Use gst-info struct and util functions from nnstreamer.
Remove unnecessary functions for ml-info.
Signed-off-by: Jaeyun Jung <jy1210.jung@samsung.com>
#include <stdio.h>
#include <stdarg.h>
#include <glib.h>
-
+#include <nnstreamer_plugin_api_util.h>
#include "nnstreamer.h"
#include "nnstreamer-tizen-internal.h"
#include "ml-api-internal.h"
}
/**
+ * @brief Convert the type from ml_tensor_type_e to tensor_type.
+ * @note This code is based on the same order between NNS type and ML type.
+ * The index should be the same in case of adding a new type.
+ */
+static tensor_type
+convert_tensor_type_from (ml_tensor_type_e type)
+{
+ if (type < ML_TENSOR_TYPE_INT32 || type >= ML_TENSOR_TYPE_UNKNOWN) {
+ _ml_error_report
+ ("Failed to convert the type. Input ml_tensor_type_e %d is invalid.",
+ type);
+ return _NNS_END;
+ }
+
+ return (tensor_type) type;
+}
+
+/**
+ * @brief Convert the type from tensor_type to ml_tensor_type_e.
+ * @note This code is based on the same order between NNS type and ML type.
+ * The index should be the same in case of adding a new type.
+ */
+static ml_tensor_type_e
+convert_ml_tensor_type_from (tensor_type type)
+{
+ if (type < _NNS_INT32 || type >= _NNS_END) {
+ _ml_error_report
+ ("Failed to convert the type. Input tensor_type %d is invalid.", type);
+ return ML_TENSOR_TYPE_UNKNOWN;
+ }
+
+ return (ml_tensor_type_e) type;
+}
+
+/**
* @brief Gets the version string of machine-learning API.
*/
char *
}
/**
- * @brief Allocates memory in given tensors_info for extra tensor infos.
- */
-gboolean
-_ml_tensors_info_create_extra (ml_tensors_info_s * ml_info)
-{
- ml_tensor_info_s *new;
- guint i;
-
- if (!ml_info)
- _ml_error_report_return (FALSE, "The parameter, ml_info, is NULL.");
-
- if (ml_info->extra) {
- return TRUE;
- }
-
- new = g_try_new0 (ml_tensor_info_s, ML_TENSOR_SIZE_EXTRA_LIMIT);
- if (!new) {
- _ml_loge ("Failed to allocate memory for extra tensors info.");
- return FALSE;
- }
-
- for (i = 0; i < ML_TENSOR_SIZE_EXTRA_LIMIT; i++) {
- if (_ml_tensor_info_initialize (&new[i]) != ML_ERROR_NONE) {
- _ml_loge ("Failed to initialize extra tensors info.");
- g_free (new);
- return FALSE;
- }
- }
-
- ml_info->extra = new;
-
- return TRUE;
-}
-
-/**
- * @brief Initializes given tensor_info with default value.
- */
-int
-_ml_tensor_info_initialize (ml_tensor_info_s * info)
-{
- guint i;
-
- if (!info)
- _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
- "The parameter, info, is NULL. Provide a valid pointer.");
-
- info->name = NULL;
- info->type = ML_TENSOR_TYPE_UNKNOWN;
- for (i = 0; i < ML_TENSOR_RANK_LIMIT; i++) {
- info->dimension[i] = 0;
- }
-
- return ML_ERROR_NONE;
-}
-
-/**
- * @brief Initializes the tensors information with default value.
- */
-int
-_ml_tensors_info_initialize (ml_tensors_info_s * info)
-{
- guint i;
-
- if (!info)
- _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
- "The parameter, info, is NULL. Provide a valid pointer.");
-
- info->num_tensors = 0;
-
- for (i = 0; i < ML_TENSOR_SIZE_LIMIT_STATIC; i++) {
- _ml_tensor_info_initialize (&info->info[i]);
- }
-
- info->extra = NULL;
-
- return ML_ERROR_NONE;
-}
-
-/**
- * @brief Get the pointer of nth tensor info.
- */
-ml_tensor_info_s *
-ml_tensors_info_get_nth_info (ml_tensors_info_s * info, guint nth)
-{
- if (!info)
- return NULL;
-
- if (nth >= ML_TENSOR_SIZE_LIMIT) {
- _ml_loge ("The given nth is out of range. It should be less than %d.",
- ML_TENSOR_SIZE_LIMIT);
- return NULL;
- }
-
- if (nth < ML_TENSOR_SIZE_LIMIT_STATIC)
- return &info->info[nth];
-
- if (!_ml_tensors_info_create_extra (info))
- return NULL;
-
- return &info->extra[nth - ML_TENSOR_SIZE_LIMIT_STATIC];
-}
-
-/**
- * @brief Get the rank of tensor dimension.
- */
-static guint
-_ml_tensor_dimension_get_rank (const ml_tensor_dimension dim)
-{
- guint i;
-
- for (i = 0; i < ML_TENSOR_RANK_LIMIT; i++) {
- if (dim[i] == 0)
- break;
- }
-
- return i;
-}
-
-/**
- * @brief Check the tensor dimension is valid
- */
-static gboolean
-_ml_tensor_dimension_is_valid (const ml_tensor_dimension dim)
-{
- guint i;
- gboolean is_valid = FALSE;
-
- i = _ml_tensor_dimension_get_rank (dim);
- if (i == 0)
- goto done;
-
- for (; i < ML_TENSOR_RANK_LIMIT; i++) {
- if (dim[i] > 0)
- goto done;
- }
-
- is_valid = TRUE;
-
-done:
- if (!is_valid) {
- _ml_error_report
- ("Failed to validate tensor dimension. The dimension string should be in the form of d1:...:d8, d1:d2:d3:d4, d1:d2:d3, d1:d2, or d1. Here, dN is a positive integer.");
- }
-
- return is_valid;
-}
-
-/**
- * @brief Compares the given tensor info.
