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tensorNet.h
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tensorNet.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 __TENSOR_NET_H__
#define __TENSOR_NET_H__
// forward declaration of IInt8Calibrator
namespace nvinfer1 { class IInt8Calibrator; }
// includes
#include <NvInfer.h>
#include <jetson-utils/cudaUtility.h>
#include <jetson-utils/commandLine.h>
#include <jetson-utils/imageFormat.h>
#include <jetson-utils/timespec.h>
#include <jetson-utils/logging.h>
#include <vector>
#include <sstream>
#include <math.h>
#if NV_TENSORRT_MAJOR >= 6
typedef nvinfer1::Dims3 Dims3;
#define DIMS_C(x) x.d[0]
#define DIMS_H(x) x.d[1]
#define DIMS_W(x) x.d[2]
#elif NV_TENSORRT_MAJOR >= 2
typedef nvinfer1::DimsCHW Dims3;
#define DIMS_C(x) x.d[0]
#define DIMS_H(x) x.d[1]
#define DIMS_W(x) x.d[2]
#else
typedef nvinfer1::Dims3 Dims3;
#define DIMS_C(x) x.c
#define DIMS_H(x) x.h
#define DIMS_W(x) x.w
#ifndef NV_TENSORRT_MAJOR
#define NV_TENSORRT_MAJOR 1
#define NV_TENSORRT_MINOR 0
#endif
#endif
#if NV_TENSORRT_MAJOR >= 8
#define NOEXCEPT noexcept
#else
#define NOEXCEPT
#endif
/**
* Default maximum batch size
* @ingroup tensorNet
*/
#define DEFAULT_MAX_BATCH_SIZE 1
/**
* Prefix used for tagging printed log output from TensorRT.
* @ingroup tensorNet
*/
#define LOG_TRT "[TRT] "
/**
* Enumeration for indicating the desired precision that
* the network should run in, if available in hardware.
* @ingroup tensorNet
*/
enum precisionType
{
TYPE_DISABLED = 0, /**< Unknown, unspecified, or disabled type */
TYPE_FASTEST, /**< The fastest detected precision should be use (i.e. try INT8, then FP16, then FP32) */
TYPE_FP32, /**< 32-bit floating-point precision (FP32) */
TYPE_FP16, /**< 16-bit floating-point half precision (FP16) */
TYPE_INT8, /**< 8-bit integer precision (INT8) */
NUM_PRECISIONS /**< Number of precision types defined */
};
/**
* Stringize function that returns precisionType in text.
* @ingroup tensorNet
*/
const char* precisionTypeToStr( precisionType type );
/**
* Parse the precision type from a string.
* @ingroup tensorNet
*/
precisionType precisionTypeFromStr( const char* str );
/**
* Enumeration for indicating the desired device that
* the network should run on, if available in hardware.
* @ingroup tensorNet
*/
enum deviceType
{
DEVICE_GPU = 0, /**< GPU (if multiple GPUs are present, a specific GPU can be selected with cudaSetDevice() */
DEVICE_DLA, /**< Deep Learning Accelerator (DLA) Core 0 (only on Jetson Xavier) */
DEVICE_DLA_0 = DEVICE_DLA, /**< Deep Learning Accelerator (DLA) Core 0 (only on Jetson Xavier) */
DEVICE_DLA_1, /**< Deep Learning Accelerator (DLA) Core 1 (only on Jetson Xavier) */
NUM_DEVICES /**< Number of device types defined */
};
/**
* Stringize function that returns deviceType in text.
* @ingroup tensorNet
*/
const char* deviceTypeToStr( deviceType type );
/**
* Parse the device type from a string.
* @ingroup tensorNet
*/
deviceType deviceTypeFromStr( const char* str );
/**
* Enumeration indicating the format of the model that's
* imported in TensorRT (either caffe, ONNX, or UFF).
