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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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class my_Layernorm(nn.Module): |
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""" |
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Special designed layernorm for the seasonal part |
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""" |
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def __init__(self, channels): |
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super(my_Layernorm, self).__init__() |
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self.layernorm = nn.LayerNorm(channels) |
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def forward(self, x): |
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x_hat = self.layernorm(x) |
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bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1) |
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return x_hat - bias |
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class moving_avg(nn.Module): |
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""" |
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Moving average block to highlight the trend of time series |
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""" |
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def __init__(self, kernel_size, stride): |
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super(moving_avg, self).__init__() |
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self.kernel_size = kernel_size |
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self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0) |
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def forward(self, x): |
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front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1) |
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end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1) |
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x = torch.cat([front, x, end], dim=1) |
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x = self.avg(x.permute(0, 2, 1)) |
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x = x.permute(0, 2, 1) |
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return x |
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class series_decomp(nn.Module): |
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""" |
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Series decomposition block |
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""" |
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def __init__(self, kernel_size): |
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super(series_decomp, self).__init__() |
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self.moving_avg = moving_avg(kernel_size, stride=1) |
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def forward(self, x): |
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moving_mean = self.moving_avg(x) |
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res = x - moving_mean |
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return res, moving_mean |
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class series_decomp_multi(nn.Module): |
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""" |
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Multiple Series decomposition block from FEDformer |
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""" |
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def __init__(self, kernel_size): |
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super(series_decomp_multi, self).__init__() |
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self.kernel_size = kernel_size |
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self.series_decomp = [series_decomp(kernel) for kernel in kernel_size] |
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def forward(self, x): |
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moving_mean = [] |
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res = [] |
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for func in self.series_decomp: |
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sea, moving_avg = func(x) |
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moving_mean.append(moving_avg) |
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res.append(sea) |
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sea = sum(res) / len(res) |
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moving_mean = sum(moving_mean) / len(moving_mean) |
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return sea, moving_mean |
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class EncoderLayer(nn.Module): |
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""" |
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Autoformer encoder layer with the progressive decomposition architecture |
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""" |
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def __init__(self, attention, d_model, d_ff=None, moving_avg=25, dropout=0.1, activation="relu"): |
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super(EncoderLayer, self).__init__() |
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d_ff = d_ff or 4 * d_model |
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self.attention = attention |
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self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1, bias=False) |
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self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1, bias=False) |
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self.decomp1 = series_decomp(moving_avg) |
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self.decomp2 = series_decomp(moving_avg) |
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self.dropout = nn.Dropout(dropout) |
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self.activation = F.relu if activation == "relu" else F.gelu |
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def forward(self, x, attn_mask=None): |
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new_x, attn = self.attention( |
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x, x, x, |
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attn_mask=attn_mask |
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) |
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x = x + self.dropout(new_x) |
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x, _ = self.decomp1(x) |
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y = x |
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y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1)))) |
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y = self.dropout(self.conv2(y).transpose(-1, 1)) |
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res, _ = self.decomp2(x + y) |
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return res, attn |
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class Encoder(nn.Module): |
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""" |
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Autoformer encoder |
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""" |
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def __init__(self, attn_layers, conv_layers=None, norm_layer=None): |
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super(Encoder, self).__init__() |
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self.attn_layers = nn.ModuleList(attn_layers) |
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self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None |
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self.norm = norm_layer |
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def forward(self, x, attn_mask=None): |
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attns = [] |
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if self.conv_layers is not None: |
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for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers): |
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x, attn = attn_layer(x, attn_mask=attn_mask) |
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x = conv_layer(x) |
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attns.append(attn) |
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x, attn = self.attn_layers[-1](x) |
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attns.append(attn) |
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else: |
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for attn_layer in self.attn_layers: |
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x, attn = attn_layer(x, attn_mask=attn_mask) |
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attns.append(attn) |
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if self.norm is not None: |
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x = self.norm(x) |
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return x, attns |
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class DecoderLayer(nn.Module): |
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""" |
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Autoformer decoder layer with the progressive decomposition architecture |
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""" |
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def __init__(self, self_attention, cross_attention, d_model, c_out, d_ff=None, |
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moving_avg=25, dropout=0.1, activation="relu"): |
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super(DecoderLayer, self).__init__() |
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d_ff = d_ff or 4 * d_model |
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self.self_attention = self_attention |
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self.cross_attention = cross_attention |
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self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1, bias=False) |
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self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1, bias=False) |
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self.decomp1 = series_decomp(moving_avg) |
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self.decomp2 = series_decomp(moving_avg) |
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self.decomp3 = series_decomp(moving_avg) |
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self.dropout = nn.Dropout(dropout) |
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self.projection = nn.Conv1d(in_channels=d_model, out_channels=c_out, kernel_size=3, stride=1, padding=1, |
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padding_mode='circular', bias=False) |
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self.activation = F.relu if activation == "relu" else F.gelu |
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def forward(self, x, cross, x_mask=None, cross_mask=None): |
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x = x + self.dropout(self.self_attention( |
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x, x, x, |
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attn_mask=x_mask |
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)[0]) |
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x, trend1 = self.decomp1(x) |
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x = x + self.dropout(self.cross_attention( |
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x, cross, cross, |
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attn_mask=cross_mask |
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)[0]) |
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x, trend2 = self.decomp2(x) |
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y = x |
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y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1)))) |
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y = self.dropout(self.conv2(y).transpose(-1, 1)) |
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x, trend3 = self.decomp3(x + y) |
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residual_trend = trend1 + trend2 + trend3 |
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residual_trend = self.projection(residual_trend.permute(0, 2, 1)).transpose(1, 2) |
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return x, residual_trend |
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class Decoder(nn.Module): |
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""" |
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Autoformer encoder |
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""" |
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def __init__(self, layers, norm_layer=None, projection=None): |
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super(Decoder, self).__init__() |
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self.layers = nn.ModuleList(layers) |
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self.norm = norm_layer |
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self.projection = projection |
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def forward(self, x, cross, x_mask=None, cross_mask=None, trend=None): |
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for layer in self.layers: |
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x, residual_trend = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask) |
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trend = trend + residual_trend |
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if self.norm is not None: |
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x = self.norm(x) |
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if self.projection is not None: |
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x = self.projection(x) |
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return x, trend |
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