[FFmpeg-devel,v3] libavfilter/dnn_native: Add multiple padding methods in dnn native

Submitted by Xuewei Meng on May 18, 2019, 7:19 a.m.

Details

Message ID 20190518071918.525-1-xwmeng96@gmail.com
State New
Headers show

Commit Message

Xuewei Meng May 18, 2019, 7:19 a.m.
Add another two padding methods "VALID" and "SAME" as tensorflow, and keep the existing "SAME_CLAMP_TO_EDGE" method suggested by sr filter.
As "SAME_CLAMP_TO_EDGE"can keep the output with the same size as original input, and gives a slight better result as mentioned by sr filter.

Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
---
 libavfilter/dnn_backend_native.c | 52 ++++++++++++++++++++++++--------
 libavfilter/dnn_backend_native.h |  3 ++
 2 files changed, 43 insertions(+), 12 deletions(-)

Comments

Guo, Yejun May 21, 2019, 2:25 a.m.
> -----Original Message-----

> From: ffmpeg-devel [mailto:ffmpeg-devel-bounces@ffmpeg.org] On Behalf Of

> Xuewei Meng

> Sent: Saturday, May 18, 2019 3:19 PM

> To: ffmpeg-devel@ffmpeg.org

> Cc: Xuewei Meng <xwmeng96@gmail.com>

> Subject: [FFmpeg-devel] [PATCH v3] libavfilter/dnn_native: Add multiple padding

> methods in dnn native

> 

> Add another two padding methods "VALID" and "SAME" as tensorflow, and

> keep the existing "SAME_CLAMP_TO_EDGE" method suggested by sr filter.

> As "SAME_CLAMP_TO_EDGE"can keep the output with the same size as

> original input, and gives a slight better result as mentioned by sr filter.

> 

> Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>

> ---

>  libavfilter/dnn_backend_native.c | 52 ++++++++++++++++++++++++--------

>  libavfilter/dnn_backend_native.h |  3 ++

>  2 files changed, 43 insertions(+), 12 deletions(-)


looks good to me, except some trailing whitespaces.

> 

> diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c

> index 06fbdf368b..171a756385 100644

> --- a/libavfilter/dnn_backend_native.c

> +++ b/libavfilter/dnn_backend_native.c

> @@ -61,6 +61,12 @@ static DNNReturnType set_input_output_native(void

> *model, DNNInputData *input, c

>                  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;

> @@ -77,6 +83,10 @@ static DNNReturnType set_input_output_native(void

> *model, DNNInputData *input, c

>          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;

> @@ -154,13 +164,14 @@ 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);

> @@ -218,23 +229,35 @@ 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];

>                          }

>                      }

>                  }

> @@ -305,6 +328,11 @@ DNNReturnType ff_dnn_execute_model_native(const

> DNNModel *model, DNNData *output

>              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 e13a68a168..d70cd16387 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}

> DNNConvPaddingParam;

> +

>  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;

> +    DNNConvPaddingParam padding_method;

>      float *kernel;

>      float *biases;

>  } ConvolutionalParams;

> --

> 2.17.1

> 

> _______________________________________________

> ffmpeg-devel mailing list

> ffmpeg-devel@ffmpeg.org

> https://ffmpeg.org/mailman/listinfo/ffmpeg-devel

> 

> To unsubscribe, visit link above, or email

> ffmpeg-devel-request@ffmpeg.org with subject "unsubscribe".
Steven Liu May 21, 2019, 4:56 a.m.
Guo, Yejun <yejun.guo@intel.com> 于2019年5月21日周二 上午10:25写道:
>
>
>
> > -----Original Message-----
> > From: ffmpeg-devel [mailto:ffmpeg-devel-bounces@ffmpeg.org] On Behalf Of
> > Xuewei Meng
> > Sent: Saturday, May 18, 2019 3:19 PM
> > To: ffmpeg-devel@ffmpeg.org
> > Cc: Xuewei Meng <xwmeng96@gmail.com>
> > Subject: [FFmpeg-devel] [PATCH v3] libavfilter/dnn_native: Add multiple padding
> > methods in dnn native
> >
> > Add another two padding methods "VALID" and "SAME" as tensorflow, and
> > keep the existing "SAME_CLAMP_TO_EDGE" method suggested by sr filter.
> > As "SAME_CLAMP_TO_EDGE"can keep the output with the same size as
> > original input, and gives a slight better result as mentioned by sr filter.
> >
> > Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
> > ---
> >  libavfilter/dnn_backend_native.c | 52 ++++++++++++++++++++++++--------
> >  libavfilter/dnn_backend_native.h |  3 ++
> >  2 files changed, 43 insertions(+), 12 deletions(-)
>
> looks good to me, except some trailing whitespaces.
Fixed trailing whitespaces, fixed code style and pushed.


Thanks
>
> >
> > diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c
> > index 06fbdf368b..171a756385 100644
> > --- a/libavfilter/dnn_backend_native.c
> > +++ b/libavfilter/dnn_backend_native.c
> > @@ -61,6 +61,12 @@ static DNNReturnType set_input_output_native(void
> > *model, DNNInputData *input, c
> >                  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;
> > @@ -77,6 +83,10 @@ static DNNReturnType set_input_output_native(void
> > *model, DNNInputData *input, c
> >          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;
> > @@ -154,13 +164,14 @@ 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);
> > @@ -218,23 +229,35 @@ 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];
> >                          }
> >                      }
> >                  }
> > @@ -305,6 +328,11 @@ DNNReturnType ff_dnn_execute_model_native(const
> > DNNModel *model, DNNData *output
> >              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 e13a68a168..d70cd16387 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}
> > DNNConvPaddingParam;
> > +
> >  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;
> > +    DNNConvPaddingParam padding_method;
> >      float *kernel;
> >      float *biases;
> >  } ConvolutionalParams;
> > --
> > 2.17.1
> >
> > _______________________________________________
> > ffmpeg-devel mailing list
> > ffmpeg-devel@ffmpeg.org
> > https://ffmpeg.org/mailman/listinfo/ffmpeg-devel
> >
> > To unsubscribe, visit link above, or email
> > ffmpeg-devel-request@ffmpeg.org with subject "unsubscribe".
> _______________________________________________
> ffmpeg-devel mailing list
> ffmpeg-devel@ffmpeg.org
> https://ffmpeg.org/mailman/listinfo/ffmpeg-devel
>
> To unsubscribe, visit link above, or email
> ffmpeg-devel-request@ffmpeg.org with subject "unsubscribe".

Patch hide | download patch | download mbox

diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c
index 06fbdf368b..171a756385 100644
--- a/libavfilter/dnn_backend_native.c
+++ b/libavfilter/dnn_backend_native.c
@@ -61,6 +61,12 @@  static DNNReturnType set_input_output_native(void *model, DNNInputData *input, c
                 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;
@@ -77,6 +83,10 @@  static DNNReturnType set_input_output_native(void *model, DNNInputData *input, c
         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;
@@ -154,13 +164,14 @@  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);
@@ -218,23 +229,35 @@  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];
                         }
                     }
                 }
@@ -305,6 +328,11 @@  DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
             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 e13a68a168..d70cd16387 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} DNNConvPaddingParam;
+
 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;
+    DNNConvPaddingParam padding_method;
     float *kernel;
     float *biases;
 } ConvolutionalParams;