InceptionNet.py

import torch
import torch.nn as nn

class Googlenet(nn.Module):
    def __init__(self,in_channels =3 ,num_classes = 1000):
        super(Googlenet, self).__init__()
        self.conv1 = conv_block(in_channels=in_channels,out_channels=64, kernel_size = 7, stride = 2, padding=3)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride = 2, padding=1)
        self.conv2 = conv_block(64,192,kernel_size = 3, stride = 1, padding =1)

        self.inception3a = Inception_block(192,64,96,128,16,32,32)
        self.inception3b = Inception_block(256,128,128,192,32,96,64)

        self.inception4a = Inception_block(480,192,96,208,16,48,64)
        self.inception4b = Inception_block(512,160,112,224,24,64,64)
        self.inception4c = Inception_block(512,128,128,256,24,64,64)
        self.inception4d = Inception_block(512,112,144,288,32,64,64)
        self.inception4e = Inception_block(528,256,160,320,32,128,128)

        self.inception5a = Inception_block(832,256,160,320,32,128,128)
        self.inception5b = Inception_block(832,384,192,384,48,128,128)
        self.avg_pool = nn.AvgPool2d(kernel_size= 7 ,stride=1)
        self.dropout = nn.Dropout(p=0.4)
        self.fc1 = nn.Linear(1024,1000)

    def forward(self,x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.conv2(x)
        x = self.maxpool(x)

        x = self.inception3a(x)
        x = self.inception3b(x)
        x = self.maxpool(x)

        x = self.inception4a(x)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = self.maxpool(x)

        x = self.inception5a(x)
        x = self.inception5b(x)
        x = self.avg_pool(x)
        x = x.reshape(x.shape[0], -1)
        x = self.dropout(x)
        x = self.fc1(x)
        return x

        


class Inception_block(nn.Module):
    def __init__(self,in_channels,out_1x1,red_3x3, out_3x3, red_5x5, out_5x5, out_1x1pool):
        super(Inception_block, self).__init__()

        self.branch1 = conv_block(in_channels,out_1x1, kernel_size = 1)

        self.branch2 = nn.Sequential(conv_block(in_channels, red_3x3, kernel_size = 1),
                                     conv_block(red_3x3 , out_3x3, kernel_size = 3, stride = 1, padding=1))

        self.branch3 = nn.Sequential(
            conv_block(in_channels, red_5x5, kernel_size = 1),
            conv_block(red_5x5, out_5x5, kernel_size = 5, padding= 2 )
        )

        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size= 3 , stride=1 ,padding =1),
            conv_block(in_channels, out_1x1pool, kernel_size =1)
        )

    def forward(self,x):
        # N * Filters * 28 * 28
        return torch.cat([self.branch1(x),self.branch2(x), self.branch3(x), self.branch4(x)],1)

class conv_block(nn.Module):
    def __init__(self,in_channels, out_channels, **kwargs):
        super(conv_block,self).__init__()
        self.conv = nn.Conv2d(in_channels = in_channels,out_channels= out_channels, **kwargs)
        self.batchnorm = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()

    def forward(self,x):
        return self.relu(self.batchnorm(self.conv(x)))
    


if __name__ == "__main__":
    x = torch.randn(3,3,224,224)
    model = Googlenet()
    print(model(x).shape)