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!36637 [MS][Micro] change benchmark metric to cos-sim 

Merge pull request !36637 from lz/master
This commit is contained in:
i-robot 2022-07-04 06:00:05 +00:00 committed by Gitee
parent 78f18e3a10 353fbf5adc
commit 8bd8aa103a
No known key found for this signature in database
GPG Key ID: 173E9B9CA92EEF8F
4 changed files with 56 additions and 23 deletions
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66
mindspore/lite/tools/converter/micro/coder/generator/component/const_blocks/calib_output.cc
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@ -159,8 +159,8 @@ int CompareOutputs(MSTensorHandleArray outputs, CalibTensor **calib_tensors, int
printf("error, outputs and calibs size is mismatch\n");
return kMSStatusLiteError;
}
float total_error = 0;
size_t outputs_num = outputs.handle_num;
bool is_success = true;
for (size_t i = 0; i < outputs_num; ++i) {
MicroTensor *output = (MicroTensor *)outputs.handle_list[i];
if (!output || !output->data) {
@ -178,6 +178,7 @@ int CompareOutputs(MSTensorHandleArray outputs, CalibTensor **calib_tensors, int
printf("error, output elements num is not equal to calib\n");
return kMSStatusLiteError;
}
float cosin = 0.f, dot = 0.f, normx = 0.f, normy = 0.f;
switch (output->type) {
case kMSDataTypeNumberTypeFloat32: {
float *float_output = (float *)output->data;
@ -187,21 +188,27 @@ int CompareOutputs(MSTensorHandleArray outputs, CalibTensor **calib_tensors, int
printf("error, output data is nan or inf\n");
return kMSStatusLiteError;
}
total_error += fabsf(float_output[j] - calib[i].data_[j]);
dot += float_output[j] * calib[i].data_[j];
normx += float_output[j] * float_output[j];
normy += calib[i].data_[j] * calib[i].data_[j];
}
break;
}
case kMSDataTypeNumberTypeInt8: {
int8_t *int_output = (int8_t *)output->data;
for (size_t j = 0; j < elements; ++j) {
total_error += fabsf(int_output[j] - calib[i].data_[j]);
dot += (float) (int_output[j] * calib[i].data_[j]);
normx += (float) (int_output[j] * int_output[j]);
normy += (float)(calib[i].data_[j] * calib[i].data_[j]);
}
break;
}
case kMSDataTypeNumberTypeUInt8: {
uint8_t *int_output = (uint8_t *)output->data;
for (size_t j = 0; j < elements; ++j) {
total_error += fabsf(int_output[j] - calib[i].data_[j]);
dot += (float) (int_output[j] * calib[i].data_[j]);
normx += (float) (int_output[j] * int_output[j]);
normy += (float)(calib[i].data_[j] * calib[i].data_[j]);
}
break;
}
@ -209,20 +216,29 @@ int CompareOutputs(MSTensorHandleArray outputs, CalibTensor **calib_tensors, int
case kMSDataTypeNumberTypeUInt32: {
int32_t *int_output = (int32_t *)output->data;
for (size_t j = 0; j < elements; ++j) {
total_error += fabsf(int_output[j] - calib[i].data_[j]);
dot += (float) (int_output[j] * calib[i].data_[j]);
normx += (float) (int_output[j] * int_output[j]);
normy += (float)(calib[i].data_[j] * calib[i].data_[j]);
}
break;
}
default: {
printf("unsupported tensor data type\n");
printf("unsupported tensor data type.\n");
}
}
cosin = dot / (sqrt(normx) * sqrt(normy));
if (cosin < 0.9999) {
printf("cos-similarity of %s is %f, less than 0.9999.\n", output->name, cosin);
is_success = false;
} else {
printf("cos-similarity of %s is %f.\n", output->name, cosin);