- */
-static gboolean
-ml_tensor_info_compare (const ml_tensor_info_s * i1,
- const ml_tensor_info_s * i2)
-{
- guint i;
-
- if (i1 == NULL || i2 == NULL)
- return FALSE;
-
- if (i1->type != i2->type)
- return FALSE;
-
- if (!_ml_tensor_dimension_is_valid (i1->dimension)
- || !_ml_tensor_dimension_is_valid (i2->dimension))
- return FALSE;
-
- for (i = 0; i < ML_TENSOR_RANK_LIMIT; i++) {
- if (i1->dimension[i] != i2->dimension[i]) {
- /* Supposed dimension is same if remained dimension is 1. */
- if (i1->dimension[i] > 1 || i2->dimension[i] > 1)
- return FALSE;
- }
- }
-
- return TRUE;
-}
-
-/**
- * @brief Validates the given tensor info is valid.
- * @note info should be locked by caller if nolock == 0.
- */
-static gboolean
-ml_tensor_info_validate (const ml_tensor_info_s * info, bool is_extended)
-{
- guint i;
-
- if (!info)
- return FALSE;
-
- if (info->type < 0 || info->type >= ML_TENSOR_TYPE_UNKNOWN)
- return FALSE;
-
- if (!is_extended) {
- for (i = ML_TENSOR_RANK_LIMIT_PREV; i < ML_TENSOR_RANK_LIMIT; i++) {
- if (info->dimension[i] > 1)
- return FALSE;
- }
- }
-
- return TRUE;
-}
-
-/**
- * @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
- */
-static int
-_ml_tensors_info_validate_nolock (const ml_tensors_info_s * info, bool *valid)
-{
- guint i;
-
- G_VERIFYLOCK_UNLESS_NOLOCK (*info);
- /* init false */
- *valid = false;
-
- if (info->num_tensors < 1) {
- _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
- "The given tensors_info to be validated has invalid num_tensors (%u). It should be 1 or more.",
- info->num_tensors);
- }
-
- for (i = 0; i < info->num_tensors; i++) {
- ml_tensor_info_s *tensor_info =
- ml_tensors_info_get_nth_info ((ml_tensors_info_s *) info, i);
- if (!ml_tensor_info_validate (tensor_info, info->is_extended))
- goto done;
- }
-
- *valid = true;
-
-done:
- return ML_ERROR_NONE;
-}
-
-/**
* @brief Validates the given tensors info is valid.
*/
int
ml_tensors_info_validate (const ml_tensors_info_h info, bool *valid)
{
ml_tensors_info_s *tensors_info;
- int ret = ML_ERROR_NONE;
check_feature_state (ML_FEATURE);
"The input parameter, tensors_info, is NULL. It should be a valid ml_tensors_info_h, which is usually created by ml_tensors_info_create().");
G_LOCK_UNLESS_NOLOCK (*tensors_info);
-
- ret = _ml_tensors_info_validate_nolock (info, valid);
-
+ *valid = gst_tensors_info_validate (&tensors_info->info);
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
- return ret;
+
+ return ML_ERROR_NONE;
}
/**
const ml_tensors_info_h info2, bool *equal)
{
ml_tensors_info_s *i1, *i2;
- guint i;
check_feature_state (ML_FEATURE);
i2 = (ml_tensors_info_s *) info2;
G_LOCK_UNLESS_NOLOCK (*i2);
- /* init false */
- *equal = false;
-
- if (i1->num_tensors != i2->num_tensors)
- goto done;
+ *equal = gst_tensors_info_is_equal (&i1->info, &i2->info);
- for (i = 0; i < i1->num_tensors; i++) {
- ml_tensor_info_s *ti1 = ml_tensors_info_get_nth_info (i1, i);
- ml_tensor_info_s *ti2 = ml_tensors_info_get_nth_info (i2, i);
- if (!ml_tensor_info_compare (ti1, ti2))
- goto done;
- }
-
- *equal = true;
-
-done:
G_UNLOCK_UNLESS_NOLOCK (*i2);
G_UNLOCK_UNLESS_NOLOCK (*i1);
return ML_ERROR_NONE;
tensors_info = (ml_tensors_info_s *) info;
/* This is atomic. No need for locks */
- tensors_info->num_tensors = count;
+ tensors_info->info.num_tensors = count;
return ML_ERROR_NONE;
}
tensors_info = (ml_tensors_info_s *) info;
/* This is atomic. No need for locks */
- *count = tensors_info->num_tensors;
+ *count = tensors_info->info.num_tensors;
return ML_ERROR_NONE;
}
unsigned int index, const char *name)
{
ml_tensors_info_s *tensors_info;
- ml_tensor_info_s *_tensor_info;
+ GstTensorInfo *_info;
check_feature_state (ML_FEATURE);
tensors_info = (ml_tensors_info_s *) info;
G_LOCK_UNLESS_NOLOCK (*tensors_info);
- if (tensors_info->num_tensors <= index) {
+ if (tensors_info->info.num_tensors <= index) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The number of tensors in 'info' parameter is %u, which is not larger than the given 'index' %u. Thus, we cannot get %u'th tensor from 'info'. Please set the number of tensors of 'info' correctly or check the value of the given 'index'.",
- tensors_info->num_tensors, index, index);
+ tensors_info->info.num_tensors, index, index);
}
- _tensor_info = ml_tensors_info_get_nth_info (tensors_info, index);
- if (!_tensor_info) {
+ _info = gst_tensors_info_get_nth_info (&tensors_info->info, index);
+ if (!_info) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_INVALID_PARAMETER;
}
- if (_tensor_info->name) {
- g_free (_tensor_info->name);
- _tensor_info->name = NULL;
- }
-
- if (name)
- _tensor_info->name = g_strdup (name);
+ g_free (_info->name);
+ _info->name = g_strdup (name);
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_NONE;
unsigned int index, char **name)
{
ml_tensors_info_s *tensors_info;
- ml_tensor_info_s *_tensor_info;
+ GstTensorInfo *_info;
check_feature_state (ML_FEATURE);
tensors_info = (ml_tensors_info_s *) info;
G_LOCK_UNLESS_NOLOCK (*tensors_info);
- if (tensors_info->num_tensors <= index) {
+ if (tensors_info->info.num_tensors <= index) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The number of tensors in 'info' parameter is %u, which is not larger than the given 'index' %u. Thus, we cannot get %u'th tensor from 'info'. Please set the number of tensors of 'info' correctly or check the value of the given 'index'.",
