AttributeError: 'CustomLayerMaxPooling1D' object has no attribute 'kernel'

[konstantinatopali]

Hi, when I am trying Quantization Aware Training on my model, I get the following error in my 'CustomLayerMaxPooling1D' :

AttributeError Traceback (most recent call last)
Cell In [62], line 9
1 # quantize_apply requires mentioning MyLSTMQuantizeConfig with quantize_scope
2 # as well as the custom Keras layer.
3 with tfmot.quantization.keras.quantize_scope({'MyLSTMQuantizeConfig': MyLSTMQuantizeConfig, 'CustomLayerLSTM': CustomLayerLSTM,
4 'MyConv1DQuantizeConfig': MyConv1DQuantizeConfig,
5 'CustomLayerConv1D': CustomLayerConv1D,
6 'MyMaxPooling1DQuantizeConfig': MyMaxPooling1DQuantizeConfig,
7 'CustomLayerMaxPooling1D': CustomLayerMaxPooling1D}):
8 # Use quantize_apply to actually make the model quantization aware.
----> 9 quant_aware_model = tfmot.quantization.keras.quantize_apply(new_model)
11 quant_aware_model.summary()

File ~\AppData\Roaming\Python\Python39\site-packages\tensorflow_model_optimization\python\core\keras\metrics.py:74, in MonitorBoolGauge.call..inner(*args, **kwargs)
72 except Exception as error:
73 self.bool_gauge.get_cell(MonitorBoolGauge._FAILURE_LABEL).set(True)
---> 74 raise error

File ~\AppData\Roaming\Python\Python39\site-packages\tensorflow_model_optimization\python\core\keras\metrics.py:69, in MonitorBoolGauge.call..inner(*args, **kwargs)
66 @functools.wraps(func)
67 def inner(*args, **kwargs):
68 try:
---> 69 results = func(*args, **kwargs)
70 self.bool_gauge.get_cell(MonitorBoolGauge._SUCCESS_LABEL).set(True)
71 return results

File ~\AppData\Roaming\Python\Python39\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quantize.py:496, in quantize_apply(model, scheme, quantized_layer_name_prefix)
490 quantize_registry = scheme.get_quantize_registry()
492 # 4. Actually quantize all the relevant layers in the model. This is done by
493 # wrapping the layers with QuantizeWrapper, and passing the associated
494 # QuantizeConfig.
--> 496 return keras.models.clone_model(
497 transformed_model, input_tensors=None, clone_function=_quantize)

File ~\anaconda3\lib\site-packages\keras\models\cloning.py:502, in clone_model(model, input_tensors, clone_function)
499 clone_function = _clone_layer
501 if isinstance(model, Sequential):
--> 502 return _clone_sequential_model(
503 model, input_tensors=input_tensors, layer_fn=clone_function
504 )
505 else:
506 return _clone_functional_model(
507 model, input_tensors=input_tensors, layer_fn=clone_function
508 )

File ~\anaconda3\lib\site-packages\keras\models\cloning.py:372, in _clone_sequential_model(model, input_tensors, layer_fn)
367 layers, ancillary_layers = _remove_ancillary_layers(
368 model, layer_map, layers
369 )
371 if input_tensors is None:
--> 372 cloned_model = Sequential(layers=layers, name=model.name)
373 elif len(generic_utils.to_list(input_tensors)) != 1:
374 raise ValueError(
375 "To clone a Sequential model, we expect at most one tensor as "
376 f"part of input_tensors. Received: input_tensors={input_tensors}"
377 )

File ~\anaconda3\lib\site-packages\tensorflow\python\trackable\base.py:205, in no_automatic_dependency_tracking.._method_wrapper(self, *args, **kwargs)
203 self._self_setattr_tracking = False # pylint: disable=protected-access
204 try:
--> 205 result = method(self, *args, **kwargs)
206 finally:
207 self._self_setattr_tracking = previous_value # pylint: disable=protected-access

File ~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.traceback)
68 # To get the full stack trace, call:
69 # tf.debugging.disable_traceback_filtering()
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb

File ~\AppData\Roaming\Python\Python39\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quantize_wrapper.py:251, in QuantizeWrapperV2.build(self, input_shape)
249 def build(self, input_shape):
250 self._trainable_weights.extend(self.layer.trainable_weights)
--> 251 super(QuantizeWrapperV2, self).build(input_shape)

File ~\AppData\Roaming\Python\Python39\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quantize_wrapper.py:110, in QuantizeWrapper.build(self, input_shape)
102 self.optimizer_step = self.add_weight(
103 'optimizer_step',
104 initializer=tf.keras.initializers.Constant(-1),
105 dtype=tf.dtypes.int32,
106 trainable=False)
108 self._weight_vars = []
109 for weight, quantizer in (
--> 110 self.quantize_config.get_weights_and_quantizers(self.layer)):
111 quantizer_vars = quantizer.build(weight.shape,
112 self._weight_name(weight.name), self)
114 self._weight_vars.append((weight, quantizer, quantizer_vars))

