| import os | |
| import torch | |
| from torch.utils.data import Dataset, DataLoader | |
| from sklearn.model_selection import train_test_split | |
| class CustomDataset(Dataset): | |
| def __init__(self, file_paths, transform=None): | |
| self.file_paths = file_paths | |
| self.transform = transform | |
| self.file_names = [os.path.basename(path) for path in file_paths] | |
| def __len__(self): | |
| return len(self.file_paths) | |
| def __getitem__(self, idx): | |
| data = torch.load(self.file_paths[idx], weights_only=True) | |
| images = data[:6] | |
| labels = data[6] | |
| images = images.float() | |
| labels = labels.long() | |
| if self.transform: | |
| images = self.transform(images) | |
| labels = self.transform(labels) | |
| return images, labels, self.file_names[idx] | |
| def generate_file_paths(base_path): | |
| file_paths = [] | |
| for frame in os.listdir(base_path): | |
| frame_path = os.path.join(base_path, frame) | |
| if frame_path.endswith('.mat.pt'): | |
| file_paths.append(frame_path) | |
| return [path for path in file_paths if os.path.exists(path)] | |
| def load_data(base_path, batch_size=4, num_workers=2, test_size=0.2): | |
| file_paths = generate_file_paths(base_path) | |
| train_paths, test_paths = train_test_split(file_paths, test_size=test_size, random_state=42) | |
| train_dataset = CustomDataset(file_paths=train_paths) | |
| test_dataset = CustomDataset(file_paths=test_paths) | |
| train_loader = DataLoader( | |
| train_dataset, | |
| batch_size=batch_size, | |
| shuffle=True, | |
| num_workers=num_workers, | |
| drop_last=True | |
| ) | |
| test_loader = DataLoader( | |
| test_dataset, | |
| batch_size=batch_size, | |
| shuffle=False, | |
| num_workers=num_workers, | |
| drop_last=True | |
| ) | |
| return train_loader, test_loader | |