/* * Copyright (c) 2018 Sergey Lavrushkin * * 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_conv2d.h" #define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x))) int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size) { ConvolutionalParams *conv_params; int kernel_size; int dnn_size = 0; conv_params = av_malloc(sizeof(*conv_params)); if (!conv_params) return 0; conv_params->dilation = (int32_t)avio_rl32(model_file_context); conv_params->padding_method = (int32_t)avio_rl32(model_file_context); conv_params->activation = (int32_t)avio_rl32(model_file_context); conv_params->input_num = (int32_t)avio_rl32(model_file_context); conv_params->output_num = (int32_t)avio_rl32(model_file_context); conv_params->kernel_size = (int32_t)avio_rl32(model_file_context); conv_params->has_bias = (int32_t)avio_rl32(model_file_context); dnn_size += 28; kernel_size = conv_params->input_num * conv_params->output_num * conv_params->kernel_size * conv_params->kernel_size; dnn_size += kernel_size * 4; if (conv_params->has_bias) dnn_size += conv_params->output_num * 4; if (dnn_size > file_size || conv_params->input_num <= 0 || conv_params->output_num <= 0 || conv_params->kernel_size <= 0){ av_freep(&conv_params); return 0; } conv_params->kernel = av_malloc(kernel_size * sizeof(float)); if (!conv_params->kernel) { av_freep(&conv_params); return 0; } for (int i = 0; i < kernel_size; ++i) { conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context)); } conv_params->biases = NULL; if (conv_params->has_bias) { conv_params->biases = av_malloc(conv_params->output_num * sizeof(float)); if (!conv_params->biases){ av_freep(&conv_params->kernel); av_freep(&conv_params); return 0; } for (int i = 0; i < conv_params->output_num; ++i){ conv_params->biases[i] = av_int2float(avio_rl32(model_file_context)); } } layer->params = conv_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; return dnn_size; } int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, const void *parameters) { 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 ConvolutionalParams *conv_params = (const ConvolutionalParams *)parameters; 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->data_type = operands[input_operand_index].data_type; 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) { if (conv_params->has_bias) output[n_filter] = conv_params->biases[n_filter]; else output[n_filter] = 0.f; 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; }