Implementing Device Training Based On C++ Interface

View Source On Gitee

MindSpore has unified the end-to-side cloud inference API. If you want to continue to use the MindSpore Lite independent API for training, you can refer to here.

Overview

Here we will demonstrate the code that trains a LeNet model using MindSpore Training-on-Device infrastructure. The code segments that are given below are provided fully in train_lenet_cpp.

The completed training procedure is as follows:

  1. Constructing your training model based on MindSpore Lite Architecture and Export it into MindIR model file.

  2. Converting MindIR model file to the MS ToD model file by using MindSpore Lite Converter tool.

  3. Loading MS model file and executing model training by calling MindSpore Lite training API.

Details will be told after environment deployed and model training by running prepared shell scripts.

Environment Preparing

Ubuntu 18.04 64-bit operating system on x86 platform is recommended.

Environment Requirements

  • The compilation environment supports Linux x86_64 only. Ubuntu 18.04.02 LTS is recommended.

  • Software dependency

    • GCC >= 7.3.0

    • CMake >= 3.18.3

    • Git >= 2.28.0

    • Android_NDK >= r20

      • Configure environment variables: export ANDROID_NDK=NDK path.

DataSet

The MNIST dataset used in this example consists of 10 classes of 28 x 28 pixels grayscale images. It has a training set of 60,000 examples, and a test set of 10,000 examples.

Download the MNIST dataset at http://yann.lecun.com/exdb/mnist/. This page provides four download links of dataset files. The first two links are training dataset and training label, while the last two links are test dataset and test label.

Download and decompress the files to /PATH/MNIST_Data/train and /PATH/MNIST_Data/test separately.

The directory structure is as follows:

./MNIST_Data/
├── test
│   ├── t10k-images-idx3-ubyte
│   └── t10k-labels-idx1-ubyte
└── train
    ├── train-images-idx3-ubyte
    └── train-labels-idx1-ubyte

Installing MindSpore

MindSpore can be installed by source code or using pip. Refer MindSpore installation guide for more details.

Downloading and Installing MindSpore Lite

Use git to clone the source code, the command in Linux is as follows:

git clone https://gitee.com/mindspore/mindspore.git -b {version}
cd ./mindspore

The mindspore/lite/examples/train_lenet_cpp directory relative to the MindSpore Lite source code contains this demo’s source code. The version is consistent with that of MindSpore Lite Download Page below. If -b the master is specified, you need to obtain the corresponding installation package through compile from source.

Go to the MindSpore Lite Download Page to download the mindspore-lite-{version}-linux-x64.tar.gz and mindspore-lite-{version}-android-aarch64.tar.gz. The mindspore-lite-{version}-linux-x64.tar.gz is the MindSpore Lite install package for x86 platform, it contains the converter tool converter_lite, this demo uses it to converte MIDIR model to .ms which is supported by MindSpore Lite; The mindspore-lite-{version}-android-aarch64.tar.gz is the MindSpore Lite install package for Android, it contains training runtime library libmindspore-lite.so, this demo uses it to train model. Then put the files to the output directory relative to MindSpore Lite source code（if there is no output directory，you should create it).

Suppose these packags are downloaded in /Downloads directory, Linux commands for operations above is as follows:

mkdir output
cp /Downloads/mindspore-lite-{version}-linux-x64.tar.gz output/mindspore-lite-{version}-linux-x64.tar.gz
cp /Downloads/mindspore-lite-{version}-android-aarch64.tar.gz output/mindspore-lite-{version}-android-aarch64.tar.gz

You can also compile from source to generate the training package for x86 platform mindspore-lite-{version}-linux-x64.tar.gz and for Andorid platform mindspore-lite-{version}-android-aarch64.tar.gz. These packages will directly generated in output directory and you should make sure that in the output directory both the two packages exist.

Connecting Android Device

Turning on the ‘USB debugging’ mode of your Android device and connect it with your PC by using adb debugging tool (runsudo apt install adb in Ubuntu OS command line).

