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# 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Training example of suppress-based privacy.
"""
import os
import mindspore.nn as nn
from mindspore import context
from mindspore.train.callback import ModelCheckpoint
from mindspore.train.callback import CheckpointConfig
from mindspore.train.callback import LossMonitor
from mindspore.nn.metrics import Accuracy
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
from mindspore.dataset.vision.utils import Inter
import mindspore.common.dtype as mstype
from examples.common.networks.lenet5.lenet5_net import LeNet5
from sup_privacy_config import mnist_cfg as cfg
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
from mindarmour.utils.logger import LogUtil
LOGGER = LogUtil.get_instance()
LOGGER.set_level('INFO')
TAG = 'Lenet5_Suppress_train'
def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1, samples=None, num_parallel_workers=1, sparse=True):
"""
create dataset for training or testing
"""
# define dataset
ds1 = ds.MnistDataset(data_path, num_samples=samples)
# define operation parameters
resize_height, resize_width = 32, 32
rescale = 1.0 / 255.0
shift = 0.0
# define map operations
resize_op = CV.Resize((resize_height, resize_width),
interpolation=Inter.LINEAR)
rescale_op = CV.Rescale(rescale, shift)
hwc2chw_op = CV.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32)
# apply map operations on images
if not sparse:
one_hot_enco = C.OneHot(10)
ds1 = ds1.map(input_columns="label", operations=one_hot_enco, num_parallel_workers=num_parallel_workers)
type_cast_op = C.TypeCast(mstype.float32)
ds1 = ds1.map(input_columns="label", operations=type_cast_op,
num_parallel_workers=num_parallel_workers)
ds1 = ds1.map(input_columns="image", operations=resize_op,
num_parallel_workers=num_parallel_workers)
ds1 = ds1.map(input_columns="image", operations=rescale_op,
num_parallel_workers=num_parallel_workers)
ds1 = ds1.map(input_columns="image", operations=hwc2chw_op,
num_parallel_workers=num_parallel_workers)
# apply DatasetOps
buffer_size = 10000
ds1 = ds1.shuffle(buffer_size=buffer_size)
ds1 = ds1.batch(batch_size, drop_remainder=True)
ds1 = ds1.repeat(repeat_size)
return ds1
def mnist_suppress_train(epoch_size=10, start_epoch=3, lr=0.05, samples=10000, mask_times=1000,
sparse_thd=0.90, sparse_start=0.0, masklayers=None):
"""
local train by suppress-based privacy
"""
networks_l5 = LeNet5()
suppress_ctrl_instance = SuppressPrivacyFactory().create(networks_l5,
masklayers,
policy="local_train",
end_epoch=epoch_size,
batch_num=(int)(samples/cfg.batch_size),
start_epoch=start_epoch,
mask_times=mask_times,
lr=lr,
sparse_end=sparse_thd,
sparse_start=sparse_start)
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
net_opt = nn.SGD(networks_l5.trainable_params(), lr)
config_ck = CheckpointConfig(save_checkpoint_steps=(int)(samples/cfg.batch_size),
keep_checkpoint_max=10)
# Create the SuppressModel model for training.
model_instance = SuppressModel(network=networks_l5,
loss_fn=net_loss,
optimizer=net_opt,
metrics={"Accuracy": Accuracy()})
model_instance.link_suppress_ctrl(suppress_ctrl_instance)
# Create a Masker for Suppress training. The function of the Masker is to
# enforce suppress operation while training.
suppress_masker = SuppressMasker(model=model_instance, suppress_ctrl=suppress_ctrl_instance)
mnist_path = "./MNIST_unzip/" #"../../MNIST_unzip/"
ds_train = generate_mnist_dataset(os.path.join(mnist_path, "train"),
batch_size=cfg.batch_size, repeat_size=1, samples=samples)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
directory="./trained_ckpt_file/",
config=config_ck)
print("============== Starting SUPP Training ==============")
model_instance.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(), suppress_masker],
dataset_sink_mode=False)
print("============== Starting SUPP Testing ==============")
ds_eval = generate_mnist_dataset(os.path.join(mnist_path, 'test'),
batch_size=cfg.batch_size)
acc = model_instance.eval(ds_eval, dataset_sink_mode=False)
print("============== SUPP Accuracy: %s ==============", acc)
suppress_ctrl_instance.print_paras()
if __name__ == "__main__":
# This configure can run in pynative mode
context.set_context(mode=context.PYNATIVE_MODE, device_target=cfg.device_target)
masklayers_lenet5 = [] # determine which layer should be masked
masklayers_lenet5.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
masklayers_lenet5.append(MaskLayerDes("conv2.weight", 1, False, True, 150))
masklayers_lenet5.append(MaskLayerDes("fc1.weight", 2, True, False, -1))
masklayers_lenet5.append(MaskLayerDes("fc2.weight", 4, True, False, -1))
masklayers_lenet5.append(MaskLayerDes("fc3.weight", 6, True, False, 50))
# do suppreess privacy train, with stronger privacy protection and better performance than Differential Privacy
mnist_suppress_train(10, 3, 0.10, 60000, 1000, 0.95, 0.0, masklayers=masklayers_lenet5) # used
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