S100 OpenExplorer

# #Board-side Deployment

After completing a series of conversion and compilation operations of the aforementioned model, on the development board side, we provide a Unify Compute Platform(UCP), to help you quickly complete the deployment of the model, and provide relevant samples.

For board-side deployment of the model, it can be simply divided into three steps, building a board-side run sample, board-side run preparation and board-side run after deployment.

## #Building Board-side Running Sample

The following preparations are required to run the sample phase on the board side:

1. To prepare the board-side sample with dependent libraries, we have provided the corresponding content in `samples/ucp_tutorial/deps_aarch64` in the OE package, and we recommend you to use the dependent libraries in this directory to complete this part of the preparation work quickly.

2. Prepare the board-side sample development-related files (main.cc).

Attention

The main.cc file needs to be prepared by adapting the main function and input Tensor to the model input.

3. Prepare to cross-compile to generate a board-side executable program (CMakeLists.txt), which needs to cover the configuration of compilation parameters, dependent header files and dynamic libraries for GCC.

### #Pyramid Input Model main.cc Preparation

For the Pyramid input model, we provide you with the corresponding sample under the `samples/ucp_tutorial/dnn/basic_samples/code/00_quick_start` path of the OE package.

Here we parse the main function and prepare the input and output tensor in main.cc:

`/** * Step1: get model handle * Step2: prepare input and output tensor * Step3: set input data to input tensor * Step4: run inference * Step5: do postprocess with output data * Step6: release resources */ #include <fstream> #include <iostream> #include <vector> #include <cstring> #include "hobot/dnn/hb_dnn.h" #include "hobot/hb_ucp.h" #include "hobot/hb_ucp_sys.h" #define ALIGN(value, alignment) (((value) + ((alignment)-1)) & ~((alignment)-1)) #define ALIGN_32(value) ALIGN(value, 32) const char* hbm_path = "model.hbm"; std::string data_y_path = "ILSVRC2012_val_00000001_y.bin"; std::string data_uv_path = "ILSVRC2012_val_00000001_uv.bin"; // Read binary input file int read_binary_file(std::string file_path, char **bin, int *length) { std::ifstream ifs(file_path, std::ios::in | std::ios::binary); ifs.seekg(0, std::ios::end); *length = ifs.tellg(); ifs.seekg(0, std::ios::beg); *bin = new char[sizeof(char) * (*length)]; ifs.read(*bin, *length); ifs.close(); return 0; } int prepare_tensor(hbDNNTensor *input_tensor, hbDNNTensor *output_tensor, hbDNNHandle_t dnn_handle); int main() { // Get model handle hbDNNPackedHandle_t packed_dnn_handle; hbDNNHandle_t dnn_handle; hbDNNInitializeFromFiles(&packed_dnn_handle, &hbm_path, 1); const char **model_name_list; int model_count = 0; hbDNNGetModelNameList(&model_name_list, &model_count, packed_dnn_handle); hbDNNGetModelHandle(&dnn_handle, packed_dnn_handle, model_name_list[0]); // Prepare input and output tensor std::vector<hbDNNTensor> input_tensors; std::vector<hbDNNTensor> output_tensors; int input_count = 0; int output_count = 0; hbDNNGetInputCount(&input_count, dnn_handle); hbDNNGetOutputCount(&output_count, dnn_handle); input_tensors.resize(input_count); output_tensors.resize(output_count); // Initialize and malloc the tensor prepare_tensor(input_tensors.data(), output_tensors.data(), dnn_handle); // Copy binary input data to input tensor int32_t