mmdeploy/tests/test_csrc/capi/test_classifier.cpp

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;
MMDEPLOY_INFO("model_path: {}", model_path);
for (auto i = 0; i < (int)mats.size(); ++i) {
MMDEPLOY_INFO("the {}-th classification result: ", i);
for (int j = 0; j < *result_count; ++j, ++result_ptr) {
MMDEPLOY_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(fs::path{"mmcls"} / "images");
REQUIRE(!img_lists.empty());
for (auto& backend : gResources.backends()) {
DYNAMIC_SECTION("loop backend: " << backend) {
auto model_list = gResources.LocateModelResources(fs::path{"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);
}
}
}
}
}