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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 DataEmbedding_inverted(nn.Module):
def __init__(self, c_in, d_model, 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)
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