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Convert directory fbcode/vision to use the Ruff Formatter

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Summary:
Converts the directory specified to use the Ruff formatter in pyfmt

ruff_dog

If this diff causes merge conflicts when rebasing, please run
`hg status -n -0 --change . -I '**/*.{py,pyi}' | xargs -0 arc pyfmt`
on your diff, and amend any changes before rebasing onto latest.
That should help reduce or eliminate any merge conflicts.

allow-large-files

Reviewed By: bottler

Differential Revision: D66472063

fbshipit-source-id: 35841cb397e4f8e066e2159550d2f56b403b1bef
This commit is contained in:
Thomas Polasek
2024-11-26 02:38:20 -08:00
committed by Facebook GitHub Bot
parent f6c2ca6bfc
commit 055ab3a2e3
92 changed files with 121 additions and 191 deletions
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1
tests/benchmarks/bm_ball_query.py
View File

@@ -11,7 +11,6 @@ from tests.test_ball_query import TestBallQuery
def bm_ball_query() -> None:
backends = ["cpu", "cuda:0"]
kwargs_list = []

1
tests/benchmarks/bm_cameras_alignment.py
View File

@@ -11,7 +11,6 @@ from tests.test_cameras_alignment import TestCamerasAlignment
def bm_cameras_alignment() -> None:
case_grid = {
"batch_size": [10, 100, 1000],
"mode": ["centers", "extrinsics"],

1
tests/benchmarks/bm_knn.py
View File

@@ -11,7 +11,6 @@ from tests.test_knn import TestKNN
def bm_knn() -> None:
backends = ["cpu", "cuda:0"]
kwargs_list = []

1
tests/benchmarks/bm_point_mesh_distance.py
View File

@@ -12,7 +12,6 @@ from tests.test_point_mesh_distance import TestPointMeshDistance
def bm_point_mesh_distance() -> None:
backend = ["cuda:0"]
kwargs_list = []

2
tests/benchmarks/bm_points_alignment.py
View File

@@ -12,7 +12,6 @@ from tests.test_points_alignment import TestCorrespondingPointsAlignment, TestIC
def bm_iterative_closest_point() -> None:
case_grid = {
"batch_size": [1, 10],
"dim": [3, 20],
@@ -43,7 +42,6 @@ def bm_iterative_closest_point() -> None:
def bm_corresponding_points_alignment() -> None:
case_grid = {
"allow_reflection": [True, False],
"batch_size": [1, 10, 100],

1
tests/benchmarks/bm_pulsar.py
View File

@@ -5,6 +5,7 @@
# LICENSE file in the root directory of this source tree.
"""Test render speed."""
import logging
import sys
from os import path

1
tests/benchmarks/bm_sample_pdf.py
View File

@@ -11,7 +11,6 @@ from tests.test_sample_pdf import TestSamplePDF
def bm_sample_pdf() -> None:
backends = ["python_cuda", "cuda", "python_cpu", "cpu"]
kwargs_list = []

1
tests/benchmarks/bm_sample_points_from_meshes.py
View File

@@ -13,7 +13,6 @@ from tests.test_sample_points_from_meshes import TestSamplePoints
def bm_sample_points() -> None:
backend = ["cpu"]
if torch.cuda.is_available():
backend.append("cuda:0")

1
tests/implicitron/test_config.py
View File

@@ -576,7 +576,6 @@ class TestConfig(unittest.TestCase):
a: int = 9
for Unprocessed in [UnprocessedConfigurable, UnprocessedReplaceable]:
self.assertFalse(_is_actually_dataclass(Unprocessed))
unprocessed = Unprocessed()
self.assertTrue(_is_actually_dataclass(Unprocessed))

1
tests/implicitron/test_evaluation.py
View File

@@ -71,7 +71,6 @@ class TestEvaluation(unittest.TestCase):
for diff in 10 ** torch.linspace(-5, 0, 6):
for crop in (0, 5):
pred = gt + (torch.rand_like(gt) - 0.5) * 2 * diff
# scaled prediction test

