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googlenet_v3.py
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googlenet_v3.py
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# coding:utf8
import sys
import torch
import torch.nn as nn
import torchvision.models
class InceptionA(nn.Module):
def __init__(self, in_planes, b1_1, b2t_1, b2t_2, b3t_1, b3t_2, b3t_3, b4_1):
super(InceptionA, self).__init__()
self.branch1 = nn.Sequential(nn.Conv2d(in_planes, 64, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True))
self.branch2 = nn.Sequential(nn.Conv2d(in_planes, 48, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(48),
nn.ReLU(inplace=True),
nn.Conv2d(48, 64, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True))
self.branch3 = nn.Sequential(nn.Conv2d(in_planes, 64, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 96, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(96),
nn.ReLU(inplace=True),
nn.Conv2d(96, 96, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(96),
nn.ReLU(inplace=True))
self.branch4 = nn.Sequential(nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(in_planes, b4_1, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(b4_1),
nn.ReLU(inplace=True))
def forward(self, x):
print x.shape
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
x4 = self.branch4(x)
print x1.shape, x2.shape, x3.shape, x4.shape
return torch.cat([x1, x2, x3, x4], dim=1)
class InceptionB(nn.Module):
def __init__(self, in_planes, b1_1, b3t_1, b3t_2, b3t_3):
super(InceptionB, self).__init__()
self.branch1 = nn.Sequential(nn.Conv2d(in_planes, 384, kernel_size=3, stride=2, padding=0),
nn.BatchNorm2d(384),
nn.ReLU(inplace=True))
self.branch2 = nn.Sequential(nn.Conv2d(in_planes, 64, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 96, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(96),
nn.ReLU(inplace=True),
nn.Conv2d(96, 96, kernel_size=3, stride=2, padding=0),
nn.BatchNorm2d(96),
nn.ReLU(inplace=True))
self.branch3 = nn.Sequential(nn.MaxPool2d(kernel_size=3, stride=2, padding=0))
def forward(self, x):
print x.shape
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
print x1.shape, x2.shape, x3.shape
return torch.cat([x1, x2, x3], dim=1)
class InceptionC(nn.Module):
def __init__(self, in_planes, b1_1, b2t_1, b2t_2, b2t_3, b3t_1, b3t_2, b3t_3, b3t_4, b3t_5, b4_1):
super(InceptionC, self).__init__()
self.branch1 = nn.Sequential(nn.Conv2d(in_planes, 192, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True))
self.branch2 = nn.Sequential(nn.Conv2d(in_planes, b2t_1, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(b2t_1),
nn.ReLU(inplace=True),
nn.Conv2d(b2t_1, b2t_1, kernel_size=(1, 7), stride=1, padding=(0, 3)),
nn.BatchNorm2d(b2t_1),
nn.ReLU(inplace=True),
nn.Conv2d(b2t_1, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True)
)
self.branch3 = nn.Sequential(nn.Conv2d(in_planes, b3t_1, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(b3t_1),
nn.ReLU(inplace=True),
nn.Conv2d(b3t_1, b3t_1, kernel_size=(7, 1), stride=1, padding=(3, 0)),
nn.BatchNorm2d(b3t_1),
nn.ReLU(inplace=True),
nn.Conv2d(b3t_1, b3t_1, kernel_size=(1, 7), stride=1, padding=(0, 3)),
nn.BatchNorm2d(b3t_1),
nn.ReLU(inplace=True),
nn.Conv2d(b3t_1, b3t_1, kernel_size=(7, 1), stride=1, padding=(3, 0)),
nn.BatchNorm2d(b3t_1),
nn.ReLU(inplace=True),
nn.Conv2d(b3t_1, 192, kernel_size=(1, 7), stride=1, padding=(0, 3)),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True)
)
self.branch4 = nn.Sequential(nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(in_planes, b4_1, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(b4_1),
nn.ReLU(inplace=True))
def forward(self, x):
print x.shape
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
x4 = self.branch4(x)
print x1.shape, x2.shape, x3.shape, x4.shape
return torch.cat([x1, x2, x3, x4], dim=1)
class InceptionD(nn.Module):
def __init__(self, in_planes, b1_1, b1_2, b3t_1, b3t_2, b3t_3, b3t_4):
super(InceptionD, self).__init__()
self.branch1 = nn.Sequential(nn.Conv2d(in_planes, 192, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True),
nn.Conv2d(192, 320, kernel_size=3, stride=2, padding=0),
nn.BatchNorm2d(320),
nn.ReLU(inplace=True)
)
self.branch2 = nn.Sequential(nn.Conv2d(in_planes, 192, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True),
nn.Conv2d(192, 192, kernel_size=(1, 7), stride=1, padding=(0, 3)),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True),
nn.Conv2d(192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True),
nn.Conv2d(192, 192, kernel_size=3, stride=2, padding=0),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True))
self.branch3 = nn.Sequential(nn.MaxPool2d(kernel_size=3, stride=2, padding=0))
