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import torch
import torch.nn as nn
import torch.nn.functional as F
class IEBlock(nn.Module):
def __init__(self, input_dim, hid_dim, output_dim, num_node):
super(IEBlock, self).__init__()
self.input_dim = input_dim
self.hid_dim = hid_dim
self.output_dim = output_dim
self.num_node = num_node
self._build()
def _build(self):
self.spatial_proj = nn.Sequential(
nn.Linear(self.input_dim, self.hid_dim),
nn.LeakyReLU(),
nn.Linear(self.hid_dim, self.hid_dim // 4)
)
self.channel_proj = nn.Linear(self.num_node, self.num_node)
torch.nn.init.eye_(self.channel_proj.weight)
self.output_proj = nn.Linear(self.hid_dim // 4, self.output_dim)
def forward(self, x):
x = self.spatial_proj(x.permute(0, 2, 1))
x = x.permute(0, 2, 1) + self.channel_proj(x.permute(0, 2, 1))
x = self.output_proj(x.permute(0, 2, 1))
x = x.permute(0, 2, 1)
return x
class Model(nn.Module):
"""
Paper link: https://arxiv.org/abs/2207.01186
"""
def __init__(self, configs, chunk_size=24):
"""
chunk_size: int, reshape T into [num_chunks, chunk_size]
"""
super(Model, self).__init__()
self.task_name = configs.task_name
self.seq_len = configs.seq_len
if self.task_name == 'classification' or self.task_name == 'anomaly_detection' or self.task_name == 'imputation':
self.pred_len = configs.seq_len
else:
self.pred_len = configs.pred_len
if configs.task_name == 'long_term_forecast' or configs.task_name == 'short_term_forecast':
self.chunk_size = min(configs.pred_len, configs.seq_len, chunk_size)
else:
self.chunk_size = min(configs.seq_len, chunk_size)
# assert (self.seq_len % self.chunk_size == 0)
if self.seq_len % self.chunk_size != 0:
self.seq_len += (self.chunk_size - self.seq_len % self.chunk_size) # padding in order to ensure complete division
self.num_chunks = self.seq_len // self.chunk_size
self.d_model = configs.d_model
self.enc_in = configs.enc_in
self.dropout = configs.dropout
if self.task_name == 'classification':
self.act = F.gelu
self.dropout = nn.Dropout(configs.dropout)
self.projection = nn.Linear(configs.enc_in * configs.seq_len, configs.num_class)
self._build()
def _build(self):
self.layer_1 = IEBlock(
input_dim=self.chunk_size,
hid_dim=self.d_model // 4,
output_dim=self.d_model // 4,
num_node=self.num_chunks
)
self.chunk_proj_1 = nn.Linear(self.num_chunks, 1)
self.layer_2 = IEBlock(
input_dim=self.chunk_size,
hid_dim=self.d_model // 4,
output_dim=self.d_model // 4,
num_node=self.num_chunks
)
self.chunk_proj_2 = nn.Linear(self.num_chunks, 1)
self.layer_3 = IEBlock(
input_dim=self.d_model // 2,
hid_dim=self.d_model // 2,
output_dim=self.pred_len,
num_node=self.enc_in
)
self.ar = nn.Linear(self.seq_len, self.pred_len)
def encoder(self, x):
B, T, N = x.size()
highway = self.ar(x.permute(0, 2, 1))
highway = highway.permute(0, 2, 1)
# continuous sampling
x1 = x.reshape(B, self.num_chunks, self.chunk_size, N)
x1 = x1.permute(0, 3, 2, 1)
x1 = x1.reshape(-1, self.chunk_size, self.num_chunks)
x1 = self.layer_1(x1)
x1 = self.chunk_proj_1(x1).squeeze(dim=-1)
# interval sampling
x2 = x.reshape(B, self.chunk_size, self.num_chunks, N)
x2 = x2.permute(0, 3, 1, 2)
x2 = x2.reshape(-1, self.chunk_size, self.num_chunks)
x2 = self.layer_2(x2)
x2 = self.chunk_proj_2(x2).squeeze(dim=-1)
x3 = torch.cat([x1, x2], dim=-1)
x3 = x3.reshape(B, N, -1)
x3 = x3.permute(0, 2, 1)
out = self.layer_3(x3)
out = out + highway
return out
def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
return self.encoder(x_enc)
def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
return self.encoder(x_enc)
def anomaly_detection(self, x_enc):
return self.encoder(x_enc)
def classification(self, x_enc, x_mark_enc):
# padding
x_enc = torch.cat([x_enc, torch.zeros((x_enc.shape[0], self.seq_len-x_enc.shape[1], x_enc.shape[2])).to(x_enc.device)], dim=1)
enc_out = self.encoder(x_enc)
# Output
output = enc_out.reshape(enc_out.shape[0], -1) # (batch_size, seq_length * d_model)
output = self.projection(output) # (batch_size, num_classes)
return output
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)
return dec_out[:, -self.pred_len:, :] # [B, L, D]
if self.task_name == 'imputation':
dec_out = self.imputation(x_enc, x_mark_enc, x_dec, x_mark_dec, mask)
return dec_out # [B, L, D]
if self.task_name == 'anomaly_detection':
dec_out = self.anomaly_detection(x_enc)
return dec_out # [B, L, D]
if self.task_name == 'classification':
dec_out = self.classification(x_enc, x_mark_enc)
return dec_out # [B, N]
return None
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