├── pygmi ├── nn │ ├── nerf.py │ ├── __init__.py │ ├── deepsdf.py │ ├── encoder.py │ └── siren.py ├── data │ ├── __init__.py │ ├── dataset │ │ ├── __init__.py │ │ ├── core.py │ │ └── sdf.py │ ├── sources │ │ ├── __init__.py │ │ ├── pyg.py │ │ ├── core.py │ │ └── misc.py │ └── preprocess │ │ ├── __init__.py │ │ ├── core.py │ │ └── sdf.py ├── __version__.py ├── __init__.py ├── types │ ├── __init__.py │ ├── core.py │ └── sdf.py ├── utils │ ├── math │ │ ├── __init__.py │ │ └── diffops.py │ ├── extract │ │ ├── __init__.py │ │ └── core.py │ ├── visual │ │ ├── __init__.py │ │ └── core.py │ ├── __init__.py │ ├── files.py │ └── misc.py └── tasks │ ├── __init__.py │ ├── core.py │ ├── distance_regression.py │ └── distance_eikonal_ivp.py ├── setup.cfg ├── setup.py ├── examples ├── surface_reconstruction.py └── encode_faust_sdf.py ├── .gitignore ├── README.md └── LICENSE /pygmi/nn/nerf.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /pygmi/data/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /pygmi/__version__.py: -------------------------------------------------------------------------------- 1 | __version__ = '0.1.0' -------------------------------------------------------------------------------- /pygmi/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.__version__ import __version__ -------------------------------------------------------------------------------- /pygmi/types/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.types.sdf import SDF 2 | from pygmi.types.core import ImplicitFunction -------------------------------------------------------------------------------- /pygmi/utils/math/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.utils.math.diffops import gradient, jacobian, hessian, divergence -------------------------------------------------------------------------------- /pygmi/utils/extract/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.utils.extract.core import extract_level_set, grid_evaluation, marching_cubes -------------------------------------------------------------------------------- /pygmi/utils/visual/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.utils.visual.core import validate_figure, plot_trisurf, isosurf_animation, plot_isosurfaces, make_3d_subplots -------------------------------------------------------------------------------- /pygmi/data/dataset/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.data.dataset.core import MultiSourceData 2 | from pygmi.data.dataset.sdf import SDFUnsupervisedData, SDFSupervisedData -------------------------------------------------------------------------------- /pygmi/data/sources/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.data.sources.pyg import PyGDataSource 2 | from pygmi.data.sources.misc import PLYDataSource, PNGDataSource, TXTArrayDataSource -------------------------------------------------------------------------------- /pygmi/nn/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.nn.deepsdf import SmoothDeepSDFNet, DeepReLUSDFNet 2 | from pygmi.nn.encoder import PointNet2Encoder, Autodecoder 3 | from pygmi.nn.siren import SirenMLP, SirenSDF -------------------------------------------------------------------------------- /pygmi/data/preprocess/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.data.preprocess.sdf import get_distance_values, upsample_with_normals, center_point_cloud 2 | from pygmi.data.preprocess.core import gather_fnames, process_source -------------------------------------------------------------------------------- /pygmi/tasks/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.tasks.core import TaskBaseModule 2 | from pygmi.tasks.distance_regression import SupervisedDistanceRegression 3 | from pygmi.tasks.distance_eikonal_ivp import EikonalIVPOptimization -------------------------------------------------------------------------------- /pygmi/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from pygmi.utils.files import validate_fnames 2 | from pygmi.utils.misc import make_grid, cat_points_latent, sphere_sdf, label_to_interval 3 | from pygmi.utils.math