File size: 2,803 Bytes
ff4becd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | #__all__ = ['Transpose', 'get_activation_fn', 'moving_avg', 'series_decomp', 'PositionalEncoding', 'SinCosPosEncoding', 'Coord2dPosEncoding', 'Coord1dPosEncoding', 'positional_encoding']
__all__ = ['moving_avg', 'series_decomp', 'Flatten_Head']
import torch
from torch import nn
import math
# decomposition
class moving_avg(nn.Module):
"""
Moving average block to highlight the trend of time series
"""
def __init__(self, kernel_size, stride):
super(moving_avg, self).__init__()
self.kernel_size = kernel_size
self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
def forward(self, x):
# padding on the both ends of time series
front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
x = torch.cat([front, x, end], dim=1)
x = self.avg(x.permute(0, 2, 1))
x = x.permute(0, 2, 1)
return x
class series_decomp(nn.Module):
"""
Series decomposition block
"""
def __init__(self, kernel_size):
super(series_decomp, self).__init__()
self.moving_avg = moving_avg(kernel_size, stride=1)
def forward(self, x):
moving_mean = self.moving_avg(x)
res = x - moving_mean
return res, moving_mean
# forecast task head
class Flatten_Head(nn.Module):
def __init__(self, individual, n_vars, nf, target_window, head_dropout=0):
super(Flatten_Head, self).__init__()
self.individual = individual
self.n_vars = n_vars
if self.individual:
self.linears = nn.ModuleList()
self.dropouts = nn.ModuleList()
self.flattens = nn.ModuleList()
for i in range(self.n_vars):
self.flattens.append(nn.Flatten(start_dim=-2))
self.linears.append(nn.Linear(nf, target_window))
self.dropouts.append(nn.Dropout(head_dropout))
else:
self.flatten = nn.Flatten(start_dim=-2)
self.linear = nn.Linear(nf, target_window)
self.dropout = nn.Dropout(head_dropout)
def forward(self, x): # x: [bs x nvars x d_model x patch_num]
if self.individual:
x_out = []
for i in range(self.n_vars):
z = self.flattens[i](x[:, i, :, :]) # z: [bs x d_model * patch_num]
z = self.linears[i](z) # z: [bs x target_window]
z = self.dropouts[i](z)
x_out.append(z)
x = torch.stack(x_out, dim=1) # x: [bs x nvars x target_window]
else:
x = self.flatten(x)
x = self.linear(x)
x = self.dropout(x)
return x |