[FFmpeg-devel] [PATCH] libavfilter: Add more operation supports in FFmpeg dnn native mode.

xwmeng at pku.edu.cn xwmeng at pku.edu.cn
Mon Apr 29 06:06:37 EEST 2019




> -----原始邮件-----
> 发件人: "Pedro Arthur" <bygrandao at gmail.com>
> 发送时间: 2019-04-29 10:42:42 (星期一)
> 收件人: "FFmpeg development discussions and patches" <ffmpeg-devel at ffmpeg.org>
> 抄送: 
> 主题: Re: [FFmpeg-devel] [PATCH] libavfilter: Add more operation supports in FFmpeg dnn native mode.
> 
> Em dom, 28 de abr de 2019 às 23:07, Guo, Yejun <yejun.guo at intel.com> escreveu:
> >
> >
> >
> > > -----Original Message-----
> > > From: ffmpeg-devel [mailto:ffmpeg-devel-bounces at ffmpeg.org] On Behalf Of
> > > xwmeng at pku.edu.cn
> > > Sent: Sunday, April 28, 2019 5:27 PM
> > > To: ffmpeg development discussions and patches <ffmpeg-devel at ffmpeg.org>
> > > Subject: [FFmpeg-devel] [PATCH] libavfilter: Add more operation supports in
> > > FFmpeg dnn native mode.
> > >
> > > This patch is for the support of derain filter project in GSoC. It adds supports for
> > > the following operations:
> > >
> > >
> > >
> > >
> > >  (1) Conv padding method: "SAME" and "VALID"
> > >
> > >  (2) Dilation
> > >
> > >  (3) Activation: "NONE" and "LEAKY_RELU"
> >
> > how about separate this single patch into 3 patches.
> >
> > >
> > >
> > >
> > >
> > > These operations are all needed in derain filter. And if modify the dnn native
> > > mode in FFmpeg, the generation process of Super Resolution model should be
> > > changed accordingly, e.g. add padding method parameter (= 0) and dilation
> > > parameter (= 1).
> >
> > you can create a PR at https://github.com/HighVoltageRocknRoll/sr
> >
> > >
> > >
> > >
> > >
> > > In addition, I have a question about the Super Resulotion implementation. The
> > > model training process of SR uses "VALID" method. According to my
> > > understanding of "VALID" mode in tensorflow, the size of output image should
> > > be smaller than the current design in SR. Because pixels near the boundary are
> > > not processed in "VALID" mode, however, these unprocessed pixels are filled
> > > with adjacent pixels in current dnn native mode. I wonder why to do like this
> > > here.
> >
> > I have the same concern that why the native model is not exactly the same as tf model,
> > the pad layer is missed, and the native model also change the behavior of pad parameter of conv layer.
> >
> > it is only suitable for vf_sr, and not general for other models.
> >
> I think for training these filters the preferred method is VALID as it
> uses only the data available (without filling the borders) and gives
> the best possible result.
> However for inference usually one expects to output an image with the
> same size of the original (imagine the case of chained filters where
> each one reduces the image by a few pixels, in the end one may have a
> useless output).
> Therefore it makes perfect sense to use different padding methods for
> training/inference.
> 
> The clamp_to_edge padding was introduced before the TF backend thus it
> stayed in the native backend even after the introduction of the TF
> backend.
> Indeed the clamp_to_edge is simpler than the other padding methods and
> also gives a slight better result, If I remember correct the student
> which implemented the TF backend did not find an equivalent padding
> method in TF, thats why it uses different paddings.
> 
Yes, I think clamp_to_edge is a good method to keep the output with the same size as input. However, I don't think "VALID" is the best method giving best possible result. So, for "VALID" mode, maybe we can use the clamp_to_edge method in the current dnn native mode? And then, we should also add "SAME" option to support other filters.

