def __init__(self, root: str, cache_dir: str, categories: list = ['chair'], train: bool = True,
split: float = .7, resolutions=[128, 32], no_progress: bool = False):
self.root = Path(root)
self.cache_dir = Path(cache_dir) / 'voxels'
self.cache_transforms = {}
self.params = {
'resolutions': resolutions,
}
mesh_dataset = ShapeNet_Meshes(root=root,
categories=categories,
train=train,
split=split,
no_progress=no_progress)
self.names = mesh_dataset.names
self.synset_idxs = mesh_dataset.synset_idxs
self.synsets = mesh_dataset.synsets
self.labels = mesh_dataset.labels
for res in self.params['resolutions']:
self.cache_transforms[res] = tfs.CacheCompose([
tfs.TriangleMeshToVoxelGrid(res, normalize=False, vertex_offset=0.5),
tfs.FillVoxelGrid(thresh=0.5),
tfs.ExtractProjectOdmsFromVoxelGrid()
], self.cache_dir)
desc = 'converting to voxels'
for idx in tqdm(range(len(mesh_dataset)), desc=desc, disable=no_progress):
name = mesh_dataset.names[idx]
if name not in self.cache_transforms[res].cached_ids:
sample = mesh_dataset[idx]
mesh = TriangleMesh.from_tensors(sample['data']['vertices'],
sample['data']['faces'])
self.cache_transforms[res](name, mesh)
def __init__(self,
basedir: str,
cache_dir: Optional[str] = None,
split: Optional[str] = 'train',
categories: list = ['bed'],
resolutions: List[int] = [32],
device: Optional[Union[torch.device, str]] = 'cpu'):
self.basedir = basedir
self.device = torch.device(device)
self.cache_dir = cache_dir if cache_dir is not None else os.path.join(
basedir, 'cache')
self.params = {'resolutions': resolutions}
self.cache_transforms = {}
mesh_dataset = ModelNet(basedir=basedir,
split=split,
categories=categories,
device=device)
self.names = mesh_dataset.names
self.categories = mesh_dataset.categories
self.cat_idxs = mesh_dataset.cat_idxs
for res in self.params['resolutions']:
self.cache_transforms[res] = tfs.CacheCompose([
tfs.TriangleMeshToVoxelGrid(
res, normalize=True, vertex_offset=0.5),
tfs.FillVoxelGrid(thresh=0.5),
tfs.ExtractProjectOdmsFromVoxelGrid()
], self.cache_dir)
desc = 'converting to voxels to resolution {0}'.format(res)
for idx in tqdm(range(len(mesh_dataset)), desc=desc,
disable=False):
name = mesh_dataset.names[idx]
if name not in self.cache_transforms[res].cached_ids:
mesh, _ = mesh_dataset[idx]
mesh.to(device=device)
self.cache_transforms[res](name, mesh)