def test_Meshes():
meshes1 = shapenet.ShapeNet_Meshes(root=SHAPENET_ROOT,
categories=['can'],
train=True,
split=.7)
assert len(meshes1) > 0
for mesh in meshes1:
assert Path(mesh['attributes']['path']).is_file()
assert mesh['data']['vertices'].shape[0] > 0
meshes2 = shapenet.ShapeNet_Meshes(root=SHAPENET_ROOT,
categories=['can', 'bowl'],
train=True,
split=.7)
assert len(meshes2) > len(meshes1)
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')
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)
valid_set = shapenet.ShapeNet_Combination(
[points_set_valid, images_set_valid, meshes_set_valid])
dataloader_val = DataLoader(valid_set,
batch_size=args.batchsize,
shuffle=False,
collate_fn=collate_fn,
num_workers=0)
# Model
mesh = kal.rep.TriangleMesh.from_obj('386.obj', enable_adjacency=True)
mesh.cuda()
normalize_adj(mesh)
# Data
points_set_valid = shapenet.ShapeNet_Points(root=args.shapenet_root,
cache_dir=args.cache_dir,
categories=args.categories,
train=False,
split=.7,
num_points=5000)
images_set_valid = shapenet.ShapeNet_Images(root=args.shapenet_images_root,
categories=args.categories,
train=False,
split=.7,
views=1,
transform=preprocess)
meshes_set_valid = shapenet.ShapeNet_Meshes(root=args.shapenet_root,
categories=args.categories,
train=False,
split=.7)
valid_set = shapenet.ShapeNet_Combination(
[points_set_valid, images_set_valid, meshes_set_valid])
dataloader_val = DataLoader(valid_set,
batch_size=args.batch_size,
collate_fn=collate_fn,
shuffle=False,
num_workers=8)
# Model
meshes = setup_meshes(filename='meshes/386.obj', device=args.device)
encoders = [Encoder().to(args.device) for i in range(3)]
mesh_update_kernels = [963, 1091, 1091]