Table of Contents

Class SpyNetLayer<T>

Namespace
AiDotNet.NeuralNetworks.Layers
Assembly
AiDotNet.dll

SPyNet (Spatial Pyramid Network) layer for optical flow estimation.

public class SpyNetLayer<T> : LayerBase<T>, ILayer<T>, IJitCompilable<T>, IDiagnosticsProvider, IWeightLoadable<T>, IDisposable, IChainableComputationGraph<T>

Type Parameters

T

The numeric type used for calculations.

Inheritance
SpyNetLayer<T>
Implements
Inherited Members

Remarks

SPyNet uses a coarse-to-fine spatial pyramid approach to estimate optical flow between two consecutive video frames. It's widely used in video super-resolution and frame interpolation models.

For Beginners: Optical flow tells us how pixels move between two frames. SPyNet is a lightweight network that estimates this motion efficiently by processing the images at multiple scales (pyramid levels).

The network works by:

  1. Building image pyramids at different resolutions
  2. Estimating flow at the coarsest level first
  3. Refining the flow at each finer level
  4. Combining all levels for the final flow

Reference: Ranjan and Black, "Optical Flow Estimation using a Spatial Pyramid Network", CVPR 2017. https://arxiv.org/abs/1611.00850

Constructors

SpyNetLayer(int, int, int, int, IEngine?)

Creates a new SPyNet layer for optical flow estimation.

public SpyNetLayer(int inputHeight, int inputWidth, int inputChannels = 3, int numLevels = 5, IEngine? engine = null)

Parameters

inputHeight int

Height of input frames.

inputWidth int

Width of input frames.

inputChannels int

Number of input channels (typically 3 for RGB).

numLevels int

Number of pyramid levels (default: 5).

engine IEngine

Optional computation engine (CPU or GPU). If null, uses default CPU engine.

Properties

SupportsGpuExecution

Indicates whether this layer supports GPU execution.

protected override bool SupportsGpuExecution { get; }

Property Value

bool

SupportsJitCompilation

Gets whether this layer supports JIT compilation.

public override bool SupportsJitCompilation { get; }

Property Value

bool

True if the layer can be JIT compiled, false otherwise.

Remarks

This property indicates whether the layer has implemented ExportComputationGraph() and can benefit from JIT compilation. All layers MUST implement this property.

For Beginners: JIT compilation can make inference 5-10x faster by converting the layer's operations into optimized native code.

Layers should return false if they:

  • Have not yet implemented a working ExportComputationGraph()
  • Use dynamic operations that change based on input data
  • Are too simple to benefit from JIT compilation

When false, the layer will use the standard Forward() method instead.

SupportsTraining

Gets a value indicating whether this layer supports training.

public override bool SupportsTraining { get; }

Property Value

bool

true if the layer has trainable parameters and supports backpropagation; otherwise, false.

Remarks

This property indicates whether the layer can be trained through backpropagation. Layers with trainable parameters such as weights and biases typically return true, while layers that only perform fixed transformations (like pooling or activation layers) typically return false.

For Beginners: This property tells you if the layer can learn from data.

A value of true means:

  • The layer has parameters that can be adjusted during training
  • It will improve its performance as it sees more data
  • It participates in the learning process

A value of false means:

  • The layer doesn't have any adjustable parameters
  • It performs the same operation regardless of training
  • It doesn't need to learn (but may still be useful)

Methods

Backward(Tensor<T>)

Performs the backward pass of the layer.

public override Tensor<T> Backward(Tensor<T> gradOutput)

Parameters

gradOutput Tensor<T>

Returns

Tensor<T>

The gradient of the loss with respect to the layer's input.

Remarks

This abstract method must be implemented by derived classes to define the backward pass of the layer. The backward pass propagates error gradients from the output of the layer back to its input, and calculates gradients for any trainable parameters.

For Beginners: This method is used during training to calculate how the layer's input should change to reduce errors.

During the backward pass:

  1. The layer receives information about how its output contributed to errors
  2. It calculates how its parameters should change to reduce errors
  3. It calculates how its input should change, which will be used by earlier layers

This is the core of how neural networks learn from their mistakes during training.

