def test_SDF_Points():
sdf_points = shapenet.ShapeNet_SDF_Points(root=SHAPENET_ROOT,
cache_dir=CACHE_DIR,
categories=['can'],
train=True,
split=.1,
resolution=100,
smoothing_iterations=3,
num_points=5000,
occ=False,
sample_box=True)
assert len(sdf_points) == 10
assert sdf_points.cache_dir.exists()
assert len(list(sdf_points.cache_dir.rglob('*.p'))) == 10
for obj in sdf_points:
assert set(obj['data']['sdf_points'].shape) == set([5000, 3])
assert set(obj['data']['sdf_distances'].shape) == set([5000])
shutil.rmtree('tests/datasets/cache/sdf_points')
sdf_points = shapenet.ShapeNet_SDF_Points(root=SHAPENET_ROOT,
cache_dir=CACHE_DIR,
categories=['can'],
train=True,
split=.1,
resolution=100,
smoothing_iterations=3,
num_points=5000,
occ=True,
sample_box=True)
assert len(sdf_points) == 10
assert sdf_points.cache_dir.exists()
assert len(list(sdf_points.cache_dir.rglob('*.p'))) == 10
for obj in sdf_points:
assert set(obj['data']['occ_points'].shape) == set([5000, 3])
assert set(obj['data']['occ_values'].shape) == set([5000])
shutil.rmtree('tests/datasets/cache/sdf_points')
shutil.rmtree('tests/datasets/cache/voxels')
shutil.rmtree('tests/datasets/cache/surface_meshes')
def test_Combination():
dataset_params = {
'root': SHAPENET_ROOT,
'categories': ['can'],
'train': True,
'split': .8,
}
# images = shapenet.ShapeNet_Images(root=SHAPENET_ROOT, cache_dir=CACHE_DIR,
# categories=['bowl'], views=1, train=True, split=.8)
meshes = shapenet.ShapeNet_Meshes(**dataset_params)
voxels = shapenet.ShapeNet_Voxels(**dataset_params,
cache_dir=CACHE_DIR,
resolutions=[32])
sdf_points = shapenet.ShapeNet_SDF_Points(**dataset_params,
cache_dir=CACHE_DIR,
smoothing_iterations=3,
num_points=500,
occ=False,
sample_box=True)
points = shapenet.ShapeNet_Points(**dataset_params,
cache_dir=CACHE_DIR,
resolution=100,
smoothing_iterations=3,
num_points=500,
surface=False,
normals=True)
dataset = shapenet.ShapeNet_Combination([voxels, sdf_points, points])
for obj in dataset:
obj_data = obj['data']
assert set(obj['data']['sdf_points'].shape) == set([500, 3])
assert set(obj['data']['sdf_distances'].shape) == set([500])
assert set(obj['data']['32'].shape) == set([32, 32, 32])
assert set(obj['data']['points'].shape) == set([500, 3])
assert set(obj['data']['normals'].shape) == set([500, 3])
train_loader = DataLoader(dataset,
batch_size=2,
shuffle=True,
num_workers=8)
for batch in train_loader:
assert set(batch['data']['sdf_points'].shape) == set([2, 500, 3])
assert set(batch['data']['sdf_distances'].shape) == set([2, 500])
assert set(batch['data']['32'].shape) == set([2, 32, 32, 32])
assert set(batch['data']['points'].shape) == set([2, 500, 3])
assert set(batch['data']['normals'].shape) == set([2, 500, 3])
shutil.rmtree('tests/datasets/cache/sdf_points')
shutil.rmtree('tests/datasets/cache/points')
shutil.rmtree('tests/datasets/cache/voxels')
shutil.rmtree('tests/datasets/cache/surface_meshes')
parser.add_argument('-logdir',
type=str,
default='log',
help='Directory to log data to.')
parser.add_argument('-save-model',
action='store_true',
help='Saves the model and a snapshot \
of the optimizer state.')
args = parser.parse_args()
"""
Dataset
"""
sdf_set = shapenet.ShapeNet_SDF_Points(
root='/media/archana/Local/Datasets/ShapeNetCore.v1.zip/ShapeNetCore.v1',
cache_dir='/media/archana/Local/Datasets/ShapeNetCore.v1.zip/cache/',
categories=args.categories,
train=True,
split=.7,
num_points=3000)
point_set = shapenet.ShapeNet_Points(
root='/media/archana/Local/Datasets/ShapeNetCore.v1.zip/ShapeNetCore.v1',
cache_dir='/media/archana/Local/Datasets/ShapeNetCore.v1.zip/cache/',
categories=args.categories,
train=True,
split=.7,
num_points=3000)
images_set = shapenet.ShapeNet_Images(
root='/media/archana/Local/Datasets/ShapeNetRendering',
categories=args.categories,
train=True,
split=.7,