Training and Evaluation

Enter the target directory and run the training bash script. The Linux command is as follows:

cd mindspore/lite/examples/train_lenet_cpp
bash prepare_and_run.sh -D /PATH/MNIST_Data -t arm64

/PATH/MNIST_Data is the absolute mnist dataset path in your machine, -t arm64 represents that we will train and run the model on an Android device, if the work computer is connected to multiple mobile devices, you can use -i devices_id to specify the running device.

The script prepare_and_run.sh has done the following works:

  1. Export the lenet_tod.mindir model file.

  2. Calling the converter tool in the last section and convert the MINDIR file to the ms file.

  3. Push the lenet.ms model file, MNIST dataset and the related library files to your Android device.

  4. Train, save and infer the model.

The model will be trained on your device and print training loss and accuracy value every epoch. The trained model will be saved as ‘lenet_tod.ms’ file. The 10 epochs training result of lenet is shown below (the classification accuracy varies in devices):

======Training Locally=========
1.100:  Loss is 1.19449
1.200:  Loss is 0.477986
1.300:  Loss is 0.440362
1.400:  Loss is 0.165605
1.500:  Loss is 0.368853
1.600:  Loss is 0.179764
1.700:  Loss is 0.173386
1.800:  Loss is 0.0767713
1.900:  Loss is 0.493
1.1000: Loss is 0.460352
1.1100: Loss is 0.262044
1.1200: Loss is 0.222022
1.1300: Loss is 0.058006
1.1400: Loss is 0.0794117
1.1500: Loss is 0.0241433
1.1600: Loss is 0.127109
1.1700: Loss is 0.0557566
1.1800: Loss is 0.0698758
Epoch (1):      Loss is 0.384778
Epoch (1):      Training Accuracy is 0.8702
2.100:  Loss is 0.0538642
2.200:  Loss is 0.444504
2.300:  Loss is 0.0806976
2.400:  Loss is 0.0495807
2.500:  Loss is 0.178903
2.600:  Loss is 0.265705
2.700:  Loss is 0.0933796
2.800:  Loss is 0.0880472
2.900:  Loss is 0.0480734
2.1000: Loss is 0.241272
2.1100: Loss is 0.0920451
2.1200: Loss is 0.371406
2.1300: Loss is 0.0365746
2.1400: Loss is 0.0784372
2.1500: Loss is 0.207537
2.1600: Loss is 0.442626
2.1700: Loss is 0.0814725
2.1800: Loss is 0.12081
Epoch (2):      Loss is 0.176118
Epoch (2):      Training Accuracy is 0.94415
......
10.1000:        Loss is 0.0984653
10.1100:        Loss is 0.189702
10.1200:        Loss is 0.0896037
10.1300:        Loss is 0.0138191
10.1400:        Loss is 0.0152357
10.1500:        Loss is 0.12785
10.1600:        Loss is 0.026495
10.1700:        Loss is 0.436495
10.1800:        Loss is 0.157564
Epoch (5):     Loss is 0.102652
Epoch (5):     Training Accuracy is 0.96805
AvgRunTime: 18980.5 ms
Total allocation: 125829120
Accuracy is 0.965244

===Evaluating trained Model=====
Total allocation: 20971520
Accuracy is 0.965244

===Running Inference Model=====
There are 1 input tensors with sizes:
tensor 0: shape is [32 32 32 1]
There are 1 output tensors with sizes:
tensor 0: shape is [32 10]
The predicted classes are:
4, 0, 2, 8, 9, 4, 5, 6, 3, 5, 2, 1, 4, 6, 8, 0, 5, 7, 3, 5, 8, 3, 4, 1, 9, 8, 7, 3, 0, 2, 3, 6,

If the Android device is not available on your hand, you could also exectute bash prepare_and_run.sh -D /PATH/MNIST_Data -t x86 and run it on the x86 platform.

