import torch.nn as nn
from configs.ocr import num_classes

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(32),
            nn.Conv2d(32, 32, 3, stride=2, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(32),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25)
        )
        
        self.conv2 = nn.Sequential(
            nn.Conv2d(32, 64, 3, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.Conv2d(64, 64, 3, stride=2, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25)
        )
        
        self.conv3 = nn.Sequential(
            nn.Conv2d(64, 128, 3, padding=1),
            nn.ReLU(),
            nn.BatchNorm2d(128),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25)
        )
        
        self.fc = nn.Sequential(
            nn.Linear(128, num_classes),
        )
                
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        
        x = x.view(x.size(0), -1)
        return self.fc(x)