ML C-API interfaces and implementations are being refactored for better relations.
ml-common will be no longer depending on nnstreamer.
Then, in the future, nntrainer will be no longer depending on nnstreamer except for its nnstreamer-nntrainer subplugin.
1. Renamed headers and sources (except for Tizen Studio's files, which we need to tend later)
2. Refactored inter-file dependency and relations.
Signed-off-by: MyungJoo Ham <myungjoo.ham@samsung.com>
- Ubuntu:
+## Design
+
+
+Target Design (there are a little work-to-do to follow this design)
+```(impl)``` and ```'''``` or ```:``` = Implements
+``` < V ``` and ```---``` or ```|``` = Depends
+``` [ ] ``` = interface (header)
+``` ( ) ``` = implementation (source)
+
+### Overview with other repos
+```
++----- API.git -----------------------------------------------------------+
+| |
+| [........ ml-api-common ...........]<''''(impl)'''( ml-api-common-impl) |
+| ^ ^ |
+| | | |
+| [ ml-api-training ] [ ml-api-inference ] <---- [ ml-api-service ] |
+| ^ (impl) ^ (impl) ^ (impl) |
+| : : : |
+| ( ml-api-tr-impl ) ( ml-api-inf-impl (p/s) ) ( ml-api-serv-impl ) |
++----|---------------------------|----------------------------------------+
+ V V
++----+- nntrainer.git --+ +------+-------- nnstreamer.git ----------------+
+| | | + GStreamer |
++-----------------------+ +-----------------------------------------------+
+```
+- As of 2021-10-20, ml-api-tr-impl is not yet migrated from nntrainer.git.
+- As of 2021-10-20, ml-api-service and ml-api-serv-impl do not exist, yet.
+
+### Internal overview (trainer not included)
+```
+
+ [ ml-api-common (pub) ]<---+-----------------------------------+
+ ^ | |
+ : [ nnstreamer.h == ml-api-inf-pipeline (pub) ]<-- | -------------------+
+ : ^ | |
+ : : [ nnstreamer-single.h == ml-api-inf-single (pub) ]<----+
+ : : ^ |
+ : : : [ nnstreamer-tizen-internal.h == ml-api-private (platform only) ]
+ : : : ^
+ ( ml-api-common ) : +'''''''''''''+
+ : +''' : ''''''''''''+
+ : ( ml-api-common-tiz* ) : :
+ : : : :
+ : : : :
+ V V : :
+ [ ml-api-internal (int) ] : +''''''''''''+
+ ^ : :
+ | ( ml-api-inference-pipeline ) :
+ | | :
+ | | ( ml-api-inference-single )
+ | V |
+[ ml-api-inference-internal (int) ]<-----------+
+ ^
+ :
+( ml-api-inference-internal )
+
+```
+- (pub): public header / interface, exposed to SDK and applications
+- (platform only): private header / interface, exposed to platform components
+- (int): internal header / interface, exposed to api.git components only.
+
+- ml-trainer APIs may depend on ml-api-internal.
+
+### Headers
+
+/c/include/*.h : Exported headers, accessed by other packages.
+ - Public (ml-api-common.h, nnstreamer-single.h, nnstreamer.h)
+ - Supposed to be accessed by application via SDK (e.g., Tizen Studio, Android Studio)
+ - Platform Only (nnstreamer-tizen-internal.h)
+ - Supposed to be accessed by platform package (e.g., Tizen middleware)
+/c/src/*.h : Not exported headers. Cannot be accessed by other packages.
Description: ML common API for Tizen
Version: @VERSION@
Requires: @ML_COMMON_REQUIRE@
-Libs: -L${libdir}
+Libs: -L${libdir} -lcapi-ml-common
Cflags: -I${includedir}/nnstreamer
+++ /dev/null
-/* SPDX-License-Identifier: Apache-2.0 */
-/**
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved.
- *
- * @file nnstreamer-capi-private.h
- * @date 07 March 2019
- * @brief NNStreamer/Pipeline(main) C-API Private Header.
- * This file should NOT be exported to SDK or devel package.
- * @see https://github.com/nnstreamer/nnstreamer
- * @author MyungJoo Ham <myungjoo.ham@samsung.com>
- * @bug No known bugs except for NYI items
- */
-
-#ifndef __NNSTREAMER_CAPI_PRIVATE_H__
-#define __NNSTREAMER_CAPI_PRIVATE_H__
-
-#include <glib.h>
-#include <gst/gst.h>
-
-#include "nnstreamer.h"
-#include "nnstreamer-single.h"
-#include <tensor_typedef.h>
-#include <nnstreamer_log.h>
-
-/* Tizen ML feature */
-#if defined (__TIZEN__)
-#include "nnstreamer-tizen-internal.h"
-
-typedef enum
-{
- NOT_CHECKED_YET = -1,
- NOT_SUPPORTED = 0,
- SUPPORTED = 1
-} feature_state_t;
-
-#if defined (__FEATURE_CHECK_SUPPORT__)
-#define check_feature_state() \
- do { \
- int feature_ret = ml_tizen_get_feature_enabled (); \
- if (ML_ERROR_NONE != feature_ret) \
- return feature_ret; \
- } while (0);
-
-#define set_feature_state(...) ml_tizen_set_feature_state(__VA_ARGS__)
-#else
-#define check_feature_state()
-#define set_feature_state(...)
-#endif /* __FEATURE_CHECK_SUPPORT__ */
-
-#if defined (__PRIVILEGE_CHECK_SUPPORT__)
-
-#define convert_tizen_element(...) ml_tizen_convert_element(__VA_ARGS__)
-
-#if (TIZENVERSION >= 5) && (TIZENVERSION < 9999)
-#define get_tizen_resource(...) ml_tizen_get_resource(__VA_ARGS__)
-#define release_tizen_resource(...) ml_tizen_release_resource(__VA_ARGS__)
-#define TIZEN5PLUS 1
-
-#if ((TIZENVERSION > 6) || (TIZENVERSION == 6 && TIZENVERSIONMINOR >= 5))
-#define TIZENMMCONF 1
-#endif
-
-#elif (TIZENVERSION < 5)
-#define get_tizen_resource(...) (0)
-#define release_tizen_resource(...) do { } while (0)
-typedef void * mm_resource_manager_h;
-typedef enum { MM_RESOURCE_MANAGER_RES_TYPE_MAX } mm_resource_manager_res_type_e;
-
-#else
-#error Tizen version is not defined.
-#endif
-
-#else
-
-#define convert_tizen_element(...) ML_ERROR_NONE
-#define get_tizen_resource(...) ML_ERROR_NONE
-#define release_tizen_resource(...)
-
-#endif /* __PRIVILEGE_CHECK_SUPPORT__ */
-
-#else
-#define check_feature_state()
-#define set_feature_state(...)
-#define convert_tizen_element(...) ML_ERROR_NONE
-#define get_tizen_resource(...) ML_ERROR_NONE
-#define release_tizen_resource(...)
-#endif /* __TIZEN__ */
-
-#ifndef TIZEN5PLUS
-#define TIZEN5PLUS 0
-#endif /* TIZEN5PLUS */
-#ifndef TIZENMMCONF
-#define TIZENMMCONF 0
-#endif /* TIZENMMCONF */
-
-#define EOS_MESSAGE_TIME_LIMIT 100
-#define WAIT_PAUSED_TIME_LIMIT 100
-
-#ifdef __cplusplus
-extern "C" {
-#endif /* __cplusplus */
-
-/**
- * @brief Internal private representation of custom filter handle.
- */
-typedef struct {
- char *name;
- unsigned int ref_count;
- GMutex lock;
- ml_tensors_info_h in_info;
- ml_tensors_info_h out_info;
- ml_custom_easy_invoke_cb cb;
- void *pdata;
-} ml_custom_filter_s;
-
-/**
- * @brief Data structure for tensor information.
- * @since_tizen 5.5
- */
-typedef struct {
- char *name; /**< Name of each element in the tensor. */
- ml_tensor_type_e type; /**< Type of each element in the tensor. */
- ml_tensor_dimension dimension; /**< Dimension information. */
-} ml_tensor_info_s;
-
-/**
- * @brief Data structure for tensors information, which contains multiple tensors.
- * @since_tizen 5.5
- */
-typedef struct {
- unsigned int num_tensors; /**< The number of tensors. */
- ml_tensor_info_s info[ML_TENSOR_SIZE_LIMIT]; /**< The list of tensor info. */
- GMutex lock; /**< Lock for thread safety */
- int nolock; /**< Set non-zero to avoid using m (giving up thread safety) */
-} ml_tensors_info_s;
-
-/**
- * @brief Macro to control private lock with nolock condition (lock)
- * @param sname The name of struct (ml_tensors_info_s or ml_tensors_data_s)
- */
-#define G_LOCK_UNLESS_NOLOCK(sname) \
- do { \
- GMutex *l = (GMutex *) &(sname).lock; \
- if (!(sname).nolock) \
- g_mutex_lock (l); \
- } while (0)
-
-/**
- * @brief Macro to control private lock with nolock condition (unlock)
- * @param sname The name of struct (ml_tensors_info_s or ml_tensors_data_s)
- */
-#define G_UNLOCK_UNLESS_NOLOCK(sname) \
- do { \
- GMutex *l = (GMutex *) &(sname).lock; \
- if (!(sname).nolock) \
- g_mutex_unlock (l); \
- } while (0)
-
-/**
- * @brief Macro to verify private lock acquired with nolock condition (lock)
- * @param sname The name of struct (ml_tensors_info_s or ml_tensors_data_s)
- */
-#define G_VERIFYLOCK_UNLESS_NOLOCK(sname) \
- do { \
- GMutex *l = (GMutex *) &(sname).lock; \
- if (!(sname).nolock) { \
- if (g_mutex_trylock(l)) { \
- g_mutex_unlock(l); \
- return ML_ERROR_INVALID_PARAMETER; \
- } \
- } \
- } while (0)
-
-
-/**
- * @brief The function to be called when destroying the allocated handle.
- * @since_tizen 6.5
- * @param[in] handle The handle created for ML API.
- * @param[in,out] user_data The user data to pass to the callback function.
- * @return @c 0 on success. Otherwise a negative error value.
- */
-typedef int (*ml_handle_destroy_cb) (void *handle, void *user_data);
-
-/**
- * @brief An instance of a single input or output frame.
- * @since_tizen 5.5
- */
-typedef struct {
- void *tensor; /**< The instance of tensor data. */
- size_t size; /**< The size of tensor. */
-} ml_tensor_data_s;
-
-/**
- * @brief An instance of input or output frames. #ml_tensors_info_h is the handle for tensors metadata.
- * @since_tizen 5.5
- */
-typedef struct {
- unsigned int num_tensors; /**< The number of tensors. */
- ml_tensor_data_s tensors[ML_TENSOR_SIZE_LIMIT]; /**< The list of tensor data. NULL for unused tensors. */
-
- /* private */
- ml_tensors_info_h info;
- void *user_data; /**< The user data to pass to the callback function */
- ml_handle_destroy_cb destroy; /**< The function to be called to release the allocated buffer */
- GMutex lock; /**< Lock for thread safety */
- int nolock; /**< Set non-zero to avoid using m (giving up thread safety) */
-} ml_tensors_data_s;
-
-/**
- * @brief Internal private representation of tensor_if custom conditon.
- * @since_tizen 6.5
- */
-typedef struct {
- char *name;
- unsigned int ref_count;
- GMutex lock;
- ml_pipeline_if_custom_cb cb;
- void *pdata;
-} ml_if_custom_s;
-
-/**
- * @brief Possible controls on elements of a pipeline.
- */
-typedef enum {
- ML_PIPELINE_ELEMENT_UNKNOWN = 0x0,
- ML_PIPELINE_ELEMENT_SINK = 0x1,
- ML_PIPELINE_ELEMENT_APP_SRC = 0x2,
- ML_PIPELINE_ELEMENT_APP_SINK = 0x3,
- ML_PIPELINE_ELEMENT_VALVE = 0x4,
- ML_PIPELINE_ELEMENT_SWITCH_INPUT = 0x8,
- ML_PIPELINE_ELEMENT_SWITCH_OUTPUT = 0x9,
- ML_PIPELINE_ELEMENT_COMMON = 0xB,
-} ml_pipeline_element_e;
-
-/**
- * @brief Internal private representation of pipeline handle.
- */
-typedef struct _ml_pipeline ml_pipeline;
-
-/**
- * @brief An element that may be controlled individually in a pipeline.
- */
-typedef struct _ml_pipeline_element {
- GstElement *element; /**< The Sink/Src/Valve/Switch element */
- ml_pipeline *pipe; /**< The main pipeline */
- char *name;
- ml_pipeline_element_e type;
- GstPad *src;
- GstPad *sink; /**< Unref this at destroy */
- ml_tensors_info_s tensors_info;
- size_t size;
-
- GList *handles;
- int maxid; /**< to allocate id for each handle */
- gulong handle_id;
-
- GMutex lock; /**< Lock for internal values */
- gboolean is_media_stream;
- gboolean is_flexible_tensor;
-
- ml_handle_destroy_cb custom_destroy;
- gpointer custom_data;
-} ml_pipeline_element;
-
-/**
- * @brief Internal data structure for the pipeline state callback.
- */
-typedef struct {
- ml_pipeline_state_cb cb; /**< Callback to notify the change of pipeline state */
- void *user_data; /**< The user data passed when calling the state change callback */
-} pipeline_state_cb_s;
-
-/**
- * @brief Internal data structure for the resource.
- */
-typedef struct {
- gchar *type; /**< resource type */
- gpointer handle; /**< pointer to resource handle */
-} pipeline_resource_s;
-
-/**
- * @brief Internal private representation of pipeline handle.
- * @details This should not be exposed to applications
- */
-struct _ml_pipeline {
- GstElement *element; /**< The pipeline itself (GstPipeline) */
- GstBus *bus; /**< The bus of the pipeline */
- gulong signal_msg; /**< The message signal (connected to bus) */
- GMutex lock; /**< Lock for pipeline operations */
- gboolean isEOS; /**< The pipeline is EOS state */
- ml_pipeline_state_e pipe_state; /**< The state of pipeline */
- GHashTable *namednodes; /**< hash table of "element"s. */
- GHashTable *resources; /**< hash table of resources to construct the pipeline */
- pipeline_state_cb_s state_cb; /**< Callback to notify the change of pipeline state */
-};
-
-/**
- * @brief Internal private representation sink callback function for GstTensorSink and GstAppSink
- * @details This represents a single instance of callback registration. This should not be exposed to applications.
- */
-typedef struct {
- ml_pipeline_sink_cb sink_cb;
- ml_pipeline_src_callbacks_s src_cb;
- void *pdata;
-} callback_info_s;
-
-/**
- * @brief Internal private representation of common element handle (All GstElement except AppSink and TensorSink)
- * @details This represents a single instance of registration. This should not be exposed to applications.
- */
-typedef struct _ml_pipeline_common_elem {
- ml_pipeline *pipe;
- ml_pipeline_element *element;
- guint32 id;
- callback_info_s *callback_info; /**< Callback function information. If element is not GstTensorSink or GstAppSink, then it should be NULL. */
-} ml_pipeline_common_elem;
-
-/**
- * @brief An information to create single-shot instance.
- */
-typedef struct {
- ml_tensors_info_h input_info; /**< The input tensors information. */
- ml_tensors_info_h output_info; /**< The output tensors information. */
- ml_nnfw_type_e nnfw; /**< The neural network framework. */
- ml_nnfw_hw_e hw; /**< The type of hardware resource. */
- char *models; /**< Comma separated neural network model files. */
- char *custom_option; /**< Custom option string for neural network framework. */
-} ml_single_preset;
-
-/**
- * @brief Opens an ML model with the custom options and returns the instance as a handle.
- * This is internal function to handle various options in public APIs.
- */
-int ml_single_open_custom (ml_single_h *single, ml_single_preset *info);
-
-/**
- * @brief Macro to check the availability of given NNFW.
- */
-#define ml_nnfw_is_available(f,h) ({bool a; (ml_check_nnfw_availability ((f), (h), &a) == ML_ERROR_NONE && a);})
-
-/**
- * @brief Macro to check the availability of given element.
- */
-#define ml_element_is_available(e) ({bool a; (ml_check_element_availability ((e), &a) == ML_ERROR_NONE && a);})
-
-/**
- * @brief Macro to check the tensors info is valid.
- */
-#define ml_tensors_info_is_valid(i) ({bool v; (ml_tensors_info_validate ((i), &v) == ML_ERROR_NONE && v);})
-
-/**
- * @brief Macro to compare the tensors info.
- */
-#define ml_tensors_info_is_equal(i1,i2) ({bool e; (ml_tensors_info_compare ((i1), (i2), &e) == ML_ERROR_NONE && e);})
-
-/**
- * @brief Gets the byte size of the given tensor info.
- * @note This is not thread safe.
- */
-size_t ml_tensor_info_get_size (const ml_tensor_info_s *info);
-
-/**
- * @brief Initializes the tensors information with default value.
- * @since_tizen 5.5
- * @param[in] info The tensors info pointer to be initialized.
- * @return @c 0 on success. Otherwise a negative error value.
- * @retval #ML_ERROR_NONE Successful
- * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
- */
-int ml_tensors_info_initialize (ml_tensors_info_s *info);
-
-/**
- * @brief Compares the given tensors information.
- * @details If the function returns an error, @a equal is not changed.
- * @since_tizen 6.0
- * @param[in] info1 The handle of tensors information to be compared.
- * @param[in] info2 The handle of tensors information to be compared.
- * @param[out] equal @c true if given tensors information is equal, @c false if it's not equal.
- * @return @c 0 on success. Otherwise a negative error value.
- * @retval #ML_ERROR_NONE Successful
- * @retval #ML_ERROR_NOT_SUPPORTED Not supported.
- * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
- */
-int ml_tensors_info_compare (const ml_tensors_info_h info1, const ml_tensors_info_h info2, bool *equal);
-
-/**
- * @brief Frees and initialize the data in tensors info.
- * @since_tizen 5.5
- * @param[in] info The tensors info pointer to be freed.
- */
-void ml_tensors_info_free (ml_tensors_info_s *info);
-
-/**
- * @brief Allocates a tensors information handle from gst info.
- */
-int ml_tensors_info_create_from_gst (ml_tensors_info_h *ml_info, GstTensorsInfo *gst_info);
-
-/**
- * @brief Copies tensor metadata from gst tensors info.
- */
-void ml_tensors_info_copy_from_gst (ml_tensors_info_s *ml_info, const GstTensorsInfo *gst_info);
-
-/**
- * @brief Copies tensor metadata from ml tensors info.
- */
-void ml_tensors_info_copy_from_ml (GstTensorsInfo *gst_info, const ml_tensors_info_s *ml_info);
-
-/**
- * @brief Frees the tensors data handle and its data.