- tensors_info->num_tensors, index, index);
+ tensors_info->info.num_tensors, index, index);
}
- _tensor_info = ml_tensors_info_get_nth_info (tensors_info, index);
- if (!_tensor_info) {
+ _info = gst_tensors_info_get_nth_info (&tensors_info->info, index);
+ if (!_info) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_INVALID_PARAMETER;
}
- *name = g_strdup (_tensor_info->name);
-
+ *name = g_strdup (_info->name);
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_NONE;
unsigned int index, const ml_tensor_type_e type)
{
ml_tensors_info_s *tensors_info;
- ml_tensor_info_s *_tensor_info;
+ GstTensorInfo *_info;
check_feature_state (ML_FEATURE);
tensors_info = (ml_tensors_info_s *) info;
G_LOCK_UNLESS_NOLOCK (*tensors_info);
- if (tensors_info->num_tensors <= index) {
+ if (tensors_info->info.num_tensors <= index) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The number of tensors in 'info' parameter is %u, which is not larger than the given 'index' %u. Thus, we cannot get %u'th tensor from 'info'. Please set the number of tensors of 'info' correctly or check the value of the given 'index'.",
- tensors_info->num_tensors, index, index);
+ tensors_info->info.num_tensors, index, index);
}
- _tensor_info = ml_tensors_info_get_nth_info (tensors_info, index);
- if (!_tensor_info) {
+ _info = gst_tensors_info_get_nth_info (&tensors_info->info, index);
+ if (!_info) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_INVALID_PARAMETER;
}
- _tensor_info->type = type;
+ _info->type = convert_tensor_type_from (type);
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_NONE;
unsigned int index, ml_tensor_type_e * type)
{
ml_tensors_info_s *tensors_info;
- ml_tensor_info_s *_tensor_info;
+ GstTensorInfo *_info;
check_feature_state (ML_FEATURE);
tensors_info = (ml_tensors_info_s *) info;
G_LOCK_UNLESS_NOLOCK (*tensors_info);
- if (tensors_info->num_tensors <= index) {
+ if (tensors_info->info.num_tensors <= index) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The number of tensors in 'info' parameter is %u, which is not larger than the given 'index' %u. Thus, we cannot get %u'th tensor from 'info'. Please set the number of tensors of 'info' correctly or check the value of the given 'index'.",
- tensors_info->num_tensors, index, index);
+ tensors_info->info.num_tensors, index, index);
}
- _tensor_info = ml_tensors_info_get_nth_info (tensors_info, index);
- if (!_tensor_info) {
+ _info = gst_tensors_info_get_nth_info (&tensors_info->info, index);
+ if (!_info) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_INVALID_PARAMETER;
}
- *type = _tensor_info->type;
+
+ *type = convert_ml_tensor_type_from (_info->type);
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_NONE;
unsigned int index, const ml_tensor_dimension dimension)
{
ml_tensors_info_s *tensors_info;
- ml_tensor_info_s *_tensor_info;
+ GstTensorInfo *_info;
guint i;
check_feature_state (ML_FEATURE);
tensors_info = (ml_tensors_info_s *) info;
G_LOCK_UNLESS_NOLOCK (*tensors_info);
- if (tensors_info->num_tensors <= index) {
+ if (tensors_info->info.num_tensors <= index) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The number of tensors in 'info' parameter is %u, which is not larger than the given 'index' %u. Thus, we cannot get %u'th tensor from 'info'. Please set the number of tensors of 'info' correctly or check the value of the given 'index'.",
- tensors_info->num_tensors, index, index);
+ tensors_info->info.num_tensors, index, index);
}
- _tensor_info = ml_tensors_info_get_nth_info (tensors_info, index);
- if (!_tensor_info) {
+ _info = gst_tensors_info_get_nth_info (&tensors_info->info, index);
+ if (!_info) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_INVALID_PARAMETER;
}
for (i = 0; i < ML_TENSOR_RANK_LIMIT_PREV; i++) {
- _tensor_info->dimension[i] = dimension[i];
+ _info->dimension[i] = dimension[i];
}
for (i = ML_TENSOR_RANK_LIMIT_PREV; i < ML_TENSOR_RANK_LIMIT; i++) {
- _tensor_info->dimension[i] = (tensors_info->is_extended ? dimension[i] : 0);
+ _info->dimension[i] = (tensors_info->is_extended ? dimension[i] : 0);
}
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
unsigned int index, ml_tensor_dimension dimension)
{
ml_tensors_info_s *tensors_info;
- ml_tensor_info_s *_tensor_info;
+ GstTensorInfo *_info;
guint i, valid_rank = ML_TENSOR_RANK_LIMIT;
check_feature_state (ML_FEATURE);
tensors_info = (ml_tensors_info_s *) info;
G_LOCK_UNLESS_NOLOCK (*tensors_info);
- if (tensors_info->num_tensors <= index) {
+ if (tensors_info->info.num_tensors <= index) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The number of tensors in 'info' parameter is %u, which is not larger than the given 'index' %u. Thus, we cannot get %u'th tensor from 'info'. Please set the number of tensors of 'info' correctly or check the value of the given 'index'.",
- tensors_info->num_tensors, index, index);
+ tensors_info->info.num_tensors, index, index);
}
- _tensor_info = ml_tensors_info_get_nth_info (tensors_info, index);
- if (!_tensor_info) {
+ _info = gst_tensors_info_get_nth_info (&tensors_info->info, index);
+ if (!_info) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_INVALID_PARAMETER;
}
valid_rank = ML_TENSOR_RANK_LIMIT_PREV;
for (i = 0; i < valid_rank; i++) {
- dimension[i] = _tensor_info->dimension[i];
+ dimension[i] = _info->dimension[i];
}
for (; i < ML_TENSOR_RANK_LIMIT; i++)
}
/**
- * @brief Gets the byte size of the given tensor info.
