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attention.py
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attention.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import math
class AttentionConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, bias=False):
super(AttentionConv, self).__init__()
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.groups = groups
assert self.out_channels % self.groups == 0, "out_channels should be divided by groups. (example: out_channels: 40, groups: 4)"
self.rel_h = nn.Parameter(torch.randn(out_channels // 2, 1, 1, kernel_size, 1), requires_grad=True)
self.rel_w = nn.Parameter(torch.randn(out_channels // 2, 1, 1, 1, kernel_size), requires_grad=True)
self.key_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias)
self.query_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias)
self.value_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias)
self.reset_parameters()
def forward(self, x):
batch, channels, height, width = x.size()
padded_x = F.pad(x, [self.padding, self.padding, self.padding, self.padding])
q_out = self.query_conv(x)
k_out = self.key_conv(padded_x)
v_out = self.value_conv(padded_x)
k_out = k_out.unfold(2, self.kernel_size, self.stride).unfold(3, self.kernel_size, self.stride)
v_out = v_out.unfold(2, self.kernel_size, self.stride).unfold(3, self.kernel_size, self.stride)
k_out_h, k_out_w = k_out.split(self.out_channels // 2, dim=1)
k_out = torch.cat((k_out_h + self.rel_h, k_out_w + self.rel_w), dim=1)
k_out = k_out.contiguous().view(batch, self.groups, self.out_channels // self.groups, height, width, -1)
v_out = v_out.contiguous().view(batch, self.groups, self.out_channels // self.groups, height, width, -1)
q_out = q_out.view(batch, self.groups, self.out_channels // self.groups, height, width, 1)
out = q_out * k_out
out = F.softmax(out, dim=-1)
out = torch.einsum('bnchwk,bnchwk -> bnchw', out, v_out).view(batch, -1, height, width)
return out
def reset_parameters(self):
init.kaiming_normal_(self.key_conv.weight, mode='fan_out', nonlinearity='relu')
init.kaiming_normal_(self.value_conv.weight, mode='fan_out', nonlinearity='relu')
init.kaiming_normal_(self.query_conv.weight, mode='fan_out', nonlinearity='relu')
init.normal_(self.rel_h, 0, 1)
init.normal_(self.rel_w, 0, 1)
class AttentionStem(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, m=4, bias=False):
super(AttentionStem, self).__init__()
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.groups = groups
self.m = m
assert self.out_channels % self.groups == 0, "out_channels should be divided by groups. (example: out_channels: 40, groups: 4)"
self.emb_a = nn.Parameter(torch.randn(out_channels // groups, kernel_size), requires_grad=True)
self.emb_b = nn.Parameter(torch.randn(out_channels // groups, kernel_size), requires_grad=True)
self.emb_mix = nn.Parameter(torch.randn(m, out_channels // groups), requires_grad=True)
self.key_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias)
self.query_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias)
self.value_conv = nn.ModuleList([nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias) for _ in range(m)])
self.reset_parameters()
def forward(self, x):
batch, channels, height, width = x.size()
padded_x = F.pad(x, [self.padding, self.padding, self.padding, self.padding])
q_out = self.query_conv(x)
k_out = self.key_conv(padded_x)
v_out = torch.stack([self.value_conv[_](padded_x) for _ in range(self.m)], dim=0)
k_out = k_out.unfold(2, self.kernel_size, self.stride).unfold(3, self.kernel_size, self.stride)
v_out = v_out.unfold(3, self.kernel_size, self.stride).unfold(4, self.kernel_size, self.stride)
k_out = k_out[:, :, :height, :width, :, :]
v_out = v_out[:, :, :, :height, :width, :, :]
emb_logit_a = torch.einsum('mc,ca->ma', self.emb_mix, self.emb_a)
emb_logit_b = torch.einsum('mc,cb->mb', self.emb_mix, self.emb_b)
emb = emb_logit_a.unsqueeze(2) + emb_logit_b.unsqueeze(1)
emb = F.softmax(emb.view(self.m, -1), dim=0).view(self.m, 1, 1, 1, 1, self.kernel_size, self.kernel_size)
v_out = emb * v_out
k_out = k_out.contiguous().view(batch, self.groups, self.out_channels // self.groups, height, width, -1)
v_out = v_out.contiguous().view(self.m, batch, self.groups, self.out_channels // self.groups, height, width, -1)
v_out = torch.sum(v_out, dim=0).view(batch, self.groups, self.out_channels // self.groups, height, width, -1)
q_out = q_out.view(batch, self.groups, self.out_channels // self.groups, height, width, 1)
out = q_out * k_out
out = F.softmax(out, dim=-1)
out = torch.einsum('bnchwk,bnchwk->bnchw', out, v_out).view(batch, -1, height, width)
return out
def reset_parameters(self):
init.kaiming_normal_(self.key_conv.weight, mode='fan_out', nonlinearity='relu')
init.kaiming_normal_(self.query_conv.weight, mode='fan_out', nonlinearity='relu')
for _ in self.value_conv:
init.kaiming_normal_(_.weight, mode='fan_out', nonlinearity='relu')
init.normal_(self.emb_a, 0, 1)
init.normal_(self.emb_b, 0, 1)
init.normal_(self.emb_mix, 0, 1)
# temp = torch.randn((2, 3, 32, 32))
# conv = AttentionConv(3, 16, kernel_size=3, padding=1)
# print(conv(temp).size())