import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 5)
        self.conv2 = nn.Conv2d(32, 64, 5)
        self.conv3 = nn.Conv2d(64, 128, 5)
        self.conv4 = nn.Conv2d(128, 256, 5)
        self.conv5 = nn.Conv2d(256, 512, 5)

        self.fc1 = None
        self.fc2 = nn.Linear(512, 128)
        self.fc3 = nn.Linear(128, 64)
        self.fc4 = nn.Linear(64, 2)

    def forward(self, x):
        x = x.float()
        """ x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv5(x))
        x = F.max_pool2d(x, 2) """
        
        x = F.max_pool2d(F.relu(self.conv1(x)), 2)
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = F.max_pool2d(F.relu(self.conv3(x)), 2)
        x = F.max_pool2d(F.relu(self.conv4(x)), 2)
        x = F.max_pool2d(F.relu(self.conv5(x)), 2)

        #x = x.view(x.size(0), -1)
        x = torch.flatten(x, 1)
        
        if self.fc1 is None:
            self.fc1 = nn.Linear(x.shape[1], 512).to(x.device)
        
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = self.fc4(x)
        return x