使用tensorflow的簡單剪切增廣，為張量賦值的問題

我想在我的圖像上做一個簡單的剪切增強，但我對張量不太熟悉，所以我不知道怎么做。

代碼如下：

def augment_cutout(image, label, size=68, n_squares=1):
    h, w, channels = image.shape
    new_image = image
    for _ in range(n_squares):
        y = np.random.randint(h)
        x = np.random.randint(w)
        y1 = np.clip(y - size // 2, 0, h)
        y2 = np.clip(y + size // 2, 0, h)
        x1 = np.clip(x - size // 2, 0, w)
        x2 = np.clip(x + size // 2, 0, w)
        new_image[y1:y2,x1:x2,:] = 0
    return tf.cast(new_image, tf.float32), label

train_dataset = train_dataset.map(map_func = augment_cutout, num_parallel_calls=AUTOTUNE)

上述代碼導致以下錯誤

<ipython-input-209-308483a55fd4>:59 augment_cutout  *
        new_image = new_image[y1:y2,x1:x2,:].assign(0)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py:401 __getattr__
        self.__getattribute__(name)

    AttributeError: 'Tensor' object has no attribute 'assign'

有人知道怎么解決這個問題嗎？

編輯：我試過tensorflow插件的隨機剪切功能，但也不起作用。

import tensorflow_addons as tfa

def random_cut_out(images, labels):
    return tfa.image.random_cutout(images, (64, 64), constant_values = 1), labels

train_dataset = train_ds.map(map_func = preprocess_img, num_parallel_calls=AUTOTUNE) 
train_dataset = train_dataset.map(map_func = augment, num_parallel_calls=AUTOTUNE)
train_dataset = train_dataset.map(random_cut_out)

我得到以下錯誤：

    ValueError: slice index 3 of dimension 0 out of bounds. for '{{node 

cutout/strided_slice_2}} = StridedSlice[Index=DT_INT32, T=DT_INT32, 

begin_mask=0, ellipsis_mask=0, end_mask=0, new_axis_mask=0, 

shrink_axis_mask=1](cutout/Shape, cutout/strided_slice_2/stack, 

cutout/strided_slice_2/stack_1, cutout/strided_slice_2/stack_2)' with input

 shapes: [3], [1], [1], [1] and with computed input tensors: input[1] = <3>,

 input[2] = <4>, input[3] = <1>.

有關如何創建數據集的詳細信息：

import tensorflow_datasets as tfds
builder = tfds.ImageFolder('/content/dataset2')
print(builder.info) 
train_ds, val_ds, test_ds = builder.as_dataset(split=['train', 'val', 'test'], shuffle_files=True, as_supervised=True)

def preprocess_img(image, label, img_shape=160):
  image = tf.image.resize(image, [img_shape, img_shape]) # reshape to img_shape
  return tf.cast(image, tf.float32), label # return (float32_image, label) tuple

BATCH_SIZE = 256
AUTOTUNE = tf.data.AUTOTUNE


train_dataset = train_ds.map(map_func = preprocess_img, num_parallel_calls=AUTOTUNE)

train_dataset = train_dataset.shuffle(buffer_size=1000).batch(batch_size=BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)