File size: 3,647 Bytes
09d8e80 | 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | # Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests for optax.tree_utils._random."""
from absl.testing import absltest
import chex
import jax
from jax import tree_util as jtu
import jax.numpy as jnp
import numpy as np
from optax import tree_utils as otu
class RandomTest(absltest.TestCase):
def setUp(self):
super().setUp()
rng = np.random.RandomState(0)
self.rng_jax = jax.random.PRNGKey(0)
self.tree_a = (rng.randn(20, 10) + 1j * rng.randn(20, 10), rng.randn(20))
self.tree_b = (rng.randn(20, 10), rng.randn(20))
self.tree_a_dict = jtu.tree_map(
jnp.asarray,
(
1.0,
{'k1': 1.0, 'k2': (1.0, 1.0)},
1.0
)
)
self.tree_b_dict = jtu.tree_map(
jnp.asarray,
(
1.0,
{'k1': 2.0, 'k2': (3.0, 4.0)},
5.0
)
)
self.array_a = rng.randn(20) + 1j * rng.randn(20)
self.array_b = rng.randn(20)
self.tree_a_dict_jax = jtu.tree_map(jnp.array, self.tree_a_dict)
self.tree_b_dict_jax = jtu.tree_map(jnp.array, self.tree_b_dict)
def test_tree_random_like(self, eps=1e-6):
"""Test for `tree_random_like`.
Args:
eps: amount of noise.
Tests that `tree_random_like` generates a tree of the proper structure,
that it can be added to a target tree with a small multiplicative factor
without errors, and that the resulting addition is close to the original.
"""
rand_tree_a = otu.tree_random_like(self.rng_jax, self.tree_a)
rand_tree_b = otu.tree_random_like(self.rng_jax, self.tree_b)
rand_tree_a_dict = otu.tree_random_like(self.rng_jax, self.tree_a_dict_jax)
rand_tree_b_dict = otu.tree_random_like(self.rng_jax, self.tree_b_dict_jax)
rand_array_a = otu.tree_random_like(self.rng_jax, self.array_a)
rand_array_b = otu.tree_random_like(self.rng_jax, self.array_b)
sum_tree_a = otu.tree_add_scalar_mul(self.tree_a, eps, rand_tree_a)
sum_tree_b = otu.tree_add_scalar_mul(self.tree_b, eps, rand_tree_b)
sum_tree_a_dict = otu.tree_add_scalar_mul(self.tree_a_dict,
eps,
rand_tree_a_dict)
sum_tree_b_dict = otu.tree_add_scalar_mul(self.tree_b_dict,
eps,
rand_tree_b_dict)
sum_array_a = otu.tree_add_scalar_mul(self.array_a, eps, rand_array_a)
sum_array_b = otu.tree_add_scalar_mul(self.array_b, eps, rand_array_b)
tree_sums = [sum_tree_a,
sum_tree_b,
sum_tree_a_dict,
sum_tree_b_dict,
sum_array_a,
sum_array_b]
trees = [self.tree_a,
self.tree_b,
self.tree_a_dict,
self.tree_b_dict,
self.array_a,
self.array_b]
chex.assert_trees_all_close(trees, tree_sums, atol=1e-5)
if __name__ == '__main__':
absltest.main()
|