* @ingroup tensorNet
*/
enum modelType
{
MODEL_CUSTOM = 0, /**< Created directly with TensorRT API */
MODEL_CAFFE, /**< caffemodel */
MODEL_ONNX, /**< ONNX */
MODEL_UFF, /**< UFF */
MODEL_ENGINE /**< TensorRT engine/plan */
};
/**
* Stringize function that returns modelType in text.
* @ingroup tensorNet
*/
const char* modelTypeToStr( modelType type );
/**
* Parse the model format from a string.
* @ingroup tensorNet
*/
modelType modelTypeFromStr( const char* str );
/**
* Parse the model format from a file path.
* @ingroup tensorNet
*/
modelType modelTypeFromPath( const char* path );
/**
* Profiling queries
* @see tensorNet::GetProfilerTime()
* @ingroup tensorNet
*/
enum profilerQuery
{
PROFILER_PREPROCESS = 0,
PROFILER_NETWORK,
PROFILER_POSTPROCESS,
PROFILER_VISUALIZE,
PROFILER_TOTAL,
};
/**
* Stringize function that returns profilerQuery in text.
* @ingroup tensorNet
*/
const char* profilerQueryToStr( profilerQuery query );
/**
* Profiler device
* @ingroup tensorNet
*/
enum profilerDevice
{
PROFILER_CPU = 0, /**< CPU walltime */
PROFILER_CUDA, /**< CUDA kernel time */
};
/**
* Abstract class for loading a tensor network with TensorRT.
* For example implementations, @see imageNet and @see detectNet
* @ingroup tensorNet
*/
class tensorNet
{
public:
/**
* Destory
*/
virtual ~tensorNet();
/**
* Load a new network instance
* @param prototxt File path to the deployable network prototxt
* @param model File path to the caffemodel
* @param mean File path to the mean value binary proto (NULL if none)
* @param input_blob The name of the input blob data to the network.
* @param output_blob The name of the output blob data from the network.
* @param maxBatchSize The maximum batch size that the network will be optimized for.
*/
bool LoadNetwork( const char* prototxt, const char* model, const char* mean=NULL,
const char* input_blob="data", const char* output_blob="prob",
uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST,
deviceType device=DEVICE_GPU, bool allowGPUFallback=true,
nvinfer1::IInt8Calibrator* calibrator=NULL, cudaStream_t stream=NULL );
/**
* Load a new network instance with multiple output layers
* @param prototxt File path to the deployable network prototxt
* @param model File path to the caffemodel
* @param mean File path to the mean value binary proto (NULL if none)
* @param input_blob The name of the input blob data to the network.
* @param output_blobs List of names of the output blobs from the network.
* @param maxBatchSize The maximum batch size that the network will be optimized for.
*/
bool LoadNetwork( const char* prototxt, const char* model, const char* mean,
const char* input_blob, const std::vector<std::string>& output_blobs,
uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST,
deviceType device=DEVICE_GPU, bool allowGPUFallback=true,
nvinfer1::IInt8Calibrator* calibrator=NULL, cudaStream_t stream=NULL );
/**
* Load a new network instance with multiple input layers.
* @param prototxt File path to the deployable network prototxt
* @param model File path to the caffemodel
* @param mean File path to the mean value binary proto (NULL if none)
* @param input_blobs List of names of the inputs blob data to the network.
* @param output_blobs List of names of the output blobs from the network.
* @param maxBatchSize The maximum batch size that the network will be optimized for.
*/
bool LoadNetwork( const char* prototxt, const char* model, const char* mean,
const std::vector<std::string>& input_blobs,
const std::vector<std::string>& output_blobs,
uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE,
precisionType precision=TYPE_FASTEST,
deviceType device=DEVICE_GPU, bool allowGPUFallback=true,
nvinfer1::IInt8Calibrator* calibrator=NULL, cudaStream_t stream=NULL );
/**
* Load a new network instance (this variant is used for UFF models)
* @param prototxt File path to the deployable network prototxt
* @param model File path to the caffemodel
* @param mean File path to the mean value binary proto (NULL if none)
* @param input_blob The name of the input blob data to the network.
