kaira.metrics.BaseMetric
Inheritance diagram for BaseMetric
- class kaira.metrics.BaseMetric(name: str | None = None, *args: Any, **kwargs: Any)[source]
-
Base Metric Module.
This is an abstract base class for defining metrics to evaluate the performance of a communication system. Subclasses should implement the forward method to calculate the metric.
Methods
Initialize the metric.
Compute metric with mean and standard deviation.
Forward pass through the metric.
Examples using
kaira.metrics.BaseMetric
Original DeepJSCC Model (Bourtsoulatze 2019) with Training
Original DeepJSCC Model (Bourtsoulatze 2019) with Training
Deep Joint Source-Channel Coding (DeepJSCC) Model - Bourtsoulatze2019 Implementation
Deep Joint Source-Channel Coding (DeepJSCC) Model - Bourtsoulatze2019 Implementation- __init__(name: str | None = None, *args: Any, **kwargs: Any)[source]
Initialize the metric.
- Parameters:
name (Optional[str]) – Name of the metric
*args – Variable length argument list.
**kwargs – Arbitrary keyword arguments.
- abstractmethod forward(x: Tensor, y: Tensor, *args: Any, **kwargs: Any) Tensor[source]
Forward pass through the metric.
- Parameters:
x (torch.Tensor) – The first input tensor (typically predictions)
y (torch.Tensor) – The second input tensor (typically targets)
*args – Variable length argument list.
**kwargs – Arbitrary keyword arguments.
- Returns:
The calculated metric value
- Return type:
- compute_with_stats(x: Tensor, y: Tensor, *args: Any, **kwargs: Any) Tuple[Tensor, Tensor][source]
Compute metric with mean and standard deviation.
- Parameters:
x (torch.Tensor) – The first input tensor (typically predictions)
y (torch.Tensor) – The second input tensor (typically targets)
*args – Variable length argument list.
**kwargs – Arbitrary keyword arguments.
- Returns:
Mean and standard deviation of the metric
- Return type:
Tuple[torch.Tensor, torch.Tensor]
