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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>
53 lines
1.5 KiB
C++
53 lines
1.5 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "graph/inference.h"
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#include "archive/json_archive.h"
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#include "archive/value_archive.h"
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#include "core/operator.h"
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#include "graph/common.h"
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namespace mmdeploy::graph {
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Inference::Inference(const Value& cfg) : BaseNode(cfg) {
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auto& model_value = cfg["params"]["model"];
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if (model_value.is_any<Model>()) {
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model_ = model_value.get<Model>();
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} else if (model_value.is_string()) {
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auto model_path = model_value.get<std::string>();
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model_ = Model(model_path);
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} else {
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MMDEPLOY_ERROR("unsupported model specification");
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throw_exception(eInvalidArgument);
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}
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auto pipeline_json = model_.ReadFile("pipeline.json").value();
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auto json = nlohmann::json::parse(pipeline_json);
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auto context = cfg.value("context", Value(ValueType::kObject));
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context["model"] = model_;
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auto value = from_json<Value>(json);
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value["context"] = context;
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pipeline_ = std::make_unique<Pipeline>(value);
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if (!pipeline_) {
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MMDEPLOY_ERROR("failed to create pipeline");
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throw_exception(eFail);
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}
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}
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void Inference::Build(TaskGraph& graph) { pipeline_->Build(graph); }
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class InferenceNodeCreator : public Creator<Node> {
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public:
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const char* GetName() const override { return "Inference"; }
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int GetVersion() const override { return 0; }
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std::unique_ptr<Node> Create(const Value& value) override {
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return std::make_unique<Inference>(value);
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}
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};
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REGISTER_MODULE(Node, InferenceNodeCreator);
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} // namespace mmdeploy::graph
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