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/* |
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* Copyright (c) 2020 |
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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_dense.h" |
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int ff_dnn_load_layer_dense(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num) |
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{ |
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DenseParams *dense_params; |
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int kernel_size; |
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int dnn_size = 0; |
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dense_params = av_malloc(sizeof(*dense_params)); |
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if (!dense_params) |
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return 0; |
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dense_params->activation = (int32_t)avio_rl32(model_file_context); |
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dense_params->input_num = (int32_t)avio_rl32(model_file_context); |
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dense_params->output_num = (int32_t)avio_rl32(model_file_context); |
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dense_params->has_bias = (int32_t)avio_rl32(model_file_context); |
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dnn_size += 16; |
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kernel_size = dense_params->input_num * dense_params->output_num; |
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dnn_size += kernel_size * 4; |
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if (dense_params->has_bias) |
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dnn_size += dense_params->output_num * 4; |
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if (dnn_size > file_size || dense_params->input_num <= 0 || |
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dense_params->output_num <= 0){ |
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av_freep(&dense_params); |
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return 0; |
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} |
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dense_params->kernel = av_malloc(kernel_size * sizeof(float)); |
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if (!dense_params->kernel) { |
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av_freep(&dense_params); |
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return 0; |
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} |
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for (int i = 0; i < kernel_size; ++i) { |
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dense_params->kernel[i] = av_int2float(avio_rl32(model_file_context)); |
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} |
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dense_params->biases = NULL; |
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if (dense_params->has_bias) { |
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dense_params->biases = av_malloc(dense_params->output_num * sizeof(float)); |
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if (!dense_params->biases){ |
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av_freep(&dense_params->kernel); |
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av_freep(&dense_params); |
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return 0; |
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} |
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for (int i = 0; i < dense_params->output_num; ++i){ |
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dense_params->biases[i] = av_int2float(avio_rl32(model_file_context)); |
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} |
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} |
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layer->params = dense_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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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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return dnn_size; |
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} |
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int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operand_indexes, |
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int32_t output_operand_index, const void *parameters, NativeContext *ctx) |
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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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const DenseParams *dense_params = (const DenseParams *)parameters; |
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int src_linesize = width * channel; |
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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; |
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output_operand->dims[2] = width; |
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output_operand->dims[3] = dense_params->output_num; |
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output_operand->data_type = operands[input_operand_index].data_type; |
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output_operand->length = ff_calculate_operand_data_length(output_operand); |
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if (output_operand->length <= 0) { |
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av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n"); |
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return DNN_ERROR; |
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} |
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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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av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n"); |
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return DNN_ERROR; |
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} |
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output = output_operand->data; |
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av_assert0(channel == dense_params->input_num); |
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for (int y = 0; y < height; ++y) { |
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for (int x = 0; x < width; ++x) { |
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for (int n_filter = 0; n_filter < dense_params->output_num; ++n_filter) { |
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if (dense_params->has_bias) |
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output[n_filter] = dense_params->biases[n_filter]; |
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else |
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output[n_filter] = 0.f; |
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for (int ch = 0; ch < dense_params->input_num; ++ch) { |
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float input_pel; |
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input_pel = input[y * src_linesize + x * dense_params->input_num + ch]; |
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output[n_filter] += input_pel * dense_params->kernel[n_filter*dense_params->input_num + ch]; |
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} |
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switch (dense_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 += dense_params->output_num; |
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} |
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} |
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return 0; |
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} |