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imageNet.h
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imageNet.h
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/*
* Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*/
#ifndef __IMAGE_NET_H__
#define __IMAGE_NET_H__
#include "tensorNet.h"
/**
* Name of default input blob for imageNet model.
* @ingroup imageNet
*/
#define IMAGENET_DEFAULT_INPUT "data"
/**
* Name of default output confidence values for imageNet model.
* @ingroup imageNet
*/
#define IMAGENET_DEFAULT_OUTPUT "prob"
/**
* Standard command-line options able to be passed to imageNet::Create()
* @ingroup imageNet
*/
#define IMAGENET_USAGE_STRING "imageNet arguments: \n" \
" --network=NETWORK pre-trained model to load, one of the following:\n" \
" * alexnet\n" \
" * googlenet (default)\n" \
" * googlenet-12\n" \
" * resnet-18\n" \
" * resnet-50\n" \
" * resnet-101\n" \
" * resnet-152\n" \
" * vgg-16\n" \
" * vgg-19\n" \
" * inception-v4\n" \
" --model=MODEL path to custom model to load (caffemodel, uff, or onnx)\n" \
" --prototxt=PROTOTXT path to custom prototxt to load (for .caffemodel only)\n" \
" --labels=LABELS path to text file containing the labels for each class\n" \
" --input-blob=INPUT name of the input layer (default is '" IMAGENET_DEFAULT_INPUT "')\n" \
" --output-blob=OUTPUT name of the output layer (default is '" IMAGENET_DEFAULT_OUTPUT "')\n" \
" --batch-size=BATCH maximum batch size (default is 1)\n" \
" --profile enable layer profiling in TensorRT\n\n"
/**
* Image recognition with classification networks, using TensorRT.
* @ingroup imageNet
*/
class imageNet : public tensorNet
{
public:
/**
* Network choice enumeration.
*/
enum NetworkType
{
CUSTOM, /**< Custom model provided by the user */
ALEXNET, /**< AlexNet trained on 1000-class ILSVRC12 */
GOOGLENET, /**< GoogleNet trained 1000-class ILSVRC12 */
GOOGLENET_12, /**< GoogleNet trained on 12-class subset of ImageNet ILSVRC12 from the tutorial */
RESNET_18, /**< ResNet-18 trained on 1000-class ILSVRC15 */
RESNET_50, /**< ResNet-50 trained on 1000-class ILSVRC15 */
RESNET_101, /**< ResNet-101 trained on 1000-class ILSVRC15 */
RESNET_152, /**< ResNet-50 trained on 1000-class ILSVRC15 */
VGG_16, /**< VGG-16 trained on 1000-class ILSVRC14 */
VGG_19, /**< VGG-19 trained on 1000-class ILSVRC14 */
INCEPTION_V4, /**< Inception-v4 trained on 1000-class ILSVRC12 */
};
/**
* Parse a string to one of the built-in pretrained models.
* Valid names are "alexnet", "googlenet", "googlenet-12", or "googlenet_12", ect.
* @returns one of the imageNet::NetworkType enums, or imageNet::CUSTOM on invalid string.
*/
static NetworkType NetworkTypeFromStr( const char* model_name );
/**
* Convert a NetworkType enum to a string.
*/
static const char* NetworkTypeToStr( NetworkType network );
/**
* Load a new network instance
*/
static imageNet* Create( NetworkType networkType=GOOGLENET, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE,
precisionType precision=TYPE_FASTEST,
deviceType device=DEVICE_GPU, bool allowGPUFallback=true );
/**
* Load a new network instance
* @param prototxt_path File path to the deployable network prototxt
* @param model_path File path to the caffemodel
* @param mean_binary File path to the mean value binary proto (can be NULL)
* @param class_labels File path to list of class name labels
* @param input Name of the input layer blob.
* @param output Name of the output layer blob.
* @param maxBatchSize The maximum batch size that the network will support and be optimized for.
*/
static imageNet* Create( const char* prototxt_path, const char* model_path,
const char* mean_binary, const char* class_labels,
const char* input=IMAGENET_DEFAULT_INPUT,
const char* output=IMAGENET_DEFAULT_OUTPUT,
uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE,
precisionType precision=TYPE_FASTEST,
deviceType device=DEVICE_GPU, bool allowGPUFallback=true );
/**
* Load a new network instance by parsing the command line.
