv.org/pdf/1902.08153.pdf>`__
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* - :ref:`NewPtqQuantizer`
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- Post training quantizaiton. Collect quantization information during calibration with observers.
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