data_length = 0; char *y_data = nullptr; read_binary_file(data_y_path, &y_data, &data_length); // Copy y_data to input tensor memcpy(reinterpret_cast<char *>(input_tensors[0].sysMem.virAddr), y_data, input_tensors[0].sysMem.memSize); free(y_data); // Refresh the cached system memory hbUCPMemFlush(&(input_tensors[0].sysMem), HB_SYS_MEM_CACHE_CLEAN); char *uv_data = nullptr; read_binary_file(data_uv_path, &uv_data, &data_length); // Copy uv_data to input tensor memcpy(reinterpret_cast<char *>(input_tensors[1].sysMem.virAddr), uv_data, input_tensors[1].sysMem.memSize); free(uv_data); // Refresh the cached system memory hbUCPMemFlush(&(input_tensors[1].sysMem), HB_SYS_MEM_CACHE_CLEAN); // Submit task and wait till it completed hbUCPTaskHandle_t task_handle{nullptr}; hbDNNTensor *output = output_tensors.data(); // Generate task handle hbDNNInferV2(&task_handle, output, input_tensors.data(), dnn_handle); // Submit task hbUCPSchedParam ctrl_param; HB_UCP_INITIALIZE_SCHED_PARAM(&ctrl_param); ctrl_param.backend = HB_UCP_BPU_CORE_ANY; hbUCPSubmitTask(task_handle, &ctrl_param); // Wait task completed hbUCPWaitTaskDone(task_handle, 0); // Parse inference result and calculate TOP1 hbUCPMemFlush(&output_tensors[0].sysMem, HB_SYS_MEM_CACHE_INVALIDATE); auto result = reinterpret_cast<float *>(output_tensors[0].sysMem.virAddr); float max_score = 0.0; int label = -1; // Find the max score and corresponding label for (auto i = 0; i < 1000; i++) { float score = result[i]; if (score > max_score) { label = i; max_score = score; } } // Output the result std::cout << "label: " << label << std::endl; hbUCPReleaseTask(task_handle); // Free input memory for (int i = 0; i < input_count; i++) { hbUCPFree(&(input_tensors[i].sysMem)); } // Free input memory for (int i = 0; i < output_count; i++) { hbUCPFree(&(output_tensors[i].sysMem)); } // Release model hbDNNRelease(packed_dnn_handle); } // Prepare input tensor and output tensor int prepare_tensor(hbDNNTensor *input_tensor, hbDNNTensor *output_tensor, hbDNNHandle_t dnn_handle) { // Get input and output tensor counts int input_count = 0; int output_count = 0; hbDNNGetInputCount(&input_count, dnn_handle); hbDNNGetOutputCount(&output_count, dnn_handle); hbDNNTensor *input = input_tensor; // Get the properties of the input tensor for (int i = 0; i < input_count; i++) { hbDNNGetInputTensorProperties(&input[i].properties, dnn_handle, i); // Calculate the stride of the input tensor auto dim_len = input[i].properties.validShape.numDimensions; for (int32_t dim_i = dim_len - 1; dim_i >= 0; --dim_i) { if (input[i].properties.stride[dim_i] == -1) { auto cur_stride = input[i].properties.stride[dim_i + 1] * input[i].properties.validShape.dimensionSize[dim_i + 1]; input[i].properties.stride[dim_i] = ALIGN_32(cur_stride); } } // Calculate the memory size of the input tensor and allocate cache memory int input_memSize = input[i].properties.stride[0] * input[i].properties.validShape.dimensionSize[0]; hbUCPMallocCached(&input[i].sysMem, input_memSize, 0); } hbDNNTensor *output = output_tensor; // Get the properties of the input tensor for (int i = 0; i < output_count; i++) { hbDNNGetOutputTensorProperties(&output[i].properties, dnn_handle, i); // Calculate the memory size of the output tensor and allocate cache memory int output_memSize = output[i].properties.alignedByteSize; hbUCPMallocCached(&output[i].sysMem, output_memSize, 0); // Show how to get output name const char *output_name; hbDNNGetOutputName(&output_name, dnn_handle, i); } return 0; }`

