adding a mobilenet_ssd_uint8 example application on timvx (#1270)

* fix missing 'fprintf' 'stderr' problem

* fix missing <cstdlib> header problem.

* updated en doc to compile for NXP i.MX 8M Plus linux.

* adding timvx version mobilenet ssd

* updated makefile
Co-authored-by: NWang Feng <feng.wang1@hexagon.com>

examples/CMakeLists.txt

@@ -93,6 +93,7 @@ IF(TENGINE_ENABLE_TIM_VX)
     TENGINE_EXAMPLE (tm_classification_timvx     tm_classification_timvx.c)
     TENGINE_EXAMPLE (tm_landmark_timvx           tm_landmark_timvx.cpp)
     TENGINE_EXAMPLE (tm_retinaface_timvx         tm_retinaface_timvx.cpp)
+    TENGINE_EXAMPLE (tm_mobilenet_ssd_uint8_timvx      tm_mobilenet_ssd_uint8_timvx.cpp)
 ENDIF()
 
 IF(TENGINE_ENABLE_VULKAN)

examples/tm_mobilenet_ssd_uint8_timvx.cpp

+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*
+ * Copyright (c) 2020, OPEN AI LAB
+ * Author: qtang@openailab.com
+ */
+
+#include <iostream>
+#include <string>
+#include <vector>
+#include "tengine_operations.h"
+#include "tengine/c_api.h"
+#include "common.h"
+
+#define DEFAULT_REPEAT_COUNT 1
+#define DEFAULT_THREAD_COUNT 1
+
+typedef struct Box
+{
+    int x0;
+    int y0;
+    int x1;
+    int y1;
+    int class_idx;
+    float score;
+} Box_t;
+
+void get_input_uint_data_ssd(const char* image_file, uint8_t* input_data, int img_h, int img_w, float input_scale,
+                             int zero_point)
+{
+    float mean[3] = {127.5f, 127.5f, 127.5f};
+    float scales[3] = {1 / 127.5f, 1 / 127.5f, 1 / 127.5f};
+    image img = imread_process(image_file, img_w, img_h, mean, scales);
+
+    float* image_data = (float*)img.data;
+
+    for (int i = 0; i < img_w * img_h * 3; i++)
+    {
+        int udata = round(image_data[i] / input_scale + zero_point);
+        if (udata > 255)
+            udata = 255;
+        else if (udata < 0)
+            udata = 0;
+
+        input_data[i] = udata;
+    }
+
+    free_image(img);
+}
+
+void post_process_ssd(const char* image_file, float threshold, float* outdata, int num)
+{
+    const char* class_names[] = {"background", "aeroplane", "bicycle", "bird", "boat", "bottle",
+                                 "bus", "car", "cat", "chair", "cow", "diningtable",
+                                 "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
+                                 "sofa", "train", "tvmonitor"};
+
+    image im = imread(image_file);
+
+    int raw_h = im.h;
+    int raw_w = im.w;
+    std::vector<Box> boxes;
+    printf("detect result num: %d \n", num);
+    for (int i = 0; i < num; i++)
+    {
+        if (outdata[1] >= threshold)
+        {
+            Box box;
+            box.class_idx = round(outdata[0]);
+            box.score = outdata[1];
+            box.x0 = outdata[2] * raw_w;
+            box.y0 = outdata[3] * raw_h;
+            box.x1 = outdata[4] * raw_w;
+            box.y1 = outdata[5] * raw_h;
+            boxes.push_back(box);
+            printf("%s\t:%.2f\n", class_names[box.class_idx], box.score * 100.f);
+            printf("BOX:( %d , %d ),( %d , %d )\n", (int)box.x0, (int)box.y0, (int)box.x1, (int)box.y1);
+        }
+        outdata += 6;
+    }
+    for (int i = 0; i < (int)boxes.size(); i++)
+    {
+        Box box = boxes[i];
+        draw_box(im, box.x0, box.y0, box.x1, box.y1, 2, 125, 0, 125);
+    }
+
+    save_image(im, "mobilenet_ssd_uint8_timvx_out");
+    free_image(im);
+    std::cout << "======================================\n";
+    std::cout << "[DETECTED IMAGE SAVED]:\t"
+              << "Mobilenet_SSD"
+              << "\n";
+    std::cout << "======================================\n";
+}
+
+void show_usage()
+{
+    fprintf(stderr, "[Usage]:  [-h]\n    [-m model_file] [-i image_file] [-r repeat_count] [-t thread_count]\n");
+}
+
+int main(int argc, char* argv[])
+{
+    int repeat_count = DEFAULT_REPEAT_COUNT;
+    int num_thread = DEFAULT_THREAD_COUNT;
+    char* model_file = nullptr;
+    char* image_file = nullptr;
+    int img_h = 300;
+    int img_w = 300;
+    float mean[3] = {127.5f, 127.5f, 127.5f};
+    float scale[3] = {0.007843f, 0.007843f, 0.007843f};
+    float show_threshold = 0.5f;
+    int ret;
+    while ((ret = getopt(argc, argv, "m:i:r:t:h:")) != -1)
+    {
+        switch (ret)
+        {
+        case 'm':
+            model_file = optarg;
+            break;
+        case 'i':
+            image_file = optarg;
+            break;
+        case 'r':
+            repeat_count = atoi(optarg);
+            break;
+        case 't':
+            num_thread = atoi(optarg);