Details

Folder Structure

The demo project folder structure:

train_lenet_cpp/
  ├── model
  │   ├── lenet_export.py
  │   ├── prepare_model.sh
  │   └── train_utils.py
  │
  ├── scripts
  │   ├── batch_of32.dat
  │   ├── eval.sh
  │   ├── infer.sh
  │   └── train.sh
  │
  ├── src
  │   ├── inference.cc
  │   ├── net_runner.cc
  │   ├── net_runner.h
  │   └── utils.h
  │
  ├── Makefile
  ├── README.md
  ├── README_CN.md
  └── prepare_and_run.sh

Model Exporting

Whether it is an off-the-shelf prepared model, or a custom written model, the model needs to be exported to a .mindir file. Here we use the already-implemented LeNet model.

Import and instantiate a LeNet5 model and set the model to train mode:

import numpy as np
import mindspore as ms
from lenet import LeNet5
from train_utils import TrainWrap

n = LeNet5()
n.set_train()
ms.set_context(mode=ms.GRAPH_MODE, device_target="CPU", save_graphs=False)

Set MindSpore context and initialize the data and label tensors. In this case we use a MindSpore that was compiled for CPU. We define a batch size of 32 and initialize the tensors according to MNIST data – single channel 32x32 images.

The tensors does not need to be loaded with relevant data, but the shape and type must be correct. Note also, that this export code runs on the server, and in this case uses the CPU device. However, the Training on Device will run according to the context

BATCH_SIZE = 32
x = ms.Tensor(np.ones((BATCH_SIZE, 1, 32, 32)), ms.float32)
label = ms.Tensor(np.zeros([BATCH_SIZE]).astype(np.int32))
net = TrainWrap(n)

Wrapping the network with a loss layer and an optimizer and export it to a MindIR file. TrainWrap is provided in the example as:

from mindspore import nn
import mindspore as ms

def train_wrap(net, loss_fn=None, optimizer=None, weights=None):
    """
    train_wrap
    """
    if loss_fn is None:
        loss_fn = nn.SoftmaxCrossEntropyWithLogits(reduction='mean', sparse=True)
    loss_net = nn.WithLossCell(net, loss_fn)
    loss_net.set_train()
    if weights is None:
        weights = ms.ParameterTuple(net.trainable_params())
    if optimizer is None:
        optimizer = nn.Adam(weights, learning_rate=0.003, beta1=0.9, beta2=0.999, eps=1e-5, use_locking=False, use_nesterov=False, weight_decay=4e-5, loss_scale=1.0)
    train_net = nn.TrainOneStepCell(loss_net, optimizer)
    return train_net

Finally, exporting the defined model.

ms.export(net, x, label, file_name="lenet_tod", file_format='MINDIR')
print("finished exporting")

Model Transferring

To convert the model simply use the converter as explained in the Convert Section, the command is:

./converter_lite --fmk=MINDIR --trainModel=true --modelFile=lenet_tod.mindir --outputFile=lenet_tod

The exported file lenet_tod.ms is under the folder ./train_lenet_cpp/model.

Model Training

The model training progress is in net_runner.cc.

The main code continues as follows:

int NetRunner::Main() {
  // Load model and create session
  InitAndFigureInputs();
  // initialize the dataset
  InitDB();
  // Execute the training
  TrainLoop();
  // Evaluate the trained model
  CalculateAccuracy();

  if (epochs_ > 0) {
    auto trained_fn = ms_file_.substr(0, ms_file_.find_last_of('.')) + "_trained.ms";
    mindspore::Serialization::ExportModel(*model_, mindspore::kMindIR, trained_fn, mindspore::kNoQuant, false);
    trained_fn = ms_file_.substr(0, ms_file_.find_last_of('.')) + "_infer.ms";
    mindspore::Serialization::ExportModel(*model_, mindspore::kMindIR, trained_fn, mindspore::kNoQuant, true);
  }
  return 0;
}

  1. Loading Model

    InitAndFigureInputs creates the TrainSession instance from the .ms file, then sets the input tensors indices for the .ms model.