* @param input_dims The dimensions of the input blob (used for UFF).
* @param output_blobs List of names of the output blobs from the network.
* @param maxBatchSize The maximum batch size that the network will be optimized for.
*/
bool LoadNetwork( const char* prototxt, const char* model, const char* mean,
const char* input_blob, const Dims3& input_dims,
const std::vector<std::string>& output_blobs,
uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE,
precisionType precision=TYPE_FASTEST,
deviceType device=DEVICE_GPU, bool allowGPUFallback=true,
nvinfer1::IInt8Calibrator* calibrator=NULL, cudaStream_t stream=NULL );
/**
* Load a new network instance with multiple input layers (used for UFF models)
* @param prototxt File path to the deployable network prototxt
* @param model File path to the caffemodel
* @param mean File path to the mean value binary proto (NULL if none)
* @param input_blobs List of names of the inputs blob data to the network.
* @param input_dims List of the dimensions of the input blobs (used for UFF).
* @param output_blobs List of names of the output blobs from the network.
* @param maxBatchSize The maximum batch size that the network will be optimized for.
*/
bool LoadNetwork( const char* prototxt, const char* model, const char* mean,
const std::vector<std::string>& input_blobs,
const std::vector<Dims3>& input_dims,
const std::vector<std::string>& output_blobs,
uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE,
precisionType precision=TYPE_FASTEST,
deviceType device=DEVICE_GPU, bool allowGPUFallback=true,
nvinfer1::IInt8Calibrator* calibrator=NULL, cudaStream_t stream=NULL );
/**
* Load a network instance from a serialized engine plan file.
* @param engine_filename path to the serialized engine plan file.
* @param input_blobs List of names of the inputs blob data to the network.
* @param output_blobs List of names of the output blobs from the network.
*/
bool LoadEngine( const char* engine_filename,
const std::vector<std::string>& input_blobs,
const std::vector<std::string>& output_blobs,
nvinfer1::IPluginFactory* pluginFactory=NULL,
deviceType device=DEVICE_GPU,
cudaStream_t stream=NULL );
/**
* Load a network instance from a serialized engine plan file.
* @param engine_stream Memory containing the serialized engine plan file.
* @param engine_size Size of the serialized engine stream (in bytes).
* @param input_blobs List of names of the inputs blob data to the network.
* @param output_blobs List of names of the output blobs from the network.
*/
bool LoadEngine( char* engine_stream, size_t engine_size,
const std::vector<std::string>& input_blobs,
const std::vector<std::string>& output_blobs,
nvinfer1::IPluginFactory* pluginFactory=NULL,
deviceType device=DEVICE_GPU,
cudaStream_t stream=NULL );
/**
* Load network resources from an existing TensorRT engine instance.
* @param engine_stream Memory containing the serialized engine plan file.
* @param engine_size Size of the serialized engine stream (in bytes).
* @param input_blobs List of names of the inputs blob data to the network.
* @param output_blobs List of names of the output blobs from the network.
*/
bool LoadEngine( nvinfer1::ICudaEngine* engine,
const std::vector<std::string>& input_blobs,
const std::vector<std::string>& output_blobs,
deviceType device=DEVICE_GPU,
cudaStream_t stream=NULL );
/**
* Load a serialized engine plan file into memory.
*/
bool LoadEngine( const char* filename, char** stream, size_t* size );
/**
* Manually enable layer profiling times.
*/
void EnableLayerProfiler();
/**
* Manually enable debug messages and synchronization.
*/
void EnableDebug();
/**
* Return true if GPU fallback is enabled.
*/
inline bool AllowGPUFallback() const { return mAllowGPUFallback; }
/**
* Retrieve the device being used for execution.
*/
inline deviceType GetDevice() const { return mDevice; }
/**
* Retrieve the type of precision being used.
