import torch
import torch.nn as nn


class AlexNetCustom(nn.Module):
    def __init__(self,
                 in_channels: int = 3,
                 out_classes: int=1_000):
        super().__init__()
        
        self._out_classes = out_classes
        
        self.feature = nn.Sequential(
            nn.Conv2d(in_channels, 96, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            
            nn.Conv2d(96, 256, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            
            nn.Conv2d(256, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            
            nn.Conv2d(384, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2)
        )
        
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6)) 
        
        self.mlp = nn.Sequential(
            nn.Dropout(p=0.5),
            nn.Linear(256*6*6, 4096),
            nn.ReLU(inplace=True),
            
            nn.Dropout(p=0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            
            nn.Linear(4096, self._out_classes)
        )
        
    def forward(self, image):
        x = self.feature(image)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.mlp(x)
        
        return x