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[C-Api/Single] remove rank list
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We implemented 0-initialized dimension in nnstreamer.
Remove unnecessary rank list from single handle.

Signed-off-by: Jaeyun Jung <[email protected]>
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@jaeyun-jung
jaeyun-jung committed Nov 16, 2023
1 parent 2b2b1ad commit 2a5b9c5
Showing 1 changed file with 4 additions and 112 deletions.
116 changes: 4 additions & 112 deletions c/src/ml-api-inference-single.c
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Original file line number Diff line number Diff line change
Expand Up @@ -134,10 +134,6 @@ typedef struct
ml_tensors_data_s in_tensors; /**< input tensor wrapper for processing */
ml_tensors_data_s out_tensors; /**< output tensor wrapper for processing */

/** @todo Use only ml_tensor_info_s dimension instead of saving ranks value */
guint input_ranks[ML_TENSOR_SIZE_LIMIT]; /**< the rank list of input tensors, it is calculated based on the dimension string. */
guint output_ranks[ML_TENSOR_SIZE_LIMIT]; /**< the rank list of output tensors, it is calculated based on the dimension string. */

GList *destroy_data_list; /**< data to be freed by filter */
} ml_single;

Expand Down Expand Up @@ -457,44 +453,6 @@ __process_output (ml_single * single_h, ml_tensors_data_h output)
}
}

/**
* @brief Initializes the rank information with default value.
*/
static int
_ml_tensors_rank_initialize (guint * rank)
{
guint i;

if (!rank)
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The parameter, rank, is NULL. Provide a valid pointer.");

for (i = 0; i < ML_TENSOR_RANK_LIMIT; i++) {
rank[i] = 0;
}

return ML_ERROR_NONE;
}

/**
* @brief Sets the rank information with given value.
*/
static int
_ml_tensors_set_rank (guint * rank, guint val)
{
guint i;

if (!rank)
_ml_error_report_return (ML_ERROR_INVALID_PARAMETER,
"The parameter, rank, is NULL. Provide a valid pointer.");

for (i = 0; i < ML_TENSOR_RANK_LIMIT; i++) {
rank[i] = val;
}

return ML_ERROR_NONE;
}

/**
* @brief thread to execute calls to invoke
*
Expand Down Expand Up @@ -865,8 +823,6 @@ ml_single_create_handle (ml_nnfw_type_e nnfw)

_ml_tensors_info_initialize (&single_h->in_info);
_ml_tensors_info_initialize (&single_h->out_info);
_ml_tensors_rank_initialize (single_h->input_ranks);
_ml_tensors_rank_initialize (single_h->output_ranks);
g_mutex_init (&single_h->mutex);
g_cond_init (&single_h->cond);

Expand Down Expand Up @@ -1143,18 +1099,14 @@ ml_single_open_custom (ml_single_h * single, ml_single_preset * info)
/* Setup input and output memory buffers for invoke */
if (in_tensors_info && in_tensors_info->is_extended) {
ml_tensors_info_create_extended (&single_h->in_tensors.info);
_ml_tensors_set_rank (single_h->input_ranks, ML_TENSOR_RANK_LIMIT);
} else {
ml_tensors_info_create (&single_h->in_tensors.info);
_ml_tensors_set_rank (single_h->input_ranks, ML_TENSOR_RANK_LIMIT_PREV);
}

if (out_tensors_info && out_tensors_info->is_extended) {
ml_tensors_info_create_extended (&single_h->out_tensors.info);
_ml_tensors_set_rank (single_h->output_ranks, ML_TENSOR_RANK_LIMIT);
} else {
ml_tensors_info_create (&single_h->out_tensors.info);
_ml_tensors_set_rank (single_h->output_ranks, ML_TENSOR_RANK_LIMIT_PREV);
}

__setup_in_out_tensors (single_h);
Expand Down Expand Up @@ -1797,33 +1749,8 @@ ml_single_set_property (ml_single_h single, const char *name, const char *value)
num = gst_tensors_info_parse_types_string (&gst_info, value);
else if (g_str_has_suffix (name, "name"))
num = gst_tensors_info_parse_names_string (&gst_info, value);
else {
guint *rank;
gchar **str_dims;
guint i;

if (is_input) {
rank = single_h->input_ranks;
} else {
rank = single_h->output_ranks;
}

str_dims = g_strsplit_set (value, ",.", -1);
num = g_strv_length (str_dims);

if (num > ML_TENSOR_SIZE_LIMIT) {
_ml_error_report ("Invalid param, dimensions (%d) max (%d)\n",
num, ML_TENSOR_SIZE_LIMIT);

num = ML_TENSOR_SIZE_LIMIT;
}

for (i = 0; i < num; ++i) {
rank[i] = gst_tensor_parse_dimension (str_dims[i],
gst_tensors_info_get_nth_info (&gst_info, i)->dimension);
}
g_strfreev (str_dims);
}
else
num = gst_tensors_info_parse_dimensions_string (&gst_info, value);

if (num == gst_info.num_tensors) {
ml_tensors_info_h ml_info;
Expand Down Expand Up @@ -1885,7 +1812,8 @@ ml_single_get_property (ml_single_h single, const char *name, char **value)

ML_SINGLE_GET_VALID_HANDLE_LOCKED (single_h, single, 0);

if (g_str_equal (name, "inputtype") || g_str_equal (name, "inputname") ||
if (g_str_equal (name, "input") || g_str_equal (name, "output") ||
g_str_equal (name, "inputtype") || g_str_equal (name, "inputname") ||
g_str_equal (name, "inputlayout") || g_str_equal (name, "outputtype") ||
g_str_equal (name, "outputname") || g_str_equal (name, "outputlayout") ||
g_str_equal (name, "accelerator") || g_str_equal (name, "custom")) {
Expand All @@ -1897,42 +1825,6 @@ ml_single_get_property (ml_single_h single, const char *name, char **value)
/* boolean */
g_object_get (G_OBJECT (single_h->filter), name, &bool_value, NULL);
*value = (bool_value) ? g_strdup ("true") : g_strdup ("false");
} else if (g_str_equal (name, "input") || g_str_equal (name, "output")) {
gchar *dim_str = NULL;
const guint *rank;
gboolean is_input = g_str_has_prefix (name, "input");
GstTensorsInfo gst_info;

if (is_input) {
rank = single_h->input_ranks;
} else {
rank = single_h->output_ranks;
}

ml_single_get_gst_info (single_h, is_input, &gst_info);

if (gst_info.num_tensors > 0) {
guint i;
GString *dimensions = g_string_new (NULL);

for (i = 0; i < gst_info.num_tensors; ++i) {
dim_str =
gst_tensor_get_rank_dimension_string (gst_tensors_info_get_nth_info
(&gst_info, i)->dimension, *(rank + i));
g_string_append (dimensions, dim_str);

if (i < gst_info.num_tensors - 1) {
g_string_append (dimensions, ",");
}
g_free (dim_str);
}
dim_str = g_string_free (dimensions, FALSE);
} else {
dim_str = g_strdup ("");
}
*value = dim_str;

gst_tensors_info_free (&gst_info);
} else {
_ml_error_report
("The property key, '%s', is not available for get_property and not recognized by the API. It should be one of {input, inputtype, inputname, inputlayout, output, outputtype, outputname, outputlayout, accelerator, custom, is-updatable}.",
Expand Down

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