}
}
if (total_error > kToleranceVal) {
printf("compare outputs failed, total error: %f\n", total_error);
if (!is_success) {
printf("compare outputs failed.\n");
return kMSStatusLiteError;
}
printf("compare outputs success, total error: %f\n", total_error);
printf("compare outputs success.\n");
return kMSStatusSuccess;
}
@ -393,10 +409,11 @@ int CompareOutputs(MSTensorHandleArray *outputs, TensorArray *tensor_array) {
printf("error, calib tensor is NULL.\n");
return kMSStatusLiteError;
}
float total_error = 0;
bool is_success = true;
size_t outputs_num = outputs->handle_num;
for (size_t i = 0; i < outputs_num; ++i) {
MicroTensor *output = (MicroTensor *)outputs->handle_list[i];
float cosin = 0.f, dot = 0.f, normx = 0.f, normy = 0.f;
if (output == NULL){
printf("error, output is nullptr.\n");
return kMSStatusLiteError;
@ -429,21 +446,27 @@ int CompareOutputs(MSTensorHandleArray *outputs, TensorArray *tensor_array) {
printf("error, output data is nan or inf\n");
return kMSStatusLiteError;
}
total_error += fabsf(float_output[j] - calib_tensors[i].data_[j]);
dot += float_output[j] * calib_tensors[i].data_[j];
normx += float_output[j] * float_output[j];
normy += calib_tensors[i].data_[j] * calib_tensors[i].data_[j];
}
break;
}
case kMSDataTypeNumberTypeInt8: {
int8_t *int_output = (int8_t *)output->data;
for (size_t j = 0; j < elements; ++j) {
total_error += fabsf(int_output[j] - calib_tensors[i].data_[j]);
dot += (float) (int_output[j] * calib_tensors[i].data_[j]);
normx += (float) (int_output[j] * int_output[j]);
normy += (float)(calib_tensors[i].data_[j] * calib_tensors[i].data_[j]);
}
break;
}
case kMSDataTypeNumberTypeUInt8: {
uint8_t *int_output = (uint8_t *)output->data;
for (size_t j = 0; j < elements; ++j) {
total_error += fabsf(int_output[j] - calib_tensors[i].data_[j]);
dot += (float) (int_output[j] * calib_tensors[i].data_[j]);
normx += (float) (int_output[j] * int_output[j]);
normy += (float)(calib_tensors[i].data_[j] * calib_tensors[i].data_[j]);
}
break;
}
@ -451,7 +474,9 @@ int CompareOutputs(MSTensorHandleArray *outputs, TensorArray *tensor_array) {
case kMSDataTypeNumberTypeUInt32: {
int32_t *int_output = (int32_t *)output->data;
for (size_t j = 0; j < elements; ++j) {
total_error += fabsf(int_output[j] - calib_tensors[i].data_[j]);
dot += (float) (int_output[j] * calib_tensors[i].data_[j]);
normx += (float) (int_output[j] * int_output[j]);
normy += (float)(calib_tensors[i].data_[j] * calib_tensors[i].data_[j]);
}
break;
}
@ -459,12 +484,19 @@ int CompareOutputs(MSTensorHandleArray *outputs, TensorArray *tensor_array) {
printf("unsupported tensor data type\n");
}
}
cosin = dot / (sqrt(normx) * sqrt(normy));
if (cosin < 0.9999) {
printf("cos-similarity of %s is %f, less than 0.9999.\n", output->name, cosin);
is_success = false;
} else {
printf("cos-similarity of %s is %f.\n", output->name, cosin);
}
}
if (total_error > kToleranceVal) {
printf("compare outputs failed, total error: %f\n", total_error);
if (!is_success) {
printf("compare outputs failed.\n");
return kMSStatusLiteError;
}
printf("compare outputs success, total error: %f.\n", total_error);
printf("compare outputs success.\n");
return kMSStatusSuccess;
}
)RAW";

