90 lines
3.1 KiB
C++
90 lines
3.1 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include <fstream>
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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 "archive/json_archive.h"
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#include "core/mat.h"
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#include "core/registry.h"
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#include "core/utils/formatter.h"
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#include "json.hpp"
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#include "preprocess/cpu/opencv_utils.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 mmdeploy::test;
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using namespace std;
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using nlohmann::json;
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void TestCpuCompose(const Value& cfg, const cv::Mat& mat) {
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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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}
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void TestCudaCompose(const Value& cfg, const cv::Mat& mat) {
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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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REQUIRE(transform != nullptr);
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}
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TEST_CASE("compose", "[compose]") {
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const char* img_path = "../../tests/data/images/ocr.jpg";
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cv::Mat bgr_mat = cv::imread(img_path, cv::IMREAD_COLOR);
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auto src_mat = cpu::CVMat2Mat(bgr_mat, PixelFormat::kBGR);
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Value input{{"ori_img", src_mat}};
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auto config_path{"../../config/text-detector/dbnet18_t4-cuda11.1-trt7.2-fp16/pipeline.json"};
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ifstream ifs(config_path);
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std::string config(istreambuf_iterator<char>{ifs}, istreambuf_iterator<char>{});
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auto json = json::parse(config);
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auto transform_json = json["pipeline"]["tasks"][0]["transforms"];
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auto cfg = ::mmdeploy::from_json<Value>(transform_json);
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Value compose_cfg{{"type", "Compose"}, {"transforms", cfg}};
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INFO("cfg: {}", compose_cfg);
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Device cpu_device{"cpu"};
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Stream cpu_stream{cpu_device};
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auto cpu_transform = CreateTransform(compose_cfg, cpu_device, cpu_stream);
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REQUIRE(cpu_transform != nullptr);
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auto cpu_result = cpu_transform->Process({{"ori_img", src_mat}});
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REQUIRE(!cpu_result.has_error());
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auto _cpu_result = cpu_result.value();
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auto cpu_tensor = _cpu_result["img"].get<Tensor>();
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INFO("cpu_tensor.shape: {}", cpu_tensor.shape());
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cpu_tensor.Reshape(
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{cpu_tensor.shape(0), cpu_tensor.shape(2), cpu_tensor.shape(3), cpu_tensor.shape(1)});
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auto ref_mat = mmdeploy::cpu::Tensor2CVMat(cpu_tensor);
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INFO("ref_mat, h:{}, w:{}, c:{}", ref_mat.rows, ref_mat.cols, ref_mat.channels());
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Device cuda_device{"cuda"};
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Stream cuda_stream{cuda_device};
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auto gpu_transform = CreateTransform(compose_cfg, cuda_device, cuda_stream);
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REQUIRE(gpu_transform != nullptr);
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auto gpu_result = gpu_transform->Process({{"ori_img", src_mat}});
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REQUIRE(!gpu_result.has_error());
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auto _gpu_result = gpu_result.value();
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auto gpu_tensor = _gpu_result["img"].get<Tensor>();
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Device _device{"cpu"};
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auto host_tensor = MakeAvailableOnDevice(gpu_tensor, _device, cuda_stream).value();
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REQUIRE(cuda_stream.Wait());
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INFO("host_tensor.shape: {}", host_tensor.shape());
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host_tensor.Reshape(
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{host_tensor.shape(0), host_tensor.shape(2), host_tensor.shape(3), host_tensor.shape(1)});
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auto res_mat = mmdeploy::cpu::Tensor2CVMat(host_tensor);
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INFO("res_mat, h:{}, w:{}, c:{}", res_mat.rows, res_mat.cols, res_mat.channels());
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REQUIRE(mmdeploy::cpu::Compare(ref_mat, res_mat));
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}
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