def forward(self, x):
print x.shape
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
print x1.shape, x2.shape, x3.shape
return torch.cat([x1, x2, x3], dim=1)
class InceptionE(nn.Module):
def __init__(self, in_planes):
super(InceptionE, self).__init__()
self.branch1 = nn.Sequential(nn.Conv2d(in_planes, 320, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(320),
nn.ReLU(inplace=True))
self.branch2 = nn.Sequential(nn.Conv2d(in_planes, 384, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(384),
nn.ReLU(inplace=True))
self.branch2_1 = nn.Sequential(nn.Conv2d(384, 384, kernel_size=(1, 3), stride=1, padding=(0, 1)),
nn.BatchNorm2d(384),
nn.ReLU(inplace=True))
self.branch2_2 = nn.Sequential(nn.Conv2d(384, 384, kernel_size=(3, 1), stride=1, padding=(1, 0)),
nn.BatchNorm2d(384),
nn.ReLU(inplace=True))
self.branch3 = nn.Sequential(nn.Conv2d(in_planes, 448, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(448),
nn.ReLU(inplace=True),
nn.Conv2d(448, 384, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(384),
nn.ReLU(inplace=True))
self.branch3_1 = nn.Sequential(nn.Conv2d(384, 384, kernel_size=(1, 3), stride=1, padding=(0, 1)),
nn.BatchNorm2d(384),
nn.ReLU(inplace=True))
self.branch3_2 = nn.Sequential(nn.Conv2d(384, 384, kernel_size=(3, 1), stride=1, padding=(1, 0)),
nn.BatchNorm2d(384),
nn.ReLU(inplace=True))
self.branch4 = nn.Sequential(nn.AvgPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(in_planes, 192, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True))
def forward(self, x):
print x.shape
x1 = self.branch1(x)
x2 = self.branch2(x)
x2_1 = self.branch2_1(x2)
x2_2 = self.branch2_2(x2)
x3 = self.branch3(x)
x3_1 = self.branch3_1(x3)
x3_2 = self.branch3_2(x3)
x4 = self.branch4(x)
print x1.shape, x2.shape, x3.shape, x2_1.shape, x2_2.shape, x3_1.shape, x3_2.shape, x4.shape
return torch.cat([x1, x2_1, x2_2, x3_1, x3_2, x4], dim=1)
class GoogLeNetV3(nn.Module):
def __init__(self):
super(GoogLeNetV3, self).__init__()
self.conv1 = nn.Sequential(nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=0),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True))
self.conv2 = nn.Sequential(nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=0),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True))
self.conv3 = nn.Sequential(nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True))
self.pool3 = nn.Sequential(nn.MaxPool2d(kernel_size=3, stride=2, padding=0))
self.conv4 = nn.Sequential(nn.Conv2d(64, 80, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(80),
nn.ReLU(inplace=True))
self.conv5 = nn.Sequential(nn.Conv2d(80, 192, kernel_size=3, stride=1, padding=0),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True))
# self.conv6 = nn.Sequential(nn.Conv2d(192, 288, kernel_size=3, stride=1, padding=0),
# nn.BatchNorm2d(288),
# nn.ReLU(inplace=True))
self.pool6 = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
self.inceptionA1 = InceptionA(192, 64, 48, 64, 64, 96, 96, 32)
self.inceptionA2 = InceptionA(256, 64, 48, 64, 64, 96, 96, 64)
self.inceptionA3 = InceptionA(288, 64, 48, 64, 64, 96, 96, 64)
self.inceptionB = InceptionB(288, 384, 64, 96, 96)
self.inceptionC1 = InceptionC(768, 192, 128, 128, 192, 128, 128, 128, 128, 192, 192)
self.inceptionC2 = InceptionC(768, 192, 160, 160, 192, 160, 160, 160, 160, 192, 192)
self.inceptionC3 = InceptionC(768, 192, 160, 160, 192, 160, 160, 160, 160, 192, 192)
self.inceptionC4 = InceptionC(768, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192)
self.inceptionD = InceptionD(768, 192, 320, 192, 192, 192, 192)
self.inceptionE1 = InceptionE(1280)
self.inceptionE2 = InceptionE(2048)
self.gloab_pool = nn.AvgPool2d(kernel_size=8, stride=1, padding=0)
self.fc = nn.Linear(2048, 1000)
def forward(self, x):
x = self.conv1(x)
print x.shape
x = self.conv2(x)
print x.shape
x = self.conv3(x)
print x.shape
x = self.pool3(x)
print x.shape
x = self.conv4(x)
print x.shape
x = self.conv5(x)
print x.shape
x = self.pool6(x)
print x.shape
x = self.inceptionA1(x)
print x.shape
x = self.inceptionA2(x)
print x.shape
x = self.inceptionA3(x)
print x.shape
x = self.inceptionB(x)
print x.shape
x = self.inceptionC1(x)
print x.shape
x = self.inceptionC2(x)
print x.shape
x = self.inceptionC3(x)
print x.shape
x = self.inceptionC4(x)
print x.shape
x = self.inceptionD(x)
print x.shape
print 'inceptionD'
x = self.inceptionE1(x)
print x.shape
print 'inceptionE'
x = self.inceptionE2(x)
print x.shape
x = self.gloab_pool(x)
print x.shape
x = x.view(x.size()[0], -1)
print x.shape
x = self.fc(x)
print x.shape
return x
if __name__ == '__main__':
input_tensor = torch.randn((1, 3, 299, 299))
input_var = torch.autograd.Variable(input_tensor)
model = GoogLeNetV3()
output = model(input_var)