import * 4 | from pygmi.utils.extract import * 5 | from pygmi.utils.visual import * -------------------------------------------------------------------------------- /pygmi/tasks/core.py: -------------------------------------------------------------------------------- 1 | import pytorch_lightning as pl 2 | from pygmi.types import ImplicitFunction 3 | 4 | 5 | 6 | class TaskBaseModule(pl.LightningModule): 7 | 8 | def __init__(self, geom_repr: ImplicitFunction): 9 | """Instantiates a `TaskBaseModule`, an abstract `LightningModule` 10 | optimizing an `ImplicitFunction` 11 | 12 | Parameters 13 | ---------- 14 | geom_repr : ImplicitFunction 15 | The implicit geometry of the scene(s) to optimize 16 | """ 17 | super(TaskBaseModule, self).__init__() 18 | self.geometry = geom_repr 19 | 20 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | name = pygmi 3 | version = attr: pygmi.__version__.__version__ 4 | description = PyGMI - A library for neural implicit geometry 5 | url = https://github.com/daniele-baieri/PyGMI 6 | long_description = file: README.md 7 | author = Daniele Baieri 8 | author_email = daniele.baieri@gmail.com 9 | license = GNU General Public License v3.0 10 | 11 | [options] 12 | packages = find: 13 | install_requires = 14 | numpy 15 | torch 16 | torchvision 17 | pytorch-lightning 18 | cgal 19 | wandb 20 | scikit-image 21 | plotly 22 | trimesh 23 | typing-extensions; python_version < "3.8" 24 | zip_safe = False 25 | 26 | [options.packages.find] 27 | exclude = 28 | bin* 29 | tmp* -------------------------------------------------------------------------------- /pygmi/data/sources/pyg.py: -------------------------------------------------------------------------------- 1 | import torch_geometric.datasets as pygdst 2 | from typing import Dict, List 3 | from pygmi.data.sources.core import DataSource 4 | 5 | 6 | 7 | class PyGDataSource(DataSource): 8 | 9 | def __init__(self, source: str, idx_select: List[int] = None, **pyg_kwargs: Dict): 10 | """Initializes a data source from a PyTorch Geometric dataset. 11 | kwargs are passed to the PyG initializer. 12 | 13 | Parameters 14 | ---------- 15 | source : str 16 | Name of the PyG dataset to use. 17 | idx_select : List[int] 18 | indices of data objects to select, by default None 19 | """ 20 | self.source = getattr(pygdst, source)(**pyg_kwargs) 21 | super(PyGDataSource, self).__init__(indices=idx_select) 22 | -------------------------------------------------------------------------------- /pygmi/utils/files.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import List 3 | 4 | 5 | 6 | def mkdir_ifnotexists(path: str) -> None: 7 | """Creates directory at given path, if it does not exist. 8 | 9 | Parameters 10 | ---------- 11 | path : str 12 | Path to directory to create 13 | """ 14 | if not os.path.isdir(path): 15 | os.mkdir(path) 16 | 17 | def validate_fnames(paths: List[str]) -> bool: 18 | """ 19 | Verifies that a list of paths exists as files in memory. 20 | 21 | Parameters 22 | ---------- 23 | paths : str 24 | A list of file paths 25 | 26 | Returns 27 | ------- 28 | bool 29 | True if every path leads to a file, False ow 30 | """ 31 | for f in paths: 32 | if not os.path.isfile(f): 33 | return False 34 | return True -------------------------------------------------------------------------------- /pygmi/data/sources/core.py: -------------------------------------------------------------------------------- 1 | from typing import List, Any 2 | 3 | 4 | class DataSource: 5 | 6 | def __init__(self, indices: List[int] = None): 7 | """Initialize abstract data source. 8 | If no indices are selected, use all the available data. 9 | 10 | Parameters 11 | ---------- 12 | indices : List[int], optional 13 | indices of data objects to select, by default None 14 | """ 15 | self.indices = range(len(self.source)) if indices is None else indices 16 | 17 | def __getitem__(self, idx: int) -> Any: 18 | return self.process(self.source[self.indices[idx]]) 19 | 20 | def __len__(self) -> int: 21 | return len(self.indices) 22 | 23 | def process(self, obj: Any) -> Any: 24 | """Make raw object ready for preprocessing. 