Prepare train loader and validation loader from train dataset

fd04.py

from torch.utils.data.sampler import SubsetRandomSampler

def prepare_train_valid_loaders(trainset, valid_size=0.2, 
                                batch_size=128):
    '''
    Split trainset data and prepare DataLoader for training and validation
    
    Args:
        trainset (Dataset): data 
        valid_size (float): validation size, defalut=0.2
        batch_size (int) : batch size, default=128
    ''' 
    
    # obtain training indices that will be used for validation
    num_train = len(trainset)
    indices = list(range(num_train))
    np.random.shuffle(indices)
    split = int(np.floor(valid_size * num_train))
    train_idx, valid_idx = indices[split:], indices[:split]
    
    # define samplers for obtaining training and validation batches
    train_sampler = SubsetRandomSampler(train_idx)
    valid_sampler = SubsetRandomSampler(valid_idx)
    
    # prepare data loaders
    train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                               sampler=train_sampler)
    valid_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                               sampler=valid_sampler)
    
    return train_loader, valid_loader