I’m trying to create a model that performs 3D cropping on an input tensor based on given bounding box coordinates, however, I keep receiving the following error message:

TypeError: Exception encountered when calling layer "tf.__operators__.getitem_6" (type SlicingOpLambda).

Only integers, slices (`:`), ellipsis (`...`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indices, got <tf.Tensor 'Shape:0' shape=(6,) dtype=int32>

Call arguments received by layer "tf.__operators__.getitem_6" (type SlicingOpLambda):
  • tensor=tf.Tensor(shape=(None, 100, 100, 100, 3), dtype=float32)
  • slice_spec=({'start': 'None', 'stop': 'None', 'step': 'None'}, {'start': 'tf.Tensor(shape=(6,), dtype=int32)', 'stop': 'tf.Tensor(shape=(6,), dtype=int32)', 'step': 'None'}, {'start': 'tf.Tensor(shape=(6,), dtype=int32)', 'stop': 'tf.Tensor(shape=(6,), dtype=int32)', 'step': 'None'}, {'start': 'tf.Tensor(shape=(6,), dtype=int32)', 'stop': 'tf.Tensor(shape=(6,), dtype=int32)', 'step': 'None'}, {'start': 'None', 'stop': 'None', 'step': 'None'})
  • var=None

Here’s a simplified version of my code:

import tensorflow as tf

def crop_3d(x: tf.Tensor, bounding_box: tf.Tensor):
    bounding_box = tf.cast(bounding_box, dtype=tf.int32)
    z_offset = bounding_box[0]
    y_offset = bounding_box[1]
    x_offset = bounding_box[2]
    z_size = bounding_box[3]
    y_size = bounding_box[4]
    x_size = bounding_box[5]
    cropped = x[:, z_offset:z_offset + z_size, y_offset:y_offset + y_size, x_offset:x_offset + x_size, :]
    return cropped

input_image = tf.keras.layers.Input(shape=(100, 100, 100, 3), name="inputs")
bounding_box = tf.keras.layers.Input(shape=(6,), name="bounding_box", dtype=tf.int32)
x = crop_3d(input_image, bounding_box)
model = tf.keras.Model(inputs=[input_image, bounding_box], outputs=x)

If it makes any difference, the extracted bounding box’s size is constant, I only want specify the offset values for each batch item.