1
tests/implicitron/test_forward_pass.py
View File

@@ -74,7 +74,6 @@ class TestGenericModel(unittest.TestCase):
eval_test: bool = True,
bw_test: bool = True,
):
R, T = look_at_view_transform(azim=torch.rand(n_train_cameras) * 360)
cameras = PerspectiveCameras(R=R, T=T, device=device)

4
tests/implicitron/test_json_index_dataset_provider_v2.py
View File

@@ -171,9 +171,7 @@ def _make_random_json_dataset_map_provider_v2_data(
Image.fromarray(
(mask * 255.0).astype(np.uint8),
mode="L",
).convert(
"L"
).save(mask_path)
).convert("L").save(mask_path)
fa = FrameAnnotation(
sequence_name=seq_name,

1
tests/implicitron/test_model_visualize.py
View File

@@ -65,7 +65,6 @@ class TestModelVisualize(unittest.TestCase):
visdom_show_preds = Visdom().check_connection()
for load_dataset_pointcloud in [True, False]:
model = _PointcloudRenderingModel(
train_dataset,
show_sequence_name,

1
tests/implicitron/test_viewsampling.py
View File

@@ -142,7 +142,6 @@ class TestViewsampling(unittest.TestCase):
expand_args_fields(ViewSampler)
for masked_sampling in (True, False):
view_sampler = ViewSampler(masked_sampling=masked_sampling)
feats_sampled, masks_sampled = view_sampler(

1
tests/implicitron/test_voxel_grids.py
View File

@@ -275,7 +275,6 @@ class TestVoxelGrids(TestCaseMixin, unittest.TestCase):
return torch.cat(result)
def test_interpolation(self):
with self.subTest("1D interpolation"):
points = self.get_random_normalized_points(
n_grids=4, n_points=5, dimension=1

1
tests/pulsar/create_multiview.py
View File

@@ -6,6 +6,7 @@
# LICENSE file in the root directory of this source tree.
"""Create multiview data."""
import sys
from os import path

1
tests/pulsar/test_channels.py
View File

@@ -5,6 +5,7 @@
# LICENSE file in the root directory of this source tree.
"""Test number of channels."""
import logging
import sys
import unittest

1
tests/pulsar/test_depth.py
View File

@@ -5,6 +5,7 @@
# LICENSE file in the root directory of this source tree.
"""Test the sorting of the closest spheres."""
import logging
import os
import sys

1
tests/pulsar/test_forward.py
View File

@@ -5,6 +5,7 @@
# LICENSE file in the root directory of this source tree.
"""Basic rendering test."""
import logging
import os
import sys

1
tests/pulsar/test_hands.py
View File

@@ -5,6 +5,7 @@
# LICENSE file in the root directory of this source tree.
"""Test right hand/left hand system compatibility."""
import logging
import sys
import unittest

1
tests/pulsar/test_ortho.py
View File

@@ -5,6 +5,7 @@
# LICENSE file in the root directory of this source tree.
"""Tests for the orthogonal projection."""
import logging
import sys
import unittest

1
tests/pulsar/test_small_spheres.py
View File

@@ -6,6 +6,7 @@
# LICENSE file in the root directory of this source tree.
"""Test right hand/left hand system compatibility."""
import sys
import unittest
from os import path

5
tests/test_blending.py
View File

@@ -401,7 +401,10 @@ class TestBlending(TestCaseMixin, unittest.TestCase):
dists1 = torch.randn(size=(N, S, S, K), requires_grad=True, device=device)
zbuf = torch.randn(size=(N, S, S, K), requires_grad=True, device=device)
fragments = Fragments(
pix_to_face=pix_to_face, bary_coords=empty, zbuf=zbuf, dists=dists1 # dummy
pix_to_face=pix_to_face,
bary_coords=empty,
zbuf=zbuf,
dists=dists1, # dummy
)
blend_params = BlendParams(sigma=1e-3)

