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36124f6205
* 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>
71 lines
1.5 KiB
C++
71 lines
1.5 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 <fstream>
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#include <numeric>
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#include <opencv2/imgcodecs.hpp>
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#include "archive/json_archive.h"
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#include "core/graph.h"
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#include "core/mat.h"
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#include "core/registry.h"
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const auto json_str = R"({
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"pipeline": {
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"tasks": [
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{
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"name": "load",
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"type": "Task",
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"module": "LoadImage",
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"input": ["input"],
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"output": ["img"]
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},
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{
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"name": "cls",
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"type": "Inference",
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"params": {
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"model": "../../config/text-recognizer/crnn",
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"batch_size": 1
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},
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"input": ["img"],
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"output": ["text"]
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}
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],
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"input": ["input"],
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"output": ["img", "text"]
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}
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}
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)";
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TEST_CASE("test crnn", "[crnn]") {
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using namespace mmdeploy;
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auto json = nlohmann::json::parse(json_str);
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auto value = mmdeploy::from_json<mmdeploy::Value>(json);
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value["context"]["device"] = Device(0);
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value["context"]["stream"] = Stream::GetDefault(Device(0));
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auto pipeline = Registry<graph::Node>::Get().GetCreator("Pipeline")->Create(value);
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REQUIRE(pipeline);
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graph::TaskGraph graph;
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pipeline->Build(graph);
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const auto img_list = "../crnn/imglist.txt";
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Device device{"cpu"};
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auto stream = Stream::GetDefault(device);
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std::ifstream ifs(img_list);
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std::string path;
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for (int image_id = 0; ifs >> path; ++image_id) {
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auto output = graph.Run({{{"filename", path}}});
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REQUIRE(output);
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INFO("output: {}", output.value());
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}
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}
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