#!/usr/bin/env python
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# Copyright 2022 Brainchip Holdings Ltd.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
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"""
Common utility methods used in quantization models.
"""
__all__ = ['load_model', 'deep_clone_model', 'apply_weights_to_model']
import warnings
from keras.models import clone_model, load_model as kload_model
[docs]def load_model(model_path, custom_layers=None, compile_model=True):
"""Loads a model with quantizeml custom layers.
Args:
model_path (str): path of the model to load
custom_layers (dict, optional): custom layers to add to the model. Defaults to None.
compile_model (bool, optional): whether to compile the model. Defaults to True.
Returns:
keras.Model: the loaded model
"""
return kload_model(model_path, custom_objects=custom_layers, compile=compile_model)
[docs]def deep_clone_model(model, *args, **kwargs):
"""Clone a model, assign variable to variable. Useful when a clone function is used,
and new layers have not the same number of parameters as the original layer.
Args:
model (keras.Model): model to be cloned
args, kwargs (optional): arguments pass to :func:`keras.models.clone_model` function
Returns:
keras.Model: the cloned model
"""
new_model = clone_model(model, *args, **kwargs)
variables_dict = {var.name: var for var in model.variables}
apply_weights_to_model(new_model, variables_dict, False)
return new_model
[docs]def apply_weights_to_model(model, weights, verbose=True):
"""Loads weights from a dictionary and apply it to a model.
Go through the dictionary of weights, find the corresponding variable in the
model and partially load its weights.
Args:
model (keras.Model): the model to update
weights (dict): the dictionary of weights
verbose (bool, optional): if True, throw warning messages if a dict item is not found in the
model. Defaults to True.
"""
if len(weights) == 0:
warnings.warn("There is no weight to apply to the model.")
return
# Go through the dictionary of weights with each item
for key, value in weights.items():
value_applied = False
for dest_var in model.variables:
if key == dest_var.name:
# Apply the current item value
dest_var.assign(value)
value_applied = True
break
if not value_applied and verbose:
warnings.warn(f"Variable '{key}' not found in the model.")