25 | For this abstract base class, simply return the object. 26 | 27 | Parameters 28 | ---------- 29 | obj : Any 30 | Raw object belonging to data source `self`. 31 | 32 | Returns 33 | ------- 34 | Any 35 | Returns `obj` 36 | """ 37 | return obj 38 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import os 2 | import warnings 3 | import subprocess 4 | from setuptools import setup 5 | 6 | 7 | NO_ADD_DEPS_KEY = 'PYGMI_NO_ADD_DEPS' 8 | DEPS = 'torch-scatter torch-sparse torch-geometric -f https://data.pyg.org/whl/torch-{}+{}.html"' 9 | 10 | 11 | if __name__ == "__main__": 12 | 13 | setup() 14 | 15 | no_additional = os.environ[NO_ADD_DEPS_KEY] if NO_ADD_DEPS_KEY in os.environ.keys() else None 16 | 17 | 18 | if not no_additional or no_additional is None: 19 | try: 20 | import torch 21 | try: 22 | import torch_geometric 23 | warnings.warn('PyG already found, skipping.') 24 | except ImportError: 25 | vrs = torch.__version__ 26 | cuda = 'cu' + torch.version.cuda.replace('.', '') if torch.cuda.is_available() else 'cpu' 27 | deps = DEPS.format(vrs, cuda) 28 | subprocess.call(['pip', 'install'] + deps.split(' ')) 29 | except: 30 | warnings.warn('PyTorch not available. PyTorch Geometric will not be installed.') 31 | 32 | else: 33 | warnings.warn('Additional dependencies install is disabled. Exiting.') 34 | 35 | # PyGMI 36 | -------------------------------------------------------------------------------- /pygmi/data/preprocess/core.py: -------------------------------------------------------------------------------- 1 | from typing import Callable, Dict, List 2 | from tqdm import tqdm 3 | from pygmi.data.sources.core import DataSource 4 | 5 | 6 | def gather_fnames(out_dir: str, source_name: str, n: int) -> List[str]: 7 | """Creates a list of filenames for storing processed data points. 8 | 9 | Parameters 10 | ---------- 11 | out_dir : str 12 | Root dir of preprocessing output 13 | source_name : str 14 | Name of data source being preprocessed 15 | n : int 16 | Size of data source being preprocessed 17 | 18 | Returns 19 | ------- 20 | List[str] 21 | List of filepaths to write preprocessed data 22 | """ 23 | return [out_dir + '/{}_{}.pth'.format(source_name, i) for i in range(n)] 24 | 25 | def process_source(data: DataSource, fnames: List[str], fn: Callable, fn_kwargs: Dict) -> None: 26 | """Runs a preprocessing function for each element in a data source. 27 | 28 | Parameters 29 | ---------- 30 | data : DataSource 31 | Collection of data points (inputs to fn) 32 | fnames : List[str] 33 | List of output locations on resident memory 34 | fn : Callable 35 | Preprocessing function 36 | fn_kwargs : Dict 37 | Additional arguments to preprocessing function 38 | """ 39 | for i in tqdm(range(len(fnames)), desc='Preprocessing data with {}'.format(fn.__name__)): 40 | fn(data[i], fnames[i], **fn_kwargs) -------------------------------------------------------------------------------- /pygmi/types/core.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | from typing import Any, Callable 3 | from torch import Tensor 4 | from pygmi.utils import cat_points_latent 5 | 6 | 7 | class ImplicitFunction(nn.Module): 8 | 9 | def __init__(self, approximator: Callable): 10 | """Base class for objects representing implicit functions 11 | 12 | Parameters 13 | ---------- 14 | approximator : Callable 15 | Callable objects computing the function (e.g. a neural net) 16 | """ 17 | super(ImplicitFunction, self).__init__() 18 | self.F = approximator 19 | 20 | def forward(self, coords: Tensor, condition: Tensor = None, *args, **kwargs) -> Any: 21 | """Computes the represented function on a set of points. args and kwargs are 22 | forwarded to `self.F`, while a condition vector can be paired to each given point. 