高效的错误处理程序

def rle_decode(mask_rle, shape=(1280, 1918, 1)):    '''    mask_rle: run-length as string formated (start length)    shape: (height,width) of array to return     returns numpy array, 1 - mask, 0 - background    '''    img = np.zeros(shape[0]*shape[1], dtype=np.uint8)    s = mask_rle.split()    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]    starts -= 1    ends = starts + lengths        for lo, hi in zip(starts, ends):        img[lo:hi] = 1    img = img.reshape(shape)    return img

def keras_generator(gen_df, batch_size):    while true:        x_batch = []        y_batch = []        for i in range(batch_size):            img_name, mask_rle = gen_df.sample(1).values[0]             img = cv2.imread('data/train/{}'.format(img_name))            mask = rle_decode(mask_rle)            img = cv2.resize(img, (256, 256))             mask = cv2.resize(mask, (256, 256))            x_batch += [img]             y_batch += [mask]        x_batch = np.array(x_batch) / 255.         y_batch = np.array(y_batch)        yield x_batch, np.expand_dims(y_batch, -1)

im_id = 5fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(25, 25))axes[0].imshow(x[im_id]) axes[1].imshow(pred[im_id, ..., 0] > 0.5) plt.show()