226 lines
13 KiB
C++
226 lines
13 KiB
C++
/**
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* \brief main function to run fix Inferencer
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* \author Mingzhe Jiang
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* \copyright 2020 Kneron Inc. All right reserved.
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*/
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#include "KL520WrapperFactory.h"
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#include "KL720WrapperFactory.h"
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#include "KL530WrapperFactory.h"
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#include "KL630WrapperFactory.h"
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#include "KL730WrapperFactory.h"
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#include "KL540WrapperFactory.h"
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#include <math.h>
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//example code, in case users want to use dump and parser functions in this header
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//#include "run_fix_inferencer.h"
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#include "iostream"
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using namespace std;
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using std::string;
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using std::vector;
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using std::unordered_map;
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using std::unique_ptr;
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using std::shared_ptr;
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using namespace dynasty::inferencer;
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using namespace dynasty::inferencer::fix_point;
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/**
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* This is an example of how to use dynamic fixed point inference.
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* The inferencer output will contain all layer dump if dump level >0, else only the output layer
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* user might need to write his own dump function for different dump purpose, can take run_fix_reference as an example
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* configration parser is also up to user's choice. Can be json or command line params.
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* @param argc
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* @param argv
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* @return
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*/
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int main(int argc, char *argv[]) {
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{
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bool cuda_en = false; //enable cuda if cuda library is installed. don't enable if no cuda ata ll
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string model_file_540 = "res/KL540/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/output/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2.piano.kdp540.scaled.onnx";
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string model_file_530 = "res/KL530/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/output/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2.piano.kdp530.scaled.onnx";
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string model_file_630 = "res/KL630/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/output/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2.piano.kdp630.scaled.onnx";
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string model_file_730 = "res/KL730/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/output/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2.piano.kdp730.scaled.onnx";
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string model_file_720 = "res/conv_8bit_3x3_base/output/conv_8bit_3x3_base.piano.kdp720.scaled.onnx";
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string model_file_520 = "res/conv_8bit_3x3_base/output/conv_8bit_3x3_base.piano.kdp520.scaled.onnx";
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string radixFile_520 = "res/conv_8bit_3x3_base/output/conv_8bit_3x3_base.piano.kdp520.scaled.onnx.json";
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string radixFile_720 = "res/conv_8bit_3x3_base/output/conv_8bit_3x3_base.piano.kdp720.scaled.onnx.json";
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string radixFile_540 = "res/KL540/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/output/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2.piano.kdp540.scaled.onnx.json";
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string radixFile_530 = "res/KL530/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/output/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2.piano.kdp530.scaled.onnx.json";
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string radixFile_630 = "res/KL630/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/output/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2.piano.kdp630.scaled.onnx.json";
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string radixFile_730 = "res/KL730/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/output/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2.piano.kdp730.scaled.onnx.json";
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//get only output node with dump_level = 0; get all layer dump with dump_level = 2
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int dump_level = 0;
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const float EPSILON = 0.8; // Max difference tolerance between 520 result and 720 result
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unordered_map<string, string> inference_input_540;
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string input_name_540 = "0";
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string input_path_540 = "res/KL540/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/input/simulator_input/test_input.txt";
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inference_input_540[input_name_540] = input_path_540;
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// Inference with 540: cuda disabled
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InferencerUniquePtr<float> inferencer_540 = kl540::InferencerImplWrapperFactory::create(model_file_540, radixFile_540, cuda_en);
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unordered_map<std::string, std::vector<float>> float_inputs;
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inferencer_540->convertInput(inference_input_540, float_inputs);
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unordered_map<std::string, std::vector<float>> float_outputs_540 = inferencer_540->Inference(float_inputs, dump_level);
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unordered_map<std::string, std::vector<int>> fix_outputs_540 = inferencer_540->ConvertFloatToInt(float_outputs_540, dump_level);
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auto builder = kl540::InferencerImplWrapperFactory::GetBuilder(inferencer_540.get());
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auto input_dim_540 = builder->GetInputDimensions();
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for(auto& item: input_dim_540) {
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cout << "input node name: " << item.first << ", shape: ";
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for(auto num: item.second) cout<< num <<", ";
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cout << endl;
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}
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cout << "inference with 540 completed" << endl;
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unordered_map<string, string> inference_input_630;
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string input_name_630 = "0";
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string input_path_630 = "res/KL630/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/input/simulator_input/test_input.txt";
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inference_input_630[input_name_630] = input_path_630;
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// Inference with 630: cuda disabled
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InferencerUniquePtr<float> inferencer_630 = kl630::InferencerImplWrapperFactory::create(model_file_630, radixFile_630, cuda_en);
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inferencer_630->convertInput(inference_input_630, float_inputs);
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unordered_map<std::string, std::vector<float>> float_outputs_630 = inferencer_630->Inference(float_inputs, dump_level);
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unordered_map<std::string, std::vector<int>> fix_outputs_630 = inferencer_630->ConvertFloatToInt(float_outputs_630, dump_level);
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builder = kl630::InferencerImplWrapperFactory::GetBuilder(inferencer_630.get());
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auto input_dim_630 = builder->GetInputDimensions();
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for(auto& item: input_dim_630) {
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cout << "input node name: " << item.first << ", shape: ";
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for(auto num: item.second) cout<< num <<", ";
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cout << endl;
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}