- * @param[in] data The handle of tensors data.
- * @param[in] free_data The flag to free the buffers in handle.
- * @return @c 0 on success. Otherwise a negative error value.
- */
-int ml_tensors_data_destroy_internal (ml_tensors_data_h data, gboolean free_data);
-
-/**
- * @brief Creates a tensor data frame without buffer with the given tensors information.
- * @details If @a info is null, this allocates data handle with empty tensor data.
- * @param[in] info The handle of tensors information for the allocation.
- * @param[out] data The handle of tensors data.
- * @return @c 0 on success. Otherwise a negative error value.
- */
-int ml_tensors_data_create_no_alloc (const ml_tensors_info_h info, ml_tensors_data_h *data);
-
-/**
- * @brief Creates a tensor data frame without allocating new buffer cloning the given tensors data.
- * @details If @a data_src is null, this returns error.
- * @param[in] data_src The handle of tensors data to be cloned.
- * @param[out] data The handle of tensors data.
- * @return @c 0 on success. Otherwise a negative error value.
- * @retval #ML_ERROR_NONE Successful
- * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
- * @retval #ML_ERROR_OUT_OF_MEMORY Failed to allocate required memory.
- */
-int ml_tensors_data_clone_no_alloc (const ml_tensors_data_s * data_src, ml_tensors_data_h * data);
-
-/**
- * @brief Initializes the GStreamer library. This is internal function.
- */
-int ml_initialize_gstreamer (void);
-
-/**
- * @brief Validates the nnfw model file.
- * @since_tizen 5.5
- * @param[in] model List of model file paths.
- * @param[in] num_models The number of model files. There are a few frameworks that require multiple model files for a single model.
- * @param[in/out] nnfw The type of NNFW.
- * @return @c 0 on success. Otherwise a negative error value.
- * @retval #ML_ERROR_NONE Successful
- * @retval #ML_ERROR_NOT_SUPPORTED Not supported.
- * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
- */
-int ml_validate_model_file (const char * const *model, const unsigned int num_models, ml_nnfw_type_e * nnfw);
-
-/**
- * @brief Checks the availability of the plugin.
- */
-int ml_check_plugin_availability (const char *plugin_name, const char *element_name);
-
-/**
- * @brief Internal function to convert accelerator as tensor_filter property format.
- * @note returned value must be freed by the caller
- */
-char* ml_nnfw_to_str_prop (ml_nnfw_hw_e hw);
-
-/**
- * @brief Internal function to get the sub-plugin name.
- */
-const char* ml_get_nnfw_subplugin_name (ml_nnfw_type_e nnfw);
-
-/**
- * @brief Internal function to get the nnfw type.
- */
-ml_nnfw_type_e ml_get_nnfw_type_by_subplugin_name (const char *name);
-
-/**
- * @brief Gets the element of pipeline itself (GstElement).
- * @details With the returned reference, you can use GStreamer functions to handle the element in pipeline.
- * Note that caller should release the returned reference using gst_object_unref().
- * @return The reference of pipeline itself. Null if the pipeline is not constructed or closed.
- */
-GstElement* ml_pipeline_get_gst_element (ml_pipeline_h pipe);
-
-/**
- * @brief Validates the given tensors info is valid without acquiring lock
- * @note This function assumes that lock on ml_tensors_info_s has already been acquired
- */
-int
-_ml_tensors_info_validate_nolock (const ml_tensors_info_s * info, bool *valid);
-
-#if defined (__TIZEN__)
-/**
- * @brief Checks whether machine_learning.inference feature is enabled or not.
- */
-int ml_tizen_get_feature_enabled (void);
-
-/**
- * @brief Set the feature status of machine_learning.inference.
- * This is only used for Unit test.
- */
-int ml_tizen_set_feature_state (int state);
-
-/**
- * @brief Releases the resource handle of Tizen.
- */
-void ml_tizen_release_resource (gpointer handle, const gchar * res_type);
-
-/**
- * @brief Gets the resource handle of Tizen.
- */
-int ml_tizen_get_resource (ml_pipeline_h pipe, const gchar * res_type);
-
-/**
- * @brief Converts predefined element for Tizen.
- */
-int ml_tizen_convert_element (ml_pipeline_h pipe, gchar ** result, gboolean is_internal);
-#endif
-
-#ifdef __cplusplus
-}
-#endif /* __cplusplus */
-#endif /* __NNSTREAMER_CAPI_PRIVATE_H__ */
#define __TIZEN_MACHINELEARNING_NNSTREAMER_INTERNAL_H__
#include <nnstreamer.h>
+#include <nnstreamer-single.h>
#ifdef __cplusplus
extern "C" {
*/
int ml_pipeline_construct_internal (const char *pipeline_description, ml_pipeline_state_cb cb, void *user_data, ml_pipeline_h *pipe);
+/**
+ * @brief An information to create single-shot instance.
+ */
+typedef struct {
+ ml_tensors_info_h input_info; /**< The input tensors information. */
+ ml_tensors_info_h output_info; /**< The output tensors information. */
+ ml_nnfw_type_e nnfw; /**< The neural network framework. */
+ ml_nnfw_hw_e hw; /**< The type of hardware resource. */
+ char *models; /**< Comma separated neural network model files. */
+ char *custom_option; /**< Custom option string for neural network framework. */
+} ml_single_preset;
+
+/**
+ * @brief Opens an ML model with the custom options and returns the instance as a handle.
+ * This is internal function to handle various options in public APIs.
+ */
+int ml_single_open_custom (ml_single_h *single, ml_single_preset *info);
+
#ifdef __cplusplus
}
#endif /* __cplusplus */
nns_capi_include = []
nns_capi_include += include_directories('include')
+nns_capi_include += include_directories('src')
if not get_option('enable-tizen')
nns_capi_include += include_directories('include/platform')
endif
nns_capi_srcs = []
-nns_capi_srcs += join_paths(meson.current_source_dir(), 'src', 'nnstreamer-capi-pipeline.c')
-nns_capi_srcs += join_paths(meson.current_source_dir(), 'src', 'nnstreamer-capi-util.c')
-nns_capi_srcs += join_paths(meson.current_source_dir(), 'src', 'nnstreamer-capi-single.c')
+nns_capi_common_srcs = []
+nns_capi_srcs += join_paths(meson.current_source_dir(), 'src', 'ml-api-inference-pipeline.c')
+nns_capi_srcs += join_paths(meson.current_source_dir(), 'src', 'ml-api-inference-single.c')
+nns_capi_srcs += join_paths(meson.current_source_dir(), 'src', 'ml-api-inference-internal.c')
+nns_capi_common_srcs += join_paths(meson.current_source_dir(), 'src', 'ml-api-common.c')
if get_option('enable-tizen')
if get_option('enable-tizen-feature-check')
- nns_capi_srcs += join_paths(meson.current_source_dir(), 'src', 'nnstreamer-capi-tizen-feature-check.c')
+ nns_capi_common_srcs += join_paths(meson.current_source_dir(), 'src', 'ml-api-common-tizen-feature-check.c')
endif
if get_option('enable-tizen-privilege-check')
- nns_capi_srcs += join_paths(meson.current_source_dir(), 'src', 'nnstreamer-capi-tizen-privilege-check.c')
+ nns_capi_srcs += join_paths(meson.current_source_dir(), 'src', 'ml-api-inference-tizen-privilege-check.c')
endif
endif
nns_capi_headers += join_paths(meson.current_source_dir(), 'include', 'platform', 'tizen_error.h')
endif
+
# Dependencies
nns_capi_deps = [nnstreamer_dep, glib_dep, gmodule_dep, gst_dep, gst_app_dep]
+nns_capi_common_deps = [glib_dep, gmodule_dep, gst_dep]
if (get_option('enable-tizen'))
message('C-API is in Tizen mode')
endif
nns_capi_deps += tizen_deps
+ nns_capi_common_deps += tizen_deps
+endif
+
+# Build ML-API Common Lib First.
+nns_capi_common_shared_lib = shared_library ('capi-ml-common',
+ nns_capi_common_srcs,
+ dependencies: nns_capi_common_deps,
+ include_directories: nns_capi_include,
+ install: true,
+ install_dir: api_install_libdir,
+)
+nns_capi_common_static_lib = static_library ('capi-ml-common',
+ nns_capi_common_srcs,
+ dependencies: nns_capi_common_deps,
+ include_directories: nns_capi_include,
+ install: true,
+ install_dir: api_install_libdir,
+)
+nns_capi_common_lib = nns_capi_common_shared_lib
+if get_option('default_library') == 'static'
+ nns_capi_common_lib = nns_capi_common_static_lib
endif
+nns_capi_common_dep = declare_dependency(link_with: nns_capi_common_lib)
+nns_capi_deps += nns_capi_common_dep
nns_capi_shared_lib = shared_library ('capi-nnstreamer',
nns_capi_srcs,
/**
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved.
*
- * @file nnstreamer-capi-tizen-feature-check.c
+ * @file ml-api-common-tizen-feature-check.c
* @date 21 July 2020
- * @brief NNStreamer/C-API Tizen dependent functions.
+ * @brief NNStreamer/C-API Tizen dependent functions, common for ML APIs, common for ML APIs.
* @see https://github.com/nnstreamer/nnstreamer
* @author MyungJoo Ham <myungjoo.ham@samsung.com>
* @bug No known bugs except for NYI items
#include <glib.h>
#include <system_info.h>
-#include "nnstreamer-capi-private.h"
+#include "ml-api-internal.h"
/**
* @brief Tizen ML feature.
g_mutex_unlock (&feature_info->mutex);
if (NOT_SUPPORTED == feature_enabled) {
- ml_loge ("machine_learning.inference NOT supported");
+ mlapi_loge ("machine_learning.inference NOT supported");
return ML_ERROR_NOT_SUPPORTED;
} else if (NOT_CHECKED_YET == feature_enabled) {
bool ml_inf_supported = false;
system_info_get_platform_bool (ML_INF_FEATURE_PATH, &ml_inf_supported);
if (0 == ret) {
if (false == ml_inf_supported) {
- ml_loge ("machine_learning.inference NOT supported");
+ mlapi_loge ("machine_learning.inference NOT supported");
ml_tizen_set_feature_state (NOT_SUPPORTED);
return ML_ERROR_NOT_SUPPORTED;
}
} else {
switch (ret) {
case SYSTEM_INFO_ERROR_INVALID_PARAMETER:
- ml_loge
+ mlapi_loge
("failed to get feature value because feature key is not vaild");
ret = ML_ERROR_NOT_SUPPORTED;
break;
case SYSTEM_INFO_ERROR_IO_ERROR:
- ml_loge ("failed to get feature value because of input/output error");
+ mlapi_loge
+ ("failed to get feature value because of input/output error");
ret = ML_ERROR_NOT_SUPPORTED;
break;
case SYSTEM_INFO_ERROR_PERMISSION_DENIED:
- ml_loge ("failed to get feature value because of permission denied");
+ mlapi_loge
+ ("failed to get feature value because of permission denied");
ret = ML_ERROR_PERMISSION_DENIED;
break;
default:
- ml_loge ("failed to get feature value because of unknown error");
+ mlapi_loge ("failed to get feature value because of unknown error");
ret = ML_ERROR_NOT_SUPPORTED;
break;
}
#include <string.h>
#include "nnstreamer.h"
-#include "nnstreamer-capi-private.h"
-#include "nnstreamer_plugin_api.h"
-#include "nnstreamer_plugin_api_filter.h"
-#include "nnstreamer_internal.h"
-
-/**
- * @brief The name of sub-plugin for defined neural net frameworks.
- * @note The sub-plugin for Android is not declared (e.g., snap)
- */
-static const char *ml_nnfw_subplugin_name[] = {
- [ML_NNFW_TYPE_ANY] = "any", /* DO NOT use this name ('any') to get the sub-plugin */
- [ML_NNFW_TYPE_CUSTOM_FILTER] = "custom",
- [ML_NNFW_TYPE_TENSORFLOW_LITE] = "tensorflow-lite",
- [ML_NNFW_TYPE_TENSORFLOW] = "tensorflow",
- [ML_NNFW_TYPE_NNFW] = "nnfw",
- [ML_NNFW_TYPE_MVNC] = "movidius-ncsdk2",
- [ML_NNFW_TYPE_OPENVINO] = "openvino",
- [ML_NNFW_TYPE_VIVANTE] = "vivante",
- [ML_NNFW_TYPE_EDGE_TPU] = "edgetpu",
- [ML_NNFW_TYPE_ARMNN] = "armnn",
- [ML_NNFW_TYPE_SNPE] = "snpe",
- [ML_NNFW_TYPE_PYTORCH] = "pytorch",
- [ML_NNFW_TYPE_NNTR_INF] = "nntrainer",
- [ML_NNFW_TYPE_VD_AIFW] = "vd_aifw",
- [ML_NNFW_TYPE_TRIX_ENGINE] = "trix-engine",
- NULL
-};
+#include "ml-api-internal.h"
/**
* @brief Allocates a tensors information handle with default value.
*info = tensors_info = g_new0 (ml_tensors_info_s, 1);
if (tensors_info == NULL) {
- ml_loge ("Failed to allocate the tensors info handle.");
+ mlapi_loge ("Failed to allocate the tensors info handle.");
return ML_ERROR_OUT_OF_MEMORY;
}
g_mutex_init (&tensors_info->lock);
}
/**
- * @brief Allocates a tensors information handle from gst info.
- */
-int
-ml_tensors_info_create_from_gst (ml_tensors_info_h * ml_info,
- GstTensorsInfo * gst_info)
-{
- int status;
-
- if (!ml_info || !gst_info)
- return ML_ERROR_INVALID_PARAMETER;
-
- status = ml_tensors_info_create (ml_info);
- if (status != ML_ERROR_NONE)
- return status;
-
- ml_tensors_info_copy_from_gst (*ml_info, gst_info);
- return ML_ERROR_NONE;
-}
-
-/**
* @brief Frees the given handle of a tensors information.
*/
int
}
/**
- * @brief Validates the given tensor info is valid.
- * @note info should be locked by caller if nolock == 0.
+ * @brief Compares the given tensor info.
*/
static gboolean
-ml_tensor_info_validate (const ml_tensor_info_s * info)
+ml_tensor_info_compare (const ml_tensor_info_s * i1,
+ const ml_tensor_info_s * i2)
{
guint i;
- if (!info)
+ if (i1 == NULL || i2 == NULL)
return FALSE;
- if (info->type < 0 || info->type >= ML_TENSOR_TYPE_UNKNOWN)
+ if (i1->type != i2->type)
return FALSE;
for (i = 0; i < ML_TENSOR_RANK_LIMIT; i++) {
- if (info->dimension[i] == 0)
+ if (i1->dimension[i] != i2->dimension[i])
return FALSE;
}
}
/**
- * @brief Compares the given tensor info.
+ * @brief Validates the given tensor info is valid.
+ * @note info should be locked by caller if nolock == 0.
*/
static gboolean
-ml_tensor_info_compare (const ml_tensor_info_s * i1,
- const ml_tensor_info_s * i2)
+ml_tensor_info_validate (const ml_tensor_info_s * info)
{
guint i;
- if (i1 == NULL || i2 == NULL)
+ if (!info)
return FALSE;
- if (i1->type != i2->type)
+ if (info->type < 0 || info->type >= ML_TENSOR_TYPE_UNKNOWN)
return FALSE;
for (i = 0; i < ML_TENSOR_RANK_LIMIT; i++) {
- if (i1->dimension[i] != i2->dimension[i])
+ if (info->dimension[i] == 0)
return FALSE;
}
* @brief Validates the given tensors info is valid without acquiring lock
* @note This function assumes that lock on ml_tensors_info_h has already been acquired
*/
-int
+static int
_ml_tensors_info_validate_nolock (const ml_tensors_info_s * info, bool *valid)
{
guint i;
tensor_size = 8;
break;
default:
- ml_loge ("In the given param, tensor type is invalid.");
+ mlapi_loge ("In the given param, tensor type is invalid.");
return 0;
}
_data = g_new0 (ml_tensors_data_s, 1);
if (!_data) {
- ml_loge ("Failed to allocate the tensors data handle.");
+ mlapi_loge ("Failed to allocate the tensors data handle.");
return ML_ERROR_OUT_OF_MEMORY;
}
g_mutex_init (&_data->lock);
return ML_ERROR_INVALID_PARAMETER;
if (!ml_tensors_info_is_valid (info)) {
- nns_loge ("Given tensors information is invalid.");
+ mlapi_loge ("Given tensors information is invalid.");
return ML_ERROR_INVALID_PARAMETER;
}
}
g_free (_data);
- ml_loge ("Failed to allocate the memory block.");
+ mlapi_loge ("Failed to allocate the memory block.");
return status;
}
}
/**
- * @brief Copies tensor meta info from gst tensors info.
- * @bug Thread safety required. Check its internal users first!
- */
-void
-ml_tensors_info_copy_from_gst (ml_tensors_info_s * ml_info,
- const GstTensorsInfo * gst_info)
-{
- guint i, j;
- guint max_dim;
-
- if (!ml_info || !gst_info)
- return;
-
- ml_tensors_info_initialize (ml_info);
- max_dim = MIN (ML_TENSOR_RANK_LIMIT, NNS_TENSOR_RANK_LIMIT);
-
- ml_info->num_tensors = gst_info->num_tensors;
-
- for (i = 0; i < gst_info->num_tensors; i++) {
- /* Copy name string */
- if (gst_info->info[i].name) {
- ml_info->info[i].name = g_strdup (gst_info->info[i].name);
- }
-
- /* Set tensor type */
- switch (gst_info->info[i].type) {
- case _NNS_INT32:
- ml_info->info[i].type = ML_TENSOR_TYPE_INT32;
- break;
- case _NNS_UINT32:
- ml_info->info[i].type = ML_TENSOR_TYPE_UINT32;
- break;
- case _NNS_INT16:
- ml_info->info[i].type = ML_TENSOR_TYPE_INT16;
- break;
- case _NNS_UINT16:
- ml_info->info[i].type = ML_TENSOR_TYPE_UINT16;
- break;
- case _NNS_INT8:
- ml_info->info[i].type = ML_TENSOR_TYPE_INT8;
- break;
- case _NNS_UINT8:
- ml_info->info[i].type = ML_TENSOR_TYPE_UINT8;
- break;
- case _NNS_FLOAT64:
- ml_info->info[i].type = ML_TENSOR_TYPE_FLOAT64;
- break;
- case _NNS_FLOAT32:
- ml_info->info[i].type = ML_TENSOR_TYPE_FLOAT32;
- break;
- case _NNS_INT64:
- ml_info->info[i].type = ML_TENSOR_TYPE_INT64;
- break;
- case _NNS_UINT64:
- ml_info->info[i].type = ML_TENSOR_TYPE_UINT64;
- break;
- default:
- ml_info->info[i].type = ML_TENSOR_TYPE_UNKNOWN;
- break;
- }
-
- /* Set dimension */
- for (j = 0; j < max_dim; j++) {
- ml_info->info[i].dimension[j] = gst_info->info[i].dimension[j];
- }
-
- for (; j < ML_TENSOR_RANK_LIMIT; j++) {
- ml_info->info[i].dimension[j] = 1;
- }
- }
-}
-
-/**
- * @brief Copies tensor meta info from gst tensors info.