5
tests/test_cameras.py
View File

@@ -495,8 +495,8 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
screen_cam_params["image_size"] = image_size
screen_cam_params["focal_length"] = fcl * scale
screen_cam_params["principal_point"] = (
image_size[:, [1, 0]]
) / 2.0 - prc * scale
(image_size[:, [1, 0]]) / 2.0 - prc * scale
)
screen_cam_params["in_ndc"] = False
else:
raise ValueError(str(cam_type))
@@ -615,7 +615,6 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
OrthographicCameras,
PerspectiveCameras,
):
# init the cameras
cameras = init_random_cameras(cam_type, batch_size)
# xyz - the ground truth point cloud

16
tests/test_compositing.py
View File

@@ -17,7 +17,6 @@ from .common_testing import get_random_cuda_device, TestCaseMixin
class TestAccumulatePoints(TestCaseMixin, unittest.TestCase):
# NAIVE PYTHON IMPLEMENTATIONS (USED FOR TESTING)
@staticmethod
def accumulate_alphacomposite_python(points_idx, alphas, features):
@@ -63,7 +62,6 @@ class TestAccumulatePoints(TestCaseMixin, unittest.TestCase):
for c in range(0, C):
for i in range(0, W):
for j in range(0, H):
for k in range(0, K):
n_idx = points_idx[b, k, j, i]
@@ -237,16 +235,16 @@ class TestAccumulatePoints(TestCaseMixin, unittest.TestCase):
[
# fmt: off
[
[0, 0, 0, 0], # noqa: E241, E201
[0, 0, 0, 0], # noqa: E241, E201
[0, -1, -1, -1], # noqa: E241, E201
[0, 1, 1, 0], # noqa: E241, E201
[0, 0, 0, 0], # noqa: E241, E201
[0, 1, 1, 0], # noqa: E241, E201
[0, 0, 0, 0], # noqa: E241, E201
],
[
[2, 2, 2, 2], # noqa: E241, E201
[2, 3, 3, 2], # noqa: E241, E201
[2, 3, 3, 2], # noqa: E241, E201
[2, 2, -1, 2], # noqa: E241, E201
[2, 2, 2, 2], # noqa: E241, E201
[2, 3, 3, 2], # noqa: E241, E201
[2, 3, 3, 2], # noqa: E241, E201
[2, 2, -1, 2], # noqa: E241, E201
],
# fmt: on
]

1
tests/test_iou_box3d.py
View File

@@ -95,7 +95,6 @@ class TestIoU3D(TestCaseMixin, unittest.TestCase):
return ious
def _test_iou(self, overlap_fn, device):
box1 = torch.tensor(
UNIT_BOX,
dtype=torch.float32,

1
tests/test_marching_cubes.py
View File

@@ -23,7 +23,6 @@ def convert_to_local(verts, volume_dim):
class TestCubeConfiguration(TestCaseMixin, unittest.TestCase):
# Test single cubes. Each case corresponds to the corresponding
# cube vertex configuration in each case here (0-indexed):
# https://en.wikipedia.org/wiki/Marching_cubes#/media/File:MarchingCubes.svg

2
tests/test_meshes.py
View File

@@ -335,7 +335,6 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
Meshes(verts=verts_padded, faces=faces_padded)
def test_simple_random_meshes(self):
# Define the test mesh object either as a list or tensor of faces/verts.
for lists_to_tensors in (False, True):
N = 10
@@ -1120,7 +1119,6 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
self.assertClose(face_areas, expected_areas)
def test_compute_normals(self):
# Simple case with one mesh where normals point in either +/- ijk
verts = torch.tensor(
[

1
tests/test_pointclouds.py
View File

@@ -1072,7 +1072,6 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
for with_normals in (True, False):
for run_padded in (True, False):
for run_packed in (True, False):
clouds = TestPointclouds.init_cloud(
3,
100,

