Add dilation parameter in dnn native to support dilated convolution.
Signed-off-by: Xuewei Meng <xwmen...@gmail.com>
---
libavfilter/dnn_backend_native.c | 17 +++++++++--------
libavfilter/dnn_backend_native.h | 1 +
2 files changed, 10 insertions(+), 8 deletions(-)
diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c
index 3c8465a283..82e900bd8c 100644
--- a/libavfilter/dnn_backend_native.c
+++ b/libavfilter/dnn_backend_native.c
@@ -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;
}
diff --git a/libavfilter/dnn_backend_native.h b/libavfilter/dnn_backend_native.h
index 7e4e943137..5917955733 100644
--- a/libavfilter/dnn_backend_native.h
+++ b/libavfilter/dnn_backend_native.h
@@ -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;
--
2.17.1
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