Trivial, fix indent and remove unnecessary space.
Signed-off-by: Jaeyun Jung <jy1210.jung@samsung.com>
- /** reset the buffer with alpha 0 / black */
+ /* reset the buffer with alpha 0 / black */
memset (out_info.data, 0, size);
if (_check_mode_is_mobilenet_ssd (bdata->mode)) {
memset (out_info.data, 0, size);
if (_check_mode_is_mobilenet_ssd (bdata->mode)) {
#include <nnstreamer_util.h>
#include "tensordecutil.h"
#include <nnstreamer_util.h>
#include "tensordecutil.h"
-void init_dv (void) __attribute__ ((constructor));
-void fini_dv (void) __attribute__ ((destructor));
+void init_dv (void) __attribute__((constructor));
+void fini_dv (void) __attribute__((destructor));
#define DECODER_DV_VIDEO_CAPS_STR \
GST_VIDEO_CAPS_MAKE ("{ GRAY8, RGB, BGR, RGBx, BGRx, xRGB, xBGR, RGBA, BGRA, ARGB, ABGR, GRAY16_BE, GRAY16_LE }") \
#define DECODER_DV_VIDEO_CAPS_STR \
GST_VIDEO_CAPS_MAKE ("{ GRAY8, RGB, BGR, RGBx, BGRx, xRGB, xBGR, RGBA, BGRA, ARGB, ABGR, GRAY16_BE, GRAY16_LE }") \
_get_video_xraw_bufsize (const tensor_dim dim, gsize data_size)
{
/* dim[0] is bpp and there is zeropadding only when dim[0]%4 > 0 */
_get_video_xraw_bufsize (const tensor_dim dim, gsize data_size)
{
/* dim[0] is bpp and there is zeropadding only when dim[0]%4 > 0 */
- return (size_t)((dim[0] * dim[1] - 1) / 4 + 1) * 4 * dim[2] * data_size;
+ return (size_t) ((dim[0] * dim[1] - 1) / 4 + 1) * 4 * dim[2] * data_size;
}
/** @brief tensordec-plugin's GstTensorDecoderDef callback */
}
/** @brief tensordec-plugin's GstTensorDecoderDef callback */
UNUSED (othercaps);
if (direction == GST_PAD_SINK)
UNUSED (othercaps);
if (direction == GST_PAD_SINK)
- transform_size = _get_video_xraw_bufsize (dim, data_size);
+ transform_size = _get_video_xraw_bufsize (dim, data_size);
* ]|
*
* Total number of data to be received is 1000((num-training-samples + num-validation-samples) * epochs)
* ]|
*
* Total number of data to be received is 1000((num-training-samples + num-validation-samples) * epochs)
* output tensors : dimensions=1:1:4, types=float64.
* values are training loss, training accuracy, validation loss and validation accuracy.
* -INFINITY value is stored if the value fetched from the sub-plugin is not greater than 0.
* output tensors : dimensions=1:1:4, types=float64.
* values are training loss, training accuracy, validation loss and validation accuracy.
* -INFINITY value is stored if the value fetched from the sub-plugin is not greater than 0.
- * @brief Check if current epochs is complete,
+ * @brief Check if current epochs is complete,
* tensor_trainer wait for one of epochs to complete before getting the results from the subplugin
*/
static gboolean
* tensor_trainer wait for one of epochs to complete before getting the results from the subplugin
*/
static gboolean
*
* @note This API is intended to be used by gst_tensor_parse_config_file ()
*/
*
* @note This API is intended to be used by gst_tensor_parse_config_file ()
*/
static void
set_property_value (GValue * prop_value, const GParamSpec * param_spec,
const gchar * property_value)
static void
set_property_value (GValue * prop_value, const GParamSpec * param_spec,
const gchar * property_value)
return 0;
/* remove spaces */
return 0;
/* remove spaces */
- dim_string = g_strdup (dimstr);
- g_strstrip (dim_string);
+ dim_string = g_strstrip (g_strdup (dimstr));
strv = g_strsplit (dim_string, ":", NNS_TENSOR_RANK_LIMIT);
num_dims = g_strv_length (strv);
strv = g_strsplit (dim_string, ":", NNS_TENSOR_RANK_LIMIT);
num_dims = g_strv_length (strv);
$(NNSTREAMER_EXT_HOME)/tensor_decoder/tensordecutil.c \
$(NNSTREAMER_EXT_HOME)/tensor_decoder/tensordec-font.c
$(NNSTREAMER_EXT_HOME)/tensor_decoder/tensordecutil.c \
$(NNSTREAMER_EXT_HOME)/tensor_decoder/tensordec-font.c
NNSTREAMER_DECODER_TR_SRCS := \
$(NNSTREAMER_EXT_HOME)/tensor_decoder/tensordec-tensor_region.c \
$(NNSTREAMER_EXT_HOME)/tensor_decoder/tensordecutil.c \
NNSTREAMER_DECODER_TR_SRCS := \
$(NNSTREAMER_EXT_HOME)/tensor_decoder/tensordec-tensor_region.c \
$(NNSTREAMER_EXT_HOME)/tensor_decoder/tensordecutil.c \
assert (0 < num_tensors && num_tensors <= NNS_TENSOR_SIZE_LIMIT); \
for (i = 0; i < num_tensors; ++i) { \
_info->info[i] = _data->info[i]; \
assert (0 < num_tensors && num_tensors <= NNS_TENSOR_SIZE_LIMIT); \
for (i = 0; i < num_tensors; ++i) { \
_info->info[i] = _data->info[i]; \
- _info->info[i].name = g_strdup(_data->info[i].name); \
+ _info->info[i].name = g_strdup (_data->info[i].name); \