I’m trying to train a graph cnn on a sample data of 8 meshes. I’m facing out of memory error in dataloader after few epochs. My dataset and dataloader are
class CustomDataset(Dataset):
def init(self, root, transform=None, pre_transform=None):
super(CustomDataset, self).init(root, transform, pre_transform)
self.root=root
self.df = pd.read_csv(root,header=None)
def len(self):
    return len(self.df)

def get(self, idx):


    # Load Meshes and RBG Image
    x = np.load(self.df.iloc[idx,0])['v']

    #gt_pts = np.load(self.df.iloc[idx,1])['g']
    gt_pts = np.load('buff_gt/'+str(idx)+'/gt.npz')['g']

    edge_index = np.load(self.df.iloc[idx,2])['e']

    faces = np.load(self.df.iloc[idx,3])['f']

    # Generate data to be returned
    edge_index = torch.from_numpy(edge_index).type(torch.LongTensor)
    faces = torch.from_numpy(faces).type(torch.LongTensor)
    x = torch.from_numpy(x).type(torch.float)
    gt_pts = torch.from_numpy(gt_pts).type(torch.float)

    data = Data(x = x, edge_index = edge_index,gt = gt_pts, faces = faces)
    return data

dataset = CustomDataset(’/home/snehith/Pixel2Mesh/buff_train_data.csv’)
train_data_loader = DataListLoader(dataset, batch_size = self.options.train.batch_size, num_workers=40, shuffle = True,pin_memory=False)
TIA