Error when creating dataset with mindscore

Creating a dataset by yourself will cause errors

【 Operation steps & Problem phenomenon 】

I want to create a super-resolution dataset , This data set has two columns ,data and target,data Is a low resolution image ,target High resolution image . My idea is to create a data and target Image like data set , And then to data Reduce the image in , But an error is reported in the process of creating the dataset

My code

from os import listdir
from os.path import join
import os
from PIL import Image
import random
import numpy as np
import mindspore.dataset as ds
import mindspore.dataset.vision.py_transforms as py_vision
import mindspore.dataset.transforms.py_transforms as py_transforms

def is_image_file(filename):
    return any(filename.endswith(extension) for extension in ['.png', '.jpg', '.jpeg', '.PNG', '.JPG', '.JPEG'])
    
def make_dataset(dir_path, max_dataset_size=float("inf")):
    """Return image list in dir."""
    images = []
    assert os.path.isdir(dir_path), '%s is not a valid directory' % dir_path

    for root, _, fnames in sorted(os.walk(dir_path)):
        for fname in fnames:
            if is_image_file(fname):
                path = os.path.join(root, fname)
                images.append(path)
    return images[:min(max_dataset_size, len(images))]
    

class MakeDataset:

    def __init__(self, dataset_dir, max_dataset_size=float("inf")):
       self.dir_A = os.path.join(dataset_dir,'data')
       self.dir_B = os.path.join(dataset_dir,'target')
       self.A_paths = sorted(make_dataset(self.dir_A, max_dataset_size))
       self.B_paths = sorted(make_dataset(self.dir_B, max_dataset_size))
       self.A_size = len(self.A_paths)
       self.B_size = len(self.B_paths)
    def __getitem__(self, index):
       A_path = self.A_paths[index % self.A_size]
       B_path = self.B_paths[index % self.B_size]
       A_img = np.array(Image.open(A_path).convert('RGB'))
       B_img = np.array(Image.open(B_path).convert('RGB'))
       print(A_img.shape)
       print(B_img.shape)
       return A_img,B_img
    def __len__(self):
       return self.A_size
        
dataset=MakeDataset(dataset_dir="/data/xjtu/wys/image/")
DS=ds.GeneratorDataset(dataset,column_names=["LR,HR"])
for data in DS.create_dict_iterator():
    print(data["LR"].shape)

Error message

Traceback (most recent call last):
  File "/data/xjtu/wys/SRGAN/dataset.py", line 49, in <module>
    for data in DS.create_dict_iterator():
  File "/root/archiconda3/envs/xjtu/lib/python3.7/site-packages/mindspore/dataset/engine/iterators.py", line 263, in __next__
    data = self.get_next()
  File "/root/archiconda3/envs/xjtu/lib/python3.7/site-packages/mindspore/dataset/engine/iterators.py", line 344, in get_next
    return {k: Tensor(v.as_array()) for k, v in self.depipeline.GetNextAsMap().items()}
RuntimeError: Thread ID 281472224702944 Exception thrown from PyFunc. Invalid parameter, Generator should return same number of numpy arrays as specified in column names.
Line of code : 122
File         : /home/jenkins/agent-working-dir/workspace/Compile_Ascend_ARM_EulerOS/mindspore/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/generator_op.cc

1. Report errors  Generator should return same number of numpy arrays as specified in column names., Description means that the returned field does not match the number of specified column names .

2. I see in the code ：class MakeDataset:  ->  def __getitem__(self, index):  ->  return A_img,B_img  Two fields are returned .

3. however  DS=ds.GeneratorDataset(dataset,column_names=["LR,HR"]) As defined in column_names Need to change to  column_names=["LR", "HR"], Expressed as two fields .