Tensorrt_mnist_onnx_tensort mnist mnist.onnx

7.4 mnist_onnx

#include "common/argsParser.h"
#include "common/buffers.h"
#include "common/common.h"
#include "common/logger.h"
#include "common/parserOnnxConfig.h"

#include "NvInfer.h"
#include <cuda_runtime_api.h>


#include <cstdlib>
#include <chrono>
#include <fstream>
#include <iostream>
#include <sstream>
const std::string gSampleName = "TensorRT.sample_onnx_mnist";
class SampleOnnxMNIST
{
    template<typename T>
    using SampleUniquePtr = std::unique_ptr<T,samplesCommon::InferDeleter>;
public:
    SampleOnnxMNIST(const samplesCommon::OnnxSampleParams& params)
        :mParams(params)
        , mEngine(nullptr)
    {

    }
    bool build();
    bool infer();
private:
    samplesCommon::OnnxSampleParams mParams;
    nvinfer1::Dims mInputDims;
    nvinfer1::Dims mOutputDims;

    int mNumber;

    std::shared_ptr<nvinfer1::ICudaEngine> mEngine;

    bool constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder,
                          SampleUniquePtr<nvinfer1::INetworkDefinition>& network,
                          SampleUniquePtr<nvinfer1::IBuilderConfig>& config,
                          SampleUniquePtr<nvonnxparser::IParser>& parser);
    bool processInput(const samplesCommon::BufferManager& buffers);
    bool verifyOutput(const samplesCommon::BufferManager& buffers)const;
};
bool SampleOnnxMNIST::build()
{
    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
    if(!builder)
    {
        return false;
    }
    const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
    auto network = SampleUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(explicitBatch));
    if(!network)
    {
        return false;
    }
    auto config = SampleUniquePtr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
    if(!config)
 {
        return false;
    }
    auto parser = SampleUniquePtr<nvonnxparser::IParser>(nvonnxparser::createParser(*network,sample::gLogger.getTRTLogger()));
    if(!parser)
    {
        return false;
    }

    auto constructed = constructNetwork(builder,network, config,parser);
    if(!constructed)
    {
        return false;
    }
    mEngine = std::shared_ptr<nvinfer1::ICudaEngine>(builder->buildEngineWithConfig(*network,*config),samplesCommon::InferDeleter());
    if(!mEngine)
    {
        return false;
    }
    assert(network->getNbInputs() == 1);
    mInputDims = network->getInput(0)->getDimensions();
    assert(mInputDims.nbDims == 4);

    assert(network->getNbOutputs() == 1);
    mOutputDims = network->getOutput(0)->getDimensions();
    assert(mOutputDims.nbDims == 2);
    return true;
}
bool SampleOnnxMNIST::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder,SampleUniquePtr<nvinfer1::INetworkDefinition>& network,SampleUniquePtr<nvinfer1::IBuilderConfig>& config,SampleUniquePtr<nvonnxparser::IParser>& parser)
{
    auto parserd = parser->parseFromFile(locateFile(mParams.onnxFileName,mParams.dataDirs).c_str(),
         static_cast<int>(sample::gLogger.getReportableSeverity()));
    if(!parserd)
    {
        return false;
    }
    config->setMaxWorkspaceSize(16_MiB);
    if(mParams.fp16)
    {
        config->setFlag(BuilderFlag::kFP16);
    }
    if(mParams.int8)
    {
        config->setFlag(BuilderFlag::kINT8);
        samplesCommon::setAllTensorScales(network.get(),127.0f,127.0f);
    }
    samplesCommon::enableDLA(builder.get(),config.get(),mParams.dlaCore);
    return true;

}
bool SampleOnnxMNIST::infer()
{
    // Create RAII buffer manager object
    samplesCommon::BufferManager buffers(mEngine);

    auto context = SampleUniquePtr<nvinfer1::IExecutionContext>(mEngine->createExecutionContext());
    if (!context)
    {
        return false;
    }

    // Read the input data into the managed buffers
    assert(mParams.inputTensorNames.size() == 1);
    if (!processInput(buffers))
    {
        return false;
    }

    // Memcpy from host input buffers to device input buffers
    buffers.copyInputToDevice();

    bool status = context->executeV2(buffers.getDeviceBindings().data());
    if (!status)
    {
        return false;
    }

    // Memcpy from device output buffers to host output buffers
    buffers.copyOutputToHost();