BuildComputationGraph(ComputationNode<T>, string)

Builds the computation graph for this layer using the provided input node.

public ComputationNode<T> BuildComputationGraph(ComputationNode<T> inputNode, string namePrefix)

Parameters

inputNode ComputationNode<T>

The input computation node from the parent layer.

namePrefix string

Prefix for naming internal nodes (for debugging/visualization).

Returns

ComputationNode<T>

The output computation node representing this layer's computation.

Remarks

Unlike ILayer<T>.ExportComputationGraph, this method does NOT create a new input variable. Instead, it uses the provided inputNode as its input, allowing the parent layer to chain multiple sub-layers together in a single computation graph.

The namePrefix parameter should be used to prefix all internal node names to avoid naming conflicts when multiple instances of the same layer type are used.

Dispose(bool)

Releases resources used by this layer.

protected override void Dispose(bool disposing)

Parameters

disposing bool

True if called from Dispose(), false if called from finalizer.

Remarks

Override this method in derived classes to release layer-specific resources. Always call base.Dispose(disposing) after releasing your resources.

For Beginners: When creating a custom layer with resources: 

protected override void Dispose(bool disposing)
{
    if (disposing)
    {
        // Release your managed resources here
        _myGpuHandle?.Dispose();
        _myGpuHandle = null;
    }
    base.Dispose(disposing);
}

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

Estimates optical flow between two frames using separate tensors.

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

Parameters

frame1 Tensor<T>

First frame tensor.

frame2 Tensor<T>

Second frame tensor.

Returns

Tensor<T>

Optical flow tensor [2, H, W] representing (dx, dy) per pixel.

ExportComputationGraph(List<ComputationNode<T>>)

Exports the layer's computation graph for JIT compilation.

public override ComputationNode<T> ExportComputationGraph(List<ComputationNode<T>> inputNodes)

Parameters

inputNodes List<ComputationNode<T>>

List to populate with input computation nodes.

Returns

ComputationNode<T>

The output computation node representing the layer's operation.

Remarks

This method constructs a computation graph representation of the layer's forward pass that can be JIT compiled for faster inference. All layers MUST implement this method to support JIT compilation.

For Beginners: JIT (Just-In-Time) compilation converts the layer's operations into optimized native code for 5-10x faster inference.

To support JIT compilation, a layer must:

  1. Implement this method to export its computation graph
  2. Set SupportsJitCompilation to true
  3. Use ComputationNode and TensorOperations to build the graph

All layers are required to implement this method, even if they set SupportsJitCompilation = false.

Forward(Tensor<T>)

Performs the forward pass of the layer.

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

Parameters

input Tensor<T>

The input tensor to process.

Returns

Tensor<T>

The output tensor after processing.

Remarks

This abstract method must be implemented by derived classes to define the forward pass of the layer. The forward pass transforms the input tensor according to the layer's operation and activation function.

For Beginners: This method processes your data through the layer.

The forward pass:

  • Takes input data from the previous layer or the network input
  • Applies the layer's specific transformation (like convolution or matrix multiplication)
  • Applies any activation function
  • Passes the result to the next layer

This is where the actual data processing happens during both training and prediction.

ForwardGpu(params IGpuTensor<T>[])

Performs the forward pass of the layer on GPU.

public override IGpuTensor<T> ForwardGpu(params IGpuTensor<T>[] inputs)

Parameters

inputs IGpuTensor<T>[]

The GPU-resident input tensor(s).

Returns

IGpuTensor<T>

The GPU-resident output tensor.

Remarks

This method performs the layer's forward computation entirely on GPU. The input and output tensors remain in GPU memory, avoiding expensive CPU-GPU transfers.

For Beginners: This is like Forward() but runs on the graphics card.

The key difference:

  • Forward() uses CPU tensors that may be copied to/from GPU
  • ForwardGpu() keeps everything on GPU the whole time

Override this in derived classes that support GPU acceleration.

Exceptions

NotSupportedException

Thrown when the layer does not support GPU execution.

GetInputShape()

Gets the input shape for this layer.

public override int[] GetInputShape()

Returns

int[]

The input shape as an array of integers.

