eVision.EasyDeepLearning.EDeepLearningTool represents the common operations of deep learning tools. More...

Inheritance diagram for eVision.EasyDeepLearning.EDeepLearningTool:

[legend]

Public Member Functions
None	Load (self, str filePath)
	Loads a deep learning tool.
None	Load (self, eVision.ESerializer\|None serializer)
	Loads a deep learning tool.
eVision.EasyDeepLearning.EDeepLearningTool\|None	Clone (self)
	Clones this tool.
None	Save (self, str filePath, bool includeTrainingModel)
	Saves the settings, inference and training model of the deep learning tool.
None	Save (self, str filePath)
	Saves the settings, inference and training model of the deep learning tool.
None	Save (self, eVision.ESerializer\|None serializer, bool includeTrainingModel)
	Saves the settings, inference and training model of the deep learning tool.
None	Save (self, eVision.ESerializer\|None serializer)
	Saves the settings, inference and training model of the deep learning tool.
None	SerializeSettings (self, eVision.ESerializer\|None serializer)
	Serializes the tool settings.
None	SerializeInferenceModel (self, eVision.ESerializer\|None serializer)
	Serializes the inference model of the tool.
None	SerializeTrainingModel (self, eVision.ESerializer\|None serializer)
	Serializes the training model of the tool.
None	SaveSettings (self, str filePath)
	Saves the settings of the tool to a file.
None	SaveInferenceModel (self, str filePath)
	Saves the tool's inference model to a file.
None	SaveTrainingModel (self, str filePath)
	Saves the tool's training model to a file.
None	LoadSettings (self, str filePath)
	Loads the settings of a tool.
None	LoadInferenceModel (self, str filePath)
	Loads the tool's inference model.
None	LoadTrainingModel (self, str filePath)
	Loads the tool's training model.
None	UnloadInferenceModel (self)
	Unloads the inference model.
None	UnloadTrainingModel (self)
	Unloads the training model.
None	UnloadModels (self)
	Unloads the inference and training model.
bool	HasInferenceModel (self)
	Whether the inference model is loaded or created when the tool is not yet trained.
bool	HasTrainingModel (self)
	Whether the training model is loaded or created when the tool is not yet trained.
bool	IsTrained (self)
	Tells whether the deep learning tool has been trained.
str	GetAvailableDeviceName (self, int i)
	Name of the i-th device of the current engine.
eVision.EasyDeepLearning.EDeepLearningDevice	GetAvailableDevice (self, int i)
	i-th eVision.EasyDeepLearning.EDeepLearningDevice of the current engine.
None	SetActiveDevice (self, eVision.EasyDeepLearning.EDeepLearningDevice device)
	Sets the device with which to run or train the model.
eVision.EasyDeepLearning.EDeepLearningDevice	GetActiveDevice (self, int i)
	Gets the active device.
eVision.EasyDeepLearning.EDeepLearningDevice	GetActiveDevice (self)
	Gets the active device.
bool	HasEngineSetupData (self)
	Whether the inference model has engine setup data.
None	ClearEngineSetupData (self)
	Clears the engine data saved in the inference model.
bool	IsTraining (self)
	Indicates whether the object is currently training.
int	WaitForTrainingCompletion (self, int timeout)
	Waits until the training is complete or the timeout is expired.
int	WaitForTrainingCompletion (self)
	Waits until the training is complete or the timeout is expired.
int	WaitForIterationCompletion (self)
	Waits until an iteration is complete.
int	StopTraining (self, bool wait)
	Stops training and returns the last completed iteration.
None	SetLearningRate (self, eVision.EasyDeepLearning.EDeepLearningLearningRateType type, list[float] parameters)
	Specifies how the value of the learning rate and how it evolves during training.
None	InitializeInference (self, int width, int height, eVision.EImageType imageType, int numImages)
	Initializes the neural network for inference (i.e.
None	InitializeInference (self, eVision.EBaseROI img)
	Initializes the neural network for inference (i.e.
None	InitializeInference (self, list[eVision._eVision.EBaseROI\|None] imgList)
	Initializes the neural network for inference (i.e.
str	GetLabel (self, int index)
	Gets the label from its index.
None	SetLabel (self, int index, str label)
	Changes a label predicted by the tool.
None	SetLabelWeight (self, int index, float weight)
	Sets the label weight.
float	GetLabelWeight (self, int index)
	Gets the label weight.
None	SetLabelColor (self, int index, eVision.ERGBColor c)
	Changes a label color.
eVision.ERGBColor	GetLabelColor (self, int index)
	Label color.
None	SetLabelOpacity (self, int index, int opacity)
	Changes a label opacity (between 0 for fully transparent and 255 for opaque color).
int	GetLabelOpacity (self, int index)
	Gets the label opacity (between 0 for fully transparent and 255 for opaque color).
int	GetOptimalNumImagesForBatchSize (self, eVision.EBaseROI sampleImage)
	Optimal number of images to process together for inference given the batch size and eVision.EasyDeepLearning.EDeepLearningTool.GetNumPatchesForImage.
int	GetOptimalNumImagesForBatchSize (self, int imageWidth, int imageHeight)
	Optimal number of images to process together for inference given the batch size and eVision.EasyDeepLearning.EDeepLearningTool.GetNumPatchesForImage.
int	GetNumPatchesForImage (self, eVision.EBaseROI sampleImage)
	Number of patches that will be extracted from an input image to perform inference.
int	GetNumPatchesForImage (self, int imageWidth, int imageHeight)
	Number of patches that will be extracted from an input image to perform inference.
None	Clean (self)
	Reset the engine and clean CPU and GPU memory allocated for running the model.
eVision.EasyDeepLearning.EDeepLearningToolType	ToolType (self)
	Type of the deep learning tool.
bool	EnableGPU (self)
	Enable the use of a GPU for training and inference of the deep learning tool.
	EnableGPU (self, bool value)
	Enable the use of a GPU for training and inference of the deep learning tool.
list[int]	GPUIndexes (self)
	Indexes of the GPUs to use for computations.
	GPUIndexes (self, list[int] value)
	Indexes of the GPUs to use for computations.
int	NumGPUs (self)
	Number of detected NVIDIA GPU.
str	Engine (self)
	Sets the engine with which to execute or train the neural network.
	Engine (self, str value)
	Sets the engine with which to execute or train the neural network.
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	InferencePrecision (self)
	Inference precision.
	InferencePrecision (self, eVision.EasyDeepLearning.EDeepLearningInferencePrecision value)
	Inference precision.
int	NumAvailableDevices (self)
	Available devices for the current eVision.EasyDeepLearning.EDeepLearningTool.Engine.
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	AvailableDevices (self)
	Get all available device for the current engine.
int	NumActiveDevices (self)
	Gets the number of devices with which to run or train the model.
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	ActiveDevices (self)
	Devices with which to run or train the model.
	ActiveDevices (self, list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice] value)
	Devices with which to run or train the model.
eVision.EasyDeepLearning.EDeepLearningExecutionSettings	ExecutionSettings (self)
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
	ExecutionSettings (self, eVision.EasyDeepLearning.EDeepLearningExecutionSettings value)
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
eVision.EasyDeepLearning.EDeepLearningDevice	EngineSetupDataDevice (self)
	Device corresponding to the engine data contained in the tool.
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	EngineSetupDataInferencePrecision (self)
	Inference precision corresponding to the engine data contained in the tool.
int	EngineSetupDataBatchSize (self)
	Batch size corresponding to the engine data contained in the tool.
bool	OptimizeBatchSize (self)
	Indicates whether to optimize the batch size (see eVision.EasyDeepLearning.EDeepLearningTool.BatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.
	OptimizeBatchSize (self, bool value)
	Indicates whether to optimize the batch size (see eVision.EasyDeepLearning.EDeepLearningTool.BatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.
int	BatchSizeForMaximumInferenceSpeed (self)
	Computes the batch size that will maximize the inference speed on NVIDIA GPU.
int	BatchSize (self)
	Batch size.
	BatchSize (self, int value)
	Batch size.
float	CurrentTrainingProgression (self)
	Current training progression as a percentage (between 0 and 1).
int	CurrentTrainingFinishedIterations (self)
	Number of iterations that are already finished in the current training.
int	CurrentTrainingNumIterations (self)
	Total number of training iterations that will be performed during the current training of the deep learning tool.
int	NumTrainedIterations (self)
	Number of iterations that were performed to train this deep learning tool.
int	BestIteration (self)
	Iteration at which the minimum validation error was reached.
int	ImageCacheSize (self)
	Size in byte of the image cache.
	ImageCacheSize (self, int value)
	Size in byte of the image cache.
str	Path (self)
	Path of the tool when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.Save or eVision.EasyDeepLearning.EDeepLearningTool.Load.
str	InferenceModelPath (self)
	Path of the inference model when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.SaveInferenceModel, eVision.EasyDeepLearning.EDeepLearningTool.LoadInferenceModel or eVision.EasyDeepLearning.EDeepLearningTool.SerializeInferenceModel.
str	TrainingModelPath (self)
	Path of the training model when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.SaveTrainingModel, eVision.EasyDeepLearning.EDeepLearningTool.LoadTrainingModel or eVision.EasyDeepLearning.EDeepLearningTool.SerializeTrainingModel.
str	SettingsPath (self)
	Path of the settings when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.SaveSettings, eVision.EasyDeepLearning.EDeepLearningTool.LoadSettings or eVision.EasyDeepLearning.EDeepLearningTool.SerializeSettings.
int	AutoSavePeriod (self)
	Auto-save period.
	AutoSavePeriod (self, int value)
	Auto-save period.
eVision.EasyDeepLearning.EDeepLearningLearningRateType	LearningRateType (self)
	Learning rate type.
list[float]	LearningRateParameters (self)
	Parameters of the learning rate.
bool	EnableDeterministicTraining (self)
	Whether to use deterministic training algorithm that allows to repeatable training results.
	EnableDeterministicTraining (self, bool value)
	Whether to use deterministic training algorithm that allows to repeatable training results.
int	DeterministicTrainingRandomSeed (self)
	Random seed to use when eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining is set to true.
	DeterministicTrainingRandomSeed (self, int value)
	Random seed to use when eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining is set to true.
int	NumLabels (self)
	Number of labels of this tool.

