模型集成AI Express结果不对

应用开发 算法工具链
1

我也遇到同这个帖子一样的问题，请问你们提供的修改方案是怎样的？

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2

请描述下具体的版本，解决方案，与出现的问题

3

参考的是这篇帖子。

https://developer.horizon.ai/forumDetail/136488103547258627

4

您好，可以参照一下代码进行修改：

/**-
* Copyright (c) 2020 Horizon Robotics. All rights reserved.-
* @File: yolov3_predict_method.cc-
* @Brief: definition of the Yolov3PredictMethod-
* @Author: zhe.sun-
* @Email: zhe.sun@horizon.ai-
* @Date: 2020-12-23 11:12:18-
* @Last Modified by: zhe.sun-
* @Last Modified time: 2020-12-23 21:21:33-
*/-

#include “Yolov3PredictMethod/yolov3_predict_method.h”-
#include -
#include -
#include -
#include “opencv2/highgui/highgui.hpp”-
#include “hobotxstream/profiler.h”-
#include “opencv2/imgproc/imgproc.hpp”-
#include “horizon/vision_type/vision_type.hpp”-
#include “hobotxstream/image_tools.h”-
#include “hobotlog/hobotlog.hpp”-
#include “DnnAsyncData.h”-
using namespace std;-
using namespace cv;-
#ifdef X3-
#include “./bpu_predict_x3.h”-
#endif-
namespace xstream {-

int Yolov3PredictMethod::Init(const std::string &cfg_path) {-
DnnPredictMethod::Init(cfg_path);-
pyramid_layer_ = config_[“pyramid_layer”].isInt() ?-
config_[“pyramid_layer”].asInt() : pyramid_layer_;-
return 0;-
}-

int Yolov3PredictMethod::GetSrcImageSize(-
const std::vector<BaseDataPtr> &input, int &src_image_height,-
int &src_image_width) {-
HOBOT_CHECK(input.size() == 1); // image-
auto xstream_img = std::static_pointer_cast<-
XStreamData<std::shared_ptrhobot::vision::ImageFrame>>(input[0]);-

std::string img_type = xstream_img->value->type;-
if (img_type == “PymImageFrame”) {-
auto pyramid_image = std::static_pointer_cast<-
hobot::vision::PymImageFrame>(xstream_img->value);-
#ifdef X2-
src_image_height = pyramid_image->img.src_img.height;-
src_image_width = pyramid_image->img.src_img.width;-
#endif-

#ifdef X3-
src_image_height = pyramid_image->down_scale[0].height;-
src_image_width = pyramid_image->down_scale[0].width;-
#endif-
} else {-
LOGE << "not support " << img_type;-
return -1;-
}-
LOGD << "src image height: " << src_image_height-
<< ", src image width: " << src_image_width;-
return 0;-
}-

int Yolov3PredictMethod::PrepareInputData(-
const std::vector<BaseDataPtr> &input,-
const InputParamPtr param,-
std::vector<std::vector<BPU_TENSOR_S>> &input_tensors,-
std::vector<std::vector<BPU_TENSOR_S>> &output_tensors) {-
LOGD << “Yolov3PredictMethod PrepareInputData”;-
HOBOT_CHECK(input.size() == 1); // image-

auto xstream_img = std::static_pointer_cast<XStreamData<-
std::shared_ptrhobot::vision::ImageFrame>>(input[0]); // image-

std::string img_type = xstream_img->value->type;-
HOBOT_CHECK(img_type == “PymImageFrame”) << "not support " << img_type;-

auto pyramid_image = std::static_pointer_cast<-
hobot::vision::PymImageFrame>(xstream_img->value);-

// 全图检测对应一次预测-
input_tensors.resize(1);-
output_tensors.resize(1);-

#ifdef X2-
auto input_img = pyramid_image->img.down_scale[pyramid_layer_];-
#endif-
#ifdef X3-
auto input_img = pyramid_image->down_scale[pyramid_layer_];-
#endif-
// 期望金字塔图像宽高都大于模型输入，避免padding影响算法效果-
if (input_img.height < model_input_height_ ||-
input_img.width < model_input_width_) {-
LOGE << “pyramid image size smaller than model_input size, “-
<< “please check pyramid_layer”;-
return -1;-
}-
// cv::imwrite(”/userdata/000.jpg”,input_img);-
// 1. alloc input_tensors-
int ret = AllocInputTensor(input_tensors[0]);-
if (ret != 0) {-
LOGE << “Alloc InputTensor failed!”;-
return -1;-
}-
// 2. alloc output_tensors-
ret = AllocOutputTensor(output_tensors[0]);-
if (ret != 0) {-
LOGE << “Alloc OutputTensor failed!”;-
return -1;-
}-

