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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
"""
Channel Attention and Spaitial Attention from
Woo, S., Park, J., Lee, J.Y., & Kweon, I. CBAM: Convolutional Block Attention Module. ECCV2018.
"""
class ChannelAttention(nn.Module):
def __init__(self, in_planes, ratio=8):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.sharedMLP = nn.Sequential(
nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False),
nn.ReLU(),
nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False))
self.sigmoid = nn.Sigmoid()
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight.data, gain=0.02)
def forward(self, x):
avgout = self.sharedMLP(self.avg_pool(x))
maxout = self.sharedMLP(self.max_pool(x))
return self.sigmoid(avgout + maxout)
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
assert kernel_size in (3, 7), "kernel size must be 3 or 7"
padding = 3 if kernel_size == 7 else 1
self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
self.sigmoid = nn.Sigmoid()
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight.data, gain=0.02)
def forward(self, x):
avgout = torch.mean(x, dim=1, keepdim=True)
maxout, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avgout, maxout], dim=1)
x = self.conv(x)
return self.sigmoid(x)
"""
The following modules are modified based on https://github.com/heykeetae/Self-Attention-GAN
"""
class Self_Attn(nn.Module):
""" Self attention Layer"""
def __init__(self, in_dim, out_dim=None, add=False, ratio=8):
super(Self_Attn, self).__init__()
self.chanel_in = in_dim
self.add = add
if out_dim is None:
out_dim = in_dim
self.out_dim = out_dim
# self.activation = activation
self.query_conv = nn.Conv2d(
in_channels=in_dim, out_channels=in_dim//ratio, kernel_size=1)
self.key_conv = nn.Conv2d(
in_channels=in_dim, out_channels=in_dim//ratio, kernel_size=1)
self.value_conv = nn.Conv2d(
in_channels=in_dim, out_channels=out_dim, kernel_size=1)
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
"""
inputs :
x : input feature maps( B X C X W X H)
returns :
out : self attention value + input feature
attention: B X N X N (N is Width*Height)
"""
m_batchsize, C, width, height = x.size()
proj_query = self.query_conv(x).view(
m_batchsize, -1, width*height).permute(0, 2, 1) # B X C X(N)
proj_key = self.key_conv(x).view(
m_batchsize, -1, width*height) # B X C x (*W*H)
energy = torch.bmm(proj_query, proj_key) # transpose check
attention = self.softmax(energy) # BX (N) X (N)
proj_value = self.value_conv(x).view(
m_batchsize, -1, width*height) # B X C X N
out = torch.bmm(proj_value, attention.permute(0, 2, 1))
out = out.view(m_batchsize, self.out_dim, width, height)
if self.add:
out = self.gamma*out + x
else:
out = self.gamma*out
return out # , attention
class CrossModalAttention(nn.Module):
""" CMA attention Layer"""
def __init__(self, in_dim, activation=None, ratio=8, cross_value=True):
super(CrossModalAttention, self).__init__()
self.chanel_in = in_dim
self.activation = activation
self.cross_value = cross_value
self.query_conv = nn.Conv2d(
in_channels=in_dim, out_channels=in_dim//ratio, kernel_size=1)
self.key_conv = nn.Conv2d(
in_channels=in_dim, out_channels=in_dim//ratio, kernel_size=1)
self.value_conv = nn.Conv2d(
in_channels=in_dim, out_channels=in_dim, kernel_size=1)
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight.data, gain=0.02)
def forward(self, x, y):
"""
inputs :
x : input feature maps( B X C X W X H)
returns :
out : self attention value + input feature
attention: B X N X N (N is Width*Height)
"""
B, C, H, W = x.size()
proj_query = self.query_conv(x).view(
B, -1, H*W).permute(0, 2, 1) # B , HW, C
proj_key = self.key_conv(y).view(
B, -1, H*W) # B X C x (*W*H)
energy = torch.bmm(proj_query, proj_key) # B, HW, HW
attention = self.softmax(energy) # BX (N) X (N)
if self.cross_value:
proj_value = self.value_conv(y).view(
B, -1, H*W) # B , C , HW
else:
proj_value = self.value_conv(x).view(
B, -1, H*W) # B , C , HW
out = torch.bmm(proj_value, attention.permute(0, 2, 1))
out = out.view(B, C, H, W)
out = self.gamma*out + x
if self.activation is not None:
out = self.activation(out)
return out # , attention
class DualCrossModalAttention(nn.Module):
""" Dual CMA attention Layer"""
def __init__(self, in_dim, activation=None, size=16, ratio=8, ret_att=False):
super(DualCrossModalAttention, self).__init__()
self.chanel_in = in_dim
self.activation = activation
self.ret_att = ret_att
# query conv
self.key_conv1 = nn.Conv2d(
in_channels=in_dim, out_channels=in_dim//ratio, kernel_size=1)
self.key_conv2 = nn.Conv2d(
in_channels=in_dim, out_channels=in_dim//ratio, kernel_size=1)
self.key_conv_share = nn.Conv2d(
in_channels=in_dim//ratio, out_channels=in_dim//ratio, kernel_size=1)
self.linear1 = nn.Linear(size*size, size*size)
self.linear2 = nn.Linear(size*size, size*size)
# separated value conv
self.value_conv1 = nn.Conv2d(
in_channels=in_dim, out_channels=in_dim, kernel_size=1)
self.gamma1 = nn.Parameter(torch.zeros(1))
self.value_conv2 = nn.Conv2d(
in_channels=in_dim, out_channels=in_dim, kernel_size=1)
self.gamma2 = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight.data, gain=0.02)
if isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight.data, gain=0.02)
def forward(self, x, y):
"""
inputs :
x : input feature maps( B X C X W X H)
returns :
out : self attention value + input feature
attention: B X N X N (N is Width*Height)
"""
B, C, H, W = x.size()
def _get_att(a, b):
proj_key1 = self.key_conv_share(self.key_conv1(a)).view(
B, -1, H*W).permute(0, 2, 1) # B, HW, C
proj_key2 = self.key_conv_share(self.key_conv2(b)).view(
B, -1, H*W) # B X C x (*W*H)
energy = torch.bmm(proj_key1, proj_key2) # B, HW, HW
attention1 = self.softmax(self.linear1(energy))
attention2 = self.softmax(self.linear2(
energy.permute(0, 2, 1))) # BX (N) X (N)
return attention1, attention2
att_y_on_x, att_x_on_y = _get_att(x, y)
proj_value_y_on_x = self.value_conv2(y).view(
B, -1, H*W) # B, C, HW
out_y_on_x = torch.bmm(proj_value_y_on_x, att_y_on_x.permute(0, 2, 1))
out_y_on_x = out_y_on_x.view(B, C, H, W)
out_x = self.gamma1*out_y_on_x + x
proj_value_x_on_y = self.value_conv1(x).view(
B, -1, H*W) # B , C , HW
out_x_on_y = torch.bmm(proj_value_x_on_y, att_x_on_y.permute(0, 2, 1))
out_x_on_y = out_x_on_y.view(B, C, H, W)
out_y = self.gamma2*out_x_on_y + y
if self.ret_att:
return out_x, out_y, att_y_on_x, att_x_on_y
return out_x, out_y # , attention
if __name__ == "__main__":
x = torch.rand(10, 768, 16, 16)
y = torch.rand(10, 768, 16, 16)
dcma = DualCrossModalAttention(768, ret_att=True)
out_x, out_y, att_y_on_x, att_x_on_y = dcma(x, y)
print(out_y.size())
print(att_x_on_y.size())
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