Remarks

This method returns the input shape of the layer. If the layer has multiple input shapes, it returns the first one.

For Beginners: This method tells you what shape of data the layer expects.

The input shape:

  • Shows the dimensions of data this layer processes
  • Is needed to connect this layer with previous layers
  • Helps verify the network structure is correct

For layers with multiple inputs, this returns just the first input shape.

GetOutputShape()

Gets the output shape for this layer (2 channels for optical flow: dx, dy).

public int[] GetOutputShape()

Returns

int[]

GetParameterGradients()

Gets the gradients of all trainable parameters in this layer.

public override Vector<T> GetParameterGradients()

Returns

Vector<T>

A vector containing the gradients of all trainable parameters.

Remarks

This method returns the gradients of all trainable parameters in the layer. If the gradients haven't been calculated yet, it initializes a new vector of the appropriate size.

For Beginners: This method provides the current adjustment values for all parameters.

The parameter gradients:

  • Show how each parameter should be adjusted during training
  • Are calculated during the backward pass
  • Guide the optimization process

These gradients are usually passed to an optimizer like SGD or Adam, which uses them to update the parameters in a way that reduces errors.

GetParameters()

Gets all trainable parameters of the layer as a single vector.

public override Vector<T> GetParameters()

Returns

Vector<T>

A vector containing all trainable parameters.

Remarks

This abstract method must be implemented by derived classes to provide access to all trainable parameters of the layer as a single vector. This is useful for optimization algorithms that operate on all parameters at once, or for saving and loading model weights.

For Beginners: This method collects all the learnable values from the layer.

The parameters:

  • Are the numbers that the neural network learns during training
  • Include weights, biases, and other learnable values
  • Are combined into a single long list (vector)

This is useful for:

  • Saving the model to disk
  • Loading parameters from a previously trained model
  • Advanced optimization techniques that need access to all parameters

ResetState()

Resets the internal state of the layer.

public override void ResetState()

Remarks

This abstract method must be implemented by derived classes to reset any internal state the layer maintains between forward and backward passes. This is useful when starting to process a new sequence or when implementing stateful recurrent networks.

For Beginners: This method clears the layer's memory to start fresh.

When resetting the state:

  • Cached inputs and outputs are cleared
  • Any temporary calculations are discarded
  • The layer is ready to process new data without being influenced by previous data

This is important for:

  • Processing a new, unrelated sequence
  • Preventing information from one sequence affecting another
  • Starting a new training episode

SetParameters(Vector<T>)

Sets the trainable parameters of the layer.

public override void SetParameters(Vector<T> parameters)

Parameters

parameters Vector<T>

A vector containing all parameters to set.

Remarks

This method sets all the trainable parameters of the layer from a single vector of parameters. The parameters vector must have the correct length to match the total number of parameters in the layer. By default, it simply assigns the parameters vector to the Parameters field, but derived classes may override this to handle the parameters differently.

For Beginners: This method updates all the learnable values in the layer.

When setting parameters:

  • The input must be a vector with the correct length
  • The layer parses this vector to set all its internal parameters
  • Throws an error if the input doesn't match the expected number of parameters

This is useful for:

  • Loading a previously saved model
  • Transferring parameters from another model
  • Setting specific parameter values for testing

Exceptions

ArgumentException

Thrown when the parameters vector has incorrect length.

UpdateParameters(T)

Updates the parameters of the layer using the calculated gradients.

public override void UpdateParameters(T learningRate)

Parameters

learningRate T

The learning rate to use for the parameter updates.

Remarks

This abstract method must be implemented by derived classes to define how the layer's parameters are updated during training. The learning rate controls the size of the parameter updates.

For Beginners: This method updates the layer's internal values during training.

When updating parameters:

  • The weights, biases, or other parameters are adjusted to reduce prediction errors
  • The learning rate controls how big each update step is
  • Smaller learning rates mean slower but more stable learning
  • Larger learning rates mean faster but potentially unstable learning

This is how the layer "learns" from data over time, gradually improving its ability to extract useful patterns from inputs.

Edit this page