mmdeploy/csrc/core/graph.cpp

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Merge sdk (#251) * 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>
2021-12-07 10:57:55 +08:00
// Copyright (c) OpenMMLab. All rights reserved.
#include "core/graph.h"
#include "archive/value_archive.h"
namespace mmdeploy::graph {
TaskGraph::Handle* TaskGraph::Add(TaskFunction fn) {
function_.push_back(std::move(fn));
handle_.push_back(std::make_unique<Handle>());
return handle_.back().get();
}
TaskGraph::~TaskGraph() {
for (int i = 0; i < time_.size(); ++i) {
INFO("node {} ({}): {} ms", i, handle_[i]->name(), static_cast<float>(time_[i]) / count_);
}
}
Result<Value> TaskGraph::Run(Value inputs) {
Context ctx(this);
ctx.push(std::move(inputs));
time_.resize(function_.size());
for (int i = 0; i < function_.size(); ++i) {
auto t0 = std::chrono::high_resolution_clock::now();
OUTCOME_TRY(function_[i](ctx));
auto t1 = std::chrono::high_resolution_clock::now();
auto dt = std::chrono::duration<double, std::milli>(t1 - t0).count();
time_[i] += dt;
}
count_ += 1;
return ctx.pop();
}
std::vector<Result<Value>> TaskGraph::Execute(Span<std::function<Result<Value>()>> tasks) {
#if MMDEPLOY_USE_TASKFLOW
std::vector<tf::Future<std::optional<Result<Value>>>> futures;
futures.reserve(tasks.size());
for (auto& task : tasks) {
futures.push_back(executor_.async(task));
}
executor_.wait_for_all();
std::vector<Result<Value>> rets;
rets.reserve(tasks.size());
for (auto& future : futures) {
Result<Value> ret = Status(eUnknown);
try {
ret = *future.get();
} catch (...) {
ret = Status(eFail);
}
rets.push_back(std::move(ret));
}
return rets;
#else
std::vector<Result<Value>> rets;
rets.reserve(tasks.size());
for (auto& task : tasks) {
Result<Value> ret = Status(eUnknown);
try {
ret = task();
} catch (const Exception& e) {
ret = failure(e.code());
} catch (...) {
ret = Status(eFail);
}
rets.push_back(std::move(ret));
}
return rets;
#endif
}
std::vector<Result<Value>> Context::Execute(Span<std::function<Result<Value>()>> tasks) {
return graph_->Execute(tasks);
}
} // namespace mmdeploy::graph