GCC Code Coverage Report
Directory: ../../../ffmpeg/ Exec Total Coverage
File: src/libavfilter/dnn/dnn_backend_native_layer_conv2d.c Lines: 79 141 56.0 %
Date: 2020-09-25 23:16:12 Branches: 39 92 42.4 %

Line Branch Exec Source
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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 "libavutil/thread.h"
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#include "libavutil/cpu.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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//struct to pass parameters
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typedef struct thread_common_param{
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    DnnOperand *operands;
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    const int32_t *input_operand_indexes;
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    int32_t output_operand_index;
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    const void *parameters;
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    NativeContext *ctx;
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    float *output_data;
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} thread_common_param;
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typedef struct thread_param{
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    thread_common_param *thread_common_param;
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    int thread_start, thread_end;
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} thread_param;
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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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{
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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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    conv_params->has_bias = (int32_t)avio_rl32(model_file_context);
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    dnn_size += 28;
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    kernel_size = conv_params->input_num * conv_params->output_num *
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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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    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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    conv_params->kernel = av_malloc(kernel_size * sizeof(float));
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    if (!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 < kernel_size; ++i) {
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        conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
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    }
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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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    }
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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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    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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static void * dnn_execute_layer_conv2d_thread(void *threadarg)
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{
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    //pass parameters
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    thread_param *thread_param = (struct thread_param *)threadarg;
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    thread_common_param *thread_common_param = thread_param->thread_common_param;
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    DnnOperand *operands = thread_common_param->operands;
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    int32_t input_operand_index = thread_common_param->input_operand_indexes[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 ConvolutionalParams *conv_params = (const ConvolutionalParams *)(thread_common_param->parameters);
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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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    float *output = thread_common_param->output_data;
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    output += (conv_params->output_num) * (width - 2 * pad_size) * (thread_param->thread_start - pad_size);
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    av_assert0(channel == conv_params->input_num);
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    for (int y = thread_param->thread_start; y < thread_param->thread_end; ++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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                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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                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 (void *)DNN_SUCCESS;
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}
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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, NativeContext *ctx)
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{
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    int thread_num = (ctx->options.conv2d_threads <= 0 || ctx->options.conv2d_threads > av_cpu_count())
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        ? (av_cpu_count() + 1) : (ctx->options.conv2d_threads);
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#if HAVE_PTHREAD_CANCEL
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    pthread_t *thread_id = av_malloc(thread_num * sizeof(pthread_t));
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    int thread_stride;
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#endif
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    thread_param **thread_param = av_malloc(thread_num * sizeof(*thread_param));
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    thread_common_param thread_common_param;
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    const ConvolutionalParams *conv_params = (const ConvolutionalParams *)(parameters);
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    int height = operands[input_operand_indexes[0]].dims[1];
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    int width = operands[input_operand_indexes[0]].dims[2];
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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] = operands[input_operand_indexes[0]].dims[0];
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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->data_type = operands[input_operand_indexes[0]].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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    thread_common_param.output_data = output_operand->data;
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    thread_common_param.operands = operands;
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    thread_common_param.input_operand_indexes = input_operand_indexes;
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    thread_common_param.output_operand_index = output_operand_index;
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    thread_common_param.parameters = parameters;
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    thread_common_param.ctx = ctx;
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#if HAVE_PTHREAD_CANCEL
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    thread_stride = (height - pad_size * 2) / thread_num;
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    //create threads
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    for (int i = 0; i < thread_num; i++){
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        thread_param[i] = av_malloc(sizeof(**thread_param));
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        thread_param[i]->thread_common_param = &thread_common_param;
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        thread_param[i]->thread_start = thread_stride * i + pad_size;
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        thread_param[i]->thread_end = (i == thread_num - 1) ? (height - pad_size) : (thread_param[i]->thread_start + thread_stride);
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        pthread_create(&thread_id[i], NULL, dnn_execute_layer_conv2d_thread, (void *)thread_param[i]);
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    }
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    //join threads, res gets function return
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    for (int i = 0; i < thread_num; i++){
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        pthread_join(thread_id[i], NULL);
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    }
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    //release memory
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    av_free(thread_id);
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    for (int i = 0; i < thread_num; i++){
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        av_free(thread_param[i]);
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    }
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#else
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    thread_param[0] = av_malloc(sizeof(**thread_param));
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    thread_param[0]->thread_common_param = &thread_common_param;
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    thread_param[0]->thread_start = pad_size;
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    thread_param[0]->thread_end = height - pad_size;
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    dnn_execute_layer_conv2d_thread((void *)thread_param[0]);
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    av_free(thread_param[0]);
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#endif
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    av_free(thread_param);
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    return DNN_SUCCESS;
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}