[FFmpeg-devel] [PATCH] libavfilter: Add multiple padding methods in FFmpeg dnn native mode.
xwmeng at pku.edu.cn
xwmeng at pku.edu.cn
Thu May 9 16:00:16 EEST 2019
> -----原始邮件-----
> 发件人: "Guo, Yejun" <yejun.guo at intel.com>
> 发送时间: 2019-05-09 09:52:46 (星期四)
> 收件人: "FFmpeg development discussions and patches" <ffmpeg-devel at ffmpeg.org>
> 抄送:
> 主题: Re: [FFmpeg-devel] [PATCH] libavfilter: Add multiple padding methods in FFmpeg dnn native mode.
>
>
>
> > -----Original Message-----
> > From: ffmpeg-devel [mailto:ffmpeg-devel-bounces at ffmpeg.org] On Behalf Of
> > xwmeng at pku.edu.cn
> > Sent: Wednesday, May 08, 2019 5:34 PM
> > To: ffmpeg-devel at ffmpeg.org
> > Subject: [FFmpeg-devel] [PATCH] libavfilter: Add multiple padding methods in
> > FFmpeg dnn native mode.
> >
> >
> >
> >
> > This patch is for the support of derain filter project in GSoC. It adds supports for
> > the following operations:
>
> it is a general patch, not special for derain, so I think we don't need mention derain here,
> just explain it in general.
>
> >
> >
> >
> >
> > (1) Conv padding method: "SAME", "VALID" and "SAME_CLAMP_TO_EDGE"
> >
> >
> >
> >
> > These operations are all needed in derain filter. As we discussed before, the
> > "SAME_CLAMP_TO_EDGE" method is the same as dnn native padding method
> > in the current implementation. And the sr model generation code should be
> > So I sent a PR
> > (https://github.com/HighVoltageRocknRoll/sr/pull/4)to the original sr
> > repo(https://github.com/HighVoltageRocknRoll/sr).
> >
> >
> >
> > From c0724bb304a6f4c3ca935cccda5b810e5c4eceb1 Mon Sep 17 00:00:00
> > 2001
> > From: Xuewei Meng <xwmeng at pku.edu.cn>
> > Date: Wed, 8 May 2019 17:32:30 +0800
> > Subject: [PATCH] Add multiple padding method in dnn native
> >
> > Signed-off-by: Xuewei Meng <xwmeng at pku.edu.cn>
> > ---
> > libavfilter/dnn_backend_native.c | 52 ++++++++++++++++++++++++--------
> > libavfilter/dnn_backend_native.h | 3 ++
> > 2 files changed, 43 insertions(+), 12 deletions(-)
> >
> > diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c
> > index 70d857f5f2..b7c0508d91 100644
> > --- a/libavfilter/dnn_backend_native.c
> > +++ b/libavfilter/dnn_backend_native.c
> > @@ -59,6 +59,12 @@ static DNNReturnType set_input_output_native(void
> > *model, DNNData *input, DNNDat
> > return DNN_ERROR;
> > }
> > cur_channels = conv_params->output_num;
> > +
> > + if(conv_params->padding_method == VALID){
> > + int pad_size = conv_params->kernel_size - 1;
> > + cur_height -= pad_size;
> > + cur_width -= pad_size;
> > + }
> > break;
> > case DEPTH_TO_SPACE:
> > depth_to_space_params = (DepthToSpaceParams
> > *)network->layers[layer].params;
> > @@ -75,6 +81,10 @@ static DNNReturnType set_input_output_native(void
> > *model, DNNData *input, DNNDat
> > if (network->layers[layer].output){
> > av_freep(&network->layers[layer].output);
> > }
> > +
> > + if(cur_height <= 0 || cur_width <= 0)
> > + return DNN_ERROR;
> > +
> > network->layers[layer].output = av_malloc(cur_height * cur_width *
> > cur_channels * sizeof(float));
> > if (!network->layers[layer].output){
> > return DNN_ERROR;
> > @@ -157,13 +167,14 @@ DNNModel *ff_dnn_load_model_native(const char
> > *model_filename)
> > ff_dnn_free_model_native(&model);
> > return NULL;
> > }
> > + conv_params->padding_method =
> > (int32_t)avio_rl32(model_file_context);
> > conv_params->activation =
> > (int32_t)avio_rl32(model_file_context);
> > conv_params->input_num =
> > (int32_t)avio_rl32(model_file_context);
> > conv_params->output_num =
> > (int32_t)avio_rl32(model_file_context);
> > conv_params->kernel_size =
> > (int32_t)avio_rl32(model_file_context);
> > kernel_size = conv_params->input_num *
> > conv_params->output_num *
> > conv_params->kernel_size *
> > conv_params->kernel_size;
> > - dnn_size += 16 + (kernel_size + conv_params->output_num <<
> > 2);
> > + dnn_size += 20 + (kernel_size + conv_params->output_num <<
> > 2);
> > if (dnn_size > file_size || conv_params->input_num <= 0 ||
> > conv_params->output_num <= 0 ||
> > conv_params->kernel_size <= 0){
> > avio_closep(&model_file_context);
> > @@ -221,23 +232,35 @@ DNNModel *ff_dnn_load_model_native(const char
> > *model_filename)
> >
> > static void convolve(const float *input, float *output, const
> > ConvolutionalParams *conv_params, int width, int height)
> > {
> > - int y, x, n_filter, ch, kernel_y, kernel_x;
> > int radius = conv_params->kernel_size >> 1;
> > int src_linesize = width * conv_params->input_num;
> > int filter_linesize = conv_params->kernel_size *
> > conv_params->input_num;
> > int filter_size = conv_params->kernel_size * filter_linesize;
> > + int pad_size = (conv_params->padding_method == VALID) ?
