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net.h
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// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
#ifndef NCNN_NET_H
#define NCNN_NET_H
#include <stdio.h>
#include <vector>
#include "blob.h"
#include "layer.h"
#include "mat.h"
#include "platform.h"
namespace ncnn {
class Extractor;
class Net
{
public:
// empty init
Net();
// clear and destroy
~Net();
#if NCNN_STRING
// register custom layer by layer type name
// return 0 if success
int register_custom_layer(const char* type, layer_creator_func creator);
#endif // NCNN_STRING
// register custom layer by layer type
// return 0 if success
int register_custom_layer(int index, layer_creator_func creator);
#if NCNN_STDIO
#if NCNN_STRING
// load network structure from plain param file
// return 0 if success
int load_param(FILE* fp);
int load_param(const char* protopath);
int load_param_mem(const char* mem);
#endif // NCNN_STRING
// load network structure from binary param file
// return 0 if success
int load_param_bin(FILE* fp);
int load_param_bin(const char* protopath);
// load network weight data from model file
// return 0 if success
int load_model(FILE* fp);
int load_model(const char* modelpath);
#endif // NCNN_STDIO
// load network structure from external memory
// memory pointer must be 32-bit aligned
// return bytes consumed
int load_param(const unsigned char* mem);
// reference network weight data from external memory
// weight data is not copied but referenced
// so external memory should be retained when used
// memory pointer must be 32-bit aligned
// return bytes consumed
int load_model(const unsigned char* mem);
// unload network structure and weight data
void clear();
// construct an Extractor from network
Extractor create_extractor() const;
public:
// enable winograd convolution optimization
// improve convolution 3x3 stride1 performace, may consume more memory
// changes should be applied before loading network structure and weight
// enabled by default
int use_winograd_convolution;
// enable sgemm convolution optimization
// improve convolution 1x1 stride1 performace, may consume more memory
// changes should be applied before loading network structure and weight
// enabled by default
int use_sgemm_convolution;
// enable quantized int8 inference
// use low-precision int8 path for quantized model
// changes should be applied before loading network structure and weight
// enabled by default
int use_int8_inference;
protected:
friend class Extractor;
#if NCNN_STRING
int find_blob_index_by_name(const char* name) const;
int find_layer_index_by_name(const char* name) const;
int custom_layer_to_index(const char* type);
Layer* create_custom_layer(const char* type);
#endif // NCNN_STRING
Layer* create_custom_layer(int index);
int forward_layer(int layer_index, std::vector<Mat>& blob_mats, Option& opt) const;
protected:
std::vector<Blob> blobs;
std::vector<Layer*> layers;
std::vector<layer_registry_entry> custom_layer_registry;
};
class Extractor
{
public:
// enable light mode
// intermediate blob will be recycled when enabled
// enabled by default
void set_light_mode(bool enable);
// set thread count for this extractor
// this will overwrite the global setting
// default count is system depended
void set_num_threads(int num_threads);
// set blob memory allocator
void set_blob_allocator(Allocator* allocator);
// set workspace memory allocator
void set_workspace_allocator(Allocator* allocator);
#if NCNN_STRING
// set input by blob name
// return 0 if success
int input(const char* blob_name, const Mat& in);
// get result by blob name
// return 0 if success
int extract(const char* blob_name, Mat& feat);
#endif // NCNN_STRING
// set input by blob index
// return 0 if success
int input(int blob_index, const Mat& in);
// get result by blob index
// return 0 if success
int extract(int blob_index, Mat& feat);
protected:
friend Extractor Net::create_extractor() const;
Extractor(const Net* net, int blob_count);
private:
const Net* net;
std::vector<Mat> blob_mats;
Option opt;
};
} // namespace ncnn
#endif // NCNN_NET_H