PyTorch实现的Inception-v3
PyTorch实现的Inception-v3
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=5, padding=2),
)
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=[1,7],paddings=[0,3]),
ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_sizes=[7,1],paddings=[3, 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=[7, 1], paddings=[3, 0]),
ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3reduce,kernel_sizes=[1, 7], paddings=[0, 3]),
ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3reduce,kernel_sizes=[7, 1], paddings=[3, 0]),
ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3,kernel_sizes=[1, 7], paddings=[0, 3]),
)
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 InceptionV2ModuleD(nn.Module):
def __init__(self, in_channels,out_channels1,out_channels2reduce, out_channels2):
super(InceptionV2ModuleD, self).__init__()
self.branch1 = ConvBNReLU(in_channels=in_channels, out_channels=out_channels1, kernel_size=3,stride=2)
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),
)
self.branch3 = nn.MaxPool2d(kernel_size=3,stride=2)
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 InceptionV2ModuleE(nn.Module):
def __init__(self, in_channels, out_channels1reduce,out_channels1, out_channels2reduce, out_channels2):
super(InceptionV2ModuleE, 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),
)
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, 7], paddings=[0, 3]),
ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2reduce,kernel_sizes=[7, 1], paddings=[3, 0]),
ConvBNReLU(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_size=3, stride=2),
)
self.branch3 = nn.MaxPool2d(kernel_size=3, stride=2)
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 InceptionV3(nn.Module):
def __init__(self, num_classes=1000, stage='train'):
super(InceptionV3, self).__init__()
self.stage = stage
self.block1 = nn.Sequential(
ConvBNReLU(in_channels=3, out_channels=32, kernel_size=3, stride=2),
ConvBNReLU(in_channels=32, out_channels=32, kernel_size=3, stride=1),
ConvBNReLU(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.MaxPool2d(kernel_size=3, stride=2)
)
self.block2 = nn.Sequential(
ConvBNReLU(in_channels=64, out_channels=80, kernel_size=3, stride=1),
ConvBNReLU(in_channels=80, out_channels=192, kernel_size=3, stride=1, padding=1),
nn.MaxPool2d(kernel_size=3, stride=2)
)
self.block3 = nn.Sequential(
InceptionV2ModuleA(in_channels=192, out_channels1=64,out_channels2reduce=48, out_channels2=64, out_channels3reduce=64, out_channels3=96, out_channels4=32),
InceptionV2ModuleA(in_channels=256, out_channels1=64,out_channels2reduce=48, out_channels2=64, out_channels3reduce=64, out_channels3=96, out_channels4=64),
InceptionV2ModuleA(in_channels=288, out_channels1=64,out_channels2reduce=48, out_channels2=64, out_channels3reduce=64, out_channels3=96, out_channels4=64)
)
self.block4 = nn.Sequential(
InceptionV2ModuleD(in_channels=288,out_channels1=384,out_channels2reduce=64, out_channels2=96),
InceptionV2ModuleB(in_channels=768, out_channels1=192, out_channels2reduce=128, out_channels2=192, out_channels3reduce=128,out_channels3=192, out_channels4=192),
InceptionV2ModuleB(in_channels=768, out_channels1=192, out_channels2reduce=160, out_channels2=192,out_channels3reduce=160, out_channels3=192, out_channels4=192),
InceptionV2ModuleB(in_channels=768, out_channels1=192, out_channels2reduce=160, out_channels2=192,out_channels3reduce=160, out_channels3=192, out_channels4=192),
InceptionV2ModuleB(in_channels=768, out_channels1=192, out_channels2reduce=192, out_channels2=192,out_channels3reduce=192, out_channels3=192, out_channels4=192),
)
if self.stage=='train':
self.aux_logits = InceptionAux(in_channels=768,out_channels=num_classes)
self.block5 = nn.Sequential(
InceptionV2ModuleE(in_channels=768, out_channels1reduce=192,out_channels1=320, out_channels2reduce=192, out_channels2=192),
InceptionV2ModuleC(in_channels=1280, out_channels1=320, out_channels2reduce=384, out_channels2=384, out_channels3reduce=448,out_channels3=384, out_channels4=192),
InceptionV2ModuleC(in_channels=2048, out_channels1=320, out_channels2reduce=384, out_channels2=384,out_channels3reduce=448, out_channels3=384, out_channels4=192),
)
self.max_pool = nn.MaxPool2d(kernel_size=8,stride=1)
self.dropout = nn.Dropout(p=0.5)
self.linear = nn.Linear(2048, num_classes)
def forward(self, x):
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
aux = 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)
if self.stage == 'train':
aux = self.aux_logits(aux)
return aux,out
else:
return out
if __name__=='__main__':
model = InceptionV3()
print(model)
input = torch.randn(1, 3, 299, 299)
aux,out = model(input)
print(aux.shape)
print(out.shape)