2019-09-05 06:00:28 +00:00
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/*
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* Copyright (c) 2018 Sergey Lavrushkin
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*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with FFmpeg; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*/
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#include "libavutil/avassert.h"
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#include "dnn_backend_native_layer_conv2d.h"
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#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
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2020-06-10 05:36:11 +00:00
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int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
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2019-10-09 14:08:18 +00:00
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{
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ConvolutionalParams *conv_params;
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int kernel_size;
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int dnn_size = 0;
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conv_params = av_malloc(sizeof(*conv_params));
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if (!conv_params)
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return 0;
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conv_params->dilation = (int32_t)avio_rl32(model_file_context);
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conv_params->padding_method = (int32_t)avio_rl32(model_file_context);
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conv_params->activation = (int32_t)avio_rl32(model_file_context);
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conv_params->input_num = (int32_t)avio_rl32(model_file_context);
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conv_params->output_num = (int32_t)avio_rl32(model_file_context);
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conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
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2019-10-21 12:38:03 +00:00
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conv_params->has_bias = (int32_t)avio_rl32(model_file_context);
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dnn_size += 28;
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2019-10-09 14:08:18 +00:00
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kernel_size = conv_params->input_num * conv_params->output_num *
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2019-10-21 12:38:03 +00:00
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conv_params->kernel_size * conv_params->kernel_size;
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dnn_size += kernel_size * 4;
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if (conv_params->has_bias)
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dnn_size += conv_params->output_num * 4;
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2019-10-09 14:08:18 +00:00
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if (dnn_size > file_size || conv_params->input_num <= 0 ||
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conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
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av_freep(&conv_params);
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return 0;
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}
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2019-10-21 12:38:03 +00:00
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2019-10-09 14:08:18 +00:00
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conv_params->kernel = av_malloc(kernel_size * sizeof(float));
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2019-10-21 12:38:03 +00:00
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if (!conv_params->kernel) {
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2019-10-09 14:08:18 +00:00
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av_freep(&conv_params);
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return 0;
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}
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2019-10-21 12:38:03 +00:00
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for (int i = 0; i < kernel_size; ++i) {
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2019-10-09 14:08:18 +00:00
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conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
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}
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2019-10-21 12:38:03 +00:00
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conv_params->biases = NULL;
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if (conv_params->has_bias) {
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conv_params->biases = av_malloc(conv_params->output_num * sizeof(float));
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if (!conv_params->biases){
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av_freep(&conv_params->kernel);
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av_freep(&conv_params);
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return 0;
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}
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for (int i = 0; i < conv_params->output_num; ++i){
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conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
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}
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2019-10-09 14:08:18 +00:00
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}
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layer->params = conv_params;
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layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
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layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
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dnn_size += 8;
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2020-06-10 05:36:11 +00:00
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if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
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return 0;
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}
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2019-10-09 14:08:18 +00:00
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return dnn_size;
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}
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2019-10-09 14:08:11 +00:00
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int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
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int32_t output_operand_index, const void *parameters)
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2019-09-05 06:00:28 +00:00
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{
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float *output;
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int32_t input_operand_index = input_operand_indexes[0];
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int number = operands[input_operand_index].dims[0];
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int height = operands[input_operand_index].dims[1];
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int width = operands[input_operand_index].dims[2];
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int channel = operands[input_operand_index].dims[3];
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const float *input = operands[input_operand_index].data;
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2019-10-09 14:08:11 +00:00
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const ConvolutionalParams *conv_params = (const ConvolutionalParams *)parameters;
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2019-09-05 06:00:28 +00:00
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int radius = conv_params->kernel_size >> 1;
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int src_linesize = width * conv_params->input_num;
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int filter_linesize = conv_params->kernel_size * conv_params->input_num;
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int filter_size = conv_params->kernel_size * filter_linesize;
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int pad_size = (conv_params->padding_method == VALID) ? (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;
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DnnOperand *output_operand = &operands[output_operand_index];
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output_operand->dims[0] = number;
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output_operand->dims[1] = height - pad_size * 2;
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output_operand->dims[2] = width - pad_size * 2;
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output_operand->dims[3] = conv_params->output_num;
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2019-10-21 12:38:10 +00:00
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output_operand->data_type = operands[input_operand_index].data_type;
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2019-09-05 06:00:28 +00:00
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output_operand->length = calculate_operand_data_length(output_operand);
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2020-07-06 07:32:17 +00:00
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if (output_operand->length <= 0)
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2020-08-25 03:47:49 +00:00
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return DNN_ERROR;
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2019-09-05 06:00:28 +00:00
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output_operand->data = av_realloc(output_operand->data, output_operand->length);
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if (!output_operand->data)
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2020-08-25 03:47:49 +00:00
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return DNN_ERROR;
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2019-09-05 06:00:28 +00:00
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output = output_operand->data;
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av_assert0(channel == conv_params->input_num);
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for (int y = pad_size; y < height - pad_size; ++y) {
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for (int x = pad_size; x < width - pad_size; ++x) {
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for (int n_filter = 0; n_filter < conv_params->output_num; ++n_filter) {
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2019-10-21 12:38:03 +00:00
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if (conv_params->has_bias)
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output[n_filter] = conv_params->biases[n_filter];
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else
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output[n_filter] = 0.f;
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2019-09-05 06:00:28 +00:00
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for (int ch = 0; ch < conv_params->input_num; ++ch) {
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for (int kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y) {
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for (int kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x) {
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float input_pel;
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if (conv_params->padding_method == SAME_CLAMP_TO_EDGE) {
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int y_pos = CLAMP_TO_EDGE(y + (kernel_y - radius) * conv_params->dilation, height);
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int x_pos = CLAMP_TO_EDGE(x + (kernel_x - radius) * conv_params->dilation, width);
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input_pel = input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
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} else {
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int y_pos = y + (kernel_y - radius) * conv_params->dilation;
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int x_pos = x + (kernel_x - radius) * conv_params->dilation;
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input_pel = (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) ? 0.0 :
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input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
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}
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output[n_filter] += input_pel * conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
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kernel_x * conv_params->input_num + ch];
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}
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}
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}
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switch (conv_params->activation){
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case RELU:
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output[n_filter] = FFMAX(output[n_filter], 0.0);
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break;
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case TANH:
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output[n_filter] = 2.0f / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
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break;
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case SIGMOID:
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output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
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break;
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case NONE:
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break;
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case LEAKY_RELU:
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output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
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}
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}
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output += conv_params->output_num;
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}
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}
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return 0;
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}
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