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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
from itertools import product
from typing import Tuple
from unittest.mock import patch
import torch
from pytorch3d.common.compat import meshgrid_ij
from pytorch3d.implicitron.models.renderer.base import EvaluationMode
from pytorch3d.implicitron.models.renderer.ray_sampler import (
AdaptiveRaySampler,
compute_radii,
NearFarRaySampler,
)
from pytorch3d.renderer.cameras import (
CamerasBase,
FoVOrthographicCameras,
FoVPerspectiveCameras,
OrthographicCameras,
PerspectiveCameras,
)
from pytorch3d.renderer.implicit.utils import HeterogeneousRayBundle
from tests.common_camera_utils import init_random_cameras
from tests.common_testing import TestCaseMixin
CAMERA_TYPES = (
FoVPerspectiveCameras,
FoVOrthographicCameras,
OrthographicCameras,
PerspectiveCameras,
)
def unproject_xy_grid_from_ndc_to_world_coord(
cameras: CamerasBase, xy_grid: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Unproject a xy_grid from NDC coordinates to world coordinates.
Args:
cameras: CamerasBase.
xy_grid: A tensor of shape `(..., H*W, 2)` representing the
x, y coords.
Returns:
A tensor of shape `(..., H*W, 3)` representing the
"""
batch_size = xy_grid.shape[0]
n_rays_per_image = xy_grid.shape[1:-1].numel()
xy = xy_grid.view(batch_size, -1, 2)
xyz = torch.cat([xy, xy_grid.new_ones(batch_size, n_rays_per_image, 1)], dim=-1)
plane_at_depth1 = cameras.unproject_points(xyz, from_ndc=True)
return plane_at_depth1.view(*xy_grid.shape[:-1], 3)
class TestRaysampler(TestCaseMixin, unittest.TestCase):
def test_ndc_raysampler_n_ray_total_is_none(self):
sampler = NearFarRaySampler()
message = (
"If you introduce the support of `n_rays_total` for {0}, please handle the "
"packing and unpacking logic for the radii and lengths computation."
)
self.assertIsNone(
sampler._training_raysampler._n_rays_total, message.format(type(sampler))
)
self.assertIsNone(
sampler._evaluation_raysampler._n_rays_total, message.format(type(sampler))
)
sampler = AdaptiveRaySampler()
self.assertIsNone(
sampler._training_raysampler._n_rays_total, message.format(type(sampler))
)
self.assertIsNone(
sampler._evaluation_raysampler._n_rays_total, message.format(type(sampler))
)
def test_catch_heterogeneous_exception(self):
cameras = init_random_cameras(FoVPerspectiveCameras, 1, random_z=True)
class FakeSampler:
def __init__(self):
self.min_x, self.max_x = 1, 2
self.min_y, self.max_y = 1, 2
def __call__(self, **kwargs):
return HeterogeneousRayBundle(
torch.rand(3), torch.rand(3), torch.rand(3), torch.rand(1)
)
with patch(
"pytorch3d.implicitron.models.renderer.ray_sampler.NDCMultinomialRaysampler",
return_value=FakeSampler(),
):
for sampler in [
AdaptiveRaySampler(cast_ray_bundle_as_cone=True),
NearFarRaySampler(cast_ray_bundle_as_cone=True),
]:
with self.assertRaises(TypeError):
_ = sampler(cameras, EvaluationMode.TRAINING)
for sampler in [
AdaptiveRaySampler(cast_ray_bundle_as_cone=False),
NearFarRaySampler(cast_ray_bundle_as_cone=False),
]:
_ = sampler(cameras, EvaluationMode.TRAINING)
def test_compute_radii(self):
batch_size = 1
image_height, image_width = 20, 10
min_y, max_y, min_x, max_x = -1.0, 1.0, -1.0, 1.0
y, x = meshgrid_ij(
torch.linspace(min_y, max_y, image_height, dtype=torch.float32),
torch.linspace(min_x, max_x, image_width, dtype=torch.float32),
)
xy_grid = torch.stack([x, y], dim=-1).view(-1, 2)
pixel_width = (max_x - min_x) / (image_width - 1)
pixel_height = (max_y - min_y) / (image_height - 1)
for cam_type in CAMERA_TYPES:
# init a batch of random cameras
cameras = init_random_cameras(cam_type, batch_size, random_z=True)
# This method allow us to compute the radii whithout having
# access to the full grid. Raysamplers during the training
# will sample random rays from the grid.
radii = compute_radii(
cameras, xy_grid, pixel_hw_ndc=(pixel_height, pixel_width)
)
plane_at_depth1 = unproject_xy_grid_from_ndc_to_world_coord(
cameras, xy_grid
)
