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!12837 update interface for yolov4 310 infer

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From: @lihongkang1
Reviewed-by: 
Signed-off-by:
pull/12837/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 01eca19f33 f851d88a97
commit 578f3b851e
3 changed files with 204 additions and 205 deletions
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4
model_zoo/official/cv/yolov4/ascend310_infer/inc/utils.h
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@ -27,6 +27,6 @@
std::vector<std::string> GetAllFiles(std::string_view dirName);
DIR *OpenDir(std::string_view dirName);
std::string RealPath(std::string_view path);
std::shared_ptr<mindspore::api::Tensor> ReadFileToTensor(const std::string &file);
int WriteResult(const std::string& imageFile, const std::vector<mindspore::api::Buffer> &outputs);
mindspore::MSTensor ReadFileToTensor(const std::string &file);
int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
#endif

72
model_zoo/official/cv/yolov4/ascend310_infer/src/main.cc
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@ -27,23 +27,22 @@
#include "include/api/model.h"
#include "include/api/serialization.h"
#include "include/api/context.h"
#include "minddata/dataset/include/minddata_eager.h"
#include "include/minddata/dataset/include/execute.h"
#include "include/minddata/dataset/include/vision_ascend.h"
#include "../inc/utils.h"
#include "include/api/types.h"
#include "minddata/dataset/include/vision.h"
using mindspore::api::Context;
using mindspore::api::Serialization;
using mindspore::api::Model;
using mindspore::api::kModelOptionInsertOpCfgPath;
using mindspore::api::kModelOptionPrecisionMode;
using mindspore::api::kModelOptionOpSelectImplMode;
using mindspore::api::Status;
using mindspore::api::MindDataEager;
using mindspore::api::Buffer;
using mindspore::api::ModelType;
using mindspore::api::GraphCell;
using mindspore::api::SUCCESS;
using mindspore::Context;
using mindspore::GlobalContext;
using mindspore::ModelContext;
using mindspore::Serialization;
using mindspore::Model;
using mindspore::Status;
using mindspore::dataset::Execute;
using mindspore::MSTensor;
using mindspore::ModelType;
using mindspore::GraphCell;
using mindspore::kSuccess;
using mindspore::dataset::vision::DvppDecodeResizeJpeg;
DEFINE_string(mindir_path, "", "mindir path");
@ -51,8 +50,6 @@ DEFINE_string(dataset_path, ".", "dataset path");
DEFINE_int32(device_id, 0, "device id");
DEFINE_string(precision_mode, "allow_fp32_to_fp16", "precision mode");
DEFINE_string(op_select_impl_mode, "", "op select impl mode");
DEFINE_string(input_shape, "img_data:1, 3, 768, 1280; img_info:1, 4", "input shape");
DEFINE_string(input_format, "nchw", "input format");
DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
int main(int argc, char **argv) {
@ -66,28 +63,30 @@ int main(int argc, char **argv) {
return 1;
}
Context::Instance().SetDeviceTarget("Ascend310").SetDeviceID(FLAGS_device_id);
GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
GlobalContext::SetGlobalDeviceID(FLAGS_device_id);
auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
Model model((GraphCell(graph)));
auto model_context = std::make_shared<Context>();
std::map<std::string, std::string> build_options;
if (!FLAGS_precision_mode.empty()) {
build_options.emplace(kModelOptionPrecisionMode, FLAGS_precision_mode);
ModelContext::SetPrecisionMode(model_context, FLAGS_precision_mode);
}
if (!FLAGS_op_select_impl_mode.empty()) {
build_options.emplace(kModelOptionOpSelectImplMode, FLAGS_op_select_impl_mode);
ModelContext::SetOpSelectImplMode(model_context, FLAGS_op_select_impl_mode);
}
if (!FLAGS_aipp_path.empty()) {
build_options.emplace(kModelOptionInsertOpCfgPath, FLAGS_aipp_path);
ModelContext::SetInsertOpConfigPath(model_context, FLAGS_aipp_path);
}
Status ret = model.Build(build_options);
if (ret != SUCCESS) {
Model model(GraphCell(graph), model_context);
Status ret = model.Build();
if (ret != kSuccess) {
std::cout << "EEEEEEEERROR Build failed." << std::endl;
return 1;
}
std::vector<MSTensor> model_inputs = model.GetInputs();
auto all_files = GetAllFiles(FLAGS_dataset_path);
if (all_files.empty()) {
std::cout << "ERROR: no input data." << std::endl;
@ -96,25 +95,34 @@ int main(int argc, char **argv) {
std::map<double, double> costTime_map;
size_t size = all_files.size();
MindDataEager SingleOp({DvppDecodeResizeJpeg({608, 608})});
Execute preprocess(std::shared_ptr<DvppDecodeResizeJpeg>(new DvppDecodeResizeJpeg({608, 608})));
for (size_t i = 0; i < size; ++i) {
struct timeval start = {0};
struct timeval end = {0};
double startTime_ms;
double endTime_ms;
std::vector<Buffer> inputs;
std::vector<Buffer> outputs;
std::vector<MSTensor> inputs;
std::vector<MSTensor> outputs;
std::cout << "Start predict input files:" << all_files[i] << std::endl;
auto imgDvpp = SingleOp(ReadFileToTensor(all_files[i]));
auto img = MSTensor();
ret = preprocess(ReadFileToTensor(all_files[i]), &img);
if (ret != kSuccess) {
std::cout << "preprocess " << all_files[i] << " failed." << std::endl;
return 1;
}
std::vector<float> input_shape = {608, 608};
inputs.clear();
inputs.emplace_back(imgDvpp->Data(), imgDvpp->DataSize());
inputs.emplace_back(input_shape.data(), input_shape.size() * sizeof(float));
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
img.Data().get(), img.DataSize());
inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
input_shape.data(), input_shape.size() * sizeof(float));
gettimeofday(&start, NULL);
ret = model.Predict(inputs, &outputs);
gettimeofday(&end, NULL);
if (ret != SUCCESS) {
if (ret != kSuccess) {
std::cout << "Predict " << all_files[i] << " failed." << std::endl;
return 1;
}