    // Verify results
    if (!verifyOutput(buffers))
    {
        return false;
    }

    return true;
}
bool SampleOnnxMNIST::processInput(const samplesCommon::BufferManager& buffers) 
{
    const int inputH = mInputDims.d[2];
    const int inputW = mInputDims.d[3];

    // Read a random digit file
    srand(unsigned(time(nullptr)));
    std::vector<uint8_t> fileData(inputH * inputW);
    mNumber = rand() % 10;
    readPGMFile(locateFile(std::to_string(mNumber) + ".pgm", mParams.dataDirs), fileData.data(), inputH,inputW);

    // Print ASCII representation of digit image
    sample::gLogInfo << "\nInput:\n" << std::endl;
    for (int i = 0; i <inputH * inputW; i++)
    {
        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
    }
    sample::gLogInfo << std::endl;

    float* hostDataBuffer = static_cast<float*>(buffers.getHostBuffer(mParams.inputTensorNames[0]));
    for (int i = 0; i < inputH * inputW; i++)
    {
        hostDataBuffer[i] =1.0- float(fileData[i]/255.0);
    }

    return true;

}
bool SampleOnnxMNIST::verifyOutput(const samplesCommon::BufferManager& buffers) const
{
    const int outputSize = mOutputDims.d[1];
    float* output = static_cast<float*>(buffers.getHostBuffer(mParams.outputTensorNames[0]));
    float val{0.0f};
    int idx{0};

    // Calculate Softmax
    float sum{0.0f};
    for (int i = 0; i < outputSize; i++)
    {
        output[i] = exp(output[i]);
        sum += output[i];
    }

    sample::gLogInfo << "Output:" << std::endl;
    for (int i = 0; i < outputSize; i++)
    {
        output[i] /= sum;
        val = std::max(val, output[i]);
        if (val == output[i])
        {
            idx = i;
        }

        sample::gLogInfo << " Prob " << i << "  " << std::fixed << std::setw(5) << std::setprecision(4) << output[i]
                         << " "
                         << "Class " << i << ": " << std::string(int(std::floor(output[i] * 10 + 0.5f)), '*')
                         << std::endl;
    }
    sample::gLogInfo << std::endl;

    return idx == mNumber && val > 0.9f;
}

samplesCommon::OnnxSampleParams initializeSampleParams(const samplesCommon::Args& args)
{
    samplesCommon::OnnxSampleParams params;
    if(args.dataDirs.empty())
    {
        params.dataDirs.push_back("./data/");
//        params.dataDirs.push_back("./data/");
    }
    else
    {
        params.dataDirs = args.dataDirs;
    }

    params.onnxFileName = "mnist.onnx";
    params.inputTensorNames.push_back("Input3");
    params.outputTensorNames.push_back("Plus214_Output_0");
    params.dlaCore = args.useDLACore;
    params.int8 = args.runInInt8;
    params.fp16 = args.runInFp16;

    return params;

}
void printHelpInfo()
{
    std::cout
        << "Usage: ./sample_mnist [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]\n";
    std::cout << "--help          Display help information\n";
    std::cout << "--datadir       Specify path to a data directory, overriding the default. This option can be used "
                 "multiple times to add multiple directories. If no data directories are given, the default is to use "
                 "(data/samples/mnist/, data/mnist/)"
              << std::endl;
    std::cout << "--useDLACore=N  Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, "
                 "where n is the number of DLA engines on the platform."
              << std::endl;
    std::cout << "--int8          Run in Int8 mode.\n";
    std::cout << "--fp16          Run in FP16 mode.\n";
}
int main(int argc,char** argv)
{
    samplesCommon::Args args;
    bool argsOK= samplesCommon::parseArgs(args,argc,argv);
    if(!argsOK)
    {
        sample::gLogError << "Invalid arguments" << std::endl;
        printHelpInfo();
        return EXIT_FAILURE;
    }
    if (args.help)
    {
        printHelpInfo();
        return EXIT_SUCCESS;
    }
    auto sampleTest=sample::gLogger.defineTest(gSampleName,argc,argv);
    sample::gLogger.reportTestStart(sampleTest);

    SampleOnnxMNIST sample(initializeSampleParams(args));
    sample::gLogInfo <<"Building and running a GPU inference engine for MNIST" << std::endl;
    if (!sample.build())
    {
        return sample::gLogger.reportFail(sampleTest);
    }

    if (!sample.infer())
    {
        return sample::gLogger.reportFail(sampleTest);
    }

    return sample::gLogger.reportPass(sampleTest);
}

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282