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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import numpy as np
import torch
from pytorch3d.implicitron.models.renderer.base import (
approximate_conical_frustum_as_gaussians,
compute_3d_diagonal_covariance_gaussian,
conical_frustum_to_gaussian,
ImplicitronRayBundle,
)
from pytorch3d.implicitron.models.renderer.ray_sampler import AbstractMaskRaySampler
from tests.common_testing import TestCaseMixin
class TestRendererBase(TestCaseMixin, unittest.TestCase):
def test_implicitron_from_bins(self) -> None:
bins = torch.randn(2, 3, 4, 5)
ray_bundle = ImplicitronRayBundle(
origins=None,
directions=None,
lengths=None,
xys=None,
bins=bins,
)
self.assertClose(ray_bundle.lengths, 0.5 * (bins[..., 1:] + bins[..., :-1]))
self.assertClose(ray_bundle.bins, bins)
def test_implicitron_raise_value_error_bins_is_set_and_try_to_set_lengths(
self,
) -> None:
ray_bundle = ImplicitronRayBundle(
origins=torch.rand(2, 3, 4, 3),
directions=torch.rand(2, 3, 4, 3),
lengths=None,
xys=torch.rand(2, 3, 4, 2),
bins=torch.rand(2, 3, 4, 14),
)
with self.assertRaisesRegex(
ValueError,
"If the bins attribute is not None you cannot set the lengths attribute.",
):
ray_bundle.lengths = torch.empty(2)
def test_implicitron_raise_value_error_if_bins_dim_equal_1(self) -> None:
with self.assertRaisesRegex(
ValueError, "The last dim of bins must be at least superior or equal to 2."
):
ImplicitronRayBundle(
origins=torch.rand(2, 3, 4, 3),
directions=torch.rand(2, 3, 4, 3),
lengths=None,
xys=torch.rand(2, 3, 4, 2),
bins=torch.rand(2, 3, 4, 1),
)
def test_implicitron_raise_value_error_if_neither_bins_or_lengths_provided(
self,
) -> None:
with self.assertRaisesRegex(
ValueError,
"Please set either bins or lengths to initialize an ImplicitronRayBundle.",
):
ImplicitronRayBundle(
origins=torch.rand(2, 3, 4, 3),
directions=torch.rand(2, 3, 4, 3),
lengths=None,
xys=torch.rand(2, 3, 4, 2),
bins=None,
)
def test_conical_frustum_to_gaussian(self) -> None:
origins = torch.zeros(3, 3, 3)
directions = torch.tensor(
[
[[0, 0, 0], [1, 0, 0], [3, 0, 0]],
[[0, 0.25, 0], [1, 0.25, 0], [3, 0.25, 0]],
[[0, 1, 0], [1, 1, 0], [3, 1, 0]],
]
)
bins = torch.tensor(
[
[[0.5, 1.5], [0.3, 0.7], [0.3, 0.7]],
[[0.5, 1.5], [0.3, 0.7], [0.3, 0.7]],
[[0.5, 1.5], [0.3, 0.7], [0.3, 0.7]],
]
)
# see test_compute_pixel_radii_from_ray_direction
radii = torch.tensor(
[
[1.25, 2.25, 2.25],
[1.75, 2.75, 2.75],
[1.75, 2.75, 2.75],
]
)
