在 zip(*image, *mask)中取得正確的資料 in Unet多類別分類

我在使用unet進行分類任務(8通道,5x1通道 1x3通道)

但是在這裡我不確定img, mask有沒有正確的取得到train_generators的資料

        for i in range(6):
            img, mask = data[i], data[i + len(image_folder)]   

我認為"for i in range(6):" 這部分是對的 因為我有print出來，shape是正確的，而且更改range會造成 concatenating shape的錯誤

主要問題是在這邊

            img, mask = data[i], data[i + len(image_folder)]

我不確定img,mask有沒有正確的取得到train_generators的資料

main.py

folders = [

    'data/syu/train1day/auto_correlation_reverse', #1channel
    'data/syu/train1day/energy',                   #1channel
    'data/syu/train1day/entropy_reverse',          #1channel
    'data/syu/train1day/homogeneity',              #1channel
    'data/syu/train1day/temprature',               #1channel
    'data/syu/train1day/clahe',                    #3channel  
    'data/syu/train2day/auto_correlation_reverse',
    'data/syu/train2day/energy',
    'data/syu/train2day/entropy_reverse',
    'data/syu/train2day/homogeneity',
    'data/syu/train2day/temprature',
    'data/syu/train2day/clahe',
    'data/syu/train3day/auto_correlation_reverse',
    'data/syu/train3day/energy',
    'data/syu/train3day/entropy_reverse',
    'data/syu/train3day/homogeneity',
    'data/syu/train3day/temprature',
    'data/syu/train3day/clahe',
    'data/syu/train4day/auto_correlation_reverse',
    'data/syu/train4day/energy',
    'data/syu/train4day/entropy_reverse',
    'data/syu/train4day/homogeneity',
    'data/syu/train4day/temprature',
    'data/syu/train4day/clahe',

]
myGene = trainGenerator(1, folders, ['image1','image2','image3','image4'], ['label1','label2','label3','label4'],data_gen_args,save_to_dir = None)

data.py

def trainGenerator(batch_size, train_paths, image_folder, mask_folder, ...)
    image_datagen = ImageDataGenerator(**aug_dict)
    mask_datagen = ImageDataGenerator(**aug_dict)
    image_generators = []
    mask_generators = []
    for train_path in train_paths:
        image_generator = image_datagen.flow_from_directory(
            train_path,
            ...)
        mask_generator = mask_datagen.flow_from_directory(
            train_path,
            ...)
        image_generators.append(image_generator)
        mask_generators.append(mask_generator)

    train_generators = zip(*image_generators, *mask_generators)

    for data in train_generators:   
        images = []
        masks = []
        for i in range(6):
            img, mask = data[i], data[i + len(image_folder)]   
            images.append(img)
            masks.append(mask)
        img_batch = np.concatenate(images, axis=3) #(1, 512, 512, 8)
        mask_batch = np.concatenate(masks, axis=3) #(1, 512, 512, 6)