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7180154 | 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 46 | # Copyright 2024 DeepMind Technologies Limited.
#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Constructors for MLPs."""
import haiku as hk
import jax
import jax.numpy as jnp
# TODO(aelkadi): Move the mlp factory here from `deep_typed_graph_net.py`.
class LinearNormConditioning(hk.Module):
"""Module for norm conditioning.
Conditions the normalization of "inputs" by applying a linear layer to the
"norm_conditioning" which produces the scale and variance which are applied to
each channel (across the last dim) of "inputs".
"""
def __init__(self, name="norm_conditioning"):
super().__init__(name=name)
def __call__(self, inputs: jax.Array, norm_conditioning: jax.Array):
feature_size = inputs.shape[-1]
conditional_linear_layer = hk.Linear(
output_size=2 * feature_size,
w_init=hk.initializers.TruncatedNormal(stddev=1e-8),
)
conditional_scale_offset = conditional_linear_layer(norm_conditioning)
scale_minus_one, offset = jnp.split(conditional_scale_offset, 2, axis=-1)
scale = scale_minus_one + 1.
return inputs * scale + offset
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