31
model_zoo/official/cv/yolov4/ascend310_infer/src/utils.cc
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@ -19,9 +19,8 @@
#include <algorithm>
#include <iostream>
using mindspore::api::Tensor;
using mindspore::api::Buffer;
using mindspore::api::DataType;
using mindspore::MSTensor;
using mindspore::DataType;
std::vector<std::string> GetAllFiles(std::string_view dirName) {
struct dirent *filename;
@ -47,11 +46,11 @@ std::vector<std::string> GetAllFiles(std::string_view dirName) {
return res;
}
int WriteResult(const std::string& imageFile, const std::vector<Buffer> &outputs) {
int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
std::string homePath = "./result_Files";
for (size_t i = 0; i < outputs.size(); ++i) {
size_t outputSize;
const void * netOutput;
std::shared_ptr<const void> netOutput;
netOutput = outputs[i].Data();
outputSize = outputs[i].DataSize();
@ -60,7 +59,7 @@ int WriteResult(const std::string& imageFile, const std::vector<Buffer> &outputs
fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
std::string outFileName = homePath + "/" + fileName;
FILE * outputFile = fopen(outFileName.c_str(), "wb");
fwrite(netOutput, outputSize, sizeof(char), outputFile);
fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
fclose(outputFile);
outputFile = nullptr;
@ -68,39 +67,31 @@ int WriteResult(const std::string& imageFile, const std::vector<Buffer> &outputs
return 0;
}
std::shared_ptr<Tensor> ReadFileToTensor(const std::string &file) {
auto buffer = std::make_shared<Tensor>();
MSTensor ReadFileToTensor(const std::string &file) {
if (file.empty()) {
std::cout << "Pointer file is nullptr" << std::endl;
return buffer;
return MSTensor();
}
std::ifstream ifs(file);
if (!ifs.good()) {
std::cout << "File: " << file << " is not exist" << std::endl;
return buffer;
return MSTensor();
}
if (!ifs.is_open()) {
std::cout << "File: " << file << "open failed" << std::endl;
return buffer;
return MSTensor();
}
ifs.seekg(0, std::ios::end);
size_t size = ifs.tellg();
buffer->ResizeData(size);
if (buffer->DataSize() != size) {
std::cout << "Malloc buf failed, file: " << file << std::endl;
ifs.close();
return buffer;
}
MSTensor buffer(file, DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
ifs.seekg(0, std::ios::beg);
ifs.read(reinterpret_cast<char *>(buffer->MutableData()), size);
ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
ifs.close();
buffer->SetDataType(DataType::kMsUint8);
buffer->SetShape({static_cast<int64_t>(size)});
return buffer;
}

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