*/
inline precisionType GetPrecision() const { return mPrecision; }
/**
* Check if a particular precision is being used.
*/
inline bool IsPrecision( precisionType type ) const { return (mPrecision == type); }
/**
* Resolve a desired precision to a specific one that's available.
*/
static precisionType SelectPrecision( precisionType precision, deviceType device=DEVICE_GPU, bool allowInt8=true );
/**
* Determine the fastest native precision on a device.
*/
static precisionType FindFastestPrecision( deviceType device=DEVICE_GPU, bool allowInt8=true );
/**
* Detect the precisions supported natively on a device.
*/
static std::vector<precisionType> DetectNativePrecisions( deviceType device=DEVICE_GPU );
/**
* Detect if a particular precision is supported natively.
*/
static bool DetectNativePrecision( const std::vector<precisionType>& nativeTypes, precisionType type );
/**
* Detect if a particular precision is supported natively.
*/
static bool DetectNativePrecision( precisionType precision, deviceType device=DEVICE_GPU );
/**
* Retrieve the stream that the device is operating on.
*/
inline cudaStream_t GetStream() const { return mStream; }
/**
* Create and use a new stream for execution.
*/
cudaStream_t CreateStream( bool nonBlocking=true );
/**
* Set the stream that the device is operating on.
*/
void SetStream( cudaStream_t stream );
/**
* Retrieve the path to the network prototxt file.
*/
inline const char* GetPrototxtPath() const { return mPrototxtPath.c_str(); }
/**
* Retrieve the path to the network model file.
*/
inline const char* GetModelPath() const { return mModelPath.c_str(); }
/**
* Retrieve the format of the network model.
*/
inline modelType GetModelType() const { return mModelType; }
/**
* Return true if the model is of the specified format.
*/
inline bool IsModelType( modelType type ) const { return (mModelType == type); }
/**
* Retrieve the number of input layers to the network.
*/
inline uint32_t GetInputLayers() const { return mInputs.size(); }
/**
* Retrieve the number of output layers to the network.
*/
inline uint32_t GetOutputLayers() const { return mOutputs.size(); }
/**
* Retrieve the dimensions of network input layer.
*/
inline Dims3 GetInputDims( uint32_t layer=0 ) const { return mInputs[layer].dims; }
/**
* Retrieve the width of network input layer.
*/
inline uint32_t GetInputWidth( uint32_t layer=0 ) const { return DIMS_W(mInputs[layer].dims); }
/**
* Retrieve the height of network input layer.
*/
inline uint32_t GetInputHeight( uint32_t layer=0 ) const { return DIMS_H(mInputs[layer].dims); }
/**
* Retrieve the size (in bytes) of network input layer.
*/
inline uint32_t GetInputSize( uint32_t layer=0 ) const { return mInputs[layer].size; }
/**
* Retrieve the dimensions of network output layer.
*/
inline Dims3 GetOutputDims( uint32_t layer=0 ) const { return mOutputs[layer].dims; }
/**
* Retrieve the width of network output layer.
*/
inline uint32_t GetOutputWidth( uint32_t layer=0 ) const { return DIMS_W(mOutputs[layer].dims); }
/**
* Retrieve the height of network output layer.
*/
inline uint32_t GetOutputHeight( uint32_t layer=0 ) const { return DIMS_H(mOutputs[layer].dims); }
/**
* Retrieve the size (in bytes) of network output layer.
*/
inline uint32_t GetOutputSize( uint32_t layer=0 ) const { return mOutputs[layer].size; }
/**
* Retrieve the network frames per second (FPS).
*/
inline float GetNetworkFPS() { return 1000.0f / GetNetworkTime(); }
/**
* Retrieve the network runtime (in milliseconds).
*/
inline float GetNetworkTime() { return GetProfilerTime(PROFILER_NETWORK, PROFILER_CUDA); }
/**
* Retrieve the profiler runtime (in milliseconds).
