GCC Code Coverage Report
Directory: ../../../ffmpeg/ Exec Total Coverage
File: src/libavfilter/dnn/dnn_backend_native_layer_dense.c Lines: 0 90 0.0 %
Date: 2020-10-23 17:01:47 Branches: 0 52 0.0 %

Line Branch Exec Source
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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 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 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 = 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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}