23 | 24 | Parameters 25 | ---------- 26 | coords : Tensor 27 | A Tensor of point coordinates, shape `B_1 x ... x B_n x S x D` 28 | condition : Tensor, optional 29 | A condition vector to be paired to each sample of points, 30 | shape `B_1 x ... x B_n x N`, by default None 31 | 32 | Returns 33 | ------- 34 | Any 35 | Function computed over point set `coords` 36 | """ 37 | x = coords if condition is None else cat_points_latent(coords, condition) 38 | return self.F(x, *args, **kwargs) -------------------------------------------------------------------------------- /examples/surface_reconstruction.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import pytorch_lightning as pl 3 | from pytorch_lightning.loggers import WandbLogger 4 | from pygmi.data.dataset import SDFUnsupervisedData 5 | from pygmi.tasks import EikonalIVPOptimization 6 | from pygmi.types import SDF 7 | from pygmi.nn import SirenSDF 8 | from pygmi.utils.extract import grid_evaluation 9 | from pygmi.utils.visual import isosurf_animation 10 | 11 | 12 | """Learns a SDF from a single .txt point cloud, using a Siren 13 | network and unsupervised training. The result is plotted after 14 | optimization. 15 | """ 16 | 17 | if __name__ == "__main__": 18 | 19 | logging = False 20 | gpu = 1 if torch.cuda.is_available() else 0 21 | 22 | has_normal_data = True 23 | data = SDFUnsupervisedData( 24 | train_source_conf=[ 25 | dict( 26 | type='TXTArrayDataSource', 27 | source_conf=dict( 28 | source='/path/to/dir/containing/txt/file', 29 | idx_select=None 30 | ) 31 | ) 32 | ], 33 | preprocessing_conf=dict( 34 | do_preprocessing=True, 35 | out_dir='/path/to/data/output/', 36 | script='center_point_cloud', 37 | conf=dict(mnfld_sigma=True) 38 | ), 39 | batch_size=dict(train=1, val=1, test=1), 40 | use_normals=has_normal_data, 41 | surf_sample=30000, 42 | global_space_sample=3750 43 | ) 44 | 45 | net = SirenSDF() 46 | sdf = SDF(net) 47 | task = EikonalIVPOptimization(sdf, lr_sdf=1e-4, lr_sched_step=None, lr_sched_gamma=None) 48 | 49 | epochs = 5000 50 | if logging is True: 51 | logger = WandbLogger(project='PyGMI Task Logs') 52 | else: 53 | logger = False 54 | trainer = pl.Trainer(logger=logger, max_epochs=epochs, accelerator='gpu' if gpu == 1 else 'cpu', devices=gpu) 55 | trainer.fit(task, data) 56 | 57 | net = net.to('cuda' if gpu == 1 else 'cpu') 58 | volume = grid_evaluation(sdf, 3, 100, 1.2, 'cuda' if gpu == 1 else 'cpu') 59 | fig = isosurf_animation(volume, axes=[-1.2, 1.2] * 3, steps=10, min_level=-0.5, max_level=0.7) 60 | fig.show() -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | *.pyc 30 | 31 | # PyInstaller 32 | # Usually these files are written by a python script from a template 33 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 34 | *.manifest 35 | *.spec 36 | 37 | # Installer logs 38 | pip-log.txt 39 | pip-delete-this-directory.txt 40 | 41 | # Unit test / coverage reports 42 | htmlcov/ 43 | .tox/ 44 | .nox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *.cover 51 | *.py,cover 52 | .hypothesis/ 53 | .pytest_cache/ 54 | 55 | # Translations 56 | *.mo 57 | *.pot 58 | 59 | # Django stuff: 60 | *.log 61 | local_settings.py 62 | db.sqlite3 63 | db.sqlite3-journal 64 | 65 | # Flask stuff: 66 | instance/ 67 | .webassets-cache 68 | 69 | # Scrapy stuff: 70 | .scrapy 71 | 72 | # Sphinx documentation 73 | docs/_build/ 74 | 75 | # PyBuilder 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | .python-version 87 | 88 | # pipenv 89 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 90 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 91 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 92 | # install all needed dependencies. 93 | #Pipfile.lock 94 | 95 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 96 | __pypackages__/ 97 | 98 | # Celery stuff 99 | celerybeat-schedule 100 | celerybeat.pid 101 | 102 | # SageMath parsed files 103 | *.sage.py 104 | 105 | # Environments 106 | .env 107 | .venv 108 | env/ 109 | venv/ 110 | ENV/ 111 | env.bak/ 112 | venv.bak/ 113 | 114 | # Spyder project settings 115 | .spyderproject 116 | .spyproject 117 | 118 | # Rope project settings 119 | .ropeproject 120 | 121 | # mkdocs documentation 122 | /site 123 | 124 | # mypy 125 | .mypy_cache/ 126 | .dmypy.json 127 | dmypy.json 128 | 129 | # Pyre type checker 130 | .pyre/ 131 | 132 | 133 | tmp/ 134 | bin/ 135 | data/ -------------------------------------------------------------------------------- /examples/encode_faust_sdf.