- */
-size_t
-_ml_tensor_info_get_size (const ml_tensor_info_s * info, bool is_extended)
-{
- size_t tensor_size;
- gint i, valid_rank = ML_TENSOR_RANK_LIMIT;
-
- if (!info)
- return 0;
-
- switch (info->type) {
- case ML_TENSOR_TYPE_INT8:
- case ML_TENSOR_TYPE_UINT8:
- tensor_size = 1;
- break;
- case ML_TENSOR_TYPE_INT16:
- case ML_TENSOR_TYPE_UINT16:
- case ML_TENSOR_TYPE_FLOAT16:
- tensor_size = 2;
- break;
- case ML_TENSOR_TYPE_INT32:
- case ML_TENSOR_TYPE_UINT32:
- case ML_TENSOR_TYPE_FLOAT32:
- tensor_size = 4;
- break;
- case ML_TENSOR_TYPE_FLOAT64:
- case ML_TENSOR_TYPE_INT64:
- case ML_TENSOR_TYPE_UINT64:
- tensor_size = 8;
- break;
- default:
- _ml_loge ("In the given param, tensor type is invalid.");
- return 0;
- }
-
- if (!is_extended)
- valid_rank = ML_TENSOR_RANK_LIMIT_PREV;
-
- for (i = 0; i < valid_rank; i++) {
- if (info->dimension[i] == 0)
- break;
- tensor_size *= info->dimension[i];
- }
-
- return tensor_size;
-}
-
-/**
* @brief Gets the byte size of the given handle of tensors information.
*/
int
int index, size_t *data_size)
{
ml_tensors_info_s *tensors_info;
- ml_tensor_info_s *_tensor_info;
check_feature_state (ML_FEATURE);
/* init 0 */
*data_size = 0;
- if (index >= 0 && tensors_info->num_tensors <= index) {
+ if (index >= 0 && tensors_info->info.num_tensors <= index) {
G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The number of tensors in 'info' parameter is %u, which is not larger than the given 'index' %u. Thus, we cannot get %u'th tensor from 'info'. Please set the number of tensors of 'info' correctly or check the value of the given 'index'.",
- tensors_info->num_tensors, index, index);
+ tensors_info->info.num_tensors, index, index);
}
- if (index < 0) {
- guint i;
+ *data_size = gst_tensors_info_get_size (&tensors_info->info, index);
- /* get total byte size */
- for (i = 0; i < tensors_info->num_tensors; i++) {
- _tensor_info = ml_tensors_info_get_nth_info (tensors_info, i);
- if (!_tensor_info) {
- G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
- return ML_ERROR_INVALID_PARAMETER;
- }
- *data_size +=
- _ml_tensor_info_get_size (_tensor_info, tensors_info->is_extended);
- }
- } else {
- _tensor_info = ml_tensors_info_get_nth_info (tensors_info, index);
- if (!_tensor_info) {
- G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
- return ML_ERROR_INVALID_PARAMETER;
- }
+ G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
+ return ML_ERROR_NONE;
+}
- *data_size =
- _ml_tensor_info_get_size (_tensor_info, tensors_info->is_extended);
- }
+/**
+ * @brief Initializes the tensors information with default value.
+ */
+int
+_ml_tensors_info_initialize (ml_tensors_info_s * info)
+{
+ if (!info)
+ _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
+ "The parameter, info, is NULL. Provide a valid pointer.");
+
+ gst_tensors_info_init (&info->info);
- G_UNLOCK_UNLESS_NOLOCK (*tensors_info);
return ML_ERROR_NONE;
}
void
_ml_tensors_info_free (ml_tensors_info_s * info)
{
- gint i;
-
if (!info)
return;
- for (i = 0; i < ML_TENSOR_SIZE_LIMIT_STATIC; i++) {
- if (info->info[i].name) {
- g_free (info->info[i].name);
- }
- }
-
- if (info->extra) {
- for (i = 0; i < ML_TENSOR_SIZE_EXTRA_LIMIT; i++) {
- if (info->extra[i].name) {
- g_free (info->extra[i].name);
- }
- }
-
- g_free (info->extra);
- }
-
- _ml_tensors_info_initialize (info);
+ gst_tensors_info_free (&info->info);
}
/**
ml_tensors_info_clone (_data->info, info);
G_LOCK_UNLESS_NOLOCK (*_info);
- _data->num_tensors = _info->num_tensors;
+ _data->num_tensors = _info->info.num_tensors;
for (i = 0; i < _data->num_tensors; i++) {
- ml_tensor_info_s *_tensor_info = ml_tensors_info_get_nth_info (_info, i);
- _data->tensors[i].size =
- _ml_tensor_info_get_size (_tensor_info, _info->is_extended);
+ _data->tensors[i].size = gst_tensors_info_get_size (&_info->info, i);
_data->tensors[i].data = NULL;
}
G_UNLOCK_UNLESS_NOLOCK (*_info);
ml_tensors_info_clone (ml_tensors_info_h dest, const ml_tensors_info_h src)
{
ml_tensors_info_s *dest_info, *src_info;
- guint i, j;
- bool valid;
int status = ML_ERROR_NONE;
check_feature_state (ML_FEATURE);
G_LOCK_UNLESS_NOLOCK (*dest_info);
G_LOCK_UNLESS_NOLOCK (*src_info);
- status = _ml_tensors_info_validate_nolock (src_info, &valid);
- if (status != ML_ERROR_NONE) {
- _ml_error_report_continue
- ("Cannot check the validity of src. Maybe src is not valid or its internal data is not consistent.");
- goto done;
- }
- if (!valid) {
+ if (gst_tensors_info_validate (&src_info->info)) {
+ dest_info->is_extended = src_info->is_extended;
+ gst_tensors_info_copy (&dest_info->info, &src_info->info);
+ } else {
_ml_error_report
("The parameter, src, is a ml_tensors_info_h handle without valid data. Every tensor-info of tensors-info should have a valid type and dimension information and the number of tensors should be between 1 and %d.",
ML_TENSOR_SIZE_LIMIT);
status = ML_ERROR_INVALID_PARAMETER;
- goto done;
- }
-
- _ml_tensors_info_initialize (dest_info);
-
- dest_info->num_tensors = src_info->num_tensors;
- dest_info->is_extended = src_info->is_extended;
-
- for (i = 0; i < dest_info->num_tensors; i++) {
- ml_tensor_info_s *dest_tensor_info =
- ml_tensors_info_get_nth_info (dest_info, i);
- ml_tensor_info_s *src_tensor_info =
- ml_tensors_info_get_nth_info (src_info, i);
-
- if (!dest_tensor_info || !src_tensor_info) {
- _ml_error_report
- ("Cannot get the %u'th tensor info from src or dest. Maybe src or dest is not valid or its internal data is not consistent.",
- i);
- status = ML_ERROR_INVALID_PARAMETER;
- goto done;
- }
-
- dest_tensor_info->name =
- (src_tensor_info->name) ? g_strdup (src_tensor_info->name) : NULL;
- dest_tensor_info->type = src_tensor_info->type;
-
- for (j = 0; j < ML_TENSOR_RANK_LIMIT; j++) {
- dest_tensor_info->dimension[j] = src_tensor_info->dimension[j];
- }
}
-done:
G_UNLOCK_UNLESS_NOLOCK (*src_info);
G_UNLOCK_UNLESS_NOLOCK (*dest_info);
#include "ml-api-internal.h"
/**
- * @brief Convert the type from ml_tensor_type_e to tensor_type.
