未绑定本地错误：分配前引用的局部变量'zipped'：生成器错误

Question

我正在尝试在Keras中构建语义分割模型。由于我使用了自定义数据，因此我决定编写一个自定义生成器将其馈送到keras函数_.fit_generator，并且在求解此发电机错误时达到了一个死端，该错误说

UnboundLocalError: local variable 'zipped' referenced before assignment

经过一些Github和Stackover的研究，我发现" https://github.com/keras-team/keras/keras/sissues/1638#issuecomment-182139908" ANS ANS" ANS与我的问题相似，即使是该解决方案也不是没有工作再次扔同样的错误

def image_segmentation_generator( PROC_DATA, target_size, 
batch_size , gen, do_augment=False):
    if gen=='train':
        images_path = PROC_DATA+'/train_images/images'
        segs_path = PROC_DATA+'/train_labels/labels'
    elif 'val':
        images_path = PROC_DATA+'/val_images/images'
        segs_path = PROC_DATA+'/val_labels/labels'
    img_seg_pairs = get_pairs_from_paths( images_path , segs_path)
    random.shuffle( img_seg_pairs )
    zipped = itertools.cycle( img_seg_pairs  )
    while True:
        X = []
        Y = []
        for _ in range( batch_size) :
            im , seg = next(zipped)
            im = cv2.imread(im , 1 )
            im = cv2.resize(im,(target_size[0],target_size[1]))
            seg = cv2.imread(seg , 0 )
            seg = ia.imresize_single_image(seg, (target_size), 
interpolation='nearest')
            if do_augment:
                img , seg[:,:,0] = augment_seg( img , seg[:,:,0] )
            X.append( get_image_arr(im )  )
            Y.append( get_segmentation_arr( seg )  )
        yield np.array(X) , np.array(Y) 
my_gen = image_segmentation_generator(...)

my_gen.next()应该给我给定批量大小的图像和标签框架。

堆栈跟踪：im，seg = next(zpipped(unboundlocalerror：分配前引用的本地变量'

我该如何解决？

Answer 1

供将来参考。我为此找到了解决方法。当创建为生成器时，该功能对象看起来应该是自我维护的，而不必依赖额外的参数。因此，应沿函数传递带有地址的迭代器对象。

zipped =在调用image_sementation_generator_1

之前创建的对象=创建对象

def image_segmentation_generator_1(zipped = zipped，target_size =(1242，378(，batch_size = 4，do_augment = false(： 而真：

    image_list = []
    label_list = []
    _image_list = []
    _label_list = []
    for _ in range((batch_size/2)) :
        im , seg = next(zipped)
        im = cv2.imread(im , 1 )
        im = cv2.resize(im,(target_size[0],target_size[1]))
        seg = cv2.imread(seg , 0 )
        seg = ia.imresize_single_image(seg, (target_size[0],target_size[1]), interpolation='nearest')
        if do_augment:
            _image_list , _label_list = augment_seg( im , seg)
        else :
            _image_list = im
            _label_list = seg
        _image_list = get_image_arr(_image_list)
        image_list = image_list + _image_list
        label_list = label_list + _label_list
    yield np.array(image_list) , np.array(label_list)