- * @bug Thread safety required. Check its internal users first!
- */
-void
-ml_tensors_info_copy_from_ml (GstTensorsInfo * gst_info,
- const ml_tensors_info_s * ml_info)
-{
- guint i, j;
- guint max_dim;
-
- if (!gst_info || !ml_info)
- return;
-
- G_LOCK_UNLESS_NOLOCK (*ml_info);
-
- gst_tensors_info_init (gst_info);
- max_dim = MIN (ML_TENSOR_RANK_LIMIT, NNS_TENSOR_RANK_LIMIT);
-
- gst_info->num_tensors = ml_info->num_tensors;
-
- for (i = 0; i < ml_info->num_tensors; i++) {
- /* Copy name string */
- if (ml_info->info[i].name) {
- gst_info->info[i].name = g_strdup (ml_info->info[i].name);
- }
-
- /* Set tensor type */
- switch (ml_info->info[i].type) {
- case ML_TENSOR_TYPE_INT32:
- gst_info->info[i].type = _NNS_INT32;
- break;
- case ML_TENSOR_TYPE_UINT32:
- gst_info->info[i].type = _NNS_UINT32;
- break;
- case ML_TENSOR_TYPE_INT16:
- gst_info->info[i].type = _NNS_INT16;
- break;
- case ML_TENSOR_TYPE_UINT16:
- gst_info->info[i].type = _NNS_UINT16;
- break;
- case ML_TENSOR_TYPE_INT8:
- gst_info->info[i].type = _NNS_INT8;
- break;
- case ML_TENSOR_TYPE_UINT8:
- gst_info->info[i].type = _NNS_UINT8;
- break;
- case ML_TENSOR_TYPE_FLOAT64:
- gst_info->info[i].type = _NNS_FLOAT64;
- break;
- case ML_TENSOR_TYPE_FLOAT32:
- gst_info->info[i].type = _NNS_FLOAT32;
- break;
- case ML_TENSOR_TYPE_INT64:
- gst_info->info[i].type = _NNS_INT64;
- break;
- case ML_TENSOR_TYPE_UINT64:
- gst_info->info[i].type = _NNS_UINT64;
- break;
- default:
- gst_info->info[i].type = _NNS_END;
- break;
- }
-
- /* Set dimension */
- for (j = 0; j < max_dim; j++) {
- gst_info->info[i].dimension[j] = ml_info->info[i].dimension[j];
- }
-
- for (; j < NNS_TENSOR_RANK_LIMIT; j++) {
- gst_info->info[i].dimension[j] = 1;
- }
- }
- G_UNLOCK_UNLESS_NOLOCK (*ml_info);
-}
-
-/**
- * @brief Initializes the GStreamer library. This is internal function.
- */
-int
-ml_initialize_gstreamer (void)
-{
- GError *err = NULL;
-
- if (!gst_init_check (NULL, NULL, &err)) {
- if (err) {
- ml_loge ("GStreamer has the following error: %s", err->message);
- g_clear_error (&err);
- } else {
- ml_loge ("Cannot initialize GStreamer. Unknown reason.");
- }
-
- return ML_ERROR_STREAMS_PIPE;
- }
-
- return ML_ERROR_NONE;
-}
-
-/**
- * @brief Internal helper function to validate model files.
- */
-static int
-_ml_validate_model_file (const char *const *model,
- const unsigned int num_models, gboolean * is_dir)
-{
- guint i;
-
- if (!model || num_models < 1) {
- ml_loge ("The required param, model is not provided (null).");
- return ML_ERROR_INVALID_PARAMETER;
- }
-
- if (g_file_test (model[0], G_FILE_TEST_IS_DIR)) {
- *is_dir = TRUE;
- return ML_ERROR_NONE;
- }
-
- for (i = 0; i < num_models; i++) {
- if (!model[i] || !g_file_test (model[i], G_FILE_TEST_IS_REGULAR)) {
- ml_loge ("The given param, model path [%s] is invalid or not given.",
- GST_STR_NULL (model[i]));
- return ML_ERROR_INVALID_PARAMETER;
- }
- }
-
- return ML_ERROR_NONE;
-}
-
-/**
- * @brief Validates the nnfw model file.
- * @since_tizen 5.5
- * @param[in] model The path of model file.
- * @param[in/out] nnfw The type of NNFW.
- * @return @c 0 on success. Otherwise a negative error value.
- * @retval #ML_ERROR_NONE Successful
- * @retval #ML_ERROR_NOT_SUPPORTED Not supported, or framework to support this model file is unavailable in the environment.
- * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
- */
-int
-ml_validate_model_file (const char *const *model,
- const unsigned int num_models, ml_nnfw_type_e * nnfw)
-{
- int status = ML_ERROR_NONE;
- ml_nnfw_type_e detected = ML_NNFW_TYPE_ANY;
- gboolean is_dir = FALSE;
- gchar *pos, *fw_name;
- gchar **file_ext = NULL;
- guint i;
-
- if (!nnfw)
- return ML_ERROR_INVALID_PARAMETER;
-
- status = _ml_validate_model_file (model, num_models, &is_dir);
- if (status != ML_ERROR_NONE)
- return status;
-
- /**
- * @note detect-fw checks the file ext and returns proper fw name for given models.
- * If detected fw and given nnfw are same, we don't need to check the file extension.
- * If any condition for auto detection is added later, below code also should be updated.
- */
- fw_name = gst_tensor_filter_detect_framework (model, num_models, TRUE);
- detected = ml_get_nnfw_type_by_subplugin_name (fw_name);
- g_free (fw_name);
-
- if (*nnfw == ML_NNFW_TYPE_ANY) {
- if (detected == ML_NNFW_TYPE_ANY) {
- ml_loge ("The given model has unknown or not supported extension.");
- status = ML_ERROR_INVALID_PARAMETER;
- } else {
- ml_logi ("The given model is supposed a %s model.",
- ml_get_nnfw_subplugin_name (detected));
- *nnfw = detected;
- }
-
- goto done;
- } else if (is_dir && *nnfw != ML_NNFW_TYPE_NNFW) {
- /* supposed it is ONE if given model is directory */
- ml_loge ("The given model is directory, check model and framework.");
- status = ML_ERROR_INVALID_PARAMETER;
- goto done;
- } else if (detected == *nnfw) {
- /* Expected framework, nothing to do. */
- goto done;
- }
-
- /* Handle mismatched case, check file extension. */
- file_ext = g_malloc0 (sizeof (char *) * (num_models + 1));
- for (i = 0; i < num_models; i++) {
- if ((pos = strrchr (model[i], '.')) == NULL) {
- ml_loge ("The given model [%s] has invalid extension.", model[i]);
- status = ML_ERROR_INVALID_PARAMETER;
- goto done;
- }
-
- file_ext[i] = g_ascii_strdown (pos, -1);
- }
-
- /** @todo Make sure num_models is correct for each nnfw type */
- switch (*nnfw) {
- case ML_NNFW_TYPE_NNFW:
- /**
- * We cannot check the file ext with NNFW.
- * NNFW itself will validate metadata and model file.
- */
- break;
- case ML_NNFW_TYPE_MVNC:
- case ML_NNFW_TYPE_OPENVINO:
- case ML_NNFW_TYPE_EDGE_TPU:
- /** @todo Need to check method to validate model */
- ml_loge ("Given NNFW is not supported yet.");
- status = ML_ERROR_NOT_SUPPORTED;
- break;
- case ML_NNFW_TYPE_VD_AIFW:
- if (!g_str_equal (file_ext[0], ".nb") &&
- !g_str_equal (file_ext[0], ".ncp") &&
- !g_str_equal (file_ext[0], ".bin")) {
- status = ML_ERROR_INVALID_PARAMETER;
- }
- break;
- case ML_NNFW_TYPE_SNAP:
-#if !defined (__ANDROID__)
- ml_loge ("SNAP only can be included in Android (arm64-v8a only).");
- status = ML_ERROR_NOT_SUPPORTED;
-#endif
- /* SNAP requires multiple files, set supported if model file exists. */
- break;
- case ML_NNFW_TYPE_ARMNN:
- if (!g_str_equal (file_ext[0], ".caffemodel") &&
- !g_str_equal (file_ext[0], ".tflite") &&
- !g_str_equal (file_ext[0], ".pb") &&
- !g_str_equal (file_ext[0], ".prototxt")) {
- status = ML_ERROR_INVALID_PARAMETER;
+ * @brief Replaces string.
+ * This function deallocates the input source string.
+ * This is copied from nnstreamer/tensor_common.c by the nnstreamer maintainer.
+ * @param[in] source The input string. This will be freed when returning the replaced string.
+ * @param[in] what The string to search for.
+ * @param[in] to The string to be replaced.
+ * @param[in] delimiters The characters which specify the place to split the string. Set NULL to replace all matched string.
+ * @param[out] count The count of replaced. Set NULL if it is unnecessary.
+ * @return Newly allocated string. The returned string should be freed with g_free().
+ */
+gchar *
+ml_replace_string (gchar * source, const gchar * what, const gchar * to,
+ const gchar * delimiters, guint * count)
+{
+ GString *builder;
+ gchar *start, *pos, *result;
+ guint changed = 0;
+ gsize len;
+
+ g_return_val_if_fail (source, NULL);
+ g_return_val_if_fail (what && to, source);
+
+ len = strlen (what);
+ start = source;
+
+ builder = g_string_new (NULL);
+ while ((pos = g_strstr_len (start, -1, what)) != NULL) {
+ gboolean skip = FALSE;
+
+ if (delimiters) {
+ const gchar *s;
+ gchar *prev, *next;
+ gboolean prev_split, next_split;
+
+ prev = next = NULL;
+ prev_split = next_split = FALSE;
+
+ if (pos != source)
+ prev = pos - 1;
+ if (*(pos + len) != '\0')
+ next = pos + len;
+
+ for (s = delimiters; *s != '\0'; ++s) {
+ if (!prev || *s == *prev)
+ prev_split = TRUE;
+ if (!next || *s == *next)
+ next_split = TRUE;
+ if (prev_split && next_split)
+ break;
}
- break;
- default:
- status = ML_ERROR_INVALID_PARAMETER;
- break;
- }
-done:
- if (status == ML_ERROR_NONE) {
- if (!ml_nnfw_is_available (*nnfw, ML_NNFW_HW_ANY)) {
- ml_loge ("%s is not available.", ml_get_nnfw_subplugin_name (*nnfw));
- status = ML_ERROR_NOT_SUPPORTED;
+ if (!prev_split || !next_split)
+ skip = TRUE;
}
- } else {
- ml_loge ("The given model file is invalid.");
- }
-
- g_strfreev (file_ext);
- return status;
-}
-/**
- * @brief Convert c-api based hw to internal representation
- */
-static accl_hw
-ml_nnfw_to_accl_hw (const ml_nnfw_hw_e hw)
-{
- switch (hw) {
- case ML_NNFW_HW_ANY:
- return ACCL_DEFAULT;
- case ML_NNFW_HW_AUTO:
- return ACCL_AUTO;
- case ML_NNFW_HW_CPU:
- return ACCL_CPU;
-#if defined (__aarch64__) || defined (__arm__)
- case ML_NNFW_HW_CPU_NEON:
- return ACCL_CPU_NEON;
-#else
- case ML_NNFW_HW_CPU_SIMD:
- return ACCL_CPU_SIMD;
-#endif
- case ML_NNFW_HW_GPU:
- return ACCL_GPU;
- case ML_NNFW_HW_NPU:
- return ACCL_NPU;
- case ML_NNFW_HW_NPU_MOVIDIUS:
- return ACCL_NPU_MOVIDIUS;
- case ML_NNFW_HW_NPU_EDGE_TPU:
- return ACCL_NPU_EDGE_TPU;
- case ML_NNFW_HW_NPU_VIVANTE:
- return ACCL_NPU_VIVANTE;
- case ML_NNFW_HW_NPU_SLSI:
- return ACCL_NPU_SLSI;
- case ML_NNFW_HW_NPU_SR:
- /** @todo how to get srcn npu */
- return ACCL_NPU_SR;
- default:
- return ACCL_AUTO;
- }
-}
-
-/**
- * @brief Internal function to convert accelerator as tensor_filter property format.
- * @note returned value must be freed by the caller
- * @note More details on format can be found in gst_tensor_filter_install_properties() in tensor_filter_common.c.
- */
-char *
-ml_nnfw_to_str_prop (const ml_nnfw_hw_e hw)
-{
- const gchar *hw_name;
- const gchar *use_accl = "true:";
- gchar *str_prop = NULL;
-
- hw_name = get_accl_hw_str (ml_nnfw_to_accl_hw (hw));
- str_prop = g_strdup_printf ("%s%s", use_accl, hw_name);
-
- return str_prop;
-}
+ builder = g_string_append_len (builder, start, pos - start);
-/**
- * @brief Internal function to get the sub-plugin name.
- */
-const char *
-ml_get_nnfw_subplugin_name (ml_nnfw_type_e nnfw)
-{
- /* check sub-plugin for android */
- if (nnfw == ML_NNFW_TYPE_SNAP)
- return "snap";
-
- return ml_nnfw_subplugin_name[nnfw];
-}
-
-/**
- * @brief Internal function to get the nnfw type.
- */
-ml_nnfw_type_e
-ml_get_nnfw_type_by_subplugin_name (const char *name)
-{
- ml_nnfw_type_e nnfw_type = ML_NNFW_TYPE_ANY;
- int idx = -1;
-
- if (name == NULL)
- return ML_NNFW_TYPE_ANY;
-
- idx = find_key_strv (ml_nnfw_subplugin_name, name);
- if (idx < 0) {
- /* check sub-plugin for android */
- if (g_ascii_strcasecmp (name, "snap") == 0)
- nnfw_type = ML_NNFW_TYPE_SNAP;
+ /* replace string if found */
+ if (skip)
+ builder = g_string_append_len (builder, pos, len);
else
- ml_logw ("Cannot find nnfw, %s is invalid name.", GST_STR_NULL (name));
- } else {
- nnfw_type = (ml_nnfw_type_e) idx;
- }
-
- return nnfw_type;
-}
-
-/**
- * @brief Checks the availability of the given execution environments with custom option.
- */
-int
-ml_check_nnfw_availability_full (ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw,
- const char *custom, bool *available)
-{
- const char *fw_name = NULL;
-
- check_feature_state ();
-
- if (!available)
- return ML_ERROR_INVALID_PARAMETER;
-
- /* init false */
- *available = false;
-
- if (nnfw == ML_NNFW_TYPE_ANY)
- return ML_ERROR_INVALID_PARAMETER;
-
- fw_name = ml_get_nnfw_subplugin_name (nnfw);
-
- if (fw_name) {
- if (nnstreamer_filter_find (fw_name) != NULL) {
- accl_hw accl = ml_nnfw_to_accl_hw (hw);
-
- if (gst_tensor_filter_check_hw_availability (fw_name, accl, custom)) {
- *available = true;
- } else {
- ml_logw ("%s is supported but not with the specified hardware.",
- fw_name);
- }
- } else {
- ml_logw ("%s is not supported.", fw_name);
- }
- }
-
- return ML_ERROR_NONE;
-}
-
-/**
- * @brief Checks the availability of the given execution environments.
- */
-int
-ml_check_nnfw_availability (ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw,
- bool *available)
-{
- return ml_check_nnfw_availability_full (nnfw, hw, NULL, available);
-}
-
-/**
- * @brief Checks the element is registered and available on the pipeline.
- */
-int
-ml_check_element_availability (const char *element_name, bool *available)
-{
- GstElementFactory *factory;
- int status;
-
- check_feature_state ();
-
- if (!element_name || !available)
- return ML_ERROR_INVALID_PARAMETER;
-
- status = ml_initialize_gstreamer ();
- if (status != ML_ERROR_NONE)
- return status;
+ builder = g_string_append (builder, to);
- /* init false */
- *available = false;
-
- factory = gst_element_factory_find (element_name);
- if (factory) {
- GstPluginFeature *feature = GST_PLUGIN_FEATURE (factory);
- const gchar *plugin_name = gst_plugin_feature_get_plugin_name (feature);
-
- /* check restricted element */
- status = ml_check_plugin_availability (plugin_name, element_name);
- if (status == ML_ERROR_NONE)
- *available = true;
-
- gst_object_unref (factory);
+ start = pos + len;
+ if (!skip)
+ changed++;
}
- return ML_ERROR_NONE;
-}
+ /* append remains */
+ builder = g_string_append (builder, start);
+ result = g_string_free (builder, FALSE);
-/**
- * @brief Checks the availability of the plugin.
- */
-int
-ml_check_plugin_availability (const char *plugin_name, const char *element_name)
-{
- static gboolean list_loaded = FALSE;
- static gchar **restricted_elements = NULL;
-
- if (!plugin_name || !element_name) {
- ml_loge ("The name is invalid, failed to check the availability.");
- return ML_ERROR_INVALID_PARAMETER;
- }
-
- if (!list_loaded) {
- gboolean restricted;
-
- restricted =
- nnsconf_get_custom_value_bool ("element-restriction",
- "enable_element_restriction", FALSE);
- if (restricted) {
- gchar *elements;
-
- /* check white-list of available plugins */
- elements =
- nnsconf_get_custom_value_string ("element-restriction",
- "restricted_elements");
- if (elements) {
- restricted_elements = g_strsplit_set (elements, " ,;", -1);
- g_free (elements);
- }
- }
+ if (count)
+ *count = changed;
- list_loaded = TRUE;
- }
-
- /* nnstreamer elements */
- if (g_str_has_prefix (plugin_name, "nnstreamer") &&
- g_str_has_prefix (element_name, "tensor_")) {
- return ML_ERROR_NONE;
- }
-
- if (restricted_elements &&
- find_key_strv ((const gchar **) restricted_elements, element_name) < 0) {
- ml_logw ("The element %s is restricted.", element_name);
- return ML_ERROR_NOT_SUPPORTED;
- }
-
- return ML_ERROR_NONE;
+ g_free (source);
+ return result;
}
--- /dev/null
+/* SPDX-License-Identifier: Apache-2.0 */
+/**
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved.
