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


# TODO: Would be cool to use a simple Transformer model... then I could use the Trainer API 👀
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
        self.conv2 = nn.Conv2d(32, 32, kernel_size=5)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=5)
        self.fc1 = nn.Linear(3 * 3 * 64, 256)
        self.fc2 = nn.Linear(256, 10)

    def forward(self, x, eval=False):
        x = F.relu(self.conv1(x))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = F.dropout(x, p=0.5, training=not eval)
        x = F.relu(F.max_pool2d(self.conv3(x), 2))
        x = F.dropout(x, p=0.5, training=not eval)
        x = x.view(-1, 3 * 3 * 64)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, p=0.5, training=not eval)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)