    void NetRunner::InitAndFigureInputs() {
auto context = std::make_shared<mindspore::Context>();
auto cpu_context = std::make_shared<mindspore::CPUDeviceInfo>();
cpu_context->SetEnableFP16(enable_fp16_);
context->MutableDeviceInfo().push_back(cpu_context);

graph_ = new mindspore::Graph();
auto status = mindspore::Serialization::Load(ms_file_, mindspore::kMindIR, graph_);
if (status != mindspore::kSuccess) {
    std::cout << "Error " << status << " during serialization of graph " << ms_file_;
    MS_ASSERT(status != mindspore::kSuccess);
}

auto cfg = std::make_shared<mindspore::TrainCfg>();
if (enable_fp16_) {
    cfg.get()->optimization_level_ = mindspore::kO2;
}

model_ = new mindspore::Model();
status = model_->Build(mindspore::GraphCell(*graph_), context, cfg);
if (status != mindspore::kSuccess) {
    std::cout << "Error " << status << " during build of model " << ms_file_;
    MS_ASSERT(status != mindspore::kSuccess);
}

acc_metrics_ = std::shared_ptr<AccuracyMetrics>(new AccuracyMetrics);
model_->InitMetrics({acc_metrics_.get()});

auto inputs = model_->GetInputs();
MS_ASSERT(inputs.size() >= 1);
auto nhwc_input_dims = inputs.at(0).Shape();

batch_size_ = nhwc_input_dims.at(0);
h_ = nhwc_input_dims.at(1);
w_ = nhwc_input_dims.at(2);
}

  2. Dataset Processing

    InitDB initializes the MNIST dataset and loads it into the memory. MindData has provided the data preprocessing API, the user could refer to the C++ API Docs for more details.

    int NetRunner::InitDB() {
train_ds_ = Mnist(data_dir_ + "/train", "all", std::make_shared<SequentialSampler>(0, 0));

TypeCast typecast_f(mindspore::DataType::kNumberTypeFloat32);
Resize resize({h_, w_});
train_ds_ = train_ds_->Map({&resize, &typecast_f}, {"image"});

TypeCast typecast(mindspore::DataType::kNumberTypeInt32);
train_ds_ = train_ds_->Map({&typecast}, {"label"});

train_ds_ = train_ds_->Batch(batch_size_, true);

if (verbose_) {
    std::cout << "DatasetSize is " << train_ds_->GetDatasetSize() << std::endl;
}
if (train_ds_->GetDatasetSize() == 0) {
    std::cout << "No relevant data was found in " << data_dir_ << std::endl;
    MS_ASSERT(train_ds_->GetDatasetSize() != 0);
}
return 0;
}

  3. Execute Training

    The TrainLoop method is the core of the training procedure. We first display its code then review it.

    int NetRunner::TrainLoop() {
mindspore::LossMonitor lm(100);
mindspore::TrainAccuracy am(1);

mindspore::CkptSaver cs(kSaveEpochs, std::string("lenet"));
Rescaler rescale(kScalePoint);
Measurement measure(epochs_);

if (virtual_batch_ > 0) {
    model_->Train(epochs_, train_ds_, {&rescale, &lm, &cs, &measure});
} else {
    struct mindspore::StepLRLambda step_lr_lambda(1, kGammaFactor);
    mindspore::LRScheduler step_lr_sched(mindspore::StepLRLambda, static_cast<void *>(&step_lr_lambda), 1);
    model_->Train(epochs_, train_ds_, {&rescale, &lm, &cs, &am, &step_lr_sched, &measure});
}

return 0;
}

  4. Execute Evaluating

    To eval the model accuracy, the CalculateAccuracy method is being called. Within which, the model is switched to Eval mode, and the method runs a cycle of test tensors through the trained network to measure the current accuracy rate.

    float NetRunner::CalculateAccuracy(int max_tests) {
test_ds_ = Mnist(data_dir_ + "/test", "all");
TypeCast typecast_f(mindspore::DataType::kNumberTypeFloat32);
Resize resize({h_, w_});
test_ds_ = test_ds_->Map({&resize, &typecast_f}, {"image"});

TypeCast typecast(mindspore::DataType::kNumberTypeInt32);
test_ds_ = test_ds_->Map({&typecast}, {"label"});
test_ds_ = test_ds_->Batch(batch_size_, true);

model_->Evaluate(test_ds_, {});
std::cout << "Accuracy is " << acc_metrics_->Eval() << std::endl;

return 0.0;
}