*/
inline float2 GetProfilerTime( profilerQuery query ) { PROFILER_QUERY(query); return mProfilerTimes[query]; }
/**
* Retrieve the profiler runtime (in milliseconds).
*/
inline float GetProfilerTime( profilerQuery query, profilerDevice device ) { PROFILER_QUERY(query); return (device == PROFILER_CPU) ? mProfilerTimes[query].x : mProfilerTimes[query].y; }
/**
* Print the profiler times (in millseconds).
*/
inline void PrintProfilerTimes()
{
LogInfo("\n");
LogInfo(LOG_TRT "------------------------------------------------\n");
LogInfo(LOG_TRT "Timing Report %s\n", GetModelPath());
LogInfo(LOG_TRT "------------------------------------------------\n");
for( uint32_t n=0; n <= PROFILER_TOTAL; n++ )
{
const profilerQuery query = (profilerQuery)n;
if( PROFILER_QUERY(query) )
LogInfo(LOG_TRT "%-12s CPU %9.5fms CUDA %9.5fms\n", profilerQueryToStr(query), mProfilerTimes[n].x, mProfilerTimes[n].y);
}
LogInfo(LOG_TRT "------------------------------------------------\n\n");
static bool first_run=true;
if( first_run )
{
LogWarning(LOG_TRT "note -- when processing a single image, run 'sudo jetson_clocks' before\n"
" to disable DVFS for more accurate profiling/timing measurements\n\n");
first_run = false;
}
}
protected:
/**
* Constructor.
*/
tensorNet();
/**
* Execute processing of the network.
* @param sync if true (default), the device will be synchronized after processing
* and the thread/function will block until processing is complete.
* if false, the function will return immediately after the processing
* has been enqueued to the CUDA stream indicated by GetStream().
*/
bool ProcessNetwork( bool sync=true );
/**
* Create and output an optimized network model
* @note this function is automatically used by LoadNetwork, but also can
* be used individually to perform the network operations offline.
* @param deployFile name for network prototxt
* @param modelFile name for model
* @param outputs network outputs
* @param maxBatchSize maximum batch size
* @param modelStream output model stream
*/
bool ProfileModel( const std::string& deployFile, const std::string& modelFile,
const std::vector<std::string>& inputs, const std::vector<Dims3>& inputDims,
const std::vector<std::string>& outputs, uint32_t maxBatchSize,
precisionType precision, deviceType device, bool allowGPUFallback,
nvinfer1::IInt8Calibrator* calibrator, char** engineStream, size_t* engineSize );
/**
* Configure builder options
*/
#if NV_TENSORRT_MAJOR >= 8
bool ConfigureBuilder( nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config,
uint32_t maxBatchSize, uint32_t workspaceSize, precisionType precision,
deviceType device, bool allowGPUFallback,
nvinfer1::IInt8Calibrator* calibrator );
#else
bool ConfigureBuilder( nvinfer1::IBuilder* builder, uint32_t maxBatchSize,
uint32_t workspaceSize, precisionType precision,
deviceType device, bool allowGPUFallback,
nvinfer1::IInt8Calibrator* calibrator );
#endif
/**
* Logger class for GIE info/warning/errors
*/
class Logger : public nvinfer1::ILogger
{
public:
void log( Severity severity, const char* msg ) NOEXCEPT override
{
if( severity == Severity::kWARNING )
{
LogWarning(LOG_TRT "%s\n", msg);
}
else if( severity == Severity::kINFO )
{