- * @note This code is based on the same order between NNS type and ML type.
- * The index should be the same in case of adding a new type.
- */
-static tensor_type
-convert_tensor_type_from (ml_tensor_type_e type)
-{
- if (type < ML_TENSOR_TYPE_INT32 || type >= ML_TENSOR_TYPE_UNKNOWN) {
- _ml_error_report
- ("Failed to convert the type. Input ml_tensor_type_e %d is invalid.",
- type);
- return _NNS_END;
- }
-
- return (tensor_type) type;
-}
-
-/**
- * @brief Convert the type from tensor_type to ml_tensor_type_e.
- * @note This code is based on the same order between NNS type and ML type.
- * The index should be the same in case of adding a new type.
- */
-static ml_tensor_type_e
-convert_ml_tensor_type_from (tensor_type type)
-{
- if (type < _NNS_INT32 || type >= _NNS_END) {
- _ml_error_report
- ("Failed to convert the type. Input tensor_type %d is invalid.", type);
- return ML_TENSOR_TYPE_UNKNOWN;
- }
-
- return (ml_tensor_type_e) type;
-}
-
-/**
* @brief Check tensor-info has extended rank value.
*/
static gboolean
* @bug Thread safety required. Check its internal users first!
*/
int
-_ml_tensors_info_copy_from_gst (ml_tensors_info_s * ml_info,
+_ml_tensors_info_copy_from_gst (ml_tensors_info_h ml_info,
const GstTensorsInfo * gst_info)
{
- guint i, j;
- guint max_dim;
+ ml_tensors_info_s *_info;
if (!ml_info)
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The parmater, gst_info, is NULL. It should be a valid GstTensorsInfo instance. This is probably an internal bug of ML API.");
- _ml_tensors_info_initialize (ml_info);
-
- max_dim = MIN (ML_TENSOR_RANK_LIMIT, NNS_TENSOR_RANK_LIMIT);
+ _info = (ml_tensors_info_s *) ml_info;
- ml_info->num_tensors = gst_info->num_tensors;
- ml_info->is_extended = gst_info_is_extended (gst_info);
- if (gst_info->extra) {
- /* create ml_info_extra in ml_tensors_info_s */
- _ml_tensors_info_create_extra (ml_info);
- }
+ G_LOCK_UNLESS_NOLOCK (*_info);
+ _info->is_extended = gst_info_is_extended (gst_info);
+ gst_tensors_info_copy (&_info->info, gst_info);
+ G_UNLOCK_UNLESS_NOLOCK (*_info);
- for (i = 0; i < gst_info->num_tensors; i++) {
- GstTensorInfo *_gst_tensor_info =
- gst_tensors_info_get_nth_info ((GstTensorsInfo *) gst_info, i);
- ml_tensor_info_s *_ml_tensor_info =
- ml_tensors_info_get_nth_info (ml_info, i);
- if (!_gst_tensor_info) {
- _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
- "The parameter, gst_info, is invalid. It should be a valid GstTensorsInfo instance. This is probably an internal bug of ML API.");
- }
-
- if (!_ml_tensor_info) {
- _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
- "The parameter, ml_info, is invalid. It should be a valid ml_tensors_info_s instance, usually created by ml_tensors_info_create(). This is probably an internal bug of ML API.");
- }
-
- if (_gst_tensor_info->name) {
- _ml_tensor_info->name = g_strdup (_gst_tensor_info->name);
- }
-
- _ml_tensor_info->type =
- convert_ml_tensor_type_from (_gst_tensor_info->type);
-
- /* Set dimension */
- for (j = 0; j < max_dim; j++) {
- _ml_tensor_info->dimension[j] = _gst_tensor_info->dimension[j];
- }
-
- for (; j < ML_TENSOR_RANK_LIMIT; j++) {
- _ml_tensor_info->dimension[j] = 0U;
- }
-
- if (!ml_info->is_extended) {
- for (j = ML_TENSOR_RANK_LIMIT_PREV; j < ML_TENSOR_RANK_LIMIT; j++) {
- _ml_tensor_info->dimension[j] = 0U;
- }
- }
- }
return ML_ERROR_NONE;
}
*/
int
_ml_tensors_info_copy_from_ml (GstTensorsInfo * gst_info,
- const ml_tensors_info_s * ml_info)
+ const ml_tensors_info_h ml_info)
{
- guint i, j;
- guint max_dim;
+ ml_tensors_info_s *_info;
if (!ml_info)
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The parmater, gst_info, is NULL. It should be a valid GstTensorsInfo instance. This is probably an internal bug of ML API.");
- 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++) {
- ml_tensor_info_s *_ml_tensor_info =
- ml_tensors_info_get_nth_info ((ml_tensors_info_s *) ml_info, i);
- GstTensorInfo *_gst_tensor_info =
- gst_tensors_info_get_nth_info (gst_info, i);
-
- if (!_gst_tensor_info) {
- G_UNLOCK_UNLESS_NOLOCK (*ml_info);
- _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
- "The parameter, gst_info, is invalid. It should be a valid GstTensorsInfo instance. This is probably an internal bug of ML API.");
- }
-
- if (!_ml_tensor_info) {
- G_UNLOCK_UNLESS_NOLOCK (*ml_info);
- _ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
- "The parameter, ml_info, is invalid. It should be a valid ml_tensors_info_s instance, usually created by ml_tensors_info_create(). This is probably an internal bug of ML API.");
- }
-
- /* Copy name string */
- if (_ml_tensor_info->name) {
- _gst_tensor_info->name = g_strdup (_ml_tensor_info->name);
- }
-
- /* Copy type */
- _gst_tensor_info->type = convert_tensor_type_from (_ml_tensor_info->type);
-
- /* Set dimension */
- for (j = 0; j < max_dim; j++) {
- _gst_tensor_info->dimension[j] = _ml_tensor_info->dimension[j];
- }
-
- for (; j < NNS_TENSOR_RANK_LIMIT; j++) {
- _gst_tensor_info->dimension[j] = 0;
- }
-
- if (!ml_info->is_extended) {
- for (j = ML_TENSOR_RANK_LIMIT_PREV; j < NNS_TENSOR_RANK_LIMIT; j++) {
- _gst_tensor_info->dimension[j] = 0;
- }
- }
- }
- G_UNLOCK_UNLESS_NOLOCK (*ml_info);
+ _info = (ml_tensors_info_s *) ml_info;
+
+ G_LOCK_UNLESS_NOLOCK (*_info);
+ gst_tensors_info_copy (gst_info, &_info->info);
+ G_UNLOCK_UNLESS_NOLOCK (*_info);
return ML_ERROR_NONE;
}
/**
* @brief Copies tensor metadata from gst tensors info.
