mirror of
https://github.com/open-mmlab/mmdeploy.git
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* minor changes * support windows * fix GCC build * fix lint * reformat * fix Windows build * fix GCC build * search backend ops for onnxruntime * fix lint * fix lint * code clean-up * code clean-up * fix clang build * fix trt support * fix cmake for ncnn * fix cmake for openvino * fix SDK Python API * handle ops for other backends (ncnn, trt) * handle SDK Python API library location * robustify linkage * fix cuda * minor fix for openvino & ncnn * use CMAKE_CUDA_ARCHITECTURES if set * fix cuda preprocessor * fix misc * fix pplnn & pplcv, drop support for pplcv<0.6.0 * robustify cmake * update build.md (#2) * build dynamic modules as module library & fix demo (partially) * fix candidate path for mmdeploy_python * move "enable CUDA" to cmake config for demo * refine demo cmake * add comment * fix ubuntu build * revert docs/en/build.md * fix C API * fix lint * Windows build doc (#3) * check in docs related to mmdeploy build on windows * update build guide on windows platform * update build guide on windows platform * make path of thirdparty libraries consistent * make path consistency * correct build command for custom ops * correct build command for sdk * update sdk build instructions * update doc * correct build command * fix lint * correct build command and fix lint Co-authored-by: lvhan <lvhan@pjlab.org> * trailing whitespace (#4) * minor fix * fix sr sdk model * fix type deduction * fix cudaFree after driver shutting down * update ppl.cv installation warning (#5) * fix device allocator threshold & fix lint * update doc (#6) * update ppl.cv installation warning * missing 'git clone' Co-authored-by: chenxin <chenxin2@sensetime.com> Co-authored-by: zhangli <zhangli@sensetime.com> Co-authored-by: lvhan028 <lvhan_028@163.com> Co-authored-by: lvhan <lvhan@pjlab.org>
86 lines
2.1 KiB
C++
86 lines
2.1 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "core/graph.h"
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#include "archive/value_archive.h"
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#include "core/registry.h"
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namespace mmdeploy {
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namespace graph {
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TaskGraph::Handle* TaskGraph::Add(TaskFunction fn) {
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function_.push_back(std::move(fn));
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handle_.push_back(std::make_unique<Handle>());
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return handle_.back().get();
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}
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TaskGraph::~TaskGraph() {
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for (int i = 0; i < time_.size(); ++i) {
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MMDEPLOY_INFO("node {} ({}): {} ms", i, handle_[i]->name(),
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static_cast<float>(time_[i]) / count_);
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}
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}
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Result<Value> TaskGraph::Run(Value inputs) {
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Context ctx(this);
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ctx.push(std::move(inputs));
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time_.resize(function_.size());
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for (int i = 0; i < function_.size(); ++i) {
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auto t0 = std::chrono::high_resolution_clock::now();
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OUTCOME_TRY(function_[i](ctx));
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auto t1 = std::chrono::high_resolution_clock::now();
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auto dt = std::chrono::duration<double, std::milli>(t1 - t0).count();
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time_[i] += dt;
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}
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count_ += 1;
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return ctx.pop();
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}
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std::vector<Result<Value>> TaskGraph::Execute(Span<std::function<Result<Value>()>> tasks) {
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#if MMDEPLOY_USE_TASKFLOW
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std::vector<tf::Future<std::optional<Result<Value>>>> futures;
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futures.reserve(tasks.size());
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for (auto& task : tasks) {
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futures.push_back(executor_.async(task));
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}
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executor_.wait_for_all();
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std::vector<Result<Value>> rets;
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rets.reserve(tasks.size());
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for (auto& future : futures) {
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Result<Value> ret = Status(eUnknown);
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try {
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ret = *future.get();
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} catch (...) {
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ret = Status(eFail);
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}
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rets.push_back(std::move(ret));
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}
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return rets;
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#else
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std::vector<Result<Value>> rets;
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rets.reserve(tasks.size());
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for (auto& task : tasks) {
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Result<Value> ret = Status(eUnknown);
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try {
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ret = task();
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} catch (const Exception& e) {
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ret = failure(e.code());
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} catch (...) {
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ret = Status(eFail);
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}
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rets.push_back(std::move(ret));
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}
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return rets;
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#endif
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}
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std::vector<Result<Value>> Context::Execute(Span<std::function<Result<Value>()>> tasks) {
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return graph_->Execute(tasks);
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}
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} // namespace graph
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MMDEPLOY_DEFINE_REGISTRY(graph::Node);
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} // namespace mmdeploy
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