轻量级网络论文：GhostNet: More Features from Cheap Operations及PyTorch其实现
import torch
import torch.nn as nn
import torchvision
def DW_Conv3x3BNReLU(in_channels,out_channels,stride,groups=1):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1,groups=groups, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True)
)
class SqueezeAndExcite(nn.Module):
def __init__(self, in_channels, out_channels, divide=4):
super(SqueezeAndExcite, self).__init__()
mid_channels = in_channels // divide
self.pool = nn.AdaptiveAvgPool2d(1)
self.SEblock = nn.Sequential(
nn.Linear(in_features=in_channels, out_features=mid_channels),
nn.ReLU6(inplace=True),
nn.Linear(in_features=mid_channels, out_features=out_channels),
nn.ReLU6(inplace=True),
)
def forward(self, x):
b, c, h, w = x.size()
out = self.pool(x)
out = out.view(b, -1)
out = self.SEblock(out)
out = out.view(b, c, 1, 1)
return out * x
class GhostNet(nn.Module):
def __init__(self, num_classes=1000):
super(GhostNet, self).__init__()
self.first_conv = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(16),
nn.ReLU6(inplace=True),
)
self.features = nn.Sequential(
GhostBottleneck(in_channels=16, mid_channels=16, out_channels=16, kernel_size=3, stride=1, use_se=False),
GhostBottleneck(in_channels=16, mid_channels=64, out_channels=24, kernel_size=3, stride=2, use_se=False),
GhostBottleneck(in_channels=24, mid_channels=72, out_channels=24, kernel_size=3, stride=1, use_se=False),
GhostBottleneck(in_channels=24, mid_channels=72, out_channels=40, kernel_size=5, stride=2, use_se=True, se_kernel_size=28),
GhostBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1, use_se=True, se_kernel_size=28),
GhostBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1, use_se=True, se_kernel_size=28),
GhostBottleneck(in_channels=40, mid_channels=240, out_channels=80, kernel_size=3, stride=1, use_se=False),
GhostBottleneck(in_channels=80, mid_channels=200, out_channels=80, kernel_size=3, stride=1, use_se=False),
GhostBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=2, use_se=False),
GhostBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=1, use_se=False),
GhostBottleneck(in_channels=80, mid_channels=480, out_channels=112, kernel_size=3, stride=1, use_se=True, se_kernel_size=14),
GhostBottleneck(in_channels=112, mid_channels=672, out_channels=112, kernel_size=3, stride=1, use_se=True, se_kernel_size=14),
GhostBottleneck(in_channels=112, mid_channels=672, out_channels=160, kernel_size=5, stride=2, use_se=True,se_kernel_size=7),
GhostBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1, use_se=True,se_kernel_size=7),
GhostBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1, use_se=True,se_kernel_size=7),
)
self.last_stage = nn.Sequential(
nn.Conv2d(in_channels=160, out_channels=960, kernel_size=1, stride=1),
nn.BatchNorm2d(960),
nn.ReLU6(inplace=True),
nn.AvgPool2d(kernel_size=7, stride=1),
nn.Conv2d(in_channels=960, out_channels=1280, kernel_size=1, stride=1),
nn.ReLU6(inplace=True),
)
self.classifier = nn.Linear(in_features=1280,out_features=num_classes)
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear) or isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.first_conv(x)
x = self.features(x)
x= self.last_stage(x)
x = x.view(x.size(0), -1)
out = self.classifier(x)
return out
if __name__ == '__main__':
model = GhostNet()
print(model)
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)