mindarmour.privacy.sup_privacy

This module provides Suppress Privacy feature to protect user privacy.

class mindarmour.privacy.sup_privacy.MaskLayerDes(layer_name, grad_idx, is_add_noise, is_lower_clip, min_num, upper_bound=1.2)

Describe the layer that need to be suppressed.

Parameters

• layer_name (str) –

  Layer name, get the name of one layer as following:

  for layer in networks.get_parameters(expand=True):
      if layer.name == "conv": ...
  

• grad_idx (int) – Grad layer index, get mask layer’s index in grad tuple.You can refer to the construct function of TrainOneStepCell in mindarmour/privacy/sup_privacy/train/model.py to get the index of some specified grad layers (print in PYNATIVE_MODE).

• is_add_noise (bool) – If True, the weight of this layer can add noise. If False, the weight of this layer can not add noise.

• is_lower_clip (bool) – If true, the weights of this layer would be clipped to greater than an lower bound value. If False, the weights of this layer won’t be clipped.

• min_num (int) – The number of weights left that not be suppressed. If min_num is smaller than (parameter num*SupperssCtrl.sparse_end), min_num has not effect.

• upper_bound (Union[float, int]) – max abs value of weight in this layer, default: 1.20.

class mindarmour.privacy.sup_privacy.SuppressCtrl(networks, mask_layers, end_epoch, batch_num, start_epoch, mask_times, lr, sparse_end, sparse_start)

Parameters

• networks (Cell) – The training network.

• mask_layers (list) – Description of those layers that need to be suppressed.

• end_epoch (int) – The last epoch in suppress operations.

• batch_num (int) – The num of grad operation in an epoch.

• start_epoch (int) – The first epoch in suppress operations.

• mask_times (int) – The num of suppress operations.

• lr (Union[float, int]) – Learning rate.

• sparse_end (float) – The sparsity to reach.

• sparse_start (Union[float, int]) – The sparsity to start.

calc_actual_sparse_for_conv(networks)

Compute actually sparsity of network for conv1 layer and conv2 layer.

Parameters

networks (Cell) – The training network.

calc_actual_sparse_for_layer(networks, layer_name)

Compute actually sparsity of one network layer

Parameters

• networks (Cell) – The training network.

• layer_name (str) – The name of target layer.

calc_theoretical_sparse_for_conv()

Compute actually sparsity of mask matrix for conv1 layer and conv2 layer.

reset_zeros()

Set add mask arrays to be zero.

update_mask(networks, cur_step)

Update add mask arrays and multiply mask arrays of network layers.

Parameters

• networks (Cell) – The training network.

• cur_step (int) – Current epoch of the whole training process.

update_mask_layer(weight_array_flat, sparse_weight_thd, sparse_stop_pos, weight_abs_max, layer_index)

Update add mask arrays and multiply mask arrays of one single layer.

Parameters

• weight_array_flat (numpy.ndarray) – The weight array of layer’s parameters.

• sparse_weight_thd (float) – The weight threshold of sparse operation.

• sparse_stop_pos (int) – The maximum number of elements to be suppressed.

• weight_abs_max (float) – The maximum absolute value of weights.

• layer_index (int) – The index of target layer.

update_mask_layer_approximity(weight_array_flat, weight_array_flat_abs, actual_stop_pos, layer_index)

Update add mask arrays and multiply mask arrays of one single layer with many parameter.

disable clipping loweer, clipping, adding noise operation

Parameters

• weight_array_flat (numpy.ndarray) – The weight array of layer’s parameters.

• weight_array_flat_abs (numpy.ndarray) – The abs weight array of layer’s parameters.

• actual_stop_pos (int) – The actually para num should be suppressed.

• layer_index (int) – The index of target layer.

update_status(cur_epoch, cur_step, cur_step_in_epoch)

Update the suppress operation status.

Parameters

• cur_epoch (int) – Current epoch of the whole training process.

• cur_step (int) – Current step of the whole training process.

• cur_step_in_epoch (int) – Current step of the current epoch.

class mindarmour.privacy.sup_privacy.SuppressMasker(model, suppress_ctrl)

Parameters

• 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)

step_end(run_context)

Update mask matrix tensor used for SuppressModel instance.

Parameters

run_context (RunContext) – Include some information of the model.

class mindarmour.privacy.sup_privacy.SuppressModel(network, loss_fn, optimizer, **kwargs)

This class is overload mindspore.train.model.Model.

Parameters

• network (Cell) – The training network.

• loss_fn (Cell) – Computes softmax cross entropy between logits and labels.

• optimizer (Optimizer) – optimizer instance.

• metrics (Union[dict, set]) – Calculates the accuracy for classification and multilabel data.

• kwargs – Keyword parameters used for creating a suppress model.

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)

link_suppress_ctrl(suppress_pri_ctrl)

Link self and SuppressCtrl instance.

Parameters

suppress_pri_ctrl (SuppressCtrl) – SuppressCtrl instance.

class mindarmour.privacy.sup_privacy.SuppressPrivacyFactory

Factory class of SuppressCtrl mechanisms

static create(networks, mask_layers, policy='local_train', end_epoch=10, batch_num=20, start_epoch=3, mask_times=1000, lr=0.05, sparse_end=0.9, sparse_start=0.0)

Parameters

• networks (Cell) – The training network.

• mask_layers (list) – Description of the training network layers that need to be suppressed.

• policy (str) – Training policy for suppress privacy training. Default: “local_train”, means local training.

• end_epoch (int) – The last epoch in suppress operations, 0<start_epoch<=end_epoch<=100. Default: 10. This end_epoch parameter should be the same as ‘epoch’ parameter of mindspore.train.model.train().

• batch_num (int) – The num of batch in an epoch, should be equal to num_samples/batch_size. Default: 20.

• start_epoch (int) – The first epoch in suppress operations, 0<start_epoch<=end_epoch<=100. Default: 3.

• mask_times (int) – The num of suppress operations. Default: 1000.

• lr (Union[float, int]) – Learning rate, should be unchanged during training. 0<lr<=0.50. Default: 0.05. This lr parameter should be the same as ‘learning_rate’ parameter of mindspore.nn.SGD().

• sparse_end (float) – The sparsity to reach, 0.0<=sparse_start<sparse_end<1.0. Default: 0.90.

• sparse_start (Union[float, int]) – The sparsity to start, 0.0<=sparse_start<sparse_end<1.0. Default: 0.0.

Returns

SuppressCtrl, class of Suppress Privavy Mechanism.

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)