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benchmark support multi outputs node & detection_post_process use stable sort & add model detect.tflite

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pull/5825/head
wangzhe 4 years ago
parent a2b329828d
commit a758a882d6
6 changed files with 61 additions and 47 deletions
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1
mindspore/lite/nnacl/detection_post_process_parameter.h
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@ -37,6 +37,7 @@ typedef struct DetectionPostProcessParameter {
void *decoded_boxes_;
void *nms_candidate_;
void *indexes_;
void *selected_;
void *score_with_class_;
void *score_with_class_all_;

13
mindspore/lite/nnacl/fp32/detection_post_process.c
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@ -27,7 +27,7 @@ int ScoreWithIndexCmp(const void *a, const void *b) {
} else if (pa->score < pb->score) {
return 1;
} else {
return 0;
return pa->index - pb->index;
}
}
@ -108,6 +108,7 @@ int NmsMultiClassesRegular(const int num_boxes, const int num_classes_with_bg, c
int all_classes_sorted_num = 0;
int all_classes_output_num = 0;
ScoreWithIndex *score_with_index_all = (ScoreWithIndex *)(param->score_with_class_all_);
int *indexes = (int *)(param->indexes_);
for (int j = first_class_index; j < num_classes_with_bg; ++j) {
int candidate_num = 0;
// process single class
@ -120,15 +121,23 @@ int NmsMultiClassesRegular(const int num_boxes, const int num_classes_with_bg, c
}
int selected_num = NmsSingleClass(candidate_num, decoded_boxes, param->detections_per_class_,
score_with_index_single, selected, param);
for (int i = 0; i < all_classes_sorted_num; ++i) {
indexes[i] = score_with_index_all[i].index;
score_with_index_all[i].index = i;
}
// process all classes
for (int i = 0; i < selected_num; ++i) {
// store class to index
score_with_index_all[all_classes_sorted_num].index = selected[i] * num_classes_with_bg + j;
indexes[all_classes_sorted_num] = selected[i] * num_classes_with_bg + j;
score_with_index_all[all_classes_sorted_num].index = all_classes_sorted_num;
score_with_index_all[all_classes_sorted_num++].score = input_scores[selected[i] * num_classes_with_bg + j];
}
all_classes_output_num =
all_classes_sorted_num < param->max_detections_ ? all_classes_sorted_num : param->max_detections_;
qsort(score_with_index_all, all_classes_sorted_num, sizeof(ScoreWithIndex), ScoreWithIndexCmp);
for (int i = 0; i < all_classes_output_num; ++i) {
score_with_index_all[i].index = indexes[score_with_index_all[i].index];
}
all_classes_sorted_num = all_classes_output_num;
}
for (int i = 0; i < param->max_detections_ * param->max_classes_per_detection_; ++i) {

4
mindspore/lite/src/runtime/kernel/arm/fp32/detection_post_process.cc
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@ -91,6 +91,7 @@ int DetectionPostProcessCPUKernel::Run() {
if (parameter->use_regular_nms_) {
parameter->score_with_class_all_ =
context_->allocator->Malloc((num_boxes + parameter->max_detections_) * sizeof(ScoreWithIndex));
parameter->indexes_ = context_->allocator->Malloc((num_boxes + parameter->max_detections_) * sizeof(int));
} else {
parameter->score_with_class_all_ =
context_->allocator->Malloc((num_boxes * parameter->num_classes_) * sizeof(ScoreWithIndex));
@ -102,6 +103,9 @@ int DetectionPostProcessCPUKernel::Run() {
context_->allocator->Free(parameter->selected_);
context_->allocator->Free(parameter->score_with_class_);
context_->allocator->Free(parameter->score_with_class_all_);
if (parameter->use_regular_nms_) {
context_->allocator->Free(parameter->indexes_);
}
return RET_OK;
}