### #Resizer Input Model main.cc Preparation

For the Resizer input model, we provide you with the corresponding sample under the `samples/ucp_tutorial/dnn/basic_samples/code/02_advanced_samples/roi_infer` path of the OE package.

Here we parse the main function and prepare the input and output tensor in main.cc:

`/** * Step1: get model handle * Step2: setinput data to nv12 * Step3: prepare roi mem * Step4: prepare input and output tensor * Step5: run inference * Step6: do postprocess with output data for every task * Step7: release resources */ #include <fstream> #include <cstring> #include <iostream> #include <map> #include <vector> #include "hobot/dnn/hb_dnn.h" #include "hobot/hb_ucp.h" #include "hobot/hb_ucp_sys.h" const char *model_file = "model.hbm"; std::string data_y_path = "ILSVRC2012_val_00000001_y.bin"; std::string data_uv_path = "ILSVRC2012_val_00000001_uv.bin"; typedef struct Roi { int32_t left; int32_t top; int32_t right; int32_t bottom; } Roi; int read_image_2_nv12(std::string &y_path, std::string &uv_path, std::vector<hbUCPSysMem> &image_mem, int &input_h, int &input_w); int prepare_roi_mem(const std::vector<Roi> &rois, std::vector<hbUCPSysMem> &roi_mem); int prepare_image_tensor(const std::vector<hbUCPSysMem> &image_mem, int input_h, int input_w, hbDNNHandle_t dnn_handle, std::vector<hbDNNTensor> &input_tensor); int read_binary_file(std::string file_path, char **bin, int *length); /** * prepare roi tensor * @param[in] roi_mem: roi mem info * @param[in] dnn_handle: dnn handle * @param[in] roi_tensor_id: tensor id of roi input in model * @param[out] roi_tensor: roi tensor */ int prepare_roi_tensor(const hbUCPSysMem *roi_mem, hbDNNHandle_t dnn_handle, int32_t roi_tensor_id, hbDNNTensor *roi_tensor); /** * prepare out tensor * @param[in] dnn_handle: dnn handle * @param[out] output: output tensor */ int prepare_output_tensor(hbDNNHandle_t dnn_handle, std::vector<hbDNNTensor> &output); int main(int argc, char **argv) { // load model hbDNNPackedHandle_t packed_dnn_handle; hbDNNHandle_t dnn_handle; const char **model_name_list; int model_count = 0; // Step1: get model handle hbDNNInitializeFromFiles(&packed_dnn_handle, &model_file, 1); hbDNNGetModelNameList(&model_name_list, &model_count, packed_dnn_handle); hbDNNGetModelHandle(&dnn_handle, packed_dnn_handle, model_name_list[0]); // Step2: set input data to nv12 // In the sample, since the input is a same image, can allocate a memory for // reusing. image_mems is to save image data for y and uv. std::vector<hbUCPSysMem> image_mems(2); // image input size int input_h = 224; int input_w = 224; read_image_2_nv12(data_y_path, data_uv_path, image_mems, input_h, input_w); // Step3: prepare roi mem /** * Suppose to infer 2 roi tasks of data, the number of ROIs to be prepared is * also 2. */ // left = 0, top = 0 right = 223, bottom = 223 Roi roi_1 = {0, 0, 223, 223}; // left = 1, top = 1, right = 223, bottom = 223 Roi roi_2 = {1, 1, 223, 223}; std::vector<Roi> rois; rois.push_back(roi_1); rois.push_back(roi_2); int roi_num = 2; std::vector<hbUCPSysMem> roi_mems(2); prepare_roi_mem(rois, roi_mems); // Step4: prepare input and output tensor std::vector<std::vector<hbDNNTensor>> input_tensors(roi_num); std::vector<std::vector<hbDNNTensor>> output_tensors(roi_num); for (int i = 0; i < roi_num; ++i) { // prepare input tensor int input_count = 0; hbDNNGetInputCount(&input_count, dnn_handle); input_tensors[i].resize(input_count); // prepare image tensor /** Tips: * In the sample, all tasks use the same image, so allocate memory to * save image. all input tensor can reuse the memory. if your model has * different input image, please allocate different memory for all inputs. * */ prepare_image_tensor(image_mems, input_h, input_w, dnn_handle, input_tensors[i]); auto roi_tensor_id = 2; prepare_roi_tensor(&roi_mems[i], dnn_handle, roi_tensor_id, &input_tensors[i][roi_tensor_id]); // prepare output tensor int output_count = 0; hbDNNGetOutputCount(&output_count, dnn_handle); output_tensors[i].resize(output_count); prepare_output_tensor(dnn_handle, output_tensors[i]); } // Step5: run inference hbUCPTaskHandle_t task_handle{nullptr}; /** Tips: * In the sample, submit multiple tasks at the same time * when taskHandle is nullptr, here create a new task，and * when taskHandle is created but not submitted yet, attach new task to the previous which represents multi model task * */ for (int i = 0; i < roi_num; ++i) { hbDNNInferV2(&task_handle, output_tensors[i].data(), input_tensors[i].data(), dnn_handle); } // submit multi tasks hbUCPSchedParam infer_ctrl_param; HB_UCP_INITIALIZE_SCHED_PARAM(&infer_ctrl_param); hbUCPSubmitTask(task_handle, &infer_ctrl_param); // wait task done hbUCPWaitTaskDone(task_handle, 0); // Step6: do postprocess with output data