forked from mindspore-Ecosystem/mindspore
353 lines
12 KiB
Python
353 lines
12 KiB
Python
# Copyright 2020-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Test export"""
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import os
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from io import BytesIO
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import numpy as np
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import mindspore
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import mindspore.nn as nn
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import mindspore.dataset as ds
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import mindspore.dataset.vision as CV
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import mindspore.dataset.transforms as CT
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from mindspore.dataset.vision import Inter
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from mindspore import context
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from mindspore.common import dtype as mstype
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from mindspore.common.tensor import Tensor
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.common.parameter import ParameterTuple
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore.train.serialization import export, _get_mindir_inputs, convert_model
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def weight_variable():
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return TruncatedNormal(0.02)
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def create_dataset():
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# define dataset
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mnist_ds = ds.MnistDataset("../data/dataset/testMnistData")
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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shift = 0.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
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rescale_op = CV.Rescale(rescale, shift)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = CT.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label")
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mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image")
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mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image")
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mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image")
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mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image")
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# apply DatasetOps
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mnist_ds = mnist_ds.batch(batch_size=32, drop_remainder=True)
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return mnist_ds
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class LeNet5(nn.Cell):
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def __init__(self):
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super(LeNet5, self).__init__()
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self.batch_size = 32
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self.conv1 = conv(1, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, 10)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.reshape = P.Reshape()
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.reshape(x, (self.batch_size, -1))
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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class InputNet1(nn.Cell):
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def construct(self, x):
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return x
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class InputNet2(nn.Cell):
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def construct(self, x, y):
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return x, y
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class WithLossCell(nn.Cell):
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def __init__(self, network):
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super(WithLossCell, self).__init__(auto_prefix=False)
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self.loss = nn.SoftmaxCrossEntropyWithLogits()
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self.network = network
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def construct(self, x, label):
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predict = self.network(x)
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return self.loss(predict, label)
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class TrainOneStepCell(nn.Cell):
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def __init__(self, network):
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super(TrainOneStepCell, self).__init__(auto_prefix=False)
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self.network = network
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self.network.set_train()
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self.weights = ParameterTuple(network.trainable_params())
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self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
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self.hyper_map = C.HyperMap()
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self.grad = C.GradOperation(get_by_list=True)
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def construct(self, x, label):
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weights = self.weights
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grads = self.grad(self.network, weights)(x, label)
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return self.optimizer(grads)
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def encrypt_func(model_stream, key):
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plain_data = BytesIO()
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plain_data.write(model_stream)
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return plain_data.getvalue()
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def test_export_lenet_grad_mindir():
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"""
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Feature: Export LeNet to MindIR
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Description: Test export API to save network into MindIR
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Expectation: save successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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network = LeNet5()
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network.set_train()
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predict = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.zeros([32, 10]).astype(np.float32))
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net = TrainOneStepCell(WithLossCell(network))
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file_name = "lenet_grad"
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export(net, predict, label, file_name=file_name, file_format='MINDIR')
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verify_name = file_name + ".mindir"
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assert os.path.exists(verify_name)
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os.remove(verify_name)
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def test_get_mindir_inputs1():
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"""
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Feature: Get MindIR input.
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Description: Test get mindir input.
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Expectation: Successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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net = InputNet1()
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input1 = Tensor(np.zeros([32, 10]).astype(np.float32))
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file_name = "input1.mindir"
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export(net, input1, file_name=file_name, file_format='MINDIR')
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input_tensor = _get_mindir_inputs(file_name)
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assert os.path.exists(file_name)
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assert input_tensor.shape == (32, 10)
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assert input_tensor.dtype == mindspore.float32
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os.remove(file_name)
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def test_get_mindir_inputs2():
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"""
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Feature: Get MindIR input.
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Description: Test get mindir input.
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Expectation: Successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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net = InputNet2()
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input1 = Tensor(np.zeros(1).astype(np.float16))
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input2 = Tensor(np.zeros([10, 20]), dtype=mstype.int32)
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file_name = "input2.mindir"
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export(net, input1, input2, file_name=file_name, file_format='MINDIR')
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input_tensor = _get_mindir_inputs(file_name)
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assert os.path.exists(file_name)
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assert len(input_tensor) == 2
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assert input_tensor[0].shape == (1,)
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assert input_tensor[0].dtype == mindspore.float16
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assert input_tensor[1].shape == (10, 20)
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assert input_tensor[1].dtype == mindspore.int32
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os.remove(file_name)
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def test_convert_model():
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"""
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Feature: Convert mindir to onnx.
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Description: Test convert.
