64 lines
2.1 KiB
C++
64 lines
2.1 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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// clang-format off
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#include "catch.hpp"
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// clang-format on
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#include "apis/c/classifier.h"
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#include "core/logger.h"
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#include "opencv2/opencv.hpp"
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#include "test_resource.h"
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using namespace std;
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TEST_CASE("test classifier's c api", "[classifier]") {
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auto test = [](const std::string& device_name, const std::string& model_path,
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const std::vector<std::string>& img_list) {
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mm_handle_t handle{nullptr};
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auto ret =
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mmdeploy_classifier_create_by_path(model_path.c_str(), device_name.c_str(), 0, &handle);
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REQUIRE(ret == MM_SUCCESS);
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vector<cv::Mat> cv_mats;
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vector<mm_mat_t> mats;
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for (auto& img_path : img_list) {
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cv::Mat mat = cv::imread(img_path);
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REQUIRE(!mat.empty());
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cv_mats.push_back(mat);
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mats.push_back({mat.data, mat.rows, mat.cols, mat.channels(), MM_BGR, MM_INT8});
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}
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mm_class_t* results{nullptr};
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int* result_count{nullptr};
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ret = mmdeploy_classifier_apply(handle, mats.data(), (int)mats.size(), &results, &result_count);
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REQUIRE(ret == MM_SUCCESS);
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auto result_ptr = results;
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MMDEPLOY_INFO("model_path: {}", model_path);
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for (auto i = 0; i < (int)mats.size(); ++i) {
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MMDEPLOY_INFO("the {}-th classification result: ", i);
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for (int j = 0; j < *result_count; ++j, ++result_ptr) {
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MMDEPLOY_INFO("\t label: {}, score: {}", result_ptr->label_id, result_ptr->score);
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}
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}
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mmdeploy_classifier_release_result(results, result_count, (int)mats.size());
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mmdeploy_classifier_destroy(handle);
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};
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auto gResources = MMDeployTestResources::Get();
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auto img_lists = gResources.LocateImageResources(fs::path{"mmcls"} / "images");
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REQUIRE(!img_lists.empty());
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for (auto& backend : gResources.backends()) {
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DYNAMIC_SECTION("loop backend: " << backend) {
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auto model_list = gResources.LocateModelResources(fs::path{"mmcls/"} / backend);
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REQUIRE(!model_list.empty());
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for (auto& model_path : model_list) {
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for (auto& device_name : gResources.device_names(backend)) {
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test(device_name, model_path, img_lists);
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}
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}
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}
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}
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}
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