File size: 3,001 Bytes
e12111a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | """Test basic functions for model."""
from functools import partial
import chex
import jax
import jax.numpy as jnp
from absl.testing import parameterized
from chex._src import fake
from imgx.model.basic import MLP, InstanceNorm, sinusoidal_positional_embedding
# Set `FLAGS.chex_n_cpu_devices` CPU devices for all tests.
def setUpModule() -> None: # pylint: disable=invalid-name
"""Fake multi-devices."""
fake.set_n_cpu_devices(2)
class TestInstanceNorm(chex.TestCase):
"""Test the function sinusoidal_positional_embedding."""
@chex.all_variants()
@parameterized.named_parameters(
(
"1d",
(2,),
),
(
"2d",
(2, 3),
),
)
def test_shapes(
self,
in_shape: tuple[int, ...],
) -> None:
"""Test output shapes under different device condition.
Args:
in_shape: input shape.
"""
rng = {"params": jax.random.PRNGKey(0)}
norm = InstanceNorm()
x = jax.random.uniform(
jax.random.PRNGKey(0),
shape=in_shape,
)
out, _ = self.variant(norm.init_with_output)(rng, x)
chex.assert_shape(out, in_shape)
class TestSinusoidalPositionalEmbedding(chex.TestCase):
"""Test the function sinusoidal_positional_embedding."""
@chex.all_variants()
@parameterized.named_parameters(
("1d case 1", (2,), 4, 5),
(
"1d case 2",
(2,),
8,
10000,
),
(
"2d",
(2, 3),
8,
10000,
),
)
def test_shapes(self, in_shape: tuple[int, ...], dim: int, max_period: int) -> None:
"""Test output shapes under different device condition.
Args:
in_shape: input shape.
dim: embedding dimension, assume to be evenly divided by two.
max_period: controls the minimum frequency of the embeddings.
"""
rng = jax.random.PRNGKey(0)
x = jax.random.uniform(
rng,
shape=in_shape,
)
out = self.variant(
partial(sinusoidal_positional_embedding, dim=dim, max_period=max_period)
)(x)
chex.assert_shape(out, (*in_shape, dim))
class TestMLP(chex.TestCase):
"""Test MLP."""
emb_size: int = 4
output_size: int = 8
@chex.all_variants()
@parameterized.product(
in_shape=[(3, 4, 5), (3, 4), (3,)],
remat=[True, False],
)
def test_shapes(
self,
in_shape: tuple[int, ...],
remat: bool,
) -> None:
"""Test output shapes."""
rng = {"params": jax.random.PRNGKey(0)}
mlp = MLP(
emb_size=self.emb_size,
output_size=self.output_size,
remat=remat,
)
out, _ = self.variant(mlp.init_with_output)(rng, jnp.ones(in_shape))
chex.assert_shape(out, (*in_shape[:-1], self.output_size))
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