Static Public Member Functions
eVision.EasyDeepLearning.EDeepLearningTool\|None	Create (str path)
	Factory method to open a deep learning tool.
eVision.EasyDeepLearning.EDeepLearningTool\|None	Create (eVision.ESerializer\|None serializer)
	Factory method to open a deep learning tool.
eVision.EasyDeepLearning.EDeepLearningTool\|None	Create (eVision.EasyDeepLearning.EDeepLearningToolType type)
	Factory method to open a deep learning tool.
int	GetNumAvailableEngines ()
	Number of available engines.
str	GetAvailableEngineName (int id)
	Gets the name of the engine given its id.
list[str]	GetAvailableEngines ()
	Available engines.
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	GetAvailableDevicesForEngine (str engine)
	Available devices for the specified engine.

Protected Member Functions
	_ActiveDeviceByName__setter (self, str value)
	Sets the device with which to run the model using its name.
	_ActiveDeviceById__setter (self, int value)
	Sets the device with which to run the model using its index in the list returned by eVision.EasyDeepLearning.EDeepLearningTool.AvailableDevices.
	_ActiveDevicesById__setter (self, list[int] value)
	Sets the devices with which to run the model using their index in the list returned by eVision.EasyDeepLearning.EDeepLearningTool.AvailableDevices.

Properties
	ActiveDeviceByName
	ActiveDeviceById
	ActiveDevicesById

Detailed Description

eVision.EasyDeepLearning.EDeepLearningTool represents the common operations of deep learning tools.

The class is responsible for handling CPU/GPU settings and training. Computing the result for an image is called inference and is the responsibility of the actual deep learning tools (see eVision.EasyDeepLearning.EClassifier).

Remarks:

License(s): EasyClassify or EasySegment or EasyLocate or EasyClassifyInference or EasySegmentInference or EasyLocateInference

Member Function Documentation

◆ _ActiveDeviceById__setter()

eVision.EasyDeepLearning.EDeepLearningTool._ActiveDeviceById__setter	(		self,
		int	value )

protected

Sets the device with which to run the model using its index in the list returned by eVision.EasyDeepLearning.EDeepLearningTool.AvailableDevices.

◆ _ActiveDeviceByName__setter()

eVision.EasyDeepLearning.EDeepLearningTool._ActiveDeviceByName__setter	(		self,
		str	value )

protected

Sets the device with which to run the model using its name.

◆ _ActiveDevicesById__setter()

eVision.EasyDeepLearning.EDeepLearningTool._ActiveDevicesById__setter	(		self,
		list[int]	value )

protected

Sets the devices with which to run the model using their index in the list returned by eVision.EasyDeepLearning.EDeepLearningTool.AvailableDevices.

◆ ActiveDevices() [1/2]

list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice] eVision.EasyDeepLearning.EDeepLearningTool.ActiveDevices ( self )

Devices with which to run or train the model.

Only multiple GPUs with the default engine is supported.

◆ ActiveDevices() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.ActiveDevices	(		self,
		list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	value )

Devices with which to run or train the model.

