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libavfilter/dnn: Add scale and mean preprocess to openvino backend

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Dnn models has different data preprocess requirements. Scale and mean
parameters are added to preprocess input data.

Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
This commit is contained in:
Wenbin Chen 2023-09-21 09:26:32 +08:00 committed by Guo Yejun
parent 74ce1d2d11
commit c8c925dc29
3 changed files with 108 additions and 19 deletions
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43
libavfilter/dnn/dnn_backend_openvino.c
View File

@ -46,6 +46,8 @@ typedef struct OVOptions{
int batch_size;
int input_resizable;
DNNLayout layout;
float scale;
float mean;
} OVOptions;
typedef struct OVContext {
@ -105,6 +107,8 @@ static const AVOption dnn_openvino_options[] = {
{ "none", "none", 0, AV_OPT_TYPE_CONST, { .i64 = DL_NONE }, 0, 0, FLAGS, "layout"},
{ "nchw", "nchw", 0, AV_OPT_TYPE_CONST, { .i64 = DL_NCHW }, 0, 0, FLAGS, "layout"},
{ "nhwc", "nhwc", 0, AV_OPT_TYPE_CONST, { .i64 = DL_NHWC }, 0, 0, FLAGS, "layout"},
{ "scale", "Add scale preprocess operation. Divide each element of input by specified value.", OFFSET(options.scale), AV_OPT_TYPE_FLOAT, { .dbl = 0 }, INT_MIN, INT_MAX, FLAGS},
{ "mean", "Add mean preprocess operation. Subtract specified value from each element of input.", OFFSET(options.mean), AV_OPT_TYPE_FLOAT, { .dbl = 0 }, INT_MIN, INT_MAX, FLAGS},
{ NULL }
};
@ -209,6 +213,7 @@ static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
ie_blob_t *input_blob = NULL;
#endif
memset(&input, 0, sizeof(input));
lltask = ff_queue_peek_front(ov_model->lltask_queue);
av_assert0(lltask);
task = lltask->task;
@ -280,6 +285,9 @@ static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
// all models in openvino open model zoo use BGR as input,
// change to be an option when necessary.
input.order = DCO_BGR;
// We use preprocess_steps to scale input data, so disable scale and mean here.
input.scale = 1;
input.mean = 0;
for (int i = 0; i < ctx->options.batch_size; ++i) {
lltask = ff_queue_pop_front(ov_model->lltask_queue);
@ -350,6 +358,7 @@ static void infer_completion_callback(void *args)
ov_shape_t output_shape = {0};
ov_element_type_e precision;
memset(&output, 0, sizeof(output));
status = ov_infer_request_get_output_tensor_by_index(request->infer_request, 0, &output_tensor);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR,
@ -418,6 +427,8 @@ static void infer_completion_callback(void *args)
#endif
output.dt = precision_to_datatype(precision);
output.layout = ctx->options.layout;
output.scale = ctx->options.scale;
output.mean = ctx->options.mean;
av_assert0(request->lltask_count >= 1);
for (int i = 0; i < request->lltask_count; ++i) {
@ -561,7 +572,9 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
ie_config_t config = {NULL, NULL, NULL};
char *all_dev_names = NULL;
#endif
// We scale pixel by default when do frame processing.
if (fabsf(ctx->options.scale) < 1e-6f)
ctx->options.scale = ov_model->model->func_type == DFT_PROCESS_FRAME ? 255 : 1;
// batch size
if (ctx->options.batch_size <= 0) {
ctx->options.batch_size = 1;
@ -628,15 +641,37 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
goto err;
}
status = ov_preprocess_input_tensor_info_set_element_type(input_tensor_info, U8);
if (ov_model->model->func_type != DFT_PROCESS_FRAME)
//set precision only for detect and classify
status = ov_preprocess_input_tensor_info_set_element_type(input_tensor_info, U8);
status |= ov_preprocess_output_set_element_type(output_tensor_info, F32);
status |= ov_preprocess_output_set_element_type(output_tensor_info, F32);
else if (fabsf(ctx->options.scale - 1) > 1e-6f || fabsf(ctx->options.mean) > 1e-6f)
status |= ov_preprocess_output_set_element_type(output_tensor_info, F32);
else
status |= ov_preprocess_output_set_element_type(output_tensor_info, U8);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set input/output element type\n");
ret = ov2_map_error(status, NULL);
goto err;
}
// set preprocess steps.
if (fabsf(ctx->options.scale - 1) > 1e-6f || fabsf(ctx->options.mean) > 1e-6f) {
ov_preprocess_preprocess_steps_t* input_process_steps = NULL;
status = ov_preprocess_input_info_get_preprocess_steps(ov_model->input_info, &input_process_steps);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get preprocess steps\n");
ret = ov2_map_error(status, NULL);
goto err;
}
status = ov_preprocess_preprocess_steps_convert_element_type(input_process_steps, F32);
status |= ov_preprocess_preprocess_steps_mean(input_process_steps, ctx->options.mean);
status |= ov_preprocess_preprocess_steps_scale(input_process_steps, ctx->options.scale);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set preprocess steps\n");
ret = ov2_map_error(status, NULL);
goto err;
}
ov_preprocess_preprocess_steps_free(input_process_steps);
}
//update model
if(ov_model->ov_model)

82
libavfilter/dnn/dnn_io_proc.c
View File

@ -24,6 +24,20 @@
#include "libavutil/avassert.h"
#include "libavutil/detection_bbox.h"
static int get_datatype_size(DNNDataType dt)
{
switch (dt)
{
case DNN_FLOAT:
return sizeof(float);
case DNN_UINT8:
return sizeof(uint8_t);
default:
av_assert0(!"not supported yet.");
return 1;
}
}
int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
{
struct SwsContext *sws_ctx;
@ -33,14 +47,26 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
void *middle_data = NULL;
uint8_t *planar_data[4] = { 0 };
int plane_size = frame->width * frame->height * sizeof(uint8_t);
enum AVPixelFormat src_fmt = AV_PIX_FMT_NONE;
int src_datatype_size = get_datatype_size(output->dt);
int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
if (bytewidth < 0) {
return AVERROR(EINVAL);
}
if (output->dt != DNN_FLOAT) {
avpriv_report_missing_feature(log_ctx, "data type rather than DNN_FLOAT");
/* scale == 1 and mean == 0 and dt == UINT8: passthrough */
if (fabsf(output->scale - 1) < 1e-6f && fabsf(output->mean) < 1e-6 && output->dt == DNN_UINT8)
src_fmt = AV_PIX_FMT_GRAY8;
/* (scale == 255 or scale == 0) and mean == 0 and dt == FLOAT: normalization */
else if ((fabsf(output->scale - 255) < 1e-6f || fabsf(output->scale) < 1e-6f) &&
fabsf(output->mean) < 1e-6 && output->dt == DNN_FLOAT)
src_fmt = AV_PIX_FMT_GRAYF32;
else {
av_log(log_ctx, AV_LOG_ERROR, "dnn_process output data doesn't type: UINT8 "
"scale: %f, mean: %f\n", output->scale, output->mean);
return AVERROR(ENOSYS);
}
dst_data = (void **)frame->data;
linesize[0] = frame->linesize[0];
if (output->layout == DL_NCHW) {
@ -58,7 +84,7 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
case AV_PIX_FMT_BGR24:
sws_ctx = sws_getContext(frame->width * 3,
frame->height,
AV_PIX_FMT_GRAYF32,
src_fmt,
frame->width * 3,
frame->height,
AV_PIX_FMT_GRAY8,
@ -66,13 +92,13 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
if (!sws_ctx) {
av_log(log_ctx, AV_LOG_ERROR, "Impossible to create scale context for the conversion "
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32), frame->width * 3, frame->height,
av_get_pix_fmt_name(src_fmt), frame->width * 3, frame->height,
av_get_pix_fmt_name(AV_PIX_FMT_GRAY8), frame->width * 3, frame->height);
ret = AVERROR(EINVAL);
goto err;
}
sws_scale(sws_ctx, (const uint8_t *[4]){(const uint8_t *)output->data, 0, 0, 0},
(const int[4]){frame->width * 3 * sizeof(float), 0, 0, 0}, 0, frame->height,
(const int[4]){frame->width * 3 * src_datatype_size, 0, 0, 0}, 0, frame->height,
(uint8_t * const*)dst_data, linesize);
sws_freeContext(sws_ctx);
// convert data from planar to packed
@ -131,13 +157,13 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
if (!sws_ctx) {
av_log(log_ctx, AV_LOG_ERROR, "Impossible to create scale context for the conversion "
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32), frame->width, frame->height,
av_get_pix_fmt_name(src_fmt), frame->width, frame->height,
av_get_pix_fmt_name(AV_PIX_FMT_GRAY8), frame->width, frame->height);
ret = AVERROR(EINVAL);
goto err;
}
sws_scale(sws_ctx, (const uint8_t *[4]){(const uint8_t *)output->data, 0, 0, 0},
(const int[4]){frame->width * sizeof(float), 0, 0, 0}, 0, frame->height,
(const int[4]){frame->width * src_datatype_size, 0, 0, 0}, 0, frame->height,
(uint8_t * const*)frame->data, frame->linesize);
sws_freeContext(sws_ctx);
break;
@ -161,12 +187,22 @@ int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
void *middle_data = NULL;
uint8_t *planar_data[4] = { 0 };
int plane_size = frame->width * frame->height * sizeof(uint8_t);
enum AVPixelFormat dst_fmt = AV_PIX_FMT_NONE;
int dst_datatype_size = get_datatype_size(input->dt);
int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
if (bytewidth < 0) {
return AVERROR(EINVAL);
}
if (input->dt != DNN_FLOAT) {
