mirror of https://github.com/FFmpeg/FFmpeg.git
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>pull/323/head
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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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int convolve(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, const ConvolutionalParams *conv_params) |
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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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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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output_operand->length = calculate_operand_data_length(output_operand); |
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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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return -1; |
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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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output[n_filter] = conv_params->biases[n_filter]; |
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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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} |
@ -0,0 +1,39 @@ |
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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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#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_CONV2D_H |
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#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_CONV2D_H |
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#include "dnn_backend_native.h" |
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typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc; |
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typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam; |
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typedef struct ConvolutionalParams{ |
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int32_t input_num, output_num, kernel_size; |
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DNNActivationFunc activation; |
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DNNConvPaddingParam padding_method; |
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int32_t dilation; |
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float *kernel; |
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float *biases; |
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} ConvolutionalParams; |
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int convolve(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, const ConvolutionalParams *conv_params); |
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#endif |
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