Only multiple GPUs with the default engine is supported.

◆ AutoSavePeriod() [1/2]

int eVision.EasyDeepLearning.EDeepLearningTool.AutoSavePeriod ( self )

Auto-save period.

The auto-save period is the number of training iterations between each automatic save of the model.

In order for the model to be automatically saved during the training, the tool must have been loaded or saved to a file. The path where the tool will be saved is either indicated by eVision.EasyDeepLearning.EDeepLearningTool.Path or the three paths eVision.EasyDeepLearning.EDeepLearningTool.SettingsPath, eVision.EasyDeepLearning.EDeepLearningTool.InferenceModelPath, and eVision.EasyDeepLearning.EDeepLearningTool.TrainingModelPath. If one of these path is empty, the corresponding part of the tool will not be saved automatically during training.

By default, auto save is disabled and the period is equal to 0.

◆ AutoSavePeriod() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.AutoSavePeriod	(		self,
		int	value )

Auto-save period.

The auto-save period is the number of training iterations between each automatic save of the model.

In order for the model to be automatically saved during the training, the tool must have been loaded or saved to a file. The path where the tool will be saved is either indicated by eVision.EasyDeepLearning.EDeepLearningTool.Path or the three paths eVision.EasyDeepLearning.EDeepLearningTool.SettingsPath, eVision.EasyDeepLearning.EDeepLearningTool.InferenceModelPath, and eVision.EasyDeepLearning.EDeepLearningTool.TrainingModelPath. If one of these path is empty, the corresponding part of the tool will not be saved automatically during training.

By default, auto save is disabled and the period is equal to 0.

◆ AvailableDevices()

list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice] eVision.EasyDeepLearning.EDeepLearningTool.AvailableDevices ( self )

Get all available device for the current engine.

◆ BatchSize() [1/2]

int eVision.EasyDeepLearning.EDeepLearningTool.BatchSize ( self )

Batch size.

The batch size is the number of images that are processed together during training and batch inference.

When using multi-GPUs processing (see eVision.EasyDeepLearning.EDeepLearningTool.GPUIndexes), the batch size is the number of images that each GPU will process at once.

A large batch size will increase the processing speed on GPU but also the memory requirements.

The batch size must be bigger or equal to 1 and it is commonly chosen to be a power of 2.

Remarks:

If eVision.EasyDeepLearning.EDeepLearningTool.OptimizeBatchSize is 'true', then the value of this property will be optimized automatically for training or inference according to the situation.

◆ BatchSize() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.BatchSize	(		self,
		int	value )

Batch size.

The batch size is the number of images that are processed together during training and batch inference.

When using multi-GPUs processing (see eVision.EasyDeepLearning.EDeepLearningTool.GPUIndexes), the batch size is the number of images that each GPU will process at once.

A large batch size will increase the processing speed on GPU but also the memory requirements.

The batch size must be bigger or equal to 1 and it is commonly chosen to be a power of 2.

Remarks:

If eVision.EasyDeepLearning.EDeepLearningTool.OptimizeBatchSize is 'true', then the value of this property will be optimized automatically for training or inference according to the situation.

◆ BatchSizeForMaximumInferenceSpeed()

int eVision.EasyDeepLearning.EDeepLearningTool.BatchSizeForMaximumInferenceSpeed ( self )

Computes the batch size that will maximize the inference speed on NVIDIA GPU.

For all other devices, it will return 1.

Remarks:

This value is given as an indication and should not necessarily be used in practice.

You must choose a tradeoff between the overall inference speed (also called the throughput), which is limited by this value, and the time it takes to compute the result of a whole batch (also called the latency), which is minimized by making inference image per image (i.e. a batch size of 1). The tradeoff depends on your particular application.

Throw EError_DeepLearningToolNotTrained if the tool is not trained.

◆ BestIteration()

int eVision.EasyDeepLearning.EDeepLearningTool.BestIteration ( self )

Iteration at which the minimum validation error was reached.

After training, the tool is in the state it was at this best iteration.

◆ Clean()

None eVision.EasyDeepLearning.EDeepLearningTool.Clean ( self )

Reset the engine and clean CPU and GPU memory allocated for running the model.

Remarks:

This method does not clean all GPU memory. For example, some program data (CUDA kernels) will be kept in GPU memory.

◆ ClearEngineSetupData()

None eVision.EasyDeepLearning.EDeepLearningTool.ClearEngineSetupData ( self )

Clears the engine data saved in the inference model.

Use this method when your engine fails.

◆ Clone()

eVision.EasyDeepLearning.EDeepLearningTool | None eVision.EasyDeepLearning.EDeepLearningTool.Clone ( self )

Clones this tool.

◆ Create() [1/3]

eVision.EasyDeepLearning.EDeepLearningTool | None eVision.EasyDeepLearning.EDeepLearningTool.Create ( eVision.EasyDeepLearning.EDeepLearningToolType type )

static

Factory method to open a deep learning tool.

Parameters

type	Type of tool to create

◆ Create() [2/3]

eVision.EasyDeepLearning.EDeepLearningTool | None eVision.EasyDeepLearning.EDeepLearningTool.Create ( eVision.ESerializer | None serializer )

static

Factory method to open a deep learning tool.

Parameters

serializer A serializer created for reading.

◆ Create() [3/3]

eVision.EasyDeepLearning.EDeepLearningTool | None eVision.EasyDeepLearning.EDeepLearningTool.Create ( str path )

static

Factory method to open a deep learning tool.

Parameters

path	Path to a deep learning tool.

◆ CurrentTrainingFinishedIterations()

int eVision.EasyDeepLearning.EDeepLearningTool.CurrentTrainingFinishedIterations ( self )

Number of iterations that are already finished in the current training.

◆ CurrentTrainingNumIterations()

int eVision.EasyDeepLearning.EDeepLearningTool.CurrentTrainingNumIterations ( self )

Total number of training iterations that will be performed during the current training of the deep learning tool.

After the end of the training, this number is the actual number of iterations performed during the training which can be lower than the number of iterations given to the training method (for example if the user called eVision.EasyDeepLearning.EDeepLearningTool.StopTraining).

◆ CurrentTrainingProgression()

float eVision.EasyDeepLearning.EDeepLearningTool.CurrentTrainingProgression ( self )

Current training progression as a percentage (between 0 and 1).

◆ DeterministicTrainingRandomSeed() [1/2]

int eVision.EasyDeepLearning.EDeepLearningTool.DeterministicTrainingRandomSeed ( self )

Random seed to use when eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining is set to true.

The default value is 42.

When eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining, the random seed is generated using a random non-deterministic source.

◆ DeterministicTrainingRandomSeed() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.DeterministicTrainingRandomSeed	(		self,
		int	value )

Random seed to use when eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining is set to true.

