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diff --git a/src/topfd/ecscore/score/onnx_ecscore.cpp b/src/topfd/ecscore/score/onnx_ecscore.cpp
index b0772c1..7a5ece3 100644
--- a/src/topfd/ecscore/score/onnx_ecscore.cpp
+++ b/src/topfd/ecscore/score/onnx_ecscore.cpp
@@ -16,7 +16,7 @@
#include <cmath>
#include "common/util/logger.hpp"
-#include "onnx/onnxruntime_cxx_api.h"
+#include "onnxruntime/onnxruntime_cxx_api.h"
#include "common/util/file_util.hpp"
#include "topfd/ecscore/score/onnx_ecscore.hpp"
@@ -34,7 +34,7 @@ std::vector<const char*> input_node_names = {"input"};
std::vector<const char*> output_node_names = {"output"};
void initModel(const std::string &dir_name, int thread_num) {
- std::string file_name = dir_name
+ std::string file_name = dir_name
+ file_util::getFileSeparator() + "ecscore_models"
+ file_util::getFileSeparator() + "ecscore_seven_attr.onnx";
@@ -54,9 +54,9 @@ void initModel(const std::string &dir_name, int thread_num) {
std::vector<double> predict(int feat_num, std::vector<float> &input_tensor_values) {
- std::vector<int64_t> input_node_dims {feat_num, 7};
- size_t input_tensor_size;
- input_tensor_size = feat_num * 7;
+ std::vector<int64_t> input_node_dims {feat_num, 7};
+ size_t input_tensor_size;
+ input_tensor_size = feat_num * 7;
// create input tensor object from data values
auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
@@ -79,7 +79,7 @@ std::vector<double> predict(int feat_num, std::vector<float> &input_tensor_value
double zero = float_arr[2*i];
double one = float_arr[2*i+1];
//softmax
- double score = std::exp(one)/ (std::exp(zero) + std::exp(one));
+ double score = std::exp(one)/ (std::exp(zero) + std::exp(one));
pred_results.push_back(score);
}
return pred_results;
diff --git a/src/topfd/envcnn/onnx_env_cnn.cpp b/src/topfd/envcnn/onnx_env_cnn.cpp
index ebaac03..3001a09 100644
--- a/src/topfd/envcnn/onnx_env_cnn.cpp
+++ b/src/topfd/envcnn/onnx_env_cnn.cpp
@@ -20,7 +20,7 @@
#include <iostream>
#include <cmath>
-#include "onnx/onnxruntime_cxx_api.h"
+#include "onnxruntime/onnxruntime_cxx_api.h"
#include "common/util/logger.hpp"
#include "common/util/file_util.hpp"
@@ -39,7 +39,7 @@ std::vector<const char*> input_node_names = {"input"};
std::vector<const char*> output_node_names = {"output"};
void initModel(const std::string &dir_name, int thread_num) {
- std::string file_name = dir_name
+ std::string file_name = dir_name
+ file_util::getFileSeparator() + "envcnn_models"
+ file_util::getFileSeparator() + "envcnn_two_block.onnx";
@@ -83,7 +83,7 @@ void extractTheoPeakData(EnvPtr &theo_env,
}
}
-void getTheoEnvData(MatchEnvPtr &ori_env, std::vector<double> &theo_mass,
+void getTheoEnvData(MatchEnvPtr &ori_env, std::vector<double> &theo_mass,
std::vector<double> &theo_intes,
double &max_inte, double &theo_mono_mz) {
EnvPtr theo_env = ori_env->getTheoEnvPtr();
@@ -96,7 +96,7 @@ void getTheoEnvData(MatchEnvPtr &ori_env, std::vector<double> &theo_mass,
}
void getExpIntervaledPeakData(const PeakPtrVec &peak_list, double real_mono_mz,
- std::vector<double> &theo_mass,
+ std::vector<double> &theo_mass,
std::vector<double> &peak_mass,
std::vector<double> &peak_intes) {
double max_theo_mass = *std::max_element(std::begin(theo_mass), std::end(theo_mass));
@@ -112,9 +112,9 @@ void getExpIntervaledPeakData(const PeakPtrVec &peak_list, double real_mono_mz,
}
}
-void getExpEnvData(const PeakPtrVec &peak_list, MatchEnvPtr &ori_env,
+void getExpEnvData(const PeakPtrVec &peak_list, MatchEnvPtr &ori_env,
std::vector<double> &theo_mass,
- std::vector<double> &peak_mass,
+ std::vector<double> &peak_mass,
std::vector<double> &peak_intes) {
ExpEnvPtr real_env = ori_env->getExpEnvPtr();
double real_mono_mz = real_env->getMonoMz();
@@ -236,8 +236,8 @@ std::vector<float> getTensor(const PeakPtrVec &peak_list, MatchEnvPtr env) {
getExpEnvData(peak_list, env, theo_masses, peak_masses, peak_intes);
// init matrix
- size_t row = 4;
- size_t col = 300;
+ size_t row = 4;
+ size_t col = 300;
std::vector<std::vector<float>> matrix = initializeMatrix(row, col);
double tolerance = 0.02;
@@ -265,7 +265,7 @@ std::vector<float> getTensor(const PeakPtrVec &peak_list, MatchEnvPtr env) {
std::vector<float> getBatchTensor(const PeakPtrVec &peak_list, MatchEnvPtrVec &envs){
std::vector<float> result;
for (size_t i = 0; i < envs.size(); i++) {
- std::vector<float> env_result = getTensor(peak_list, envs[i]);
+ std::vector<float> env_result = getTensor(peak_list, envs[i]);
result.insert(std::end(result), std::begin(env_result), std::end(env_result));
}
return result;
@@ -276,15 +276,15 @@ void predict(MatchEnvPtrVec &envs, std::vector<float> &input_tensor_values) {
std::vector<double> pred_results = predict(env_num, input_tensor_values);
for (int i = 0; i < env_num; i++) {
- envs[i]->setEnvcnnScore(pred_results[i]);
- LOG_DEBUG(" msdeconv " << envs[i]->getMsdeconvScore() << " predict " << pred_results[i]);
+ envs[i]->setEnvcnnScore(pred_results[i]);
+ LOG_DEBUG(" msdeconv " << envs[i]->getMsdeconvScore() << " predict " << pred_results[i]);
}
}
std::vector<double> predict(int env_num, std::vector<float> &input_tensor_values) {
- std::vector<int64_t> input_node_dims {env_num, 4, 300};
- size_t input_tensor_size;
- input_tensor_size = env_num * 4 * 300;
+ std::vector<int64_t> input_node_dims {env_num, 4, 300};
+ size_t input_tensor_size;
+ input_tensor_size = env_num * 4 * 300;
// create input tensor object from data values
auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
@@ -307,7 +307,7 @@ std::vector<double> predict(int env_num, std::vector<float> &input_tensor_values
double zero = float_arr[2*i];
double one = float_arr[2*i+1];
//softmax
- double score = std::exp(one)/ (std::exp(zero) + std::exp(one));
+ double score = std::exp(one)/ (std::exp(zero) + std::exp(one));
pred_results.push_back(score);
}
return pred_results;
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