def test_copyright(self):
test_dir = get_tests_dir()
root_dir = test_dir.parent
extensions = ("py", "cu", "cuh", "cpp", "h", "hpp", "sh")
expect = ("Copyright (c) Facebook, Inc. and its affiliates." +
" All rights reserved.\n")
files_missing_copyright_header = []
for extension in extensions:
for path in root_dir.glob(f"**/*.{extension}"):
if str(path).endswith(
"pytorch3d/transforms/external/kornia_angle_axis_to_rotation_matrix.py"
):
continue
if str(path).endswith(
"pytorch3d/csrc/pulsar/include/fastermath.h"):
continue
with open(path) as f:
firstline = f.readline()
if firstline.startswith(("# -*-", "#!")):
firstline = f.readline()
if not firstline.endswith(expect):
files_missing_copyright_header.append(str(path))
if len(files_missing_copyright_header) != 0:
self.fail("\n".join(files_missing_copyright_header))
def test_name_clash(self):
# For setup.py, all translation units need distinct names, so we
# cannot have foo.cu and foo.cpp, even in different directories.
test_dir = get_tests_dir()
source_dir = test_dir.parent / "pytorch3d"
stems = []
for extension in [".cu", ".cpp"]:
files = source_dir.glob(f"**/*{extension}")
stems.extend(f.stem for f in files)
counter = Counter(stems)
for k, v in counter.items():
self.assertEqual(v, 1, f"Too many files with stem {k}.")
def test_multinomial_weights(self):
"""
Confirm that torch.multinomial does not sample elements which have
zero probability using a real example of input from a training run.
"""
weights = torch.load(get_tests_dir() / "weights.pt")
S = 4096
num_trials = 100
for _ in range(0, num_trials):
weights[weights < 0] = 0.0
samples = weights.multinomial(S, replacement=True)
sampled_weights = weights[samples]
assert sampled_weights.min() > 0
if sampled_weights.min() <= 0:
return False
return True
from pytorch3d.renderer import TexturesVertex
from pytorch3d.renderer.cameras import FoVPerspectiveCameras, look_at_view_transform
from pytorch3d.renderer.mesh.rasterize_meshes import barycentric_coordinates
from pytorch3d.renderer.points import (
NormWeightedCompositor,
PointsRasterizationSettings,
PointsRasterizer,
PointsRenderer,
)
from pytorch3d.structures import Meshes, Pointclouds
from pytorch3d.utils.ico_sphere import ico_sphere
# If DEBUG=True, save out images generated in the tests for debugging.
# All saved images have prefix DEBUG_
DEBUG = False
DATA_DIR = get_tests_dir() / "data"
class TestSamplePoints(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
super().setUp()
torch.manual_seed(1)
@staticmethod
def init_meshes(
num_meshes: int = 10,
num_verts: int = 1000,
num_faces: int = 3000,
device: str = "cpu",
add_texture: bool = False,
):
def setUp(self) -> None:
super().setUp()
torch.manual_seed(42)
np.random.seed(42)
trimesh_results_path = get_tests_dir() / "data/icp_data.pth"
self.trimesh_results = torch.load(trimesh_results_path)
from pytorch3d.implicitron.models.view_pooling.feature_aggregation import (
AngleWeightedIdentityFeatureAggregator,
AngleWeightedReductionFeatureAggregator,
)
from pytorch3d.implicitron.tools.config import (
get_default_args,
remove_unused_components,
)
if os.environ.get("FB_TEST", False):
from common_testing import get_tests_dir
else:
from tests.common_testing import get_tests_dir
DATA_DIR = get_tests_dir() / "implicitron/data"
DEBUG: bool = False
# Tests the use of the config system in implicitron
class TestGenericModel(unittest.TestCase):
def setUp(self):
self.maxDiff = None
def test_create_gm(self):
args = get_default_args(GenericModel)
gm = GenericModel(**args)
self.assertIsInstance(gm.renderer, MultiPassEmissionAbsorptionRenderer)
self.assertIsInstance(
gm.feature_aggregator, AngleWeightedReductionFeatureAggregator
