Source code for pypots.nn.modules.informer.autoencoder

""" """

# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause

import torch.nn as nn




class InformerEncoder(nn.Module):
    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
        super().__init__()
        self.attn_layers = nn.ModuleList(attn_layers)
        self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
        self.norm = norm_layer



    def forward(self, x, attn_mask=None):
        attns = []
        if self.conv_layers is not None:
            for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
                x, attn = attn_layer(x, attn_mask=attn_mask)
                x = conv_layer(x)
                attns.append(attn)
            x, attn = self.attn_layers[-1](x, attn_mask=attn_mask)
            attns.append(attn)
        else:
            for attn_layer in self.attn_layers:
                x, attn = attn_layer(x, attn_mask=attn_mask)
                attns.append(attn)

        if self.norm is not None:
            x = self.norm(x)

        return x, attns






class InformerDecoder(nn.Module):
    def __init__(self, layers, norm_layer=None, projection=None):
        super().__init__()
        self.layers = nn.ModuleList(layers)
        self.norm = norm_layer
        self.projection = projection



    def forward(self, x, cross, x_mask=None, cross_mask=None, trend=None):
        for layer in self.layers:
            x, residual_trend = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)
            trend = trend + residual_trend

        if self.norm is not None:
            x = self.norm(x)

        if self.projection is not None:
            x = self.projection(x)
        return x, trend