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import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvLayer(nn.Module):
def __init__(self, c_in):
super(ConvLayer, self).__init__()
self.downConv = nn.Conv1d(in_channels=c_in,
out_channels=c_in,
kernel_size=3,
padding=2,
padding_mode='circular')
self.norm = nn.BatchNorm1d(c_in)
self.activation = nn.ELU()
self.maxPool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
def forward(self, x):
x = self.downConv(x.permute(0, 2, 1))
x = self.norm(x)
x = self.activation(x)
x = self.maxPool(x)
x = x.transpose(1, 2)
return x
class EncoderLayer(nn.Module):
def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
super(EncoderLayer, self).__init__()
d_ff = d_ff or 4 * d_model
self.attention = attention
self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = F.relu if activation == "relu" else F.gelu
def forward(self, x, attn_mask=None, tau=None, delta=None):
new_x, attn = self.attention(
x, x, x,
attn_mask=attn_mask,
tau=tau, delta=delta
)
x = x + self.dropout(new_x)
y = x = self.norm1(x)
y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
y = self.dropout(self.conv2(y).transpose(-1, 1))
return self.norm2(x + y), attn
class Encoder(nn.Module):
def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
super(Encoder, self).__init__()
self.attn_layers = nn.ModuleList(attn_layers)
self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
self.norm = norm_layer
def forward(self, x, attn_mask=None, tau=None, delta=None):
# x [B, L, D]
attns = []
if self.conv_layers is not None:
for i, (attn_layer, conv_layer) in enumerate(zip(self.attn_layers, self.conv_layers)):
delta = delta if i == 0 else None
x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
x = conv_layer(x)
attns.append(attn)
x, attn = self.attn_layers[-1](x, tau=tau, delta=None)
attns.append(attn)
else:
for attn_layer in self.attn_layers:
x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
attns.append(attn)
if self.norm is not None:
x = self.norm(x)
return x, attns
class DecoderLayer(nn.Module):
def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
dropout=0.1, activation="relu"):
super(DecoderLayer, self).__init__()
d_ff = d_ff or 4 * d_model
self.self_attention = self_attention
self.cross_attention = cross_attention
self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = F.relu if activation == "relu" else F.gelu
def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
x = x + self.dropout(self.self_attention(
x, x, x,
attn_mask=x_mask,
tau=tau, delta=None
)[0])
x = self.norm1(x)
x = x + self.dropout(self.cross_attention(
x, cross, cross,
attn_mask=cross_mask,
tau=tau, delta=delta
)[0])
y = x = self.norm2(x)
y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
y = self.dropout(self.conv2(y).transpose(-1, 1))
return self.norm3(x + y)
class Decoder(nn.Module):
def __init__(self, layers, norm_layer=None, projection=None):
super(Decoder, self).__init__()
self.layers = nn.ModuleList(layers)
self.norm = norm_layer
self.projection = projection
def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
for layer in self.layers:
x = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask, tau=tau, delta=delta)
if self.norm is not None:
x = self.norm(x)
if self.projection is not None:
x = self.projection(x)
return x
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