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import pytorch_lightning as pl 3 | from pytorch_lightning.loggers import WandbLogger 4 | from pygmi.data.dataset import SDFSupervisedData 5 | from pygmi.tasks import SupervisedDistanceRegression 6 | from pygmi.types import SDF 7 | from pygmi.nn import DeepReLUSDFNet 8 | from pygmi.utils.extract import grid_evaluation 9 | from pygmi.utils.visual import isosurf_animation 10 | 11 | 12 | """Learns a parametric SDF for the FAUST train + test dataset. 13 | Preprocesses the data from its PyG counterpart, then runs 14 | sign agnostic distance regression. Finally, the result for one 15 | of the training shapes is plotted. 16 | """ 17 | 18 | if __name__ == "__main__": 19 | 20 | logging = False 21 | num_shapes = 100 22 | latent_dim = 256 23 | gpu = 1 if torch.cuda.is_available() else 0 24 | 25 | data = SDFSupervisedData( 26 | train_source_conf=[ 27 | dict( 28 | type='PyGDataSource', 29 | source_conf=dict( 30 | source='FAUST', 31 | idx_select=None, 32 | root='/path/to/FAUST/dir', 33 | train=True 34 | ) 35 | ), 36 | dict( 37 | type='PyGDataSource', 38 | source_conf=dict( 39 | source='FAUST', 40 | idx_select=None, 41 | root='/path/to/FAUST/dir', 42 | train=False 43 | ) 44 | ) 45 | ], 46 | preprocessing_conf=dict( 47 | do_preprocessing=True, 48 | out_dir='/path/to/data/output/', 49 | script='get_distance_values', 50 | conf=dict(sample=500000) 51 | ), 52 | batch_size=dict(train=10, val=1, test=1), 53 | use_normals=False 54 | ) 55 | 56 | net = DeepReLUSDFNet(input_dim = 3 + latent_dim) 57 | sdf = SDF(net) 58 | task = SupervisedDistanceRegression(sdf, num_shapes=num_shapes, condition_size=latent_dim) 59 | 60 | epochs = 2000 61 | if logging is True: 62 | logger = WandbLogger(project='PyGMI Task Logs') 63 | else: 64 | logger = False 65 | trainer = pl.Trainer(logger=logger, max_epochs=epochs, accelerator='gpu' if gpu==1 else 'cpu', devices=gpu) 66 | trainer.fit(task, data) 67 | 68 | device = 'cuda' if gpu == 1 else 'cpu' 69 | shape_to_plot = 16 70 | latent = task.autodecoder(shape_to_plot).to(device) 71 | net = net.to(device) 72 | volume = grid_evaluation(sdf, 3, 100, 1.2, device, condition=latent) 73 | fig = isosurf_animation(volume, axes=[-1.2, 1.2] * 3, steps=10, min_level=-0.5, max_level=0.7) 74 | fig.show() -------------------------------------------------------------------------------- /pygmi/types/sdf.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pygmi.utils.math.diffops as diffops 3 | import pygmi.utils.extract as extract 4 | from typing import Callable, Literal, Tuple 5 | from torch import Tensor 6 | from pygmi.types.core import ImplicitFunction 7 | 8 | 9 | class SDF(ImplicitFunction): 10 | 11 | def __init__(self, approximator: Callable, dim: int = 3): 12 | """A utility class for objects representing signed distance fields. 13 | 14 | Parameters 15 | ---------- 16 | approximator : Callable 17 | Function computing the SDF of given points 18 | dim : int, optional 19 | Dimensionality of domain, by default 3 20 | """ 21 | super(SDF, self).__init__(approximator) 22 | self.dim = dim 23 | 24 | def normal(self, coords: Tensor, condition: Tensor = None) -> Tensor: 25 | """Computes SDF normals in query points `coords`, using the 26 | normalized gradient of the SDF 27 | 28 | Parameters 29 | ---------- 30 | coords : Tensor 31 | A Tensor of point coordinates, shape `B_1 x ... x B_n x S x D` 32 | condition : Tensor, optional 33 | A condition vector to be paired to each sample of points, 34 | shape `B_1 x ... x B_n x N`, by default None 35 | 36 | Returns 37 | ------- 38 | Tensor 39 | Shape `B_1 x ... x B_n x S x D`, normals of each point in `coords` 40 | """ 41 | x = coords.requires_grad_() 42 | d = self(x, condition=condition) 43 | n = diffops.gradient(x, d, dim=self.dim) 44 | return n / n.norm(dim=-1, keepdim=True) 45 | 46 | def to_mesh( 47 | self, 48 | condition: Tensor = None, 49 | res: int = 100, 50 | max_coord: float = 1.0, 51 | device: Literal['cpu', 'cuda'] = 'cpu' 52 | ) -> Tuple[np.ndarray, np.ndarray]: 53 | """Converts `self` to a mesh using Marching Cubes. 