mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
3be1779e66
* fix sdk model's pipeline.json * resize INT64 mask * refactor unit tests * fix api in model.h * remove 'customs' from meta info * fix zip model * fix clang-format issue * put tc on each backend into a SECTION * change SECTION title * add DYNAMIC_SECTION for capi unit test * change 'devices' to 'device_names' * change trt to tensorrt * remove uncessary check * add color_type 'color_ignore_orientation' which is used in ocr * 'min_width', 'max_width' and 'backend' might be null in pipeline config * fix clang-format issue * remove useless code
64 lines
2.1 KiB
C++
64 lines
2.1 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
|
|
|
|
// clang-format off
|
|
#include "catch.hpp"
|
|
// clang-format on
|
|
|
|
#include "apis/c/classifier.h"
|
|
#include "core/logger.h"
|
|
#include "opencv2/opencv.hpp"
|
|
#include "test_resource.h"
|
|
|
|
using namespace std;
|
|
|
|
TEST_CASE("test classifier's c api", "[classifier]") {
|
|
auto test = [](const std::string& device_name, const std::string& model_path,
|
|
const std::vector<std::string>& img_list) {
|
|
mm_handle_t handle{nullptr};
|
|
auto ret =
|
|
mmdeploy_classifier_create_by_path(model_path.c_str(), device_name.c_str(), 0, &handle);
|
|
REQUIRE(ret == MM_SUCCESS);
|
|
|
|
vector<cv::Mat> cv_mats;
|
|
vector<mm_mat_t> mats;
|
|
for (auto& img_path : img_list) {
|
|
cv::Mat mat = cv::imread(img_path);
|
|
REQUIRE(!mat.empty());
|
|
cv_mats.push_back(mat);
|
|
mats.push_back({mat.data, mat.rows, mat.cols, mat.channels(), MM_BGR, MM_INT8});
|
|
}
|
|
|
|
mm_class_t* results{nullptr};
|
|
int* result_count{nullptr};
|
|
ret = mmdeploy_classifier_apply(handle, mats.data(), (int)mats.size(), &results, &result_count);
|
|
REQUIRE(ret == MM_SUCCESS);
|
|
auto result_ptr = results;
|
|
INFO("model_path: {}", model_path);
|
|
for (auto i = 0; i < (int)mats.size(); ++i) {
|
|
INFO("the {}-th classification result: ", i);
|
|
for (int j = 0; j < *result_count; ++j, ++result_ptr) {
|
|
INFO("\t label: {}, score: {}", result_ptr->label_id, result_ptr->score);
|
|
}
|
|
}
|
|
|
|
mmdeploy_classifier_release_result(results, result_count, (int)mats.size());
|
|
mmdeploy_classifier_destroy(handle);
|
|
};
|
|
|
|
auto gResources = MMDeployTestResources::Get();
|
|
auto img_lists = gResources.LocateImageResources("mmcls/images");
|
|
REQUIRE(!img_lists.empty());
|
|
|
|
for (auto& backend : gResources.backends()) {
|
|
DYNAMIC_SECTION("loop backend: " << backend) {
|
|
auto model_list = gResources.LocateModelResources("mmcls/" + backend);
|
|
REQUIRE(!model_list.empty());
|
|
for (auto& model_path : model_list) {
|
|
for (auto& device_name : gResources.device_names(backend)) {
|
|
test(device_name, model_path, img_lists);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|