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avfilter/dnn: unify the layer execution function in native mode
Signed-off-by: Guo, Yejun <yejun.guo@intel.com> Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
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@ -1,5 +1,6 @@
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OBJS-$(CONFIG_DNN) += dnn/dnn_interface.o
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native.o
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layers.o
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_pad.o
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_conv2d.o
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OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_depth2space.o
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@ -29,6 +29,7 @@
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#include "dnn_backend_native_layer_conv2d.h"
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#include "dnn_backend_native_layer_depth2space.h"
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#include "dnn_backend_native_layer_maximum.h"
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#include "dnn_backend_native_layers.h"
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static DNNReturnType set_input_output_native(void *model, DNNInputData *input, const char *input_name, const char **output_names, uint32_t nb_output)
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{
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@ -331,10 +332,6 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
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{
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ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
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int32_t layer;
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ConvolutionalParams *conv_params;
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DepthToSpaceParams *depth_to_space_params;
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LayerPadParams *pad_params;
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DnnLayerMaximumParams *maximum_params;
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uint32_t nb = FFMIN(nb_output, network->nb_output);
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if (network->layers_num <= 0 || network->operands_num <= 0)
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@ -343,30 +340,11 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
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return DNN_ERROR;
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for (layer = 0; layer < network->layers_num; ++layer){
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switch (network->layers[layer].type){
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case DLT_CONV2D:
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conv_params = (ConvolutionalParams *)network->layers[layer].params;
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convolve(network->operands, network->layers[layer].input_operand_indexes,
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network->layers[layer].output_operand_index, conv_params);
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break;
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case DLT_DEPTH_TO_SPACE:
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depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
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depth_to_space(network->operands, network->layers[layer].input_operand_indexes,
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network->layers[layer].output_operand_index, depth_to_space_params->block_size);
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break;
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case DLT_MIRROR_PAD:
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pad_params = (LayerPadParams *)network->layers[layer].params;
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dnn_execute_layer_pad(network->operands, network->layers[layer].input_operand_indexes,
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network->layers[layer].output_operand_index, pad_params);
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break;
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case DLT_MAXIMUM:
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maximum_params = (DnnLayerMaximumParams *)network->layers[layer].params;
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dnn_execute_layer_maximum(network->operands, network->layers[layer].input_operand_indexes,
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network->layers[layer].output_operand_index, maximum_params);
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break;
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case DLT_INPUT:
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return DNN_ERROR;
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}
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DNNLayerType layer_type = network->layers[layer].type;
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layer_funcs[layer_type](network->operands,
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network->layers[layer].input_operand_indexes,
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network->layers[layer].output_operand_index,
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network->layers[layer].params);
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}
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for (uint32_t i = 0; i < nb; ++i) {
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@ -33,13 +33,15 @@
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/**
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* the enum value of DNNLayerType should not be changed,
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* the same values are used in convert_from_tensorflow.py
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* and, it is used to index the layer execution function pointer.
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*/
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typedef enum {
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DLT_INPUT = 0,
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DLT_CONV2D = 1,
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DLT_DEPTH_TO_SPACE = 2,
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DLT_MIRROR_PAD = 3,
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DLT_MAXIMUM = 4
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DLT_MAXIMUM = 4,
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DLT_COUNT
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} DNNLayerType;
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typedef enum {DOT_INPUT = 1, DOT_OUTPUT = 2, DOT_INTERMEDIATE = DOT_INPUT | DOT_INPUT} DNNOperandType;
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@ -23,7 +23,8 @@
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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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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)
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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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@ -32,6 +33,7 @@ int convolve(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t
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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 *)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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@ -35,5 +35,6 @@ typedef struct ConvolutionalParams{
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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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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);
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#endif
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@ -27,9 +27,12 @@
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#include "libavutil/avassert.h"
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#include "dnn_backend_native_layer_depth2space.h"
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int depth_to_space(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, int block_size)
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int dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_operand_indexes,
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int32_t output_operand_index, const void *parameters)
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{
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float *output;
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const DepthToSpaceParams *params = (const DepthToSpaceParams *)parameters;
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int block_size = params->block_size;
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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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@ -34,6 +34,7 @@ typedef struct DepthToSpaceParams{
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int block_size;
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} DepthToSpaceParams;
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int depth_to_space(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, int block_size);
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int dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_operand_indexes,