The default value is 42.

When eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining, the random seed is generated using a random non-deterministic source.

◆ EnableDeterministicTraining() [1/2]

bool eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining ( self )

Whether to use deterministic training algorithm that allows to repeatable training results.

Default value: false.

Remarks:

Deterministic training is not guaranteed when:

using multi-core processing is enabled (see eVision.Easy.SetMaxNumberOfProcessingThreads) regardless of the value of eVision.EasyDeepLearning.EDeepLearningTool.EnableGPU).
performing several training on the same tool in succession.

◆ EnableDeterministicTraining() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining	(		self,
		bool	value )

Whether to use deterministic training algorithm that allows to repeatable training results.

Default value: false.

Remarks:

Deterministic training is not guaranteed when:

using multi-core processing is enabled (see eVision.Easy.SetMaxNumberOfProcessingThreads) regardless of the value of eVision.EasyDeepLearning.EDeepLearningTool.EnableGPU).
performing several training on the same tool in succession.

◆ EnableGPU() [1/2]

bool eVision.EasyDeepLearning.EDeepLearningTool.EnableGPU ( self )

Enable the use of a GPU for training and inference of the deep learning tool.

If the active engine is not the default engine, eVision.EasyDeepLearning.EDeepLearningTool.EnableGPU will be false even if the active device has a GPU device type. Setting eVision.EasyDeepLearning.EDeepLearningTool.EnableGPU to any value when the active engine is not the default one will throw an exception.

Deprecated: Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ EnableGPU() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.EnableGPU	(		self,
		bool	value )

Enable the use of a GPU for training and inference of the deep learning tool.

If the active engine is not the default engine, eVision.EasyDeepLearning.EDeepLearningTool.EnableGPU will be false even if the active device has a GPU device type. Setting eVision.EasyDeepLearning.EDeepLearningTool.EnableGPU to any value when the active engine is not the default one will throw an exception.

Deprecated: Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ Engine() [1/2]

str eVision.EasyDeepLearning.EDeepLearningTool.Engine ( self )

Sets the engine with which to execute or train the neural network.

◆ Engine() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.Engine	(		self,
		str	value )

Sets the engine with which to execute or train the neural network.

◆ EngineSetupDataBatchSize()

int eVision.EasyDeepLearning.EDeepLearningTool.EngineSetupDataBatchSize ( self )

Batch size corresponding to the engine data contained in the tool.

◆ EngineSetupDataDevice()

eVision.EasyDeepLearning.EDeepLearningDevice eVision.EasyDeepLearning.EDeepLearningTool.EngineSetupDataDevice ( self )

Device corresponding to the engine data contained in the tool.

◆ EngineSetupDataInferencePrecision()

eVision.EasyDeepLearning.EDeepLearningInferencePrecision eVision.EasyDeepLearning.EDeepLearningTool.EngineSetupDataInferencePrecision ( self )

Inference precision corresponding to the engine data contained in the tool.

◆ ExecutionSettings() [1/2]

eVision.EasyDeepLearning.EDeepLearningExecutionSettings eVision.EasyDeepLearning.EDeepLearningTool.ExecutionSettings ( self )

Execution settings of the tool (engine, device(s), inference precision, batch size settings).

◆ ExecutionSettings() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.ExecutionSettings	(		self,
		eVision.EasyDeepLearning.EDeepLearningExecutionSettings	value )

Execution settings of the tool (engine, device(s), inference precision, batch size settings).

◆ GetActiveDevice() [1/2]

eVision.EasyDeepLearning.EDeepLearningDevice eVision.EasyDeepLearning.EDeepLearningTool.GetActiveDevice ( self )

Gets the active device.

By default, it returns the first active device.

◆ GetActiveDevice() [2/2]

eVision.EasyDeepLearning.EDeepLearningDevice eVision.EasyDeepLearning.EDeepLearningTool.GetActiveDevice	(		self,
		int	i )

Gets the active device.

By default, it returns the first active device.

Parameters

i	Index of the active devices between 0 and eVision.EasyDeepLearning.EDeepLearningTool.NumActiveDevices.

◆ GetAvailableDevice()

eVision.EasyDeepLearning.EDeepLearningDevice eVision.EasyDeepLearning.EDeepLearningTool.GetAvailableDevice	(		self,
		int	i )

i-th eVision.EasyDeepLearning.EDeepLearningDevice of the current engine.

Parameters

i	Index of the device between 0 and eVision.EasyDeepLearning.EDeepLearningTool.NumAvailableDevices - 1.

◆ GetAvailableDeviceName()

str eVision.EasyDeepLearning.EDeepLearningTool.GetAvailableDeviceName	(		self,
		int	i )

Name of the i-th device of the current engine.

Parameters

i	Index of the device between 0 and eVision.EasyDeepLearning.EDeepLearningTool.NumAvailableDevices - 1.

◆ GetAvailableDevicesForEngine()

list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice] eVision.EasyDeepLearning.EDeepLearningTool.GetAvailableDevicesForEngine ( str engine )

static

Available devices for the specified engine.

Parameters

engine Engine for which to get the devices

◆ GetAvailableEngineName()

str eVision.EasyDeepLearning.EDeepLearningTool.GetAvailableEngineName ( int id )

static

Gets the name of the engine given its id.

Parameters

id	Index of the available engine between 0 and eVision.EasyDeepLearning.EDeepLearningTool.GetNumAvailableEngines - 1.

◆ GetAvailableEngines()

list[str] eVision.EasyDeepLearning.EDeepLearningTool.GetAvailableEngines ( )

static

Available engines.

◆ GetLabel()

str eVision.EasyDeepLearning.EDeepLearningTool.GetLabel	(		self,
		int	index )

Gets the label from its index.

If the tool is not trained, the method may throw an exception.

Parameters

index Index of the label

◆ GetLabelColor()

eVision.ERGBColor eVision.EasyDeepLearning.EDeepLearningTool.GetLabelColor	(		self,
		int	index )

Label color.

The label colors affect how the results will be displayed.

The default label colors are the one specified in the dataset used for training.

Parameters

index Index of the label between 0 and eVision.EasyDeepLearning.EDeepLearningTool.NumLabels

◆ GetLabelOpacity()

int eVision.EasyDeepLearning.EDeepLearningTool.GetLabelOpacity	(		self,
		int	index )

Gets the label opacity (between 0 for fully transparent and 255 for opaque color).

The label opacities affect with the label colors how the results will be displayed.

The default label opacities are the one specified in the dataset used for training.

