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/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include "libavutil/avassert.h"
#include "dnn_backend_native_layer_dense.h"
int ff_dnn_load_layer_dense(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
DenseParams *dense_params;
int kernel_size;
int dnn_size = 0;
dense_params = av_malloc(sizeof(*dense_params));
if (!dense_params)
return 0;
dense_params->activation = (int32_t)avio_rl32(model_file_context);
dense_params->input_num = (int32_t)avio_rl32(model_file_context);
dense_params->output_num = (int32_t)avio_rl32(model_file_context);
dense_params->has_bias = (int32_t)avio_rl32(model_file_context);
dnn_size += 16;
kernel_size = dense_params->input_num * dense_params->output_num;
dnn_size += kernel_size * 4;
if (dense_params->has_bias)
dnn_size += dense_params->output_num * 4;
if (dnn_size > file_size || dense_params->input_num <= 0 ||
dense_params->output_num <= 0){
av_freep(&dense_params);
return 0;
}
dense_params->kernel = av_malloc(kernel_size * sizeof(float));
if (!dense_params->kernel) {
av_freep(&dense_params);
return 0;
}
for (int i = 0; i < kernel_size; ++i) {
dense_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
}
dense_params->biases = NULL;
if (dense_params->has_bias) {
dense_params->biases = av_malloc(dense_params->output_num * sizeof(float));
if (!dense_params->biases){
av_freep(&dense_params->kernel);
av_freep(&dense_params);
return 0;
}
for (int i = 0; i < dense_params->output_num; ++i){
dense_params->biases[i] = av_int2float(avio_rl32(model_file_context));
}
}
layer->params = dense_params;
layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
return 0;
}
return dnn_size;
}
int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
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;
const DenseParams *dense_params = parameters;
int src_linesize = width * channel;
DnnOperand *output_operand = &operands[output_operand_index];
output_operand->dims[0] = number;
output_operand->dims[1] = height;
output_operand->dims[2] = width;
output_operand->dims[3] = dense_params->output_num;
output_operand->data_type = operands[input_operand_index].data_type;
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return AVERROR(EINVAL);
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return AVERROR(ENOMEM);
}
output = output_operand->data;
av_assert0(channel == dense_params->input_num);
for (int y = 0; y < height; ++y) {
for (int x = 0; x < width; ++x) {
for (int n_filter = 0; n_filter < dense_params->output_num; ++n_filter) {
if (dense_params->has_bias)
output[n_filter] = dense_params->biases[n_filter];
else
output[n_filter] = 0.f;
for (int ch = 0; ch < dense_params->input_num; ++ch) {
float input_pel;
input_pel = input[y * src_linesize + x * dense_params->input_num + ch];
output[n_filter] += input_pel * dense_params->kernel[n_filter*dense_params->input_num + ch];
}
switch (dense_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 += dense_params->output_num;
}
}
return DNN_SUCCESS;
}
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