> 在 2019年5月8日，下午5:33，xwm...@pku.edu.cn 写道：
>
>
>
>
> This patch is for the support of derain filter project in GSoC. It adds
> supports for the following operations:
>
>
>
>
> (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 changed if
> mutiple padding method supports added. So I sent a PR
> (https://github.com/HighVoltageRocknRoll/sr/pull/4)to the original sr
> repo(https://github.com/HighVoltageRocknRoll/sr).
Cannot sure Sergey Lavrushkin is maintaining that repo, maybe Pedro Arthur can
tick he.
>
>
>
> From c0724bb304a6f4c3ca935cccda5b810e5c4eceb1 Mon Sep 17 00:00:00 2001
> From: Xuewei Meng <xwm...@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 <xwm...@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{
attention all the code style above section. : } else { and for (…) {
> + 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;
> +
> 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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