RKNN前传报错 - Toybrick - Powered by Discuz!

2017-08-05 23:04:05.362 20048-20233/com.rockchip.gpadc.ssddemo E/rkssd4j: 查找的设备[0]: type=USB_DEVICE, id=0123456789ABCDEF
2017-08-05 23:04:09.608 20048-20233/com.rockchip.gpadc.ssddemo E/rkssd4j: model input num: 1, output num: 1
2017-08-05 23:04:26.675 20048-20233/com.rockchip.gpadc.ssddemo E/rkssd4j: model input[0] : , dim: 4, fmt: 0, type: 3, qnt_type: 2
# 测试代码
std::string lands_model_path = "/data/user/0/com.rockchip.gpadc.ssddemo/cache/shuffle-stage.rknn";
FILE *fp_lands = fopen(lands_model_path.c_str(), "rb");
if(fp_lands == NULL) {
LOGE("fopen %s fail!\n", lands_model_path.c_str());
return;
}
fseek(fp_lands, 0, SEEK_END);
int lands_model_len = ftell(fp_lands);
void *lands_model = malloc(lands_model_len);
fseek(fp_lands, 0, SEEK_SET);
if(lands_model_len != fread(lands_model, 1, lands_model_len, fp_lands)) {
LOGE("fread %s fail!\n", lands_model_path.c_str());
free(lands_model);
fclose(fp_lands);
return;
}
fclose(fp_lands);
// RKNN_FLAG_ASYNC_MASK: enable async mode to use NPU efficiently.
rknn_context lands_ctx = 0;
int lands_ret = rknn_init(&lands_ctx, lands_model, lands_model_len, RKNN_FLAG_PRIOR_HIGH);
free(lands_model);
if(lands_ret < 0) {
LOGE("rknn_init shuffle_stage landmarks fail! ret=%d\n", ret);
return;
}
// 查询sdk版本信息
rknn_sdk_version version;
lands_ret = rknn_query(lands_ctx, RKNN_QUERY_SDK_VERSION, &version,
sizeof(rknn_sdk_version));
LOGE("api version: %s\n", version.api_version);
LOGE("driver version: %s\n", version.drv_version);
// 查找设备
rknn_devices_id devids;
ret = rknn_find_devices (&devids);
LOGE("查找设备: n_devices = %d\n", devids.n_devices);
for(int i=0; i<devids.n_devices; i++) {
LOGE("查找的设备[%d]: type=%s, id=%s\n", i, devids.types[i], devids.ids[i]);
}
// 查询输入Tensor的属性
rknn_input_output_num io_num;
ret = rknn_query(lands_ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));
if(ret < 0) {
LOGE("rknn_query fail! ret=%d\n", ret);
}
LOGE("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output);
// 查询Input的Tensor属性
rknn_tensor_attr input_attrs[io_num.n_input];
memset(input_attrs, 0, sizeof(input_attrs));
for (int i = 0; i < io_num.n_input; i++) {
input_attrs[i].index = i;
ret = rknn_query(lands_ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]),
sizeof(rknn_tensor_attr));
LOGE("model input[%d] : %s, dim: %d, fmt: %d, type: %d, qnt_type: %d \n", i, \
input_attrs[i].name, input_attrs[i].n_dims, input_attrs[i].fmt, input_attrs[i].type,\
input_attrs[i].qnt_type);
// 打印各维度值就是shape
}
// 查询Ouput的Tensor属性
rknn_tensor_attr output_attrs[io_num.n_output];
memset(output_attrs, 0, sizeof(output_attrs));
for (int i = 0; i < io_num.n_output; i++) {
output_attrs[i].index = i;
ret = rknn_query(lands_ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]),
sizeof(rknn_tensor_attr));
LOGE("model output[%d] : %s, dim: %d, fmt: %d, type: %d, qnt_type: %d \n", i, \
output_attrs[i].name, output_attrs[i].n_dims, output_attrs[i].fmt, output_attrs[i].type,\
output_attrs[i].qnt_type);
}
// 设置输入
/// 1. 读取图像作为BGR输入 112x112x3 NHWC格式
std::string lands_img_path = "/data/user/0/com.rockchip.gpadc.ssddemo/cache/npu_test_lands.png";
cv::Mat img = cv::imread(lands_img_path.c_str());
/// 2. 将图像传入RKNN_INPUT_TENSOR
rknn_input inputs[io_num.n_input];
memset(inputs, 0, sizeof(inputs));
for (uint32_t i = 0; i < io_num.n_input; i++){
inputs[i].index = i;
inputs[i].type = RKNN_TENSOR_UINT8;
inputs[i].size = (uint32_t)img.cols*img.rows*img.channels();
inputs[i].pass_through = (uint8_t)false; // 非直通模式，将当前数据类型传递到模型中，并在模型内部转换；
inputs[i].fmt = RKNN_TENSOR_NHWC;
inputs[i].buf = img.data;
}
ret = rknn_inputs_set(lands_ctx, io_num.n_input, inputs);
// 运行rknn模型
ret = rknn_run(lands_ctx, NULL);
if(ret != RKNN_SUCC){
LOGE("rknn_run fail! ret=%d\n", ret);
}
// 获取rknn结果信息
rknn_output outputs[io_num.n_output];
memset(outputs, 0, sizeof(outputs));
for (int i = 0; i < io_num.n_output; i++) {
outputs[i].want_float = (uint8_t)true;
outputs[i].is_prealloc = (uint8_t)false;
}
ret = rknn_outputs_get(lands_ctx, io_num.n_output, outputs, NULL);
// 绘制人脸图像并保存人脸结果
float *pf_src = (float *)outputs[0].buf;
for (int i = 0; i < output_attrs[0].dims[2]; ++i) {
float x = pf_src[2*i];
float y = pf_src[2*i + 1];
float x_w = x * img.cols;
float y_h = y * img.rows;
cv::circle(img,cv::Point(x_w, y_h),2, cv::Scalar(0,0,255),1, 8);
}
std::string lands_img_out = "/data/user/0/com.rockchip.gpadc.ssddemo/cache/npu_res_lands.png";
cv::imwrite(lands_img_out.c_str(), img);
// 释放输出结果的内存
ret = rknn_outputs_release(lands_ctx, io_num.n_output, outputs);
// 释放模型
rknn_destroy(lands_ctx);
2017-08-05 23:04:26.675 20048-20233/com.rockchip.gpadc.ssddemo E/rkssd4j: model output[0] : , dim: 4, fmt: 0, type: 3, qnt_type: 2