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

class MovieposterNet(nn.Module):

    def __init__(self, num_classes=10):
        super(MovieposterNet, self).__init__()
        
        # Bloc 1 : 224 -> 112
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        
        # Bloc 2 : 112 -> 56
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        
        # Bloc 3 : 56 -> 28
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(64)
        
        # Bloc 4 : 28 -> 14
        self.conv4 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn4 = nn.BatchNorm2d(128)
        
        self.pool = nn.MaxPool2d(2, 2)
        
        # Taille après 4 poolings : 224 / 2^4 = 14
        # Entrée fc1 : 128 canaux * 14 * 14 = 25088
        self.fc1 = nn.Linear(128 * 14 * 14, 512)
        self.dropout = nn.Dropout(0.5) # Limite le sur-apprentissage
        self.fc2 = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.pool(F.relu(self.bn1(self.conv1(x))))
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        x = self.pool(F.relu(self.bn3(self.conv3(x))))
        x = self.pool(F.relu(self.bn4(self.conv4(x))))
        
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

    def get_features(self, x):
        x = self.pool(F.relu(self.bn1(self.conv1(x))))
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        x = self.pool(F.relu(self.bn3(self.conv3(x))))
        x = self.pool(F.relu(self.bn4(self.conv4(x))))
        return torch.flatten(x, 1)