> > >
> > >
> > >
> > >
> > > From 4d92ef21a5acf064122c51f442d0e2f5437b3343 Mon Sep 17 00:00:00
> > > 2001
> > > From: Xuewei Meng <xwmeng at pku.edu.cn>
> > > Date: Sun, 28 Apr 2019 17:21:35 +0800
> > > Subject: [PATCH] Add operation supports in dnn_native
> > >
> > > Signed-off-by: Xuewei Meng <xwmeng at pku.edu.cn>
> > > ---
> > >  libavfilter/dnn_backend_native.c | 36 +++++++++++++++++++++-----------
> > >  libavfilter/dnn_backend_native.h |  6 +++++-
> > >  2 files changed, 29 insertions(+), 13 deletions(-)
> > >
> > > diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c
> > > index 70d857f5f2..0e3ef5d64d 100644
> > > --- a/libavfilter/dnn_backend_native.c
> > > +++ b/libavfilter/dnn_backend_native.c
> > > @@ -157,13 +157,15 @@ DNNModel *ff_dnn_load_model_native(const char
> > > *model_filename)
> > >                  ff_dnn_free_model_native(&model);
> > >                  return NULL;
> > >              }
> > > +            conv_params->dilation =
> > > (int32_t)avio_rl32(model_file_context);
> > > +            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 += 24 + (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 +223,28 @@ 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 * conv_params->dilation : 0;
> >
> > for parameter 'valid', the size of feature map is changed, it should be reflected at function set_input_output_native,
> > for example, the size of network->layers[layer].output should be changed, and we might add the size info into struct Layer.
> >
> > >
> > > -    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]
> >
> > to compatible with vf_sr.c, as a step by step method, we can keep clamp_to_edge at the first step.
> >
> > it means that we can support 3 parameters for conv pad, same, valid, and this extra same_clamp_to_edge,
> > we can remove same_clamp_to_edge after all the things are settled.
> >
> > > *
> > > -
> > > 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){
> > > +                            int y_pos = y + (kernel_y - radius) *
> > > conv_params->dilation;
> > > +                            int x_pos = x + (kernel_x - radius) *
> > > conv_params->dilation;
> > > +
> > > +                            float 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];
> > >                          }
> > >                      }
> > >                  }
> > > @@ -250,6 +257,11 @@ static void convolve(const float *input, float *output,
> > > const ConvolutionalParam
> > >                      break;
> > >                  case SIGMOID:
> > >                      output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
> > > +                    break;
> > > +                case NONE:
> > > +                    break;
> > > +                case LEAKY_RELU:
> > > +                    output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 *
> > > FFMIN(output[n_filter], 0.0);
> > >                  }
> > >              }
> > >              output += conv_params->output_num;
> > > diff --git a/libavfilter/dnn_backend_native.h b/libavfilter/dnn_backend_native.h
> > > index 51d4cac955..f7d4eb823b 100644
> > > --- a/libavfilter/dnn_backend_native.h
> > > +++ b/libavfilter/dnn_backend_native.h
> > > @@ -32,7 +32,9 @@
> > >
> > >  typedef enum {INPUT, CONV, DEPTH_TO_SPACE} DNNLayerType;
> > >
> > > -typedef enum {RELU, TANH, SIGMOID} DNNActivationFunc;
> > > +typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU}
> > > DNNActivationFunc;
> > > +
> > > +typedef enum {VALID, SAME} DNNPaddingFunc;
> > >
> > >  typedef struct Layer{
> > >      DNNLayerType type;
> > > @@ -43,6 +45,8 @@ typedef struct Layer{
> > >  typedef struct ConvolutionalParams{
> > >      int32_t input_num, output_num, kernel_size;
> > >      DNNActivationFunc activation;
> > > +    DNNPaddingFunc padding_method;
> > > +    int32_t dilation;
> > >      float *kernel;
> > >      float *biases;
> > >  } ConvolutionalParams;
> > > --
> > > 2.17.1
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > > _______________________________________________
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