nncf/experimental/torch/nas/bootstrapNAS/elasticity/filter_reorder.py
def apply_reordering_indexes(self) -> None:
        """
        Applying propagated masks (which encodes indexes to reorder filters) for all nodes in topological order:
        1. running input_reorder method for this node
        2. running output_reorder method for this node
        """
        pruned_node_modules = []
        with torch.no_grad():
            for node in self._graph.topological_sort():
                node_cls = self.get_meta_operation_by_type_name(node.node_type)
                node_module = self._model.get_containing_module(node.node_name)
                if node_module not in pruned_node_modules:
                    node_cls.input_reorder(self._model, node, self._graph)
                    node_cls.output_reorder(self._model, node, self._graph)
                    pruned_node_modules.append(node_module)
            nncf_logger.debug('Finished mask applying step')

        Applying propagated masks (which encodes indexes to reorder filters) for all nodes in topological order:
        1. running input_reorder method for this node
        2. running output_reorder method for this node
        
Applying propagated masks (which encodes indexes to reorder filters) for all nodes in topological order:
1. running input_reorder method for this node
2. running output_reorder method for this node
Applying propagated masks ( which encodes indexes to reorder filters ) for all nodes in topological order : 1 . running input_reorder method for this node 2 . running output_reorder method for this node
def apply_reordering_indexes(self) -> None:
        pruned_node_modules = []
        with torch.no_grad():
            for node in self._graph.topological_sort():
                node_cls = self.get_meta_operation_by_type_name(node.node_type)
                node_module = self._model.get_containing_module(node.node_name)
                if node_module not in pruned_node_modules:
                    node_cls.input_reorder(self._model, node, self._graph)
                    node_cls.output_reorder(self._model, node, self._graph)
                    pruned_node_modules.append(node_module)
            nncf_logger.debug('Finished mask applying step')
def apply_reordering_indexes ( self ) -> None : pruned_node_modules = [ ] with torch . no_grad ( ) : for node in self . _graph . topological_sort ( ) : node_cls = self . get_meta_operation_by_type_name ( node . node_type ) node_module = self . _model . get_containing_module ( node . node_name ) if node_module not in pruned_node_modules : node_cls . input_reorder ( self . _model , node , self . _graph ) node_cls . output_reorder ( self . _model , node , self . _graph ) pruned_node_modules . append ( node_module ) nncf_logger . debug ( 'Finished mask applying step' )
Applying propagated masks (which encodes indexes to reorder filters) for all nodes in topological order:
1. running input_reorder method for this node
2. running output_reorder method for this node
Applying propagated masks ( which encodes indexes to reorder filters ) for all nodes in topological order : 1 . running input_reorder method for this node 2 . running output_reorder method for this node
"""
        Applying propagated masks (which encodes indexes to reorder filters) for all nodes in topological order:
        1. running input_reorder method for this node
        2. running output_reorder method for this node
        """
self