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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 | """Test time sampler class and related functions."""
import chex
import jax
import jax.numpy as jnp
import numpy as np
from absl.testing import parameterized
from chex._src import fake
from imgx.diffusion.time_sampler import TimeSampler, scatter_add, scatter_set
# 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 TestScatter(chex.TestCase):
"""Test scatter_add and scatter_set."""
@chex.all_variants()
@parameterized.named_parameters(
(
"add to zeros",
np.array([0.0, 0.0, 0.0, 0.0, 0.0]),
np.array([0, 3, 0]),
np.array([-1.0, 2.1, 1.2]),
np.array([0.2, 0.0, 0.0, 2.1, 0.0]),
),
(
"add to non zeros",
np.array([0.0, 1.0, 0.0, 2.0, 0.0]),
np.array([0, 1, 2]),
np.array([-1.0, 2.1, 1.2]),
np.array([-1.0, 3.1, 1.2, 2.0, 0.0]),
),
)
def test_scatter_add(
self,
x: np.ndarray,
indices: np.ndarray,
updates: np.ndarray,
expected: np.ndarray,
) -> None:
"""Test scatter_add."""
got = self.variant(scatter_add)(
x,
indices,
updates,
)
chex.assert_trees_all_close(got, expected)
@chex.all_variants()
@parameterized.named_parameters(
(
"set to zeros",
np.array([0.0, 0.0, 0.0, 0.0, 0.0]),
np.array([0, 3, 1]),
np.array([-1.0, 2.1, 1.2]),
np.array([-1.0, 1.2, 0.0, 2.1, 0.0]),
),
(
"set to non zeros",
np.array([0.0, 1.0, 0.0, 2.0, 4.0]),
np.array([0, 3, 1]),
np.array([-1.0, 2.1, 1.2]),
np.array([-1.0, 1.2, 0.0, 2.1, 4.0]),
),
)
def test_scatter_set(
self,
x: np.ndarray,
indices: np.ndarray,
updates: np.ndarray,
expected: np.ndarray,
) -> None:
"""Test scatter_set."""
got = self.variant(scatter_set)(
x,
indices,
updates,
)
chex.assert_trees_all_close(got, expected)
class TestTimeSampler(chex.TestCase):
"""Test TimeSampler."""
num_timesteps = 4
batch_size = 2
@parameterized.named_parameters(
(
"uniform",
True,
0,
3,
),
(
"importance sampling",
False,
0,
3,
),
)
def test_shapes(
self,
uniform_time_sampling: bool,
t_index_min: int,
t_index_max: int,
) -> None:
"""Test output shape."""
key = jax.random.PRNGKey(0)
sampler = TimeSampler(
num_timesteps=self.num_timesteps,
uniform_time_sampling=uniform_time_sampling,
)
loss_count_hist = jnp.ones((self.num_timesteps,), dtype=jnp.int32)
loss_sq_hist = jnp.ones((self.num_timesteps,), dtype=jnp.float32)
t, t_index, probs_t = sampler.sample(
key, self.batch_size, t_index_min, t_index_max, loss_count_hist, loss_sq_hist
)
chex.assert_shape(t, (self.batch_size,))
chex.assert_shape(t_index, (self.batch_size,))
chex.assert_shape(probs_t, (self.batch_size,))
@parameterized.named_parameters(
(
"uniform zero",
np.array([0.0, 0.0, 0.0, 0.0]),
np.array([0.25, 0.25, 0.25, 0.25]),
),
(
"uniform",
np.array([0.1, 0.1, 0.1, 0.1]),
np.array([0.25, 0.25, 0.25, 0.25]),
),
(
"non-uniform one hot",
np.array([1.0, 0.0, 0.0, 0.0]),
np.array([0.9925, 0.0025, 0.0025, 0.0025]),
),
(
"non-uniform",
np.array([9.0, 4.0, 0.0, 0.0]),
np.array([0.0025 + 0.6 * 0.99, 0.0025 + 0.4 * 0.99, 0.0025, 0.0025]),
),
)
def test_t_probs_from_loss_sq(self, loss_sq_hist: np.ndarray, expected: np.ndarray) -> None:
"""Test t_probs_from_loss_sq."""
sampler = TimeSampler(
num_timesteps=self.num_timesteps,
uniform_time_sampling=False,
)
got = sampler.t_probs_from_loss_sq(jnp.array(loss_sq_hist))
chex.assert_trees_all_close(got, jnp.array(expected))
@parameterized.named_parameters(
(
"uniform",
np.array([10.0, 10.0, 10.0, 10.0]),
np.array([0.25, 0.25, 0.25, 0.25]),
),
(
"uniform zero",
np.array([0.0, 0.0, 0.0, 0.0]),
np.array([0.25, 0.25, 0.25, 0.25]),
),
(
"non-uniform",
np.array([4.0, 6.0, 5.0, 10.0]),
np.array(
[
6.0 / 15.0,
4.0 / 15.0,
5.0 / 15.0,
0.0,
]
),
),
)
def test_t_probs_from_loss_count(
self, loss_count_hist: np.ndarray, expected: np.ndarray
) -> None:
"""Test t_probs_from_loss_count."""
sampler = TimeSampler(
num_timesteps=self.num_timesteps,
uniform_time_sampling=False,
)
got = sampler.t_probs_from_loss_count(jnp.array(loss_count_hist))
chex.assert_trees_all_close(got, jnp.array(expected))
def test_sample(self) -> None:
"""Test sample make sure that all time steps are sampled after enough steps."""
batch_size = 4
sampler = TimeSampler(
num_timesteps=self.num_timesteps,
uniform_time_sampling=False,
)
loss_count_hist = jnp.zeros((self.num_timesteps,), dtype=jnp.int32)
loss_sq_hist = jnp.zeros((self.num_timesteps,), dtype=jnp.float32)
# the coefficient 1.1 is to ensure over-sampling since probs has 0.01 uniform noise
for i in range(int(sampler.warmup_steps * self.num_timesteps // batch_size * 1.1)):
_, t_index, probs_t = sampler.sample(
key=jax.random.PRNGKey(i),
batch_size=batch_size,
t_index_min=0,
t_index_max=self.num_timesteps,
loss_count_hist=loss_count_hist,
loss_sq_hist=loss_sq_hist,
)
loss_count_hist, loss_sq_hist = sampler.update_stats(
loss_batch=jnp.ones((batch_size,)),
t_index=t_index,
loss_count_hist=loss_count_hist,
loss_sq_hist=loss_sq_hist,
)
min_loss_count_hist = jnp.min(loss_count_hist)
if min_loss_count_hist < sampler.warmup_steps:
chex.assert_trees_all_close(probs_t, jnp.ones_like(probs_t) / self.num_timesteps)
min_loss_count_hist = jnp.min(loss_count_hist)
assert min_loss_count_hist >= sampler.warmup_steps
max_loss_count_hist = jnp.max(loss_count_hist)
assert max_loss_count_hist > sampler.warmup_steps
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