def __call__(self, inputs, are_samples=False, **kwargs):
"""
Forward data through this hidden GP layer. The output is a MultitaskMultivariateNormal distribution
(or MultivariateNormal distribution is output_dims=None).
If the input is >=2 dimensional Tensor (e.g. `n x d`), we pass the input through each hidden GP,
resulting in a `n x h` multitask Gaussian distribution (where all of the `h` tasks represent an
output dimension and are independent from one another). We then draw `s` samples from these Gaussians,
resulting in a `s x n x h` MultitaskMultivariateNormal distribution.
If the input is a >=3 dimensional Tensor, and the `are_samples=True` kwarg is set, then we assume that
the outermost batch dimension is a samples dimension. The output will have the same number of samples.
For example, a `s x b x n x d` input will result in a `s x b x n x h` MultitaskMultivariateNormal distribution.
The goal of these last two points is that if you have a tensor `x` that is `n x d`, then:
>>> hidden_gp2(hidden_gp(x))
will just work, and return a tensor of size `s x n x h2`, where `h2` is the output dimensionality of
hidden_gp2. In this way, hidden GP layers are easily composable.
"""
deterministic_inputs = not are_samples
if isinstance(inputs, MultitaskMultivariateNormal):
inputs = torch.distributions.Normal(
loc=inputs.mean, scale=inputs.variance.sqrt()).rsample()
deterministic_inputs = False
if settings.debug.on():
if not torch.is_tensor(inputs):
raise ValueError(
"`inputs` should either be a MultitaskMultivariateNormal or a Tensor, got "
f"{inputs.__class__.__Name__}")
if inputs.size(-1) != self.input_dims:
raise RuntimeError(
f"Input shape did not match self.input_dims. Got total feature dims [{inputs.size(-1)}],"
f" expected [{self.input_dims}]")
# Repeat the input for all possible outputs
if self.output_dims is not None:
inputs = inputs.unsqueeze(-3)
inputs = inputs.expand(*inputs.shape[:-3], self.output_dims,
*inputs.shape[-2:])
# Now run samples through the GP
output = ApproximateGP.__call__(self, inputs)
if self.output_dims is not None:
mean = output.loc.transpose(-1, -2)
covar = BlockDiagLazyTensor(output.lazy_covariance_matrix,
block_dim=-3)
output = MultitaskMultivariateNormal(mean,
covar,
interleaved=False)
# Maybe expand inputs?
if deterministic_inputs:
output = output.expand(
torch.Size([settings.num_likelihood_samples.value()]) +
output.batch_shape)
return output
def __call__(self, inputs, are_samples=False, **kwargs):
deterministic_inputs = not are_samples
if isinstance(inputs, MultitaskMultivariateNormal):
inputs = torch.distributions.Normal(
loc=inputs.mean, scale=inputs.variance.sqrt()).rsample()
deterministic_inputs = False
if settings.debug.on():
if not torch.is_tensor(inputs):
raise ValueError(
"`inputs` should either be a MultitaskMultivariateNormal or a Tensor, got "
f"{inputs.__class__.__Name__}")
if inputs.size(-1) != self.input_dims:
raise RuntimeError(
f"Input shape did not match self.input_dims. Got total feature dims [{inputs.size(-1)}],"
f" expected [{self.input_dims}]")
# Repeat the input for all possible outputs
if self.output_dims is not None:
inputs = inputs.unsqueeze(-3)
inputs = inputs.expand(*inputs.shape[:-3], self.output_dims,
*inputs.shape[-2:])
# Now run samples through the GP
output = ApproximateGP.__call__(self, inputs)
if self.output_dims is not None:
mean = output.loc.transpose(-1, -2)
covar = BlockDiagLazyTensor(output.lazy_covariance_matrix,
block_dim=-3)
output = MultitaskMultivariateNormal(mean,
covar,
interleaved=False)
# Maybe expand inputs?
if deterministic_inputs:
output = output.expand(
torch.Size([settings.num_likelihood_samples.value()]) +
output.batch_shape)
return output