+ *
+ * @file ml-api-inference-internal.c
+ * @date 19 October 2021
+ * @brief ML-API Internal Utility Functions for inference implementations
+ * @see https://github.com/nnstreamer/api
+ * @author MyungJoo Ham <myungjoo.ham@samsung.com>
+ * @bug No known bugs except for NYI items
+ */
+
+#include <string.h>
+
+#include <nnstreamer_internal.h>
+#include <nnstreamer_log.h>
+#include <nnstreamer_plugin_api.h>
+#include <nnstreamer_plugin_api_filter.h>
+#include <tensor_typedef.h>
+#include "ml-api-inference-internal.h"
+#include "ml-api-internal.h"
+
+/**
+ * @brief The name of sub-plugin for defined neural net frameworks.
+ * @note The sub-plugin for Android is not declared (e.g., snap)
+ */
+static const char *ml_nnfw_subplugin_name[] = {
+ [ML_NNFW_TYPE_ANY] = "any", /* DO NOT use this name ('any') to get the sub-plugin */
+ [ML_NNFW_TYPE_CUSTOM_FILTER] = "custom",
+ [ML_NNFW_TYPE_TENSORFLOW_LITE] = "tensorflow-lite",
+ [ML_NNFW_TYPE_TENSORFLOW] = "tensorflow",
+ [ML_NNFW_TYPE_NNFW] = "nnfw",
+ [ML_NNFW_TYPE_MVNC] = "movidius-ncsdk2",
+ [ML_NNFW_TYPE_OPENVINO] = "openvino",
+ [ML_NNFW_TYPE_VIVANTE] = "vivante",
+ [ML_NNFW_TYPE_EDGE_TPU] = "edgetpu",
+ [ML_NNFW_TYPE_ARMNN] = "armnn",
+ [ML_NNFW_TYPE_SNPE] = "snpe",
+ [ML_NNFW_TYPE_PYTORCH] = "pytorch",
+ [ML_NNFW_TYPE_NNTR_INF] = "nntrainer",
+ [ML_NNFW_TYPE_VD_AIFW] = "vd_aifw",
+ [ML_NNFW_TYPE_TRIX_ENGINE] = "trix-engine",
+ NULL
+};
+
+/**
+ * @brief Internal function to get the sub-plugin name.
+ */
+const char *
+ml_get_nnfw_subplugin_name (ml_nnfw_type_e nnfw)
+{
+ /* check sub-plugin for android */
+ if (nnfw == ML_NNFW_TYPE_SNAP)
+ return "snap";
+
+ return ml_nnfw_subplugin_name[nnfw];
+}
+
+/**
+ * @brief Allocates a tensors information handle from gst info.
+ */
+int
+ml_tensors_info_create_from_gst (ml_tensors_info_h * ml_info,
+ GstTensorsInfo * gst_info)
+{
+ int status;
+
+ if (!ml_info || !gst_info)
+ return ML_ERROR_INVALID_PARAMETER;
+
+ status = ml_tensors_info_create (ml_info);
+ if (status != ML_ERROR_NONE)
+ return status;
+
+ ml_tensors_info_copy_from_gst (*ml_info, gst_info);
+ return ML_ERROR_NONE;
+}
+
+/**
+ * @brief Copies tensor meta info from gst tensors info.
+ * @bug Thread safety required. Check its internal users first!
+ */
+void
+ml_tensors_info_copy_from_gst (ml_tensors_info_s * ml_info,
+ const GstTensorsInfo * gst_info)
+{
+ guint i, j;
+ guint max_dim;
+
+ if (!ml_info || !gst_info)
+ return;
+
+ ml_tensors_info_initialize (ml_info);
+ max_dim = MIN (ML_TENSOR_RANK_LIMIT, NNS_TENSOR_RANK_LIMIT);
+
+ ml_info->num_tensors = gst_info->num_tensors;
+
+ for (i = 0; i < gst_info->num_tensors; i++) {
+ /* Copy name string */
+ if (gst_info->info[i].name) {
+ ml_info->info[i].name = g_strdup (gst_info->info[i].name);
+ }
+
+ /* Set tensor type */
+ switch (gst_info->info[i].type) {
+ case _NNS_INT32:
+ ml_info->info[i].type = ML_TENSOR_TYPE_INT32;
+ break;
+ case _NNS_UINT32:
+ ml_info->info[i].type = ML_TENSOR_TYPE_UINT32;
+ break;
+ case _NNS_INT16:
+ ml_info->info[i].type = ML_TENSOR_TYPE_INT16;
+ break;
+ case _NNS_UINT16:
+ ml_info->info[i].type = ML_TENSOR_TYPE_UINT16;
+ break;
+ case _NNS_INT8:
+ ml_info->info[i].type = ML_TENSOR_TYPE_INT8;
+ break;
+ case _NNS_UINT8:
+ ml_info->info[i].type = ML_TENSOR_TYPE_UINT8;
+ break;
+ case _NNS_FLOAT64:
+ ml_info->info[i].type = ML_TENSOR_TYPE_FLOAT64;
+ break;
+ case _NNS_FLOAT32:
+ ml_info->info[i].type = ML_TENSOR_TYPE_FLOAT32;
+ break;
+ case _NNS_INT64:
+ ml_info->info[i].type = ML_TENSOR_TYPE_INT64;
+ break;
+ case _NNS_UINT64:
+ ml_info->info[i].type = ML_TENSOR_TYPE_UINT64;
+ break;
+ default:
+ ml_info->info[i].type = ML_TENSOR_TYPE_UNKNOWN;
+ break;
+ }
+
+ /* Set dimension */
+ for (j = 0; j < max_dim; j++) {
+ ml_info->info[i].dimension[j] = gst_info->info[i].dimension[j];
+ }
+
+ for (; j < ML_TENSOR_RANK_LIMIT; j++) {
+ ml_info->info[i].dimension[j] = 1;
+ }
+ }
+}
+
+/**
+ * @brief Copies tensor meta info from gst tensors info.
+ * @bug Thread safety required. Check its internal users first!
+ */
+void
+ml_tensors_info_copy_from_ml (GstTensorsInfo * gst_info,
+ const ml_tensors_info_s * ml_info)
+{
+ guint i, j;
+ guint max_dim;
+
+ if (!gst_info || !ml_info)
+ return;
+
+ G_LOCK_UNLESS_NOLOCK (*ml_info);
+
+ gst_tensors_info_init (gst_info);
+ max_dim = MIN (ML_TENSOR_RANK_LIMIT, NNS_TENSOR_RANK_LIMIT);
+
+ gst_info->num_tensors = ml_info->num_tensors;
+
+ for (i = 0; i < ml_info->num_tensors; i++) {
+ /* Copy name string */
+ if (ml_info->info[i].name) {
+ gst_info->info[i].name = g_strdup (ml_info->info[i].name);
+ }
+
+ /* Set tensor type */
+ switch (ml_info->info[i].type) {
+ case ML_TENSOR_TYPE_INT32:
+ gst_info->info[i].type = _NNS_INT32;
+ break;
+ case ML_TENSOR_TYPE_UINT32:
+ gst_info->info[i].type = _NNS_UINT32;
+ break;
+ case ML_TENSOR_TYPE_INT16:
+ gst_info->info[i].type = _NNS_INT16;
+ break;
+ case ML_TENSOR_TYPE_UINT16:
+ gst_info->info[i].type = _NNS_UINT16;
+ break;
+ case ML_TENSOR_TYPE_INT8:
+ gst_info->info[i].type = _NNS_INT8;
+ break;
+ case ML_TENSOR_TYPE_UINT8:
+ gst_info->info[i].type = _NNS_UINT8;
+ break;
+ case ML_TENSOR_TYPE_FLOAT64:
+ gst_info->info[i].type = _NNS_FLOAT64;
+ break;
+ case ML_TENSOR_TYPE_FLOAT32:
+ gst_info->info[i].type = _NNS_FLOAT32;
+ break;
+ case ML_TENSOR_TYPE_INT64:
+ gst_info->info[i].type = _NNS_INT64;
+ break;
+ case ML_TENSOR_TYPE_UINT64:
+ gst_info->info[i].type = _NNS_UINT64;
+ break;
+ default:
+ gst_info->info[i].type = _NNS_END;
+ break;
+ }
+
+ /* Set dimension */
+ for (j = 0; j < max_dim; j++) {
+ gst_info->info[i].dimension[j] = ml_info->info[i].dimension[j];
+ }
+
+ for (; j < NNS_TENSOR_RANK_LIMIT; j++) {
+ gst_info->info[i].dimension[j] = 1;
+ }
+ }
+ G_UNLOCK_UNLESS_NOLOCK (*ml_info);
+}
+
+/**
+ * @brief Initializes the GStreamer library. This is internal function.
+ */
+int
+ml_initialize_gstreamer (void)
+{
+ GError *err = NULL;
+
+ if (!gst_init_check (NULL, NULL, &err)) {
+ if (err) {
+ mlapi_loge ("GStreamer has the following error: %s", err->message);
+ g_clear_error (&err);
+ } else {
+ mlapi_loge ("Cannot initialize GStreamer. Unknown reason.");
+ }
+
+ return ML_ERROR_STREAMS_PIPE;
+ }
+
+ return ML_ERROR_NONE;
+}
+
+/**
+ * @brief Internal helper function to validate model files.
+ */
+static int
+_ml_validate_model_file (const char *const *model,
+ const unsigned int num_models, gboolean * is_dir)
+{
+ guint i;
+
+ if (!model || num_models < 1) {
+ mlapi_loge ("The required param, model is not provided (null).");
+ return ML_ERROR_INVALID_PARAMETER;
+ }
+
+ if (g_file_test (model[0], G_FILE_TEST_IS_DIR)) {
+ *is_dir = TRUE;
+ return ML_ERROR_NONE;
+ }
+
+ for (i = 0; i < num_models; i++) {
+ if (!model[i] || !g_file_test (model[i], G_FILE_TEST_IS_REGULAR)) {
+ mlapi_loge ("The given param, model path [%s] is invalid or not given.",
+ GST_STR_NULL (model[i]));
+ return ML_ERROR_INVALID_PARAMETER;
+ }
+ }
+
+ return ML_ERROR_NONE;
+}
+
+/**
+ * @brief Internal function to get the nnfw type.
+ */
+ml_nnfw_type_e
+ml_get_nnfw_type_by_subplugin_name (const char *name)
+{
+ ml_nnfw_type_e nnfw_type = ML_NNFW_TYPE_ANY;
+ int idx = -1;
+
+ if (name == NULL)
+ return ML_NNFW_TYPE_ANY;
+
+ idx = find_key_strv (ml_nnfw_subplugin_name, name);
+ if (idx < 0) {
+ /* check sub-plugin for android */
+ if (g_ascii_strcasecmp (name, "snap") == 0)
+ nnfw_type = ML_NNFW_TYPE_SNAP;
+ else
+ mlapi_logw ("Cannot find nnfw, %s is invalid name.", GST_STR_NULL (name));
+ } else {
+ nnfw_type = (ml_nnfw_type_e) idx;
+ }
+
+ return nnfw_type;
+}
+
+/**
+ * @brief Validates the nnfw model file.
+ * @since_tizen 5.5
+ * @param[in] model The path of model file.
+ * @param[in/out] nnfw The type of NNFW.
+ * @return @c 0 on success. Otherwise a negative error value.
+ * @retval #ML_ERROR_NONE Successful
+ * @retval #ML_ERROR_NOT_SUPPORTED Not supported, or framework to support this model file is unavailable in the environment.
+ * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
+ */
+int
+ml_validate_model_file (const char *const *model,
+ const unsigned int num_models, ml_nnfw_type_e * nnfw)
+{
+ int status = ML_ERROR_NONE;
+ ml_nnfw_type_e detected = ML_NNFW_TYPE_ANY;
+ gboolean is_dir = FALSE;
+ gchar *pos, *fw_name;
+ gchar **file_ext = NULL;
+ guint i;
+
+ if (!nnfw)
+ return ML_ERROR_INVALID_PARAMETER;
+
+ status = _ml_validate_model_file (model, num_models, &is_dir);
+ if (status != ML_ERROR_NONE)
+ return status;
+
+ /**
+ * @note detect-fw checks the file ext and returns proper fw name for given models.
+ * If detected fw and given nnfw are same, we don't need to check the file extension.
+ * If any condition for auto detection is added later, below code also should be updated.
+ */
+ fw_name = gst_tensor_filter_detect_framework (model, num_models, TRUE);
+ detected = ml_get_nnfw_type_by_subplugin_name (fw_name);
+ g_free (fw_name);
+
+ if (*nnfw == ML_NNFW_TYPE_ANY) {
+ if (detected == ML_NNFW_TYPE_ANY) {
+ mlapi_loge ("The given model has unknown or not supported extension.");
+ status = ML_ERROR_INVALID_PARAMETER;
+ } else {
+ mlapi_logi ("The given model is supposed a %s model.",
+ ml_get_nnfw_subplugin_name (detected));
+ *nnfw = detected;
+ }
+
+ goto done;
+ } else if (is_dir && *nnfw != ML_NNFW_TYPE_NNFW) {
+ /* supposed it is ONE if given model is directory */
+ mlapi_loge ("The given model is directory, check model and framework.");
+ status = ML_ERROR_INVALID_PARAMETER;
+ goto done;
+ } else if (detected == *nnfw) {
+ /* Expected framework, nothing to do. */
+ goto done;
+ }
+
+ /* Handle mismatched case, check file extension. */
+ file_ext = g_malloc0 (sizeof (char *) * (num_models + 1));
+ for (i = 0; i < num_models; i++) {
+ if ((pos = strrchr (model[i], '.')) == NULL) {
+ mlapi_loge ("The given model [%s] has invalid extension.", model[i]);
+ status = ML_ERROR_INVALID_PARAMETER;
+ goto done;
+ }
+
+ file_ext[i] = g_ascii_strdown (pos, -1);
+ }
+
+ /** @todo Make sure num_models is correct for each nnfw type */
+ switch (*nnfw) {
+ case ML_NNFW_TYPE_NNFW:
+ /**
+ * We cannot check the file ext with NNFW.
+ * NNFW itself will validate metadata and model file.
+ */
+ break;
+ case ML_NNFW_TYPE_MVNC:
+ case ML_NNFW_TYPE_OPENVINO:
+ case ML_NNFW_TYPE_EDGE_TPU:
+ /** @todo Need to check method to validate model */
+ mlapi_loge ("Given NNFW is not supported yet.");
+ status = ML_ERROR_NOT_SUPPORTED;
+ break;
+ case ML_NNFW_TYPE_VD_AIFW:
+ if (!g_str_equal (file_ext[0], ".nb") &&
+ !g_str_equal (file_ext[0], ".ncp") &&
+ !g_str_equal (file_ext[0], ".bin")) {
+ status = ML_ERROR_INVALID_PARAMETER;
+ }
+ break;
+ case ML_NNFW_TYPE_SNAP:
+#if !defined (__ANDROID__)
+ mlapi_loge ("SNAP only can be included in Android (arm64-v8a only).");
+ status = ML_ERROR_NOT_SUPPORTED;
+#endif
+ /* SNAP requires multiple files, set supported if model file exists. */
+ break;
+ case ML_NNFW_TYPE_ARMNN:
+ if (!g_str_equal (file_ext[0], ".caffemodel") &&
+ !g_str_equal (file_ext[0], ".tflite") &&
+ !g_str_equal (file_ext[0], ".pb") &&
+ !g_str_equal (file_ext[0], ".prototxt")) {
+ status = ML_ERROR_INVALID_PARAMETER;
+ }
+ break;
+ default:
+ status = ML_ERROR_INVALID_PARAMETER;
+ break;
+ }
+
+done:
+ if (status == ML_ERROR_NONE) {
+ if (!ml_nnfw_is_available (*nnfw, ML_NNFW_HW_ANY)) {
+ mlapi_loge ("%s is not available.", ml_get_nnfw_subplugin_name (*nnfw));
+ status = ML_ERROR_NOT_SUPPORTED;
+ }
+ } else {
+ mlapi_loge ("The given model file is invalid.");
+ }
+
+ g_strfreev (file_ext);
+ return status;
+}
+
+/**
+ * @brief Convert c-api based hw to internal representation
+ */
+accl_hw
+ml_nnfw_to_accl_hw (const ml_nnfw_hw_e hw)
+{
+ switch (hw) {
+ case ML_NNFW_HW_ANY:
+ return ACCL_DEFAULT;
+ case ML_NNFW_HW_AUTO:
+ return ACCL_AUTO;
+ case ML_NNFW_HW_CPU:
+ return ACCL_CPU;
+#if defined (__aarch64__) || defined (__arm__)
+ case ML_NNFW_HW_CPU_NEON:
+ return ACCL_CPU_NEON;
+#else
+ case ML_NNFW_HW_CPU_SIMD:
+ return ACCL_CPU_SIMD;
+#endif
+ case ML_NNFW_HW_GPU:
+ return ACCL_GPU;
+ case ML_NNFW_HW_NPU:
+ return ACCL_NPU;
+ case ML_NNFW_HW_NPU_MOVIDIUS:
+ return ACCL_NPU_MOVIDIUS;
+ case ML_NNFW_HW_NPU_EDGE_TPU:
+ return ACCL_NPU_EDGE_TPU;
+ case ML_NNFW_HW_NPU_VIVANTE:
+ return ACCL_NPU_VIVANTE;
+ case ML_NNFW_HW_NPU_SLSI:
+ return ACCL_NPU_SLSI;
+ case ML_NNFW_HW_NPU_SR:
+ /** @todo how to get srcn npu */
+ return ACCL_NPU_SR;
+ default:
+ return ACCL_AUTO;
+ }
+}
+
+/**
+ * @brief Internal function to convert accelerator as tensor_filter property format.
+ * @note returned value must be freed by the caller
+ * @note More details on format can be found in gst_tensor_filter_install_properties() in tensor_filter_common.c.
+ */
+char *
+ml_nnfw_to_str_prop (const ml_nnfw_hw_e hw)
+{
+ const gchar *hw_name;
+ const gchar *use_accl = "true:";
+ gchar *str_prop = NULL;
+
+ hw_name = get_accl_hw_str (ml_nnfw_to_accl_hw (hw));
+ str_prop = g_strdup_printf ("%s%s", use_accl, hw_name);
+
+ return str_prop;
+}
+
+/**
+ * @brief Checks the element is registered and available on the pipeline.
