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!321 Add API examples for suppress privacy module 

Merge pull request !321 from 张澍坤/master
tags/v1.8.0
i-robot Gitee 3 years ago
parent
32689ee2e7 a479e3d232
commit
525c0fb809
No known key found for this signature in database GPG Key ID: 173E9B9CA92EEF8F
5 changed files with 144 additions and 137 deletions
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  1. +25
    -19
      mindarmour/privacy/evaluation/inversion_attack.py
  2. +42
    -11
      mindarmour/privacy/evaluation/membership_inference.py
  3. +37
    -22
      mindarmour/privacy/sup_privacy/mask_monitor/masker.py
  4. +39
    -55
      mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py
  5. +1
    -30
      mindarmour/privacy/sup_privacy/train/model.py

+ 25
- 19
mindarmour/privacy/evaluation/inversion_attack.py View File

@@ -111,6 +111,31 @@ class ImageInversionAttack:
TypeError: If the type of network is not Cell.
ValueError: If any value of input_shape is not positive int.
ValueError: If any value of loss_weights is not positive value.

Examples:
>>> import mindspore.ops.operations as P
>>> from mindspore.nn import Cell
>>> from mindarmour.privacy.evaluation.inversion_attack import ImageInversionAttack
>>> class Net(Cell):
... def __init__(self):
... super(Net, self).__init__()
... self._softmax = P.Softmax()
... self._reduce = P.ReduceSum()
... self._squeeze = P.Squeeze(1)
... def construct(self, inputs):
... out = self._softmax(inputs)
... out = self._reduce(out, 2)
... return self._squeeze(out)
>>> net = Net()
>>> original_images = np.random.random((2,1,10,10)).astype(np.float32)
>>> target_features = np.random.random((2,10)).astype(np.float32)
>>> inversion_attack = ImageInversionAttack(net,
... input_shape=(1, 10, 10),
... input_bound=(0, 1),
... loss_weights=[1, 0.2, 5])
>>> inversion_images = inversion_attack.generate(target_features, iters=10)
>>> evaluate_result = inversion_attack.evaluate(original_images, inversion_images)
>>> print(evaluate_result)
"""
def __init__(self, network, input_shape, input_bound, loss_weights=(1, 0.2, 5)):
self._network = check_param_type('network', network, Cell)
@@ -144,15 +169,6 @@ class ImageInversionAttack:
Raises:
TypeError: If the type of target_features is not numpy.ndarray.
ValueError: If any value of iters is not positive int.Z

Examples:
>>> net = LeNet5()
>>> inversion_attack = ImageInversionAttack(net, input_shape=(1, 32, 32), input_bound=(0, 1),
>>> loss_weights=[1, 0.2, 5])
>>> features = np.random.random((2, 10)).astype(np.float32)
>>> images = inversion_attack.generate(features, iters=10)
>>> print(images.shape)
(2, 1, 32, 32)
"""
target_features = check_numpy_param('target_features', target_features)
iters = check_int_positive('iters', iters)
@@ -203,16 +219,6 @@ class ImageInversionAttack:
- float, average ssim value.

- Union[float, None], average confidence. It would be None if labels or new_network is None.

Examples:
>>> net = LeNet5()
>>> inversion_attack = ImageInversionAttack(net, input_shape=(1, 32, 32), input_bound=(0, 1),
>>> loss_weights=[1, 0.2, 5])
>>> features = np.random.random((2, 10)).astype(np.float32)
>>> inver_images = inversion_attack.generate(features, iters=10)
>>> ori_images = np.random.random((2, 1, 32, 32))
>>> result = inversion_attack.evaluate(ori_images, inver_images)
>>> print(len(result))
"""
check_numpy_param('original_images', original_images)
check_numpy_param('inversion_images', inversion_images)


+ 42
- 11
mindarmour/privacy/evaluation/membership_inference.py View File

@@ -106,21 +106,52 @@ class MembershipInference:
n_jobs (int): Number of jobs run in parallel. -1 means using all processors,
otherwise the value of n_jobs must be a positive integer.

