Is it possible to randomize the augmentation in the Dataset class?
def __init__(self, subdict, num_labels, params=None, isTransform=None, isplot=None):
    """
    :param subdict: dictionary of 3D MR images e.g. ['img_sub'] = '/user/318_T1w.nii.gz'
    :param num_labels: number of segmentation labels
    """
    self.subdict = subdict
    self.num_labels = num_labels
    self.img_subs = subdict['img_subs']
    self.img_files = subdict['img_files']
    self.seg_subs = subdict['seg_subs']
    self.seg_files = subdict['seg_files']
    self.isTransfom = isTransform
    self.isplot = isplot
    self.params = params

def __getitem__(self, index):      
    sub_name = self.img_subs[index]
    if self.isTransfom:                         
        img, seg = self.imaugment(imgnp, segnp) 

def imaugment(self, X, Y):                                             
    """                                                                
    Preprocess the tuple (image, mask) and then apply if selected:      
        augmentation techniques adapted from Keras ImageDataGenerator  
        elastic deformation                                            
    """                                                                
    if Y is not None and X.shape != Y.shape:                           
        raise ValueError("image and mask should have the same size")   
    if self.params["augmentation"][0] == True:                         
        X, Y = random_transform(X, Y, **self.params["random_deform"])  
    if self.params["augmentation"][1] == True:                         
        X, Y = deform_pixel(X, Y, **self.params["e_deform_p"])         
    if self.params["augmentation"][2] == True:                         
        X, Y = deform_grid(X, Y, **self.params["e_deform_g"])          
    return X, Y                                                        

This way, I can only choose True/False, which entirely turns the augmentation on or off for the whole dataset.
What I want to augment some index/sample and not to others.
Thanks for the help.