// Copyright (c) OpenMMLab. All rights reserved. #include "segmentor.h" #include "codebase/mmseg/mmseg.h" #include "core/device.h" #include "core/graph.h" #include "core/mat.h" #include "core/tensor.h" #include "core/utils/formatter.h" #include "handle.h" using namespace std; using namespace mmdeploy; namespace { Value& config_template() { // clang-format off static Value v{ { "pipeline", { {"input", {"img"}}, {"output", {"mmsegmentation-fcn_output"}}, { "tasks", { { {"name", "mmsegmentation-fcn"}, {"type", "Inference"}, {"params", {{"model", "TBD"}}}, {"input", {"img"}}, {"output", {"mmsegmentation-fcn_output"}} } } } } } }; // clang-format on return v; } template int mmdeploy_segmentor_create_impl(ModelType&& m, const char* device_name, int device_id, mm_handle_t* handle) { try { auto value = config_template(); value["pipeline"]["tasks"][0]["params"]["model"] = std::forward(m); auto segmentor = std::make_unique(device_name, device_id, std::move(value)); *handle = segmentor.release(); return MM_SUCCESS; } catch (const std::exception& e) { ERROR("exception caught: {}", e.what()); } catch (...) { ERROR("unknown exception caught"); } return MM_E_FAIL; } } // namespace MM_SDK_API int mmdeploy_segmentor_create(mm_model_t model, const char* device_name, int device_id, mm_handle_t* handle) { return mmdeploy_segmentor_create_impl(*static_cast(model), device_name, device_id, handle); } MM_SDK_API int mmdeploy_segmentor_create_by_path(const char* model_path, const char* device_name, int device_id, mm_handle_t* handle) { return mmdeploy_segmentor_create_impl(model_path, device_name, device_id, handle); } MM_SDK_API int mmdeploy_segmentor_apply(mm_handle_t handle, const mm_mat_t* mats, int mat_count, mm_segment_t** results) { if (handle == nullptr || mats == nullptr || mat_count == 0 || results == nullptr) { return MM_E_INVALID_ARG; } try { auto segmentor = static_cast(handle); Value input{Value::kArray}; for (int i = 0; i < mat_count; ++i) { mmdeploy::Mat _mat{mats[i].height, mats[i].width, PixelFormat(mats[i].format), DataType(mats->type), mats[i].data, Device{"cpu"}}; input.front().push_back({{"ori_img", _mat}}); } auto output = segmentor->Run(std::move(input)).value().front(); auto deleter = [&](mm_segment_t* p) { mmdeploy_segmentor_release_result(p, mat_count); }; unique_ptr _results(new mm_segment_t[mat_count]{}, deleter); auto results_ptr = _results.get(); for (auto i = 0; i < mat_count; ++i, ++results_ptr) { auto& output_item = output[i]; DEBUG("the {}-th item in output: {}", i, output_item); auto segmentor_output = from_value(output_item); results_ptr->height = segmentor_output.height; results_ptr->width = segmentor_output.width; results_ptr->classes = segmentor_output.classes; results_ptr->mask = new int[results_ptr->height * results_ptr->width]; segmentor_output.mask.CopyTo(results_ptr->mask, segmentor->stream()).value(); } segmentor->stream().Wait().value(); *results = _results.release(); return MM_SUCCESS; } catch (const std::exception& e) { ERROR("exception caught: {}", e.what()); } catch (...) { ERROR("unknown exception caught"); } return MM_E_FAIL; } MM_SDK_API void mmdeploy_segmentor_release_result(mm_segment_t* results, int count) { if (results == nullptr) { return; } for (auto i = 0; i < count; ++i) { delete[] results[i].mask; } delete[] results; } MM_SDK_API void mmdeploy_segmentor_destroy(mm_handle_t handle) { if (handle != nullptr) { auto segmentor = static_cast(handle); delete segmentor; } }