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* check in cmake * move backend_ops to csrc/backend_ops * check in preprocess, model, some codebase and their c-apis * check in CMakeLists.txt * check in parts of test_csrc * commit everything else * add readme * update core's BUILD_INTERFACE directory * skip codespell on third_party * update trt_net and ort_net's CMakeLists * ignore clion's build directory * check in pybind11 * add onnx.proto. Remove MMDeploy's dependency on ncnn's source code * export MMDeployTargets only when MMDEPLOY_BUILD_SDK is ON * remove useless message * target include directory is wrong * change target name from mmdeploy_ppl_net to mmdeploy_pplnn_net * skip install directory * update project's cmake * remove useless code * set CMAKE_BUILD_TYPE to Release by force if it isn't set by user * update custom ops CMakeLists * pass object target's source lists * fix lint end-of-file * fix lint: trailing whitespace * fix codespell hook * remove bicubic_interpolate to csrc/backend_ops/ * set MMDEPLOY_BUILD_SDK OFF * change custom ops build command * add spdlog installation command * update docs on how to checkout pybind11 * move bicubic_interpolate to backend_ops/tensorrt directory * remove useless code * correct cmake * fix typo * fix typo * fix install directory * correct sdk's readme * set cub dir when cuda version < 11.0 * change directory where clang-format will apply to * fix build command * add .clang-format * change clang-format style from google to file * reformat csrc/backend_ops * format sdk's code * turn off clang-format for some files * add -Xcompiler=-fno-gnu-unique * fix trt topk initialize * check in config for sdk demo * update cmake script and csrc's readme * correct config's path * add cuda include directory, otherwise compile failed in case of tensorrt8.2 * clang-format onnx2ncnn.cpp Co-authored-by: zhangli <lzhang329@gmail.com> Co-authored-by: grimoire <yaoqian@sensetime.com>
179 lines
6.2 KiB
C++
179 lines
6.2 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "catch.hpp"
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#include "core/mat.h"
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#include "preprocess/cpu/opencv_utils.h"
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#include "preprocess/transform/transform.h"
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#include "preprocess/transform/transform_utils.h"
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#include "test_utils.h"
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using namespace mmdeploy;
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using namespace std;
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using namespace mmdeploy::test;
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tuple<int, int, int, int> CenterCropArea(const cv::Mat& mat, int crop_height, int crop_width) {
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auto img_height = mat.rows;
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auto img_width = mat.cols;
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auto y1 = max(0, int(round((img_height - crop_height) / 2.)));
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auto x1 = max(0, int(round((img_width - crop_width) / 2.)));
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auto y2 = min(img_height, y1 + crop_height) - 1;
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auto x2 = min(img_width, x1 + crop_width) - 1;
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return {y1, x1, y2, x2};
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}
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void TestCpuCenterCrop(const Value& cfg, const cv::Mat& mat, int crop_height, int crop_width) {
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Device device{"cpu"};
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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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auto [top, left, bottom, right] = CenterCropArea(mat, crop_height, crop_width);
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auto ref_mat = mmdeploy::cpu::Crop(mat, top, left, bottom, right);
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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_mat = mmdeploy::cpu::Tensor2CVMat(res.value()["img"].get<Tensor>());
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REQUIRE(mmdeploy::cpu::Compare(ref_mat, res_mat));
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REQUIRE(Shape(res.value(), "img_shape") ==
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vector<int64_t>{1, ref_mat.rows, ref_mat.cols, ref_mat.channels()});
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}
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void TestCudaCenterCrop(const Value& cfg, const cv::Mat& mat, int crop_height, int crop_width) {
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Device device{"cuda"};
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Stream stream{device};
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auto transform = CreateTransform(cfg, device, stream);
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if (transform == nullptr) {
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return;
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}
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auto [top, left, bottom, right] = CenterCropArea(mat, crop_height, crop_width);
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auto ref_mat = mmdeploy::cpu::Crop(mat, top, left, bottom, right);
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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().is_device());
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Device _device{"cpu"};
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auto host_tensor = MakeAvailableOnDevice(res_tensor, _device, 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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// cv::imwrite("ref.jpg",ref_mat);
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// cv::imwrite("res.jpg", res_mat);
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REQUIRE(mmdeploy::cpu::Compare(ref_mat, res_mat));
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REQUIRE(Shape(res.value(), "img_shape") ==
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vector<int64_t>{1, ref_mat.rows, ref_mat.cols, ref_mat.channels()});
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}
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TEST_CASE("test transform crop (cpu) process", "[crop]") {
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std::string transform_type("CenterCrop");
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const char* img_path = "../../tests/preprocess/data/imagenet_banner.jpeg";
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cv::Mat bgr_mat = cv::imread(img_path, cv::IMREAD_COLOR);
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cv::Mat gray_mat = cv::imread(img_path, cv::IMREAD_GRAYSCALE);
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cv::Mat bgr_float_mat;
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cv::Mat gray_float_mat;
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bgr_mat.convertTo(bgr_float_mat, CV_32FC3);
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gray_mat.convertTo(gray_float_mat, CV_32FC1);
