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))
def create_transform(c, h, w, levels, hidden_channels, steps_per_level, alpha,
num_bits, preprocessing, multi_scale):
if not isinstance(hidden_channels, list):
hidden_channels = [hidden_channels] * levels
if multi_scale:
mct = transforms.MultiscaleCompositeTransform(num_transforms=levels)
for level, level_hidden_channels in zip(range(levels),
hidden_channels):
squeeze_transform = transforms.SqueezeTransform()
c, h, w = squeeze_transform.get_output_shape(c, h, w)
transform_level = transforms.CompositeTransform(
[squeeze_transform] + [
create_transform_step(c, level_hidden_channels)
for _ in range(steps_per_level)
] + [transforms.OneByOneConvolution(c)
] # End each level with a linear transformation.
)
new_shape = mct.add_transform(transform_level, (c, h, w))
if new_shape: # If not last layer
c, h, w = new_shape
else:
all_transforms = []
for level, level_hidden_channels in zip(range(levels),
hidden_channels):
squeeze_transform = transforms.SqueezeTransform()
c, h, w = squeeze_transform.get_output_shape(c, h, w)
transform_level = transforms.CompositeTransform(
[squeeze_transform] + [
create_transform_step(c, level_hidden_channels)
for _ in range(steps_per_level)
] + [transforms.OneByOneConvolution(c)
] # End each level with a linear transformation.
)
all_transforms.append(transform_level)
all_transforms.append(
transforms.ReshapeTransform(input_shape=(c, h, w),
output_shape=(c * h * w, )))
mct = transforms.CompositeTransform(all_transforms)
# Inputs to the model in [0, 2 ** num_bits]
if preprocessing == 'glow':
# Map to [-0.5,0.5]
preprocess_transform = transforms.AffineScalarTransform(
scale=(1. / 2**num_bits), shift=-0.5)
elif preprocessing == 'realnvp':
preprocess_transform = transforms.CompositeTransform([
# Map to [0,1]
transforms.AffineScalarTransform(scale=(1. / 2**num_bits)),
# Map into unconstrained space as done in RealNVP
transforms.AffineScalarTransform(shift=alpha, scale=(1 - alpha)),
transforms.Logit()
])
elif preprocessing == 'realnvp_2alpha':
preprocess_transform = transforms.CompositeTransform([
transforms.AffineScalarTransform(scale=(1. / 2**num_bits)),
transforms.AffineScalarTransform(shift=alpha,
scale=(1 - 2. * alpha)),
transforms.Logit()
])
else:
raise RuntimeError(
'Unknown preprocessing type: {}'.format(preprocessing))
return transforms.CompositeTransform([preprocess_transform, mct])