LogInfo(LOG_TRT "%s\n", msg);
}
#if NV_TENSORRT_MAJOR >= 6
else if( severity == Severity::kVERBOSE )
{
LogVerbose(LOG_TRT "%s\n", msg);
}
#endif
else
{
LogError(LOG_TRT "%s\n", msg);
}
}
} static gLogger;
/**
* Profiler interface for measuring layer timings
*/
class Profiler : public nvinfer1::IProfiler
{
public:
Profiler() : timingAccumulator(0.0f) { }
virtual void reportLayerTime(const char* layerName, float ms) NOEXCEPT
{
LogVerbose(LOG_TRT "layer %s - %f ms\n", layerName, ms);
timingAccumulator += ms;
}
float timingAccumulator;
} gProfiler;
/**
* Begin a profiling query, before network is run
*/
inline void PROFILER_BEGIN( profilerQuery query )
{
const uint32_t evt = query*2;
const uint32_t flag = (1 << query);
CUDA(cudaEventRecord(mEventsGPU[evt], mStream));
timestamp(&mEventsCPU[evt]);
mProfilerQueriesUsed |= flag;
mProfilerQueriesDone &= ~flag;
}
/**
* End a profiling query, after the network is run
*/
inline void PROFILER_END( profilerQuery query )
{
const uint32_t evt = query*2+1;
CUDA(cudaEventRecord(mEventsGPU[evt]));
timestamp(&mEventsCPU[evt]);
timespec cpuTime;
timeDiff(mEventsCPU[evt-1], mEventsCPU[evt], &cpuTime);
mProfilerTimes[query].x = timeFloat(cpuTime);
if( mEnableProfiler && query == PROFILER_NETWORK )
{
LogVerbose(LOG_TRT "layer network time - %f ms\n", gProfiler.timingAccumulator);
gProfiler.timingAccumulator = 0.0f;
LogWarning(LOG_TRT "note -- when processing a single image, run 'sudo jetson_clocks' before\n"
" to disable DVFS for more accurate profiling/timing measurements\n");
}
}
/**
* Query the CUDA part of a profiler query.
*/
inline bool PROFILER_QUERY( profilerQuery query )
{
const uint32_t flag = (1 << query);
if( query == PROFILER_TOTAL )
{
mProfilerTimes[PROFILER_TOTAL].x = 0.0f;
mProfilerTimes[PROFILER_TOTAL].y = 0.0f;
for( uint32_t n=0; n < PROFILER_TOTAL; n++ )
{
if( PROFILER_QUERY((profilerQuery)n) )
{
mProfilerTimes[PROFILER_TOTAL].x += mProfilerTimes[n].x;
mProfilerTimes[PROFILER_TOTAL].y += mProfilerTimes[n].y;
}
}
return true;
}
else if( mProfilerQueriesUsed & flag )
{
if( !(mProfilerQueriesDone & flag) )
{
const uint32_t evt = query*2;
float cuda_time = 0.0f;
CUDA(cudaEventElapsedTime(&cuda_time, mEventsGPU[evt], mEventsGPU[evt+1]));
mProfilerTimes[query].y = cuda_time;
mProfilerQueriesDone |= flag;
//mProfilerQueriesUsed &= ~flag;
}
return true;
}
return false;
}
protected:
/* Member Variables */
std::string mPrototxtPath;
std::string mModelPath;
std::string mMeanPath;
std::string mCacheEnginePath;
std::string mCacheCalibrationPath;
deviceType mDevice;
precisionType mPrecision;
modelType mModelType;
cudaStream_t mStream;
cudaEvent_t mEventsGPU[PROFILER_TOTAL * 2];
timespec mEventsCPU[PROFILER_TOTAL * 2];
nvinfer1::IRuntime* mInfer;
nvinfer1::ICudaEngine* mEngine;
nvinfer1::IExecutionContext* mContext;
float2 mProfilerTimes[PROFILER_TOTAL + 1];
uint32_t mProfilerQueriesUsed;
uint32_t mProfilerQueriesDone;
uint32_t mWorkspaceSize;
uint32_t mMaxBatchSize;
bool mEnableProfiler;
bool mEnableDebug;
bool mAllowGPUFallback;
void** mBindings;
struct layerInfo
{
std::string name;
Dims3 dims;
uint32_t size;
uint32_t binding;
float* CPU;
float* CUDA;
};
std::vector<layerInfo> mInputs;
std::vector<layerInfo> mOutputs;
};
#endif