--- /dev/null
+/**
+ * Copyright (c) 2021 Samsung Electronics Co., Ltd All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+#include <iostream>
+#include <glib.h>
+#include <glib/gprintf.h>
+#include <argp.h>
+#include <string.h>
+#include <tuple>
+#include <map>
+#include <fcntl.h>
+#include <unistd.h>
+#include <queue>
+#include <algorithm>
+
+#include <ctype.h>
+#include <stdlib.h>
+#include <stdio.h>
+
+#include "inference_engine_private_type.h"
+#include "inference_engine_type.h"
+#include "inference_engine_error.h"
+#include "inference_engine_common_impl.h"
+#include "inference_engine_test_common.h"
+
+extern "C"
+{
+#include <dlog.h>
+
+#ifdef LOG_TAG
+#undef LOG_TAG
+#endif
+
+#define LOG_TAG "INFERENCE_ENGINE_CLTUNER"
+}
+#define ARRAY_SIZE(x) (sizeof((x)) / sizeof((x)[0]))
+using namespace InferenceEngineInterface::Common;
+
+static gboolean process();
+GMainLoop *g_loop;
+inference_engine_cltuner cltuner;
+inference_engine_tensor_info input_tensor_info;
+inference_engine_tensor_info output_tensor_info;
+int target_device;
+size_t height;
+size_t width;
+size_t ch;
+int tensor_type;
+int tensor_shape;
+std::vector<std::string> image_paths;
+std::vector<std::string> input_layers;
+std::vector<std::string> output_layers;
+std::vector<std::string> model_paths;
+std::vector<std::string> models;
+inference_engine_layer_property input_property;
+inference_engine_layer_property output_property;
+
+int menu_idx=0;
+
+static void show_menu(const char *title, int idx){
+ g_print("*********************************************\n");
+ g_print("* %38s *\n", title);
+ g_print("*-------------------------------------------*\n");
+ if (idx == 0) {
+ g_print("* %2i. %34s *\n", 0, "INFERENCE_ENGINE_CLTUNER_READ");
+ g_print("* %2i. %34s *\n", 1, "INFERENCE_ENGINE_CLTUNER_EXHAUSTIVE");
+ g_print("* %2i. %34s *\n", 2, "INFERENCE_ENGINE_CLTUNER_NORMAL");
+ g_print("* %2i. %34s *\n", 3, "INFERENCE_ENGINE_CLTUNER_RAPID");
+ g_print("* %2c. %34s *\n", 'q', "Exit");
+ } else if (idx == 1) {
+ g_print("* %2i. %34s *\n", 1, "ic_tflite_model.tflite");
+ g_print("* %2i. %34s *\n", 2, "ic_tflite_q_model.tflite");
+ g_print("* %2i. %34s *\n", 3, "od_tflite_model.tflite");
+ g_print("* %2i. %34s *\n", 4, "fd_tflite_model1.tflite");
+ g_print("* %2i. %34s *\n", 5, "ped_tflite_model.tflite");
+ g_print("* %2i. %34s *\n", 6, "posenet1_lite_224.tflite");
+ g_print("* %2i. %34s *\n", 7, "posenet2_lite_224.tflite");
+ g_print("* %2c. %34s *\n", 'q', "Exit");
+ }
+ g_print("*********************************************\n\n");
+}
+
+static gboolean __interpret(char *cmd)
+{
+ g_strsplit(cmd, " ", 0);
+ if(strncmp(cmd, "", 1) != 0) {
+ if (strncmp(cmd, "q", 1) == 0) {
+ g_main_loop_quit(g_loop);
+ } else {
+ if (menu_idx == 0) {
+ cltuner.tuning_mode = (inference_engine_cltuner_mode_e)atoi(cmd);
+ g_print("tuning_mode is %d\n",cltuner.tuning_mode);
+ menu_idx = 1;
+ show_menu("Select Model", menu_idx);
+ } else if (menu_idx == 1) {
+ int idx = atoi(cmd);
+ switch (idx) {
+ case 1 :
+ g_print("ic_tflite_model.tflite is selected\n");
+ height=224;
+ width=224;
+ ch=3;
+ tensor_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
+ tensor_shape = INFERENCE_TENSOR_SHAPE_NCHW;
+ image_paths.push_back("/opt/usr/images/image_classification.bin");
+ input_layers.push_back("input_2");
+ output_layers.push_back("dense_3/Softmax");
+ model_paths.push_back("/usr/share/capi-media-vision/models/IC/tflite/ic_tflite_model.tflite");
+ break;
+ case 2 :
+ g_print("ic_tflite_q_model.tflite is selected\n");
+ height=224;
+ width=224;
+ ch=3;
+ tensor_type = INFERENCE_TENSOR_DATA_TYPE_UINT8;
+ tensor_shape = INFERENCE_TENSOR_SHAPE_NCHW;
+ image_paths.push_back("/opt/usr/images/image_classification_q.bin");
+ input_layers.push_back("input");
+ output_layers.push_back("MobilenetV1/Predictions/Reshape_1");
+ model_paths.push_back("/usr/share/capi-media-vision/models/IC_Q/tflite/ic_tflite_q_model.tflite");
