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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class Splitting(nn.Module):
def __init__(self):
super(Splitting, self).__init__()
def even(self, x):
return x[:, ::2, :]
def odd(self, x):
return x[:, 1::2, :]
def forward(self, x):
# return the odd and even part
return self.even(x), self.odd(x)
class CausalConvBlock(nn.Module):
def __init__(self, d_model, kernel_size=5, dropout=0.0):
super(CausalConvBlock, self).__init__()
module_list = [
nn.ReplicationPad1d((kernel_size - 1, kernel_size - 1)),
nn.Conv1d(d_model, d_model,
kernel_size=kernel_size),
nn.LeakyReLU(negative_slope=0.01, inplace=True),
nn.Dropout(dropout),
nn.Conv1d(d_model, d_model,
kernel_size=kernel_size),
nn.Tanh()
]
self.causal_conv = nn.Sequential(*module_list)
def forward(self, x):
return self.causal_conv(x) # return value is the same as input dimension
class SCIBlock(nn.Module):
def __init__(self, d_model, kernel_size=5, dropout=0.0):
super(SCIBlock, self).__init__()
self.splitting = Splitting()
self.modules_even, self.modules_odd, self.interactor_even, self.interactor_odd = [CausalConvBlock(d_model) for _ in range(4)]
def forward(self, x):
x_even, x_odd = self.splitting(x)
x_even = x_even.permute(0, 2, 1)
x_odd = x_odd.permute(0, 2, 1)
x_even_temp = x_even.mul(torch.exp(self.modules_even(x_odd)))
x_odd_temp = x_odd.mul(torch.exp(self.modules_odd(x_even)))
x_even_update = x_even_temp + self.interactor_even(x_odd_temp)
x_odd_update = x_odd_temp - self.interactor_odd(x_even_temp)
return x_even_update.permute(0, 2, 1), x_odd_update.permute(0, 2, 1)
class SCINet(nn.Module):
def __init__(self, d_model, current_level=3, kernel_size=5, dropout=0.0):
super(SCINet, self).__init__()
self.current_level = current_level
self.working_block = SCIBlock(d_model, kernel_size, dropout)
if current_level != 0:
self.SCINet_Tree_odd = SCINet(d_model, current_level-1, kernel_size, dropout)
self.SCINet_Tree_even = SCINet(d_model, current_level-1, kernel_size, dropout)
def forward(self, x):
odd_flag = False
if x.shape[1] % 2 == 1:
odd_flag = True
x = torch.cat((x, x[:, -1:, :]), dim=1)
x_even_update, x_odd_update = self.working_block(x)
if odd_flag:
x_odd_update = x_odd_update[:, :-1]
if self.current_level == 0:
return self.zip_up_the_pants(x_even_update, x_odd_update)
else:
return self.zip_up_the_pants(self.SCINet_Tree_even(x_even_update), self.SCINet_Tree_odd(x_odd_update))
def zip_up_the_pants(self, even, odd):
even = even.permute(1, 0, 2)
odd = odd.permute(1, 0, 2)
even_len = even.shape[0]
odd_len = odd.shape[0]
min_len = min(even_len, odd_len)
zipped_data = []
for i in range(min_len):
zipped_data.append(even[i].unsqueeze(0))
zipped_data.append(odd[i].unsqueeze(0))
if even_len > odd_len:
zipped_data.append(even[-1].unsqueeze(0))
return torch.cat(zipped_data,0).permute(1, 0, 2)
class Model(nn.Module):
def __init__(self, configs):
super(Model, self).__init__()
self.task_name = configs.task_name
self.seq_len = configs.seq_len
self.label_len = configs.label_len
self.pred_len = configs.pred_len
# You can set the number of SCINet stacks by argument "d_layers", but should choose 1 or 2.
self.num_stacks = configs.d_layers
if self.num_stacks == 1:
self.sci_net_1 = SCINet(configs.enc_in, dropout=configs.dropout)
self.projection_1 = nn.Conv1d(self.seq_len, self.seq_len + self.pred_len, kernel_size=1, stride=1, bias=False)
else:
self.sci_net_1, self.sci_net_2 = [SCINet(configs.enc_in, dropout=configs.dropout) for _ in range(2)]
self.projection_1 = nn.Conv1d(self.seq_len, self.pred_len, kernel_size=1, stride=1, bias=False)
self.projection_2 = nn.Conv1d(self.seq_len+self.pred_len, self.seq_len+self.pred_len,
kernel_size = 1, bias = False)
# For positional encoding
self.pe_hidden_size = configs.enc_in
if self.pe_hidden_size % 2 == 1:
self.pe_hidden_size += 1
num_timescales = self.pe_hidden_size // 2
max_timescale = 10000.0
min_timescale = 1.0
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
max(num_timescales - 1, 1))
inv_timescales = min_timescale * torch.exp(
torch.arange(num_timescales, dtype=torch.float32) *
-log_timescale_increment)
self.register_buffer('inv_timescales', inv_timescales)
def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec) # [B,pred_len,C]
dec_out = torch.cat([torch.zeros_like(x_enc), dec_out], dim=1)
return dec_out # [B, T, D]
return None
def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
# Normalization from Non-stationary Transformer
means = x_enc.mean(1, keepdim=True).detach()
x_enc = x_enc - means
stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
x_enc /= stdev
# position-encoding
pe = self.get_position_encoding(x_enc)
if pe.shape[2] > x_enc.shape[2]:
x_enc += pe[:, :, :-1]
else:
x_enc += self.get_position_encoding(x_enc)
# SCINet
dec_out = self.sci_net_1(x_enc)
dec_out += x_enc
dec_out = self.projection_1(dec_out)
if self.num_stacks != 1:
dec_out = torch.cat((x_enc, dec_out), dim=1)
temp = dec_out
dec_out = self.sci_net_2(dec_out)
dec_out += temp
dec_out = self.projection_2(dec_out)
# De-Normalization from Non-stationary Transformer
dec_out = dec_out * \
(stdev[:, 0, :].unsqueeze(1).repeat(
1, self.pred_len + self.seq_len, 1))
dec_out = dec_out + \
(means[:, 0, :].unsqueeze(1).repeat(
1, self.pred_len + self.seq_len, 1))
return dec_out
def get_position_encoding(self, x):
max_length = x.size()[1]
position = torch.arange(max_length, dtype=torch.float32,
device=x.device) # tensor([0., 1., 2., 3., 4.], device='cuda:0')
scaled_time = position.unsqueeze(1) * self.inv_timescales.unsqueeze(0) # 5 256
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) # [T, C]
signal = F.pad(signal, (0, 0, 0, self.pe_hidden_size % 2))
signal = signal.view(1, max_length, self.pe_hidden_size)
return signal |