[FFmpeg-devel] [PATCH v2] Add multiple padding method in dnn native
Pedro Arthur
bygrandao at gmail.com
Wed May 15 16:34:18 EEST 2019
Em qua, 15 de mai de 2019 às 04:44, Steven Liu
<lingjiujianke at gmail.com> escreveu:
>
> Xuewei Meng <xwmeng96 at gmail.com> 于2019年5月11日周六 上午11:11写道：
> >
> > ---
> > 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 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 at ffmpeg.org
> > https://ffmpeg.org/mailman/listinfo/ffmpeg-devel
> >
> > To unsubscribe, visit link above, or email
> > ffmpeg-devel-request at ffmpeg.org with subject "unsubscribe".
>
>
> The https://github.com/HighVoltageRocknRoll/sr has loss of
> communication，and the project
> https://github.com/HighVoltageRocknRoll/sr has no maintainer now, so i
> think the pull request cannot be merge.
> 1. So i recommend Xuewei fork the project to his github, and merge the
> pr to his fork project, and modify the sr document of
> libavfilter/vf_sr.c. makes GSoC derain mentor project continue.
>
I see no problem with 1, anyway he already had to make a fork to
create the pr, he can continue developing it there.
> 2. If 1st way cannot be acceptable, Xuewei should duplicate DNN code
> for the derain.
>
> Comments welcome.
>
> Thanks
>
> Steven
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