*/
-int _ml_tensors_info_copy_from_gst (ml_tensors_info_s *ml_info, const GstTensorsInfo *gst_info);
+int _ml_tensors_info_copy_from_gst (ml_tensors_info_h ml_info, const GstTensorsInfo *gst_info);
/**
* @brief Copies tensor metadata from ml tensors info.
*/
-int _ml_tensors_info_copy_from_ml (GstTensorsInfo *gst_info, const ml_tensors_info_s *ml_info);
+int _ml_tensors_info_copy_from_ml (GstTensorsInfo *gst_info, const ml_tensors_info_h ml_info);
#ifdef __cplusplus
}
#define ML_TENSOR_RANK_LIMIT_PREV (4)
/**
- * @brief The tensor size limit with static gst buffer.
- */
-#define ML_TENSOR_SIZE_LIMIT_STATIC (16)
-
-/**
- * @brief The size limit of "extra" tensors.
- */
-#define ML_TENSOR_SIZE_EXTRA_LIMIT (ML_TENSOR_SIZE_LIMIT - ML_TENSOR_SIZE_LIMIT_STATIC)
-
-/**
- * @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_STATIC]; /**< The list of tensor info. */
- ml_tensor_info_s *extra; /**< The list of extra tensor info. */
GMutex lock; /**< Lock for thread safety */
int nolock; /**< Set non-zero to avoid using m (giving up thread safety) */
bool is_extended; /**< True if tensors are extended */
+ GstTensorsInfo info;
} ml_tensors_info_s;
/**
} 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, bool is_extended);
-
-/**
- * @brief Allocates memory in given tensors_info for extra tensor infos.
- */
-gboolean _ml_tensors_info_create_extra (ml_tensors_info_s * ml_info);
-
-/**
- * @brief Initializes given tensor_info with default value.
- */
-int _ml_tensor_info_initialize (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.
void _ml_tensors_info_free (ml_tensors_info_s *info);
/**
- * @brief Get the pointer of nth tensor info.
- */
-ml_tensor_info_s * ml_tensors_info_get_nth_info (ml_tensors_info_s *info, guint nth);
-
-/**
* @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.
guint i;
info_class_info_s *icls_info;
ml_tensors_info_s *info;
+ GstTensorInfo *_info;
jobject obj_info = NULL;
g_return_val_if_fail (pipe_info, FALSE);
goto done;
}
- for (i = 0; i < info->num_tensors; i++) {
+ for (i = 0; i < info->info.num_tensors; i++) {
jstring name = NULL;
jint type;
jintArray dimension;
- if (info->info[i].name)
- name = (*env)->NewStringUTF (env, info->info[i].name);
+ _info = gst_tensors_info_get_nth_info (&info->info, i);
- type = (jint) info->info[i].type;
+ if (_info->name)
+ name = (*env)->NewStringUTF (env, _info->name);
+
+ type = (jint) _info->type;
dimension = (*env)->NewIntArray (env, ML_TENSOR_RANK_LIMIT);
(*env)->SetIntArrayRegion (env, dimension, 0, ML_TENSOR_RANK_LIMIT,
- (jint *) info->info[i].dimension);
+ (jint *) _info->dimension);
(*env)->CallVoidMethod (env, obj_info, icls_info->mid_add_info,
name, type, dimension);
guint i;
info_class_info_s *icls_info;
ml_tensors_info_s *info;
+ GstTensorInfo *_info;
jobjectArray info_arr;
g_return_val_if_fail (pipe_info, FALSE);
info_arr = (*env)->CallObjectMethod (env, obj_info, icls_info->mid_get_array);
/* number of tensors info */
- info->num_tensors = (unsigned int) (*env)->GetArrayLength (env, info_arr);
+ info->info.num_tensors = (unsigned int) (*env)->GetArrayLength (env, info_arr);
/* read tensor info */
- for (i = 0; i < info->num_tensors; i++) {
+ for (i = 0; i < info->info.num_tensors; i++) {
jobject item;
jstring name_str;
jintArray dimension;
item = (*env)->GetObjectArrayElement (env, info_arr, i);
+ _info = gst_tensors_info_get_nth_info (&info->info, i);
/* tensor name */
name_str = (jstring) (*env)->GetObjectField (env, item,
if (name_str) {
const gchar *name = (*env)->GetStringUTFChars (env, name_str, NULL);
- info->info[i].name = g_strdup (name);
+ _info->name = g_strdup (name);
(*env)->ReleaseStringUTFChars (env, name_str, name);
(*env)->DeleteLocalRef (env, name_str);
}
/* tensor type */
- info->info[i].type = (ml_tensor_type_e) (*env)->GetIntField (env, item,
+ _info->type = (tensor_type) (*env)->GetIntField (env, item,
icls_info->fid_info_type);
/* tensor dimension */
dimension = (jintArray) (*env)->GetObjectField (env, item,
icls_info->fid_info_dim);
(*env)->GetIntArrayRegion (env, dimension, 0, ML_TENSOR_RANK_LIMIT,
- (jint *) info->info[i].dimension);
+ (jint *) _info->dimension);
(*env)->DeleteLocalRef (env, dimension);
