kaira.models.image.Yilmaz2023DeepJSCCNOMAEncoder

Inheritance diagram of Yilmaz2023DeepJSCCNOMAEncoder

Inheritance diagram for Yilmaz2023DeepJSCCNOMAEncoder

class kaira.models.image.Yilmaz2023DeepJSCCNOMAEncoder(N=64, M=16, in_ch=4, csi_length=1, *args: Any, **kwargs: Any)[source]

Bases: Tung2022DeepJSCCQ2Encoder

DeepJSCC-NOMA Encoder Module [Yilmaz et al., 2023].

This encoder transforms input images into latent representations. This class extends the Tung2022DeepJSCCQ2Encoder class with parameter adaptation as used in the paper Yilmaz et al. [2023].

Methods

__init__

Initialize the DeepJSCCNOMAEncoder.

forward

Forward pass through the encoder.

from_config

Create model instance from configuration.

from_hydra_config

Create model from Hydra DictConfig.

from_pretrained_config

Create model from Hugging Face PretrainedConfig.

Attributes

bandwidth_ratio

Calculate the bandwidth ratio of the model.
__init__(N=64, M=16, in_ch=4, csi_length=1, *args: Any, **kwargs: Any)[source]

Initialize the DeepJSCCNOMAEncoder.

Parameters:

  • N (int, optional) – Number of channels in the network.

  • M (int, optional) – Latent dimension of the bottleneck representation.

  • in_ch (int, optional) – Number of input channels. Defaults to 4.

  • csi_length (int, optional) – The number of dimensions in the CSI data. Defaults to 1.

  • *args – Variable positional arguments passed to the base class.

  • **kwargs – Variable keyword arguments passed to the base class.

property bandwidth_ratio: float

Calculate the bandwidth ratio of the model.

Returns:

The bandwidth ratio.

Return type:

float

forward(x: Tensor, csi: Tensor, *args: Any, **kwargs: Any) → Tensor

Forward pass through the encoder.

Parameters:

  • x (torch.Tensor) – The input image tensor.

  • csi (torch.Tensor) – Channel State Information tensor.

  • *args – Additional positional arguments.

  • **kwargs – Additional keyword arguments.

Returns:

The encoded latent representation.

Return type:

torch.Tensor

classmethod from_config(config, **kwargs)

Create model instance from configuration.

Parameters:

  • config – Configuration object (PretrainedConfig, DictConfig, or dict)

  • **kwargs – Additional parameters to override config

Returns:

Model instance

classmethod from_hydra_config(config: DictConfig, **kwargs)

Create model from Hydra DictConfig.

Parameters:

  • config – Hydra configuration

  • **kwargs – Additional parameters

Returns:

Model instance

classmethod from_pretrained_config(config: PretrainedConfig, **kwargs)

Create model from Hugging Face PretrainedConfig.

Parameters:

  • config – PretrainedConfig instance

  • **kwargs – Additional parameters

Returns:

Model instance