105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
|
import numpy as np
|
||
|
|
import torch
|
||
|
|
from torch import nn
|
||
|
|
from torch.nn import init
|
||
|
|
|
||
|
|
|
||
|
|
class Depth_Pointwise_Conv1d(nn.Module):
|
||
|
|
def __init__(self, in_ch, out_ch, k):
|
||
|
|
super().__init__()
|
||
|
|
if (k == 1):
|
||
|
|
self.depth_conv = nn.Identity()
|
||
|
|
else:
|
||
|
|
self.depth_conv = nn.Conv1d(
|
||
|
|
in_channels=in_ch,
|
||
|
|
out_channels=in_ch,
|
||
|
|
kernel_size=k,
|
||
|
|
groups=in_ch,
|
||
|
|
padding=k // 2
|
||
|
|
)
|
||
|
|
self.pointwise_conv = nn.Conv1d(
|
||
|
|
in_channels=in_ch,
|
||
|
|
out_channels=out_ch,
|
||
|
|
kernel_size=1,
|
||
|
|
groups=1
|
||
|
|
)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
out = self.pointwise_conv(self.depth_conv(x))
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
class MUSEAttention(nn.Module):
|
||
|
|
|
||
|
|
def __init__(self, d_model, d_k, d_v, h, dropout=.1):
|
||
|
|
|
||
|
|
super(MUSEAttention, self).__init__()
|
||
|
|
self.fc_q = nn.Linear(d_model, h * d_k)
|
||
|
|
self.fc_k = nn.Linear(d_model, h * d_k)
|
||
|
|
self.fc_v = nn.Linear(d_model, h * d_v)
|
||
|
|
self.fc_o = nn.Linear(h * d_v, d_model)
|
||
|
|
self.dropout = nn.Dropout(dropout)
|
||
|
|
|
||
|
|
self.conv1 = Depth_Pointwise_Conv1d(h * d_v, d_model, 1)
|
||
|
|
self.conv3 = Depth_Pointwise_Conv1d(h * d_v, d_model, 3)
|
||
|
|
self.conv5 = Depth_Pointwise_Conv1d(h * d_v, d_model, 5)
|
||
|
|
self.dy_paras = nn.Parameter(torch.ones(3))
|
||
|
|
self.softmax = nn.Softmax(-1)
|
||
|
|
|
||
|
|
self.d_model = d_model
|
||
|
|
self.d_k = d_k
|
||
|
|
self.d_v = d_v
|
||
|
|
self.h = h
|
||
|
|
|
||
|
|
self.init_weights()
|
||
|
|
|
||
|
|
def init_weights(self):
|
||
|
|
for m in self.modules():
|
||
|
|
if isinstance(m, nn.Conv2d):
|
||
|
|
init.kaiming_normal_(m.weight, mode='fan_out')
|
||
|
|
if m.bias is not None:
|
||
|
|
init.constant_(m.bias, 0)
|
||
|
|
elif isinstance(m, nn.BatchNorm2d):
|
||
|
|
init.constant_(m.weight, 1)
|
||
|
|
init.constant_(m.bias, 0)
|
||
|
|
elif isinstance(m, nn.Linear):
|
||
|
|
init.normal_(m.weight, std=0.001)
|
||
|
|
if m.bias is not None:
|
||
|
|
init.constant_(m.bias, 0)
|
||
|
|
|
||
|
|
def forward(self, queries, keys, values, attention_mask=None, attention_weights=None):
|
||
|
|
|
||
|
|
# Self Attention
|
||
|
|
b_s, nq = queries.shape[:2]
|
||
|
|
nk = keys.shape[1]
|
||
|
|
|
||
|
|
q = self.fc_q(queries).view(b_s, nq, self.h, self.d_k).permute(0, 2, 1, 3) # (b_s, h, nq, d_k)
|
||
|
|
k = self.fc_k(keys).view(b_s, nk, self.h, self.d_k).permute(0, 2, 3, 1) # (b_s, h, d_k, nk)
|
||
|
|
v = self.fc_v(values).view(b_s, nk, self.h, self.d_v).permute(0, 2, 1, 3) # (b_s, h, nk, d_v)
|
||
|
|
|
||
|
|
att = torch.matmul(q, k) / np.sqrt(self.d_k) # (b_s, h, nq, nk)
|
||
|
|
if attention_weights is not None:
|
||
|
|
att = att * attention_weights
|
||
|
|
if attention_mask is not None:
|
||
|
|
att = att.masked_fill(attention_mask, -np.inf)
|
||
|
|
att = torch.softmax(att, -1)
|
||
|
|
att = self.dropout(att)
|
||
|
|
|
||
|
|
out = torch.matmul(att, v).permute(0, 2, 1, 3).contiguous().view(b_s, nq, self.h * self.d_v) # (b_s, nq, h*d_v)
|
||
|
|
out = self.fc_o(out) # (b_s, nq, d_model)
|
||
|
|
|
||
|
|
v2 = v.permute(0, 1, 3, 2).contiguous().view(b_s, -1, nk) # bs,dim,n
|
||
|
|
self.dy_paras = nn.Parameter(self.softmax(self.dy_paras))
|
||
|
|
out2 = self.dy_paras[0] * self.conv1(v2) + self.dy_paras[1] * self.conv3(v2) + self.dy_paras[2] * self.conv5(v2)
|
||
|
|
out2 = out2.permute(0, 2, 1) # bs.n.dim
|
||
|
|
|
||
|
|
out = out + out2
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
block = MUSEAttention(d_model=512, d_k=512, d_v=512, h=8).cuda()
|
||
|
|
input = torch.rand(64, 64, 512).cuda()
|
||
|
|
output = block(input, input, input)
|
||
|
|
print(input.size(), output.size())
|