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# SPDX-FileCopyrightText: Copyright 2024, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Rewrite functions used to return layers ready for clustering."""
from typing import Any
import tensorflow_model_optimization as tfmot
from keras.api._v2 import keras # Temporary workaround for now: MLIA-1107
from mlia.nn.rewrite.library.helper_functions import compute_conv2d_parameters
def fc_clustering_rewrite(input_shape: Any, output_shape: Any) -> keras.Model:
"""Fully connected TensorFlow Lite model ready for clustering."""
rewrite_params = {
"number_of_clusters": 4,
"cluster_centroids_init": tfmot.clustering.keras.CentroidInitialization.LINEAR,
}
model = tfmot.clustering.keras.cluster_weights(
to_cluster=keras.Sequential(
[
keras.layers.InputLayer(input_shape=input_shape),
keras.layers.Flatten(),
keras.layers.Dense(units=output_shape),
]
),
**rewrite_params
)
return model
def conv2d_clustering_rewrite(input_shape: Any, output_shape: Any) -> keras.Model:
"""Conv2d TensorFlow Lite model ready for clustering."""
rewrite_params = {
"number_of_clusters": 4,
"cluster_centroids_init": tfmot.clustering.keras.CentroidInitialization.LINEAR,
}
conv2d_parameters = compute_conv2d_parameters(
input_shape=input_shape, output_shape=output_shape
)
model = tfmot.clustering.keras.cluster_weights(
to_cluster=keras.Sequential(
[
keras.layers.InputLayer(input_shape=input_shape),
keras.layers.Conv2D(**conv2d_parameters),
keras.layers.BatchNormalization(),
keras.layers.ReLU(),
]
),
**rewrite_params
)
return model
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