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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
*/
/**
* @file
* DNN native backend implementation.
*/
#include "libavutil/avassert.h"
#include "dnn_backend_native_layer_avgpool.h"
int ff_dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
AvgPoolParams *avgpool_params;
int dnn_size = 0;
avgpool_params = av_malloc(sizeof(*avgpool_params));
if(!avgpool_params)
return 0;
avgpool_params->strides = (int32_t)avio_rl32(model_file_context);
avgpool_params->padding_method = (int32_t)avio_rl32(model_file_context);
avgpool_params->kernel_size = (int32_t)avio_rl32(model_file_context);
dnn_size += 12;
if (dnn_size > file_size || avgpool_params->kernel_size <= 0 || avgpool_params->strides <=0){
av_freep(&avgpool_params);
return 0;
}
layer->params = avgpool_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_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
float *output;
int height_end, width_end, height_radius, width_radius, output_height, output_width, kernel_area;
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 AvgPoolParams *avgpool_params = parameters;
int kernel_strides = avgpool_params->strides;
int src_linesize = width * channel;
DnnOperand *output_operand = &operands[output_operand_index];
/**
* When padding_method = SAME, the tensorflow will only padding the hald number of 0 pixels
* except the remainders.
* Eg: assuming the input height = 1080, the strides = 11, so the remainders = 1080 % 11 = 2
* and if ksize = 5: it will fill (5 - 2) >> 1 = 1 line before the first line of input image,
* and 5 - 2 - 1 = 2 lines after the last line of input image.
* and if ksize = 7: it will fill (7 - 2) >> 1 = 2 lines before the first line of input image,
* and 7 - 2 - 2 = 3 lines after the last line of input image.
*/
if (avgpool_params->padding_method == SAME) {
height_end = height;
width_end = width;
height_radius = avgpool_params->kernel_size - ((height - 1) % kernel_strides + 1);
width_radius = avgpool_params->kernel_size - ((width - 1) % kernel_strides + 1);
height_radius = height_radius < 0 ? 0 : height_radius >> 1;
width_radius = width_radius < 0 ? 0 : width_radius >> 1;
output_height = ceil(height / (kernel_strides * 1.0));
output_width = ceil(width / (kernel_strides * 1.0));
} else {
av_assert0(avgpool_params->padding_method == VALID);
height_end = height - avgpool_params->kernel_size + 1;
width_end = width - avgpool_params->kernel_size + 1;
height_radius = 0;
width_radius = 0;
output_height = ceil((height - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0));
output_width = ceil((width - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0));
}
output_operand->dims[0] = number;
output_operand->dims[1] = output_height;
output_operand->dims[2] = output_width;
// not support pooling in channel dimension now
output_operand->dims[3] = channel;
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 DNN_ERROR;
}
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 DNN_ERROR;
}
output = output_operand->data;
for (int y = 0; y < height_end; y += kernel_strides) {
for (int x = 0; x < width_end; x += kernel_strides) {
for (int n_channel = 0; n_channel < channel; ++n_channel) {
output[n_channel] = 0.0;
kernel_area = 0;
for (int kernel_y = 0; kernel_y < avgpool_params->kernel_size; ++kernel_y) {
for (int kernel_x = 0; kernel_x < avgpool_params->kernel_size; ++kernel_x) {
float input_pel;
int y_pos = y + (kernel_y - height_radius);
int x_pos = x + (kernel_x - width_radius);
if (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) {
input_pel = 0.0;
} else {
kernel_area++;
input_pel = input[y_pos * src_linesize + x_pos * channel + n_channel];
}
output[n_channel] += input_pel;
}
}
output[n_channel] /= kernel_area;
}
output += channel;
}
}
return 0;
}
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