Parameters

index Index of the label between 0 and eVision.EasyDeepLearning.EDeepLearningTool.NumLabels

◆ GetLabelWeight()

float eVision.EasyDeepLearning.EDeepLearningTool.GetLabelWeight	(		self,
		int	index )

Gets the label weight.

If the tool is not trained, the label weights are not defined. After training, they are set to the value of the training dataset label weights.

Parameters

index Index of the label

◆ GetNumAvailableEngines()

int eVision.EasyDeepLearning.EDeepLearningTool.GetNumAvailableEngines ( )

static

Number of available engines.

◆ GetNumPatchesForImage() [1/2]

int eVision.EasyDeepLearning.EDeepLearningTool.GetNumPatchesForImage	(		self,
		eVision.EBaseROI	sampleImage )

Number of patches that will be extracted from an input image to perform inference.

For EasyClassify and EasyLocate, this will always be equal to 1. For EasySegment, the number of patches will depend on the scale, patch size and sampling density parameters.

Parameters

sampleImage Image for which to get the number of patch

Reimplemented in eVision.EasyDeepLearning.ESupervisedSegmenter, and eVision.EasyDeepLearning.EUnsupervisedSegmenter.

◆ GetNumPatchesForImage() [2/2]

int eVision.EasyDeepLearning.EDeepLearningTool.GetNumPatchesForImage	(		self,
		int	imageWidth,
		int	imageHeight )

Number of patches that will be extracted from an input image to perform inference.

For EasyClassify and EasyLocate, this will always be equal to 1. For EasySegment, the number of patches will depend on the scale, patch size and sampling density parameters.

Parameters

imageWidth	Width of the image for which to get the number of patch
imageHeight	Height of the image for which to get the number of patch

Reimplemented in eVision.EasyDeepLearning.ESupervisedSegmenter, and eVision.EasyDeepLearning.EUnsupervisedSegmenter.

◆ GetOptimalNumImagesForBatchSize() [1/2]

int eVision.EasyDeepLearning.EDeepLearningTool.GetOptimalNumImagesForBatchSize	(		self,
		eVision.EBaseROI	sampleImage )

Optimal number of images to process together for inference given the batch size and eVision.EasyDeepLearning.EDeepLearningTool.GetNumPatchesForImage.

In practice, for EasyClassify and EasyLocate, this is the same as the batch size.

For EasySegment, the value will depend on the scale, patch size and sampling density parameters.

Parameters

sampleImage Image for which to get the optimal number of images

◆ GetOptimalNumImagesForBatchSize() [2/2]

int eVision.EasyDeepLearning.EDeepLearningTool.GetOptimalNumImagesForBatchSize	(		self,
		int	imageWidth,
		int	imageHeight )

Optimal number of images to process together for inference given the batch size and eVision.EasyDeepLearning.EDeepLearningTool.GetNumPatchesForImage.

In practice, for EasyClassify and EasyLocate, this is the same as the batch size.

For EasySegment, the value will depend on the scale, patch size and sampling density parameters.

Parameters

imageWidth	Width of the image for which to get the optimal number of images
imageHeight	Height of the image for which to get the optimal number of images

◆ GPUIndexes() [1/2]

list[int] eVision.EasyDeepLearning.EDeepLearningTool.GPUIndexes ( self )

Indexes of the GPUs to use for computations.

Using multiple GPUs is only possible when we can process multiple images at once, i.e. during training or batch inference.

By default, all the detected GPUs will be used.

Remarks:

The GPU are indexed from 0 to eVision.EasyDeepLearning.EDeepLearningTool.NumGPUs - 1.

Deprecated

Use eVision.EasyDeepLearning.EDeepLearningTool.GetActiveDevice or eVision.EasyDeepLearning.EDeepLearningTool.ActiveDevices.

Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ GPUIndexes() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.GPUIndexes	(		self,
		list[int]	value )

Indexes of the GPUs to use for computations.

Using multiple GPUs is only possible when we can process multiple images at once, i.e. during training or batch inference.

By default, all the detected GPUs will be used.

Remarks:

The GPU are indexed from 0 to eVision.EasyDeepLearning.EDeepLearningTool.NumGPUs - 1.

Deprecated

Use eVision.EasyDeepLearning.EDeepLearningTool.GetActiveDevice or eVision.EasyDeepLearning.EDeepLearningTool.ActiveDevices.

Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ HasEngineSetupData()

bool eVision.EasyDeepLearning.EDeepLearningTool.HasEngineSetupData ( self )

Whether the inference model has engine setup data.

Engine setup data allows for quicker configuration of the engine when reusing the same device, inference precision and batch size.

Use eVision.EasyDeepLearning.EDeepLearningTool.EngineSetupDataDevice, eVision.EasyDeepLearning.EDeepLearningTool.EngineSetupDataInferencePrecision, and eVision.EasyDeepLearning.EDeepLearningTool.EngineSetupDataBatchSize to retrieve the device (and engine), inference precision, and batch size for which the tool has engine setup data.

The engine setup data is stored in the tool after the first inference or after calling eVision.EasyDeepLearning.EDeepLearningTool.InitializeInference if the engine supports it. At the moment, only the TensorRT engine supports setup data. If you save the tool, the engine setup data will be saved with the tool and can be reused when loading the tool again.

The engine setup data will only be replaced when using another device and/or inference precision and/or batch size with an engine that supports engine setup data. Otherwise, this setup data will remain (for example, using an engine that does not supports setup data will leave the pervious engine setup data intact).

You can clear the engine setup data by using eVision.EasyDeepLearning.EDeepLearningTool.ClearEngineSetupData.

◆ HasInferenceModel()

bool eVision.EasyDeepLearning.EDeepLearningTool.HasInferenceModel ( self )

Whether the inference model is loaded or created when the tool is not yet trained.

◆ HasTrainingModel()

bool eVision.EasyDeepLearning.EDeepLearningTool.HasTrainingModel ( self )

Whether the training model is loaded or created when the tool is not yet trained.

◆ ImageCacheSize() [1/2]

int eVision.EasyDeepLearning.EDeepLearningTool.ImageCacheSize ( self )

Size in byte of the image cache.

The cache is used during training to store reformatted and normalized images. A correctly sized cache can reduce the hard drive accesses and the preprocessing time for each image.

◆ ImageCacheSize() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.ImageCacheSize	(		self,
		int	value )

Size in byte of the image cache.

The cache is used during training to store reformatted and normalized images. A correctly sized cache can reduce the hard drive accesses and the preprocessing time for each image.

◆ InferenceModelPath()

str eVision.EasyDeepLearning.EDeepLearningTool.InferenceModelPath ( self )

Path of the inference model when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.SaveInferenceModel, eVision.EasyDeepLearning.EDeepLearningTool.LoadInferenceModel or eVision.EasyDeepLearning.EDeepLearningTool.SerializeInferenceModel.