54 | 55 | Parameters 56 | ---------- 57 | condition : Tensor, optional 58 | Condition vector (for parametric SDFs), by default None 59 | res : int, optional 60 | Grid resolution, by default 100 61 | max_coord : float, optional 62 | Grid maximum absolute coordinate, by default 1.0 63 | device : Literal['cpu', 'cuda'], optional 64 | Device to run grid evaluation of SDF, by default 'cpu' 65 | 66 | Returns 67 | ------- 68 | Tuple[np.ndarray, np.ndarray] 69 | Vertices and faces of output mesh 70 | """ 71 | return extract.extract_level_set(self.forward, self.dim, res, bound=max_coord, device=device, condition=condition) 72 | 73 | -------------------------------------------------------------------------------- /pygmi/utils/misc.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import functools 3 | import time 4 | import numpy as np 5 | from torch import Tensor 6 | from typing import Callable 7 | 8 | 9 | 10 | 11 | def timer(func: Callable) -> Callable: 12 | """Decorator for timing functions. 13 | 14 | Parameters 15 | ---------- 16 | func : Callable 17 | Any function 18 | 19 | Returns 20 | ------- 21 | Callable 22 | Timing wrapper for `func` 23 | """ 24 | @functools.wraps(func) 25 | def wrapper_timer(*args, **kwargs): 26 | start_time = time.perf_counter() 27 | value = func(*args, **kwargs) 28 | end_time = time.perf_counter() 29 | run_time = end_time - start_time 30 | print(f"Timed {func.__name__!r}: {run_time:.4f}s") 31 | return value 32 | return wrapper_timer 33 | 34 | def sphere_sdf(x: Tensor, p: float = 2.0, r: float = 1.0) -> Tensor: 35 | """Signed distance function for a d-dimensional sphere. 36 | The sphere is assumed to be centered in the R^d origin. 37 | 38 | Parameters 39 | ---------- 40 | x : Tensor 41 | Points: shape `B_1 x ... x B_n x d` 42 | p : float, optional 43 | Specify which L_p norm to compute, by default 2.0 44 | r : float, optional 45 | Radius of the sphere, by default 1.0 46 | 47 | Returns 48 | ------- 49 | Tensor 50 | SDF values from the sphere for each point in `x`. 51 | """ 52 | return x.norm(dim=-1, p=p, keepdim=True) - r 53 | 54 | def make_grid(resolution: int, bound: float, dim: int = 3) -> Tensor: 55 | """Instantiate a regular voxel grid with given bounds and resolution. 56 | 57 | Parameters 58 | ---------- 59 | resolution : int 60 | Number of voxels in each dimension 61 | bound : float 62 | Maximum coordinate of voxel points 63 | dim : int, optional 64 | Number of dimensions, by default 3 65 | 66 | Returns 67 | ------- 68 | Tensor 69 | Shape `N x dim`, each point representing a voxel corner in the 70 | specified grid 71 | """ 72 | line = np.linspace(-bound, bound, resolution) 73 | grid = np.meshgrid(*([line] * dim)) 74 | return torch.tensor( 75 | np.vstack([l.ravel() for l in grid]).T, dtype=torch.float 76 | ) 77 | 78 | def cat_points_latent(points: Tensor, latent: Tensor) -> Tensor: 79 | """Utility function to concatenate latent vectors of continuous 80 | data to finite samples of continuous data. 81 | 82 | Parameters 83 | ---------- 84 | points : Tensor 85 | A coordinate Tensor, shape `B x S x D` 86 | latent : Tensor 87 | A Tensor of latent vectors, shape `B x N` 88 | 89 | Returns 90 | ------- 91 | Tensor 92 | Each point concatenated to the respective latent vector, shape `B x S x (D + N)` 93 | """ 94 | B, S = points.shape[0], points.shape[1] 95 | if len(latent.shape) == 1: 96 | latent = latent.unsqueeze(0) 97 | z_exp = torch.stack([latent[i, :].expand(S, -1) for i in range(B)]) 98 | x = torch.cat([points, z_exp], dim=-1).view(-1, points.shape[2] + latent.shape[1]) 99 | return x 100 | 101 | def label_to_interval(i: int, lo: float, hi: float, steps: int) -> float: 102 | """Maps `i` to the proper float value in the linear space with `steps` steps 103 | between `lo` and `hi`. 