import torch.nn as nn import torchvision from torch.nn import init class Flatten(nn.Module): def forward(self, x): return x.view(x.shape[0], -1) class ChannelAttention(nn.Module): def __int__(self, channel, reduction, num_layers): super(ChannelAttention, self).__init__() self.avgpool = nn.AdaptiveAvgPool2d(1) gate_channels = [channel] gate_channels += [len(channel) // reduction] * num_layers gate_channels += [channel] self.ca = nn.Sequential() self.ca.add_module('flatten', Flatten()) for i in range(len(gate_channels) - 2): self.ca.add_module('', nn.Linear(gate_channels[i], gate_channels[i + 1])) self.ca.add_module('', nn.BatchNorm1d(gate_channels[i + 1])) self.ca.add_module('', nn.ReLU()) self.ca.add_module('', nn.Linear(gate_channels[-2], gate_channels[-1])) def forward(self, x): res = self.avgpool(x) res = self.ca(res) res = res.unsqueeze(-1).unsqueeze(-1).expand_as(x) return res class SpatialAttention(nn.Module): def __int__(self, channel, reduction=16, num_lay=3, dilation=2): super(SpatialAttention).__init__() self.sa = nn.Sequential() self.sa.add_module('', nn.Conv2d(kernel_size=1, in_channels=channel, out_channels=(channel // reduction) * 3)) self.sa.add_module('', nn.BatchNorm2d(num_features=(channel // reduction))) self.sa.add_module('', nn.ReLU()) for i in range(num_lay): self.sa.add_module('', nn.Conv2d(kernel_size=3, in_channels=(channel // reduction), out_channels=(channel // reduction), padding=1, dilation=2)) self.sa.add_module('', nn.BatchNorm2d(channel // reduction)) self.sa.add_module('', nn.ReLU()) self.sa.add_module('', nn.Conv2d(channel // reduction, 1, kernel_size=1)) def forward(self, x): res = self.sa(x) res = res.expand_as(x) return res class BAMblock(nn.Module): def __init__(self, channel=512, reduction=16, dia_val=2): super(BAMblock, self).__init__() self.ca = ChannelAttention(channel, reduction) self.sa = SpatialAttention(channel, reduction, dia_val) self.sigmoid = nn.Sigmoid() def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal(m.weight, mode='fan_out') if m.bais 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, x): b, c, _, _ = x.size() sa_out = self.sa(x) ca_out = self.ca(x) weight = self.sigmoid(sa_out + ca_out) out = (1 + weight) * x return out if __name__ == "__main__": print(512 // 14)