diff --git a/example/mnist_demo/lenet5_config.py b/example/mnist_demo/lenet5_config.py index e638686..de8a29a 100644 --- a/example/mnist_demo/lenet5_config.py +++ b/example/mnist_demo/lenet5_config.py @@ -31,7 +31,7 @@ mnist_cfg = edict({ 'device_target': 'Ascend', # device used 'data_path': './MNIST_unzip', # the path of training and testing data set 'dataset_sink_mode': False, # whether deliver all training data to device one time　 - 'micro_batches': 32, # the number of small batches split from an original batch + 'micro_batches': 16, # the number of small batches split from an original batch 'l2_norm_bound': 1.0, # the clip bound of the gradients of model's training parameters 'initial_noise_multiplier': 1.5, # the initial multiplication coefficient of the noise added to training # parameters' gradients diff --git a/example/mnist_demo/lenet5_dp_model_train.py b/example/mnist_demo/lenet5_dp_model_train.py index b36ba35..638785f 100644 --- a/example/mnist_demo/lenet5_dp_model_train.py +++ b/example/mnist_demo/lenet5_dp_model_train.py @@ -124,8 +124,7 @@ if __name__ == "__main__": rdp_monitor = PrivacyMonitorFactory.create('rdp', num_samples=60000, batch_size=cfg.batch_size, - initial_noise_multiplier=cfg.initial_noise_multiplier* - cfg.l2_norm_bound, + initial_noise_multiplier=cfg.initial_noise_multiplier, per_print_times=50) # Create the DP model for training. diff --git a/tests/ut/python/defenses/test_ad.py b/tests/ut/python/defenses/test_ad.py index 0a3015f..d726c36 100644 --- a/tests/ut/python/defenses/test_ad.py +++ b/tests/ut/python/defenses/test_ad.py @@ -54,7 +54,7 @@ def test_ad(): net = Net() loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=sparse) optimizer = Momentum(learning_rate=Tensor(np.array([0.001], np.float32)), - momentum=Tensor(np.array([0.9], np.float32)), + momentum=0.9, params=net.trainable_params()) ad_defense = AdversarialDefense(net, loss_fn=loss_fn, optimizer=optimizer) diff --git a/tests/ut/python/diff_privacy/test_model_train.py b/tests/ut/python/diff_privacy/test_model_train.py index 7373967..c98084c 100644 --- a/tests/ut/python/diff_privacy/test_model_train.py +++ b/tests/ut/python/diff_privacy/test_model_train.py @@ -18,13 +18,14 @@ import pytest import numpy as np from mindspore import nn -from mindspore.model_zoo.lenet import LeNet5 from mindspore import context import mindspore.dataset as ds from mindarmour.diff_privacy import DPOptimizerClassFactory from mindarmour.diff_privacy import DPModel +from test_network import LeNet5 + def dataset_generator(batch_size, batches): data = np.random.random((batches * batch_size, 1, 32, 32)).astype(np.float32) diff --git a/tests/ut/python/diff_privacy/test_monitor.py b/tests/ut/python/diff_privacy/test_monitor.py index 530d0b8..b67a8e3 100644 --- a/tests/ut/python/diff_privacy/test_monitor.py +++ b/tests/ut/python/diff_privacy/test_monitor.py @@ -21,11 +21,12 @@ import mindspore.nn as nn import mindspore.dataset as ds from mindspore.train import Model import mindspore.context as context -from mindspore.model_zoo.lenet import LeNet5 from mindarmour.diff_privacy import PrivacyMonitorFactory from mindarmour.utils.logger import LogUtil +from test_network import LeNet5 + LOGGER = LogUtil.get_instance() TAG = 'DP-Monitor Test' diff --git a/tests/ut/python/diff_privacy/test_network.py b/tests/ut/python/diff_privacy/test_network.py new file mode 100644 index 0000000..ac5cf3b --- /dev/null +++ b/tests/ut/python/diff_privacy/test_network.py @@ -0,0 +1,63 @@ +# Copyright 2019 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. +from mindspore import nn +from mindspore.common.initializer import TruncatedNormal + + +def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): + weight = weight_variable() + return nn.Conv2d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + weight_init=weight, has_bias=False, pad_mode="valid") + + +def fc_with_initialize(input_channels, out_channels): + weight = weight_variable() + bias = weight_variable() + return nn.Dense(input_channels, out_channels, weight, bias) + + +def weight_variable(): + return TruncatedNormal(0.05) + + +class LeNet5(nn.Cell): + """ + Lenet network + """ + def __init__(self): + super(LeNet5, self).__init__() + self.conv1 = conv(1, 6, 5) + self.conv2 = conv(6, 16, 5) + self.fc1 = fc_with_initialize(16*5*5, 120) + self.fc2 = fc_with_initialize(120, 84) + self.fc3 = fc_with_initialize(84, 10) + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.flatten = nn.Flatten() + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.flatten(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.relu(x) + x = self.fc3(x) + return x diff --git a/tests/ut/python/diff_privacy/test_optimizer.py b/tests/ut/python/diff_privacy/test_optimizer.py index 978e916..74962fd 100644 --- a/tests/ut/python/diff_privacy/test_optimizer.py +++ b/tests/ut/python/diff_privacy/test_optimizer.py @@ -15,11 +15,11 @@ import pytest from mindspore import nn from mindspore import context -from mindspore.model_zoo.lenet import LeNet5 from mindspore.train.model import Model from mindarmour.diff_privacy import DPOptimizerClassFactory +from test_network import LeNet5 @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training