2
tests/test_points_alignment.py
View File

@@ -56,7 +56,6 @@ class TestICP(TestCaseMixin, unittest.TestCase):
use_pointclouds=False,
estimate_scale=False,
):
device = torch.device("cuda:0")
# initialize a ground truth point cloud
@@ -433,7 +432,6 @@ class TestCorrespondingPointsAlignment(TestCaseMixin, unittest.TestCase):
reflect=False,
random_weights=False,
):
device = torch.device("cuda:0")
# initialize a ground truth point cloud

2
tests/test_points_to_volumes.py
View File

@@ -258,9 +258,7 @@ class TestPointsToVolumes(TestCaseMixin, unittest.TestCase):
batch_size = 4
for volume_size in ([25, 25, 25], [30, 25, 15]):
for python, interp_mode in product([True, False], ["trilinear", "nearest"]):
(pointclouds, initial_volumes) = init_volume_boundary_pointcloud(
volume_size=volume_size,
n_points=int(1e5),

1
tests/test_r2n2.py
View File

@@ -7,6 +7,7 @@
"""
Sanity checks for loading R2N2.
"""
import json
import os
import unittest

3
tests/test_raysampling.py
View File

@@ -210,13 +210,11 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
device = torch.device("cuda")
for n_pts_per_ray in (100, 1):
for raysampler_type in (
MonteCarloRaysampler,
MultinomialRaysampler,
NDCMultinomialRaysampler,
):
raysampler = TestRaysampling.init_raysampler(
raysampler_type=raysampler_type,
min_x=min_x,
@@ -258,7 +256,6 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
OrthographicCameras,
PerspectiveCameras,
):
# init a batch of random cameras
cameras = init_random_cameras(
cam_type, batch_size, random_z=True

4
tests/test_render_implicit.py
View File

@@ -215,9 +215,7 @@ class TestRenderImplicit(TestCaseMixin, unittest.TestCase):
volumetric_function=spherical_volumetric_function,
sphere_centroid=sphere_centroid,
sphere_diameter=sphere_diameter,
)[
0
]
)[0]
# check that the renderer does not erase gradients
loss = images_opacities.sum()

1
tests/test_render_meshes.py
View File

@@ -8,6 +8,7 @@
"""
Sanity checks for output images from the renderer.
"""
import os
import unittest
from collections import namedtuple

1
tests/test_render_meshes_clipped.py
View File

@@ -12,6 +12,7 @@ behind the image plane. These faces are clipped and then rasterized.
See pytorch3d/renderer/mesh/clip.py for more details about the
clipping process.
"""
import unittest
import imageio

1
tests/test_render_multigpu.py
View File

@@ -134,7 +134,6 @@ class TestRenderMeshesMultiGPU(TestCaseMixin, unittest.TestCase):
self.renderer = self.init_render(device)
def init_render(self, device):
cameras = FoVPerspectiveCameras().to(device)
raster_settings = RasterizationSettings(
image_size=128, blur_radius=0.0, faces_per_pixel=1

