Table of Contents

Class RAFT<T>

Namespace
AiDotNet.Video.Motion
Assembly
AiDotNet.dll

Recurrent All-pairs Field Transforms (RAFT) for optical flow estimation.

public class RAFT<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 (e.g., float, double).

Inheritance
RAFT<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

For Beginners: RAFT is a state-of-the-art optical flow estimation model that predicts the motion between two consecutive video frames. Optical flow represents how pixels move from one frame to the next, useful for: - Motion analysis and tracking - Video stabilization - Action recognition - Video compression - Self-driving car perception

RAFT iteratively refines its flow estimate using a recurrent update mechanism, making it very accurate while remaining efficient.

Technical Details: - Feature extraction using CNN encoder - 4D correlation volumes for all-pairs matching - GRU-based iterative update operator - Multi-scale feature pyramids

Reference: Teed and Deng, "RAFT: Recurrent All-Pairs Field Transforms for Optical Flow" ECCV 2020.

Constructors

RAFT(NeuralNetworkArchitecture<T>, int, int, int, int)

Initializes a new instance of the RAFT class.

public RAFT(NeuralNetworkArchitecture<T> architecture, int numFeatures = 256, int correlationLevels = 4, int correlationRadius = 4, int numIterations = 12)

Parameters

architecture NeuralNetworkArchitecture<T>

The neural network architecture configuration.

numFeatures int

The number of features in intermediate layers.

correlationLevels int

The number of levels in the correlation pyramid.

correlationRadius int

The search radius for correlation lookup.

numIterations int

The number of GRU refinement iterations.

Properties

SupportsTraining

Gets whether training is supported.

public override bool SupportsTraining { get; }

Property Value

bool

Methods

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.

EstimateFlow(Tensor<T>, Tensor<T>)

Estimates optical flow between two frames.

public Tensor<T> EstimateFlow(Tensor<T> frame1, Tensor<T> frame2)

Parameters

frame1 Tensor<T>

The first frame tensor [C, H, W] or [B, C, H, W].

frame2 Tensor<T>

The second frame tensor [C, H, W] or [B, C, H, W].

Returns

Tensor<T>

The optical flow tensor [2, H, W] or [B, 2, H, W].

EstimateFlowIterative(Tensor<T>, Tensor<T>)

Estimates optical flow with intermediate flow predictions.

public List<Tensor<T>> EstimateFlowIterative(Tensor<T> frame1, Tensor<T> frame2)

Parameters

frame1 Tensor<T>

The first frame tensor.

frame2 Tensor<T>

The second frame tensor.

Returns

List<Tensor<T>>

List of flow predictions at each iteration.

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).

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.

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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