/
cubic.py
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/
cubic.py
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import math
import torch
from torch.nn import functional as F
import utils
from nde import transforms
DEFAULT_MIN_BIN_WIDTH = 1e-3
DEFAULT_MIN_BIN_HEIGHT = 1e-3
DEFAULT_EPS = 1e-5
DEFAULT_QUADRATIC_THRESHOLD = 1e-3
def unconstrained_cubic_spline(inputs,
unnormalized_widths,
unnormalized_heights,
unnorm_derivatives_left,
unnorm_derivatives_right,
inverse=False,
tail_bound=1.,
tails='linear',
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
eps = DEFAULT_EPS,
quadratic_threshold = DEFAULT_QUADRATIC_THRESHOLD):
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
outside_interval_mask = ~inside_interval_mask
outputs = torch.zeros_like(inputs)
logabsdet = torch.zeros_like(inputs)
if tails == 'linear':
outputs[outside_interval_mask] = inputs[outside_interval_mask]
logabsdet[outside_interval_mask] = 0
else:
raise RuntimeError('{} tails are not implemented.'.format(tails))
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = cubic_spline(
inputs=inputs[inside_interval_mask],
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
unnorm_derivatives_left=unnorm_derivatives_left[inside_interval_mask, :],
unnorm_derivatives_right=unnorm_derivatives_right[inside_interval_mask, :],
inverse=inverse,
left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
min_bin_width=min_bin_width,
min_bin_height=min_bin_height,
eps=eps,
quadratic_threshold=quadratic_threshold
)
return outputs, logabsdet
def cubic_spline(inputs,
unnormalized_widths,
unnormalized_heights,
unnorm_derivatives_left,
unnorm_derivatives_right,
inverse=False,
left=0., right=1., bottom=0., top=1.,
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
eps=DEFAULT_EPS,
quadratic_threshold=DEFAULT_QUADRATIC_THRESHOLD):
"""
References:
> Blinn, J. F. (2007). How to solve a cubic equation, part 5: Back to numerics. IEEE Computer
Graphics and Applications, 27(3):78–89.
"""
if not inverse and (torch.min(inputs) < left or torch.max(inputs) > right):
raise transforms.InputOutsideDomain()
elif inverse and (torch.min(inputs) < bottom or torch.max(inputs) > top):
raise transforms.InputOutsideDomain()
num_bins = unnormalized_widths.shape[-1]
if min_bin_width * num_bins > 1.0:
raise ValueError('Minimal bin width too large for the number of bins')
if min_bin_height * num_bins > 1.0:
raise ValueError('Minimal bin height too large for the number of bins')
if inverse:
inputs = (inputs - bottom) / (top - bottom)
else:
inputs = (inputs - left) / (right - left)
widths = F.softmax(unnormalized_widths, dim=-1)
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
cumwidths = torch.cumsum(widths, dim=-1)
cumwidths[..., -1] = 1
cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
heights = F.softmax(unnormalized_heights, dim=-1)
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
cumheights = torch.cumsum(heights, dim=-1)
cumheights[..., -1] = 1
cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
slopes = heights / widths
min_something_1 = torch.min(torch.abs(slopes[..., :-1]),
torch.abs(slopes[..., 1:]))
min_something_2 = (
0.5 * (widths[..., 1:] * slopes[..., :-1] + widths[..., :-1] * slopes[..., 1:])
/ (widths[..., :-1] + widths[..., 1:])
)
min_something = torch.min(min_something_1, min_something_2)
derivatives_left = torch.sigmoid(unnorm_derivatives_left) * 3 * slopes[..., 0][..., None]
derivatives_right = torch.sigmoid(unnorm_derivatives_right) * 3 * slopes[..., -1][..., None]
derivatives = min_something * (torch.sign(slopes[..., :-1]) + torch.sign(slopes[..., 1:]))
derivatives = torch.cat([derivatives_left,
derivatives,
derivatives_right], dim=-1)
a = (derivatives[..., :-1] + derivatives[..., 1:] - 2 * slopes) / widths.pow(2)
b = (3 * slopes - 2 * derivatives[..., :-1] - derivatives[..., 1:]) / widths
c = derivatives[..., :-1]
d = cumheights[..., :-1]
if inverse:
bin_idx = utils.searchsorted(cumheights, inputs)[..., None]
else:
bin_idx = utils.searchsorted(cumwidths, inputs)[..., None]
inputs_a = a.gather(-1, bin_idx)[..., 0]
inputs_b = b.gather(-1, bin_idx)[..., 0]
inputs_c = c.gather(-1, bin_idx)[..., 0]
inputs_d = d.gather(-1, bin_idx)[..., 0]
input_left_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
input_right_cumwidths = cumwidths.gather(-1, bin_idx + 1)[..., 0]
if inverse:
# Modified coefficients for solving the cubic.
inputs_b_ = (inputs_b / inputs_a) / 3.
