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Extended example to use logistic regression code

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This commit is contained in:
Alejandro Saucedo 2020-10-04 16:22:19 +01:00
parent 7715db8993
commit a04dabc22c
5 changed files with 234 additions and 40 deletions
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43
examples/android/android-simple/app/src/main/cpp/KomputeJniNative.cpp
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@ -16,10 +16,6 @@
#define RELEASE 1
#include <android/log.h>
//#include <android_native_app_glue.h>
//#include <cassert>
//#include <memory>
//#include <vector>
#include <unistd.h>
#include <string.h>
@ -27,19 +23,12 @@
#include "kompute/Kompute.hpp"
#include "KomputeModelML.hpp"
#ifndef KOMPUTE_VK_INIT_RETRIES
#define KOMPUTE_VK_INIT_RETRIES 5
#endif
// Android log function wrappers
static const char* kTAG = "KomputeJni";
#define LOGI(...) \
((void)__android_log_print(ANDROID_LOG_INFO, kTAG, __VA_ARGS__))
#define LOGW(...) \
((void)__android_log_print(ANDROID_LOG_WARN, kTAG, __VA_ARGS__))
#define LOGE(...) \
((void)__android_log_print(ANDROID_LOG_ERROR, kTAG, __VA_ARGS__))
static std::vector<float> jfloatArrayToVector(JNIEnv *env, const jfloatArray & fromArray) {
float *inCArray = env->GetFloatArrayElements(fromArray, NULL);
if (NULL == inCArray) return std::vector<float>();
@ -61,12 +50,12 @@ extern "C" {
JNIEXPORT jboolean JNICALL
Java_com_ethicalml_kompute_KomputeJni_initVulkan(JNIEnv *env, jobject thiz) {
LOGI("Initialising vulkan");
SPDLOG_INFO("Initialising vulkan");
uint32_t totalRetries = 0;
while (totalRetries < KOMPUTE_VK_INIT_RETRIES) {
LOGI("VULKAN LOAD TRY NUMBER: %u", totalRetries);
SPDLOG_INFO("VULKAN LOAD TRY NUMBER: %u", totalRetries);
if(InitVulkan()) {
break;
}
@ -86,31 +75,17 @@ Java_com_ethicalml_kompute_KomputeJni_kompute(
jfloatArray xjJFloatArr,
jfloatArray yJFloatArr) {
LOGI("Creating manager");
SPDLOG_INFO("Creating manager");
std::vector<float> xiVector = jfloatArrayToVector(env, xiJFloatArr);
std::vector<float> xjVector = jfloatArrayToVector(env, xjJFloatArr);
std::vector<float> yVector = jfloatArrayToVector(env, yJFloatArr);
kp::Manager mgr;
KomputeModelML kml;
kml.train(yVector, xiVector, xjVector);
auto tensorA = mgr.buildTensor(xiVector);
auto tensorB = mgr.buildTensor(xjVector);
auto tensorC = mgr.buildTensor(yVector);
std::vector<float> pred = kml.predict(xiVector, xjVector);
LOGI("Result before:");
for(const float & i : tensorC->data()) {
LOGI("%f ", i);
}
mgr.evalOpDefault<kp::OpMult<>>({tensorA, tensorB, tensorC});
mgr.evalOpDefault<kp::OpTensorSyncLocal>({tensorC});
LOGI("Result after:");
for(const float & i : tensorC->data()) {
LOGI("%f ", i);
}
return vectorToJFloatArray(env, tensorC->data());
return vectorToJFloatArray(env, pred);
}
}