PyTorch实现的Inception-v2
PyTorch实现的Inception-v2
PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks
PyTorch代码:
import torch
import torch.nn as nn
import torchvision
def ConvBNReLU(in_channels,out_channels,kernel_size,stride=1,padding=0):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,padding=padding),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True),
)
def ConvBNReLUFactorization(in_channels,out_channels,kernel_sizes,paddings):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_sizes, stride=1,padding=paddings),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True),
nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_sizes, stride=1, padding=paddings),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True),
)
class InceptionV2ModuleA(nn.Module):
def __init__(self, in_channels,out_channels1,out_channels2reduce, out_channels2, out_channels3reduce, out_channels3, out_channels4):
super(InceptionV2ModuleA, self).__init__()
self.branch1 = ConvBNReLU(in_channels=in_channels,out_channels=out_channels1,kernel_size=1)
self.branch2 = nn.Sequential(
ConvBNReLU(in_channels=in_channels, out_channels=out_channels2reduce, kernel_size=1),
ConvBNReLU(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_size=3, padding=1),
)
self.branch3 = nn.Sequential(
ConvBNReLU(in_channels=in_channels,out_channels=out_channels3reduce,kernel_size=1),
ConvBNReLU(in_channels=out_channels3reduce, out_channels=out_channels3, kernel_size=3, padding=1),
ConvBNReLU(in_channels=out_channels3, out_channels=out_channels3, kernel_size=3, padding=1),
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
ConvBNReLU(in_channels=in_channels, out_channels=out_channels4, kernel_size=1),
)
def forward(self, x):
out1 = self.branch1(x)
out2 = self.branch2(x)
out3 = self.branch3(x)
out4 = self.branch4(x)
out = torch.cat([out1, out2, out3, out4], dim=1)
return out
class InceptionV2ModuleB(nn.Module):
def __init__(self, in_channels,out_channels1,out_channels2reduce, out_channels2, out_channels3reduce, out_channels3, out_channels4):
super(InceptionV2ModuleB, self).__init__()
self.branch1 = ConvBNReLU(in_channels=in_channels,out_channels=out_channels1,kernel_size=1)
self.branch2 = nn.Sequential(
ConvBNReLU(in_channels=in_channels, out_channels=out_channels2reduce, kernel_size=1),
ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2reduce, kernel_sizes=[1,3],paddings=[0,1]),
ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_sizes=[3,1],paddings=[1, 0]),
)
self.branch3 = nn.Sequential(
ConvBNReLU(in_channels=in_channels,out_channels=out_channels3reduce,kernel_size=1),
ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3reduce,kernel_sizes=[3, 1], paddings=[1, 0]),
ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3reduce,kernel_sizes=[1, 3], paddings=[0, 1]),
ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3reduce,kernel_sizes=[3, 1], paddings=[1, 0]),
ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3,kernel_sizes=[1, 3], paddings=[0, 1]),
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
ConvBNReLU(in_channels=in_channels, out_channels=out_channels4, kernel_size=1),
)
def forward(self, x):
out1 = self.branch1(x)
out2 = self.branch2(x)
out3 = self.branch3(x)
out4 = self.branch4(x)
out = torch.cat([out1, out2, out3, out4], dim=1)
return out
class InceptionV2ModuleC(nn.Module):
def __init__(self, in_channels,out_channels1,out_channels2reduce, out_channels2, out_channels3reduce, out_channels3, out_channels4):
super(InceptionV2ModuleC, self).__init__()
self.branch1 = ConvBNReLU(in_channels=in_channels,out_channels=out_channels1,kernel_size=1)
self.branch2_conv1 = ConvBNReLU(in_channels=in_channels, out_channels=out_channels2reduce, kernel_size=1)
self.branch2_conv2a = ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_sizes=[1,3],paddings=[0,1])
self.branch2_conv2b = ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_sizes=[3,1],paddings=[1,0])
self.branch3_conv1 = ConvBNReLU(in_channels=in_channels,out_channels=out_channels3reduce,kernel_size=1)
self.branch3_conv2 = ConvBNReLU(in_channels=out_channels3reduce, out_channels=out_channels3, kernel_size=3,stride=1,padding=1)
self.branch3_conv3a = ConvBNReLUFactorization(in_channels=out_channels3, out_channels=out_channels3, kernel_sizes=[3, 1],paddings=[1, 0])
self.branch3_conv3b = ConvBNReLUFactorization(in_channels=out_channels3, out_channels=out_channels3, kernel_sizes=[1, 3],paddings=[0, 1])
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
