def test_log_prob(self):
batch_size = 10
input_shape = [2, 3, 4]
context_shape = [5, 6]
flow = base.Flow(
transform=transforms.AffineScalarTransform(scale=2.0),
distribution=distributions.StandardNormal(input_shape),
)
inputs = torch.randn(batch_size, *input_shape)
maybe_context = torch.randn(batch_size, *context_shape)
for context in [None, maybe_context]:
with self.subTest(context=context):
log_prob = flow.log_prob(inputs, context=context)
self.assertIsInstance(log_prob, torch.Tensor)
self.assertEqual(log_prob.shape, torch.Size([batch_size]))
def test_transform_to_noise(self):
batch_size = 10
context_size = 20
shape = [2, 3, 4]
context_shape = [5, 6]
flow = base.Flow(
transform=transforms.AffineScalarTransform(scale=2.0),
distribution=distributions.StandardNormal(shape),
)
inputs = torch.randn(batch_size, *shape)
maybe_context = torch.randn(context_size, *context_shape)
for context in [None, maybe_context]:
with self.subTest(context=context):
noise = flow.transform_to_noise(inputs, context=context)
self.assertIsInstance(noise, torch.Tensor)
self.assertEqual(noise.shape, torch.Size([batch_size] + shape))
def test_sample_and_log_prob(self):
num_samples = 10
input_shape = [2, 3, 4]
flow = base.Flow(
transform=transforms.AffineScalarTransform(scale=2.0),
distribution=distributions.StandardNormal(input_shape),
)
samples, log_prob_1 = flow.sample_and_log_prob(num_samples)
log_prob_2 = flow.log_prob(samples)
self.assertIsInstance(samples, torch.Tensor)
self.assertIsInstance(log_prob_1, torch.Tensor)
self.assertIsInstance(log_prob_2, torch.Tensor)
self.assertEqual(samples.shape,
torch.Size([num_samples] + input_shape))
self.assertEqual(log_prob_1.shape, torch.Size([num_samples]))
self.assertEqual(log_prob_2.shape, torch.Size([num_samples]))
self.assertEqual(log_prob_1, log_prob_2)
def test_sample_and_log_prob_with_context(self):
num_samples = 10
context_size = 20
input_shape = [2, 3, 4]
context_shape = [5, 6]
flow = base.Flow(
transform=transforms.AffineScalarTransform(scale=2.0),
distribution=distributions.StandardNormal(input_shape),
)
context = torch.randn(context_size, *context_shape)
samples, log_prob = flow.sample_and_log_prob(num_samples,
context=context)
self.assertIsInstance(samples, torch.Tensor)
self.assertIsInstance(log_prob, torch.Tensor)
self.assertEqual(samples.shape,
torch.Size([context_size, num_samples] + input_shape))
self.assertEqual(log_prob.shape,
torch.Size([context_size, num_samples]))
def test_sample(self):
num_samples = 10
context_size = 20
input_shape = [2, 3, 4]
context_shape = [5, 6]
flow = base.Flow(
transform=transforms.AffineScalarTransform(scale=2.0),
distribution=distributions.StandardNormal(input_shape),
)
maybe_context = torch.randn(context_size, *context_shape)
for context in [None, maybe_context]:
with self.subTest(context=context):
samples = flow.sample(num_samples, context=context)
self.assertIsInstance(samples, torch.Tensor)
if context is None:
self.assertEqual(samples.shape,
torch.Size([num_samples] + input_shape))
else:
self.assertEqual(
samples.shape,
torch.Size([context_size, num_samples] + input_shape))