1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
|
# SPDX-FileCopyrightText: Copyright 2024, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Example rewrite with one fully connected clustered layer."""
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_sparsity_rewrite(input_shape: Any, output_shape: Any) -> keras.Model:
"""Generate TensorFlow Lite model for rewrite."""
model = tfmot.sparsity.keras.prune_low_magnitude(
to_prune=keras.Sequential(
[
keras.layers.InputLayer(input_shape=input_shape),
keras.layers.Reshape([-1]),
keras.layers.Dense(output_shape),
]
),
sparsity_m_by_n=(2, 4),
)
return model
def conv2d_sparsity_rewrite(input_shape: Any, output_shape: Any) -> keras.Model:
"""Generate TensorFlow Lite model for rewrite."""
conv2d_parameters = compute_conv2d_parameters(
input_shape=input_shape, output_shape=output_shape
)
model = tfmot.sparsity.keras.prune_low_magnitude(
to_prune=keras.Sequential(
[
keras.layers.InputLayer(input_shape=input_shape),
keras.layers.Conv2D(**conv2d_parameters),
keras.layers.BatchNormalization(),
keras.layers.ReLU(),
]
),
sparsity_m_by_n=(2, 4),
)
return model
|