ConvBNReLU(in_channels=in_channels, out_channels=out_channels4, kernel_size=1),
)
def forward(self, x):
out1 = self.branch1(x)
x2 = self.branch2_conv1(x)
out2 = torch.cat([self.branch2_conv2a(x2), self.branch2_conv2b(x2)],dim=1)
x3 = self.branch3_conv2(self.branch3_conv1(x))
out3 = torch.cat([self.branch3_conv3a(x3), self.branch3_conv3b(x3)], dim=1)
out4 = self.branch4(x)
out = torch.cat([out1, out2, out3, out4], dim=1)
return out
class InceptionV3ModuleD(nn.Module):
def __init__(self, in_channels,out_channels1reduce,out_channels1,out_channels2reduce, out_channels2):
super(InceptionV3ModuleD, self).__init__()
self.branch1 = nn.Sequential(
ConvBNReLU(in_channels=in_channels, out_channels=out_channels1reduce, kernel_size=1),
ConvBNReLU(in_channels=out_channels1reduce, out_channels=out_channels1, kernel_size=3,stride=2,padding=1)
)
self.branch2 = nn.Sequential(
ConvBNReLU(in_channels=in_channels, out_channels=out_channels2reduce, kernel_size=1),
ConvBNReLU(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_size=3, stride=1, padding=1),
ConvBNReLU(in_channels=out_channels2, out_channels=out_channels2, kernel_size=3, stride=2,padding=1),
)
self.branch3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
def forward(self, x):
out1 = self.branch1(x)
out2 = self.branch2(x)
out3 = self.branch3(x)
out = torch.cat([out1, out2, out3], dim=1)
return out
class InceptionAux(nn.Module):
def __init__(self, in_channels,out_channels):
super(InceptionAux, self).__init__()
self.auxiliary_avgpool = nn.AvgPool2d(kernel_size=5, stride=3)
self.auxiliary_conv1 = ConvBNReLU(in_channels=in_channels, out_channels=128, kernel_size=1)
self.auxiliary_conv2 = nn.Conv2d(in_channels=128, out_channels=768, kernel_size=5,stride=1)
self.auxiliary_dropout = nn.Dropout(p=0.7)
self.auxiliary_linear1 = nn.Linear(in_features=768, out_features=out_channels)
def forward(self, x):
x = self.auxiliary_conv1(self.auxiliary_avgpool(x))
x = self.auxiliary_conv2(x)
x = x.view(x.size(0), -1)
out = self.auxiliary_linear1(self.auxiliary_dropout(x))
return out
class InceptionV2(nn.Module):
def __init__(self, num_classes=1000, stage='train'):
super(InceptionV2, self).__init__()
self.stage = stage
self.block1 = nn.Sequential(
ConvBNReLU(in_channels=3, out_channels=64, kernel_size=7,stride=2,padding=3),
nn.MaxPool2d(kernel_size=3,stride=2,padding=1),
)
self.block2 = nn.Sequential(
ConvBNReLU(in_channels=64, out_channels=192, kernel_size=3, stride=1, padding=1),
nn.MaxPool2d(kernel_size=3, stride=2,padding=1),
)
self.block3 = nn.Sequential(
InceptionV2ModuleA(in_channels=192,out_channels1=64,out_channels2reduce=64, out_channels2=64, out_channels3reduce=64, out_channels3=96, out_channels4=32),
InceptionV2ModuleA(in_channels=256, out_channels1=64, out_channels2reduce=64, out_channels2=96,out_channels3reduce=64, out_channels3=96, out_channels4=64),
InceptionV3ModuleD(in_channels=320, out_channels1reduce=128, out_channels1=160, out_channels2reduce=64,out_channels2=96),
)
self.block4 = nn.Sequential(
InceptionV2ModuleB(in_channels=576, out_channels1=224, out_channels2reduce=64, out_channels2=96,out_channels3reduce=96, out_channels3=128, out_channels4=128),
InceptionV2ModuleB(in_channels=576, out_channels1=192, out_channels2reduce=96, out_channels2=128,out_channels3reduce=96, out_channels3=128, out_channels4=128),
InceptionV2ModuleB(in_channels=576, out_channels1=160, out_channels2reduce=128, out_channels2=160,out_channels3reduce=128, out_channels3=128, out_channels4=128),
InceptionV2ModuleB(in_channels=576, out_channels1=96, out_channels2reduce=128, out_channels2=192,out_channels3reduce=160, out_channels3=160, out_channels4=128),
InceptionV3ModuleD(in_channels=576, out_channels1reduce=128, out_channels1=192, out_channels2reduce=192,out_channels2=256),
)
self.block5 = nn.Sequential(
InceptionV2ModuleC(in_channels=1024, out_channels1=352, out_channels2reduce=192, out_channels2=160,out_channels3reduce=160, out_channels3=112, out_channels4=128),
InceptionV2ModuleC(in_channels=1024, out_channels1=352, out_channels2reduce=192, out_channels2=160,
out_channels3reduce=192, out_channels3=112, out_channels4=128)
)
self.max_pool = nn.MaxPool2d(kernel_size=7, stride=1)
self.dropout = nn.Dropout(p=0.5)
self.linear = nn.Linear(1024, num_classes)
def forward(self, x):
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.max_pool(x)
x = self.dropout(x)
x = x.view(x.size(0), -1)
out = self.linear(x)
return out
if __name__=='__main__':
model = InceptionV2()
print(model)
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)