*/
static imageNet* Create( int argc, char** argv );
/**
* Load a new network instance by parsing the command line.
*/
static imageNet* Create( const commandLine& cmdLine );
/**
* Usage string for command line arguments to Create()
*/
static inline const char* Usage() { return IMAGENET_USAGE_STRING; }
/**
* Destroy
*/
virtual ~imageNet();
/**
* Determine the maximum likelihood image class.
* This function performs pre-processing to the image (apply mean-value subtraction and NCHW format), @see PreProcess()
* @param rgba input image in CUDA device memory.
* @param width width of the input image in pixels.
* @param height height of the input image in pixels.
* @param confidence optional pointer to float filled with confidence value.
* @returns Index of the maximum class, or -1 on error.
*/
template<typename T> int Classify( T* image, uint32_t width, uint32_t height, float* confidence=NULL ) { return Classify((void*)image, width, height, imageFormatFromType<T>(), confidence); }
/**
* Determine the maximum likelihood image class.
* This function performs pre-processing to the image (apply mean-value subtraction and NCHW format), @see PreProcess()
* @param rgba input image in CUDA device memory.
* @param width width of the input image in pixels.
* @param height height of the input image in pixels.
* @param confidence optional pointer to float filled with confidence value.
* @returns Index of the maximum class, or -1 on error.
*/
int Classify( void* image, uint32_t width, uint32_t height, imageFormat format, float* confidence=NULL );
/**
* Determine the maximum likelihood image class.
* This function performs pre-processing to the image (apply mean-value subtraction and NCHW format), @see PreProcess()
* @deprecated this overload of Classify() provides legacy compatibility with `float*` type (RGBA32F).
* @param rgba float4 input image in CUDA device memory.
* @param width width of the input image in pixels.
* @param height height of the input image in pixels.
* @param confidence optional pointer to float filled with confidence value.
* @returns Index of the maximum class, or -1 on error.
*/
int Classify( float* rgba, uint32_t width, uint32_t height, float* confidence=NULL, imageFormat format=IMAGE_RGBA32F );
/**
* Retrieve the number of image recognition classes (typically 1000)
*/
inline uint32_t GetNumClasses() const { return mOutputClasses; }
/**
* Retrieve the description of a particular class.
*/
inline const char* GetClassDesc( uint32_t index ) const { return mClassDesc[index].c_str(); }
/**
* Retrieve the class synset category of a particular class.
*/
inline const char* GetClassSynset( uint32_t index ) const { return mClassSynset[index].c_str(); }
/**
* Retrieve the path to the file containing the class descriptions.
*/
inline const char* GetClassPath() const { return mClassPath.c_str(); }
/**
* Retrieve the network type (alexnet or googlenet)
*/
inline NetworkType GetNetworkType() const { return mNetworkType; }
/**
* Retrieve a string describing the network name.
*/
inline const char* GetNetworkName() const { NetworkTypeToStr(mNetworkType); }
/**
* Load class descriptions from a label file.
*/
static bool LoadClassInfo( const char* filename, std::vector<std::string>& descriptions, int expectedClasses=-1 );
/**
* Load class descriptions and synset strings from a label file.
*/
static bool LoadClassInfo( const char* filename, std::vector<std::string>& descriptions, std::vector<std::string>& synsets, int expectedClasses=-1 );
protected:
imageNet();
int Classify( float* confidence=NULL );
bool PreProcess( void* image, uint32_t width, uint32_t height, imageFormat format );
bool Process();
bool init( NetworkType networkType, uint32_t maxBatchSize, precisionType precision, deviceType device, bool allowGPUFallback );
bool init(const char* prototxt_path, const char* model_path, const char* mean_binary, const char* class_path, const char* input, const char* output, uint32_t maxBatchSize, precisionType precision, deviceType device, bool allowGPUFallback );
bool loadClassInfo( const char* filename, int expectedClasses=-1 );
uint32_t mOutputClasses;
std::vector<std::string> mClassSynset; // 1000 class ID's (ie n01580077, n04325704)
std::vector<std::string> mClassDesc;
std::string mClassPath;
NetworkType mNetworkType;
};
#endif