CBAM_attention Class Reference

Inheritance diagram for CBAM_attention:

Collaboration diagram for CBAM_attention:

Public Member Functions
def	__init__ (self, channels, which_conv=SNConv2d, reduction=8, attention_kernel_size=3)
	Constructor.
def	forward (self, x)
	forward

Public Attributes
	avg_pool
	average pooling
	max_pool
	max pooling
	fc1
	fcl
	relu
	relu
	fc2
	f2c
	sigmoid_channel
	sigmoid channel
	conv_after_concat
	convolution after concatenation
	sigmoid_spatial
	sigmoid_spatial

Detailed Description

CBAM attention

Definition at line 683 of file ieagan.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			channels,
			which_conv = SNConv2d,
			reduction = 8,
			attention_kernel_size = 3
	)

Constructor.

Definition at line 687 of file ieagan.py.

    ):
        super(CBAM_attention, self).__init__()
        
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        
        self.fc1 = which_conv(
            channels, channels // reduction, kernel_size=1, padding=0
        )
        
        self.relu = nn.ReLU(inplace=True)
        
        self.fc2 = which_conv(
            channels // reduction, channels, kernel_size=1, padding=0
        )
        
        self.sigmoid_channel = nn.Sigmoid()
        
        self.conv_after_concat = which_conv(
            2,
            1,
            kernel_size=attention_kernel_size,
            stride=1,
            padding=attention_kernel_size // 2,
        )
        
        self.sigmoid_spatial = nn.Sigmoid()
 

Member Function Documentation

forward()

def forward	(		self,
			x
	)

forward

Definition at line 723 of file ieagan.py.

    def forward(self, x):
        # Channel attention module
        module_input = x
        avg = self.avg_pool(x)
        mx = self.max_pool(x)
        avg = self.fc1(avg)
        mx = self.fc1(mx)
        avg = self.relu(avg)
        mx = self.relu(mx)
        avg = self.fc2(avg)
        mx = self.fc2(mx)
        x = avg + mx
        x = self.sigmoid_channel(x)
        # Spatial attention module
        x = module_input * x
        module_input = x
        # b, c, h, w = x.size()
        avg = torch.mean(x, 1, True)
        mx, _ = torch.max(x, 1, True)
        x = torch.cat((avg, mx), 1)
        x = self.conv_after_concat(x)
        x = self.sigmoid_spatial(x)
        x = module_input * x
        return x
 
 

Member Data Documentation

avg_pool

avg_pool

average pooling

Definition at line 696 of file ieagan.py.

conv_after_concat

conv_after_concat

convolution after concatenation

Definition at line 712 of file ieagan.py.

fc1

fc1

fcl

Definition at line 700 of file ieagan.py.

fc2

fc2

f2c

Definition at line 706 of file ieagan.py.

max_pool

max_pool

max pooling

Definition at line 698 of file ieagan.py.

relu

relu

relu

Definition at line 704 of file ieagan.py.

sigmoid_channel

sigmoid_channel

sigmoid channel

Definition at line 710 of file ieagan.py.

sigmoid_spatial

sigmoid_spatial

sigmoid_spatial

Definition at line 720 of file ieagan.py.

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The documentation for this class was generated from the following file:

• pxd/scripts/pxd/background_generator/models/ieagan.py