def test_Points():
points = shapenet.ShapeNet_Points(root=SHAPENET_ROOT,
cache_dir=CACHE_DIR,
categories=['can'],
train=True,
split=.1,
resolution=100,
smoothing_iterations=3,
num_points=5000,
surface=False,
normals=False)
assert len(points) == 10
assert points.cache_dir.exists()
assert len(list(points.cache_dir.rglob('*.p'))) == 10
for obj in points:
assert set(obj['data']['points'].shape) == set([5000, 3])
assert set(obj['data']['normals'].shape) == set([5000, 3])
shutil.rmtree('tests/datasets/cache/points')
points = shapenet.ShapeNet_Points(root=SHAPENET_ROOT,
cache_dir=CACHE_DIR,
categories=['can'],
train=True,
split=.1,
resolution=100,
smoothing_iterations=3,
num_points=5000,
surface=True,
normals=True)
assert len(points) == 10
assert points.cache_dir.exists()
assert len(list(points.cache_dir.rglob('*.p'))) == 10
for obj in points:
assert set(obj['data']['points'].shape) == set([5000, 3])
assert set(obj['data']['normals'].shape) == set([5000, 3])
shutil.rmtree('tests/datasets/cache/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')
type=str,
nargs='+',
default=['chair'],
help='list of object classes to use')
parser.add_argument('-vis',
action='store_true',
help='Visualize each model while evaluating')
parser.add_argument('-batchsize', type=int, default=16, help='Batch size.')
parser.add_argument('-f_score', action='store_true', help='compute F-score')
args = parser.parse_args()
# Data
points_set_valid = 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=False,
split=.7,
num_points=5000)
images_set_valid = shapenet.ShapeNet_Images(
root='/media/archana/Local/Datasets/ShapeNetRendering',
categories=args.categories,
train=False,
split=.7,
views=1,
transform=preprocess)
meshes_set_valid = shapenet.ShapeNet_Meshes(
root='/media/archana/Local/Datasets/ShapeNetCore.v1.zip/ShapeNetCore.v1',
categories=args.categories,
train=False,
split=.7)
type=str,
default='log',
help='Directory to log data to.')
parser.add_argument(
'--resume',
choices=['best', 'recent'],
default=None,
help=
'Choose which weights to resume training from (None to start from random initialization.)'
)
args = parser.parse_args()
# Setup Dataset
points_set = shapenet.ShapeNet_Points(root=args.shapenet_root,
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,