◆ InferencePrecision() [1/2]

eVision.EasyDeepLearning.EDeepLearningInferencePrecision eVision.EasyDeepLearning.EDeepLearningTool.InferencePrecision ( self )

Inference precision.

Use eVision.EasyDeepLearning.EDeepLearningDevice.IsPrecisionSupported to check whether a device support the given precision.

◆ InferencePrecision() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.InferencePrecision	(		self,
		eVision.EasyDeepLearning.EDeepLearningInferencePrecision	value )

Inference precision.

Use eVision.EasyDeepLearning.EDeepLearningDevice.IsPrecisionSupported to check whether a device support the given precision.

◆ InitializeInference() [1/3]

None eVision.EasyDeepLearning.EDeepLearningTool.InitializeInference	(		self,
		eVision.EBaseROI	img )

Initializes the neural network for inference (i.e.

making predictions with the neural network).

You need to pass an image or a vector of images with the same characteristics (image resolution, image type and number of image) as the image or vector of images that you will use to make inference with this tool.

If eVision.EasyDeepLearning.EDeepLearningTool.OptimizeBatchSize is equal to true, the value of eVision.EasyDeepLearning.EDeepLearningTool.BatchSize will be recomputed.

Parameters

img	Image for which to prepare the tool for inference

◆ InitializeInference() [2/3]

None eVision.EasyDeepLearning.EDeepLearningTool.InitializeInference	(		self,
		int	width,
		int	height,
		eVision.EImageType	imageType,
		int	numImages )

Initializes the neural network for inference (i.e.

making predictions with the neural network).

You need to pass an image or a vector of images with the same characteristics (image resolution, image type and number of image) as the image or vector of images that you will use to make inference with this tool.

If eVision.EasyDeepLearning.EDeepLearningTool.OptimizeBatchSize is equal to true, the value of eVision.EasyDeepLearning.EDeepLearningTool.BatchSize will be recomputed.

Parameters

width	Width of the images for which to prepare the tool for inference
height	Height of the images for which to prepare the tool for inference
imageType	Type of the images for which to prepare the tool for inference
numImages	Number of images for which to prepare the tool for inference

◆ InitializeInference() [3/3]

None eVision.EasyDeepLearning.EDeepLearningTool.InitializeInference	(		self,
		list[eVision._eVision.EBaseROI \| None]	imgList )

Initializes the neural network for inference (i.e.

making predictions with the neural network).

You need to pass an image or a vector of images with the same characteristics (image resolution, image type and number of image) as the image or vector of images that you will use to make inference with this tool.

If eVision.EasyDeepLearning.EDeepLearningTool.OptimizeBatchSize is equal to true, the value of eVision.EasyDeepLearning.EDeepLearningTool.BatchSize will be recomputed.

Parameters

imgList Vector of images for which to prepare the tool for inference

◆ IsTrained()

bool eVision.EasyDeepLearning.EDeepLearningTool.IsTrained ( self )

Tells whether the deep learning tool has been trained.

◆ IsTraining()

bool eVision.EasyDeepLearning.EDeepLearningTool.IsTraining ( self )

Indicates whether the object is currently training.

◆ LearningRateParameters()

list[float] eVision.EasyDeepLearning.EDeepLearningTool.LearningRateParameters ( self )

Parameters of the learning rate.

◆ LearningRateType()

eVision.EasyDeepLearning.EDeepLearningLearningRateType eVision.EasyDeepLearning.EDeepLearningTool.LearningRateType ( self )

Learning rate type.

◆ Load() [1/2]

None eVision.EasyDeepLearning.EDeepLearningTool.Load	(		self,
		eVision.ESerializer \| None	serializer )

Loads a deep learning tool.

The given eVision.ESerializer must have been created for reading.

Parameters

serializer Pointer to the eVision.ESerializer created for reading.

◆ Load() [2/2]

None eVision.EasyDeepLearning.EDeepLearningTool.Load	(		self,
		str	filePath )

Loads a deep learning tool.

The given eVision.ESerializer must have been created for reading.

Parameters

filePath File path.

◆ LoadInferenceModel()

None eVision.EasyDeepLearning.EDeepLearningTool.LoadInferenceModel	(		self,
		str	filePath )

Loads the tool's inference model.

The inference model is required to apply the tool on new images.

Parameters

filePath File path.

◆ LoadSettings()

None eVision.EasyDeepLearning.EDeepLearningTool.LoadSettings	(		self,
		str	filePath )

Loads the settings of a tool.

Parameters

filePath File path.

◆ LoadTrainingModel()

None eVision.EasyDeepLearning.EDeepLearningTool.LoadTrainingModel	(		self,
		str	filePath )

Loads the tool's training model.

The training model is required to continue a training.

Parameters

filePath File path.

◆ NumActiveDevices()

int eVision.EasyDeepLearning.EDeepLearningTool.NumActiveDevices ( self )

Gets the number of devices with which to run or train the model.

◆ NumAvailableDevices()

int eVision.EasyDeepLearning.EDeepLearningTool.NumAvailableDevices ( self )

Available devices for the current eVision.EasyDeepLearning.EDeepLearningTool.Engine.

◆ NumGPUs()

int eVision.EasyDeepLearning.EDeepLearningTool.NumGPUs ( self )

Number of detected NVIDIA GPU.

Deprecated

Use eVision.EasyDeepLearning.EDeepLearningTool.NumAvailableDevices or parse the output of eVision.EasyDeepLearning.EDeepLearningTool.AvailableDevices to find the devices that are GPUs.

Deprecated in version 23.4. Scheduled to be removed in version 27.6.

◆ NumLabels()

int eVision.EasyDeepLearning.EDeepLearningTool.NumLabels ( self )

Number of labels of this tool.

If the tool is not trained, the method will throw an exception.

Remarks:

Some type of tools such as eVision.EasyDeepLearning.EUnsupervisedSegmenter may have no label at all. For these tools, there is a separate API to control the predicted labels.

◆ NumTrainedIterations()

int eVision.EasyDeepLearning.EDeepLearningTool.NumTrainedIterations ( self )

Number of iterations that were performed to train this deep learning tool.

Remarks:

This number of iteration may result from the addition of the iterations performed in several calls to the training method.

An iteration can also be called an epoch.

◆ OptimizeBatchSize() [1/2]

bool eVision.EasyDeepLearning.EDeepLearningTool.OptimizeBatchSize ( self )

Indicates whether to optimize the batch size (see eVision.EasyDeepLearning.EDeepLearningTool.BatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.

Default value is true.

Remarks:

The batch size will be optimized at the start of training or during eVision.EasyDeepLearning.EDeepLearningTool.InitializeInference.