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cout << "inference with 630 completed" << endl;
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unordered_map<string, string> inference_input_730;
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string input_name_730 = "0";
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string input_path_730 = "res/KL730/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/input/simulator_input/test_input.txt";
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inference_input_730[input_name_730] = input_path_730;
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// Inference with 730: cuda disabled
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InferencerUniquePtr<float> inferencer_730 = kl730::InferencerImplWrapperFactory::create(model_file_730, radixFile_730, cuda_en);
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inferencer_730->convertInput(inference_input_730, float_inputs);
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unordered_map<std::string, std::vector<float>> float_outputs_730 = inferencer_730->Inference(float_inputs, dump_level);
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unordered_map<std::string, std::vector<int>> fix_outputs_730 = inferencer_730->ConvertFloatToInt(float_outputs_730, dump_level);
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builder = kl730::InferencerImplWrapperFactory::GetBuilder(inferencer_730.get());
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auto input_dim_730 = builder->GetInputDimensions();
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for(auto& item: input_dim_730) {
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cout << "input node name: " << item.first << ", shape: ";
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for(auto num: item.second) cout<< num <<", ";
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cout << endl;
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}
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cout << "inference with 730 completed" << endl;
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unordered_map<string, string> inference_input_530;
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string input_name_530 = "0";
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string input_path_530 = "res/KL530/Conv1Ch_inputFM16x208x1_group1_kernel3x3_pads1_1_1_1_stride2x2_bias1_dilations2/input/simulator_input/test_input.txt";
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inference_input_530[input_name_530] = input_path_530;
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// Inference with 530: cuda disabled
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InferencerUniquePtr<float> inferencer_530 = kl530::InferencerImplWrapperFactory::create(model_file_530, radixFile_530, cuda_en);
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inferencer_530->convertInput(inference_input_530, float_inputs);
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unordered_map<std::string, std::vector<float>> float_outputs_530 = inferencer_530->Inference(float_inputs, dump_level);
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unordered_map<std::string, std::vector<int>> fix_outputs_530 = inferencer_530->ConvertFloatToInt(float_outputs_530, dump_level);
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builder = kl530::InferencerImplWrapperFactory::GetBuilder(inferencer_530.get());
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auto input_dim_530 = builder->GetInputDimensions();
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for(auto& item: input_dim_530) {
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cout << "input node name: " << item.first << ", shape: ";
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for(auto num: item.second) cout<< num <<", ";
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cout << endl;
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}
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cout << "inference with 530 completed" << endl;
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// Set up 520/720 inputs
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unordered_map<string, string> inference_input;
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string input_name = "input_1_o0";
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string input_path = "res/conv_8bit_3x3_base/input/simulator_input/test_input.txt";
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inference_input[input_name] = input_path;
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// Inference with 720: cuda disabled
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InferencerUniquePtr<float> inferencer_720 = kl720::InferencerImplWrapperFactory::create(model_file_720, radixFile_720, cuda_en);
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inferencer_720->convertInput(inference_input, float_inputs);
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unordered_map<std::string, std::vector<float>> float_outputs_720 = inferencer_720->Inference(float_inputs, dump_level);
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unordered_map<std::string, std::vector<int>> fix_outputs_720 = inferencer_720->ConvertFloatToInt(float_outputs_720, dump_level);
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builder = kl720::InferencerImplWrapperFactory::GetBuilder(inferencer_720.get());
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auto input_dim_720 = builder->GetInputDimensions();
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for(auto& item: input_dim_720) {
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cout << "input node name: " << item.first << ", shape: ";
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for(auto num: item.second) cout<< num <<", ";
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cout << endl;
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}
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cout << "inference with 720 completed" << endl;
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// Inference with 520: cuda disabled
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InferencerUniquePtr<float> inferencer_520 = kl520::InferencerImplWrapperFactory::create(model_file_520, radixFile_520, cuda_en);
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inferencer_520->convertInput(inference_input, float_inputs);
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unordered_map<std::string, std::vector<float>> float_outputs_520 = inferencer_520->Inference(float_inputs, dump_level);
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unordered_map<std::string, std::vector<int>> fix_outputs_520 = inferencer_520->ConvertFloatToInt(float_outputs_520, dump_level);
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builder = kl520::InferencerImplWrapperFactory::GetBuilder(inferencer_520.get());
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auto output_dim = builder->GetOutputDimensions();
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for(auto& item: output_dim) {
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cout << "output node name: " << item.first << ", shape: ";
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for(auto num: item.second) cout<< num <<", ";
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cout << endl;
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}
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cout << "inference with 520 completed" << endl;
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// compare results :
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if ( float_outputs_520.size() != float_outputs_720.size() ) {
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cout << "size is not equal" << endl;
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} else {
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auto AreSame=[EPSILON](float a, float b) {
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return fabs(a - b) < EPSILON;
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};
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for (auto& item : float_outputs_720) {
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vector<float>& result_720 = item.second;
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vector<float>& result_520 = float_outputs_520[item.first];
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cout << "....checking result for node: " << item.first << endl;
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if ( result_520.size() != result_720.size()) {
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cout << "size is not equal for : " << item.first;
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exit(-1);
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} else {
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for(size_t i=0 ; i < result_720.size();i++) {
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float r_520 = result_520[i];
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float r_720 = result_720[i];
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if (! AreSame(r_520, r_720) ) {
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cout << "r_520: " << r_520 << " , r_720: " << r_720 << endl;
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exit(-1);
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}
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}
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}
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}
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}
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cout << "Done!" << endl;
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return 0;
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}
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}
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