Autoformer encoder
| 138 | |
| 139 | |
| 140 | class Encoder(nn.Module): |
| 141 | """ |
| 142 | Autoformer encoder |
| 143 | """ |
| 144 | def __init__(self, attn_layers, conv_layers=None, norm_layer=None): |
| 145 | super(Encoder, self).__init__() |
| 146 | self.attn_layers = nn.ModuleList(attn_layers) |
| 147 | self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None |
| 148 | self.norm = norm_layer |
| 149 | |
| 150 | def forward(self, x, attn_mask=None): |
| 151 | attns = [] |
| 152 | if self.conv_layers is not None: |
| 153 | for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers): |
| 154 | x, attn = attn_layer(x, attn_mask=attn_mask) |
| 155 | x = conv_layer(x) |
| 156 | attns.append(attn) |
| 157 | x, attn = self.attn_layers[-1](x) |
| 158 | attns.append(attn) |
| 159 | else: |
| 160 | for attn_layer in self.attn_layers: |
| 161 | x, attn = attn_layer(x, attn_mask=attn_mask) |
| 162 | attns.append(attn) |
| 163 | |
| 164 | if self.norm is not None: |
| 165 | x = self.norm(x) |
| 166 | |
| 167 | return x, attns |
| 168 | |
| 169 | |
| 170 | class DecoderLayer(nn.Module): |