Apply different Transform (Data Augmentation) to Train and Validation

vision
1

My dataset folder is prepared as Train Folder and Test Folder. When I conduct experiments, I further split my Train Folder data into Train and Validation.

However, transform is applied before my split and they are the same for both my Train and Validation. My question is how to apply a different transform in this case?

Transoform Code:

data_transform = transforms.Compose([
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

Dataset Code

train_data = datasets.ImageFolder(base_path + '/train/',
                                           transform=data_transform)

Train and Validation Split and Loader Code

# obtain training indices that will be used for validation
num_train = len(train_data)
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(train_data, batch_size=batch_size,
    sampler=train_sampler, num_workers=num_workers)
valid_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,
    sampler=valid_sampler, num_workers=num_workers)
3 Likes