+ break;
+ case 3 :
+ g_print("od_tflite_model.tflite is selected\n");
+ height=300;
+ width=300;
+ ch=3;
+ tensor_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
+ tensor_shape = INFERENCE_TENSOR_SHAPE_NCHW;
+ image_paths.push_back("/opt/usr/images/object_detection.bin");
+ input_layers.push_back("normalized_input_image_tensor");
+ output_layers.push_back("TFLite_Detection_PostProcess");
+ output_layers.push_back("TFLite_Detection_PostProcess:1");
+ output_layers.push_back("TFLite_Detection_PostProcess:2");
+ output_layers.push_back("TFLite_Detection_PostProcess:3");
+ model_paths.push_back("/usr/share/capi-media-vision/models/OD/tflite/od_tflite_model.tflite");
+ break;
+ case 4 :
+ g_print("fd_tflite_model1.tflite is selected\n");
+ height=300;
+ width=300;
+ ch=3;
+ tensor_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
+ tensor_shape = INFERENCE_TENSOR_SHAPE_NCHW;
+ image_paths.push_back("/opt/usr/images/face_detection.bin");
+ input_layers.push_back("normalized_input_image_tensor");
+ output_layers.push_back("TFLite_Detection_PostProcess");
+ output_layers.push_back("TFLite_Detection_PostProcess:1");
+ output_layers.push_back("TFLite_Detection_PostProcess:2");
+ output_layers.push_back("TFLite_Detection_PostProcess:3");
+ model_paths.push_back("/usr/share/capi-media-vision/models/FD/tflite/fd_tflite_model1.tflite");
+ break;
+ case 5 :
+ g_print("ped_tflite_model.tflite is selected\n");
+ height=192;
+ width=192;
+ ch=3;
+ tensor_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
+ tensor_shape = INFERENCE_TENSOR_SHAPE_NCHW;
+ image_paths.push_back("/opt/usr/images/pose_estimation.bin");
+ input_layers.push_back("image");
+ output_layers.push_back("Convolutional_Pose_Machine/stage_5_out");
+ model_paths.push_back("/usr/share/capi-media-vision/models/PE/tflite/ped_tflite_model.tflite");
+ break;
+ case 6 :
+ g_print("posenet1_lite_224.tflite is selected\n");
+ height=224;
+ width=224;
+ ch=3;
+ tensor_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
+ tensor_shape = INFERENCE_TENSOR_SHAPE_NCHW;
+ image_paths.push_back("/opt/usr/images/hand.bin");
+ input_layers.push_back("input");
+ output_layers.push_back("mobilenetv2/boundingbox");
+ output_layers.push_back("mobilenetv2/heatmap");
+ model_paths.push_back("/usr/share/capi-media-vision/models/PE_1/tflite/posenet1_lite_224.tflite");
+ break;
+ case 7 :
+ g_print("posenet2_lite_224.tflite is selected\n");
+ height=56;
+ width=56;
+ ch=21;
+ tensor_type = INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
+ tensor_shape = INFERENCE_TENSOR_SHAPE_NCHW;
+ image_paths.push_back("/opt/usr/images/hand.bin");
+ input_layers.push_back("input");
+ output_layers.push_back("mobilenetv2/coord_refine");
+ output_layers.push_back("mobilenetv2/gesture");
+ model_paths.push_back("/usr/share/capi-media-vision/models/PE_2/tflite/posenet2_lite_224.tflite");
+ break;
+ default :
+ break;
+ }
+
+ inference_engine_tensor_info _input_tensor_info = {
+ { 1, ch, height, width },
+ (inference_tensor_shape_type_e)tensor_shape,
+ static_cast<inference_tensor_data_type_e>(tensor_type),
+ static_cast<size_t>(1 * ch * height * width)
+ };
+
+ inference_engine_tensor_info _output_tensor_info = {
+ std::vector<size_t>{1},
+ (inference_tensor_shape_type_e)tensor_shape,
+ (inference_tensor_data_type_e)tensor_type,
+ 1
+ };
+ input_tensor_info = _input_tensor_info;
+ output_tensor_info = _output_tensor_info;
+ if (!process()) {
+ g_print("Error is occurred while doing process.\n ");
+ return FALSE;
+ }
+
+ } else {
+ g_print("unknown menu_idx [%d]", menu_idx);
+ }
+ }
+ } else {
+ g_print("Please input command.\n");
+ }
+ return TRUE;
+}
+
+void CheckResult(){
+ std::string tune_file = model_paths[0];
+ tune_file.append(".tune");
+ int fd = open(tune_file.c_str(), O_RDONLY);
+ if (fd == -1) {
+ g_print("tune file generation failed!!\n");
