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libavfilter/dnn_interface: use dims to represent shapes

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For detect and classify output, width and height make no sence, so
change width, height to dims to represent the shape of tensor. Use
layout and dims to get width, height and channel.

Signed-off-by: Wenbin Chen <wenbin.chen@intel.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
This commit is contained in:
Wenbin Chen
2024-01-17 15:21:50 +08:00
committed by Guo Yejun
parent c695de56b5
commit 3de38b9da5
7 changed files with 146 additions and 90 deletions
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80
libavfilter/dnn/dnn_backend_openvino.c
View File

@@ -253,9 +253,9 @@ static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
ov_shape_free(&input_shape);
return ov2_map_error(status, NULL);
}
input.height = dims[1];
input.width = dims[2];
input.channels = dims[3];
for (int i = 0; i < input_shape.rank; i++)
input.dims[i] = dims[i];
input.layout = DL_NHWC;
input.dt = precision_to_datatype(precision);
#else
status = ie_infer_request_get_blob(request->infer_request, task->input_name, &input_blob);
@@ -278,9 +278,9 @@ static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
av_log(ctx, AV_LOG_ERROR, "Failed to get input blob buffer\n");
return DNN_GENERIC_ERROR;
}
input.height = dims.dims[2];
input.width = dims.dims[3];
input.channels = dims.dims[1];
for (int i = 0; i < input_shape.rank; i++)
input.dims[i] = dims[i];
input.layout = DL_NCHW;
input.data = blob_buffer.buffer;
input.dt = precision_to_datatype(precision);
#endif
@@ -339,8 +339,8 @@ static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
av_assert0(!"should not reach here");
break;
}
input.data = (uint8_t *)input.data
+ input.width * input.height * input.channels * get_datatype_size(input.dt);
input.data = (uint8_t *)input.data +
input.dims[1] * input.dims[2] * input.dims[3] * get_datatype_size(input.dt);
}
#if HAVE_OPENVINO2
ov_tensor_free(tensor);
@@ -403,10 +403,11 @@ static void infer_completion_callback(void *args)
goto end;
}
outputs[i].dt = precision_to_datatype(precision);
outputs[i].channels = output_shape.rank > 2 ? dims[output_shape.rank - 3] : 1;
outputs[i].height = output_shape.rank > 1 ? dims[output_shape.rank - 2] : 1;
outputs[i].width = output_shape.rank > 0 ? dims[output_shape.rank - 1] : 1;
outputs[i].layout = DL_NCHW;
outputs[i].dims[0] = 1;
outputs[i].dims[1] = output_shape.rank > 2 ? dims[output_shape.rank - 3] : 1;
outputs[i].dims[2] = output_shape.rank > 1 ? dims[output_shape.rank - 2] : 1;
outputs[i].dims[3] = output_shape.rank > 0 ? dims[output_shape.rank - 1] : 1;
av_assert0(request->lltask_count <= dims[0]);
outputs[i].layout = ctx->options.layout;
outputs[i].scale = ctx->options.scale;
@@ -445,9 +446,9 @@ static void infer_completion_callback(void *args)
return;
}
output.data = blob_buffer.buffer;
output.channels = dims.dims[1];
output.height = dims.dims[2];
output.width = dims.dims[3];
output.layout = DL_NCHW;
for (int i = 0; i < 4; i++)
output.dims[i] = dims.dims[i];
av_assert0(request->lltask_count <= dims.dims[0]);
output.dt = precision_to_datatype(precision);
output.layout = ctx->options.layout;
@@ -469,8 +470,10 @@ static void infer_completion_callback(void *args)
ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
}
} else {
task->out_frame->width = outputs[0].width;
task->out_frame->height = outputs[0].height;
task->out_frame->width =
outputs[0].dims[dnn_get_width_idx_by_layout(outputs[0].layout)];
task->out_frame->height =
outputs[0].dims[dnn_get_height_idx_by_layout(outputs[0].layout)];
}
break;
case DFT_ANALYTICS_DETECT:
@@ -501,7 +504,8 @@ static void infer_completion_callback(void *args)
av_freep(&request->lltasks[i]);
for (int i = 0; i < ov_model->nb_outputs; i++)
outputs[i].data = (uint8_t *)outputs[i].data +
outputs[i].width * outputs[i].height * outputs[i].channels * get_datatype_size(outputs[i].dt);
outputs[i].dims[1] * outputs[i].dims[2] * outputs[i].dims[3] *
get_datatype_size(outputs[i].dt);
}
end:
#if HAVE_OPENVINO2
@@ -1085,7 +1089,6 @@ static int get_input_ov(void *model, DNNData *input, const char *input_name)
#if HAVE_OPENVINO2
ov_shape_t input_shape = {0};
ov_element_type_e precision;
int64_t* dims;
ov_status_e status;
if (input_name)
status = ov_model_const_input_by_name(ov_model->ov_model, input_name, &ov_model->input_port);
@@ -1105,16 +1108,18 @@ static int get_input_ov(void *model, DNNData *input, const char *input_name)
av_log(ctx, AV_LOG_ERROR, "Failed to get input port shape.\n");
return ov2_map_error(status, NULL);
}
dims = input_shape.dims;
if (dims[1] <= 3) { // NCHW
input->channels = dims[1];
input->height = input_resizable ? -1 : dims[2];
input->width = input_resizable ? -1 : dims[3];
} else { // NHWC
input->height = input_resizable ? -1 : dims[1];
input->width = input_resizable ? -1 : dims[2];
input->channels = dims[3];
for (int i = 0; i < 4; i++)
input->dims[i] = input_shape.dims[i];
if (input_resizable) {
input->dims[dnn_get_width_idx_by_layout(input->layout)] = -1;
input->dims[dnn_get_height_idx_by_layout(input->layout)] = -1;
}
if (input_shape.dims[1] <= 3) // NCHW
input->layout = DL_NCHW;
else // NHWC
input->layout = DL_NHWC;
input->dt = precision_to_datatype(precision);
ov_shape_free(&input_shape);
return 0;
@@ -1144,15 +1149,18 @@ static int get_input_ov(void *model, DNNData *input, const char *input_name)
return DNN_GENERIC_ERROR;
}
if (dims[1] <= 3) { // NCHW
input->channels = dims[1];
input->height = input_resizable ? -1 : dims[2];
input->width = input_resizable ? -1 : dims[3];
} else { // NHWC
input->height = input_resizable ? -1 : dims[1];
input->width = input_resizable ? -1 : dims[2];
input->channels = dims[3];
for (int i = 0; i < 4; i++)
input->dims[i] = input_shape.dims[i];
if (input_resizable) {
input->dims[dnn_get_width_idx_by_layout(input->layout)] = -1;
input->dims[dnn_get_height_idx_by_layout(input->layout)] = -1;
}
if (input_shape.dims[1] <= 3) // NCHW
input->layout = DL_NCHW;
else // NHWC
input->layout = DL_NHWC;
input->dt = precision_to_datatype(precision);
return 0;
}