> > (conv_params->kernel_size - 1) / 2 : 0;
> >
> > - for (y = 0; y < height; ++y){
> > - for (x = 0; x < width; ++x){
> > - for (n_filter = 0; n_filter < conv_params->output_num;
> > ++n_filter){
> > + for (int y = pad_size; y < height - pad_size; ++y){
> > + for (int x = pad_size; x < width - pad_size; ++x){
> > + for (int n_filter = 0; n_filter < conv_params->output_num;
> > ++n_filter){
> > output[n_filter] = conv_params->biases[n_filter];
> > - for (ch = 0; ch < conv_params->input_num; ++ch){
> > - for (kernel_y = 0; kernel_y <
> > conv_params->kernel_size; ++kernel_y){
> > - for (kernel_x = 0; kernel_x <
> > conv_params->kernel_size; ++kernel_x){
> > - output[n_filter] +=
> > input[CLAMP_TO_EDGE(y + kernel_y - radius, height) * src_linesize +
> > -
> > CLAMP_TO_EDGE(x + kernel_x - radius, width) * conv_params->input_num + ch]
> > *
> > -
> > conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
> > -
> > kernel_x * conv_params->input_num + ch];
> > +
> > + for (int ch = 0; ch < conv_params->input_num; ++ch){
> > + for (int kernel_y = 0; kernel_y <
> > conv_params->kernel_size; ++kernel_y){
> > + for (int kernel_x = 0; kernel_x <
> > conv_params->kernel_size; ++kernel_x){
> > + float input_pel;
> > + if(conv_params->padding_method ==
> > SAME_CLAMP_TO_EDGE){
> > + int y_pos = CLAMP_TO_EDGE(y +
> > kernel_y - radius, height);
> > + int x_pos = CLAMP_TO_EDGE(x +
> > kernel_x - radius, width);
> > + input_pel = input[y_pos * src_linesize
> > + x_pos * conv_params->input_num + ch];
> > + }else{
> > + int y_pos = y + kernel_y - radius;
> > + int x_pos = x + kernel_x - radius;
> > + input_pel = (x_pos < 0 || x_pos >=
> > width || y_pos < 0 || y_pos >= height) ? 0.0 :
> > + input[y_pos *
> > src_linesize + x_pos * conv_params->input_num + ch];
> > + }
> > +
> > +
> > + output[n_filter] += input_pel *
> > conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
> > +
> > kernel_x * conv_params->input_num + ch];
> > }
> > }
> > }
> > @@ -308,6 +331,11 @@ DNNReturnType ff_dnn_execute_model_native(const
> > DNNModel *model)
> > conv_params = (ConvolutionalParams
> > *)network->layers[layer].params;
> > convolve(network->layers[layer - 1].output,
> > network->layers[layer].output, conv_params, cur_width, cur_height);
> > cur_channels = conv_params->output_num;
> > + if(conv_params->padding_method == VALID){
> > + int pad_size = conv_params->kernel_size - 1;
> > + cur_height -= pad_size;
> > + cur_width -= pad_size;
> > + }
> > break;
> > case DEPTH_TO_SPACE:
> > depth_to_space_params = (DepthToSpaceParams
> > *)network->layers[layer].params;
> > diff --git a/libavfilter/dnn_backend_native.h b/libavfilter/dnn_backend_native.h
> > index 51d4cac955..a609e09754 100644
> > --- a/libavfilter/dnn_backend_native.h
> > +++ b/libavfilter/dnn_backend_native.h
> > @@ -34,6 +34,8 @@ typedef enum {INPUT, CONV, DEPTH_TO_SPACE}
> > DNNLayerType;
> >
> > typedef enum {RELU, TANH, SIGMOID} DNNActivationFunc;
> >
> > +typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNPaddingFunc;
>
> DNNPaddingFunc might have conflict with the pad layer, how about rename to DNNConvPaddingParam
Yeah, DNNConvPaddingParam is better
>
> > +
> > typedef struct Layer{
> > DNNLayerType type;
> > float *output;
> > @@ -43,6 +45,7 @@ typedef struct Layer{
> > typedef struct ConvolutionalParams{
> > int32_t input_num, output_num, kernel_size;
> > DNNActivationFunc activation;
> > + DNNPaddingFunc padding_method;
> > float *kernel;
> > float *biases;
> > } ConvolutionalParams;
> > --
> > 2.17.1
> >
> >
> >
> >
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> >
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