brilliant-soilder
commented
2 years ago command:
include
include <opencv2/imgcodecs/imgcodecs.hpp>
include <opencv2/imgproc/imgproc.hpp>
include
include
include "detector.h"
stTimeRecord_t stc, startTime, finishTime; int main() {
const char* image_path = R"(F:\mmdeploy-0.6.0\test\1_0050.bmp)";
const char* model_path = R"(F:\mmdeploy-0.6.0\test\mask_rcnn_static_640)";
const char* device_name = "cuda";//int main(int argc, char *argv[]) { // if (argc != 4) { // fprintf(stderr, "usage:\n object_detection device_name model_path image_path\n"); // return 1; // } // auto device_name = argv[1]; // auto model_path = argv[2]; // auto image_path = argv[3];
cv::Mat img = cv::imread(image_path); if (!img.data) { fprintf(stderr, "failed to load image: %s\n", image_path); return 1; }
mm_handle_t detector{}; int status{}; status = mmdeploy_detector_create_by_path(model_path, device_name, 0, &detector); if (status != MM_SUCCESS) { fprintf(stderr, "failed to create detector, code: %d\n", (int)status); return 1; }
//TIME_START(startTime); // /*测试部分*****/ //TIME_END(finishTime); //CACL_TIME_PRINT(startTime, finishTime, s_tc, "The time---------: ");
TIME_START(startTime); mm_mat_t mat{img.data, img.rows, img.cols, 3, MM_BGR, MM_INT8};
mm_detect_t bboxes{}; int res_count{}; status = mmdeploy_detector_apply(detector, &mat, 1, &bboxes, &res_count); if (status != MM_SUCCESS) { fprintf(stderr, "failed to apply detector, code: %d\n", (int)status); return 1; }
fprintf(stdout, "bbox_count=%d\n", *res_count);
for (int i = 0; i < *res_count; ++i) { const auto &box = bboxes[i].bbox; const auto &mask = bboxes[i].mask;
fprintf(stdout, "box %d, left=%.2f, top=%.2f, right=%.2f, bottom=%.2f, label=%d, score=%.4f\n",
i, box.left, box.top, box.right, box.bottom, bboxes[i].label_id, bboxes[i].score);
// skip detections with invalid bbox size (bbox height or width < 1)
if ((box.right - box.left) < 1 || (box.bottom - box.top) < 1) {
continue;
}
// skip detections less than specified score threshold
if (bboxes[i].score < 0.3) {
continue;
}
// generate mask overlay if model exports masks
if (mask != nullptr) {
fprintf(stdout, "mask %d, height=%d, width=%d\n", i, mask->height, mask->width);
cv::Mat imgMask(mask->height, mask->width, CV_8UC1, &mask->data[0]);
int x0 = max(std::floor(box.left) - 1, 0.f);
int y0 = max(std::floor(box.top) - 1, 0.f);
cv::Rect roi((int)x0, (int)y0, mask->width, mask->height);
// split the RGB channels, overlay mask to a specific color channel
cv::Mat ch[3];
split(img, ch);
int col = 0; // int col = i % 3;
cv::bitwise_or(imgMask, ch[col](roi), ch[col](roi));
merge(ch, 3, img);
}
TIME_END(finishTime);
CACL_TIME_PRINT(startTime, finishTime, stc, "The time---------: ");
cv::rectangle(img, cv::Point{(int)box.left, (int)box.top},
cv::Point{(int)box.right, (int)box.bottom}, cv::Scalar{0, 255, 0});}
cv::imwrite("F:\mmdeploy-0.6.0\test\build_pictures\1_0050.bmp", img);
mmdeploy_detector_release_result(bboxes, res_count, 1);
mmdeploy_detector_destroy(detector);
return 0; }
lzhangzz
sherry521sherry
I have converted the model on Ubuntu platform. When I perform the inference on Windows platform, it shows an error as this "Serialization assertion magicTagRead == magicTag failed.Magic tag does not match". Mask_rcnn_static_640x256. I'm using 640x256 as dimensions.
