TypeError: Could not build a TypeSpec for <KerasTensor: shape=(None, None, 4) dtype=float32 (created by layer 'tf.math.truediv')> with type KerasTensor

[trueToastedCode]

My environment:

Chip: Apple M1
Python Platform: macOS-12.5.1-arm64-arm-64bit
Tensor Flow Version: 2.5.0
Keras Version: 2.5.0

Python 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:14) 
[Clang 12.0.1 ]
Pandas 1.4.4
Scikit-Learn 1.1.2
GPU is available

Error causing code: model = MaskRCNN(mode='training', model_dir="logs", config=config) Error:

TypeError                                 Traceback (most recent call last)
Input In [7], in <cell line: 2>()
      1 # define the model
----> 2 model = MaskRCNN(mode='training', model_dir="logs", config=config)

File ~/VSCode/ai1/mrcnn/model.py:1846, in MaskRCNN.__init__(self, mode, config, model_dir)
   1844 self.model_dir = model_dir
   1845 self.set_log_dir()
-> 1846 self.keras_model = self.build(mode=mode, config=config)

File ~/VSCode/ai1/mrcnn/model.py:1884, in MaskRCNN.build(self, mode, config)
   1881 input_gt_boxes = KL.Input(
   1882     shape=[None, 4], name="input_gt_boxes", dtype=tf.float32)
   1883 # Normalize coordinates
-> 1884 gt_boxes = KL.Lambda(lambda x: norm_boxes_graph(
   1885     x, K.shape(input_image)[1:3]))(input_gt_boxes)
   1886 # 3. GT Masks (zero padded)
   1887 # [batch, height, width, MAX_GT_INSTANCES]
   1888 if config.USE_MINI_MASK:

File ~/opt/miniconda3/envs/tensorflow/lib/python3.8/site-packages/keras/engine/base_layer.py:945, in Layer.__call__(self, *args, **kwargs)
    939 # Functional Model construction mode is invoked when `Layer`s are called on
    940 # symbolic `KerasTensor`s, i.e.:
    941 # >> inputs = tf.keras.Input(10)
    942 # >> outputs = MyLayer()(inputs)  # Functional construction mode.
    943 # >> model = tf.keras.Model(inputs, outputs)
    944 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
--> 945   return self._functional_construction_call(inputs, args, kwargs,
    946                                             input_list)
    948 # Maintains info about the `Layer.call` stack.
    949 call_context = base_layer_utils.call_context()

File ~/opt/miniconda3/envs/tensorflow/lib/python3.8/site-packages/keras/engine/base_layer.py:1083, in Layer._functional_construction_call(self, inputs, args, kwargs, input_list)
   1078     training_arg_passed_by_framework = True
   1080 with call_context.enter(
   1081     layer=self, inputs=inputs, build_graph=True, training=training_value):
   1082   # Check input assumptions set after layer building, e.g. input shape.
-> 1083   outputs = self._keras_tensor_symbolic_call(
   1084       inputs, input_masks, args, kwargs)
   1086   if outputs is None:
   1087     raise ValueError('A layer\'s `call` method should return a '
   1088                      'Tensor or a list of Tensors, not None '
   1089                      '(layer: ' + self.name + ').')

File ~/opt/miniconda3/envs/tensorflow/lib/python3.8/site-packages/keras/engine/base_layer.py:816, in Layer._keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
    814   return tf.nest.map_structure(keras_tensor.KerasTensor, output_signature)
    815 else:
--> 816   return self._infer_output_signature(inputs, args, kwargs, input_masks)

File ~/opt/miniconda3/envs/tensorflow/lib/python3.8/site-packages/keras/engine/base_layer.py:861, in Layer._infer_output_signature(self, inputs, args, kwargs, input_masks)
    858     self._handle_activity_regularization(inputs, outputs)
    859   self._set_mask_metadata(inputs, outputs, input_masks,
    860                           build_graph=False)
--> 861   outputs = tf.nest.map_structure(
    862       keras_tensor.keras_tensor_from_tensor, outputs)
    864 if hasattr(self, '_set_inputs') and not self.inputs:
    865   # TODO(kaftan): figure out if we need to do this at all
    866   # Subclassed network: explicitly set metadata normally set by
    867   # a call to self._set_inputs().
    868   self._set_inputs(inputs, outputs)

File ~/opt/miniconda3/envs/tensorflow/lib/python3.8/site-packages/tensorflow/python/util/nest.py:867, in map_structure(func, *structure, **kwargs)
    863 flat_structure = (flatten(s, expand_composites) for s in structure)
    864 entries = zip(*flat_structure)
    866 return pack_sequence_as(
--> 867     structure[0], [func(*x) for x in entries],
    868     expand_composites=expand_composites)

File ~/opt/miniconda3/envs/tensorflow/lib/python3.8/site-packages/tensorflow/python/util/nest.py:867, in <listcomp>(.0)
    863 flat_structure = (flatten(s, expand_composites) for s in structure)
    864 entries = zip(*flat_structure)
    866 return pack_sequence_as(
--> 867     structure[0], [func(*x) for x in entries],
    868     expand_composites=expand_composites)

File ~/opt/miniconda3/envs/tensorflow/lib/python3.8/site-packages/keras/engine/keras_tensor.py:580, in keras_tensor_from_tensor(tensor)
    577     keras_tensor_cls = cls
    578     break
--> 580 out = keras_tensor_cls.from_tensor(tensor)
    582 if hasattr(tensor, '_keras_mask'):
    583   out._keras_mask = keras_tensor_from_tensor(tensor._keras_mask)  # pylint: disable=protected-access

File ~/opt/miniconda3/envs/tensorflow/lib/python3.8/site-packages/keras/engine/keras_tensor.py:172, in KerasTensor.from_tensor(cls, tensor)
    169 else:
    170   # Fallback to the generic arbitrary-typespec KerasTensor
    171   name = getattr(tensor, 'name', None)
--> 172   type_spec = tf.type_spec_from_value(tensor)
    173   return cls(type_spec, name=name)

File ~/opt/miniconda3/envs/tensorflow/lib/python3.8/site-packages/tensorflow/python/framework/type_spec.py:579, in type_spec_from_value(value)
    575 except (ValueError, TypeError) as e:
    576   logging.vlog(
    577       3, "Failed to convert %r to tensor: %s" % (type(value).__name__, e))
--> 579 raise TypeError("Could not build a TypeSpec for %r with type %s" %
    580                 (value, type(value).__name__))

TypeError: Could not build a TypeSpec for <KerasTensor: shape=(None, None, 4) dtype=float32 (created by layer 'tf.math.truediv')> with type KerasTensor