◆ OptimizeBatchSize() [2/2]

eVision.EasyDeepLearning.EDeepLearningTool.OptimizeBatchSize	(		self,
		bool	value )

Indicates whether to optimize the batch size (see eVision.EasyDeepLearning.EDeepLearningTool.BatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.

Default value is true.

Remarks:

The batch size will be optimized at the start of training or during eVision.EasyDeepLearning.EDeepLearningTool.InitializeInference.

◆ Path()

str eVision.EasyDeepLearning.EDeepLearningTool.Path ( self )

Path of the tool when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.Save or eVision.EasyDeepLearning.EDeepLearningTool.Load.

◆ Save() [1/4]

None eVision.EasyDeepLearning.EDeepLearningTool.Save	(		self,
		eVision.ESerializer \| None	serializer )

Saves the settings, inference and training model of the deep learning tool.

The given eVision.ESerializer must have been created for writing.

Parameters

serializer Pointer to the eVision.ESerializer created for writing.

◆ Save() [2/4]

None eVision.EasyDeepLearning.EDeepLearningTool.Save	(		self,
		eVision.ESerializer \| None	serializer,
		bool	includeTrainingModel )

Saves the settings, inference and training model of the deep learning tool.

The given eVision.ESerializer must have been created for writing.

Parameters

serializer	Pointer to the eVision.ESerializer created for writing.
includeTrainingModel	Whether to save the training model. The training model is required to continue training the tool.

◆ Save() [3/4]

None eVision.EasyDeepLearning.EDeepLearningTool.Save	(		self,
		str	filePath )

Saves the settings, inference and training model of the deep learning tool.

The given eVision.ESerializer must have been created for writing.

Parameters

filePath File path.

◆ Save() [4/4]

None eVision.EasyDeepLearning.EDeepLearningTool.Save	(		self,
		str	filePath,
		bool	includeTrainingModel )

Saves the settings, inference and training model of the deep learning tool.

The given eVision.ESerializer must have been created for writing.

Parameters

filePath	File path.
includeTrainingModel	Whether to save the training model. The training model is required to continue training the tool.

◆ SaveInferenceModel()

None eVision.EasyDeepLearning.EDeepLearningTool.SaveInferenceModel	(		self,
		str	filePath )

Saves the tool's inference model to a file.

Parameters

filePath File path.

◆ SaveSettings()

None eVision.EasyDeepLearning.EDeepLearningTool.SaveSettings	(		self,
		str	filePath )

Saves the settings of the tool to a file.

Parameters

filePath File path.

◆ SaveTrainingModel()

None eVision.EasyDeepLearning.EDeepLearningTool.SaveTrainingModel	(		self,
		str	filePath )

Saves the tool's training model to a file.

Parameters

filePath File path.

◆ SerializeInferenceModel()

None eVision.EasyDeepLearning.EDeepLearningTool.SerializeInferenceModel	(		self,
		eVision.ESerializer \| None	serializer )

Serializes the inference model of the tool.

Parameters

serializer Pointer to eVision.ESerializer

◆ SerializeSettings()

None eVision.EasyDeepLearning.EDeepLearningTool.SerializeSettings	(		self,
		eVision.ESerializer \| None	serializer )

Serializes the tool settings.

Parameters

serializer Pointer to eVision.ESerializer

Reimplemented in eVision.EasyDeepLearning.EClassifier, eVision.EasyDeepLearning.EDeepOCRTool, eVision.EasyDeepLearning.EInterestPointLocator, eVision.EasyDeepLearning.ELocator, eVision.EasyDeepLearning.ELocatorBase, eVision.EasyDeepLearning.ESupervisedSegmenter, and eVision.EasyDeepLearning.EUnsupervisedSegmenter.

◆ SerializeTrainingModel()

None eVision.EasyDeepLearning.EDeepLearningTool.SerializeTrainingModel	(		self,
		eVision.ESerializer \| None	serializer )

Serializes the training model of the tool.

The training model is necessary to continue training the tool.

Parameters

serializer Pointer to eVision.ESerializer

◆ SetActiveDevice()

None eVision.EasyDeepLearning.EDeepLearningTool.SetActiveDevice	(		self,
		eVision.EasyDeepLearning.EDeepLearningDevice	device )

Sets the device with which to run or train the model.

Remarks:

For CPU device, the number of thread is initialized from eVision.Easy.SetMaxNumberOfProcessingThreads.

Note that this can change eVision.EasyDeepLearning.EDeepLearningTool.InferencePrecision if the previously set value was incompatible with the device.

Parameters

device The device with which to run or train the model.

◆ SetLabel()

None eVision.EasyDeepLearning.EDeepLearningTool.SetLabel	(		self,
		int	index,
		str	label )

Changes a label predicted by the tool.

Remarks:

Be careful when changing one of the label of a trained tool. It can create incompatibilities with previous metrics or results computed with this tool or with the dataset used to trained the tool.

Some tools may not use this label API. For example eVision.EasyDeepLearning.EUnsupervisedSegmenter uses eVision.EasyDeepLearning.EUnsupervisedSegmenter.GoodLabel instead.

Parameters

index	Index of the label
label	New label

◆ SetLabelColor()

None eVision.EasyDeepLearning.EDeepLearningTool.SetLabelColor	(		self,
		int	index,
		eVision.ERGBColor	c )

Changes a label color.

Parameters

index	Index of the label between 0 and eVision.EasyDeepLearning.EDeepLearningTool.NumLabels
c	New color for the specified label

◆ SetLabelOpacity()

None eVision.EasyDeepLearning.EDeepLearningTool.SetLabelOpacity	(		self,
		int	index,
		int	opacity )

Changes a label opacity (between 0 for fully transparent and 255 for opaque color).

Parameters

index	Index of the label between 0 and eVision.EasyDeepLearning.EDeepLearningTool.NumLabels
opacity	New opacity for the specified label

◆ SetLabelWeight()

None eVision.EasyDeepLearning.EDeepLearningTool.SetLabelWeight	(		self,
		int	index,
		float	weight )

Sets the label weight.

If the tool is not trained, the label weights are not defined. After training, they are set to the value of the training dataset label weights.

Parameters

index	Index of the label
weight	Weight

◆ SetLearningRate()

None eVision.EasyDeepLearning.EDeepLearningTool.SetLearningRate	(		self,
		eVision.EasyDeepLearning.EDeepLearningLearningRateType	type,
		list[float]	parameters )

Specifies how the value of the learning rate and how it evolves during training.

◆ SettingsPath()

str eVision.EasyDeepLearning.EDeepLearningTool.SettingsPath ( self )

Path of the settings when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.SaveSettings, eVision.EasyDeepLearning.EDeepLearningTool.LoadSettings or eVision.EasyDeepLearning.EDeepLearningTool.SerializeSettings.

