train.py

import os
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset, SubsetRandomSampler
from sklearn.model_selection import train_test_split

def load_dataset(folder_path, max_images_per_class=60, allowed_classes=None):
    dataset = {}
    
    class_names = [
        name for name in os.listdir(folder_path)
        if os.path.isdir(os.path.join(folder_path, name)) and 
        (allowed_classes is None or name in allowed_classes)
    ]
    
    if allowed_classes:
        class_names = [cls for cls in allowed_classes if cls in class_names]
    
    for class_name in class_names:
        class_path = os.path.join(folder_path, class_name)
        images = []
        
        for file_name in os.listdir(class_path):
            if len(images) >= max_images_per_class:
                break
            if file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
                img_path = os.path.join(class_path, file_name)
                img = Image.open(img_path).convert('RGB')
                images.append(np.array(img))
        
        dataset[class_name] = images
    
    return dataset

class AnimeDataset(Dataset):
    def __init__(self, images, transform=None, classes=None):
        self.images = []
        self.labels = []
        self.transform = transform
        self.classes = classes or list(images.keys())
        
        for label, class_name in enumerate(self.classes):
            class_images = images.get(class_name, [])
            self.images.extend(class_images)
            self.labels.extend([label] * len(class_images))

    def __len__(self):
        return len(self.images)
    
    def __getitem__(self, idx):
        image = Image.fromarray(self.images[idx])
        label = self.labels[idx]
        
        if self.transform:
            image = self.transform(image)
            
        return image, label

class AnimeCNN(nn.Module):
    def __init__(self, num_classes=4):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25),
            
            nn.Conv2d(32, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Dropout(0.25)
        )
        
        self.classifier = nn.Sequential(
            nn.Linear(64*16*16, 256),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(256, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

def main():
    SEED = 42
    CLASSES = ["usada_pekora", "aisaka_taiga", "megumin", "minato_aqua"]
    IMG_SIZE = 64
    BATCH_SIZE = 16 
    NUM_EPOCHS = 15  
    
    torch.manual_seed(SEED)
    np.random.seed(SEED)
    
    dataset = load_dataset("dataset", allowed_classes=CLASSES)
    
    transform = transforms.Compose([
        transforms.Resize((IMG_SIZE, IMG_SIZE)),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])
    
    anime_dataset = AnimeDataset(dataset, transform=transform, classes=CLASSES)
    
    indices = list(range(len(anime_dataset)))
    train_indices, val_indices = train_test_split(
        indices, 
        test_size=0.2,
        random_state=SEED,
        stratify=anime_dataset.labels
    )
    
    train_loader = DataLoader(
        anime_dataset,
        batch_size=BATCH_SIZE,
        sampler=SubsetRandomSampler(train_indices),
        pin_memory=True
    )
    
    val_loader = DataLoader(
        anime_dataset,
        batch_size=40,  
        sampler=SubsetRandomSampler(val_indices),
        pin_memory=True
    )
    
    model = AnimeCNN(num_classes=len(CLASSES))
    
    optimizer = optim.Adam(
        model.parameters(), 
        lr=0.001,
        weight_decay=1e-4
    )
    
    criterion = nn.CrossEntropyLoss()
    
    for epoch in range(NUM_EPOCHS):
        model.train()
        train_loss = 0.0
        
        for inputs, labels in train_loader:
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
        
        model.eval()
        val_loss = 0.0
        correct = 0
        total = 0
        
        with torch.no_grad():
            for inputs, labels in val_loader:
                outputs = model(inputs)
                loss = criterion(outputs, labels)
                val_loss += loss.item()
                
                _, predicted = torch.max(outputs, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum().item()
        
        train_loss /= len(train_loader)
        val_loss /= len(val_loader)
        val_acc = 100 * correct / total
        
        print(f"Epoch {epoch+1:02d} | "
              f"Train Loss: {train_loss:.4f} | "
              f"Val Loss: {val_loss:.4f} | "
              f"Accuracy: {val_acc:.2f}%")

    print("Model saved as model.pth")
    torch.save(model.state_dict(), "model.pth")

if __name__ == "__main__":
    main()