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36124f6205
* 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>
62 lines
2.5 KiB
C
62 lines
2.5 KiB
C
// Copyright (c) OpenMMLab. All rights reserved.
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#ifndef MMDEPLOY_CLASSIFIER_H
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#define MMDEPLOY_CLASSIFIER_H
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#include "common.h"
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typedef struct mm_class_t {
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int label_id;
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float score;
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} mm_class_t;
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MM_SDK_API int mmdeploy_classifier_create(mm_model_t model, const char* device_name, int device_id,
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mm_handle_t* handle);
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/**
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* @brief Create classifier's handle
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* @param config classifier's config which is supposed to be json
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* @param device_name name of device, such as "cpu", "cuda" and etc.
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* @param device_id id of device.
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* @param handle instance of a classifier
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* @return status of creating classifier's handle
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*/
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MM_SDK_API int mmdeploy_classifier_create_by_path(const char* model_path, const char* device_name,
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int device_id, mm_handle_t* handle);
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/**
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* @brief Use classifier created by `mmdeploy_classifier_create_by_path` to get label information
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* of each image in a batch
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* @param handle classifier's handle made by `mmdeploy_classifier_create_by_path`
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* @param mats batch images
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* @param mat_count number of images in a batch
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* @param results a consecutive buffer to save classification results of each
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* image. User has to access `result_count` to get the range of an
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* image's classification result. `results` is created inside, and should be
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* freed by calling `mmdeploy_classifier_release_result`
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* @param result_count a consecutive buffer to save the number of classification
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* results of each image. Its length is equal to `mat_count`. It is created
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* inside and should be destroyed by calling `mmdeploy_classifier_release_result`
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* @return status of inference
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*/
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MM_SDK_API int mmdeploy_classifier_apply(mm_handle_t handle, const mm_mat_t* mats, int mat_count,
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mm_class_t** results, int** result_count);
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/**
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* @brief release the inference result buffer
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* @param results a consecutive buffer to save images' classification results
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* @param result_count a consecutive buffer to save the size of classification
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* results of each image in a batch
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* @param count length of `result_count`
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*/
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MM_SDK_API void mmdeploy_classifier_release_result(mm_class_t* results, const int* result_count,
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int count);
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/**
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* @brief destroy classifier's handle
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* @param handle classifier's handle made by `mmdeploy_classifier_create_by_path`
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*/
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MM_SDK_API void mmdeploy_classifier_destroy(mm_handle_t handle);
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#endif // MMDEPLOY_CLASSIFIER_H
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