libavfilter/dnn: separate conv2d 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, 79 deletions

diff --git a/libavfilter/dnn/dnn_backend_native.c b/libavfilter/dnn/dnn_backend_native.c
index f56cd81187..5dabd151c0 100644
--- a/libavfilter/dnn/dnn_backend_native.c
+++ b/libavfilter/dnn/dnn_backend_native.c
@@ -26,6 +26,7 @@
 #include "dnn_backend_native.h"
 #include "libavutil/avassert.h"
 #include "dnn_backend_native_layer_pad.h"
+#include "dnn_backend_native_layer_conv2d.h"
 
 static DNNReturnType set_input_output_native(void *model, DNNInputData *input, const char *input_name, const char **output_names, uint32_t nb_output)
 {
@@ -281,85 +282,6 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
     return model;
 }
 
-#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
-
-static int convolve(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, const ConvolutionalParams *conv_params)
-{
-    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 channel = operands[input_operand_index].dims[3];
-    const float *input = operands[input_operand_index].data;
-
-    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 * conv_params->dilation : 0;
-
-    DnnOperand *output_operand = &operands[output_operand_index];
-    output_operand->dims[0] = number;
-    output_operand->dims[1] = height - pad_size * 2;
-    output_operand->dims[2] = width - pad_size * 2;
-    output_operand->dims[3] = conv_params->output_num;
-    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;
-
-    av_assert0(channel == conv_params->input_num);
-
-    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 (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) * 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) * 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];
-                            }
-
-
-                            output[n_filter] += input_pel * conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
-                                                                                kernel_x * conv_params->input_num + ch];
-                        }
-                    }
-                }
-                switch (conv_params->activation){
-                case RELU:
-                    output[n_filter] = FFMAX(output[n_filter], 0.0);
-                    break;
-                case TANH:
-                    output[n_filter] = 2.0f  / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
-                    break;
-                case SIGMOID:
-                    output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
-                    break;
-                case NONE:
-                    break;
-                case LEAKY_RELU:
-                    output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
-                }
-            }
-            output += conv_params->output_num;
-        }
-    }
-    return 0;
-}
-
 static int depth_to_space(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, int block_size)
 {
     float *output;