for every task // Find the max score and corresponding label for (auto roi_idx = 0; roi_idx < roi_num; roi_idx++) { auto result = reinterpret_cast<float *>(output_tensors[roi_idx][0].sysMem.virAddr); float max_score = 0.0; int label = -1; for (auto i = 0; i < 1000; i++) { float score = result[i]; if (score > max_score) { label = i; max_score = score; } } std::cout << "label: " << label << std::endl; } // Step7: release resources // release task handle hbUCPReleaseTask(task_handle); // free input mem for (auto &mem : image_mems) { hbUCPFree(&mem); } for (auto &mem : roi_mems) { hbUCPFree(&mem); } // free output mem for (auto &tensors : output_tensors) { for (auto &tensor : tensors) { hbUCPFree(&(tensor.sysMem)); } } // release model hbDNNRelease(packed_dnn_handle); return 0; } #define ALIGN(value, alignment) (((value) + ((alignment)-1)) & ~((alignment)-1)) #define ALIGN_32(value) ALIGN(value, 32) int prepare_image_tensor(const std::vector<hbUCPSysMem> &image_mem, int input_h, int input_w, hbDNNHandle_t dnn_handle, std::vector<hbDNNTensor> &input_tensor) { // y and uv tensor for (int i = 0; i < 2; i++) { hbDNNGetInputTensorProperties(&input_tensor[i].properties, dnn_handle, i); input_tensor[i].sysMem = image_mem[i]; /** Tips: * roi model should modify input valid shape to input image shape. * here the struct of y/uv shape is NHWC * */ input_tensor[i].properties.validShape.dimensionSize[1] = input_h; input_tensor[i].properties.validShape.dimensionSize[2] = input_w; if (i == 1) { // uv input input_tensor[i].properties.validShape.dimensionSize[1] /= 2; input_tensor[i].properties.validShape.dimensionSize[2] /= 2; } /** Tips: * For input tensor, stride should be set according to real padding * of the user's data. And 32 bytes alignment is the requirement of y/uv **/ input_tensor[i].properties.stride[1] = ALIGN_32(input_tensor[i].properties.stride[2] * input_tensor[i].properties.validShape.dimensionSize[2]); input_tensor[i].properties.stride[0] = input_tensor[i].properties.stride[1] * input_tensor[i].properties.validShape.dimensionSize[1]; } return 0; } int prepare_roi_tensor(const hbUCPSysMem *roi_mem, hbDNNHandle_t dnn_handle, int32_t roi_tensor_id, hbDNNTensor *roi_tensor) { hbDNNGetInputTensorProperties(&roi_tensor->properties, dnn_handle, roi_tensor_id); roi_tensor->sysMem = *roi_mem; return 0; } int prepare_output_tensor(hbDNNHandle_t dnn_handle, std::vector<hbDNNTensor> &output) { for (size_t i = 0; i < output.size(); i++) { hbDNNGetOutputTensorProperties(&output[i].properties, dnn_handle, i); hbUCPMallocCached(&output[i].sysMem, output[i].properties.alignedByteSize, 0); } return 0; } int read_binary_file(std::string file_path, char **bin, int *length) { std::ifstream ifs(file_path, std::ios::in | std::ios::binary); ifs.seekg(0, std::ios::end); *length = ifs.tellg(); ifs.seekg(0, std::ios::beg); *bin = new char[sizeof(char) * (*length)]; ifs.read(*bin, *length); ifs.close(); return 0; } /** You can define read_image_2_other_type to prepare your data **/ int read_image_2_nv12(std::string &y_path, std::string &uv_path, std::vector<hbUCPSysMem> &image_mem, int &input_h, int &input_w) { // copy y data auto w_stride = ALIGN_32(input_w); int32_t y_mem_size = input_h * w_stride; hbUCPMallocCached(&image_mem[0], y_mem_size, 0); uint8_t *y_data_dst = reinterpret_cast<uint8_t *>(image_mem[0].virAddr); int32_t y_data_length = 0; char *y_data = nullptr; read_binary_file(y_path, &y_data, &y_data_length); memcpy(reinterpret_cast<char *>(image_mem[0].virAddr), y_data, y_mem_size); // copy uv data int32_t uv_height = input_h / 2; int32_t uv_width = input_w / 2; int32_t uv_mem_size = uv_height * w_stride; hbUCPMallocCached(&image_mem[1], uv_mem_size, 0); int32_t uv_data_length = 0; char *uv_data = nullptr; read_binary_file(uv_path, &uv_data, &uv_data_length); memcpy(reinterpret_cast<char *>(image_mem[1].virAddr), uv_data, uv_mem_size); // make sure cahced mem data is flushed to DDR before inference hbUCPMemFlush(&image_mem[0], HB_SYS_MEM_CACHE_CLEAN); hbUCPMemFlush(&image_mem[1], HB_SYS_MEM_CACHE_CLEAN); free(y_data); free(uv_data); return 0; } int prepare_roi_mem(const std::vector<Roi> &rois, std::vector<hbUCPSysMem> &roi_mem) { auto roi_size = rois.size(); roi_mem.resize(roi_size); for (auto i = 0; i < roi_size; ++i) { int32_t mem_size = 4 * sizeof(int32_t); hbUCPMallocCached(&roi_mem[i], mem_size, 0); int32_t *roi_data = reinterpret_cast<int32_t *>(roi_mem[i].virAddr); // The order of filling in the corner points of roi tensor is left, top, right, bottom roi_data[0] = rois[i].left; roi_data[1] = rois[i].top; roi_data[2] = rois[i].right; roi_data[3] = rois[i].bottom; // make sure cahced mem data is flushed to DDR before inference hbUCPMemFlush(&roi_mem[i], HB_SYS_MEM_CACHE_CLEAN); } return 0; }`

