File size: 3,414 Bytes
27793b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | 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__()
padding = 1 if torch.__version__>='1.5.0' else 2
self.downConv = nn.Conv1d(in_channels=c_in,
out_channels=c_in,
kernel_size=3,
padding=padding,
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):
new_x, attn = self.attention(
x, x, x,
attn_mask = attn_mask
)
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):
# x [B, L, D]
attns = []
if self.conv_layers is not None:
for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
x, attn = attn_layer(x, attn_mask=attn_mask)
x = conv_layer(x)
attns.append(attn)
x, attn = self.attn_layers[-1](x, attn_mask=attn_mask)
attns.append(attn)
else:
for attn_layer in self.attn_layers:
x, attn = attn_layer(x, attn_mask=attn_mask)
attns.append(attn)
if self.norm is not None:
x = self.norm(x)
return x, attns
class EncoderStack(nn.Module):
def __init__(self, encoders, inp_lens):
super(EncoderStack, self).__init__()
self.encoders = nn.ModuleList(encoders)
self.inp_lens = inp_lens
def forward(self, x, attn_mask=None):
# x [B, L, D]
x_stack = []; attns = []
for i_len, encoder in zip(self.inp_lens, self.encoders):
inp_len = x.shape[1]//(2**i_len)
x_s, attn = encoder(x[:, -inp_len:, :])
x_stack.append(x_s); attns.append(attn)
x_stack = torch.cat(x_stack, -2)
return x_stack, attns
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