2
mindspore/lite/tools/converter/micro/coder/opcoders/nnacl/fp32/convolution_winograd_fp32_coder.cc
View File

@ -270,7 +270,7 @@ int ConvolutionWinogradFP32Coder::DoCode(CoderContext *const context) {
code.CodeFunction("memset", gemm_out_, "0", gemm_out_size_);
code.CodeFunction("memset", tmp_data_, "0", tmp_data_size_);
code.CodeFunction("memset", col_buffer_, "0", col_buffer_size_);
code << "\t\tfloat *tmp_buffer_address_list[4] = {" << allocator_->GetRuntimeAddr(trans_input_) << ", "
code << " float *tmp_buffer_address_list[4] = {" << allocator_->GetRuntimeAddr(trans_input_) << ", "
<< allocator_->GetRuntimeAddr(gemm_out_) << ", " << allocator_->GetRuntimeAddr(tmp_data_) << ", "
<< allocator_->GetRuntimeAddr(col_buffer_) << "};\n";
code.CodeStruct("conv_parameter", *conv_param_);

3
mindspore/lite/tools/converter/micro/coder/opcoders/serializers/nnacl_serializer/nnacl_fp32_serializer.cc
View File

@ -61,7 +61,8 @@ void NNaclFp32Serializer::CodeStruct(const std::string &name, const SoftmaxParam
}
void NNaclFp32Serializer::CodeStruct(const std::string &name, const ConvParameter &conv_parameter) {
code << "int thread_num = MSMIN(" << gThreadNum << ", " << conv_parameter.output_h_ << ");\n";
code << gThreadNum << " = 1;\n";
code << " int thread_num = MSMIN(" << gThreadNum << ", " << conv_parameter.output_h_ << ");\n";
CodeBaseStruct<false>(
"ConvParameter", name, conv_parameter.op_parameter_, "{0}", conv_parameter.kernel_h_, conv_parameter.kernel_w_,
conv_parameter.stride_h_, conv_parameter.stride_w_, conv_parameter.dilation_h_, conv_parameter.dilation_w_,

8
mindspore/lite/tools/converter/micro/coder/opcoders/serializers/nnacl_serializer/nnacl_int8_serializer.cc
View File

@ -49,7 +49,7 @@ void NNaclInt8Serializer::CodeStruct(const std::string &name, const ConvParamete
quant_arg_in, quant_arg_w, quant_arg_out, real_multiplier, left_shift, right_shift,
quant_multiplier, out_act_min, out_act_max, quant_arg.input_arg_num_, quant_arg.filter_arg_num_,
quant_arg.output_arg_num_, quant_arg.per_channel_);
code << "int thread_num = MSMIN(" << gThreadNum << ", " << conv_parameter.output_h_ << ");\n";
code << " int thread_num = MSMIN(" << gThreadNum << ", " << conv_parameter.output_h_ << ");\n";
CodeBaseStruct<false>(
"ConvParameter", name, conv_parameter.op_parameter_, conv_quant_arg, conv_parameter.kernel_h_,
conv_parameter.kernel_w_, conv_parameter.stride_h_, conv_parameter.stride_w_, conv_parameter.dilation_h_,
@ -100,10 +100,10 @@ void NNaclInt8Serializer::CodeStruct(const std::string &name, const PoolingParam
MS_CHECK_PTR_IF_NULL(in_quant_args);
MS_CHECK_PTR_IF_NULL(out_quant_args);
code << "static QuantArg " << in_quant_name << " = " << *in_quant_args << ";\n";
code << "static QuantArg " << out_quant_name << " = " << *out_quant_args << ";\n";
code << " static QuantArg " << in_quant_name << " = " << *in_quant_args << ";\n";
code << " static QuantArg " << out_quant_name << " = " << *out_quant_args << ";\n";
code << "static QuantArg *" << quant_name << "[2] = {"
code << " static QuantArg *" << quant_name << "[2] = {"
<< " &" << in_quant_name << ", "
<< " &" << out_quant_name << "};\n";