Examples:
>>> # train_1, train_2 are non-overlapping datasets from training dataset of target model.
>>> # test_1, test_2 are non-overlapping datasets from test dataset of target model.
>>> # We use train_1, test_1 to train attack model, and use train_2, test_2 to evaluate attack model.
>>> model = Model(network=net, loss_fn=loss, optimizer=opt, metrics={'acc', 'loss'})
>>> attack_model = MembershipInference(model, n_jobs=-1)
>>> config = [{"method": "KNN", "params": {"n_neighbors": [3, 5, 7]}}]
>>> attack_model.train(train_1, test_1, config)
>>> metrics = ["precision", "recall", "accuracy"]
>>> result = attack_model.eval(train_2, test_2, metrics)

Raises:
TypeError: If type of model is not mindspore.train.Model.
TypeError: If type of n_jobs is not int.
ValueError: The value of n_jobs is neither -1 nor a positive integer.


Examples:
>>> import mindspore.ops.operations as P
>>> from mindspore.nn import Cell
>>> from mindspore import Model
>>> from mindarmour.privacy.evaluation import MembershipInference
>>> def dataset_generator():
... batch_size = 16
... batches = 1
... data = np.random.randn(batches * batch_size,1,10).astype(np.float32)
... label = np.random.randint(0,10, batches * batch_size).astype(np.int32)
... for i in range(batches):
... yield data[i*batch_size:(i+1)*batch_size],\
... label[i*batch_size:(i+1)*batch_size]
>>> class Net(Cell):
... def __init__(self):
... super(Net, self).__init__()
... self._softmax = P.Softmax()
... self._Dense = nn.Dense(10,10)
... self._squeeze = P.Squeeze(1)
... def construct(self, inputs):
... out = self._softmax(inputs)
... out = self._Dense(out)
... return self._squeeze(out)
>>> net = Net()
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> model = Model(network=net, loss_fn=loss, optimizer=opt)
>>> inference_model = MembershipInference(model, 2)
>>> config = [{
... "method": "KNN",
... "params": {"n_neighbors": [3, 5, 7],}
}]
>>> ds_train = ds.GeneratorDataset(dataset_generator, ["image", "label"])
>>> ds_test = ds.GeneratorDataset(dataset_generator, ["image", "label"])
>>> inference_model.train(ds_train, ds_test, config)
>>> metrics = ["precision", "accuracy", "recall"]
>>> eval_train = ds.GeneratorDataset(dataset_generator, ["image", "label"])
>>> eval_test = ds.GeneratorDataset(dataset_generator, ["image", "label"])
>>> result = inference_model.eval(eval_train. eval_test, metrics)
>>> print(result)
"""

def __init__(self, model, n_jobs=-1):


+ 37
- 22
mindarmour/privacy/sup_privacy/mask_monitor/masker.py View File

@@ -25,39 +25,54 @@ TAG = 'suppress masker'

class SuppressMasker(Callback):
"""
For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_

Args:
model (SuppressModel): SuppressModel instance.
suppress_ctrl (SuppressCtrl): SuppressCtrl instance.

Examples:
>>> networks_l5 = LeNet5()
>>> import mindspore.nn as nn
>>> import mindspore.dataset as ds
>>> import mindspore.ops.operations as P
>>> from mindspore import context
>>> from mindspore.nn import Accuracy
>>> from mindarmour.privacy.sup_privacy import SuppressModel
>>> from mindarmour.privacy.sup_privacy import SuppressMasker
>>> from mindarmour.privacy.sup_privacy import SuppressPrivacyFactory
>>> from mindarmour.privacy.sup_privacy import MaskLayerDes
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self._softmax = P.Softmax()
... self._Dense = nn.Dense(10,10)
... self._squeeze = P.Squeeze(1)
... def construct(self, inputs):
... out = self._softmax(inputs)
... out = self._Dense(out)
... return self._squeeze(out)
>>> context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
>>> network = Net()
>>> 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)
>>> masklayers.append(MaskLayerDes("_Dense.weight", 0, False, True, 10))
>>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=network,
... mask_layers=masklayers,
... policy="local_train",
... end_epoch=10,
... batch_num=1,
... start_epoch=3,
... mask_times=10,
... lr=0.05,
... sparse_end=0.95,
... 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,
>>> net_opt = nn.SGD(network.trainable_params(), 0.05)
>>> model_instance = SuppressModel(network=network,
... 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)
>>> masker_instance = SuppressMasker(model_instance, suppress_ctrl_instance)
"""

def __init__(self, model, suppress_ctrl):