## #Preparation for Board-side Operation

When the executable program is compiled, the inputs to the model need to be prepared. If the input type is Featuremap, we can just use the calibration set as model input. If the input type is non-Featuremap, we need to process the data before using it as model input.

### #Featuremap Input

For Featuremap input, you can refer to the following command to convert the npy format of the calibration set to a bin file:

`import numpy as np data = np.load("calibration_data.npy") data.tofile("input.bin")`

### #Non-featuremap Input

For non-Featuremap input, we need to process the data first. Below we give examples of data processing through python and C++ respectively.

The processing of data using python can be found below:

`import os import cv2 import PIL import numpy as np from PIL import Image image_path = "./ILSVRC2012_val_00000001.JPEG" def resize_transformer(image_data: np.array, short_size: int): image = Image.fromarray(image_data.astype('uint8'), 'RGB') # Specify width, height w, h = image.size if (w <= h and w == short_size) or (h <= w and h == short_size): return np.array(image) # I.e., the width of the image is the short side if w < h: resize_size = (short_size, int(short_size * h / w)) # I.e., the height of the image is the short side else: resize_size = (int(short_size * w / h), short_size) # Resize the image data = np.array(image.resize(resize_size, Image.BILINEAR)) return data def center_crop_transformer(image_data: np.array, crop_size: int): image = Image.fromarray(image_data.astype('uint8'), 'RGB') image_width, image_height = image.size crop_height, crop_width = (crop_size, crop_size) crop_top = int(round((image_height - crop_height) / 2.)) crop_left = int(round((image_width - crop_width) / 2.)) image_data = image.crop((crop_left, crop_top, crop_left + crop_width, crop_top + crop_height)) return np.array(image_data).astype(np.float32) def rgb_to_nv12(image_data: np.array): r = image_data[:, :, 0] g = image_data[:, :, 1] b = image_data[:, :, 2] y = (0.299 * r + 0.587 * g + 0.114 * b) u = (-0.169 * r - 0.331 * g + 0.5 * b + 128)[::2, ::2] v = (0.5 * r - 0.419 * g - 0.081 * b + 128)[::2, ::2] uv = np.zeros(shape=(u.shape[0], u.shape[1] * 2)) for i in range(0, u.shape[0]): for j in range(0, u.shape[1]): uv[i, 2 * j] = u[i, j] uv[i, 2 * j + 1] = v[i, j] y = y.astype(np.uint8) uv = uv.astype(np.uint8) return y, uv if __name__ == '__main__': # load the image with PIL method pil_image_data = PIL.Image.open(image_path).convert('RGB') image_data = np.array(pil_image_data).astype(np.uint8) # Resize the image image_data = resize_transformer(image_data, 256) # Crop the image image_data = center_crop_transformer(image_data, 224) # Covert format from RGB to nv12 y, uv = rgb_to_nv12(image_data) y.tofile("ILSVRC2012_val_00000001_y.bin") uv.tofile("ILSVRC2012_val_00000001_uv.bin")`

The upper read mode is PIL, if you use opencv method, please refer to the following:

`import cv2 import numpy as np def image2nv12(image): image = image.astype(np.uint8) height, width = image.shape[0], image.shape[1] yuv420p = cv2.cvtColor(image, cv2.COLOR_BGR2YUV_I420).reshape((height * width * 3 // 2, )) y = yuv420p[:height * width] uv_planar = yuv420p[height * width:].reshape((2, height * width // 4)) uv = uv_planar.transpose((1, 0)).reshape((height * width // 2, )) nv12 = np.zeros_like(yuv420p) # y component nv12[:height * width] = y # uv component, UVUV alternate store nv12[height * width:] = uv # Return separately return y, uv image = cv2.imread("./image.jpg") nv12 = image2nv12(image)`

