Please see the modified code below. I am not able to convert list of strings to tensor format before loading into data model. Please help me.
import pandas as pd
import numpy as np


import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data.dataset import Dataset  

from cnn_3dmodel import CNNModel




class CustomDatasetFromCSV(Dataset):
    def __init__(self, csv_path, transform=None):
        
        self.data = pd.read_csv(csv_path)
        self.labels = np.asarray(self.data.iloc[:, 0])
        #self.points = np.asarray(self.data.iloc[:, 1])
        self.transform = transform
	
	
    def __getitem__(self, index):
        data_labels = self.labels[index]
        data_points = np.asarray(df.iloc[:, 1]) 
        mat = []
	for i in data_points:
 	  	 row = i.split(',')
   		 mat.append(row)
	print(mat)  
	point_t=torch.FloatTensor(mat)
	print(point_t)
        return (points_t, data_labels)
                

    def __len__(self):
        return len(self.data.index)


if __name__ == "__main__":

    #transformations = transforms.Compose([transforms.ToTensor()])

    #custom_b_from_csv = \
    	#CustomDatasetFromCSV('train.csv',transformations)

    b_dataset_loader = torch.utils.data.DataLoader(dataset= "train.csv",batch_size=10,shuffle=False)

    model = CNNModel()
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

    for i, (points_t, data_labels) in enumerate(b_dataset_loader):
        points = Variable(points_t)
        labels = Variable(data_labels)
        # Clear gradients
        optimizer.zero_grad()
        # Forward pass
        outputs = model(points)
        # Calculate loss
        loss = criterion(outputs, labels)
        # Backward pass
        loss.backward()
        # Update weights
        optimizer.step()
	break