104 | 105 | Parameters 106 | ---------- 107 | i : int 108 | An integer value 109 | lo : float 110 | The minimum value of the linear space 111 | hi : float 112 | The maximum value of the linear space 113 | steps : int 114 | Number of steps between `lo` and `hi` 115 | 116 | Returns 117 | ------- 118 | float 119 | The `i`-th step in the linspace 120 | """ 121 | return lo + (((hi - lo) / (steps - 1)) * i) -------------------------------------------------------------------------------- /pygmi/data/sources/misc.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import List 3 | from torch_geometric.data import Data as PyGData 4 | from torch_geometric.io import read_txt_array, read_ply 5 | from torchvision.datasets import ImageFolder 6 | from pygmi.data.sources.core import DataSource 7 | 8 | 9 | class PLYDataSource(DataSource): 10 | 11 | def __init__(self, source: str, idx_select: List[int] = None): 12 | """Initializes a data source from .ply files in a folder. 13 | 14 | Parameters 15 | ---------- 16 | source : str 17 | path to directory containing the files 18 | idx_select : List[int], optional 19 | indices of data objects to select, by default None 20 | """ 21 | self.source = [source + '/' + fp for fp in os.listdir(source)] 22 | super(PLYDataSource, self).__init__(indices=idx_select) 23 | 24 | def process(self, obj: str) -> PyGData: 25 | """Override of `process` method. Calls `torch_geometric.io.read_ply` 26 | to load a filepath pointing to a .ply file into a `torch_geometric.data.Data` 27 | object. 28 | 29 | Parameters 30 | ---------- 31 | obj : str 32 | Path to a .ply file on disk. 33 | 34 | Returns 35 | ------- 36 | PyGData 37 | A `torch_geometric.data.Data` object, representing a mesh or a point cloud. 38 | Usual attributes are `pos`, `face`, and `normal`. 39 | """ 40 | return read_ply(obj) 41 | 42 | 43 | class TXTArrayDataSource(DataSource): 44 | 45 | def __init__(self, source: str, idx_select: List[int] = None): 46 | """Initializes a data source from .txt files in a folder, 47 | containing array data. Usually convenient for point clouds, 48 | following the format: 49 | x y z u v w 50 | where each row contains point coordinates and normals. 51 | 52 | Parameters 53 | ---------- 54 | source : str 55 | path to directory containing the files 56 | idx_select : List[int], optional 57 | indices of data objects to select, by default None 58 | """ 59 | self.source = [source + '/' + fp for fp in os.listdir(source)] 60 | super(TXTArrayDataSource, self).__init__(indices=idx_select) 61 | 62 | def process(self, obj: str) -> PyGData: 63 | """Overrides the `process` method, by reading a .txt array and 64 | returning it as a `torch_geometric.data.Data` object containing 65 | point coordinates and (optionally) normals of a point cloud. 66 | 67 | Parameters 68 | ---------- 69 | obj : str 70 | Path to a .txt file containing a point cloud saved as array, 71 | with format: 72 | x y z [u v w] 73 | Where (x, y, z) are the point coordinates and (u, v, w) are the 74 | (optional) surface normals of the corresponding points. 75 | 76 | Returns 77 | ------- 78 | PyGData 79 | A `torch_geometric.data.Data` with attributes `pos` and `normal`. 80 | """ 81 | t = read_txt_array(obj) 82 | pos = t[:, :3] 83 | if t.shape[1] > 3: 84 | normal = t[:, 3:] 85 | return PyGData(pos=pos, normal=normal) 86 | 87 | 88 | class PNGDataSource(DataSource): 89 | 90 | def __init__(self, source: str, idx_select: List[int] = None): 91 | """Initializes a data source from .png files in a folder. 92 | 93 | Parameters 94 | ---------- 95 | source : str 96 | path to directory containing the files 97 | idx_select : List[int], optional 98 | indices of data objects to select, by default None 99 | """ 100 | self.source = ImageFolder(source) 101 | super(PNGDataSource, self).__init__(indices=idx_select) 102 | 103 | -------------------------------------------------------------------------------- /pygmi/nn/deepsdf.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import numpy as np 4 | from typing import List 5 | from torch import Tensor 6 | 7 | 8 | 9 | class _MLP(nn.Module): 10 | 11 | def __init__( 12 | self, 13 | num_layers: int, 14 | input_dim: int, 15 | output_dim: int, 16 | hidden_dim: int, 17 | skip_in: List[int], 18 | geometric_init: bool, 19 | activation: nn.Module 20 | ): 21 | super(_MLP, self).