avpriv_report_missing_feature(log_ctx, "data type rather than DNN_FLOAT");
/* scale == 1 and mean == 0 and dt == UINT8: passthrough */
if (fabsf(input->scale - 1) < 1e-6f && fabsf(input->mean) < 1e-6 && input->dt == DNN_UINT8)
dst_fmt = AV_PIX_FMT_GRAY8;
/* (scale == 255 or scale == 0) and mean == 0 and dt == FLOAT: normalization */
else if ((fabsf(input->scale - 255) < 1e-6f || fabsf(input->scale) < 1e-6f) &&
fabsf(input->mean) < 1e-6 && input->dt == DNN_FLOAT)
dst_fmt = AV_PIX_FMT_GRAYF32;
else {
av_log(log_ctx, AV_LOG_ERROR, "dnn_process input data doesn't support type: UINT8 "
"scale: %f, mean: %f\n", input->scale, input->mean);
return AVERROR(ENOSYS);
}
@ -223,20 +259,20 @@ int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
AV_PIX_FMT_GRAY8,
frame->width * 3,
frame->height,
AV_PIX_FMT_GRAYF32,
dst_fmt,
0, NULL, NULL, NULL);
if (!sws_ctx) {
av_log(log_ctx, AV_LOG_ERROR, "Impossible to create scale context for the conversion "
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(AV_PIX_FMT_GRAY8), frame->width * 3, frame->height,
av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32),frame->width * 3, frame->height);
av_get_pix_fmt_name(dst_fmt),frame->width * 3, frame->height);
ret = AVERROR(EINVAL);
goto err;
}
sws_scale(sws_ctx, (const uint8_t **)src_data,
linesize, 0, frame->height,
(uint8_t * const [4]){input->data, 0, 0, 0},
(const int [4]){frame->width * 3 * sizeof(float), 0, 0, 0});
(const int [4]){frame->width * 3 * dst_datatype_size, 0, 0, 0});
sws_freeContext(sws_ctx);
break;
case AV_PIX_FMT_GRAYF32:
@ -256,20 +292,20 @@ int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
AV_PIX_FMT_GRAY8,
frame->width,
frame->height,
AV_PIX_FMT_GRAYF32,
dst_fmt,
0, NULL, NULL, NULL);
if (!sws_ctx) {
av_log(log_ctx, AV_LOG_ERROR, "Impossible to create scale context for the conversion "
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(AV_PIX_FMT_GRAY8), frame->width, frame->height,
av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32),frame->width, frame->height);
av_get_pix_fmt_name(dst_fmt),frame->width, frame->height);
ret = AVERROR(EINVAL);
goto err;
}
sws_scale(sws_ctx, (const uint8_t **)frame->data,
frame->linesize, 0, frame->height,
(uint8_t * const [4]){input->data, 0, 0, 0},
(const int [4]){frame->width * sizeof(float), 0, 0, 0});
(const int [4]){frame->width * dst_datatype_size, 0, 0, 0});
sws_freeContext(sws_ctx);
break;
default:
@ -315,6 +351,14 @@ int ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index
AVFrameSideData *sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
av_assert0(sd);
/* (scale != 1 and scale != 0) or mean != 0 */
if ((fabsf(input->scale - 1) > 1e-6f && fabsf(input->scale) > 1e-6f) ||
fabsf(input->mean) > 1e-6f) {
av_log(log_ctx, AV_LOG_ERROR, "dnn_classify input data doesn't support "
"scale: %f, mean: %f\n", input->scale, input->mean);
return AVERROR(ENOSYS);
}
if (input->layout == DL_NCHW) {
av_log(log_ctx, AV_LOG_ERROR, "dnn_classify input data doesn't support layout: NCHW\n");
return AVERROR(ENOSYS);
@ -373,6 +417,14 @@ int ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_ctx)
int ret = 0;
enum AVPixelFormat fmt = get_pixel_format(input);
/* (scale != 1 and scale != 0) or mean != 0 */
if ((fabsf(input->scale - 1) > 1e-6f && fabsf(input->scale) > 1e-6f) ||
fabsf(input->mean) > 1e-6f) {
av_log(log_ctx, AV_LOG_ERROR, "dnn_detect input data doesn't support "
"scale: %f, mean: %f\n", input->scale, input->mean);
return AVERROR(ENOSYS);
}
if (input->layout == DL_NCHW) {
av_log(log_ctx, AV_LOG_ERROR, "dnn_detect input data doesn't support layout: NCHW\n");
return AVERROR(ENOSYS);

2
libavfilter/dnn_interface.h
View File

@ -69,6 +69,8 @@ typedef struct DNNData{
DNNDataType dt;
DNNColorOrder order;
DNNLayout layout;
float scale;
float mean;
} DNNData;
typedef struct DNNExecBaseParams {