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vector<cv::Mat> mats{bgr_mat, gray_mat, bgr_float_mat, gray_float_mat};
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SECTION("crop_size: int; small size") {
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constexpr int crop_size = 224;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", crop_size}};
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for (auto& mat : mats) {
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TestCpuCenterCrop(cfg, mat, crop_size, crop_size);
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}
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}
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SECTION("crop_size: int; oversize") {
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constexpr int crop_size = 800;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", crop_size}};
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for (auto& mat : mats) {
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TestCpuCenterCrop(cfg, mat, crop_size, crop_size);
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}
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}
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SECTION("crop_size: tuple") {
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constexpr int crop_height = 224;
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constexpr int crop_width = 224;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", {crop_height, crop_width}}};
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for (auto& mat : mats) {
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TestCpuCenterCrop(cfg, mat, crop_height, crop_width);
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}
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}
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SECTION("crop_size: tuple;oversize in height") {
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constexpr int crop_height = 640;
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constexpr int crop_width = 224;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", {crop_height, crop_width}}};
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for (auto& mat : mats) {
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TestCpuCenterCrop(cfg, mat, crop_height, crop_width);
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}
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}
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SECTION("crop_size: tuple;oversize in width") {
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constexpr int crop_height = 224;
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constexpr int crop_width = 800;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", {crop_height, crop_width}}};
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for (auto& mat : mats) {
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TestCpuCenterCrop(cfg, mat, crop_height, crop_width);
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}
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}
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}
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TEST_CASE("test transform crop (gpu) process", "[crop]") {
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std::string transform_type("CenterCrop");
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const char* img_path = "../../tests/preprocess/data/imagenet_banner.jpeg";
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cv::Mat bgr_mat = cv::imread(img_path, cv::IMREAD_COLOR);
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cv::Mat gray_mat = cv::imread(img_path, cv::IMREAD_GRAYSCALE);
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cv::Mat bgr_float_mat;
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cv::Mat gray_float_mat;
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bgr_mat.convertTo(bgr_float_mat, CV_32FC3);
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gray_mat.convertTo(gray_float_mat, CV_32FC1);
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vector<cv::Mat> mats{bgr_mat, gray_mat, bgr_float_mat, gray_float_mat};
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SECTION("crop_size: int; small size") {
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constexpr int crop_size = 224;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", crop_size}};
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for (auto& mat : mats) {
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TestCudaCenterCrop(cfg, mat, crop_size, crop_size);
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}
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}
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SECTION("crop_size: int; oversize") {
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constexpr int crop_size = 800;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", crop_size}};
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for (auto& mat : mats) {
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TestCudaCenterCrop(cfg, mat, crop_size, crop_size);
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}
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}
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SECTION("crop_size: tuple") {
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constexpr int crop_height = 224;
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constexpr int crop_width = 224;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", {crop_height, crop_width}}};
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for (auto& mat : mats) {
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TestCudaCenterCrop(cfg, mat, crop_height, crop_width);
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}
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}
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SECTION("crop_size: tuple;oversize in height") {
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constexpr int crop_height = 640;
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constexpr int crop_width = 224;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", {crop_height, crop_width}}};
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for (auto& mat : mats) {
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TestCpuCenterCrop(cfg, mat, crop_height, crop_width);
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}
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}
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SECTION("crop_size: tuple;oversize in width") {
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constexpr int crop_height = 224;
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constexpr int crop_width = 800;
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Value cfg{{"type", "CenterCrop"}, {"crop_size", {crop_height, crop_width}}};
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for (auto& mat : mats) {
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TestCudaCenterCrop(cfg, mat, crop_height, crop_width);
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}
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}
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}
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