mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
* 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>
79 lines
2.0 KiB
C++
79 lines
2.0 KiB
C++
// 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
|