+ return;
+ }
+ off_t fsize;
+ fsize = lseek(fd, 0, SEEK_END);
+ g_print("************TUNE FILE GENERATED**************\n");
+ g_print("Location \n[%s] \nSize \n[%lld]\n", tune_file.c_str(), fsize);
+ g_print("*-------------------------------------------*\n\n\n");
+ close(fd);
+
+ show_menu("Select Tuning Mode", menu_idx);
+}
+static gboolean __input(GIOChannel *channel,
+ GIOCondition cond,
+ gpointer data)
+{
+ char buf[200];
+ gsize read;
+ GError *error = NULL;
+ if (data != nullptr) {
+ g_print("data: %p \n",data);
+ return FALSE;
+ }
+ if (cond == G_IO_ERR) {
+ g_print("G_IO_ERR is occurred. \n");
+ return FALSE;
+ }
+
+ g_io_channel_read_chars(channel, buf, 200, &read, &error);
+ buf[read] = '\0';
+ g_strstrip(buf);
+ if (!__interpret(buf)) {
+ g_print("Error is occurred while doing __interpret.\n ");
+ return FALSE;
+ }
+ return TRUE;
+}
+
+static gboolean process(){
+ InferenceEngineCommon *mBackend;
+ inference_engine_config config = {
+ .backend_name = "armnn",
+ .backend_type = INFERENCE_BACKEND_ARMNN,
+ .target_devices = INFERENCE_TARGET_GPU
+ };
+
+ int ret = 0;
+ mBackend = new InferenceEngineCommon();
+ ret = mBackend->LoadConfigFile();
+ if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+ LOGE("LoadConfigFile(); failed");
+ return FALSE;
+ }
+ ret = mBackend->BindBackend(&config);
+ if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+ LOGE("BindBackend failed");
+ return FALSE;
+ }
+ inference_engine_capacity capacity;
+ ret = mBackend->GetBackendCapacity(&capacity);
+ if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+ LOGE("GetBackendCapacity failed");
+ return FALSE;
+ }
+
+ mBackend->SetCLTuner(&cltuner);
+
+ ret = mBackend->SetTargetDevices(config.target_devices);
+ if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+ LOGE("SetTargetDevices failed");
+ return FALSE;
+ }
+
+ int model_type = GetModelInfo(model_paths, models);
+ if (model_type <= INFERENCE_MODEL_NONE) {
+ LOGE("GetModelInfo failed");
+ return FALSE;
+ }
+
+ for (auto& input : input_layers) {
+ input_property.layers.insert(std::make_pair(input, input_tensor_info));
+ }
+
+ ret = mBackend->SetInputLayerProperty(input_property);
+ if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+ LOGE("SetInputLayerProperty failed");
+ return FALSE;
+ }
+
+
+ for (auto& layer : output_layers) {
+ output_property.layers.insert(std::make_pair(layer, output_tensor_info));
+ }
+
+ ret = mBackend->SetOutputLayerProperty(output_property);
+ if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+ LOGE("SetOutputLayerProperty failed");
+ return FALSE;
+ }
+
+ ret = mBackend->Load(models, (inference_model_format_e) model_type);
+ if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+ LOGE("Load failed");
+ return FALSE;
+ }
+
+ IETensorBuffer inputs, outputs;
+ ret = PrepareTensorBuffers(mBackend, inputs, outputs);
+ if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+ LOGE("PrepareTensorBuffers failed");
+ return FALSE;
+ }
+ int imageIndex = 0;
+ for (auto& input : inputs) {
+ CopyFileToMemory(image_paths[imageIndex++].c_str(), input.second, input.second.size);
+ }
+
+ ret = mBackend->Run(inputs, outputs);
+ if (ret != INFERENCE_ENGINE_ERROR_NONE) {
+ LOGE("Run failed");
+ return FALSE;
+ }
+
+ CleanupTensorBuffers(inputs, outputs);
+ CheckResult();
+ mBackend->UnbindBackend();
+ models.clear();
+
+ return TRUE;
+}
+
+int main ()
+{
+ int ret = 0;
+ GIOChannel *stdin_channel;
+ stdin_channel = g_io_channel_unix_new(0);
+ g_io_channel_set_flags(stdin_channel, G_IO_FLAG_NONBLOCK, NULL);
+ g_io_add_watch(stdin_channel, G_IO_IN, (GIOFunc)__input, NULL);
+
+ cltuner.active = true;
+ cltuner.update = true;
+
+
+ show_menu("Select Tuning Mode", menu_idx);
+ g_loop = g_main_loop_new(NULL, FALSE);
+ g_main_loop_run(g_loop);
+
+ return ret;
+}
projects / platform / core / multimedia / inference-engine-interface.git / commitdiff