[FFmpeg-cvslog] libavfilter/dnn_native: Add multiple padding methods in dnn native
Xuewei Meng
git at videolan.org
Tue May 21 07:54:21 EEST 2019
ffmpeg | branch: master | Xuewei Meng <xwmeng96 at gmail.com> | Sat May 18 15:19:18 2019 +0800| [e45e6005ce6adbb91bf0a0a7194a8575650f0e31] | committer: Steven Liu
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.
Reviewed-by: Guo, Yejun <yejun.guo at intel.com>
Signed-off-by: Xuewei Meng <xwmeng96 at gmail.com>
Signed-off-by: Steven Liu <lq at onvideo.cn>
> http://git.videolan.org/gitweb.cgi/ffmpeg.git/?a=commit;h=e45e6005ce6adbb91bf0a0a7194a8575650f0e31
---
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..fa3f20e4a2 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;
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