radii = radii[..., None] / 12**0.5
# The expected mean and diagonal covariance have been computed
# by hand from the official code of MipNerf.
# https://github.com/google/mipnerf/blob/84c969e0a623edd183b75693aed72a7e7c22902d/internal/mip.py#L125
# mean, cov_diag = cast_rays(length, origins, directions, radii, 'cone', diag=True)
expected_mean = torch.tensor(
[
[
[[0.0, 0.0, 0.0]],
[[0.5506329, 0.0, 0.0]],
[[1.6518986, 0.0, 0.0]],
],
[
[[0.0, 0.28846154, 0.0]],
[[0.5506329, 0.13765822, 0.0]],
[[1.6518986, 0.13765822, 0.0]],
],
[
[[0.0, 1.1538461, 0.0]],
[[0.5506329, 0.5506329, 0.0]],
[[1.6518986, 0.5506329, 0.0]],
],
]
)
expected_diag_cov = torch.tensor(
[
[
[[0.04544772, 0.04544772, 0.04544772]],
[[0.01130973, 0.03317059, 0.03317059]],
[[0.10178753, 0.03317059, 0.03317059]],
],
[
[[0.08907752, 0.00404956, 0.08907752]],
[[0.0142245, 0.04734321, 0.04955113]],
[[0.10212927, 0.04991625, 0.04955113]],
],
[
[[0.08907752, 0.0647929, 0.08907752]],
[[0.03608529, 0.03608529, 0.04955113]],
[[0.10674264, 0.05590574, 0.04955113]],
],
]
)
ray = ImplicitronRayBundle(
origins=origins,
directions=directions,
bins=bins,
lengths=None,
pixel_radii_2d=radii,
xys=None,
)
mean, diag_cov = conical_frustum_to_gaussian(ray)
self.assertClose(mean, expected_mean)
self.assertClose(diag_cov, expected_diag_cov)
def test_scale_conical_frustum_to_gaussian(self) -> None:
origins = torch.zeros(2, 2, 3)
directions = torch.Tensor(
[
[[0, 1, 0], [0, 0, 1]],
[[0, 1, 0], [0, 0, 1]],
]
)
bins = torch.Tensor(
[
[[0.5, 1.5], [0.3, 0.7]],
[[0.5, 1.5], [0.3, 0.7]],
]
)
radii = torch.ones(2, 2, 1)
ray = ImplicitronRayBundle(
origins=origins,
directions=directions,
bins=bins,
pixel_radii_2d=radii,
lengths=None,
xys=None,
)
mean, diag_cov = conical_frustum_to_gaussian(ray)
scaling_factor = 2.5
ray = ImplicitronRayBundle(
origins=origins,
directions=directions,
bins=bins * scaling_factor,
pixel_radii_2d=radii,
lengths=None,
xys=None,
)
mean_scaled, diag_cov_scaled = conical_frustum_to_gaussian(ray)
np.testing.assert_allclose(mean * scaling_factor, mean_scaled)
np.testing.assert_allclose(
diag_cov * scaling_factor**2, diag_cov_scaled, atol=1e-6
)
def test_approximate_conical_frustum_as_gaussian(self) -> None:
"""Ensure that the computation modularity in our function is well done."""
bins = torch.Tensor([[0.5, 1.5], [0.3, 0.7]])
radii = torch.Tensor([[1.0], [1.0]])
t_mean, t_var, r_var = approximate_conical_frustum_as_gaussians(bins, radii)
self.assertEqual(t_mean.shape, (2, 1))
self.assertEqual(t_var.shape, (2, 1))
self.assertEqual(r_var.shape, (2, 1))
mu = np.array([[1.0], [0.5]])
delta = np.array([[0.5], [0.2]])
np.testing.assert_allclose(
mu + (2 * mu * delta**2) / (3 * mu**2 + delta**2), t_mean.numpy()
)
np.testing.assert_allclose(
(delta**2) / 3
- (4 / 15)
* ((delta**4 * (12 * mu**2 - delta**2)) / (3 * mu**2 + delta**2) ** 2),
t_var.numpy(),
)
np.testing.assert_allclose(
radii**2
* (
(mu**2) / 4
+ (5 / 12) * delta**2
- 4 / 15 * (delta**4) / (3 * mu**2 + delta**2)
),
r_var.numpy(),
)
def test_compute_3d_diagonal_covariance_gaussian(self) -> None:
ray_directions = torch.Tensor([[0, 0, 1]])
t_var = torch.Tensor([0.5, 0.5, 1])
r_var = torch.Tensor([0.6, 0.3, 0.4])
expected_diag_cov = np.array(
[
[
# t_cov_diag + xy_cov_diag
[0.0 + 0.6, 0.0 + 0.6, 0.5 + 0.0],
[0.0 + 0.3, 0.0 + 0.3, 0.5 + 0.0],
[0.0 + 0.4, 0.0 + 0.4, 1.0 + 0.0],
]
]
)
diag_cov = compute_3d_diagonal_covariance_gaussian(ray_directions, t_var, r_var)
np.testing.assert_allclose(diag_cov.numpy(), expected_diag_cov)
def test_conical_frustum_to_gaussian_raise_valueerror(self) -> None:
lengths = torch.linspace(0, 1, steps=6)
directions = torch.tensor([0, 0, 1])
origins = torch.tensor([1, 1, 1])
ray = ImplicitronRayBundle(
origins=origins, directions=directions, lengths=lengths, xys=None
)
expected_error_message = (
"RayBundle pixel_radii_2d or bins have not been provided."
" Look at pytorch3d.renderer.implicit.renderer.ray_sampler::"
"AbstractMaskRaySampler to see how to compute them. Have you forgot to set"
"`cast_ray_bundle_as_cone` to True?"
)
with self.assertRaisesRegex(ValueError, expected_error_message):
_ = conical_frustum_to_gaussian(ray)
# Ensure message is coherent with AbstractMaskRaySampler
class FakeRaySampler(AbstractMaskRaySampler):
def _get_min_max_depth_bounds(self, *args):
return None
message_assertion = (
"If cast_ray_bundle_as_cone has been removed please update the doc"
"conical_frustum_to_gaussian"
)
self.assertIsNotNone(
getattr(FakeRaySampler(), "cast_ray_bundle_as_cone", None),
message_assertion,
)
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