Method __init__

funasr/models/lora/layers.py:395–434

Initialize Conv2d. Args: in_channels: TODO. out_channels: TODO. kernel_size: Size/dimension parameter. r: TODO. lora_alpha: TODO. lora_dropout: TODO. merge_weights: TO

(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        r: int = 0,
        lora_alpha: int = 1,
        lora_dropout: float = 0.0,
        merge_weights: bool = True,
        **kwargs
    )

class Conv2d(nn.Conv2d, LoRALayer):
    # LoRA implemented in a dense layer
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        r: int = 0,
        lora_alpha: int = 1,
        lora_dropout: float = 0.0,
        merge_weights: bool = True,
        **kwargs
    ):
        """Initialize Conv2d.
        
            Args:
                in_channels: TODO.
                out_channels: TODO.
                kernel_size: Size/dimension parameter.
                r: TODO.
                lora_alpha: TODO.
                lora_dropout: TODO.
                merge_weights: TODO.
                **kwargs: Additional keyword arguments.
            """
        nn.Conv2d.__init__(self, in_channels, out_channels, kernel_size, **kwargs)
        LoRALayer.__init__(
            self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights
        )
        assert type(kernel_size) is int
        # Actual trainable parameters
        if r > 0:
            self.lora_A = nn.Parameter(
                self.weight.new_zeros((r * kernel_size, in_channels * kernel_size))
            )
            self.lora_B = nn.Parameter(
                self.weight.new_zeros((out_channels * kernel_size, r * kernel_size))
            )
            self.scaling = self.lora_alpha / self.r
            # Freezing the pre-trained weight matrix
            self.weight.requires_grad = False
        self.reset_parameters()

    def reset_parameters(self):
        """Reset parameters."""