Table of Contents

Class XMem<T>

Namespace
AiDotNet.Video.Segmentation
Assembly
AiDotNet.dll

XMem: Long-Term Video Object Segmentation with Atkinson-Shiffrin memory model.

public class XMem<T> : NeuralNetworkBase<T>, INeuralNetworkModel<T>, INeuralNetwork<T>, IFullModel<T, Tensor<T>, Tensor<T>>, IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Tensor<T>, Tensor<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>, IGradientComputable<T, Tensor<T>, Tensor<T>>, IJitCompilable<T>, IInterpretableModel<T>, IInputGradientComputable<T>, IDisposable

Type Parameters

T

The numeric type used for calculations (typically float or double).

Inheritance
XMem<T>
Implements
IFullModel<T, Tensor<T>, Tensor<T>>
IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>
IParameterizable<T, Tensor<T>, Tensor<T>>
ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>
IGradientComputable<T, Tensor<T>, Tensor<T>>
Inherited Members
Extension Methods

Remarks

XMem is designed for tracking objects in very long videos using a three-tier memory system inspired by human memory.

For Beginners: XMem can track objects in hour-long videos without running out of memory. It uses three types of memory: - Sensory memory: Very recent frames (high detail, fast to forget) - Working memory: Important recent frames (moderate detail) - Long-term memory: Key historical frames (compressed, permanent)

Example usage (native mode for training):

var arch = new NeuralNetworkArchitecture<double>(
    inputType: InputType.ThreeDimensional,
    inputHeight: 480, inputWidth: 854, inputDepth: 3);
var model = new XMem<double>(arch);
var masks = model.TrackObjectLongTerm(videoFrames, initialMask);

Example usage (ONNX mode for inference):

var arch = new NeuralNetworkArchitecture<double>(
    inputType: InputType.ThreeDimensional,
    inputHeight: 480, inputWidth: 854, inputDepth: 3);
var model = new XMem<double>(arch, "xmem.onnx");
var masks = model.TrackObjectLongTerm(videoFrames, initialMask);

Reference: "XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model" https://arxiv.org/abs/2207.07115

Constructors

XMem(NeuralNetworkArchitecture<T>, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>?, ILossFunction<T>?, int, int, int, int)

Creates an XMem model using native layers for training and inference.

public XMem(NeuralNetworkArchitecture<T> architecture, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>? optimizer = null, ILossFunction<T>? lossFunction = null, int numFeatures = 256, int sensoryMemorySize = 3, int workingMemorySize = 10, int longTermMemorySize = 100)

Parameters

architecture NeuralNetworkArchitecture<T>
optimizer IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>
lossFunction ILossFunction<T>
numFeatures int
sensoryMemorySize int
workingMemorySize int
longTermMemorySize int

XMem(NeuralNetworkArchitecture<T>, string, int, int, int)

Creates an XMem model using a pretrained ONNX model for inference.

public XMem(NeuralNetworkArchitecture<T> architecture, string onnxModelPath, int sensoryMemorySize = 3, int workingMemorySize = 10, int longTermMemorySize = 100)

Parameters

architecture NeuralNetworkArchitecture<T>
onnxModelPath string
sensoryMemorySize int
workingMemorySize int
longTermMemorySize int

Properties

SupportsTraining

Indicates whether this network supports training (learning from data).

public override bool SupportsTraining { get; }

Property Value

bool

Remarks

For Beginners: Not all neural networks can learn. Some are designed only for making predictions with pre-set parameters. This property tells you if the network can learn from data.

Methods

ClearAllMemory()

Clears all memory banks.

public void ClearAllMemory()

CreateNewInstance()

Creates a new instance of the same type as this neural network.

protected override IFullModel<T, Tensor<T>, Tensor<T>> CreateNewInstance()

Returns

IFullModel<T, Tensor<T>, Tensor<T>>

A new instance of the same neural network type.

Remarks

For Beginners: This creates a blank version of the same type of neural network.