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Expectation: Successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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net1 = InputNet1()
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input1 = Tensor(np.ones([1, 32, 32]).astype(np.float32))
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mindir_name1 = "lenet1.mindir"
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export(net1, input1, file_name=mindir_name1, file_format='MINDIR')
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onnx_name1 = "lenet1.onnx"
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convert_model(mindir_name1, onnx_name1, "ONNX")
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assert os.path.exists(mindir_name1)
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assert os.path.exists(onnx_name1)
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os.remove(mindir_name1)
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os.remove(onnx_name1)
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net2 = InputNet2()
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input1 = Tensor(np.ones(32).astype(np.float32))
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input2 = Tensor(np.ones([32, 32]).astype(np.float32))
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mindir_name2 = "lenet2.mindir"
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export(net2, input1, input2, file_name=mindir_name2, file_format='MINDIR')
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onnx_name2 = "lenet2.onnx"
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convert_model(mindir_name2, onnx_name2, "ONNX")
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assert os.path.exists(mindir_name2)
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assert os.path.exists(onnx_name2)
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os.remove(mindir_name2)
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os.remove(onnx_name2)
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def test_export_lenet_with_dataset():
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"""
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Feature: Export LeNet with data preprocess to MindIR
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Description: Test export API to save network and dataset into MindIR
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Expectation: save successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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network = LeNet5()
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network.set_train()
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dataset = create_dataset()
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file_name = "lenet_preprocess"
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export(network, dataset, file_name=file_name, file_format='MINDIR')
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verify_name = file_name + ".mindir"
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assert os.path.exists(verify_name)
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os.remove(verify_name)
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def test_export_lenet_onnx_with_encryption():
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"""
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Feature: Export encrypted LeNet to ONNX
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Description: Test export API to save network with encryption into ONNX
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Expectation: save successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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network = LeNet5()
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network.set_train()
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file_name = "lenet_preprocess"
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input_tensor = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
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export(network, input_tensor, file_name=file_name, file_format='ONNX',
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enc_key=b'123456789', enc_mode=encrypt_func)
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verify_name = file_name + ".onnx"
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assert os.path.exists(verify_name)
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os.remove(verify_name)
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def test_export_lenet_mindir_with_encryption():
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"""
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Feature: Export encrypted LeNet to MindIR
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Description: Test export API to save network with encryption into MindIR
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Expectation: save successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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network = LeNet5()
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network.set_train()
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file_name = "lenet_preprocess"
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input_tensor = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
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export(network, input_tensor, file_name=file_name, file_format='MINDIR',
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enc_key=b'123456789', enc_mode=encrypt_func)
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verify_name = file_name + ".mindir"
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assert os.path.exists(verify_name)
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os.remove(verify_name)
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def test_export_lenet_mindir_with_aes():
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"""
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Feature: Export encrypted LeNet to MindIR
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Description: Test export API to save network with AES encryption into MindIR
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Expectation: save successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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network = LeNet5()
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file_name = "aes_encrypt"
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input_tensor = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
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export(network, input_tensor, file_name=file_name, file_format='MINDIR',
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enc_key=b'0123456789012345', enc_mode="AES-GCM")
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verify_name = file_name + ".mindir"
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assert os.path.exists(verify_name)
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load_graph = mindspore.load("aes_encrypt.mindir",
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dec_key=b'0123456789012345', dec_mode="AES-GCM")
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load_net = nn.GraphCell(load_graph)
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os.remove(verify_name)
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input_tensor = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
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export(network, input_tensor, file_name=file_name, file_format='MINDIR',
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enc_key=b'0123456789012345', enc_mode="AES-CBC")
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verify_name = file_name + ".mindir"
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assert os.path.exists(verify_name)
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load_graph = mindspore.load("aes_encrypt.mindir",
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dec_key=b'0123456789012345', dec_mode="AES-CBC")
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load_net = nn.GraphCell(load_graph)
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assert isinstance(load_net, nn.GraphCell)
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os.remove(verify_name)
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def test_export_lenet_mindir_with_sm4():
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"""
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Feature: Export encrypted LeNet to MindIR
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Description: Test export API to save network with SM4-CBC encryption into MindIR
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Expectation: save successfully
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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network = LeNet5()
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file_name = "sm4_encrypt"
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input_tensor = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
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export(network, input_tensor, file_name=file_name, file_format='MINDIR',
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enc_key=b'0123456789012345', enc_mode="SM4-CBC")
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verify_name = file_name + ".mindir"
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assert os.path.exists(verify_name)
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load_graph = mindspore.load("sm4_encrypt.mindir",
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dec_key=b'0123456789012345', dec_mode="SM4-CBC")
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load_net = nn.GraphCell(load_graph)
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assert isinstance(load_net, nn.GraphCell)
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os.remove(verify_name)
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