Method __init__

models/MultiPatchFormer.py:26–50

(
        self,
        d_model: int,
        mha: AttentionLayer,
        d_hidden: int,
        dropout: float = 0,
        channel_wise=False,
    )

class Encoder(nn.Module):
    def __init__(
        self,
        d_model: int,
        mha: AttentionLayer,
        d_hidden: int,
        dropout: float = 0,
        channel_wise=False,
    ):
        super(Encoder, self).__init__()

        self.channel_wise = channel_wise
        if self.channel_wise:
            self.conv = torch.nn.Conv1d(
                in_channels=d_model,
                out_channels=d_model,
                kernel_size=1,
                stride=1,
                padding=0,
                padding_mode="reflect",
            )
        self.MHA = mha
        self.feedforward = FeedForward(d_model=d_model, d_hidden=d_hidden)
        self.dropout = torch.nn.Dropout(p=dropout)
        self.layerNormal_1 = torch.nn.LayerNorm(d_model)
        self.layerNormal_2 = torch.nn.LayerNorm(d_model)

    def forward(self, x):
        residual = x