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34 строки
2.1 KiB
ReStructuredText
34 строки
2.1 KiB
ReStructuredText
Pruning Algorithm Supported in NNI
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==================================
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Note that not all pruners from the previous version have been migrated to the new framework yet.
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NNI has plans to migrate all pruners that were implemented in NNI 3.2.
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If you believe that a certain old pruner has not been implemented or that another pruning algorithm would be valuable,
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please feel free to contact us. We will prioritize and expedite support accordingly.
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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:`new-level-pruner`
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- Pruning the specified ratio on each weight element based on absolute value of weight element
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* - :ref:`new-l1-norm-pruner`
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- Pruning output channels with the smallest L1 norm of weights (Pruning Filters for Efficient Convnets) `Reference Paper <https://arxiv.org/abs/1608.08710>`__
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* - :ref:`new-l2-norm-pruner`
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- Pruning output channels with the smallest L2 norm of weights
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* - :ref:`new-fpgm-pruner`
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- Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration `Reference Paper <https://arxiv.org/abs/1811.00250>`__
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* - :ref:`new-slim-pruner`
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- Pruning output channels by pruning scaling factors in BN layers(Learning Efficient Convolutional Networks through Network Slimming) `Reference Paper <https://arxiv.org/abs/1708.06519>`__
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* - :ref:`new-taylor-pruner`
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- Pruning filters based on the first order taylor expansion on weights(Importance Estimation for Neural Network Pruning) `Reference Paper <http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf>`__
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* - :ref:`new-linear-pruner`
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- Sparsity ratio increases linearly during each pruning rounds, in each round, using a basic pruner to prune the model.
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* - :ref:`new-agp-pruner`
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- Automated gradual pruning (To prune, or not to prune: exploring the efficacy of pruning for model compression) `Reference Paper <https://arxiv.org/abs/1710.01878>`__
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* - :ref:`new-movement-pruner`
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- Movement Pruning: Adaptive Sparsity by Fine-Tuning `Reference Paper <https://arxiv.org/abs/2005.07683>`__
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