Pytorch实现ECA

nn.ReLU(inplace=True),

nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

)

class SE_Module(nn.Module):

def init(self, channel,ratio = 16):

super(SE_Module, self).init()

self.squeeze = nn.AdaptiveAvgPool2d(1)

self.excitation = nn.Sequential(

nn.Linear(in_features=channel, out_features=channel // ratio),

nn.ReLU(inplace=True),

nn.Linear(in_features=channel // ratio, out_features=channel),

nn.Sigmoid()

)

def forward(self, x):

b, c, _, _ = x.size()

y = self.squeeze(x).view(b, c)

z = self.excitation(y).view(b, c, 1, 1)

return x * z.expand_as(x)

class ECA_Module(nn.Module):

def init(self, channel,gamma=2, b=1):

super(ECA_Module, self).init()

self.gamma = gamma

self.b = b

t = int(abs(log(channel, 2) + self.b) / self.gamma)

k = t if t % 2 else t + 1

self.avg_pool = nn.AdaptiveAvgPool2d(1)

self.conv = nn.Conv1d(1, 1, kernel_size=k, padding=k//2, bias=False)

self.sigmoid = nn.Sigmoid()

def forward(self, x):

b, c, _, _ = x.size()

y = self.avg_pool(x)

y = self.conv(y.squeeze(-1).transpose(-1,-2))

y = y.transpose(-1,-2).unsqueeze(-1)

y = self.sigmoid(y)

return x * y.expand_as(x)

class ECA_ResNetBlock(nn.Module):

def init(self,in_places,places, stride=1,downsampling=False, expansion = 4):

super(ECA_ResNetBlock,self).init()

self.expansion = expansion

self.downsampling = downsampling

self.bottleneck = nn.Sequential(

nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False),

nn.BatchNorm2d(places),

nn.ReLU(inplace=True),

nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False),

nn.BatchNorm2d(places),

nn.ReLU(inplace=True),

nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False),

nn.BatchNorm2d(places*self.expansion),

)

if self.downsampling:

self.downsample = nn.Sequential(

nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False),

nn.BatchNorm2d(places*self.expansion)

)

self.relu = nn.ReLU(inplace=True)

def forward(self, x):

residual = x

out = self.bottleneck(x)

if self.downsampling:

residual = self.downsample(x)

out += residual

out = self.relu(out)

return out

class ECA_ResNet(nn.Module):

def init(self,blocks, num_classes=1000, expansion = 4):

super(ECA_ResNet,self).init()

self.expansion = expansion

self.conv1 = Conv1(in_planes = 3, places= 64)

self.layer1 = self.make_layer(in_places = 64, places= 64, block=blocks[0], stride=1)

self.layer2 = self.make_layer(in_places = 256,places=128, block=blocks[1], stride=2)

self.layer3 = self.make_layer(in_places=512,places=256, block=blocks[2], stride=2)

self.layer4 = self.make_layer(in_places=1024,places=512, block=blocks[3], stride=2)

self.avgpool = nn.AvgPool2d(7, stride=1)

self.fc = nn.Linear(2048,num_classes)

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