1
mindspore/lite/test/models_tflite_awaretraining.cfg
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@ -23,3 +23,4 @@ inception_v3_quant.tflite
inception_v4_299_quant.tflite
graph_8bit_1021_combine.tflite
lite-model_object_detection_mobile_object_labeler_v1_1.tflite
detect.tflite

83
mindspore/lite/tools/benchmark/benchmark.cc
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@ -100,7 +100,7 @@ int Benchmark::ReadInputFile() {
}
auto tensorDataSize = cur_tensor->Size();
if (size != tensorDataSize) {
std::cerr << "Input binary file size error, required: %zu, in fact: %zu" << tensorDataSize << size << std::endl;
std::cerr << "Input binary file size error, required: " << tensorDataSize << ", in fact: " << size << std::endl;
MS_LOG(ERROR) << "Input binary file size error, required: " << tensorDataSize << ", in fact: " << size;
delete binBuf;
return RET_ERROR;
@ -166,41 +166,40 @@ int Benchmark::ReadCalibData() {
return RET_OK;
}
int Benchmark::CompareOutput() {
std::cout << "================ Comparing Output data ================" << std::endl;
float totalBias = 0;
int totalSize = 0;
bool hasError = false;
for (const auto &calibTensor : calibData) {
std::string nodeName = calibTensor.first;
auto tensors = session->GetOutputsByNodeName(nodeName);
if (tensors.empty()) {
MS_LOG(ERROR) << "Cannot find output node: " << nodeName.c_str() << " , compare output data fail.";
std::cerr << "Cannot find output node: " << nodeName.c_str() << " , compare output data fail." << std::endl;
return RET_ERROR;
}
// make sure tensor size is 1
if (tensors.size() != 1) {
MS_LOG(ERROR) << "Only support 1 tensor with a name now.";
std::cerr << "Only support 1 tensor with a name now." << std::endl;
return RET_ERROR;
std::string nodeOrTensorName = calibTensor.first;
auto tensors = session->GetOutputsByNodeName(nodeOrTensorName);
const mindspore::tensor::MSTensor *tensor = nullptr;
if (tensors.empty() || tensors.size() != 1) {
MS_LOG(INFO) << "Cannot find output node: " << nodeOrTensorName
<< " or node has more than one output tensor, switch to GetOutputByTensorName";
tensor = session->GetOutputByTensorName(nodeOrTensorName);
if (tensor == nullptr) {
MS_LOG(ERROR) << "Cannot find output tensor " << nodeOrTensorName << ", get model output failed";
return RET_ERROR;
}
} else {
tensor = tensors.front();
}
auto &tensor = tensors.front();
MS_ASSERT(tensor->GetDataType() == DataType_DT_FLOAT);
MS_ASSERT(tensor->GetData() != nullptr);
float bias = 0;
switch (msCalibDataType) {
case TypeId::kNumberTypeFloat: {
bias = CompareData<float>(nodeName, tensor->shape(), static_cast<float *>(tensor->MutableData()));
bias = CompareData<float>(nodeOrTensorName, tensor->shape(), static_cast<float *>(tensor->MutableData()));
break;
}
case TypeId::kNumberTypeInt8: {
bias = CompareData<int8_t>(nodeName, tensor->shape(), static_cast<int8_t *>(tensor->MutableData()));
bias = CompareData<int8_t>(nodeOrTensorName, tensor->shape(), static_cast<int8_t *>(tensor->MutableData()));
break;
}
case TypeId::kNumberTypeInt32: {
bias = CompareData<int32_t>(nodeName, tensor->shape(), static_cast<int32_t *>(tensor->MutableData()));
bias = CompareData<int32_t>(nodeOrTensorName, tensor->shape(), static_cast<int32_t *>(tensor->MutableData()));
break;
}
default:
@ -224,12 +223,12 @@ int Benchmark::CompareOutput() {
meanBias = 0;
}
std::cout << "Mean bias of all nodes: " << meanBias << "%" << std::endl;