◆ StopTraining()

int eVision.EasyDeepLearning.EDeepLearningTool.StopTraining	(		self,
		bool	wait )

Stops training and returns the last completed iteration.

If the parameter 'wait' is set to true, the method will wait for the training thread to completely stop. Otherwise, the method will return immediately.

Parameters

wait	Whether to wait for the training to completely stop

◆ ToolType()

eVision.EasyDeepLearning.EDeepLearningToolType eVision.EasyDeepLearning.EDeepLearningTool.ToolType ( self )

Type of the deep learning tool.

Reimplemented in eVision.EasyDeepLearning.EClassifier, eVision.EasyDeepLearning.EDeepOCRTool, eVision.EasyDeepLearning.EInterestPointLocator, eVision.EasyDeepLearning.ELocator, eVision.EasyDeepLearning.ESupervisedSegmenter, and eVision.EasyDeepLearning.EUnsupervisedSegmenter.

◆ TrainingModelPath()

str eVision.EasyDeepLearning.EDeepLearningTool.TrainingModelPath ( self )

Path of the training model when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.SaveTrainingModel, eVision.EasyDeepLearning.EDeepLearningTool.LoadTrainingModel or eVision.EasyDeepLearning.EDeepLearningTool.SerializeTrainingModel.

◆ UnloadInferenceModel()

None eVision.EasyDeepLearning.EDeepLearningTool.UnloadInferenceModel ( self )

Unloads the inference model.

◆ UnloadModels()

None eVision.EasyDeepLearning.EDeepLearningTool.UnloadModels ( self )

Unloads the inference and training model.

◆ UnloadTrainingModel()

None eVision.EasyDeepLearning.EDeepLearningTool.UnloadTrainingModel ( self )

Unloads the training model.

◆ WaitForIterationCompletion()

int eVision.EasyDeepLearning.EDeepLearningTool.WaitForIterationCompletion ( self )

Waits until an iteration is complete.

A call to this method will block the calling thread until a training iteration in the training thread is finished. This method returns the number of trained iterations.

◆ WaitForTrainingCompletion() [1/2]

int eVision.EasyDeepLearning.EDeepLearningTool.WaitForTrainingCompletion ( self )

Waits until the training is complete or the timeout is expired.

A call to this method will block the calling thread for the shortest time between the timeout and the time it takes for the training to complete.

A negative timeout means that the method will wait until the training is complete.

The default value is set to -1.

The method returns the number of trained iterations.

◆ WaitForTrainingCompletion() [2/2]

int eVision.EasyDeepLearning.EDeepLearningTool.WaitForTrainingCompletion	(		self,
		int	timeout )

Waits until the training is complete or the timeout is expired.

A call to this method will block the calling thread for the shortest time between the timeout and the time it takes for the training to complete.

A negative timeout means that the method will wait until the training is complete.

The default value is set to -1.

The method returns the number of trained iterations.

Parameters

timeout Timeout in second

Property Documentation

◆ ActiveDeviceById

eVision.EasyDeepLearning.EDeepLearningTool.ActiveDeviceById

static

Initial value:

=  property(None, _ActiveDeviceById__setter, None,
        
    )

◆ ActiveDeviceByName

eVision.EasyDeepLearning.EDeepLearningTool.ActiveDeviceByName

static

Initial value:

=  property(None, _ActiveDeviceByName__setter, None,
        
    )

◆ ActiveDevicesById

eVision.EasyDeepLearning.EDeepLearningTool.ActiveDevicesById

static

Initial value:

=  property(None, _ActiveDevicesById__setter, None,
        
    )

Public Member Functions

Static Public Member Functions

Protected Member Functions

Properties

Detailed Description

Member Function Documentation

◆ _ActiveDeviceById__setter()

◆ _ActiveDeviceByName__setter()

◆ _ActiveDevicesById__setter()

◆ ActiveDevices() [1/2]

◆ ActiveDevices() [2/2]

◆ AutoSavePeriod() [1/2]

◆ AutoSavePeriod() [2/2]

◆ AvailableDevices()

◆ BatchSize() [1/2]

◆ BatchSize() [2/2]

◆ BatchSizeForMaximumInferenceSpeed()

◆ BestIteration()

◆ Clean()

◆ ClearEngineSetupData()

◆ Clone()

◆ Create() [1/3]

◆ Create() [2/3]

◆ Create() [3/3]

◆ CurrentTrainingFinishedIterations()

◆ CurrentTrainingNumIterations()

◆ CurrentTrainingProgression()

◆ DeterministicTrainingRandomSeed() [1/2]

◆ DeterministicTrainingRandomSeed() [2/2]

◆ EnableDeterministicTraining() [1/2]

◆ EnableDeterministicTraining() [2/2]

◆ EnableGPU() [1/2]

◆ EnableGPU() [2/2]

◆ Engine() [1/2]

◆ Engine() [2/2]

◆ EngineSetupDataBatchSize()

◆ EngineSetupDataDevice()

◆ EngineSetupDataInferencePrecision()

◆ ExecutionSettings() [1/2]

◆ ExecutionSettings() [2/2]

◆ GetActiveDevice() [1/2]

◆ GetActiveDevice() [2/2]

◆ GetAvailableDevice()

◆ GetAvailableDeviceName()

◆ GetAvailableDevicesForEngine()

◆ GetAvailableEngineName()

◆ GetAvailableEngines()

◆ GetLabel()

◆ GetLabelColor()

◆ GetLabelOpacity()

◆ GetLabelWeight()

◆ GetNumAvailableEngines()

◆ GetNumPatchesForImage() [1/2]

◆ GetNumPatchesForImage() [2/2]

◆ GetOptimalNumImagesForBatchSize() [1/2]

◆ GetOptimalNumImagesForBatchSize() [2/2]

◆ GPUIndexes() [1/2]

◆ GPUIndexes() [2/2]

◆ HasEngineSetupData()

◆ HasInferenceModel()

◆ HasTrainingModel()

◆ ImageCacheSize() [1/2]

◆ ImageCacheSize() [2/2]

◆ InferenceModelPath()

◆ InferencePrecision() [1/2]

◆ InferencePrecision() [2/2]

◆ InitializeInference() [1/3]

◆ InitializeInference() [2/3]

◆ InitializeInference() [3/3]

◆ IsTrained()

◆ IsTraining()

◆ LearningRateParameters()

◆ LearningRateType()

◆ Load() [1/2]

◆ Load() [2/2]

◆ LoadInferenceModel()

◆ LoadSettings()

◆ LoadTrainingModel()

◆ NumActiveDevices()

◆ NumAvailableDevices()