20
tests/test_render_points.py
View File

@@ -8,6 +8,7 @@
"""
Sanity checks for output images from the pointcloud renderer.
"""
import unittest
import warnings
from os import path
@@ -220,7 +221,8 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
# znear and zfar is required in this case.
self.assertRaises(
ValueError,
lambda renderer=renderer, pointclouds=pointclouds: renderer.forward(
lambda renderer=renderer,
pointclouds=pointclouds: renderer.forward(
point_clouds=pointclouds, gamma=(1e-4,)
),
)
@@ -233,7 +235,8 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
# znear and zfar must be batched.
self.assertRaises(
TypeError,
lambda renderer=renderer, pointclouds=pointclouds: renderer.forward(
lambda renderer=renderer,
pointclouds=pointclouds: renderer.forward(
point_clouds=pointclouds,
gamma=(1e-4,),
znear=1.0,
@@ -242,7 +245,8 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
)
self.assertRaises(
TypeError,
lambda renderer=renderer, pointclouds=pointclouds: renderer.forward(
lambda renderer=renderer,
pointclouds=pointclouds: renderer.forward(
point_clouds=pointclouds,
gamma=(1e-4,),
znear=(1.0,),
@@ -253,7 +257,8 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
# gamma must be batched.
self.assertRaises(
TypeError,
lambda renderer=renderer, pointclouds=pointclouds: renderer.forward(
lambda renderer=renderer,
pointclouds=pointclouds: renderer.forward(
point_clouds=pointclouds, gamma=1e-4
),
)
@@ -262,7 +267,8 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
renderer.rasterizer.raster_settings.image_size = 0
self.assertRaises(
ValueError,
lambda renderer=renderer, pointclouds=pointclouds: renderer.forward(
lambda renderer=renderer,
pointclouds=pointclouds: renderer.forward(
point_clouds=pointclouds, gamma=(1e-4,)
),
)
@@ -372,7 +378,6 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
self.assertClose(rgb, image_ref)
def test_compositor_background_color_rgba(self):
N, H, W, K, C, P = 1, 15, 15, 20, 4, 225
ptclds = torch.randn((C, P))
alphas = torch.rand((N, K, H, W))
@@ -385,7 +390,6 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
]
for compositor_class, composite_func in compositor_funcs:
compositor = compositor_class(background_color)
# run the forward method to generate masked images
@@ -423,7 +427,6 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
)
def test_compositor_background_color_rgb(self):
N, H, W, K, C, P = 1, 15, 15, 20, 3, 225
ptclds = torch.randn((C, P))
alphas = torch.rand((N, K, H, W))
@@ -436,7 +439,6 @@ class TestRenderPoints(TestCaseMixin, unittest.TestCase):
]
for compositor_class, composite_func in compositor_funcs:
compositor = compositor_class(background_color)
# run the forward method to generate masked images

3
tests/test_render_volumes.py
View File

@@ -308,7 +308,6 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
# init the boundary volume
for shape in ("sphere", "cube"):
if not DEBUG and shape == "cube":
# do not run numeric checks for the cube as the
# differences in rendering equations make the renders incomparable
@@ -515,7 +514,6 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
for shape in ("sphere", "cube"):
for sample_mode in ("bilinear", "nearest"):
volumes = init_boundary_volume(
volume_size=volume_size, batch_size=n_frames, shape=shape
)[0]
@@ -593,7 +591,6 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
for volume_size in ([25, 25, 25],):
for sample_mode in ("bilinear", "nearest"):
volume_translation = torch.zeros(4, 3)
volume_translation.requires_grad = True
volumes, volume_voxel_size, _ = init_boundary_volume(

1
tests/test_rendering_utils.py
View File

@@ -231,7 +231,6 @@ class TestTensorProperties(TestCaseMixin, unittest.TestCase):
# check both H > W and W > H
for flip_axes in [False, True]:
# non-batched version
for xy_ndc, xy_gs in xy_ndc_gs:
xy_gs_predicted = ndc_to_grid_sample_coords(

1
tests/test_sample_points_from_meshes.py
View File

@@ -301,7 +301,6 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
)
def test_outputs(self):
for add_texture in (True, False):
meshes = init_meshes(device=torch.device("cuda:0"), add_texture=add_texture)
out1 = sample_points_from_meshes(meshes, num_samples=100)

1
tests/test_shapenet_core.py
View File

@@ -7,6 +7,7 @@
"""
Sanity checks for loading ShapeNetCore.
"""
import os
import unittest

1
tests/test_struct_utils.py
View File

@@ -105,7 +105,6 @@ class TestStructUtils(TestCaseMixin, unittest.TestCase):
ndim = 2
for ndim in (2, 3, 4):
dims = [K] * ndim
x = torch.rand([N] + dims, device=device)