32
libavfilter/dnn/dnn_backend_tf.c
View File

@@ -251,7 +251,12 @@ static TF_Tensor *allocate_input_tensor(const DNNData *input)
{
TF_DataType dt;
size_t size;
int64_t input_dims[] = {1, input->height, input->width, input->channels};
int64_t input_dims[4] = { 0 };
input_dims[0] = 1;
input_dims[1] = input->dims[dnn_get_height_idx_by_layout(input->layout)];
input_dims[2] = input->dims[dnn_get_width_idx_by_layout(input->layout)];
input_dims[3] = input->dims[dnn_get_channel_idx_by_layout(input->layout)];
switch (input->dt) {
case DNN_FLOAT:
dt = TF_FLOAT;
@@ -310,9 +315,9 @@ static int get_input_tf(void *model, DNNData *input, const char *input_name)
// currently only NHWC is supported
av_assert0(dims[0] == 1 || dims[0] == -1);
input->height = dims[1];
input->width = dims[2];
input->channels = dims[3];
for (int i = 0; i < 4; i++)
input->dims[i] = dims[i];
input->layout = DL_NHWC;
return 0;
}
@@ -640,8 +645,8 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
}
infer_request = request->infer_request;
input.height = task->in_frame->height;
input.width = task->in_frame->width;
input.dims[1] = task->in_frame->height;
input.dims[2] = task->in_frame->width;
infer_request->tf_input = av_malloc(sizeof(TF_Output));
if (!infer_request->tf_input) {
@@ -731,9 +736,12 @@ static void infer_completion_callback(void *args) {
}
for (uint32_t i = 0; i < task->nb_output; ++i) {
outputs[i].height = TF_Dim(infer_request->output_tensors[i], 1);
outputs[i].width = TF_Dim(infer_request->output_tensors[i], 2);
outputs[i].channels = TF_Dim(infer_request->output_tensors[i], 3);
outputs[i].dims[dnn_get_height_idx_by_layout(outputs[i].layout)] =
TF_Dim(infer_request->output_tensors[i], 1);
outputs[i].dims[dnn_get_width_idx_by_layout(outputs[i].layout)] =
TF_Dim(infer_request->output_tensors[i], 2);
outputs[i].dims[dnn_get_channel_idx_by_layout(outputs[i].layout)] =
TF_Dim(infer_request->output_tensors[i], 3);
outputs[i].data = TF_TensorData(infer_request->output_tensors[i]);
outputs[i].dt = (DNNDataType)TF_TensorType(infer_request->output_tensors[i]);
}
@@ -747,8 +755,10 @@ static void infer_completion_callback(void *args) {
ff_proc_from_dnn_to_frame(task->out_frame, outputs, ctx);
}
} else {
task->out_frame->width = outputs[0].width;
task->out_frame->height = outputs[0].height;
task->out_frame->width =
outputs[0].dims[dnn_get_width_idx_by_layout(outputs[0].layout)];
task->out_frame->height =
outputs[0].dims[dnn_get_height_idx_by_layout(outputs[0].layout)];
}
break;
case DFT_ANALYTICS_DETECT:

30
libavfilter/dnn/dnn_io_proc.c
View File

@@ -70,7 +70,7 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
dst_data = (void **)frame->data;
linesize[0] = frame->linesize[0];
if (output->layout == DL_NCHW) {
middle_data = av_malloc(plane_size * output->channels);
middle_data = av_malloc(plane_size * output->dims[1]);
if (!middle_data) {
ret = AVERROR(ENOMEM);
goto err;
@@ -209,7 +209,7 @@ int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
src_data = (void **)frame->data;
linesize[0] = frame->linesize[0];
if (input->layout == DL_NCHW) {
middle_data = av_malloc(plane_size * input->channels);
middle_data = av_malloc(plane_size * input->dims[1]);
if (!middle_data) {
ret = AVERROR(ENOMEM);
goto err;
@@ -346,6 +346,7 @@ int ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index
int ret = 0;
enum AVPixelFormat fmt;
int left, top, width, height;
int width_idx, height_idx;
const AVDetectionBBoxHeader *header;
const AVDetectionBBox *bbox;
AVFrameSideData *sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
@@ -364,6 +365,9 @@ int ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index
return AVERROR(ENOSYS);
}
width_idx = dnn_get_width_idx_by_layout(input->layout);
height_idx = dnn_get_height_idx_by_layout(input->layout);
header = (const AVDetectionBBoxHeader *)sd->data;
bbox = av_get_detection_bbox(header, bbox_index);
@@ -374,17 +378,20 @@ int ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index
fmt = get_pixel_format(input);
sws_ctx = sws_getContext(width, height, frame->format,
input->width, input->height, fmt,
input->dims[width_idx],
input->dims[height_idx], fmt,
SWS_FAST_BILINEAR, NULL, NULL, NULL);
if (!sws_ctx) {
av_log(log_ctx, AV_LOG_ERROR, "Failed to create scale context for the conversion "
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(frame->format), width, height,
av_get_pix_fmt_name(fmt), input->width, input->height);
av_get_pix_fmt_name(fmt),
input->dims[width_idx],
input->dims[height_idx]);
return AVERROR(EINVAL);
}
ret = av_image_fill_linesizes(linesizes, fmt, input->width);
ret = av_image_fill_linesizes(linesizes, fmt, input->dims[width_idx]);
if (ret < 0) {
av_log(log_ctx, AV_LOG_ERROR, "unable to get linesizes with av_image_fill_linesizes");
sws_freeContext(sws_ctx);
@@ -414,7 +421,7 @@ int ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_ctx)
{
struct SwsContext *sws_ctx;
int linesizes[4];
int ret = 0;
int ret = 0, width_idx, height_idx;
enum AVPixelFormat fmt = get_pixel_format(input);
/* (scale != 1 and scale != 0) or mean != 0 */
@@ -430,18 +437,23 @@ int ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_ctx)
return AVERROR(ENOSYS);
}
width_idx = dnn_get_width_idx_by_layout(input->layout);
height_idx = dnn_get_height_idx_by_layout(input->layout);
sws_ctx = sws_getContext(frame->width, frame->height, frame->format,
input->width, input->height, fmt,
input->dims[width_idx],
input->dims[height_idx], fmt,
SWS_FAST_BILINEAR, 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(frame->format), frame->width, frame->height,
av_get_pix_fmt_name(fmt), input->width, input->height);
av_get_pix_fmt_name(fmt), input->dims[width_idx],
input->dims[height_idx]);
return AVERROR(EINVAL);
}
ret = av_image_fill_linesizes(linesizes, fmt, input->width);
ret = av_image_fill_linesizes(linesizes, fmt, input->dims[width_idx]);
if (ret < 0) {
av_log(log_ctx, AV_LOG_ERROR, "unable to get linesizes with av_image_fill_linesizes");
sws_freeContext(sws_ctx);