update yolov5s_timvx example to v6.0 && update EAIS-750E compile manual (#1342)

* update EAIS-750E compile manul

* update yolov5s_timvx example to v6.0 version

doc/docs_zh/source_compile/compile_timvx.md

@@ -244,32 +244,11 @@ $ cp -rf ../TIM-VX/include ./source/device/tim-vx/
 $ cp -rf ../TIM-VX/src ./source/device/tim-vx/
 ```
 
-\- 准备编译环境    编译依赖的系统库在EAIS-750E板载/usr/lib/下能搜索到，因此只需要拷贝头文件
-```bash
-$ wget -c https://github.com/VeriSilicon/TIM-VX/releases/download/v1.1.28/aarch64_A311D_D312513_A294074_R311680_T312233_O312045.tgz
-$ tar zxvf aarch64_A311D_D312513_A294074_R311680_T312233_O312045.tgz
-$ mv aarch64_A311D_D312513_A294074_R311680_T312233_O312045 prebuild-sdk-a311d
-$ cd <tengine-lite-root-dir>
-$ mkdir -p ./3rdparty/tim-vx/include
-$ mkdir -p ./3rdparty/tim-vx/lib/aarch64
-$ cp -rf ../prebuild-sdk-a311d/include/*  ./3rdparty/tim-vx/include/
-```
-
-\- 如遇到cmake版本不够，更新板子上的cmake版本
-```bash
-$ sudo apt-get autoremove cmake
-$ wget https://cmake.org/files/v3.22/cmake-3.22.0-linux-aarch64.tar.gz
-$ tar -xzvf cmake-3.22.0-linux-aarch64.tar.gz
-$ sudo mv cmake-3.22.0-linux-aarch64 /opt/cmake-3.22
-$ ln -sf /opt/cmake-3.22/bin/*  /usr/bin/
-$ cmake --version
-```
-
 \- 编译Tengine
 ```bash
 $ cd <tengine-lite-root-dir>
 $ mkdir build && cd build
-$ cmake -DTENGINE_ENABLE_TIM_VX=ON ..
+$ cmake -DTENGINE_ENABLE_TIM_VX=ON -DTENGINE_ENABLE_MODEL_CACHE=ON ..
 $ make -j`nproc` && make install
 ```
 

examples/tm_yolov5s_timvx.cpp

@@ -37,6 +37,8 @@
 #include "tengine/c_api.h"
 #include "tengine_operations.h"
 
+#define YOLOV5_VERSION6 0
+
 struct Object
 {
     cv::Rect_<float> rect;
@@ -341,6 +343,69 @@ void get_input_data_focus_uint8(const char* image_file, uint8_t* input_data, int
     }
 }
 
+void get_input_data_nofocus_uint8(const char* image_file, uint8_t* input_data, int letterbox_rows, int letterbox_cols, const float* mean,
+                                const float* scale, float input_scale, int zero_point)
+{
+    cv::Mat sample = cv::imread(image_file, 1);
+    cv::Mat img;
+
+    if (sample.channels() == 1)
+        cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
+    else
+        cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);
+
+    /* letterbox process to support different letterbox size */
+    float scale_letterbox;
+    int resize_rows;
+    int resize_cols;
+    if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols))
+    {
+        scale_letterbox = letterbox_rows * 1.0 / img.rows;
+    }
+    else
+    {
+        scale_letterbox = letterbox_cols * 1.0 / img.cols;
+    }
+    resize_cols = int(scale_letterbox * img.cols);
+    resize_rows = int(scale_letterbox * img.rows);
+
+    cv::resize(img, img, cv::Size(resize_cols, resize_rows));
+    img.convertTo(img, CV_32FC3);
+    // Generate a gray image for letterbox using opencv
+    cv::Mat img_new(letterbox_cols, letterbox_rows, CV_32FC3, cv::Scalar(0.5 / scale[0] + mean[0], 0.5 / scale[1] + mean[1], 0.5 / scale[2] + mean[2]));
+    int top = (letterbox_rows - resize_rows) / 2;
+    int bot = (letterbox_rows - resize_rows + 1) / 2;
+    int left = (letterbox_cols - resize_cols) / 2;
+    int right = (letterbox_cols - resize_cols + 1) / 2;
+    // Letterbox filling
+    cv::copyMakeBorder(img, img_new, top, bot, left, right, cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
+
+    img_new.convertTo(img_new, CV_32FC3);
+    float* img_data = (float*)img_new.data;
+
+    /* nhwc to nchw */
+    for (int h = 0; h < letterbox_rows; h++)
+    {
+        for (int w = 0; w < letterbox_cols; w++)
+        {
+            for (int c = 0; c < 3; c++)
+            {
+                int in_index = h * letterbox_cols * 3 + w * 3 + c;
+                int out_index = c * letterbox_rows * letterbox_cols + h * letterbox_cols + w;
+                int udata = (round)((img_data[in_index] - mean[c]) * scale[c] / input_scale + (float)zero_point);
+
+                if (udata > 255)
+                    udata = 255;
+                else if (udata < 0)
+                    udata = 0;
+
+                input_data[out_index] = udata;
+            }
+        }
+    }
+
+}
+
 int main(int argc, char* argv[])
 {
     const char* model_file = nullptr;
@@ -439,7 +504,11 @@ int main(int argc, char* argv[])
     }
 
     int img_size = letterbox_rows * letterbox_cols * img_c;
+#if YOLOV5_VERSION6
+    int dims[] = {1, 3, letterbox_rows, letterbox_cols};
+#else
     int dims[] = {1, 12, int(letterbox_rows / 2), int(letterbox_cols / 2)};
+#endif
     std::vector<uint8_t> input_data(img_size);
 
     tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
@@ -472,7 +541,13 @@ int main(int argc, char* argv[])
     float input_scale = 0.f;
     int input_zero_point = 0;
     get_tensor_quant_param(input_tensor, &input_scale, &input_zero_point, 1);
-    get_input_data_focus_uint8(image_file, input_data.data(), letterbox_rows, letterbox_cols, mean, scale, input_scale, input_zero_point);
+
+#if YOLOV5_VERSION6
+    get_input_data_nofocus_uint8(image_file, input_data.data(), letterbox_rows, letterbox_cols, mean, scale, input_scale, input_zero_point);
+#else
+    get_input_data_focus_uint8(image_file, input_data.data(), letterbox_rows, letterbox_cols, mean, scale,input_scale, input_zero_point);
+#endif
+
 
     /* run graph */
     double min_time = DBL_MAX;