# This method absolutely needs the full grid to work.
expected_radii = compute_pixel_radii_from_grid(
plane_at_depth1.reshape(1, image_height, image_width, 3)
)
self.assertClose(expected_radii.reshape(-1, 1), radii)
def test_forward(self):
n_rays_per_image = 16
image_height, image_width = 20, 20
kwargs = {
"image_width": image_width,
"image_height": image_height,
"n_pts_per_ray_training": 32,
"n_pts_per_ray_evaluation": 32,
"n_rays_per_image_sampled_from_mask": n_rays_per_image,
"cast_ray_bundle_as_cone": False,
}
batch_size = 2
samplers = [NearFarRaySampler(**kwargs), AdaptiveRaySampler(**kwargs)]
evaluation_modes = [EvaluationMode.TRAINING, EvaluationMode.EVALUATION]
for cam_type, sampler, evaluation_mode in product(
CAMERA_TYPES, samplers, evaluation_modes
):
cameras = init_random_cameras(cam_type, batch_size, random_z=True)
ray_bundle = sampler(cameras, evaluation_mode)
shape_out = (
(batch_size, image_width, image_height)
if evaluation_mode == EvaluationMode.EVALUATION
else (batch_size, n_rays_per_image, 1)
)
n_pts_per_ray = (
kwargs["n_pts_per_ray_evaluation"]
if evaluation_mode == EvaluationMode.EVALUATION
else kwargs["n_pts_per_ray_training"]
)
self.assertIsNone(ray_bundle.bins)
self.assertIsNone(ray_bundle.pixel_radii_2d)
self.assertEqual(
ray_bundle.lengths.shape,
(*shape_out, n_pts_per_ray),
)
self.assertEqual(ray_bundle.directions.shape, (*shape_out, 3))
self.assertEqual(ray_bundle.origins.shape, (*shape_out, 3))
def test_forward_with_use_bins(self):
n_rays_per_image = 16
image_height, image_width = 20, 20
kwargs = {
"image_width": image_width,
"image_height": image_height,
"n_pts_per_ray_training": 32,
"n_pts_per_ray_evaluation": 32,
"n_rays_per_image_sampled_from_mask": n_rays_per_image,
"cast_ray_bundle_as_cone": True,
}
batch_size = 1
samplers = [NearFarRaySampler(**kwargs), AdaptiveRaySampler(**kwargs)]
evaluation_modes = [EvaluationMode.TRAINING, EvaluationMode.EVALUATION]
for cam_type, sampler, evaluation_mode in product(
CAMERA_TYPES, samplers, evaluation_modes
):
cameras = init_random_cameras(cam_type, batch_size, random_z=True)
ray_bundle = sampler(cameras, evaluation_mode)
lengths = 0.5 * (ray_bundle.bins[..., :-1] + ray_bundle.bins[..., 1:])
self.assertClose(ray_bundle.lengths, lengths)
shape_out = (
(batch_size, image_width, image_height)
if evaluation_mode == EvaluationMode.EVALUATION
else (batch_size, n_rays_per_image, 1)
)
self.assertEqual(ray_bundle.pixel_radii_2d.shape, (*shape_out, 1))
self.assertEqual(ray_bundle.directions.shape, (*shape_out, 3))
self.assertEqual(ray_bundle.origins.shape, (*shape_out, 3))
# Helper to test compute_radii
def compute_pixel_radii_from_grid(pixel_grid: torch.Tensor) -> torch.Tensor:
"""
Compute the radii of a conical frustum given the pixel grid.
To compute the radii we first compute the translation from a pixel
to its neighbors along the x and y axis. Then, we compute the norm
of each translation along the x and y axis.
The radii are then obtained by the following formula:
(dx_norm + dy_norm) * 0.5 * 2 / 12**0.5
where 2/12**0.5 is a scaling factor to match
the variance of the pixel’s footprint.
Args:
pixel_grid: A tensor of shape `(..., H, W, dim)` representing the
full grid of rays pixel_grid.
Returns:
The radiis for each pixels and shape `(..., H, W, 1)`.
"""
# [B, H, W - 1, 3]
x_translation = torch.diff(pixel_grid, dim=-2)
# [B, H - 1, W, 3]
y_translation = torch.diff(pixel_grid, dim=-3)
# [B, H, W - 1, 1]
dx_norm = torch.linalg.norm(x_translation, dim=-1, keepdim=True)
# [B, H - 1, W, 1]
dy_norm = torch.linalg.norm(y_translation, dim=-1, keepdim=True)
# Fill the missing value [B, H, W, 1]
dx_norm = torch.concatenate([dx_norm, dx_norm[..., -1:, :]], -2)
dy_norm = torch.concatenate([dy_norm, dy_norm[..., -1:, :, :]], -3)
# Cut the distance in half to obtain the base radius: (dx_norm + dy_norm) * 0.5
# and multiply it by the scaling factor: * 2 / 12**0.5
radii = (dx_norm + dy_norm) / 12**0.5
return radii
class TestRadiiComputationOnFullGrid(TestCaseMixin, unittest.TestCase):
def test_compute_pixel_radii_from_grid(self):
pixel_grid = torch.tensor(
[
[[0.0, 0, 0], [1.0, 0.0, 0], [3.0, 0.0, 0.0]],
[[0.0, 0.25, 0], [1.0, 0.25, 0], [3.0, 0.25, 0]],
[[0.0, 1, 0], [1.0, 1.0, 0], [3.0000, 1.0, 0]],
]
)
expected_y_norm = torch.tensor(
[
[0.25, 0.25, 0.25],
[0.75, 0.75, 0.75],
[0.75, 0.75, 0.75], # duplicated from previous row
]
)
expected_x_norm = torch.tensor(
[
# 3rd column is duplicated from 2nd
[1.0, 2.0, 2.0],
[1.0, 2.0, 2.0],
[1.0, 2.0, 2.0],
]
)
expected_radii = (expected_x_norm + expected_y_norm) / 12**0.5
radii = compute_pixel_radii_from_grid(pixel_grid)
self.assertClose(radii, expected_radii[..., None])
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