def _elementwise(self, inputs, autoregressive_params, inverse=False):
batch_size = inputs.shape[0]
transform_params = autoregressive_params.view(batch_size,
self.features,
self.num_bins * 2 + 2)
unnormalized_widths = transform_params[..., :self.num_bins]
unnormalized_heights = transform_params[..., self.num_bins:2 *
self.num_bins]
derivatives = transform_params[..., 2 * self.num_bins:]
unnorm_derivatives_left = derivatives[..., 0][..., None]
unnorm_derivatives_right = derivatives[..., 1][..., None]
if hasattr(self.autoregressive_net, 'hidden_features'):
unnormalized_widths /= np.sqrt(
self.autoregressive_net.hidden_features)
unnormalized_heights /= np.sqrt(
self.autoregressive_net.hidden_features)
outputs, logabsdet = splines.cubic_spline(
inputs=inputs,
unnormalized_widths=unnormalized_widths,
unnormalized_heights=unnormalized_heights,
unnorm_derivatives_left=unnorm_derivatives_left,
unnorm_derivatives_right=unnorm_derivatives_right,
inverse=inverse)
return outputs, utils.sum_except_batch(logabsdet)
def _elementwise_inverse(self, inputs, autoregressive_params):
unconstrained_scale, shift = self._unconstrained_scale_and_shift(
autoregressive_params)
scale = torch.sigmoid(unconstrained_scale + 2.) + 1e-3
log_scale = torch.log(scale)
outputs = (inputs - shift) / scale
logabsdet = -utils.sum_except_batch(log_scale, num_batch_dims=1)
return outputs, logabsdet
def _elementwise(self, inputs, autoregressive_params, inverse=False):
batch_size = inputs.shape[0]
unnormalized_pdf = autoregressive_params.view(
batch_size, self.features, self._output_dim_multiplier())
outputs, logabsdet = splines.linear_spline(
inputs=inputs, unnormalized_pdf=unnormalized_pdf, inverse=inverse)
return outputs, utils.sum_except_batch(logabsdet)
def _elementwise(self, inputs, autoregressive_params, inverse=False):
batch_size, features = inputs.shape[0], inputs.shape[1]
transform_params = autoregressive_params.view(
batch_size, features, self._output_dim_multiplier())
unnormalized_widths = transform_params[..., :self.num_bins]
unnormalized_heights = transform_params[..., self.num_bins:2 *
self.num_bins]
unnormalized_derivatives = transform_params[..., 2 * self.num_bins:]
if hasattr(self.autoregressive_net, 'hidden_features'):
unnormalized_widths /= np.sqrt(
self.autoregressive_net.hidden_features)
unnormalized_heights /= np.sqrt(
self.autoregressive_net.hidden_features)
if self.tails is None:
spline_fn = splines.rational_quadratic_spline
spline_kwargs = {}
elif self.tails == 'linear':
spline_fn = splines.unconstrained_rational_quadratic_spline
spline_kwargs = {
'tails': self.tails,
'tail_bound': self.tail_bound
}
else:
raise ValueError
outputs, logabsdet = spline_fn(
inputs=inputs,
unnormalized_widths=unnormalized_widths,
unnormalized_heights=unnormalized_heights,
unnormalized_derivatives=unnormalized_derivatives,
inverse=inverse,
min_bin_width=self.min_bin_width,
min_bin_height=self.min_bin_height,
min_derivative=self.min_derivative,
**spline_kwargs)
return outputs, utils.sum_except_batch(logabsdet)