// 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& 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_mats; vector 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); } } } } }