BAM 주의력 메커니즘 모듈

import torch
import math
import torch.nn as nn
import torch.nn.functional as F

class Flatten(nn.Module):
    def forward(self, x):
        return x.view(x.size(0), -1)
class ChannelGate(nn.Module):
    def __init__(self, gate_channel, reduction_ratio=16, num_layers=1):
        super(ChannelGate, self).__init__()
        # self.gate_activation = gate_activation
        self.gate_c = nn.Sequential()
        self.gate_c.add_module( 'flatten', Flatten() )
        gate_channels = [gate_channel]
        gate_channels += [gate_channel // reduction_ratio] * num_layers
        gate_channels += [gate_channel]
        for i in range( len(gate_channels) - 2 ):
            self.gate_c.add_module( 'gate_c_fc_%d'%i, nn.Linear(gate_channels[i], gate_channels[i+1]) )
            self.gate_c.add_module( 'gate_c_bn_%d'%(i+1), nn.BatchNorm1d(gate_channels[i+1]) )
            self.gate_c.add_module( 'gate_c_relu_%d'%(i+1), nn.ReLU() )
        self.gate_c.add_module( 'gate_c_fc_final', nn.Linear(gate_channels[-2], gate_channels[-1]) )
    def forward(self, in_tensor):
        avg_pool = F.avg_pool2d( in_tensor, in_tensor.size(2), stride=in_tensor.size(2) )
        return self.gate_c( avg_pool ).unsqueeze(2).unsqueeze(3).expand_as(in_tensor)

class SpatialGate(nn.Module):
    def __init__(self, gate_channel, reduction_ratio=16, dilation_conv_num=2, dilation_val=4):
        super(SpatialGate, self).__init__()
        self.gate_s = nn.Sequential()
        self.gate_s.add_module( 'gate_s_conv_reduce0', nn.Conv2d(gate_channel, gate_channel//reduction_ratio, kernel_size=1))
        self.gate_s.add_module( 'gate_s_bn_reduce0',	nn.BatchNorm2d(gate_channel//reduction_ratio) )
        self.gate_s.add_module( 'gate_s_relu_reduce0',nn.ReLU() )
        for i in range( dilation_conv_num ):
            self.gate_s.add_module( 'gate_s_conv_di_%d'%i, nn.Conv2d(gate_channel//reduction_ratio, gate_channel//reduction_ratio, kernel_size=3, \
						padding=dilation_val, dilation=dilation_val) )
            self.gate_s.add_module( 'gate_s_bn_di_%d'%i, nn.BatchNorm2d(gate_channel//reduction_ratio) )
            self.gate_s.add_module( 'gate_s_relu_di_%d'%i, nn.ReLU() )
        self.gate_s.add_module( 'gate_s_conv_final', nn.Conv2d(gate_channel//reduction_ratio, 1, kernel_size=1) )
    def forward(self, in_tensor):
        return self.gate_s( in_tensor ).expand_as(in_tensor)
class BAM(nn.Module):
    def __init__(self, gate_channel):
        super(BAM, self).__init__()
        self.channel_att = ChannelGate(gate_channel)
        self.spatial_att = SpatialGate(gate_channel)
    def forward(self,in_tensor):
        # att = 1 + F.sigmoid( self.channel_att(in_tensor) * self.spatial_att(in_tensor) )
        # att = 1 + torch.nn.Sigmoid()( self.channel_att(in_tensor) * self.spatial_att(in_tensor) )
        att = 1 + torch.sigmoid( self.channel_att(in_tensor) * self.spatial_att(in_tensor) )
        return att * in_tensor

if __name__ == "__main__":
    print('  main  ...')
    bam = BAM(32)
    data_in = torch.randn(8,32,300,300)
    data_out = bam(data_in)
    print(data_in.shape)  # 
    print(data_out.shape)  # 

Windows PowerShell
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        PowerShell https://aka.ms/pscore6

              1032   。
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\my-experiments>  & 'D:\Anaconda3\envs\pytorch_1.7.1_cu102\python.exe' 'c:\Users\chenxuqi\.vscode\extensions\ms-python.python-2021.4.765268190\pythonFiles\lib\python\debugpy\launcher' '52301' '--' 'c:\Users\chenxuqi\Desktop\News4cxq\my-experiments\test.py'
  main  ...
torch.Size([8, 32, 300, 300])
torch.Size([8, 32, 300, 300])
(base) PS C:\Users\chenxuqi\Desktop\News4cxq\my-experiments> conda activate pytorch_1.7.1_cu102
(pytorch_1.7.1_cu102) PS C:\Users\chenxuqi\Desktop\News4cxq\my-experiments>