104 lines
3.2 KiB
C++
104 lines
3.2 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "catch.hpp"
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#include "mmdeploy/core/mat.h"
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#include "mmdeploy/core/utils/device_utils.h"
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#include "mmdeploy/preprocess/transform/transform.h"
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#include "opencv2/imgcodecs/imgcodecs.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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#include "opencv_utils.h"
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#include "test_resource.h"
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#include "test_utils.h"
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using namespace mmdeploy;
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using namespace mmdeploy::test;
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using namespace std;
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void TestNormalize(const Value &cfg, const cv::Mat &mat) {
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auto gResource = MMDeployTestResources::Get();
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for (auto const &device_name : gResource.device_names()) {
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Device device{device_name.c_str()};
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Stream stream{device};
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auto transform = CreateTransform(cfg, device, stream);
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REQUIRE(transform != nullptr);
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vector<float> mean;
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vector<float> std;
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for (auto &v : cfg["mean"]) {
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mean.push_back(v.get<float>());
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}
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for (auto &v : cfg["std"]) {
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std.push_back(v.get<float>());
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}
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bool to_rgb = cfg.value("to_rgb", false);
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auto _mat = mat.clone();
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auto ref_mat = mmdeploy::cpu::Normalize(_mat, mean, std, to_rgb);
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auto res = transform->Process({{"img", cpu::CVMat2Tensor(mat)}});
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REQUIRE(!res.has_error());
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auto res_tensor = res.value()["img"].get<Tensor>();
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REQUIRE(res_tensor.device() == device);
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REQUIRE(res_tensor.desc().data_type == DataType::kFLOAT);
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REQUIRE(ImageNormCfg(res.value(), "mean") == mean);
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REQUIRE(ImageNormCfg(res.value(), "std") == std);
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Device kHost{"cpu"};
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auto host_tensor = MakeAvailableOnDevice(res_tensor, kHost, stream);
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REQUIRE(stream.Wait());
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auto res_mat = mmdeploy::cpu::Tensor2CVMat(host_tensor.value());
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REQUIRE(mmdeploy::cpu::Compare(ref_mat, res_mat));
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}
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}
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TEST_CASE("transform Normalize", "[normalize]") {
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auto gResource = MMDeployTestResources::Get();
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auto img_list = gResource.LocateImageResources("transform");
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REQUIRE(!img_list.empty());
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auto img_path = img_list.front();
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cv::Mat bgr_mat = cv::imread(img_path);
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cv::Mat gray_mat;
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cv::Mat float_bgr_mat;
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cv::Mat float_gray_mat;
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cv::cvtColor(bgr_mat, gray_mat, cv::COLOR_BGR2GRAY);
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bgr_mat.convertTo(float_bgr_mat, CV_32FC3);
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gray_mat.convertTo(float_gray_mat, CV_32FC1);
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SECTION("cpu vs gpu: 3 channel mat") {
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bool to_rgb = true;
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Value cfg{{"type", "Normalize"},
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{"mean", {123.675, 116.28, 103.53}},
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{"std", {58.395, 57.12, 57.375}},
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{"to_rgb", to_rgb}};
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vector<cv::Mat> mats{bgr_mat, float_bgr_mat};
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for (auto &mat : mats) {
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TestNormalize(cfg, mat);
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}
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}
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SECTION("cpu vs gpu: 3 channel mat, to_rgb false") {
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bool to_rgb = false;
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Value cfg{{"type", "Normalize"},
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{"mean", {123.675, 116.28, 103.53}},
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{"std", {58.395, 57.12, 57.375}},
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{"to_rgb", to_rgb}};
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vector<cv::Mat> mats{bgr_mat, float_bgr_mat};
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for (auto &mat : mats) {
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TestNormalize(cfg, mat);
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}
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}
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SECTION("cpu vs gpu: 1 channel mat") {
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bool to_rgb = true;
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Value cfg{{"type", "Normalize"}, {"mean", {123.675}}, {"std", {58.395}}, {"to_rgb", to_rgb}};
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vector<cv::Mat> mats{gray_mat, float_gray_mat};
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for (auto &mat : mats) {
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TestNormalize(cfg, mat);
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}
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}
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}
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