def test_parameter_update():
original_param_values = {}
original_bn_stat_values = {}
model = get_model_for_test()
for layer in model.layers:
if get_keras_layer_metatype(
layer) == TFBatchNormalizationLayerMetatype:
original_bn_stat_values[layer] = deepcopy(
layer.non_trainable_weights)
original_param_values[layer] = deepcopy(layer.trainable_weights)
else:
original_param_values[layer] = deepcopy(layer.weights)
config = get_config_for_test()
bn_adaptation = BatchnormAdaptationAlgorithm(
**extract_bn_adaptation_init_params(config, "quantization"))
bn_adaptation.run(model)
for layer in model.layers:
if get_keras_layer_metatype(
layer) == TFBatchNormalizationLayerMetatype:
compare_params(original_bn_stat_values[layer],
layer.non_trainable_weights,
equal=False)
compare_params(original_param_values[layer],
layer.trainable_weights)
else:
compare_params(original_param_values[layer], layer.weights)
def test_all_parameter_keep():
original_all_param_values = {}
model = get_model_for_test()
for layer in model.layers:
original_all_param_values[layer] = deepcopy(layer.weights)
config = get_config_for_test(num_bn_adaptation_samples=0)
bn_adaptation = BatchnormAdaptationAlgorithm(
**extract_bn_adaptation_init_params(config, "quantization"))
bn_adaptation.run(model)
for layer in model.layers:
compare_params(original_all_param_values[layer], layer.weights)
def _run_batchnorm_adaptation(self):
if self._bn_adaptation is None:
self._bn_adaptation = BatchnormAdaptationAlgorithm(
**extract_bn_adaptation_init_params(self._config,
'magnitude_sparsity'))
self._bn_adaptation.run(self.model)
def _run_batchnorm_adaptation(self, model: tf.keras.Model) -> None:
if self._bn_adaptation is None:
self._bn_adaptation = BatchnormAdaptationAlgorithm(
**extract_bn_adaptation_init_params(self.config, self.name))
self._bn_adaptation.run(model)
def _run_batchnorm_adaptation(self):
if self._bn_adaptation is None:
self._bn_adaptation = BatchnormAdaptationAlgorithm(
**extract_bn_adaptation_init_params(self.config,
'filter_pruning'))
self._bn_adaptation.run(self.model)
def _run_batchnorm_adaptation(self):
if self._bn_adaptation is None:
self._bn_adaptation = BatchnormAdaptationAlgorithm(
**extract_bn_adaptation_init_params(self.qctrl.config,
"quantization"))
self._bn_adaptation.run(self.qctrl.model)
