model.py

import torch
import torch.nn as nn

class DeepVP(nn.Module):
    def __init__(self):
        super(DeepVP, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 3, kernel_size=11),
            nn.Conv2d(3, 96, kernel_size=5),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(96, 256, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(256, 384, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(384, 256, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(57600, 1024),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(1024, 1024),
            nn.ReLU(inplace=True),
            nn.Linear(1024, 2),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x