+ 39
- 55
mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py View File

@@ -38,6 +38,8 @@ class SuppressPrivacyFactory:
def create(networks, mask_layers, policy="local_train", end_epoch=10, batch_num=20, start_epoch=3,
mask_times=1000, lr=0.05, sparse_end=0.90, sparse_start=0.0):
"""
For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_

Args:
networks (Cell): The training network.
This networks parameter should be same as 'network' parameter of SuppressModel().
@@ -57,35 +59,45 @@ class SuppressPrivacyFactory:
SuppressCtrl, class of Suppress Privavy Mechanism.

Examples:
>>> networks_l5 = LeNet5()
>>> mask_layers = []
>>> mask_layers.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
>>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=networks_l5,
... mask_layers=mask_layers,
... 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)
>>> import mindspore.nn as nn
>>> import mindspore.dataset as ds
>>> import mindspore.ops.operations as P
>>> from mindspore import context
>>> from mindspore.nn import Accuracy
>>> from mindarmour.privacy.sup_privacy import SuppressPrivacyFactory
>>> from mindarmour.privacy.sup_privacy import MaskLayerDes
>>> from mindarmour.privacy.sup_privacy import SuppressModel
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self._softmax = P.Softmax()
... self._Dense = nn.Dense(10,10)
... self._squeeze = P.Squeeze(1)
... def construct(self, inputs):
... out = self._softmax(inputs)
... out = self._Dense(out)
... return self._squeeze(out)
>>> context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
>>> network = Net()
>>> masklayers = []
>>> masklayers.append(MaskLayerDes("_Dense.weight", 0, False, True, 10))
>>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=network,
... mask_layers=masklayers,
... policy="local_train",
... end_epoch=10,
... batch_num=1,
... start_epoch=3,
... mask_times=10,
... lr=0.05,
... sparse_end=0.95,
... 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,
>>> net_opt = nn.SGD(network.trainable_params(), 0.05)
>>> model_instance = SuppressModel(network=network,
... 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)
"""
check_param_type('policy', policy, str)
if policy == "local_train":
@@ -97,6 +109,8 @@ class SuppressPrivacyFactory:

class SuppressCtrl(Cell):
"""
For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_

Args:
networks (Cell): The training network.
mask_layers (list): Description of those layers that need to be suppressed.
@@ -107,37 +121,6 @@ class SuppressCtrl(Cell):
lr (Union[float, int]): Learning rate.
sparse_end (float): The sparsity to reach.
sparse_start (Union[float, int]): The sparsity to start.

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, networks, mask_layers, end_epoch, batch_num, start_epoch, mask_times, lr,
sparse_end, sparse_start):
@@ -776,6 +759,7 @@ class MaskLayerDes:
If parameter num is greater than 100000, upper_bound has not effect.

Examples:
>>> from mindarmour.privacy.sup_privacy import MaskLayerDes
>>> masklayers = []
>>> masklayers.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
"""


+ 1
- 30
mindarmour/privacy/sup_privacy/train/model.py View File

@@ -57,42 +57,13 @@ def tensor_grad_scale(scale, grad):
class SuppressModel(Model):
"""
This class is overload mindspore.train.model.Model.
For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html>`_

Args:
network (Cell): The training network.
loss_fn (Cell): Computes softmax cross entropy between logits and labels.
optimizer (Optimizer): optimizer instance.
kwargs: Keyword parameters used for creating a suppress model.

Examples:
>>> networks_l5 = LeNet5()
>>> mask_layers = []
>>> mask_layers.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
>>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=networks_l5,
... mask_layers=mask_layers,
... 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,


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