import torch
import torch.nn as nn

class Inception(nn.Module):
    def __init__(self, in_planes, kernel_1_x, kernel_3_in, kernel_3_x, kernel_5_in, kernel_5_x, pool_planes):
        super(Inception, self).__init__()
        # 1x1 conv branch
        self.b1 = nn.Sequential(
            nn.Conv2d(in_planes, kernel_1_x, kernel_size=1),
            nn.BatchNorm2d(kernel_1_x),
            nn.ReLU(True),
        )

        # 1x1 conv -> 3x3 conv branch
        self.b2 = nn.Sequential(
            nn.Conv2d(in_planes, kernel_3_in, kernel_size=1),
            nn.BatchNorm2d(kernel_3_in),
            nn.ReLU(True),
            nn.Conv2d(kernel_3_in, kernel_3_x, kernel_size=3, padding=1),
            nn.BatchNorm2d(kernel_3_x),
            nn.ReLU(True),
        )

        # 1x1 conv -> 5x5 conv branch
        self.b3 = nn.Sequential(
            nn.Conv2d(in_planes, kernel_5_in, kernel_size=1),
            nn.BatchNorm2d(kernel_5_in),
            nn.ReLU(True),
            nn.Conv2d(kernel_5_in, kernel_5_x, kernel_size=3, padding=1),
            nn.BatchNorm2d(kernel_5_x),
            nn.ReLU(True),
            nn.Conv2d(kernel_5_x, kernel_5_x, kernel_size=3, padding=1),
            nn.BatchNorm2d(kernel_5_x),
            nn.ReLU(True),
        )

        # 3x3 pool -> 1x1 conv branch
        self.b4 = nn.Sequential(
            nn.MaxPool2d(3, stride=1, padding=1),
            nn.Conv2d(in_planes, pool_planes, kernel_size=1),
            nn.BatchNorm2d(pool_planes),
            nn.ReLU(True),
        )

    def forward(self, x):
        y1 = self.b1(x)
        y2 = self.b2(x)
        y3 = self.b3(x)
        y4 = self.b4(x)
        return torch.cat([y1, y2, y3, y4], 1)

class GoogLeNet(nn.Module):
    def __init__(self, input_channels, output_num):
        super(GoogLeNet, self).__init__()
        self.pre_layers = nn.Sequential(
            nn.Conv2d(input_channels, 192, kernel_size=3, padding=1),
            nn.BatchNorm2d(192),
            nn.ReLU(True),
        )

        self.a3 = Inception(192, 64, 96, 128, 16, 32, 32)
        self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)
        self.max_pool = nn.MaxPool2d(3, stride=2, padding=1)
        self.a4 = Inception(480, 192, 96, 208, 16, 48, 64)
        self.b4 = Inception(512, 160, 112, 224, 24, 64, 64)
        self.c4 = Inception(512, 128, 128, 256, 24, 64, 64)
        self.d4 = Inception(512, 112, 144, 288, 32, 64, 64)
        self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)
        self.a5 = Inception(832, 256, 160, 320, 32, 128, 128)
        self.b5 = Inception(832, 384, 192, 384, 48, 128, 128)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # Adaptive pooling
        self.linear = nn.Linear(1024, output_num)

    def forward(self, x):
        x = self.pre_layers(x)
        x = self.a3(x)
        x = self.b3(x)
        x = self.max_pool(x)
        x = self.a4(x)
        x = self.b4(x)
        x = self.c4(x)
        x = self.d4(x)
        x = self.e4(x)
        x = self.max_pool(x)
        x = self.a5(x)
        x = self.b5(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.linear(x)
        return x

if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser(description='GoogLeNet Implementation')
    parser.add_argument('--input_channels', default=3, type=int)
    parser.add_argument('--output_num', default=10, type=int)
    args = parser.parse_args()
    
    model = GoogLeNet(args.input_channels, args.output_num)
    tensor = torch.rand(1, args.input_channels, 224, 224)  # Example for a larger size
    pred = model(tensor)
    pred_shape = pred.shape
    
    print(model)
    print("Predictions shape:", pred_shape)