(*env)->DeleteLocalRef (env, item);
{
bool equal;
ml_tensors_info_h info1, info2;
- ml_tensors_info_s *is;
int status = ml_tensors_info_create (&info1);
ASSERT_EQ (status, ML_ERROR_NONE);
status = ml_tensors_info_create (&info2);
ASSERT_EQ (status, ML_ERROR_NONE);
- is = (ml_tensors_info_s *) info1;
- is->num_tensors = 1;
- is = (ml_tensors_info_s *) info2;
- is->num_tensors = 2;
+ status = ml_tensors_info_set_count (info1, 1U);
+ ASSERT_EQ (status, ML_ERROR_NONE);
+ status = ml_tensors_info_set_count (info2, 2U);
+ ASSERT_EQ (status, ML_ERROR_NONE);
status = _ml_tensors_info_compare (info1, info2, &equal);
ASSERT_EQ (status, ML_ERROR_NONE);
}
/**
- * @brief Test utility function `ml_tensors_info_get_nth_info`
- */
-TEST (nnstreamer_capi_util, get_nth_info_00)
-{
- int status;
- ml_tensors_info_h info;
-
- status = ml_tensors_info_create (&info);
- ASSERT_EQ (status, ML_ERROR_NONE);
-
- status = ml_tensors_info_set_count (info, 1U);
- ASSERT_EQ (status, ML_ERROR_NONE);
-
- status = ml_tensors_info_set_tensor_type (info, 0U, ML_TENSOR_TYPE_UINT8);
- ASSERT_EQ (status, ML_ERROR_NONE);
-
- ml_tensor_dimension in_dim = { 5, 1, 1, 1 };
- status = ml_tensors_info_set_tensor_dimension (info, 0U, in_dim);
- ASSERT_EQ (status, ML_ERROR_NONE);
-
- status = ml_tensors_info_set_tensor_name (info, 0U, "test");
- ASSERT_EQ (status, ML_ERROR_NONE);
-
- ml_tensors_info_s *info_s = (ml_tensors_info_s *) info;
- ml_tensor_info_s *tinfo_s = ml_tensors_info_get_nth_info (info_s, 0U);
- EXPECT_NE (tinfo_s, nullptr);
-
- EXPECT_EQ (tinfo_s->type, ML_TENSOR_TYPE_UINT8);
- EXPECT_EQ (tinfo_s->dimension[0], 5U);
- EXPECT_STREQ (tinfo_s->name, "test");
-
- status = ml_tensors_info_destroy (info);
- ASSERT_EQ (status, ML_ERROR_NONE);
-}
-
-/**
- * @brief Test utility function `ml_tensors_info_get_nth_info`
- */
-TEST (nnstreamer_capi_util, get_nth_info_01_n)
-{
- EXPECT_EQ (ml_tensors_info_get_nth_info (NULL, 0U), nullptr);
-
- ml_tensors_info_s info;
- EXPECT_EQ (ml_tensors_info_get_nth_info (&info, ML_TENSOR_SIZE_LIMIT), nullptr);
-}
-
-/**
- * @brief Test internal util functions for extra info.
- */
-TEST (nnstreamer_capi_util, info_extra_n)
-{
- int status;
-
- status = _ml_tensor_info_initialize (NULL);
- EXPECT_EQ (status, ML_ERROR_INVALID_PARAMETER);
-
- EXPECT_FALSE (_ml_tensors_info_create_extra (NULL));
-}
-
-/**
* @brief Test utility functions (public)
*/
TEST (nnstreamer_capi_util, data_create_01_n)
status = ml_tensors_info_create (&ml_info);
EXPECT_EQ (status, ML_ERROR_NONE);
- _ml_tensors_info_copy_from_gst ((ml_tensors_info_s *) ml_info, &gst_info);
+ _ml_tensors_info_copy_from_gst (ml_info, &gst_info);
status = ml_tensors_info_get_count (ml_info, &count);
EXPECT_EQ (status, ML_ERROR_NONE);
EXPECT_EQ (count, 2U);
gst_info.info[0].type = _NNS_INT32;
gst_info.info[1].type = _NNS_UINT32;
- _ml_tensors_info_copy_from_gst ((ml_tensors_info_s *) ml_info, &gst_info);
+ _ml_tensors_info_copy_from_gst (ml_info, &gst_info);
status = ml_tensors_info_get_tensor_type (ml_info, 0, &type);
EXPECT_EQ (status, ML_ERROR_NONE);
EXPECT_EQ (type, ML_TENSOR_TYPE_INT32);
gst_info.info[0].type = _NNS_INT16;
gst_info.info[1].type = _NNS_UINT16;
- _ml_tensors_info_copy_from_gst ((ml_tensors_info_s *) ml_info, &gst_info);
+ _ml_tensors_info_copy_from_gst (ml_info, &gst_info);
status = ml_tensors_info_get_tensor_type (ml_info, 0, &type);
EXPECT_EQ (status, ML_ERROR_NONE);
EXPECT_EQ (type, ML_TENSOR_TYPE_INT16);
gst_info.info[0].type = _NNS_INT8;
gst_info.info[1].type = _NNS_UINT8;
- _ml_tensors_info_copy_from_gst ((ml_tensors_info_s *) ml_info, &gst_info);
+ _ml_tensors_info_copy_from_gst (ml_info, &gst_info);
status = ml_tensors_info_get_tensor_type (ml_info, 0, &type);
EXPECT_EQ (status, ML_ERROR_NONE);
EXPECT_EQ (type, ML_TENSOR_TYPE_INT8);
gst_info.info[0].type = _NNS_INT64;
gst_info.info[1].type = _NNS_UINT64;
- _ml_tensors_info_copy_from_gst ((ml_tensors_info_s *) ml_info, &gst_info);
+ _ml_tensors_info_copy_from_gst (ml_info, &gst_info);
status = ml_tensors_info_get_tensor_type (ml_info, 0, &type);
EXPECT_EQ (status, ML_ERROR_NONE);
EXPECT_EQ (type, ML_TENSOR_TYPE_INT64);
gst_info.info[0].type = _NNS_FLOAT64;
gst_info.info[1].type = _NNS_FLOAT32;