+            break;
+        case 'h':
+            show_usage();
+            return 0;
+        default:
+            break;
+        }
+    }
+
+    /* check files */
+    if (model_file == NULL)
+    {
+        fprintf(stderr, "Error: Tengine model file not specified!\n");
+        show_usage();
+        return -1;
+    }
+
+    if (image_file == NULL)
+    {
+        fprintf(stderr, "Error: Image file not specified!\n");
+        show_usage();
+        return -1;
+    }
+
+    if (!check_file_exist(model_file) || !check_file_exist(image_file))
+        return -1;
+
+    /* set runtime options */
+    struct options opt;
+    opt.num_thread = num_thread;
+    opt.cluster = TENGINE_CLUSTER_ALL;
+    opt.precision = TENGINE_MODE_UINT8;
+    opt.affinity = 0;
+
+    // init tengine
+    if (init_tengine() < 0)
+    {
+        std::cout << "init tengine failed\n";
+        return 1;
+    }
+
+    // create VeriSilicon TIM-VX backend
+    context_t timvx_context = create_context("timvx", 1);
+    if ( set_context_device(timvx_context, "TIMVX", nullptr, 0) < 0 )
+    {
+        fprintf(stderr, "add_context_device failed.\n" );
+        return 1;
+    }
+
+    // create graph
+    //graph_t graph = create_graph(nullptr, "tengine", model_file);
+    graph_t graph = create_graph(timvx_context, "tengine", model_file);
+    if (graph == nullptr)
+    {
+        std::cout << "Create graph failed\n";
+        return 1;
+    }
+
+    /* set the input shape to initial the graph, and prerun graph to infer shape */
+    int img_size = img_h * img_w * 3;
+    int dims[] = {1, 3, img_h, img_w}; // nchw
+    uint8_t* input_data = (uint8_t*)malloc(img_size * sizeof(uint8_t));
+
+    tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
+    if (input_tensor == NULL)
+    {
+        fprintf(stderr, "Get input tensor failed\n");
+        return -1;
+    }
+
+    if (set_tensor_shape(input_tensor, dims, 4) < 0)
+    {
+        fprintf(stderr, "Set input tensor shape failed\n");
+        return -1;
+    }
+
+    if (set_tensor_buffer(input_tensor, input_data, img_size * sizeof(uint8_t)) < 0)
+    {
+        fprintf(stderr, "Set input tensor buffer failed\n");
+        return -1;
+    }
+
+    /* prerun graph, set work options(num_thread, cluster, precision) */
+    if (prerun_graph_multithread(graph, opt) < 0)
+    {
+        fprintf(stderr, "Prerun graph failed\n");
+        return -1;
+    }
+
+    /* prepare process input data, set the data mem to input tensor */
+    float input_scale = 0.f;
+    int input_zero_point = 0;
+    get_tensor_quant_param(input_tensor, &input_scale, &input_zero_point, 1);
+    get_input_uint_data_ssd(image_file, input_data, img_h, img_w, input_scale, input_zero_point);
+
+    /* run graph */
+    double min_time = DBL_MAX;
+    double max_time = DBL_MIN;
+    double total_time = 0.;
+    for (int i = 0; i < repeat_count; i++)
+    {
+        double start = get_current_time();
+        if (run_graph(graph, 1) < 0)
+        {
+            fprintf(stderr, "Run graph failed\n");
+            return -1;
+        }
+        double end = get_current_time();
+        double cur = end - start;
+        total_time += cur;
+        if (min_time > cur)
+            min_time = cur;
+        if (max_time < cur)
+            max_time = cur;
+    }
+    fprintf(stderr, "Repeat %d times, thread %d, avg time %.2f ms, max_time %.2f ms, min_time %.2f ms\n", 1, 1,
+            total_time / 1, max_time, min_time);
+    fprintf(stderr, "--------------------------------------\n");
+
+    /* process the detection result */
+    tensor_t output_tensor = get_graph_output_tensor(graph, 0, 0); //"detection_out"
+    int out_dim[4];
+    get_tensor_shape(output_tensor, out_dim, 4);
+    int output_size = get_tensor_buffer_size(output_tensor);
+    uint8_t* output_u8 = (uint8_t*)get_tensor_buffer(output_tensor);
+    float* output_data = (float*)malloc(output_size * sizeof(float));
+
+    /* dequant */
+    float output_scale = 0.f;
+    int output_zero_point = 0;
+    get_tensor_quant_param(output_tensor, &output_scale, &output_zero_point, 1);
+    for (int i = 0; i < output_size; i++)
+        output_data[i] = ((float)output_u8[i] - (float)output_zero_point) * output_scale;
+
+    /* post_process_ssd */
+    post_process_ssd(image_file, show_threshold, output_data, out_dim[1]);
+
+    /* release tengine */
+    free(output_data);
+    free(input_data);
+    postrun_graph(graph);
+    destroy_graph(graph);
+    release_tengine();
+
+    return 0;
+}