inputs_c_ = (inputs_c / inputs_a) / 3.
inputs_d_ = (inputs_d - inputs) / inputs_a
delta_1 = -inputs_b_.pow(2) + inputs_c_
delta_2 = -inputs_c_ * inputs_b_ + inputs_d_
delta_3 = inputs_b_ * inputs_d_ - inputs_c_.pow(2)
discriminant = 4. * delta_1 * delta_3 - delta_2.pow(2)
depressed_1 = -2. * inputs_b_ * delta_1 + delta_2
depressed_2 = delta_1
three_roots_mask = discriminant >= 0 # Discriminant == 0 might be a problem in practice.
one_root_mask = discriminant < 0
outputs = torch.zeros_like(inputs)
# Deal with one root cases.
p = utils.cbrt((-depressed_1[one_root_mask] + torch.sqrt(-discriminant[one_root_mask])) / 2.)
q = utils.cbrt((-depressed_1[one_root_mask] - torch.sqrt(-discriminant[one_root_mask])) / 2.)
outputs[one_root_mask] = ((p + q)
- inputs_b_[one_root_mask]
+ input_left_cumwidths[one_root_mask])
# Deal with three root cases.
theta = torch.atan2(torch.sqrt(discriminant[three_roots_mask]), -depressed_1[three_roots_mask])
theta /= 3.
cubic_root_1 = torch.cos(theta)
cubic_root_2 = torch.sin(theta)
root_1 = cubic_root_1
root_2 = -0.5 * cubic_root_1 - 0.5 * math.sqrt(3) * cubic_root_2
root_3 = -0.5 * cubic_root_1 + 0.5 * math.sqrt(3) * cubic_root_2
root_scale = 2 * torch.sqrt(-depressed_2[three_roots_mask])
root_shift = (-inputs_b_[three_roots_mask] + input_left_cumwidths[three_roots_mask])
root_1 = root_1 * root_scale + root_shift
root_2 = root_2 * root_scale + root_shift
root_3 = root_3 * root_scale + root_shift
root1_mask = ((input_left_cumwidths[three_roots_mask] - eps) < root_1).float()
root1_mask *= (root_1 < (input_right_cumwidths[three_roots_mask] + eps)).float()
root2_mask = ((input_left_cumwidths[three_roots_mask] - eps) < root_2).float()
root2_mask *= (root_2 < (input_right_cumwidths[three_roots_mask] + eps)).float()
root3_mask = ((input_left_cumwidths[three_roots_mask] - eps) < root_3).float()
root3_mask *= (root_3 < (input_right_cumwidths[three_roots_mask] + eps)).float()
roots = torch.stack([root_1, root_2, root_3], dim=-1)
masks = torch.stack([root1_mask, root2_mask, root3_mask], dim=-1)
mask_index = torch.argsort(masks, dim=-1, descending=True)[..., 0][..., None]
outputs[three_roots_mask] = torch.gather(roots, dim=-1, index=mask_index).view(-1)
# Deal with a -> 0 (almost quadratic) cases.
quadratic_mask = inputs_a.abs() < quadratic_threshold
a = inputs_b[quadratic_mask]
b = inputs_c[quadratic_mask]
c = (inputs_d[quadratic_mask] - inputs[quadratic_mask])
alpha = (-b + torch.sqrt(b.pow(2) - 4*a*c)) / (2*a)
outputs[quadratic_mask] = alpha + input_left_cumwidths[quadratic_mask]
shifted_outputs = (outputs - input_left_cumwidths)
logabsdet = -torch.log((3 * inputs_a * shifted_outputs.pow(2) +
2 * inputs_b * shifted_outputs +
inputs_c))
else:
shifted_inputs = (inputs - input_left_cumwidths)
outputs = (inputs_a * shifted_inputs.pow(3) +
inputs_b * shifted_inputs.pow(2) +
inputs_c * shifted_inputs +
inputs_d)
logabsdet = torch.log((3 * inputs_a * shifted_inputs.pow(2) +
2 * inputs_b * shifted_inputs +
inputs_c))
if inverse:
outputs = outputs * (right - left) + left
logabsdet = logabsdet - math.log(top - bottom) + math.log(right - left)
else:
outputs = outputs * (top - bottom) + bottom
logabsdet = logabsdet + math.log(top - bottom) - math.log(right - left)
return outputs, logabsdet