- _ml_tensors_info_copy_from_gst ((ml_tensors_info_s *) ml_info, &gst_info);
+ _ml_tensors_info_copy_from_gst (ml_info, &gst_info);
status = ml_tensors_info_get_tensor_type (ml_info, 0, &type);
EXPECT_EQ (status, ML_ERROR_NONE);
EXPECT_EQ (type, ML_TENSOR_TYPE_FLOAT64);
gst_info.info[0].name = g_strdup ("tn1");
gst_info.info[1].name = g_strdup ("tn2");
- _ml_tensors_info_copy_from_gst ((ml_tensors_info_s *) ml_info, &gst_info);
+ _ml_tensors_info_copy_from_gst (ml_info, &gst_info);
status = ml_tensors_info_get_tensor_name (ml_info, 0, &name);
EXPECT_EQ (status, ML_ERROR_NONE);
EXPECT_STREQ (name, "tn1");
status = ml_tensors_info_create_extended (&ml_info);
EXPECT_EQ (status, ML_ERROR_NONE);
- _ml_tensors_info_copy_from_gst ((ml_tensors_info_s *) ml_info, &gst_info);
+ _ml_tensors_info_copy_from_gst (ml_info, &gst_info);
status = ml_tensors_info_get_count (ml_info, &count);
EXPECT_EQ (status, ML_ERROR_NONE);
EXPECT_EQ (count, 2U);
status = ml_tensors_info_create (&ml_info);
EXPECT_EQ (status, ML_ERROR_NONE);
- status = _ml_tensors_info_copy_from_gst ((ml_tensors_info_s *) ml_info, NULL);
+ status = _ml_tensors_info_copy_from_gst (ml_info, NULL);
EXPECT_NE (status, ML_ERROR_NONE);
status = ml_tensors_info_destroy (ml_info);
status = ml_tensors_info_set_tensor_dimension (ml_info, 1, dim);
EXPECT_EQ (status, ML_ERROR_NONE);
- _ml_tensors_info_copy_from_ml (&gst_info, (ml_tensors_info_s *) ml_info);
+ _ml_tensors_info_copy_from_ml (&gst_info, ml_info);
EXPECT_EQ (gst_info.num_tensors, 2U);
EXPECT_EQ (gst_info.info[0].dimension[0], 1U);
EXPECT_EQ (gst_info.info[0].dimension[1], 2U);
EXPECT_EQ (status, ML_ERROR_NONE);
status = ml_tensors_info_set_tensor_type (ml_info, 1, ML_TENSOR_TYPE_UINT32);
EXPECT_EQ (status, ML_ERROR_NONE);
- _ml_tensors_info_copy_from_ml (&gst_info, (ml_tensors_info_s *) ml_info);
+ _ml_tensors_info_copy_from_ml (&gst_info, ml_info);
EXPECT_EQ (gst_info.info[0].type, _NNS_INT32);
EXPECT_EQ (gst_info.info[1].type, _NNS_UINT32);
EXPECT_EQ (status, ML_ERROR_NONE);
status = ml_tensors_info_set_tensor_type (ml_info, 1, ML_TENSOR_TYPE_UINT16);
EXPECT_EQ (status, ML_ERROR_NONE);
- _ml_tensors_info_copy_from_ml (&gst_info, (ml_tensors_info_s *) ml_info);
+ _ml_tensors_info_copy_from_ml (&gst_info, ml_info);
EXPECT_EQ (gst_info.info[0].type, _NNS_INT16);
EXPECT_EQ (gst_info.info[1].type, _NNS_UINT16);
EXPECT_EQ (status, ML_ERROR_NONE);
status = ml_tensors_info_set_tensor_type (ml_info, 1, ML_TENSOR_TYPE_UINT8);
EXPECT_EQ (status, ML_ERROR_NONE);
- _ml_tensors_info_copy_from_ml (&gst_info, (ml_tensors_info_s *) ml_info);
+ _ml_tensors_info_copy_from_ml (&gst_info, ml_info);
EXPECT_EQ (gst_info.info[0].type, _NNS_INT8);
EXPECT_EQ (gst_info.info[1].type, _NNS_UINT8);
EXPECT_EQ (status, ML_ERROR_NONE);
status = ml_tensors_info_set_tensor_type (ml_info, 1, ML_TENSOR_TYPE_UINT64);
EXPECT_EQ (status, ML_ERROR_NONE);
- _ml_tensors_info_copy_from_ml (&gst_info, (ml_tensors_info_s *) ml_info);
+ _ml_tensors_info_copy_from_ml (&gst_info, ml_info);
EXPECT_EQ (gst_info.info[0].type, _NNS_INT64);
EXPECT_EQ (gst_info.info[1].type, _NNS_UINT64);
EXPECT_EQ (status, ML_ERROR_NONE);
status = ml_tensors_info_set_tensor_type (ml_info, 1, ML_TENSOR_TYPE_FLOAT32);
EXPECT_EQ (status, ML_ERROR_NONE);
- _ml_tensors_info_copy_from_ml (&gst_info, (ml_tensors_info_s *) ml_info);
+ _ml_tensors_info_copy_from_ml (&gst_info, ml_info);
EXPECT_EQ (gst_info.info[0].type, _NNS_FLOAT64);
EXPECT_EQ (gst_info.info[1].type, _NNS_FLOAT32);
EXPECT_EQ (status, ML_ERROR_NONE);
status = ml_tensors_info_set_tensor_name (ml_info, 1, "tn2");
EXPECT_EQ (status, ML_ERROR_NONE);
- _ml_tensors_info_copy_from_ml (&gst_info, (ml_tensors_info_s *) ml_info);
+ _ml_tensors_info_copy_from_ml (&gst_info, ml_info);
EXPECT_STREQ (gst_info.info[0].name, "tn1");
EXPECT_STREQ (gst_info.info[1].name, "tn2");
status = ml_tensors_info_create (&ml_info);
EXPECT_EQ (status, ML_ERROR_NONE);
- status = _ml_tensors_info_copy_from_ml (NULL, (ml_tensors_info_s *) ml_info);
+ status = _ml_tensors_info_copy_from_ml (NULL, ml_info);
EXPECT_NE (status, ML_ERROR_NONE);
status = ml_tensors_info_destroy (ml_info);
projects / platform / core / api / machine-learning.git / commitdiff