def test_Surface_Meshes():
surface_meshes = shapenet.ShapeNet_Surface_Meshes(root=SHAPENET_ROOT,
cache_dir=CACHE_DIR,
categories=['can'],
train=True,
split=.1,
resolution=100,
smoothing_iterations=3,
mode='Tri')
assert len(surface_meshes) == 10
assert surface_meshes.cache_dir.exists()
assert len(list(surface_meshes.cache_dir.rglob('*.p'))) == 10
for smesh in surface_meshes:
assert smesh['data']['vertices'].shape[0] > 0
assert smesh['data']['faces'].shape[1] == 3
shutil.rmtree('tests/datasets/cache/surface_meshes')
surface_meshes = shapenet.ShapeNet_Surface_Meshes(root=SHAPENET_ROOT,
cache_dir=CACHE_DIR,
categories=['can'],
train=True,
split=.1,
resolution=100,
smoothing_iterations=3,
mode='Quad')
assert len(surface_meshes) == 10
assert surface_meshes.cache_dir.exists()
assert len(list(surface_meshes.cache_dir.rglob('*.p'))) == 10
for smesh in surface_meshes:
assert smesh['data']['vertices'].shape[0] > 0
assert smesh['data']['faces'].shape[1] == 4
shutil.rmtree('tests/datasets/cache/voxels')
shutil.rmtree('tests/datasets/cache/surface_meshes')
cache_dir=args.cache_dir,
categories=args.categories,
train=True,
split=.7,
num_points=3000)
images_set = shapenet.ShapeNet_Images(root=args.shapenet_images_root,
categories=args.categories,
train=True,
split=.7,
views=23,
transform=preprocess)
if args.latent_loss:
mesh_set = shapenet.ShapeNet_Surface_Meshes(root=args.shapenet_root,
cache_dir=args.cache_dir,
categories=args.categories,
resolution=100,
train=True,
split=.7,
mode='Tri')
train_set = shapenet.ShapeNet_Combination(
[points_set, images_set, mesh_set])
dataloader_train = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
collate_fn=collate_fn,
num_workers=8)
else:
train_set = shapenet.ShapeNet_Combination([points_set, images_set])
dataloader_train = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,