libavfilter/dnn: separate depth_to_space layer from dnn_backend_native.c to a new file

the logic is that one layer in one separated source file to make
the source files simple for maintaining.

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>

1 files changed, 1 insertions, 43 deletions

diff --git a/libavfilter/dnn/dnn_backend_native.c b/libavfilter/dnn/dnn_backend_native.c
index 5dabd151c0..be548c6d46 100644
--- a/libavfilter/dnn/dnn_backend_native.c
+++ b/libavfilter/dnn/dnn_backend_native.c
@@ -27,6 +27,7 @@
 #include "libavutil/avassert.h"
 #include "dnn_backend_native_layer_pad.h"
 #include "dnn_backend_native_layer_conv2d.h"
+#include "dnn_backend_native_layer_depth2space.h"
 
 static DNNReturnType set_input_output_native(void *model, DNNInputData *input, const char *input_name, const char **output_names, uint32_t nb_output)
 {
@@ -282,49 +283,6 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
     return model;
 }
 
-static int depth_to_space(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, int block_size)
-{
-    float *output;
-    int32_t input_operand_index = input_operand_indexes[0];
-    int number = operands[input_operand_index].dims[0];
-    int height = operands[input_operand_index].dims[1];
-    int width = operands[input_operand_index].dims[2];
-    int channels = operands[input_operand_index].dims[3];
-    const float *input = operands[input_operand_index].data;
-
-    int y, x, by, bx, ch;
-    int new_channels = channels / (block_size * block_size);
-    int output_linesize = width * channels;
-    int by_linesize = output_linesize / block_size;
-    int x_linesize = new_channels * block_size;
-
-    DnnOperand *output_operand = &operands[output_operand_index];
-    output_operand->dims[0] = number;
-    output_operand->dims[1] = height * block_size;
-    output_operand->dims[2] = width * block_size;
-    output_operand->dims[3] = new_channels;
-    output_operand->length = calculate_operand_data_length(output_operand);
-    output_operand->data = av_realloc(output_operand->data, output_operand->length);
-    if (!output_operand->data)
-        return -1;
-    output = output_operand->data;
-
-    for (y = 0; y < height; ++y){
-        for (x = 0; x < width; ++x){
-            for (by = 0; by < block_size; ++by){
-                for (bx = 0; bx < block_size; ++bx){
-                    for (ch = 0; ch < new_channels; ++ch){
-                        output[by * by_linesize + x * x_linesize + bx * new_channels + ch] = input[ch];
-                    }
-                    input += new_channels;
-                }
-            }
-        }
-        output += output_linesize;
-    }
-    return 0;
-}
-
 DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
 {
     ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;