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libavfilter/dnn_native: Add support of dilated convolution in dnn_native. 

Add dilation parameter in dnn native to support dilated convolution.

Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>
Signed-off-by: Steven Liu <lq@onvideo.cn>
This commit is contained in:
Xuewei Meng 2019-05-22 21:02:58 +08:00 committed by Steven Liu
parent 8f6e651833
commit 023ea5e360
2 changed files with 10 additions and 8 deletions
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17
libavfilter/dnn_backend_native.c
View File

@ -63,7 +63,7 @@ static DNNReturnType set_input_output_native(void *model, DNNInputData *input, c
cur_channels = conv_params->output_num;
if (conv_params->padding_method == VALID) {
int pad_size = conv_params->kernel_size - 1;
int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
cur_height -= pad_size;
cur_width -= pad_size;
}
@ -164,6 +164,7 @@ 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);
@ -171,7 +172,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
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 += 20 + (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);
@ -233,7 +234,7 @@ static void convolve(const float *input, float *output, const ConvolutionalParam
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;
int pad_size = (conv_params->padding_method == VALID) ? (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;
for (int y = pad_size; y < height - pad_size; ++y) {
for (int x = pad_size; x < width - pad_size; ++x) {
@ -245,12 +246,12 @@ static void convolve(const float *input, float *output, const ConvolutionalParam
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);
int y_pos = CLAMP_TO_EDGE(y + (kernel_y - radius) * conv_params->dilation, height);
int x_pos = CLAMP_TO_EDGE(x + (kernel_x - radius) * conv_params->dilation, 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;
int y_pos = y + (kernel_y - radius) * conv_params->dilation;
int x_pos = x + (kernel_x - radius) * conv_params->dilation;
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];
}
@ -334,7 +335,7 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
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;
int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
cur_height -= pad_size;
cur_width -= pad_size;
}

1
libavfilter/dnn_backend_native.h
View File

@ -46,6 +46,7 @@ typedef struct ConvolutionalParams{
int32_t input_num, output_num, kernel_size;
DNNActivationFunc activation;
DNNConvPaddingParam padding_method;
int32_t dilation;
float *kernel;
float *biases;
} ConvolutionalParams;