// 模型输入大小：416 x 416-
// 以1080p为例，金字塔第0层1920x1080，第4层960x540-
// vio配置目标层pyramid_layer_：取第4层数据，padding到960x960，再resize到416x416-
// desired: target_pym_layer_height = 540, target_pym_layer_width = 960-

// prepare 960x960 BPU_TENSOR-
// 注意BPU_TENSOR需要释放-
BPU_TENSOR_S pre_resize_tensor;-
{-
pre_resize_tensor.data_type = BPU_TYPE_IMG_NV12_SEPARATE;-
int h_idx, w_idx, c_idx;-
HB_BPU_getHWCIndex(-
pre_resize_tensor.data_type, nullptr, &h_idx, &w_idx, &c_idx);-
pre_resize_tensor.data_shape.ndim = 4;-
pre_resize_tensor.data_shape.d[0] = 1;-
// 金字塔图像width>height, padding到width-
// pre_resize_tensor.data_shape.d[h_idx] = input_img.width;-
pre_resize_tensor.data_shape.d[h_idx] = input_img.height;-

pre_resize_tensor.data_shape.d[w_idx] = input_img.width;-
pre_resize_tensor.data_shape.d[c_idx] = 3;-
pre_resize_tensor.aligned_shape = pre_resize_tensor.data_shape;-

// alloc bpu-mem-
// 数据类型是nv12图像，y和uv分量分开alloc和存储-
// int y_length = input_img.width * input_img.width;-
int y_length = input_img.width * input_img.height;//chaged by xgs alloc half data-

int uv_length = y_length >> 1;-
cout<<"++++++++++++++++++++++++++++length of y is: “<<y_length<<” length og uv is: "<<uv_length<<endl;-
ret = HB_SYS_bpuMemAlloc(-
“in_data0”, y_length, true, &pre_resize_tensor.data);-
if (ret != 0) {-
LOGE << "bpu alloc mem failed: " << HB_BPU_getErrorName(ret);-
return -1;-
}-
ret = HB_SYS_bpuMemAlloc(-
“in_data1”, uv_length, true, &pre_resize_tensor.data_ext);-
if (ret != 0) {-
LOGE << "bpu alloc mem failed: " << HB_BPU_getErrorName(ret);-
// release alloced mem-
HB_SYS_bpuMemFree(&pre_resize_tensor.data);-
return -1;-
}-
// Copy y data to data0-

uint8_t *y = reinterpret_cast<uint8_t *>(pre_resize_tensor.data.virAddr);-
uint8_t *src_y = reinterpret_cast<uint8_t *>(input_img.y_vaddr);-
memcpy(y, src_y, input_img.width * input_img.height);-
// 将padding的底部填充0-
// memset(y + input_img.width * input_img.height, 0,-
// input_img.width * (input_img.width - input_img.height));-
HB_SYS_flushMemCache(&pre_resize_tensor.data, HB_SYS_MEM_CACHE_CLEAN);-
// Copy uv data to data_ext-
uint8_t *uv = reinterpret_cast<uint8_t *>(-
pre_resize_tensor.data_ext.virAddr);-
uint8_t *src_uv = reinterpret_cast<uint8_t *>(input_img.c_vaddr);-
int uv_height = input_img.height >> 1;-
memcpy(uv, src_uv, input_img.width * uv_height);-
// 将padding的底部填充0-
// memset(uv + input_img.width * uv_height, 0,-
// input_img.width * (input_img.width - input_img.height) >> 1);-
HB_SYS_flushMemCache(&pre_resize_tensor.data_ext, HB_SYS_MEM_CACHE_CLEAN);-
cout<<"input_img.width: “<<input_img.width<<” input_img.height is: "<<input_img.height<<endl;-

}-

// resize to 416x416-
BPU_RESIZE_CTRL_S ctrl_param = {-
BPU_RESIZE_TYPE_BILINEAR,-
BPU_TYPE_IMG_NV12_SEPARATE,-
-1};-
HOBOT_CHECK(input_tensors[0].size() == 1);-
ret = HB_BPU_resize(&pre_resize_tensor,-
&input_tensors[0][0],-
&ctrl_param);-
HB_SYS_bpuMemFree(&pre_resize_tensor.data);-
HB_SYS_bpuMemFree(&pre_resize_tensor.data_ext);-
if (ret != 0) {-
LOGE << “resize nv12 failed”;-
return -1;-
}-
return 0;-
}-
} // namespace xstream-