nncf.torch.sparsity.magnitude.algo#

Classes#

MagnitudeSparsityController

Controller for the magnitude sparsity algorithm in PT.
class nncf.torch.sparsity.magnitude.algo.MagnitudeSparsityController(target_model, sparsified_module_info, config)[source]#

Bases: nncf.torch.sparsity.base_algo.BaseSparsityAlgoController

Controller for the magnitude sparsity algorithm in PT.

Parameters:

  • target_model (nncf.torch.nncf_network.NNCFNetwork) –

  • sparsified_module_info (List[nncf.torch.sparsity.base_algo.SparseModuleInfo]) –

  • config (nncf.NNCFConfig) –

property compression_rate[source]#

Returns a float compression rate value ranging from 0 to 1 (e.g. the sparsity level, or the ratio of filters pruned).

statistics(quickly_collected_only=False)[source]#

Returns a Statistics class instance that contains compression algorithm statistics.

Parameters:

quickly_collected_only (bool) – Enables collection of the statistics that don’t take too much time to compute. Can be helpful for the case when need to keep track of statistics on each training batch/step/iteration.

Return type:

nncf.common.statistics.NNCFStatistics

freeze(freeze=True)[source]#

Freezes all sparsity masks. Sparsity masks will not be trained after calling this method.

Parameters:

freeze (bool) –

set_sparsity_level(sparsity_level, target_sparsified_module_info=None, run_batchnorm_adaptation=False)[source]#

Sets the sparsity level that should be applied to the model’s weights.

Parameters:

  • sparsity_level – Sparsity level that should be applied to the model’s weights.

  • target_sparsified_module_info (nncf.torch.sparsity.base_algo.SparseModuleInfo) –

  • run_batchnorm_adaptation (bool) –

compression_stage()[source]#

Returns the compression stage. Should be used on saving best checkpoints to distinguish between uncompressed, partially compressed, and fully compressed models.

Returns:

The compression stage of the target model.

Return type:

nncf.api.compression.CompressionStage