+ */
+int
+ml_check_element_availability (const char *element_name, bool *available)
+{
+ GstElementFactory *factory;
+ int status;
+
+ check_feature_state ();
+
+ if (!element_name || !available)
+ return ML_ERROR_INVALID_PARAMETER;
+
+ status = ml_initialize_gstreamer ();
+ if (status != ML_ERROR_NONE)
+ return status;
+
+ /* init false */
+ *available = false;
+
+ factory = gst_element_factory_find (element_name);
+ if (factory) {
+ GstPluginFeature *feature = GST_PLUGIN_FEATURE (factory);
+ const gchar *plugin_name = gst_plugin_feature_get_plugin_name (feature);
+
+ /* check restricted element */
+ status = ml_check_plugin_availability (plugin_name, element_name);
+ if (status == ML_ERROR_NONE)
+ *available = true;
+
+ gst_object_unref (factory);
+ }
+
+ return ML_ERROR_NONE;
+}
+
+/**
+ * @brief Checks the availability of the plugin.
+ */
+int
+ml_check_plugin_availability (const char *plugin_name, const char *element_name)
+{
+ static gboolean list_loaded = FALSE;
+ static gchar **restricted_elements = NULL;
+
+ if (!plugin_name || !element_name) {
+ mlapi_loge ("The name is invalid, failed to check the availability.");
+ return ML_ERROR_INVALID_PARAMETER;
+ }
+
+ if (!list_loaded) {
+ gboolean restricted;
+
+ restricted =
+ nnsconf_get_custom_value_bool ("element-restriction",
+ "enable_element_restriction", FALSE);
+ if (restricted) {
+ gchar *elements;
+
+ /* check white-list of available plugins */
+ elements =
+ nnsconf_get_custom_value_string ("element-restriction",
+ "restricted_elements");
+ if (elements) {
+ restricted_elements = g_strsplit_set (elements, " ,;", -1);
+ g_free (elements);
+ }
+ }
+
+ list_loaded = TRUE;
+ }
+
+ /* nnstreamer elements */
+ if (g_str_has_prefix (plugin_name, "nnstreamer") &&
+ g_str_has_prefix (element_name, "tensor_")) {
+ return ML_ERROR_NONE;
+ }
+
+ if (restricted_elements &&
+ find_key_strv ((const gchar **) restricted_elements, element_name) < 0) {
+ mlapi_logw ("The element %s is restricted.", element_name);
+ return ML_ERROR_NOT_SUPPORTED;
+ }
+
+ return ML_ERROR_NONE;
+}
--- /dev/null
+/* SPDX-License-Identifier: Apache-2.0 */
+/**
+ * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved.
+ *
+ * @file ml-api-internal-nnstreamer.h
+ * @date 20 October 2021
+ * @brief ML C-API internal header with NNStreamer deps.
+ * This file should NOT be exported to SDK or devel package.
+ * @see https://github.com/nnstreamer/api
+ * @author MyungJoo Ham <myungjoo.ham@samsung.com>
+ * @bug No known bugs except for NYI items
+ */
+
+#ifndef __ML_API_INTERNAL_NNSTREAMER_H__
+#define __ML_API_INTERNAL_NNSTREAMER_H__
+
+#include <glib.h>
+#include <gst/gst.h>
+#include <tensor_typedef.h>
+
+#include <nnstreamer.h>
+#include <nnstreamer-single.h>
+#include <nnstreamer_plugin_api_filter.h>
+
+#include "ml-api-internal.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/***** Wrappers of tizen-api-internal.h for pipelines *****/
+#if defined (__TIZEN__)
+#if defined (__PRIVILEGE_CHECK_SUPPORT__)
+
+#define convert_tizen_element(...) ml_tizen_convert_element(__VA_ARGS__)
+
+#if (TIZENVERSION >= 5) && (TIZENVERSION < 9999)
+#define get_tizen_resource(...) ml_tizen_get_resource(__VA_ARGS__)
+#define release_tizen_resource(...) ml_tizen_release_resource(__VA_ARGS__)
+
+#elif (TIZENVERSION < 5)
+#define get_tizen_resource(...) (0)
+#define release_tizen_resource(...) do { } while (0)
+typedef void * mm_resource_manager_h;
+typedef enum { MM_RESOURCE_MANAGER_RES_TYPE_MAX } mm_resource_manager_res_type_e;
+
+#else /* TIZENVERSION */
+#error Tizen version is not defined.
+#endif /* TIZENVERSION */
+
+#else /* __PRIVILEGE_CHECK_SUPPORT__ */
+
+#define convert_tizen_element(...) ML_ERROR_NONE
+#define get_tizen_resource(...) ML_ERROR_NONE
+#define release_tizen_resource(...)
+
+#endif /* __PRIVILEGE_CHECK_SUPPORT__ */
+
+#else /* __TIZEN */
+
+#define convert_tizen_element(...) ML_ERROR_NONE
+#define get_tizen_resource(...) ML_ERROR_NONE
+#define release_tizen_resource(...)
+
+#endif /* __TIZEN__ */
+/**
+ * @brief Internal private representation of custom filter handle.
+ */
+typedef struct {
+ char *name;
+ unsigned int ref_count;
+ GMutex lock;
+ ml_tensors_info_h in_info;
+ ml_tensors_info_h out_info;
+ ml_custom_easy_invoke_cb cb;
+ void *pdata;
+} ml_custom_filter_s;
+
+/**
+ * @brief Internal private representation of tensor_if custom conditon.
+ * @since_tizen 6.5
+ */
+typedef struct {
+ char *name;
+ unsigned int ref_count;
+ GMutex lock;
+ ml_pipeline_if_custom_cb cb;
+ void *pdata;
+} ml_if_custom_s;
+
+/**
+ * @brief Possible controls on elements of a pipeline.
+ */
+typedef enum {
+ ML_PIPELINE_ELEMENT_UNKNOWN = 0x0,
+ ML_PIPELINE_ELEMENT_SINK = 0x1,
+ ML_PIPELINE_ELEMENT_APP_SRC = 0x2,
+ ML_PIPELINE_ELEMENT_APP_SINK = 0x3,
+ ML_PIPELINE_ELEMENT_VALVE = 0x4,
+ ML_PIPELINE_ELEMENT_SWITCH_INPUT = 0x8,
+ ML_PIPELINE_ELEMENT_SWITCH_OUTPUT = 0x9,
+ ML_PIPELINE_ELEMENT_COMMON = 0xB,
+} ml_pipeline_element_e;
+
+/**
+ * @brief Internal private representation of pipeline handle.
+ */
+typedef struct _ml_pipeline ml_pipeline;
+
+/**
+ * @brief An element that may be controlled individually in a pipeline.
+ */
+typedef struct _ml_pipeline_element {
+ GstElement *element; /**< The Sink/Src/Valve/Switch element */
+ ml_pipeline *pipe; /**< The main pipeline */
+ char *name;
+ ml_pipeline_element_e type;
+ GstPad *src;
+ GstPad *sink; /**< Unref this at destroy */
+ ml_tensors_info_s tensors_info;
+ size_t size;
+
+ GList *handles;
+ int maxid; /**< to allocate id for each handle */
+ gulong handle_id;
+
+ GMutex lock; /**< Lock for internal values */
+ gboolean is_media_stream;
+ gboolean is_flexible_tensor;
+
+ ml_handle_destroy_cb custom_destroy;
+ gpointer custom_data;
+} ml_pipeline_element;
+
+/**
+ * @brief Internal data structure for the pipeline state callback.
+ */
+typedef struct {
+ ml_pipeline_state_cb cb; /**< Callback to notify the change of pipeline state */
+ void *user_data; /**< The user data passed when calling the state change callback */
+} pipeline_state_cb_s;
+
+/**
+ * @brief Internal data structure for the resource.
+ */
+typedef struct {
+ gchar *type; /**< resource type */
+ gpointer handle; /**< pointer to resource handle */
+} pipeline_resource_s;
+
+/**
+ * @brief Internal private representation of pipeline handle.
+ * @details This should not be exposed to applications
+ */
+struct _ml_pipeline {
+ GstElement *element; /**< The pipeline itself (GstPipeline) */
+ GstBus *bus; /**< The bus of the pipeline */
+ gulong signal_msg; /**< The message signal (connected to bus) */
+ GMutex lock; /**< Lock for pipeline operations */
+ gboolean isEOS; /**< The pipeline is EOS state */
+ ml_pipeline_state_e pipe_state; /**< The state of pipeline */
+ GHashTable *namednodes; /**< hash table of "element"s. */
+ GHashTable *resources; /**< hash table of resources to construct the pipeline */
+ pipeline_state_cb_s state_cb; /**< Callback to notify the change of pipeline state */
+};
+
+/**
+ * @brief Internal private representation sink callback function for GstTensorSink and GstAppSink
+ * @details This represents a single instance of callback registration. This should not be exposed to applications.
+ */
+typedef struct {
+ ml_pipeline_sink_cb sink_cb;
+ ml_pipeline_src_callbacks_s src_cb;
+ void *pdata;
+} callback_info_s;
+
+/**
+ * @brief Internal private representation of common element handle (All GstElement except AppSink and TensorSink)
+ * @details This represents a single instance of registration. This should not be exposed to applications.
+ */
+typedef struct _ml_pipeline_common_elem {
+ ml_pipeline *pipe;
+ ml_pipeline_element *element;
+ guint32 id;
+ callback_info_s *callback_info; /**< Callback function information. If element is not GstTensorSink or GstAppSink, then it should be NULL. */
+} ml_pipeline_common_elem;
+
+
+/**
+ * @brief Macro to check the availability of given NNFW.
+ */
+#define ml_nnfw_is_available(f,h) ({bool a; (ml_check_nnfw_availability ((f), (h), &a) == ML_ERROR_NONE && a);})
+
+/**
+ * @brief Macro to check the availability of given element.
+ */
+#define ml_element_is_available(e) ({bool a; (ml_check_element_availability ((e), &a) == ML_ERROR_NONE && a);})
+
+/**
+ * @brief Allocates a tensors information handle from gst info.
+ */
+int ml_tensors_info_create_from_gst (ml_tensors_info_h *ml_info, GstTensorsInfo *gst_info);
+
+/**
+ * @brief Copies tensor metadata from gst tensors info.
+ */
+void ml_tensors_info_copy_from_gst (ml_tensors_info_s *ml_info, const GstTensorsInfo *gst_info);
+
+/**
+ * @brief Copies tensor metadata from ml tensors info.
+ */
+void ml_tensors_info_copy_from_ml (GstTensorsInfo *gst_info, const ml_tensors_info_s *ml_info);
+
+/**
+ * @brief Internal function to get the sub-plugin name.
+ */
+const char * ml_get_nnfw_subplugin_name (ml_nnfw_type_e nnfw);
+
+/**
+ * @brief Convert c-api based hw to internal representation
+ */
+accl_hw ml_nnfw_to_accl_hw (const ml_nnfw_hw_e hw);
+
+/**
+ * @brief Internal function to get the nnfw type.
+ */
+ml_nnfw_type_e ml_get_nnfw_type_by_subplugin_name (const char *name);
+
+/**
+ * @brief Initializes the GStreamer library. This is internal function.
+ */
+int ml_initialize_gstreamer (void);
+
+/**
+ * @brief Validates the nnfw model file. (Internal only)
+ * @since_tizen 5.5
+ * @param[in] model List of model file paths.
+ * @param[in] num_models The number of model files. There are a few frameworks that require multiple model files for a single model.
+ * @param[in/out] nnfw The type of NNFW.
+ * @return @c 0 on success. Otherwise a negative error value.
+ * @retval #ML_ERROR_NONE Successful
+ * @retval #ML_ERROR_NOT_SUPPORTED Not supported.
+ * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
+ */
+int ml_validate_model_file (const char * const *model, const unsigned int num_models, ml_nnfw_type_e * nnfw);
+
+/**
+ * @brief Checks the availability of the plugin.
+ */
+int ml_check_plugin_availability (const char *plugin_name, const char *element_name);
+
+/**
+ * @brief Internal function to convert accelerator as tensor_filter property format.
+ * @note returned value must be freed by the caller
+ */
+char* ml_nnfw_to_str_prop (ml_nnfw_hw_e hw);
+
+/**
+ * @brief Gets the element of pipeline itself (GstElement).
+ * @details With the returned reference, you can use GStreamer functions to handle the element in pipeline.
+ * Note that caller should release the returned reference using gst_object_unref().
+ * @return The reference of pipeline itself. Null if the pipeline is not constructed or closed.
+ */
+GstElement* ml_pipeline_get_gst_element (ml_pipeline_h pipe);
+
+#if defined (__TIZEN__)
+/****** TIZEN PRIVILEGE CHECK BEGINS ******/
+/**
+ * @brief Releases the resource handle of Tizen.
+ */
+void ml_tizen_release_resource (gpointer handle, const gchar * res_type);
+
+/**
+ * @brief Gets the resource handle of Tizen.
+ */
+int ml_tizen_get_resource (ml_pipeline_h pipe, const gchar * res_type);
+
+/**
+ * @brief Converts predefined element for Tizen.
+ */
+int ml_tizen_convert_element (ml_pipeline_h pipe, gchar ** result, gboolean is_internal);
+/****** TIZEN PRIVILEGE CHECK ENDS ******/
+#endif /* __TIZEN */
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+#endif /* __ML_API_INTERNAL_NNSTREAMER_H__ */
/**
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved.
*
- * @file nnstreamer-capi-pipeline.c
+ * @file ml-api-inference-pipeline.c
* @date 11 March 2019
* @brief NNStreamer/Pipeline(main) C-API Wrapper.
* This allows to construct and control NNStreamer pipelines.
