File size: 2,192 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 | """Test Gaussian diffusion related classes and functions."""
import chex
from chex._src import fake
from imgx.task.diffusion_segmentation.gaussian_diffusion import GaussianDiffusionSegmentation
# 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 TestGaussianDiffusionSegmentation(chex.TestCase):
"""Test the class GaussianDiffusion."""
batch_size = 2
# unet
in_channels = 1
num_classes = 2
num_channels = (1, 2)
num_timesteps = 5
num_timesteps_beta = 1001
beta_schedule = "linear"
beta_start = 0.0001
beta_end = 0.02
def test_attributes(
self,
) -> None:
"""Test attribute shape."""
gd = GaussianDiffusionSegmentation.create(
classes_are_exclusive=True,
num_timesteps=self.num_timesteps,
num_timesteps_beta=self.num_timesteps_beta,
beta_schedule=self.beta_schedule,
beta_start=self.beta_start,
beta_end=self.beta_end,
model_out_type="x_start",
model_var_type="fixed_large",
)
chex.assert_shape(gd.betas, (self.num_timesteps,))
chex.assert_shape(gd.alphas_cumprod, (self.num_timesteps,))
chex.assert_shape(gd.alphas_cumprod_prev, (self.num_timesteps,))
chex.assert_shape(gd.alphas_cumprod_next, (self.num_timesteps,))
chex.assert_shape(gd.sqrt_alphas_cumprod, (self.num_timesteps,))
chex.assert_shape(gd.sqrt_one_minus_alphas_cumprod, (self.num_timesteps,))
chex.assert_shape(gd.log_one_minus_alphas_cumprod, (self.num_timesteps,))
chex.assert_shape(gd.sqrt_recip_alphas_cumprod, (self.num_timesteps,))
chex.assert_shape(gd.sqrt_recip_alphas_cumprod_minus_one, (self.num_timesteps,))
chex.assert_shape(gd.posterior_mean_coeff_start, (self.num_timesteps,))
chex.assert_shape(gd.posterior_mean_coeff_t, (self.num_timesteps,))
chex.assert_shape(gd.posterior_variance, (self.num_timesteps,))
chex.assert_shape(gd.posterior_log_variance_clipped, (self.num_timesteps,))
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