5
tests/test_transforms.py
View File

@@ -308,9 +308,7 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
t1._matrix = torch.FloatTensor(persp_proj)
points = torch.tensor(
[[0.0, 1.0, 0.0], [0.0, 0.0, 1e-5], [-1.0, 0.0, 1e-5]]
).view(
1, 3, 3
) # a set of points with z-coord very close to 0
).view(1, 3, 3) # a set of points with z-coord very close to 0
proj = t1.transform_points(points)
proj_eps = t1.transform_points(points, eps=1e-4)
@@ -323,7 +321,6 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
# generate a random chain of transforms
for _ in range(10): # 10 different tries
# list of transform matrices
ts = []

37
tests/test_volumes.py
View File

@@ -66,7 +66,6 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
volume_size=(10, 13, 8),
dtype=torch.float32,
):
device = torch.device("cuda:0")
# make sure we have at least 3 volumes to prevent indexing crash
@@ -94,7 +93,6 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
for features_, densities_ in zip(
(None, features, features_list), (densities, densities, densities_list)
):
# init the volume structure
v = Volumes(
features=features_,
@@ -205,7 +203,6 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
# try for 10 sets of different random sizes/centers/voxel_sizes
for _ in range(10):
size = torch.randint(high=10, size=(3,), low=3).tolist()
densities = torch.randn(
@@ -433,7 +430,6 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
# try for 10 sets of different random sizes/centers/voxel_sizes
for _ in range(10):
size = torch.randint(high=10, size=(3,), low=3).tolist()
center = torch.randn(num_volumes, 3, dtype=torch.float32, device=device)
@@ -449,7 +445,6 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
num_volumes, 3, size, num_channels, device, rand_sizes=None
)[0],
):
# init the volume structure
v = Volumes(
densities=densities,
@@ -794,9 +789,7 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
size=[num_volumes + 1, num_channels, *size],
device=device,
dtype=torch.float32,
).unbind(
0
), # list with diff batch size
).unbind(0), # list with diff batch size
torch.randn(
size=[num_volumes + 1, num_channels, *size],
device=device,
@@ -806,9 +799,7 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
size=[num_volumes, num_channels, *diff_size],
device=device,
dtype=torch.float32,
).unbind(
0
), # list with different size
).unbind(0), # list with different size
torch.randn(
size=[num_volumes, num_channels, *diff_size],
device=device,
@@ -823,9 +814,7 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
size=[num_volumes, num_channels, *size],
device=diff_device,
dtype=torch.float32,
).unbind(
0
), # list with different device
).unbind(0), # list with different device
]
# good ways to define features
@@ -834,9 +823,7 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
size=[num_volumes, num_channels, *size],
device=device,
dtype=torch.float32,
).unbind(
0
), # list of features of correct size
).unbind(0), # list of features of correct size
torch.randn(
size=[num_volumes, num_channels, *size],
device=device,
@@ -872,9 +859,7 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
size=[num_volumes, num_channels, *size],
device=device,
dtype=torch.float32,
).unbind(
0
), # list of features
).unbind(0), # list of features
None, # no features
]
@@ -890,9 +875,7 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
), # 4 dims
torch.randn(
size=[num_volumes, *size], device=device, dtype=torch.float32
).unbind(
0
), # list of 4 dim tensors
).unbind(0), # list of 4 dim tensors
]
# all ways to define densities
@@ -902,9 +885,7 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
), # padded tensor
torch.randn(
size=[num_volumes, 1, *size], device=device, dtype=torch.float32
).unbind(
0
), # list of densities
).unbind(0), # list of densities
]
# bad ways to define densities
@@ -915,9 +896,7 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
), # 6-dim tensor
torch.randn(
size=[num_volumes, 1, 1, *size], device=device, dtype=torch.float32
).unbind(
0
), # list of 5-dim densities
).unbind(0), # list of 5-dim densities
]
# all possible ways to define the voxels sizes