Cell In [48], line 6, in MyMaxPooling1DQuantizeConfig.get_weights_and_quantizers(self, layer)
5 def get_weights_and_quantizers(self, layer):
----> 6 return [(layer.kernel, LastValueQuantizer())]

AttributeError: 'CustomLayerMaxPooling1D' object has no attribute 'kernel'

The class MyMaxPooling1DQuantizeConfig is the following:

LastValueQuantizer = tfmot.quantization.keras.quantizers.LastValueQuantizer
MovingAverageQuantizer = tfmot.quantization.keras.quantizers.MovingAverageQuantizer

class MyMaxPooling1DQuantizeConfig(tfmot.quantization.keras.QuantizeConfig):
    def get_weights_and_quantizers(self, layer):
        return [(layer.kernel, LastValueQuantizer())]
    
    def get_activations_and_quantizers(self, layer):
        return [(layer.activation, MovingAverageQuantizer())]

    def set_quantize_activations(self, layer, quantize_activations):
        layer.activation = quantize_activations[0]
    
    def set_quantize_weights(self, layer, quantize_weights):
        layer.kernel = quantize_weights[0]

    def get_config(self):
        return {}

    def get_output_quantizers(self, layer):
        return []

The model along with the CustomLayerMaxPooling1D class is the following:

class CustomLayerMaxPooling1D(tf.keras.layers.MaxPooling1D):
    pass

new_model = tfmot.quantization.keras.quantize_annotate_model(tf.keras.Sequential([tfmot.quantization.keras.quantize_annotate_layer(CustomLayerConv1D(64, kernel_size=3, activation = 'relu', input_shape=(120,51)), MyConv1DQuantizeConfig()),
    tfmot.quantization.keras.quantize_annotate_layer(CustomLayerMaxPooling1D(pool_size=2), MyMaxPooling1DQuantizeConfig()),
    tfmot.quantization.keras.quantize_annotate_layer(CustomLayerConv1D(64, kernel_size=3, activation = 'relu'), MyConv1DQuantizeConfig()),
    tfmot.quantization.keras.quantize_annotate_layer(CustomLayerMaxPooling1D(pool_size=2), MyMaxPooling1DQuantizeConfig()),
    tfmot.quantization.keras.quantize_annotate_layer(CustomLayerLSTM(32, return_sequences=False, activation='relu'), MyLSTMQuantizeConfig()),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(actions.shape[0], activation='softmax')    
]))

And this is how I quantize the model:

with tfmot.quantization.keras.quantize_scope({'MyLSTMQuantizeConfig': MyLSTMQuantizeConfig, 'CustomLayerLSTM': 
 CustomLayerLSTM, 'MyConv1DQuantizeConfig': MyConv1DQuantizeConfig, 'CustomLayerConv1D': CustomLayerConv1D,                                       'MyMaxPooling1DQuantizeConfig': MyMaxPooling1DQuantizeConfig, 'CustomLayerMaxPooling1D': CustomLayerMaxPooling1D}):

    # Use `quantize_apply` to actually make the model quantization aware.
    quant_aware_model = tfmot.quantization.keras.quantize_apply(new_model)

Apparently sth is wrong with the function def get_weights_and_quantizers in the class MyMaxPooling1DQuantizeConfig, but I am not aware on how to fix the problem. I tried replacing layer.kernel with [ ] to the return value of the function def get_weights_and_quantizers(self, layer) as well as replaced pass in place of layer.kernel = quantize_weights[0] in function def set_quantize_weights(self, layer, quantize_weights), but it returns ValueError: not enough values to unpack (expected 2, got 0).

new_model.summary() info:

[Xhark]

Can you add how CustomLayerMaxPooling1D is implemented?
If it doesn't have kernel, than you have to change the MyMaxPooling1DQuantizeConfig as:

class MyMaxPooling1DQuantizeConfig(tfmot.quantization.keras.QuantizeConfig):
    def get_weights_and_quantizers(self, layer):
        return [] # No kernel.
        # return [(layer.kernel, LastValueQuantizer())]
    
    def get_activations_and_quantizers(self, layer):
        return [(layer.activation, MovingAverageQuantizer())]

    def set_quantize_activations(self, layer, quantize_activations):
        layer.activation = quantize_activations[0]
    
    def set_quantize_weights(self, layer, quantize_weights):
        layer.kernel = quantize_weights[0]

    def get_config(self):
        return {}

    def get_output_quantizers(self, layer):
        return []