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import math
import scipy
import torch
import torch.nn.functional as F
from torch import nn, einsum
from functools import partial
from einops import rearrange, reduce
from scipy.fftpack import next_fast_len
def exists(x):
return x is not None
def default(val, d):
if exists(val):
return val
return d() if callable(d) else d
def identity(t, *args, **kwargs):
return t
def extract(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
def Upsample(dim, dim_out=None):
return nn.Sequential(
nn.Upsample(scale_factor=2, mode="nearest"),
nn.Conv1d(dim, default(dim_out, dim), 3, padding=1),
)
def Downsample(dim, dim_out=None):
return nn.Conv1d(dim, default(dim_out, dim), 4, 2, 1)
# normalization functions
def normalize_to_neg_one_to_one(x):
return x * 2 - 1
def unnormalize_to_zero_to_one(x):
return (x + 1) * 0.5
# sinusoidal positional embeds
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
# learnable positional embeds
class LearnablePositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=1024):
super(LearnablePositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
# Each position gets its own embedding
# Since indices are always 0 ... max_len, we don't have to do a look-up
self.pe = nn.Parameter(
torch.empty(1, max_len, d_model)
) # requires_grad automatically set to True
nn.init.uniform_(self.pe, -0.02, 0.02)
def forward(self, x):
r"""Inputs of forward function
Args:
x: the sequence fed to the positional encoder model (required).
Shape:
x: [batch size, sequence length, embed dim]
output: [batch size, sequence length, embed dim]
"""
# print(x.shape)
x = x + self.pe
return self.dropout(x)
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 - math.floor((self.kernel_size - 1) // 2), 1
)
end = x[:, -1:, :].repeat(1, math.floor((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
class series_decomp_multi(nn.Module):
"""
Series decomposition block
"""
def __init__(self, kernel_size):
super(series_decomp_multi, self).__init__()
self.moving_avg = [moving_avg(kernel, stride=1) for kernel in kernel_size]
self.layer = torch.nn.Linear(1, len(kernel_size))
def forward(self, x):
moving_mean = []
for func in self.moving_avg:
moving_avg = func(x)
moving_mean.append(moving_avg.unsqueeze(-1))
moving_mean = torch.cat(moving_mean, dim=-1)
moving_mean = torch.sum(
moving_mean * nn.Softmax(-1)(self.layer(x.unsqueeze(-1))), dim=-1
)
res = x - moving_mean
return res, moving_mean
class Transpose(nn.Module):
"""Wrapper class of torch.transpose() for Sequential module."""
def __init__(self, shape: tuple):
super(Transpose, self).__init__()
self.shape = shape
def forward(self, x):
return x.transpose(*self.shape)
class Conv_MLP(nn.Module):
def __init__(self, in_dim, out_dim, resid_pdrop=0.0):
super().__init__()
self.sequential = nn.Sequential(
Transpose(shape=(1, 2)),
nn.Conv1d(in_dim, out_dim, 3, stride=1, padding=1),
nn.Dropout(p=resid_pdrop),
)
def forward(self, x):
return self.sequential(x).transpose(1, 2)
class Transformer_MLP(nn.Module):
def __init__(self, n_embd, mlp_hidden_times, act, resid_pdrop):
super().__init__()
self.sequential = nn.Sequential(
nn.Conv1d(
in_channels=n_embd,
out_channels=int(mlp_hidden_times * n_embd),
kernel_size=1,
padding=0,
),
act,
nn.Conv1d(
in_channels=int(mlp_hidden_times * n_embd),
out_channels=int(mlp_hidden_times * n_embd),
kernel_size=3,
padding=1,
),
act,
nn.Conv1d(
in_channels=int(mlp_hidden_times * n_embd),
out_channels=n_embd,
kernel_size=3,
padding=1,
),
nn.Dropout(p=resid_pdrop),
)
def forward(self, x):
return self.sequential(x)
class GELU2(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x * F.sigmoid(1.702 * x)
class AdaLayerNorm(nn.Module):
def __init__(self, n_embd):
super().__init__()
self.emb = SinusoidalPosEmb(n_embd)
self.silu = nn.SiLU()
self.linear = nn.Linear(n_embd, n_embd * 2)
self.layernorm = nn.LayerNorm(n_embd, elementwise_affine=False)
def forward(self, x, timestep, label_emb=None):
emb = self.emb(timestep)
if label_emb is not None:
# print(emb.shape, label_emb.shape)
emb = emb + label_emb
emb = self.linear(self.silu(emb)).unsqueeze(1)
scale, shift = torch.chunk(emb, 2, dim=2)
x = self.layernorm(x) * (1 + scale) + shift
return x
class AdaInsNorm(nn.Module):
def __init__(self, n_embd):
super().__init__()
self.emb = SinusoidalPosEmb(n_embd)
self.silu = nn.SiLU()
self.linear = nn.Linear(n_embd, n_embd * 2)
self.instancenorm = nn.InstanceNorm1d(n_embd)
def forward(self, x, timestep, label_emb=None):
emb = self.emb(timestep)
if label_emb is not None:
emb = emb + label_emb
emb = self.linear(self.silu(emb)).unsqueeze(1)
scale, shift = torch.chunk(emb, 2, dim=2)
x = (
self.instancenorm(x.transpose(-1, -2)).transpose(-1, -2) * (1 + scale)
+ shift
)
return x
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