"""
An implementation of GoogLeNet / InceptionNet from scratch.

Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
*    2020-04-07 Initial coding
*    2022-12-20 Update comments, code revision, checked still works with latest PyTorch version
"""

import torch
from torch import nn


class GoogLeNet(nn.Module):
    def __init__(self, aux_logits=True, num_classes=1000):
        super(GoogLeNet, self).__init__()
        assert aux_logits == True or aux_logits == False
        self.aux_logits = aux_logits

        # Write in_channels, etc, all explicit in self.conv1, rest will write to
        # make everything as compact as possible, kernel_size=3 instead of (3,3)
        self.conv1 = conv_block(
            in_channels=3,
            out_channels=64,
            kernel_size=7,
            stride=2,
            padding=3,
        )

        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.conv2 = conv_block(64, 192, kernel_size=3, stride=1, padding=1)
        self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # In this order: in_channels, out_1x1, red_3x3, out_3x3, red_5x5, out_5x5, out_1x1pool
        self.inception3a = Inception_block(192, 64, 96, 128, 16, 32, 32)
        self.inception3b = Inception_block(256, 128, 128, 192, 32, 96, 64)
        self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        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.maxpool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.inception5a = Inception_block(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = Inception_block(832, 384, 192, 384, 48, 128, 128)

        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.dropout = nn.Dropout(p=0.4)
        self.fc1 = nn.Linear(1024, num_classes)

        if self.aux_logits:
            self.aux1 = InceptionAux(512, num_classes)
            self.aux2 = InceptionAux(528, num_classes)
        else:
            self.aux1 = self.aux2 = None

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)

        x = self.inception3a(x)
        x = self.inception3b(x)
        x = self.maxpool3(x)

        x = self.inception4a(x)

        # Auxiliary Softmax classifier 1
        if self.aux_logits and self.training:
            aux1 = self.aux1(x)

        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)

        # Auxiliary Softmax classifier 2
        if self.aux_logits and self.training:
            aux2 = self.aux2(x)

        x = self.inception4e(x)
        x = self.maxpool4(x)
        x = self.inception5a(x)
        x = self.inception5b(x)
        x = self.avgpool(x)
        x = x.reshape(x.shape[0], -1)
        x = self.dropout(x)
        x = self.fc1(x)

        if self.aux_logits and self.training:
            return aux1, aux2, x
        else:
            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, 3), 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):
        return torch.cat(
            [self.branch1(x), self.branch2(x), self.branch3(x), self.branch4(x)], 1
        )


class InceptionAux(nn.Module):
    def __init__(self, in_channels, num_classes):
        super(InceptionAux, self).__init__()
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(p=0.7)
        self.pool = nn.AvgPool2d(kernel_size=5, stride=3)
        self.conv = conv_block(in_channels, 128, kernel_size=1)
        self.fc1 = nn.Linear(2048, 1024)
        self.fc2 = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.pool(x)
        x = self.conv(x)
        x = x.reshape(x.shape[0], -1)
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x


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

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


if __name__ == "__main__":
    BATCH_SIZE = 5
    x = torch.randn(BATCH_SIZE, 3, 224, 224)
    model = GoogLeNet(aux_logits=True, num_classes=1000)
    print(model(x)[2].shape)
    assert model(x)[2].shape == torch.Size([BATCH_SIZE, 1000])