Class Conv2DOp
- Namespace
- AiDotNet.JitCompiler.IR.Operations
- Assembly
- AiDotNet.dll
Represents 2D convolution in the IR.
public class Conv2DOp : IROp- Inheritance
-
Conv2DOp
- Inherited Members
Properties
HasBias
Whether this convolution has a bias term.
public bool HasBias { get; set; }Property Value
InputShape
Input shape [batch, channels, height, width] for kernel generation.
public int[] InputShape { get; set; }Property Value
- int[]
KernelSize
Kernel size [height, width].
public int[] KernelSize { get; set; }Property Value
- int[]
Padding
Padding [height, width].
public int[] Padding { get; set; }Property Value
- int[]
Stride
Stride [height, width].
public int[] Stride { get; set; }Property Value
- int[]
Methods
ToString()
Gets a string representation of this operation for debugging.
public override string ToString()Returns
- string
A string describing this operation.
Remarks
The string format is: "tOutput = OpType(tInput1, tInput2, ...) : Type [Shape]"
For Beginners: This creates a readable description of the operation.
Example outputs:
- "t2 = Add(t0, t1) : Float32 [3, 4]"
- "t5 = MatMul(t3, t4) : Float32 [128, 256]"
- "t8 = ReLU(t7) : Float32 [32, 128]"
This is super helpful for debugging - you can see exactly what each operation does and what shape tensors flow through the graph.
Validate()
Validates that this operation is correctly formed.
public override bool Validate()Returns
- bool
True if valid, false otherwise.
Remarks
Basic validation checks that the operation has required information. Derived classes can override to add operation-specific validation.
For Beginners: This checks that the operation makes sense.
Basic checks:
- Output ID is valid (non-negative)
- Has the right number of inputs
- Shapes are compatible
Specific operations add their own checks:
- MatMul: inner dimensions must match
- Conv2D: kernel size must be valid
- Reshape: total elements must be preserved
If validation fails, the operation can't be compiled.