pypots/nn/modules/informer/autoencoder.py
"""
"""
# 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