Hi, I am using Cats and Dogs dataset by microsoft. The dataset consists of 25,000 images where all the images of cats contain in cats folder and all the images of dogs contain in the Dogs folder.
However, I split the data into train,valid and test data as given below:

data_transf = transforms.Compose([
transforms.CenterCrop(233),
transforms.ToTensor(),
transforms.Normalize(mean =  [0.485, 0.456, 0.406],
std  =  [0.229, 0.224, 0.225])])
train_data = datasets.ImageFolder(root= ‘/content/PetImages’, transform = data_transf)
valid_size = 0.2
test_size  = 0.1
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)
num_train = len(train_sampler)
indices = list(range(num_train)
np.random.shuffle(indices)
split = int(np.floor(test_size * num_train))
train_idx, test_idx = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_idx)
test_sampler = SubsetRandomSampler(test_idx)
prepare data loaders
trainloader = DataLoader(dataset = train_data, batch_size=32, drop_last=True,sampler=train_sampler)
validloader = DataLoader(dataset = train_data, batch_size=32,drop_last=True,sampler=valid_sampler)
testloader = DataLoader(dataset = train_data, batch_size=32,drop_last=True,sampler=test_sampler)

Can anyone help me in applying data augmentation techniques to train data and valid data  without applying it on test data?