It's used internally by methods like DeepCopy and Clone to create the right type of network before copying the data into it.

DeserializeNetworkSpecificData(BinaryReader)

Deserializes network-specific data that was not covered by the general deserialization process.

protected override void DeserializeNetworkSpecificData(BinaryReader reader)

Parameters

reader BinaryReader

The BinaryReader to read the data from.

Remarks

This method is called at the end of the general deserialization process to allow derived classes to read any additional data specific to their implementation.

For Beginners: Continuing the suitcase analogy, this is like unpacking that special compartment. After the main deserialization method has unpacked the common items (layers, parameters), this method allows each specific type of neural network to unpack its own unique items that were stored during serialization.

GetMemoryStats()

Gets memory statistics.

public (int Sensory, int Working, int LongTerm) GetMemoryStats()

Returns

(int TrainSize, int ValidationSize, int TestSize)

GetModelMetadata()

Gets the metadata for this neural network model.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A ModelMetaData object containing information about the model.

InitializeLayers()

Initializes the layers of the neural network based on the architecture.

protected override void InitializeLayers()

Remarks

For Beginners: This method sets up all the layers in your neural network according to the architecture you've defined. It's like assembling the parts of your network before you can use it.

Predict(Tensor<T>)

Makes a prediction using the neural network.

public override Tensor<T> Predict(Tensor<T> input)

Parameters

input Tensor<T>

The input data to process.

Returns

Tensor<T>

The network's prediction.

Remarks

For Beginners: This is the main method you'll use to get results from your trained neural network. You provide some input data (like an image or text), and the network processes it through all its layers to produce an output (like a classification or prediction).

SegmentFrame(Tensor<T>)

Segments a single frame using the memory hierarchy.

public Tensor<T> SegmentFrame(Tensor<T> frame)

Parameters

frame Tensor<T>

Returns

Tensor<T>

SerializeNetworkSpecificData(BinaryWriter)

Serializes network-specific data that is not covered by the general serialization process.

protected override void SerializeNetworkSpecificData(BinaryWriter writer)

Parameters

writer BinaryWriter

The BinaryWriter to write the data to.

Remarks

This method is called at the end of the general serialization process to allow derived classes to write any additional data specific to their implementation.

For Beginners: Think of this as packing a special compartment in your suitcase. While the main serialization method packs the common items (layers, parameters), this method allows each specific type of neural network to pack its own unique items that other networks might not have.

TrackObjectLongTerm(List<Tensor<T>>, Tensor<T>)

Tracks an object through a long video sequence.

public List<Tensor<T>> TrackObjectLongTerm(List<Tensor<T>> frames, Tensor<T> initialMask)

Parameters

frames List<Tensor<T>>
initialMask Tensor<T>

Returns

List<Tensor<T>>

Train(Tensor<T>, Tensor<T>)

Trains the neural network on a single input-output pair.

public override void Train(Tensor<T> input, Tensor<T> expectedOutput)

Parameters

input Tensor<T>

The input data.

expectedOutput Tensor<T>

The expected output for the given input.

Remarks

This method performs one training step on the neural network using the provided input and expected output. It updates the network's parameters to reduce the error between the network's prediction and the expected output.

For Beginners: This is how your neural network learns. You provide: - An input (what the network should process) - The expected output (what the correct answer should be)

The network then:

  1. Makes a prediction based on the input
  2. Compares its prediction to the expected output
  3. Calculates how wrong it was (the loss)
  4. Adjusts its internal values to do better next time

After training, you can get the loss value using the GetLastLoss() method to see how well the network is learning.

UpdateParameters(Vector<T>)

Updates the network's parameters with new values.

public override void UpdateParameters(Vector<T> parameters)

Parameters

parameters Vector<T>

The new parameter values to set.

Remarks

For Beginners: During training, a neural network's internal values (parameters) get adjusted to improve its performance. This method allows you to update all those values at once by providing a complete set of new parameters.

This is typically used by optimization algorithms that calculate better parameter values based on training data.

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