You can also implement data processing via C++ in the boardside sample based on the data processing logic implemented in python above, please refer to the following:

`#include <fstream> #include <iostream> #include <vector> #include <cstring> #include "hobot/dnn/hb_dnn.h" #include "hobot/hb_ucp.h" #include "hobot/hb_ucp_sys.h" int32_t read_image_2_tensor_as_nv12(std::string &image_file, hbDNNTensor *input_tensor) { // the struct of input shape is NHWC int input_h = input_tensor[0].properties.validShape.dimensionSize[1]; int input_w = input_tensor[0].properties.validShape.dimensionSize[2]; cv::Mat bgr_mat = cv::imread(image_file, cv::IMREAD_COLOR); if (bgr_mat.empty()) { std::cout << "image file not exist!" << std::endl; return -1; } // resize cv::Mat mat; mat.create(input_h, input_w, bgr_mat.type()); cv::resize(bgr_mat, mat, mat.size(), 0, 0); // convert to YUV420 if (input_h % 2 || input_w % 2) { std::cout << "input img height and width must aligned by 2!" << std::endl; return -1; } cv::Mat yuv_mat; cv::cvtColor(mat, yuv_mat, cv::COLOR_BGR2YUV_I420); uint8_t *yuv_data = yuv_mat.ptr<uint8_t>(); uint8_t *y_data_src = yuv_data; // copy y data uint8_t *y_data_dst = reinterpret_cast<uint8_t *>(input_tensor[0].sysMem.virAddr); for (int32_t h = 0; h < input_h; ++h) { memcpy(y_data_dst, y_data_src, input_w); y_data_src += input_w; // add padding y_data_dst += input_tensor[0].properties.stride[1]; } // copy uv data int32_t uv_height = input_tensor[1].properties.validShape.dimensionSize[1]; int32_t uv_width = input_tensor[1].properties.validShape.dimensionSize[2]; uint8_t *uv_data_dst = reinterpret_cast<uint8_t *>(input_tensor[1].sysMem.virAddr); uint8_t *u_data_src = yuv_data + input_h * input_w; uint8_t *v_data_src = u_data_src + uv_height * uv_width; for (int32_t h = 0; h < uv_height; ++h) { auto *cur_data = uv_data_dst; for (int32_t w = 0; w < uv_width; ++w) { *cur_data++ = *u_data_src++; *cur_data++ = *v_data_src++; } // add padding uv_data_dst += input_tensor[1].properties.stride[1]; } // make sure memory data is flushed to DDR before inference hbUCPMemFlush(&input_tensor[0].sysMem, HB_SYS_MEM_CACHE_CLEAN); hbUCPMemFlush(&input_tensor[1].sysMem, HB_SYS_MEM_CACHE_CLEAN); return 0; }`

After completing the preparation of the model input data, which means correctly generating the input file in binary format for the board-side sample inference, you also need to make sure that you now have the following ready:

- S100 dev board for the actual execution of board-side program runs.

- The model(*.hbm) that can be used for board-side inference.

- The board-side program(main.cc file and cross-compile to generate board-side executable program).

- The board-side program depends on libraries, and in order to reduce deployment costs, you can directly use the contents of the OE package `samples/ucp_tutorial/deps_aarch64/ucp/lib` folder.

Once ready, we integrate the model file(*.hbm), input data (*.bin files), board-side program and dependent libraries as above, with the following reference directory structure:

`horizon ├── input.bin ├── lib ├── model.hbm └── run_sample`

Copy this integrated folder as a whole to the board-side environment, please refer to the following command:

`scp -r horizon/ root@{board_ip}:/map/`

## #Board-side Execution

After completing the board-side running sample and running preparation, we need to configure LD_LIBRARY_PATH on the board-side, and then run the following program to complete the board-side running inference:

`horizon@hobot:/map/horizon# export LD_LIBRARY_PATH=./lib:$LD_LIBRARY_PATH horizon@hobot:/map/horizon# ./run_sample ...`

At this point, the principles and procedures related to the entire model deployment process have been fully introduced. In the following sections, we will use the public version of ResNet18 as an example to provide practical guidance on the complete PTQ pipeline in typical scenarios for your reference.

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