Source code for mindarmour.privacy.sup_privacy.mask_monitor.masker

# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""
Masker module of suppress-based privacy..
"""
from mindspore.train.callback import Callback
from mindarmour.utils.logger import LogUtil
from mindarmour.utils._check_param import check_param_type
from mindarmour.privacy.sup_privacy.train.model import SuppressModel
from mindarmour.privacy.sup_privacy.sup_ctrl.conctrl import SuppressCtrl

LOGGER = LogUtil.get_instance()
TAG = 'suppress masker'

[docs]class SuppressMasker(Callback): """ Args: args (Union[int, float, numpy.ndarray, list, str]): Parameters used for creating a suppress privacy monitor. kwargs (Union[int, float, numpy.ndarray, list, str]): Keyword parameters used for creating a suppress privacy monitor. model (SuppressModel): SuppressModel instance. suppress_ctrl (SuppressCtrl): SuppressCtrl instance. Examples: >>> networks_l5 = LeNet5() >>> masklayers = [] >>> masklayers.append(MaskLayerDes("conv1.weight", 0, False, True, 10)) >>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=networks_l5, >>> mask_layers=masklayers, >>> policy="local_train", >>> end_epoch=10, >>> batch_num=(int)(10000/cfg.batch_size), >>> start_epoch=3, >>> mask_times=1000, >>> lr=lr, >>> sparse_end=0.90, >>> sparse_start=0.0) >>> net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") >>> net_opt = nn.Momentum(params=networks_l5.trainable_params(), learning_rate=lr, momentum=0.0) >>> config_ck = CheckpointConfig(save_checkpoint_steps=(int)(samples/cfg.batch_size), keep_checkpoint_max=10) >>> model_instance = SuppressModel(network=networks_l5, >>> loss_fn=net_loss, >>> optimizer=net_opt, >>> metrics={"Accuracy": Accuracy()}) >>> model_instance.link_suppress_ctrl(suppress_ctrl_instance) >>> ds_train = generate_mnist_dataset("./MNIST_unzip/train", >>> batch_size=cfg.batch_size, repeat_size=1, samples=samples) >>> ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", >>> directory="./trained_ckpt_file/", >>> config=config_ck) >>> model_instance.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(), suppress_masker], >>> dataset_sink_mode=False) """ def __init__(self, model, suppress_ctrl): super(SuppressMasker, self).__init__() self._model = check_param_type('model', model, SuppressModel) self._suppress_ctrl = check_param_type('suppress_ctrl', suppress_ctrl, SuppressCtrl)
[docs] def step_end(self, run_context): """ Update mask matrix tensor used for SuppressModel instance. Args: run_context (RunContext): Include some information of the model. """ cb_params = run_context.original_args() cur_step = cb_params.cur_step_num cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1 if self._suppress_ctrl is not None and self._model.network_end is not None: self._suppress_ctrl.update_status(cb_params.cur_epoch_num, cur_step, cur_step_in_epoch) if not self._suppress_ctrl.mask_initialized: raise ValueError("Not initialize network!") if self._suppress_ctrl.to_do_mask: self._suppress_ctrl.update_mask(self._suppress_ctrl.networks, cur_step) LOGGER.info(TAG, "suppress update") elif not self._suppress_ctrl.to_do_mask and self._suppress_ctrl.mask_started: self._suppress_ctrl.reset_zeros() if cur_step_in_epoch % 100 == 1: self._suppress_ctrl.calc_theoretical_sparse_for_conv() _, _, _ = self._suppress_ctrl.calc_actual_sparse_for_conv( self._suppress_ctrl.networks)