std::cout << "Mean bias of all nodes/tensors: " << meanBias << "%" << std::endl;
std::cout << "=======================================================" << std::endl << std::endl;
if (meanBias > this->_flags->accuracyThreshold) {
MS_LOG(ERROR) << "Mean bias of all nodes is too big: " << meanBias << "%";
std::cerr << "Mean bias of all nodes is too big: " << meanBias << "%" << std::endl;
MS_LOG(ERROR) << "Mean bias of all nodes/tensors is too big: " << meanBias << "%";
std::cerr << "Mean bias of all nodes/tensors is too big: " << meanBias << "%" << std::endl;
return RET_ERROR;
} else {
return RET_OK;
@ -294,26 +293,26 @@ int Benchmark::MarkAccuracy() {
MS_LOG(INFO) << "MarkAccuracy";
std::cout << "MarkAccuracy" << std::endl;
for (size_t i = 0; i < msInputs.size(); i++) {
switch (msInputs.at(i)->data_type()) {
case TypeId::kNumberTypeFloat:
PrintInputData<float>(msInputs.at(i));
break;
case TypeId::kNumberTypeFloat32:
PrintInputData<float>(msInputs.at(i));
break;
case TypeId::kNumberTypeInt8:
PrintInputData<int8_t>(msInputs.at(i));
break;
case TypeId::kNumberTypeUInt8:
PrintInputData<uint8_t>(msInputs.at(i));
break;
case TypeId::kNumberTypeInt32:
PrintInputData<int>(msInputs.at(i));
break;
default:
MS_LOG(ERROR) << "Datatype " << msInputs.at(i)->data_type() << " is not supported.";
return RET_ERROR;
}
switch (msInputs.at(i)->data_type()) {
case TypeId::kNumberTypeFloat:
PrintInputData<float>(msInputs.at(i));
break;
case TypeId::kNumberTypeFloat32:
PrintInputData<float>(msInputs.at(i));
break;
case TypeId::kNumberTypeInt8:
PrintInputData<int8_t>(msInputs.at(i));
break;
case TypeId::kNumberTypeUInt8:
PrintInputData<uint8_t>(msInputs.at(i));
break;
case TypeId::kNumberTypeInt32:
PrintInputData<int>(msInputs.at(i));
break;
default:
MS_LOG(ERROR) << "Datatype " << msInputs.at(i)->data_type() << " is not supported.";
return RET_ERROR;
}
}
auto status = session->RunGraph();
if (status != RET_OK) {
@ -355,7 +354,7 @@ int Benchmark::RunBenchmark(const std::string &deviceType) {
auto model = lite::Model::Import(graphBuf, size);
auto model_version = model->version_;
if (model_version != Version()) {
MS_LOG(WARNING) << "model version is "<< model_version << ", inference version is " << Version() << " not equal";
MS_LOG(WARNING) << "model version is " << model_version << ", inference version is " << Version() << " not equal";
}
if (model == nullptr) {
MS_LOG(ERROR) << "Import model file failed while running " << modelName.c_str();

6
mindspore/lite/tools/benchmark/benchmark.h
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@ -131,7 +131,7 @@ class MS_API Benchmark {
auto inData = reinterpret_cast<T *>(input->MutableData());
std::cout << "InData" << i++ << ": ";
for (size_t j = 0; j < 20; j++) {
std::cout << static_cast<float >(inData[j]) << " ";
std::cout << static_cast<float>(inData[j]) << " ";
}
std::cout << std::endl;
}
@ -192,9 +192,9 @@ class MS_API Benchmark {
}
if (meanError <= 0.0000001) {
std::cout << "Mean bias of node " << nodeName << " : 0%" << std::endl;
std::cout << "Mean bias of node/tensor " << nodeName << " : 0%" << std::endl;
} else {
std::cout << "Mean bias of node " << nodeName << " : " << meanError * 100 << "%" << std::endl;
std::cout << "Mean bias of node/tensor " << nodeName << " : " << meanError * 100 << "%" << std::endl;
}
return meanError;
} else {

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