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int32_t output_operand_index, const void *parameters);
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#endif
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@ -27,10 +27,12 @@
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#include "libavutil/avassert.h"
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#include "dnn_backend_native_layer_maximum.h"
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int dnn_execute_layer_maximum(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, const DnnLayerMaximumParams *params)
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int dnn_execute_layer_maximum(DnnOperand *operands, const int32_t *input_operand_indexes,
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int32_t output_operand_index, const void *parameters)
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{
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const DnnOperand *input = &operands[input_operand_indexes[0]];
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DnnOperand *output = &operands[output_operand_index];
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const DnnLayerMaximumParams *params = (const DnnLayerMaximumParams *)parameters;
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int dims_count;
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const float *src;
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float *dst;
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@ -37,6 +37,7 @@ typedef struct DnnLayerMaximumParams{
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}val;
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} DnnLayerMaximumParams;
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int dnn_execute_layer_maximum(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, const DnnLayerMaximumParams *params);
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int dnn_execute_layer_maximum(DnnOperand *operands, const int32_t *input_operand_indexes,
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int32_t output_operand_index, const void *parameters);
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#endif
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@ -48,12 +48,13 @@ static int after_get_buddy(int given, int border, LayerPadModeParam mode)
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}
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}
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int dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index,
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const LayerPadParams *params)
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int dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes,
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int32_t output_operand_index, const void *parameters)
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{
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int32_t before_paddings;
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int32_t after_paddings;
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float* output;
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const LayerPadParams *params = (const LayerPadParams *)parameters;
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// suppose format is <N, H, W, C>
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int32_t input_operand_index = input_operand_indexes[0];
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@ -36,7 +36,7 @@ typedef struct LayerPadParams{
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float constant_values;
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} LayerPadParams;
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int dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index,
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const LayerPadParams *params);
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int dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes,
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int32_t output_operand_index, const void *parameters);
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#endif
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34
libavfilter/dnn/dnn_backend_native_layers.c
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34
libavfilter/dnn/dnn_backend_native_layers.c
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@ -0,0 +1,34 @@
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/*
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* Copyright (c) 2019 Guo Yejun
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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 <string.h>
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#include "dnn_backend_native_layers.h"
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#include "dnn_backend_native_layer_pad.h"
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#include "dnn_backend_native_layer_conv2d.h"
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#include "dnn_backend_native_layer_depth2space.h"
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#include "dnn_backend_native_layer_maximum.h"
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LAYER_EXEC_FUNC layer_funcs[DLT_COUNT] = {
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NULL,
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dnn_execute_layer_conv2d,
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dnn_execute_layer_depth2space,
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dnn_execute_layer_pad,
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dnn_execute_layer_maximum,
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};
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libavfilter/dnn/dnn_backend_native_layers.h
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32
libavfilter/dnn/dnn_backend_native_layers.h
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@ -0,0 +1,32 @@
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/*
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* Copyright (c) 2019 Guo Yejun
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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_LAYERS_H
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#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYERS_H
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#include <stdint.h>
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#include "dnn_backend_native.h"
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typedef int (*LAYER_EXEC_FUNC)(DnnOperand *operands, const int32_t *input_operand_indexes,
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int32_t output_operand_index, const void *parameters);
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extern LAYER_EXEC_FUNC layer_funcs[DLT_COUNT];
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#endif
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@ -113,7 +113,7 @@ static int test_with_same_dilate(void)
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operands[1].data = NULL;
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input_indexes[0] = 0;
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convolve(operands, input_indexes, 1, ¶ms);
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dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms);
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output = operands[1].data;
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for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
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@ -212,7 +212,7 @@ static int test_with_valid(void)
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operands[1].data = NULL;
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input_indexes[0] = 0;
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convolve(operands, input_indexes, 1, ¶ms);
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dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms);
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output = operands[1].data;
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for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
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@ -48,6 +48,7 @@ static int test(void)
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print(list(output.flatten()))
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*/
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DepthToSpaceParams params;
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DnnOperand operands[2];
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int32_t input_indexes[1];
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float input[1*5*3*4] = {
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@ -79,7 +80,8 @@ static int test(void)
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operands[1].data = NULL;
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input_indexes[0] = 0;
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depth_to_space(operands, input_indexes, 1, 2);
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params.block_size = 2;
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dnn_execute_layer_depth2space(operands, input_indexes, 1, ¶ms);
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output = operands[1].data;
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for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
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