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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import math
class PositionalEmbedding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEmbedding, self).__init__()
# Compute the positional encodings once in log space.
pe = torch.zeros(max_len, d_model).float()
pe.require_grad = False
position = torch.arange(0, max_len).float().unsqueeze(1)
div_term = (torch.arange(0, d_model, 2).float()
* -(math.log(10000.0) / d_model)).exp()
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.register_buffer('pe', pe)
def forward(self, x):
return self.pe[:, :x.size(1)]
class TokenEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= '1.5.0' else 2
self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
kernel_size=3, padding=padding, padding_mode='circular', bias=False)
for m in self.modules():
if isinstance(m, nn.Conv1d):
nn.init.kaiming_normal_(
m.weight, mode='fan_in', nonlinearity='leaky_relu')
def forward(self, x):
x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
return x
class FixedEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(FixedEmbedding, self).__init__()
w = torch.zeros(c_in, d_model).float()
w.require_grad = False
position = torch.arange(0, c_in).float().unsqueeze(1)
div_term = (torch.arange(0, d_model, 2).float()
* -(math.log(10000.0) / d_model)).exp()
w[:, 0::2] = torch.sin(position * div_term)
w[:, 1::2] = torch.cos(position * div_term)
self.emb = nn.Embedding(c_in, d_model)
self.emb.weight = nn.Parameter(w, requires_grad=False)
def forward(self, x):
return self.emb(x).detach()
class TemporalEmbedding(nn.Module):
def __init__(self, d_model, embed_type='fixed', freq='h'):
super(TemporalEmbedding, self).__init__()
minute_size = 4
hour_size = 24
weekday_size = 7
day_size = 32
month_size = 13
Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding
if freq == 't':
self.minute_embed = Embed(minute_size, d_model)
self.hour_embed = Embed(hour_size, d_model)
self.weekday_embed = Embed(weekday_size, d_model)
self.day_embed = Embed(day_size, d_model)
self.month_embed = Embed(month_size, d_model)
def forward(self, x):
x = x.long()
minute_x = self.minute_embed(x[:, :, 4]) if hasattr(
self, 'minute_embed') else 0.
hour_x = self.hour_embed(x[:, :, 3])
weekday_x = self.weekday_embed(x[:, :, 2])
day_x = self.day_embed(x[:, :, 1])
month_x = self.month_embed(x[:, :, 0])
return hour_x + weekday_x + day_x + month_x + minute_x
class TimeFeatureEmbedding(nn.Module):
def __init__(self, d_model, embed_type='timeF', freq='h'):
super(TimeFeatureEmbedding, self).__init__()
freq_map = {'h': 4, 't': 5, 's': 6,
'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3, '15min' : 5}
d_inp = freq_map[freq]
self.embed = nn.Linear(d_inp, d_model, bias=False)
def forward(self, x):
return self.embed(x)
class DataEmbedding(nn.Module):
def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
super(DataEmbedding, self).__init__()
self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
self.position_embedding = PositionalEmbedding(d_model=d_model)
self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
d_model=d_model, embed_type=embed_type, freq=freq)
self.dropout = nn.Dropout(p=dropout)
def forward(self, x, x_mark):
if x_mark is None:
x = self.value_embedding(x) + self.position_embedding(x)
else:
x = self.value_embedding(
x) + self.temporal_embedding(x_mark) + self.position_embedding(x)
return self.dropout(x)
class DataEmbedding_inverted(nn.Module):
def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
super(DataEmbedding_inverted, self).__init__()
self.value_embedding = nn.Linear(c_in, d_model)
self.dropout = nn.Dropout(p=dropout)
def forward(self, x, x_mark):
x = x.permute(0, 2, 1)
# x: [Batch Variate Time]
if x_mark is None:
x = self.value_embedding(x)
else:
x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
# x: [Batch Variate d_model]
return self.dropout(x)
class DataEmbedding_wo_pos(nn.Module):
def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
super(DataEmbedding_wo_pos, self).__init__()
self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
self.position_embedding = PositionalEmbedding(d_model=d_model)
self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
d_model=d_model, embed_type=embed_type, freq=freq)
self.dropout = nn.Dropout(p=dropout)
def forward(self, x, x_mark):
if x_mark is None:
x = self.value_embedding(x)
else:
x = self.value_embedding(x) + self.temporal_embedding(x_mark)
return self.dropout(x)
class PatchEmbedding(nn.Module):
def __init__(self, d_model, patch_len, stride, padding, dropout):
super(PatchEmbedding, self).__init__()
# Patching
self.patch_len = patch_len
self.stride = stride
self.padding_patch_layer = nn.ReplicationPad1d((0, padding))
# Backbone, Input encoding: projection of feature vectors onto a d-dim vector space
self.value_embedding = nn.Linear(patch_len, d_model, bias=False)
# Positional embedding
self.position_embedding = PositionalEmbedding(d_model)
# Residual dropout
self.dropout = nn.Dropout(dropout)
def forward(self, x):
# do patching
n_vars = x.shape[1]
x = self.padding_patch_layer(x)
x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
# Input encoding
x = self.value_embedding(x) + self.position_embedding(x)
return self.dropout(x), n_vars
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