#include <glib.h>
#include <gst/gstbuffer.h>
#include <gst/app/app.h> /* To push data to pipeline */
+#include <nnstreamer_log.h>
+#include <nnstreamer_plugin_api.h>
+#include <tensor_if.h>
+#include <tensor_typedef.h>
+#include <tensor_filter_custom_easy.h>
-#include "nnstreamer-capi-private.h"
-#include "tensor_if.h"
-#include "tensor_typedef.h"
-#include "tensor_filter_custom_easy.h"
-#include "nnstreamer_plugin_api.h"
+#include <nnstreamer.h>
+#include <nnstreamer-tizen-internal.h>
+
+#include "ml-api-inference-internal.h"
+#include "ml-api-internal.h"
#define handle_init(name, h) \
int ret = ML_ERROR_NONE; \
check_feature_state (); \
if ((h) == NULL) { \
- ml_loge ("The given handle is invalid"); \
+ mlapi_loge ("The given handle is invalid"); \
return ML_ERROR_INVALID_PARAMETER; \
} \
\
p = name->pipe; \
elem = name->element; \
if (p == NULL || elem == NULL || p != elem->pipe) { \
- ml_loge ("The handle appears to be broken."); \
+ mlapi_loge ("The handle appears to be broken."); \
return ML_ERROR_INVALID_PARAMETER; \
} \
\
g_mutex_lock (&elem->lock); \
\
if (NULL == g_list_find (elem->handles, name)) { \
- ml_loge ("The handle does not exists."); \
+ mlapi_loge ("The handle does not exists."); \
ret = ML_ERROR_INVALID_PARAMETER; \
goto unlock_return; \
}
ml_pipeline_element *ret = g_new0 (ml_pipeline_element, 1);
if (ret == NULL) {
- ml_loge ("Failed to allocate memory for the pipeline.");
+ mlapi_loge ("Failed to allocate memory for the pipeline.");
return NULL;
}
num_mems = gst_buffer_n_memory (b);
if (num_mems > ML_TENSOR_SIZE_LIMIT) {
- ml_loge ("Number of memory chunks in a GstBuffer exceed the limit: %u > %u",
+ mlapi_loge
+ ("Number of memory chunks in a GstBuffer exceed the limit: %u > %u",
num_mems, ML_TENSOR_SIZE_LIMIT);
return;
}
status =
ml_tensors_data_create_no_alloc (NULL, (ml_tensors_data_h *) & _data);
if (status != ML_ERROR_NONE) {
- ml_loge ("Failed to allocate memory for tensors data in sink callback.");
+ mlapi_loge ("Failed to allocate memory for tensors data in sink callback.");
return;
}
}
if (_info->num_tensors != num_mems) {
- ml_loge
+ mlapi_loge
("The sink event of [%s] cannot be handled because the number of tensors mismatches.",
elem->name);
/* Not configured, yet. */
if (sz == 0)
- ml_loge ("The caps for sink(%s) is not configured.", elem->name);
+ mlapi_loge ("The caps for sink(%s) is not configured.",
+ elem->name);
if (sz != _data->tensors[i].size) {
- ml_loge
+ mlapi_loge
("The sink event of [%s] cannot be handled because the tensor dimension mismatches.",
elem->name);
/* Get the data! */
if (gst_buffer_get_size (b) != total_size ||
(elem->size > 0 && total_size != elem->size)) {
- ml_loge
+ mlapi_loge
("The buffersize mismatches. All the three values must be the same: %zu, %zu, %zu",
total_size, elem->size, gst_buffer_get_size (b));
goto error;
gst_message_parse_state_changed (message, &old_state, &new_state, NULL);
pipe_h->pipe_state = (ml_pipeline_state_e) new_state;
- ml_logd ("The pipeline state changed from %s to %s.",
+ mlapi_logd ("The pipeline state changed from %s to %s.",
gst_element_state_get_name (old_state),
gst_element_state_get_name (new_state));
gst_element_set_state (e->pipe->element, GST_STATE_PLAYING);
if (gst_app_src_end_of_stream (GST_APP_SRC (e->element)) != GST_FLOW_OK) {
- ml_logw ("Failed to set EOS in %s", e->name);
+ mlapi_logw ("Failed to set EOS in %s", e->name);
}
g_mutex_unlock (&e->lock);
while (!e->pipe->isEOS) {
/** check EOS every 1ms */
g_usleep (1000);
if (eos_check_cnt >= EOS_MESSAGE_TIME_LIMIT) {
- ml_loge ("Failed to get EOS message");
+ mlapi_loge ("Failed to get EOS message");
break;
}
}
status = convert_tizen_element (pipe, &converted, is_internal);
if (status == ML_ERROR_NONE) {
- ml_logd ("Converted pipeline: %s", converted);
+ mlapi_logd ("Converted pipeline: %s", converted);
*result = converted;
} else {
g_free (converted);
g_object_get (G_OBJECT (elem), "sync", &sync, NULL);
if (sync) {
- ml_logw
+ mlapi_logw
("It is recommended to apply 'sync=false' property to a sink element in most AI applications. Otherwise, inference results of large neural networks will be frequently dropped by the synchronization mechanism at the sink element.");
}
}
break;
case GST_ITERATOR_RESYNC:
case GST_ITERATOR_ERROR:
- ml_logw
+ mlapi_logw
("There is an error or a resync-event while inspecting a pipeline. However, we can still execute the pipeline.");
/* fallthrough */
case GST_ITERATOR_DONE:
/* prepare pipeline handle */
pipe_h = g_new0 (ml_pipeline, 1);
if (pipe_h == NULL) {
- ml_loge ("Failed to allocate handle for pipeline.");
+ mlapi_loge ("Failed to allocate handle for pipeline.");
return ML_ERROR_OUT_OF_MEMORY;
}
g_free (description);
if (pipeline == NULL || err) {
- ml_loge ("Cannot parse and launch the given pipeline = [%s]",
+ mlapi_loge ("Cannot parse and launch the given pipeline = [%s]",
pipeline_description);
- ml_loge (" - Error Message: %s", (err) ? err->message : "unknown reason");
+ mlapi_loge (" - Error Message: %s",
+ (err) ? err->message : "unknown reason");
g_clear_error (&err);
if (pipeline)
scret = gst_element_set_state (p->element, GST_STATE_PAUSED);
if (scret == GST_STATE_CHANGE_FAILURE) {
g_mutex_unlock (&p->lock);
- ml_loge
+ mlapi_loge
("Failed to wait until state changed PLAYING to PAUSED. For the detail, please check the GStreamer log messages.");
return ML_ERROR_STREAMS_PIPE;
}
/** check PAUSED every 1ms */
g_usleep (1000);
if (check_paused_cnt >= WAIT_PAUSED_TIME_LIMIT) {
- ml_loge ("Failed to wait until state changed to PAUSED");
+ mlapi_loge ("Failed to wait until state changed to PAUSED");
break;
}
}
scret = gst_element_set_state (p->element, GST_STATE_NULL);
if (scret != GST_STATE_CHANGE_SUCCESS) {
g_mutex_unlock (&p->lock);
- ml_loge
+ mlapi_loge
("Failed to wait until state changed to NULL(STOP). For the detail, please check the GStreamer log messages.");
return ML_ERROR_STREAMS_PIPE;
}
g_mutex_unlock (&p->lock);
if (scret == GST_STATE_CHANGE_FAILURE) {
- ml_loge
+ mlapi_loge
("Failed to get the state of the pipeline. For the detail, please check the GStreamer log messages.");
return ML_ERROR_STREAMS_PIPE;
}
scret = gst_element_set_state (p->element, GST_STATE_PLAYING);
if (scret == GST_STATE_CHANGE_FAILURE) {
- ml_loge
+ mlapi_loge
("Failed to set the state of the pipeline to PLAYING. For the detail, please check the GStreamer log messages.");
status = ML_ERROR_STREAMS_PIPE;
}
g_mutex_unlock (&p->lock);
if (scret == GST_STATE_CHANGE_FAILURE) {
- ml_loge
+ mlapi_loge
("Failed to set the state of the pipeline to PAUSED. For the detail, please check the GStreamer log messages.");
return ML_ERROR_STREAMS_PIPE;
}
if (status != ML_ERROR_NONE)
return status;
- ml_logi ("The pipeline is stopped, clear all data from the pipeline.");
+ mlapi_logi ("The pipeline is stopped, clear all data from the pipeline.");
/* send flush event to pipeline */
g_mutex_lock (&p->lock);
if (!gst_element_send_event (p->element, gst_event_new_flush_start ())) {
- ml_logw ("Error occurs while sending flush_start event.");
+ mlapi_logw ("Error occurs while sending flush_start event.");
}
if (!gst_element_send_event (p->element, gst_event_new_flush_stop (TRUE))) {
- ml_logw ("Error occurs while sending flush_stop event.");
+ mlapi_logw ("Error occurs while sending flush_stop event.");
}
g_mutex_unlock (&p->lock);
check_feature_state ();
if (h == NULL) {
- ml_loge ("The argument sink handle is not valid.");
+ mlapi_loge ("The argument sink handle is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
*h = NULL;
if (pipe == NULL) {
- ml_loge ("The first argument, pipeline handle is not valid.");
+ mlapi_loge ("The first argument, pipeline handle is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
if (sink_name == NULL) {
- ml_loge ("The second argument, sink name is not valid.");
+ mlapi_loge ("The second argument, sink name is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
if (cb == NULL) {
- ml_loge ("The callback argument, cb, is not valid.");
+ mlapi_loge ("The callback argument, cb, is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
elem = g_hash_table_lookup (p->namednodes, sink_name);
if (elem == NULL) {
- ml_loge ("There is no element named [%s] in the pipeline.", sink_name);
+ mlapi_loge ("There is no element named [%s] in the pipeline.", sink_name);
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
}
if (elem->type != ML_PIPELINE_ELEMENT_SINK &&
elem->type != ML_PIPELINE_ELEMENT_APP_SINK) {
- ml_loge ("The element [%s] in the pipeline is not a sink element.",
+ mlapi_loge ("The element [%s] in the pipeline is not a sink element.",
sink_name);
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
if (elem->handle_id > 0) {
/* no need to connect signal to sink element */
- ml_logw ("Sink callback is already registered.");
+ mlapi_logw ("Sink callback is already registered.");
} else {
/* set callback for new data */
if (elem->type == ML_PIPELINE_ELEMENT_SINK) {
}
if (elem->handle_id == 0) {
- ml_loge ("Failed to connect a signal to the element [%s].", sink_name);
+ mlapi_loge ("Failed to connect a signal to the element [%s].", sink_name);
ret = ML_ERROR_STREAMS_PIPE;
goto unlock_return;
}
sink = g_new0 (ml_pipeline_common_elem, 1);
if (sink == NULL) {
- ml_loge ("Failed to allocate the sink handle for %s.", sink_name);
+ mlapi_loge ("Failed to allocate the sink handle for %s.", sink_name);
ret = ML_ERROR_OUT_OF_MEMORY;
goto unlock_return;
}
sink->callback_info = g_new0 (callback_info_s, 1);
if (sink->callback_info == NULL) {
g_free (sink);
- ml_loge ("Failed to allocate the sink handle for %s.", sink_name);
+ mlapi_loge ("Failed to allocate the sink handle for %s.", sink_name);
ret = ML_ERROR_OUT_OF_MEMORY;
goto unlock_return;
}
elem->size = 0;
if (elem->src == NULL) {
- ml_loge
+ mlapi_loge
("Failed to get the src pad of the element[%s]. For the detail, please check the GStreamer log messages.",
elem->name);
ret = ML_ERROR_STREAMS_PIPE;
}
} else {
if (!elem->is_media_stream && !elem->is_flexible_tensor) {
- ml_logw
+ mlapi_logw
("Cannot find caps. The pipeline is not yet negotiated for src element [%s].",
elem->name);
gst_object_unref (elem->src);
check_feature_state ();
if (h == NULL) {
- ml_loge ("The argument source handle is not valid.");
+ mlapi_loge ("The argument source handle is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
*h = NULL;
if (pipe == NULL) {
- ml_loge ("The first argument, pipeline handle is not valid.");
+ mlapi_loge ("The first argument, pipeline handle is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
if (src_name == NULL) {
- ml_loge ("The second argument, source name is not valid.");
+ mlapi_loge ("The second argument, source name is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
elem = g_hash_table_lookup (p->namednodes, src_name);
if (elem == NULL) {
- ml_loge ("There is no element named [%s] in the pipeline.", src_name);
+ mlapi_loge ("There is no element named [%s] in the pipeline.", src_name);
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
}
if (elem->type != ML_PIPELINE_ELEMENT_APP_SRC) {
- ml_loge ("The element [%s] in the pipeline is not a source element.",
+ mlapi_loge ("The element [%s] in the pipeline is not a source element.",
src_name);
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
src = *h = g_new0 (ml_pipeline_common_elem, 1);
if (src == NULL) {
- ml_loge ("Failed to allocate the src handle for %s.", src_name);
+ mlapi_loge ("Failed to allocate the src handle for %s.", src_name);
ret = ML_ERROR_OUT_OF_MEMORY;
goto unlock_return;
}
_data = (ml_tensors_data_s *) data;
if (!_data) {
- ml_loge ("The given param data is invalid.");
+ mlapi_loge ("The given param data is invalid.");
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
}
G_LOCK_UNLESS_NOLOCK (*_data);
if (_data->num_tensors < 1 || _data->num_tensors > ML_TENSOR_SIZE_LIMIT) {
- ml_loge ("The tensor size is invalid. It should be 1 ~ %u; where it is %u",
+ mlapi_loge
+ ("The tensor size is invalid. It should be 1 ~ %u; where it is %u",
ML_TENSOR_SIZE_LIMIT, _data->num_tensors);
ret = ML_ERROR_INVALID_PARAMETER;
goto dont_destroy_data;
ret = ml_pipeline_src_parse_tensors_info (elem);
if (ret != ML_ERROR_NONE) {
- ml_logw
+ mlapi_logw
("The pipeline is not ready to accept inputs. The input is ignored.");
goto dont_destroy_data;
}
if (!elem->is_media_stream && !elem->is_flexible_tensor) {
if (elem->tensors_info.num_tensors != _data->num_tensors) {
- ml_loge
+ mlapi_loge
("The src push of [%s] cannot be handled because the number of tensors in a frame mismatches. %u != %u",
elem->name, elem->tensors_info.num_tensors, _data->num_tensors);
size_t sz = ml_tensor_info_get_size (&elem->tensors_info.info[i]);
if (sz != _data->tensors[i].size) {
- ml_loge
+ mlapi_loge
("The given input tensor size (%d'th, %zu bytes) mismatches the source pad (%zu bytes)",
i, _data->tensors[i].size, sz);
}
if (gret == GST_FLOW_FLUSHING) {
- ml_logw
+ mlapi_logw
("The pipeline is not in PAUSED/PLAYING. The input may be ignored.");
ret = ML_ERROR_TRY_AGAIN;
} else if (gret == GST_FLOW_EOS) {
- ml_logw ("THe pipeline is in EOS state. The input is ignored.");
+ mlapi_logw ("THe pipeline is in EOS state. The input is ignored.");
ret = ML_ERROR_STREAMS_PIPE;
}
if (src->callback_info == NULL)
src->callback_info = g_new0 (callback_info_s, 1);
if (src->callback_info == NULL) {
- ml_loge ("Failed to allocate the callback info for %s.", elem->name);
+ mlapi_loge ("Failed to allocate the callback info for %s.", elem->name);
ret = ML_ERROR_OUT_OF_MEMORY;
goto unlock_return;
}
check_feature_state ();
if (h == NULL) {
- ml_loge ("The argument switch handle is not valid.");
+ mlapi_loge ("The argument switch handle is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
*h = NULL;
if (pipe == NULL) {
- ml_loge ("The first argument, pipeline handle, is not valid.");
+ mlapi_loge ("The first argument, pipeline handle, is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
if (switch_name == NULL) {
- ml_loge ("The second argument, switch name, is not valid.");
+ mlapi_loge ("The second argument, switch name, is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
elem = g_hash_table_lookup (p->namednodes, switch_name);
if (elem == NULL) {
- ml_loge ("There is no switch element named [%s] in the pipeline.",
+ mlapi_loge ("There is no switch element named [%s] in the pipeline.",
switch_name);
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
if (type)
*type = ML_PIPELINE_SWITCH_OUTPUT_SELECTOR;
} else {
- ml_loge
+ mlapi_loge
("There is an element named [%s] in the pipeline, but it is not an input/output switch",
switch_name);
swtc = *h = g_new0 (ml_pipeline_common_elem, 1);
if (swtc == NULL) {
- ml_loge ("Failed to allocate the switch handle for %s.", switch_name);
+ mlapi_loge ("Failed to allocate the switch handle for %s.", switch_name);
ret = ML_ERROR_OUT_OF_MEMORY;
goto unlock_return;
}
handle_init (swtc, h);
if (pad_name == NULL) {
- ml_loge ("The second argument, pad name, is not valid.");
+ mlapi_loge ("The second argument, pad name, is not valid.");
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
}
active_name = gst_pad_get_name (active_pad);
if (g_strcmp0 (pad_name, active_name) == 0) {
- ml_logi ("Switch is called, but there is no effective changes: %s->%s.",
+ mlapi_logi ("Switch is called, but there is no effective changes: %s->%s.",
active_name, pad_name);
g_free (active_name);
gst_object_unref (active_pad);
new_pad = gst_element_get_static_pad (elem->element, pad_name);
if (new_pad == NULL) {
/* Not Found! */
- ml_loge ("Cannot find the pad, [%s], from the switch, [%s].",
+ mlapi_loge ("Cannot find the pad, [%s], from the switch, [%s].",
pad_name, elem->name);
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
g_object_set (G_OBJECT (elem->element), "active-pad", new_pad, NULL);
gst_object_unref (new_pad);
- ml_logi ("Switched to [%s] successfully at switch [%s].", pad_name,
+ mlapi_logi ("Switched to [%s] successfully at switch [%s].", pad_name,
elem->name);
handle_exit (h);
handle_init (swtc, h);
if (list == NULL) {
- ml_loge ("The second argument, list, is not valid.");
+ mlapi_loge ("The second argument, list, is not valid.");
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
}
else if (elem->type == ML_PIPELINE_ELEMENT_SWITCH_OUTPUT)
it = gst_element_iterate_src_pads (elem->element);
else {
- ml_loge
+ mlapi_loge
("The element, [%s], is supposed to be input/output switch, but it is not. Internal data structure is broken.",
elem->name);
ret = ML_ERROR_STREAMS_PIPE;
gst_iterator_resync (it);
break;
case GST_ITERATOR_ERROR:
- ml_loge ("Cannot access the list of pad properly of a switch, [%s].",
+ mlapi_loge ("Cannot access the list of pad properly of a switch, [%s].",
elem->name);
ret = ML_ERROR_STREAMS_PIPE;
break;
*list = g_malloc0 (sizeof (char *) * (counter + 1));
if (*list == NULL) {
- ml_loge ("Failed to allocate memory for pad list.");
+ mlapi_loge ("Failed to allocate memory for pad list.");
ret = ML_ERROR_OUT_OF_MEMORY;
goto unlock_return;
}
g_free (*list);
*list = NULL;
- ml_loge
+ mlapi_loge
("Internal data inconsistency. This could be a bug in nnstreamer. Switch [%s].",
elem->name);
ret = ML_ERROR_STREAMS_PIPE;
check_feature_state ();
if (h == NULL) {
- ml_loge ("The argument valve handle is not valid.");
+ mlapi_loge ("The argument valve handle is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
*h = NULL;
if (pipe == NULL) {
- ml_loge ("The first argument, pipeline handle, is not valid.");
+ mlapi_loge ("The first argument, pipeline handle, is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
if (valve_name == NULL) {
- ml_loge ("The second argument, valve name, is not valid.");
+ mlapi_loge ("The second argument, valve name, is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
elem = g_hash_table_lookup (p->namednodes, valve_name);
if (elem == NULL) {
- ml_loge ("There is no valve element named [%s] in the pipeline.",
+ mlapi_loge ("There is no valve element named [%s] in the pipeline.",
valve_name);
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
}
if (elem->type != ML_PIPELINE_ELEMENT_VALVE) {
- ml_loge
+ mlapi_loge
("There is an element named [%s] in the pipeline, but it is not a valve",
valve_name);
ret = ML_ERROR_INVALID_PARAMETER;
valve = *h = g_new0 (ml_pipeline_common_elem, 1);
if (valve == NULL) {
- ml_loge ("Failed to allocate the valve handle for %s.", valve_name);
+ mlapi_loge ("Failed to allocate the valve handle for %s.", valve_name);
ret = ML_ERROR_OUT_OF_MEMORY;
goto unlock_return;
}
if ((open != false) != (drop != FALSE)) {
/* Nothing to do */
- ml_logi ("Valve is called, but there is no effective changes");
+ mlapi_logi ("Valve is called, but there is no effective changes");
goto unlock_return;
}
/* Check input parameter */
if (pipe == NULL) {
- ml_loge ("The first argument, pipeline handle, is not valid.");
+ mlapi_loge ("The first argument, pipeline handle, is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
if (element_name == NULL) {
- ml_loge ("The second argument, element name, is not valid.");
+ mlapi_loge ("The second argument, element name, is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
if (elem_h == NULL) {
- ml_loge ("The argument element handle is not valid.");
+ mlapi_loge ("The argument element handle is not valid.");
return ML_ERROR_INVALID_PARAMETER;
}
*elem_h = NULL;
gst_elem = gst_bin_get_by_name (GST_BIN (p->element), element_name);
if (gst_elem == NULL) {
- ml_loge ("The element named [%s] is not found in the pipeline",
+ mlapi_loge ("The element named [%s] is not found in the pipeline",
element_name);
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
elem = construct_element (gst_elem, pipe, element_name,
ML_PIPELINE_ELEMENT_COMMON);
if (elem == NULL) {
- ml_loge ("Failed to allocate the internal memory");
+ mlapi_loge ("Failed to allocate the internal memory");
ret = ML_ERROR_OUT_OF_MEMORY;
goto unlock_return;
}
/* Type checking */
if (elem->type == ML_PIPELINE_ELEMENT_UNKNOWN) {
- ml_loge
+ mlapi_loge
("There is an element named [%s] in the pipeline, but it is unknown type.",
element_name);
ret = ML_ERROR_INVALID_PARAMETER;
common_elem = *elem_h = g_new0 (ml_pipeline_common_elem, 1);
if (common_elem == NULL) {
- ml_loge ("Failed to allocate the internal handler for %s.", element_name);
+ mlapi_loge ("Failed to allocate the internal handler for %s.",
+ element_name);
ret = ML_ERROR_OUT_OF_MEMORY;
goto unlock_return;
}
/* Check property existence */
pspec = g_object_class_find_property (class, property_name);
if (pspec == NULL) {
- ml_loge ("The property name [%s] does not exist.", property_name);
+ mlapi_loge ("The property name [%s] does not exist.", property_name);
return FALSE;
}
(type == G_TYPE_INT && G_TYPE_IS_ENUM (pspec->value_type)) ||
(type == G_TYPE_UINT && G_TYPE_IS_ENUM (pspec->value_type)) ||
(type == G_TYPE_DOUBLE && pspec->value_type == G_TYPE_FLOAT))) {
- ml_loge ("The type of property name [%s] is '%s'", property_name,
+ mlapi_loge ("The type of property name [%s] is '%s'", property_name,
g_type_name (pspec->value_type));
return FALSE;
}
/* Check the input parameter */
if (property_name == NULL) {
- ml_loge ("The second argument, property name is not valid.");
+ mlapi_loge ("The second argument, property name is not valid.");
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
}
/* Check the input parameter */
if (property_name == NULL) {
- ml_loge ("The second argument, property_name is not valid.");
+ mlapi_loge ("The second argument, property_name is not valid.");
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
}
if (pvalue == NULL) {
- ml_loge ("The third argument, value is not valid.");
+ mlapi_loge ("The third argument, value is not valid.");
ret = ML_ERROR_INVALID_PARAMETER;
goto unlock_return;
}
g_mutex_lock (&c->lock);
if (c->ref_count > 0) {
- ml_loge
+ mlapi_loge
("Failed to unregister custom filter %s, it is used in the pipeline.",
c->name);
status = ML_ERROR_INVALID_PARAMETER;
}
if (NNS_custom_easy_unregister (c->name) != 0) {
- ml_loge ("Failed to unregister custom filter %s.", c->name);
+ mlapi_loge ("Failed to unregister custom filter %s.", c->name);
status = ML_ERROR_INVALID_PARAMETER;
goto done;
}
g_mutex_lock (&c->lock);
if (c->ref_count > 0) {
- ml_loge
+ mlapi_loge
("Failed to unregister custom condition %s, it is used in the pipeline.",
c->name);
status = ML_ERROR_INVALID_PARAMETER;
}
if (nnstreamer_if_custom_unregister (c->name) != 0) {
- ml_loge ("Failed to unregister tensor_if custom condition %s.", c->name);
+ mlapi_loge ("Failed to unregister tensor_if custom condition %s.", c->name);
status = ML_ERROR_STREAMS_PIPE;
goto done;
}
/**
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved.