Here's the error information:
loading mmdeploy_execution ... loading mmdeploy_cpu_device ... loading mmdeploy_cuda_device ... loading mmdeploy_graph ... loading mmdeploy_directory_model ... [2022-07-25 01:10:55.359] [mmdeploy] [info] [model.cpp:95] Register 'DirectoryModel' loading mmdeploy_transform ... loading mmdeploy_cpu_transform_impl ... loading mmdeploy_cuda_transform_impl ... loading mmdeploy_transform_module ... loading mmdeploy_trt_net ... loading mmdeploy_net_module ... loading mmdeploy_mmcls ... loading mmdeploy_mmdet ... loading mmdeploy_mmseg ... loading mmdeploy_mmocr ... loading mmdeploy_mmedit ... loading mmdeploy_mmpose ... loading mmdeploy_mmrotate ... [2022-07-25 01:10:55.402] [mmdeploy] [info] [model.cpp:38] DirectoryModel successfully load sdk model F:\mmdeploy-0.6.0\test\mask_rcnn_static_640 [2022-07-25 01:10:55.498] [mmdeploy] [info] [common.h:29] config: { "context": { "device": "",
"stream": ""
},
"input": [
"image"
],
"name": "mmdetection",
"output": [
"det"
],
"params": {
"model": ""
},
"type": "Inference"
}
[2022-07-25 01:10:55.499] [mmdeploy] [info] [common.h:29] config: {
"context": {
"device": "",
"model": "",
"stream": ""
},
"input": [
"img"
],
"module": "Transform",
"name": "Preprocess",
"output": [
"prep_output"
],
"transforms": [
{
"type": "LoadImageFromFile"
},
{
"keep_ratio": false,
"size": [
256,
640
],
"type": "Resize"
},
{
"mean": [
123.675,
116.28,
103.53
],
"std": [
58.395,
57.12,
57.375
],
"to_rgb": true,
"type": "Normalize"
},
{
"size_divisor": 1,
"type": "Pad"
},
{
"type": "DefaultFormatBundle"
},
{
"keys": [
"img"
],
"meta_keys": [
"ori_shape",
"ori_filename",
"flip",
"flip_direction",
"scale_factor",
"filename",
"img_shape",
"valid_ratio",
"img_norm_cfg",
"pad_shape"
],
"type": "Collect"
}
],
"type": "Task"
}
[2022-07-25 01:10:55.501] [mmdeploy] [info] [common.h:29] config: {
"context": {
"device": "",
"model": "",
"stream": ""
},
"input": [
"img"
],
"module": "Transform",
"name": "Preprocess",
"output": [
"prep_output"
],
"transforms": [
{
"type": "LoadImageFromFile"
},
{
"keep_ratio": false,
"size": [
256,
640
],
"type": "Resize"
},
{
"mean": [
123.675,
116.28,
103.53
],
"std": [
58.395,
57.12,
57.375
],
"to_rgb": true,
"type": "Normalize"
},
{
"size_divisor": 1,
"type": "Pad"
},
{
"type": "DefaultFormatBundle"
},
{
"keys": [
"img"
],
"meta_keys": [
"ori_shape",
"ori_filename",
"flip",
"flip_direction",
"scale_factor",
"filename",
"img_shape",
"valid_ratio",
"img_norm_cfg",
"pad_shape"
],
"type": "Collect"
}
],
"type": "Task"
}
[2022-07-25 01:10:55.502] [mmdeploy] [info] [common.h:29] config: {
"context": {
"device": "",
"model": "",
"stream": ""
},
"input": [
"prep_output"
],
"input_map": {
"img": "input"
},
"module": "Net",
"name": "maskrcnn",
"output": [
"infer_output"
],
"type": "Task"
}
[2022-07-25 01:10:55.502] [mmdeploy] [info] [common.h:29] config: {
"context": {
"device": "",
"model": "",
"stream": ""
},
"input": [
"prep_output"
],
"input_map": {
"img": "input"
},
"module": "Net",
"name": "maskrcnn",
"output": [
"infer_output"
],
"type": "Task"
}
[2022-07-25 01:10:55.503] [mmdeploy] [info] [cuda_device.cpp:61] Default CUDA allocator initialized
[2022-07-25 01:10:56.021] [mmdeploy] [error] [trt_net.cpp:28] TRTNet: 1: [stdArchiveReader.cpp::nvinfer1::rt::StdArchiveReader::StdArchiveReader::30] Error Code 1: Serialization (Serialization assertion magicTagRead == magicTag failed.Magic tag does not match)
[2022-07-25 01:10:56.022] [mmdeploy] [error] [trt_net.cpp:28] TRTNet: 4: [runtime.cpp::nvinfer1::Runtime::deserializeCudaEngine::50] Error Code 4: Internal Error (Engine deserialization failed.)
[2022-07-25 01:10:56.022] [mmdeploy] [error] [trt_net.cpp:75] failed to deserialize TRT CUDA engine
[2022-07-25 01:10:56.029] [mmdeploy] [error] [task.cpp:67] error parsing config: {
"context": {
"device": "",
"model": "",
"stream": ""
},
"input": [
"prep_output"
],
"input_map": {
"img": "input"
},
"module": "Net",
"name": "maskrcnn",
"output": [
"infer_output"
],
"type": "Task"
}
[2022-07-25 01:10:56.029] [mmdeploy] [error] [pipeline.cpp:146] could not create maskrcnn: Task
[2022-07-25 01:10:56.030] [mmdeploy] [error] [pipeline.cpp:160] error parsing config: unknown (6) @ :0
[2022-07-25 01:10:56.030] [mmdeploy] [error] [pipeline.cpp:27] exception caught: unknown (6) @ :0
failed to create detector, code: 6
F:\mmdeploy-0.6.0\sdk\build\Release\object_detection.exe (进程 4116)已退出，代码为 1。 要在调试停止时自动关闭控制台，请启用“工具”->“选项”->“调试”->“调试停止时自动关闭控制台”。 按任意键关闭此窗口. . .