#!/usr/bin/env python
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# Copyright 2020 Brainchip Holdings Ltd.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Custom constraint for BatchNormalization layers and a method helper to apply the
constraint.
"""
import warnings
from keras.models import clone_model
from keras.utils.generic_utils import custom_object_scope
from keras.layers import BatchNormalization
from cnn2snn.min_value_constraint import MinValueConstraint
[docs]def add_gamma_constraint(model):
""" Method helper to add a MinValueConstraint to an existing model so that
gamma values of its BatchNormalization layers are above a defined minimum.
This is typically used to help having a model that will be Akida compatible
after conversion. In some cases, the mapping on hardware will fail because
of huge values for `threshold` or `act_step` with a message indicating that
a value cannot fit in a 20 bit signed or unsigned integer.
In such a case, this helper can be called to apply a constraint that can fix
the issue.
Note that in order for the constraint to be applied to the actual weights,
some training must be done: for an already trained model, it can be on a few
batches, one epoch or more depending on the impact the constraint has on
accuracy. This helper can also be called to a new model that has not been
trained yet.
Args:
model (keras.Model): the model for which gamma constraints will be
added.
Returns:
keras.Model: the same model with BatchNormalisation layers updated.
"""
def apply_gamma_constraint(layer):
constraint = MinValueConstraint()
if isinstance(layer, BatchNormalization):
if layer.gamma_constraint is not None:
warnings.warn(
f"Layer {layer.name} already has a gamma_constraint set "
f"to {layer.gamma_constraint}, it will be overwritten.")
bn = BatchNormalization.from_config(layer.get_config())
bn.gamma_constraint = constraint
return bn
return layer.__class__.from_config(layer.get_config())
with custom_object_scope({'MinValueConstraint': MinValueConstraint}):
updated_model = clone_model(model,
clone_function=apply_gamma_constraint)
updated_model.set_weights(model.get_weights())
return updated_model