*
- * @file nnstreamer-capi-single.c
+ * @file ml-api-inference-single.c
* @date 29 Aug 2019
* @brief NNStreamer/Single C-API Wrapper.
* This allows to invoke individual input frame with NNStreamer.
*/
#include <nnstreamer-single.h>
-#include <nnstreamer-capi-private.h>
+#include <nnstreamer-tizen-internal.h> /* Tizen platform header */
+#include <nnstreamer_internal.h>
+#include <nnstreamer_log.h>
#include <nnstreamer_plugin_api.h>
-
+#include <nnstreamer_plugin_api_filter.h>
#include <tensor_filter_single.h>
+#include "ml-api-inference-internal.h"
+#include "ml-api-internal.h"
+
#define ML_SINGLE_MAGIC 0xfeedfeed
/**
G_LOCK (magic); \
single_h = (ml_single *) single; \
if (G_UNLIKELY(single_h->magic != ML_SINGLE_MAGIC)) { \
- ml_loge ("The given param, single is invalid."); \
+ mlapi_loge ("The given param, single is invalid."); \
G_UNLOCK (magic); \
return ML_ERROR_INVALID_PARAMETER; \
} \
} ml_single;
/**
+ * @brief Checks the availability of the given execution environments with custom option.
+ */
+int
+ml_check_nnfw_availability_full (ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw,
+ const char *custom, bool *available)
+{
+ const char *fw_name = NULL;
+
+ check_feature_state ();
+
+ if (!available)
+ return ML_ERROR_INVALID_PARAMETER;
+
+ /* init false */
+ *available = false;
+
+ if (nnfw == ML_NNFW_TYPE_ANY)
+ return ML_ERROR_INVALID_PARAMETER;
+
+ fw_name = ml_get_nnfw_subplugin_name (nnfw);
+
+ if (fw_name) {
+ if (nnstreamer_filter_find (fw_name) != NULL) {
+ accl_hw accl = ml_nnfw_to_accl_hw (hw);
+
+ if (gst_tensor_filter_check_hw_availability (fw_name, accl, custom)) {
+ *available = true;
+ } else {
+ mlapi_logw ("%s is supported but not with the specified hardware.",
+ fw_name);
+ }
+ } else {
+ mlapi_logw ("%s is not supported.", fw_name);
+ }
+ }
+
+ return ML_ERROR_NONE;
+}
+
+/**
+ * @brief Checks the availability of the given execution environments.
+ */
+int
+ml_check_nnfw_availability (ml_nnfw_type_e nnfw, ml_nnfw_hw_e hw,
+ bool *available)
+{
+ return ml_check_nnfw_availability_full (nnfw, hw, NULL, available);
+}
+
+/**
* @brief setup input and output tensor memory to pass to the tensor_filter.
* @note this tensor memory wrapper will be reused for each invoke.
*/
ML_SINGLE_HANDLE_UNLOCK (single_h);
if (G_UNLIKELY (status != ML_ERROR_NONE))
- ml_loge ("Failed to destroy the data.");
+ mlapi_loge ("Failed to destroy the data.");
return status;
}
/** invoke the thread */
if (!single_h->klass->invoke (single_h->filter, in_tensors, out_tensors,
single_h->free_output)) {
- ml_loge ("Failed to invoke the tensors.");
+ mlapi_loge ("Failed to invoke the tensors.");
status = ML_ERROR_STREAMS_PIPE;
}
g_free (val);
if (gst_info->num_tensors != num) {
- ml_logw ("The number of tensor type is mismatched in filter.");
+ mlapi_logw ("The number of tensor type is mismatched in filter.");
}
/* get names */
g_free (val);
if (gst_info->num_tensors != num) {
- ml_logw ("The number of tensor name is mismatched in filter.");
+ mlapi_logw ("The number of tensor name is mismatched in filter.");
}
}
single_h = g_new0 (ml_single, 1);
if (single_h == NULL) {
- ml_loge ("Failed to allocate the single handle.");
+ mlapi_loge ("Failed to allocate the single handle.");
return NULL;
}
single_h->filter = g_object_new (G_TYPE_TENSOR_FILTER_SINGLE, NULL);
if (single_h->filter == NULL) {
- ml_loge ("Failed to create a new instance for filter.");
+ mlapi_loge ("Failed to create a new instance for filter.");
g_free (single_h);
return NULL;
}
single_h->klass = g_type_class_ref (G_TYPE_TENSOR_FILTER_SINGLE);
if (single_h->klass == NULL) {
- ml_loge ("Failed to get class of the filter.");
+ mlapi_loge ("Failed to get class of the filter.");
ml_single_close (single_h);
return NULL;
}
single_h->thread =
g_thread_try_new (NULL, invoke_thread, (gpointer) single_h, &error);
if (single_h->thread == NULL) {
- ml_loge ("Failed to create the invoke thread, error: %s.", error->message);
+ mlapi_loge ("Failed to create the invoke thread, error: %s.",
+ error->message);
g_clear_error (&error);
ml_single_close (single_h);
return NULL;
ml_single_preset * info)
{
if (!single) {
- ml_loge ("The given param is invalid: 'single' is NULL.");
+ mlapi_loge ("The given param is invalid: 'single' is NULL.");
return ML_ERROR_INVALID_PARAMETER;
}
if (!info) {
- ml_loge ("The given param is invalid: 'info' is NULL.");
+ mlapi_loge ("The given param is invalid: 'info' is NULL.");
return ML_ERROR_INVALID_PARAMETER;
}
/* Validate input tensor info. */
if (info->input_info && !ml_tensors_info_is_valid (info->input_info)) {
- ml_loge ("The given param, input tensor info is invalid.");
+ mlapi_loge ("The given param, input tensor info is invalid.");
return ML_ERROR_INVALID_PARAMETER;
}
/* Validate output tensor info. */
if (info->output_info && !ml_tensors_info_is_valid (info->output_info)) {
- ml_loge ("The given param, output tensor info is invalid.");
+ mlapi_loge ("The given param, output tensor info is invalid.");
return ML_ERROR_INVALID_PARAMETER;
}
if (!info->models) {
- ml_loge ("The given param, model is invalid: info->models is NULL.");
+ mlapi_loge ("The given param, model is invalid: info->models is NULL.");
return ML_ERROR_INVALID_PARAMETER;
}
* (Supposed CPU only) Support others later.
*/
if (!ml_nnfw_is_available (nnfw, hw)) {
- ml_loge ("The given nnfw is not available.");
+ mlapi_loge ("The given nnfw is not available.");
return ML_ERROR_NOT_SUPPORTED;
}
- /** Create ml_single object */
+ /** Create ml_single object */
if ((single_h = ml_single_create_handle (nnfw)) == NULL)
return ML_ERROR_OUT_OF_MEMORY;
if (status != ML_ERROR_NONE)
goto error;
} else {
- ml_loge
+ mlapi_loge
("To run the pipeline, input and output information should be initialized.");
status = ML_ERROR_INVALID_PARAMETER;
goto error;
/* 4. Start the nnfw to get inout configurations if needed */
if (!single_h->klass->start (single_h->filter)) {
- ml_loge ("Failed to start NNFW to get inout configurations.");
+ mlapi_loge ("Failed to start NNFW to get inout configurations.");
status = ML_ERROR_STREAMS_PIPE;
goto error;
}
/* 5. Set in/out configs and metadata */
if (!ml_single_set_info_in_handle (single_h, TRUE, in_tensors_info)) {
- ml_loge ("The input tensor info is invalid.");
+ mlapi_loge ("The input tensor info is invalid.");
status = ML_ERROR_INVALID_PARAMETER;
goto error;
}
if (!ml_single_set_info_in_handle (single_h, FALSE, out_tensors_info)) {
- ml_loge ("The output tensor info is invalid.");
+ mlapi_loge ("The output tensor info is invalid.");
status = ML_ERROR_INVALID_PARAMETER;
goto error;
}
check_feature_state ();
if (!single) {
- ml_loge ("The given param, single is invalid.");
+ mlapi_loge ("The given param, single is invalid.");
return ML_ERROR_INVALID_PARAMETER;
}
/** Wait until invoke process is finished */
while (invoking) {
- ml_logw ("Wait 1 ms until invoke is finished and close the handle.");
+ mlapi_logw ("Wait 1 ms until invoke is finished and close the handle.");
g_usleep (1000);
g_mutex_lock (&single_h->mutex);
invoking = single_h->invoking;
_data = (ml_tensors_data_s *) data;
if (G_UNLIKELY (!_data)) {
- ml_loge ("The data handle to invoke the model is invalid.");
+ mlapi_loge ("The data handle to invoke the model is invalid.");
return ML_ERROR_INVALID_PARAMETER;
}
_model = &single_h->out_tensors;
if (G_UNLIKELY (_data->num_tensors != _model->num_tensors)) {
- ml_loge
+ mlapi_loge
("The number of %s tensors is not compatible with model. Given: %u, Expected: %u.",
(is_input) ? "input" : "output", _data->num_tensors,
_model->num_tensors);
for (i = 0; i < _data->num_tensors; i++) {
if (G_UNLIKELY (!_data->tensors[i].tensor)) {
- ml_loge ("The %d-th input tensor is not valid.", i);
+ mlapi_loge ("The %d-th input tensor is not valid.", i);
return ML_ERROR_INVALID_PARAMETER;
}
raw_size = _model->tensors[i].size;
if (G_UNLIKELY (_data->tensors[i].size != raw_size)) {
- ml_loge
+ mlapi_loge
("The size of %d-th %s tensor is not compatible with model. Given: %zu, Expected: %zu (type: %d).",
i, (is_input) ? "input" : "output", _data->tensors[i].size, raw_size,
single_h->in_info.info[i].type);
check_feature_state ();
if (G_UNLIKELY (!single)) {
- ml_loge
+ mlapi_loge
("The first argument of ml_single_invoke() is not valid. Please check the single handle.");
return ML_ERROR_INVALID_PARAMETER;
}
if (G_UNLIKELY (!input)) {
- ml_loge
+ mlapi_loge
("The second argument of ml_single_invoke() is not valid. Please check the input data handle.");
return ML_ERROR_INVALID_PARAMETER;
}
if (G_UNLIKELY (!output)) {
- ml_loge
+ mlapi_loge
("The third argument of ml_single_invoke() is not valid. Please check the output data handle.");
return ML_ERROR_INVALID_PARAMETER;
}
ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 0);
if (G_UNLIKELY (!single_h->filter)) {
- ml_loge
+ mlapi_loge
("The tensor_filter element is not valid. It is not correctly created or already freed.");
status = ML_ERROR_INVALID_PARAMETER;
goto exit;
if (single_h->state != IDLE) {
if (G_UNLIKELY (single_h->state == JOIN_REQUESTED)) {
- ml_loge ("The handle is closed or being closed.");
+ mlapi_loge ("The handle is closed or being closed.");
status = ML_ERROR_STREAMS_PIPE;
goto exit;
}
- ml_loge ("The single invoking thread is not idle.");
+ mlapi_loge ("The single invoking thread is not idle.");
status = ML_ERROR_TRY_AGAIN;
goto exit;
}
if (g_cond_wait_until (&single_h->cond, &single_h->mutex, end_time)) {
status = single_h->status;
} else {
- ml_logw ("Wait for invoke has timed out");
+ mlapi_logw ("Wait for invoke has timed out");
status = ML_ERROR_TIMED_OUT;
/** This is set to notify invoke_thread to not process if timed out */
if (need_alloc)
exit:
if (G_UNLIKELY (status != ML_ERROR_NONE)) {
- ml_loge ("Failed to invoke the model.");
+ mlapi_loge ("Failed to invoke the model.");
} else {
if (need_alloc)
*output = single_h->output;
g_object_set (G_OBJECT (single_h->filter), name, (gboolean) FALSE,
NULL);
} else {
- ml_loge ("The property value (%s) is not available.", value);
+ mlapi_loge ("The property value (%s) is not available.", value);
status = ML_ERROR_INVALID_PARAMETER;
}
} else if (g_str_equal (name, "input") || g_str_equal (name, "inputtype")
ml_tensors_info_destroy (ml_info);
} else {
- ml_loge ("The property value (%s) is not available.", value);
+ mlapi_loge ("The property value (%s) is not available.", value);
status = ML_ERROR_INVALID_PARAMETER;
}
g_object_get (G_OBJECT (single_h->filter), name, &bool_value, NULL);
*value = (bool_value) ? g_strdup ("true") : g_strdup ("false");
} else {
- ml_loge ("The property %s is not available.", name);
+ mlapi_loge ("The property %s is not available.", name);
status = ML_ERROR_NOT_SUPPORTED;
}
/**
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved.
*
- * @file nnstreamer-capi-tizen-privilege-check.c
+ * @file ml-api-inference-tizen-privilege-check.c
* @date 22 July 2020
- * @brief NNStreamer/C-API Tizen dependent functions.
+ * @brief NNStreamer/C-API Tizen dependent functions for inference APIs.
* @see https://github.com/nnstreamer/nnstreamer
* @author MyungJoo Ham <myungjoo.ham@samsung.com>
* @bug No known bugs except for NYI items
#include <system_info.h>
#include <restriction.h> /* device policy manager */
#include <privacy_privilege_manager.h>
-#include "nnstreamer-capi-private.h"
+#include <nnstreamer.h>
+#include "ml-api-internal.h"
+#include "ml-api-inference-internal.h"
#if TIZEN5PLUS
#include <mm_resource_manager.h>
#endif
#include <mm_camcorder.h>
-#include "nnstreamer.h"
-#include "nnstreamer_plugin_api.h"
#if TIZENMMCONF
/* We can use "MMCAM_VIDEOSRC_ELEMENT_NAME and MMCAM_AUDIOSRC_ELEMENT_NAME */
priv_result == PRIVACY_PRIVILEGE_MANAGER_CHECK_RESULT_ALLOW) {
/* privilege allowed */
} else {
- ml_loge ("Failed to check the privilege %s.", privilege);
+ mlapi_loge ("Failed to check the privilege %s.", privilege);
status = ML_ERROR_PERMISSION_DENIED;
}
}
if (err != DPM_ERROR_NONE || dpm_is_allowed != 1) {
- ml_loge ("Failed, device policy is not allowed.");
+ mlapi_loge ("Failed, device policy is not allowed.");
return ML_ERROR_PERMISSION_DENIED;
}
res_key = g_strdup ("tizen_mm_res_radio");
break;
default:
- ml_logw ("The resource type %d is invalid.", type);
+ mlapi_logw ("The resource type %d is invalid.", type);
break;
}
if (!res) {
res = g_new0 (pipeline_resource_s, 1);
if (!res) {
- ml_loge ("Failed to allocate pipeline resource handle.");
+ mlapi_loge ("Failed to allocate pipeline resource handle.");
status = ML_ERROR_OUT_OF_MEMORY;
goto rm_error;
}
if (!mm_handle) {
mm_handle = g_new0 (tizen_mm_handle_s, 1);
if (!mm_handle) {
- ml_loge ("Failed to allocate media resource handle.");
+ mlapi_loge ("Failed to allocate media resource handle.");
status = ML_ERROR_OUT_OF_MEMORY;
goto rm_error;
}
if (dpm_add_policy_changed_cb (mm_handle->dpm_h, "camera",
ml_tizen_dpm_policy_changed_cb, pipe,
&mm_handle->dpm_cb_id) != DPM_ERROR_NONE) {
- ml_loge ("Failed to add device policy callback.");
+ mlapi_loge ("Failed to add device policy callback.");
status = ML_ERROR_PERMISSION_DENIED;
goto rm_error;
}
if (!mm_res) {
mm_res = g_new0 (pipeline_resource_s, 1);
if (mm_res == NULL) {
- ml_loge ("Failed to allocate media resource data.");
+ mlapi_loge ("Failed to allocate media resource data.");
g_free (res_key);
status = ML_ERROR_OUT_OF_MEMORY;
goto rm_error;
_mmcamcorder_conf_get_value_element_name (element, &src_name);
if (!src_name) {
- ml_loge ("Failed to get the name of %s.", name);
+ mlapi_loge ("Failed to get the name of %s.", name);
return ML_ERROR_STREAMS_PIPE;
}
- *description = replace_string (*description, what, src_name, " !", &changed);
+ *description =
+ ml_replace_string (*description, what, src_name, " !", &changed);
if (changed > 1) {
/* allow one src in the pipeline */
- ml_loge ("Cannot parse duplicated src node.");
+ mlapi_loge ("Cannot parse duplicated src node.");
return ML_ERROR_STREAMS_PIPE;
}
}
if ((err = mm_camcorder_create (&hcam, &cam_info)) != MM_ERROR_NONE) {
- ml_loge ("Fail to call mm_camcorder_create = %x\n", err);
+ mlapi_loge ("Fail to call mm_camcorder_create = %x\n", err);
goto mm_error;
}
#if TIZENMMCONF /* 6.5 or higher */
MMCAM_VIDEOSRC_ELEMENT_NAME, &src_name, &size, NULL);
if (err != MM_ERROR_NONE || !src_name || size < 1) {
- ml_loge ("Failed to get attributes of MMCAM_VIDEOSRC_ELEMENT_NAME.");
+ mlapi_loge ("Failed to get attributes of MMCAM_VIDEOSRC_ELEMENT_NAME.");
status = ML_ERROR_NOT_SUPPORTED;
goto mm_error;
}
- *result = replace_string (*result, ML_TIZEN_CAM_VIDEO_SRC, src_name,
+ *result = ml_replace_string (*result, ML_TIZEN_CAM_VIDEO_SRC, src_name,
" !", &changed);
if (changed > 1) {
/* Allow one src only in a pipeline */
- ml_loge ("Cannot parse duplicated Tizen video src nodes.");
+ mlapi_loge ("Cannot parse duplicated Tizen video src nodes.");
status = ML_ERROR_INVALID_PARAMETER;
goto mm_error;
}
MMCAM_AUDIOSRC_ELEMENT_NAME, &src_name, &size, NULL);
if (err != MM_ERROR_NONE || !src_name || size < 1) {
- ml_loge ("Failed to get attributes of MMCAM_AUDIOSRC_ELEMENT_NAME.");
+ mlapi_loge ("Failed to get attributes of MMCAM_AUDIOSRC_ELEMENT_NAME.");
status = ML_ERROR_NOT_SUPPORTED;
goto mm_error;
}
- *result = replace_string (*result, ML_TIZEN_CAM_AUDIO_SRC, src_name,
+ *result = ml_replace_string (*result, ML_TIZEN_CAM_AUDIO_SRC, src_name,
" !", &changed);
if (changed > 1) {
/* Allow one src only in a pipeline */
- ml_loge ("Cannot parse duplicated Tizen audio src nodes.");
+ mlapi_loge ("Cannot parse duplicated Tizen audio src nodes.");
status = ML_ERROR_INVALID_PARAMETER;
goto mm_error;
}
err =
_mmcamcorder_conf_get_info (hcam, 0, MMFW_CONFIG_MAIN_FILE, &cam_conf);
if (err != MM_ERROR_NONE || !cam_conf) {
- ml_loge ("Failed to load conf %s.", MMFW_CONFIG_MAIN_FILE);
+ mlapi_loge ("Failed to load conf %s.", MMFW_CONFIG_MAIN_FILE);
status = ML_ERROR_NOT_SUPPORTED;
goto mm_error;
}
--- /dev/null
+/* SPDX-License-Identifier: Apache-2.0 */
+/**
+ * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved.
+ *
+ * @file ml-api-internal.h
+ * @date 20 October 2021
+ * @brief ML C-API internal herader without NNStreamer deps.
+ * This file should NOT be exported to SDK or devel package.
+ * @see https://github.com/nnstreamer/nnstreamer
+ * @author MyungJoo Ham <myungjoo.ham@samsung.com>
+ * @bug No known bugs except for NYI items
+ */
+#ifndef __ML_API_INTERNAL_H__
+#define __ML_API_INTERNAL_H__
+
+#include <glib.h>
+#include <ml-api-common.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/**
+ * DO NOT USE THE LOG INFRA of NNSTREAMER.
+ * This header is supposed to be independent from nnstreamer.git
+ */
+#define MLAPI_TAG_NAME "ml-api"
+
+#if defined(__TIZEN__)
+#include <dlog.h>
+#define mlapi_loge(...) \
+ dlog_print (DLOG_ERROR, MLAPI_TAG_NAME, __VA_ARGS__)
+#define mlapi_logi(...) \
+ dlog_print (DLOG_INFO, MLAPI_TAG_NAME, __VA_ARGS__)
+#define mlapi_logw(...) \
+ dlog_print (DLOG_WARN, MLAPI_TAG_NAME, __VA_ARGS__)
+#define mlapi_logd(...) \
+ dlog_print (DLOG_DEBUG, MLAPI_TAG_NAME, __VA_ARGS__)
+#elif defined(__ANDROID__)
+#include <android/log.h>
+#define mlapi_loge(...) \
+ __android_log_print (ANDROID_LOG_ERROR, MLAPI_TAG_NAME, __VA_ARGS__)
+#define mlapi_logi(...) \
+ __android_log_print (ANDROID_LOG_INFO, MLAPI_TAG_NAME, __VA_ARGS__)
+#define mlapi_logw(...) \
+ __android_log_print (ANDROID_LOG_WARN, MLAPI_TAG_NAME, __VA_ARGS__)
+#define mlapi_logd(...) \
+ __android_log_print (ANDROID_LOG_DEBUG, MLAPI_TAG_NAME, __VA_ARGS__)
+#else /* Linux distro */
+#include <glib.h>
+#define mlapi_loge g_critical
+#define mlapi_logi g_info
+#define mlapi_logw g_warning
+#define mlapi_logd g_debug
+#endif
+
+#if defined (__TIZEN__)
+typedef enum
+{
+ NOT_CHECKED_YET = -1,
+ NOT_SUPPORTED = 0,
+ SUPPORTED = 1
+} feature_state_t;
+
+#if defined (__FEATURE_CHECK_SUPPORT__)
+#define check_feature_state() \
+ do { \
+ int feature_ret = ml_tizen_get_feature_enabled (); \
+ if (ML_ERROR_NONE != feature_ret) \
+ return feature_ret; \
+ } while (0);
+
+#define set_feature_state(...) ml_tizen_set_feature_state(__VA_ARGS__)
+#else /* __FEATURE_CHECK_SUPPORT__ */
+#define check_feature_state()
+#define set_feature_state(...)
+#endif /* __FEATURE_CHECK_SUPPORT__ */
+
+#if (TIZENVERSION >= 5) && (TIZENVERSION < 9999)
+#define TIZEN5PLUS 1
+#if ((TIZENVERSION > 6) || (TIZENVERSION == 6 && TIZENVERSIONMINOR >= 5))
+#define TIZENMMCONF 1
+#endif
+#endif
+
+#else /* __TIZEN__ */
+#define check_feature_state()
+#define set_feature_state(...)
+#endif /* __TIZEN__ */
+
+#ifndef TIZEN5PLUS
+#define TIZEN5PLUS 0
+#endif /* TIZEN5PLUS */
+#ifndef TIZENMMCONF
+#define TIZENMMCONF 0
+#endif /* TIZENMMCONF */
+
+#define EOS_MESSAGE_TIME_LIMIT 100
+#define WAIT_PAUSED_TIME_LIMIT 100
+
+/**
+ * @brief Data structure for tensor information.
+ * @since_tizen 5.5
+ */
+typedef struct {
+ char *name; /**< Name of each element in the tensor. */
+ ml_tensor_type_e type; /**< Type of each element in the tensor. */
+ ml_tensor_dimension dimension; /**< Dimension information. */
+} ml_tensor_info_s;
+
+/**
+ * @brief Data structure for tensors information, which contains multiple tensors.
+ * @since_tizen 5.5
+ */
+typedef struct {
+ unsigned int num_tensors; /**< The number of tensors. */
+ ml_tensor_info_s info[ML_TENSOR_SIZE_LIMIT]; /**< The list of tensor info. */
+ GMutex lock; /**< Lock for thread safety */
+ int nolock; /**< Set non-zero to avoid using m (giving up thread safety) */
+} ml_tensors_info_s;
+
+/**
+ * @brief Macro to control private lock with nolock condition (lock)
+ * @param sname The name of struct (ml_tensors_info_s or ml_tensors_data_s)
+ */
+#define G_LOCK_UNLESS_NOLOCK(sname) \
+ do { \
+ GMutex *l = (GMutex *) &(sname).lock; \
+ if (!(sname).nolock) \
+ g_mutex_lock (l); \
+ } while (0)
+
+/**
+ * @brief Macro to control private lock with nolock condition (unlock)
+ * @param sname The name of struct (ml_tensors_info_s or ml_tensors_data_s)
+ */
+#define G_UNLOCK_UNLESS_NOLOCK(sname) \
+ do { \
+ GMutex *l = (GMutex *) &(sname).lock; \
+ if (!(sname).nolock) \
+ g_mutex_unlock (l); \
+ } while (0)
+
+/**
+ * @brief Macro to verify private lock acquired with nolock condition (lock)
+ * @param sname The name of struct (ml_tensors_info_s or ml_tensors_data_s)
+ */
+#define G_VERIFYLOCK_UNLESS_NOLOCK(sname) \
+ do { \
+ GMutex *l = (GMutex *) &(sname).lock; \
+ if (!(sname).nolock) { \
+ if (g_mutex_trylock(l)) { \
+ g_mutex_unlock(l); \
+ return ML_ERROR_INVALID_PARAMETER; \
+ } \
+ } \
+ } while (0)
+
+/**
+ * @brief Macro to check the tensors info is valid.
+ */
+#define ml_tensors_info_is_valid(i) ({bool v; (ml_tensors_info_validate ((i), &v) == ML_ERROR_NONE && v);})
+
+/**
+ * @brief Macro to compare the tensors info.
+ */
+#define ml_tensors_info_is_equal(i1,i2) ({bool e; (ml_tensors_info_compare ((i1), (i2), &e) == ML_ERROR_NONE && e);})
+
+/**
+ * @brief The function to be called when destroying the allocated handle.
+ * @since_tizen 6.5
+ * @param[in] handle The handle created for ML API.
+ * @param[in,out] user_data The user data to pass to the callback function.
+ * @return @c 0 on success. Otherwise a negative error value.
+ */
+typedef int (*ml_handle_destroy_cb) (void *handle, void *user_data);
+
+/**
+ * @brief An instance of a single input or output frame.
+ * @since_tizen 5.5
+ */
+typedef struct {
+ void *tensor; /**< The instance of tensor data. */
+ size_t size; /**< The size of tensor. */
+} ml_tensor_data_s;
+
+/**
+ * @brief An instance of input or output frames. #ml_tensors_info_h is the handle for tensors metadata.
+ * @since_tizen 5.5
+ */
+typedef struct {
+ unsigned int num_tensors; /**< The number of tensors. */
+ ml_tensor_data_s tensors[ML_TENSOR_SIZE_LIMIT]; /**< The list of tensor data. NULL for unused tensors. */
+
+ /* private */
+ ml_tensors_info_h info;
+ void *user_data; /**< The user data to pass to the callback function */
+ ml_handle_destroy_cb destroy; /**< The function to be called to release the allocated buffer */
+ GMutex lock; /**< Lock for thread safety */
+ int nolock; /**< Set non-zero to avoid using m (giving up thread safety) */
+} ml_tensors_data_s;
+
+/**
+ * @brief Gets the byte size of the given tensor info.
+ * @note This is not thread safe.
+ */
+size_t ml_tensor_info_get_size (const ml_tensor_info_s *info);
+
+/**
+ * @brief Initializes the tensors information with default value.
+ * @since_tizen 5.5
+ * @param[in] info The tensors info pointer to be initialized.
+ * @return @c 0 on success. Otherwise a negative error value.
+ * @retval #ML_ERROR_NONE Successful
+ * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
+ */
+int ml_tensors_info_initialize (ml_tensors_info_s *info);
+
+/**
+ * @brief Frees and initialize the data in tensors info.
+ * @since_tizen 5.5
+ * @param[in] info The tensors info pointer to be freed.
+ */
+void ml_tensors_info_free (ml_tensors_info_s *info);
+
+/**
+ * @brief Creates a tensor data frame without allocating new buffer cloning the given tensors data.
+ * @details If @a data_src is null, this returns error.
+ * @param[in] data_src The handle of tensors data to be cloned.
+ * @param[out] data The handle of tensors data.
+ * @return @c 0 on success. Otherwise a negative error value.
+ * @retval #ML_ERROR_NONE Successful
+ * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
+ * @retval #ML_ERROR_OUT_OF_MEMORY Failed to allocate required memory.
+ */
+int ml_tensors_data_clone_no_alloc (const ml_tensors_data_s * data_src, ml_tensors_data_h * data);
+
+/**
+ * @brief Replaces string.
+ * This function deallocates the input source string.
+ * This is copied from nnstreamer/tensor_common.c by the nnstreamer maintainer.
+ * @param[in] source The input string. This will be freed when returning the replaced string.
+ * @param[in] what The string to search for.
+ * @param[in] to The string to be replaced.
+ * @param[in] delimiters The characters which specify the place to split the string. Set NULL to replace all matched string.
+ * @param[out] count The count of replaced. Set NULL if it is unnecessary.
+ * @return Newly allocated string. The returned string should be freed with g_free().
+ */
+gchar * ml_replace_string (gchar * source, const gchar * what, const gchar * to, const gchar * delimiters, guint * count);
+
+/**
+ * @brief Compares the given tensors information.
+ * @details If the function returns an error, @a equal is not changed.
+ * @since_tizen 6.0
+ * @param[in] info1 The handle of tensors information to be compared.
+ * @param[in] info2 The handle of tensors information to be compared.
+ * @param[out] equal @c true if given tensors information is equal, @c false if it's not equal.
+ * @return @c 0 on success. Otherwise a negative error value.
+ * @retval #ML_ERROR_NONE Successful
+ * @retval #ML_ERROR_NOT_SUPPORTED Not supported.
+ * @retval #ML_ERROR_INVALID_PARAMETER Given parameter is invalid.
+ */
+int ml_tensors_info_compare (const ml_tensors_info_h info1, const ml_tensors_info_h info2, bool *equal);
+
+/**
+ * @brief Frees the tensors data handle and its data.
+ * @param[in] data The handle of tensors data.
+ * @param[in] free_data The flag to free the buffers in handle.
+ * @return @c 0 on success. Otherwise a negative error value.
+ */
+int ml_tensors_data_destroy_internal (ml_tensors_data_h data, gboolean free_data);
+
+/**
+ * @brief Creates a tensor data frame without buffer with the given tensors information.
+ * @details If @a info is null, this allocates data handle with empty tensor data.
+ * @param[in] info The handle of tensors information for the allocation.
+ * @param[out] data The handle of tensors data.
+ * @return @c 0 on success. Otherwise a negative error value.
+ */
+int ml_tensors_data_create_no_alloc (const ml_tensors_info_h info, ml_tensors_data_h *data);
+
+#if defined (__TIZEN__)
+/****** TIZEN CHECK FEATURE BEGINS *****/
+/**
+ * @brief Checks whether machine_learning.inference feature is enabled or not.
+ */
+int ml_tizen_get_feature_enabled (void);
+
+/**
+ * @brief Set the feature status of machine_learning.inference.
+ * This is only used for Unit test.
+ */
+int ml_tizen_set_feature_state (int state);
+/****** TIZEN CHECK FEATURE ENDS *****/
+#endif /* __TIZEN__ */
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+#endif /* __ML_API_INTERNAL_H__ */
NNSTREAMER_CAPI_INCLUDES := \
$(NNSTREAMER_ROOT)/gst/nnstreamer/tensor_filter \
$(ML_API_ROOT)/c/include/platform \
- $(ML_API_ROOT)/c/include
+ $(ML_API_ROOT)/c/include \
+ $(ML_API_ROOT)/c/src
# nnstreamer and single-shot api
NNSTREAMER_SRC_FILES := \
$(NNSTREAMER_COMMON_SRCS) \
- $(ML_API_ROOT)/c/src/nnstreamer-capi-util.c \
- $(ML_API_ROOT)/c/src/nnstreamer-capi-single.c
+ $(ML_API_ROOT)/c/src/ml-api-common.c \
+ $(ML_API_ROOT)/c/src/ml-api-inference-internal.c \
+ $(ML_API_ROOT)/c/src/ml-api-inference-single.c
# pipeline api and nnstreamer plugins
ifneq ($(NNSTREAMER_API_OPTION),single)
NNSTREAMER_SRC_FILES += \
- $(ML_API_ROOT)/c/src/nnstreamer-capi-pipeline.c \
+ $(ML_API_ROOT)/c/src/ml-api-inference-pipeline.c \
$(NNSTREAMER_PLUGINS_SRCS) \
$(NNSTREAMER_SOURCE_AMC_SRCS) \
$(NNSTREAMER_DECODER_BB_SRCS) \
#include "nnstreamer.h"
#include "nnstreamer-single.h"
-#include "nnstreamer-capi-private.h"
+#include "nnstreamer-tizen-internal.h"
#include "nnstreamer_log.h"
#include "nnstreamer_plugin_api.h"
#include "nnstreamer_plugin_api_filter.h"
+#include <ml-api-internal.h>
+#include <ml-api-inference-internal.h>
#if GLIB_SIZEOF_VOID_P == 8
#define CAST_TO_LONG(p) (jlong)(p)
%description devel-static
Static library of capi-machine-learning-inference-devel package.
+%package -n capi-machine-learning-common
+Summary: Common utility functions for Tizen Machine Learning API
+Group: Machine Learning/ML Framework
+%description -n capi-machine-learning-common
+Tizen ML(Machine Learning) native API's common parts.
+
%package -n capi-machine-learning-common-devel
Summary: Common headers for Tizen Machine Learning API
Group: Machine Learning/ML Framework
+Requires: capi-machine-larning-common = %{version}-%{release}
%description -n capi-machine-learning-common-devel
Common headers for Tizen Machine Learning API.
+%package -n capi-machine-learning-common-devel-static
+Summary: Static library of common utility functions for Tizen Machine Learning API
+Group: Machine Learning/ML Framework
+Requires: capi-machine-larning-common-devel = %{version}-%{release}
+%description -n capi-machine-learning-common-devel-static
+Static library of common headers for Tizen Machine Learning API.
+
%package -n capi-machine-learning-tizen-internal-devel
Summary: Tizen internal headers for Tizen Machine Learning API
Group: Machine Learning/ML Framework
%files devel-static
%{_libdir}/libcapi-nnstreamer.a
+%files -n capi-machine-learning-common
+%{_libdir}/libcapi-ml-common.so*
+
%files -n capi-machine-learning-common-devel
%{_includedir}/nnstreamer/ml-api-common.h
%{_libdir}/pkgconfig/capi-ml-common.pc
+%files -n capi-machine-learning-common-devel-static
+%{_libdir}/libcapi-ml-common.a
+
%files -n capi-machine-learning-tizen-internal-devel
%{_includedir}/nnstreamer/nnstreamer-tizen-internal.h
/* ML API Side */
#include <nnstreamer.h>
-#include <nnstreamer-capi-private.h>
+#include <ml-api-internal.h>
/* GStreamer Side */
#include <gst/gst.h>
#include <nnstreamer.h>
#include <nnstreamer-single.h>
#include <nnstreamer_plugin_api.h>
-#include <nnstreamer-capi-private.h>
#include <nnstreamer_internal.h>
+#include <nnstreamer-tizen-internal.h>
+#include <ml-api-internal.h>
+#include <ml-api-inference-internal.h>
#if defined (__APPLE__)
#define SO_FILE_EXTENSION ".dylib"
#include <nnstreamer.h>
#include <nnstreamer-single.h>
-#include <nnstreamer-capi-private.h>
#include <nnstreamer_plugin_api.h>
#include <nnstreamer_plugin_api_filter.h>
+#include <ml-api-internal.h>
+#include <ml-api-inference-internal.h>
/**
* @brief nnstreamer invoke latency testing base class
#include <glib.h>
#include <nnstreamer.h>
#include <nnstreamer-single.h>
-#include <nnstreamer-capi-private.h>
+#include <ml-api-internal.h>
+#include <ml-api-inference-internal.h>
static GMutex g_test_mutex;
projects / platform / core / api / machine-learning.git / commitdiff