Xhark
commented
1 year ago 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 []
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_applyrequires mentioningMyLSTMQuantizeConfigwithquantize_scope2 # 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 # Usequantize_applyto 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
Sequentialmodel, we expect at most one tensor as " 376 f"part ofinput_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_tbFile ~\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:
The model along with the CustomLayerMaxPooling1D class is the following:
And this is how I quantize the 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: