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https://github.com/wassname/ETSformer.git
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gorold
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
import torch
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import torch.nn as nn
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from einops import rearrange, reduce, repeat
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class DampingLayer(nn.Module):
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def __init__(self, pred_len, nhead, dropout=0.1):
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super().__init__()
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self.pred_len = pred_len
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self.nhead = nhead
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self._damping_factor = nn.Parameter(torch.randn(1, nhead))
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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x = repeat(x, 'b 1 d -> b t d', t=self.pred_len)
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b, t, d = x.shape
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powers = torch.arange(self.pred_len).to(self._damping_factor.device) + 1
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powers = powers.view(self.pred_len, 1)
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damping_factors = self.damping_factor ** powers
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damping_factors = damping_factors.cumsum(dim=0)
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x = x.view(b, t, self.nhead, -1)
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x = self.dropout(x) * damping_factors.unsqueeze(-1)
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return x.view(b, t, d)
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@property
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def damping_factor(self):
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return torch.sigmoid(self._damping_factor)
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class DecoderLayer(nn.Module):
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def __init__(self, d_model, nhead, c_out, pred_len, dropout=0.1):
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super().__init__()
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self.d_model = d_model
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self.nhead = nhead
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self.c_out = c_out
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self.pred_len = pred_len
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self.growth_damping = DampingLayer(pred_len, nhead, dropout=dropout)
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self.dropout1 = nn.Dropout(dropout)
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def forward(self, growth, season):
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growth_horizon = self.growth_damping(growth[:, -1:])
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growth_horizon = self.dropout1(growth_horizon)
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seasonal_horizon = season[:, -self.pred_len:]
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return growth_horizon, seasonal_horizon
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class Decoder(nn.Module):
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def __init__(self, layers):
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super().__init__()
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self.d_model = layers[0].d_model
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self.c_out = layers[0].c_out
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self.pred_len = layers[0].pred_len
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self.nhead = layers[0].nhead
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self.layers = nn.ModuleList(layers)
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self.pred = nn.Linear(self.d_model, self.c_out)
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def forward(self, growths, seasons):
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growth_repr = []
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season_repr = []
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for idx, layer in enumerate(self.layers):
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growth_horizon, season_horizon = layer(growths[idx], seasons[idx])
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growth_repr.append(growth_horizon)
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season_repr.append(season_horizon)
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growth_repr = sum(growth_repr)
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season_repr = sum(season_repr)
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return self.pred(growth_repr), self.pred(season_repr)
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