__init__() 22 | self.actvn = activation 23 | hidden_sizes = [input_dim] + ([hidden_dim] * (num_layers - 1)) + [output_dim] 24 | self.num_layers = len(hidden_sizes) 25 | self.skip_conn = set(skip_in) 26 | 27 | self.linears = nn.ModuleList() 28 | for layer in range(1, self.num_layers): 29 | out_size = hidden_sizes[layer] 30 | if layer + 1 in self.skip_conn: 31 | out_size -= input_dim 32 | lin = nn.Linear(hidden_sizes[layer - 1], out_size) 33 | if geometric_init: 34 | if layer == self.num_layers - 1: 35 | torch.nn.init.normal_(lin.weight, mean=np.sqrt(np.pi) / np.sqrt(out_size), std=0.00001) 36 | torch.nn.init.constant_(lin.bias, -1.0) 37 | else: 38 | torch.nn.init.constant_(lin.bias, 0.0) 39 | torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_size)) 40 | self.linears.append(lin) 41 | 42 | def forward(self, x: Tensor) -> Tensor: 43 | h = x 44 | for idx, layer in enumerate(self.linears[:-1]): 45 | if idx + 1 in self.skip_conn: 46 | h = torch.cat([h, x], dim=-1) 47 | h = self.actvn(layer(h)) 48 | output = self.linears[-1](h) 49 | return output 50 | 51 | 52 | class SmoothDeepSDFNet(_MLP): 53 | 54 | def __init__( 55 | self, 56 | input_dim: int = 3, 57 | hidden_dim: int = 512, 58 | num_layers: int = 8, 59 | skip_conn: List[int] = [4], 60 | geom_init: bool = True 61 | ): 62 | """Softplus-activated MLP, as proposed in https://arxiv.org/abs/2002.10099. 63 | Default parameters and spherical weight initialization are as in original paper. 64 | 65 | Parameters 66 | ---------- 67 | input_dim : int, optional 68 | _description_, by default 3 69 | hidden_dim : int, optional 70 | _description_, by default 512 71 | num_layers : int, optional 72 | _description_, by default 8 73 | skip_conn : List[int], optional 74 | _description_, by default [4] 75 | """ 76 | super(SmoothDeepSDFNet, self).__init__( 77 | num_layers, input_dim, 1, hidden_dim, skip_conn, geom_init, nn.Softplus(beta=100) 78 | ) 79 | 80 | 81 | class DeepReLUSDFNet(_MLP): 82 | 83 | def __init__( 84 | self, 85 | input_dim: int = 3, 86 | hidden_dim: int = 512, 87 | num_layers: int = 8, 88 | skip_conn: List[int] = [4], 89 | geom_init: bool = True 90 | ): 91 | """ReLU-activated MLP, as proposed in https://arxiv.org/abs/1901.05103. 92 | Default parameters are as in original paper. Also features spherical 93 | weight initialization proposed in https://arxiv.org/abs/1911.10414. 94 | 95 | Parameters 96 | ---------- 97 | input_dim : int, optional 98 | _description_, by default 3 99 | hidden_dim : int, optional 100 | _description_, by default 512 101 | num_layers : int, optional 102 | _description_, by default 8 103 | skip_conn : List[int], optional 104 | _description_, by default [4] 105 | """ 106 | super(DeepReLUSDFNet, self).__init__( 107 | num_layers, input_dim, 1, hidden_dim, skip_conn, geom_init, nn.ReLU() 108 | ) -------------------------------------------------------------------------------- /pygmi/utils/extract/core.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | import pygmi.utils as utils 4 | from skimage import measure 5 | from typing import Tuple, Callable, Literal 6 | 7 | 8 | def extract_level_set( 9 | f: Callable, 10 | dim: int, 11 | res: int, 12 | bound: float = 1.0, 13 | device: Literal['cpu', 'cuda'] = 'cpu', 14 | level: float = 0.0, 15 | *f_args, **f_kwargs 16 | ) -> Tuple[np.ndarray, np.ndarray]: 17 | """Approximates a given level set of an implicit function with a mesh. 18 | The function is evaluated on a regular voxel grid and the output mesh 19 | is extracted using the Marching Cubes algorithm. 20 | 21 | Parameters 22 | ---------- 23 | f : Callable 24 | Callable representing the implicit function. Its first argument 25 | has to be a tensor of spatial coordinates of shape (B, S, dim) 26 | dim : int 27 | Dimensionality of query points for `f` 28 | res : int 29 | Grid resolution for mesh extraction 30 | bound : float, optional 31 | Maximum coordinate of voxel grid, by default 1.0 32 | device : Literal['cpu', 'cuda'], optional 33 | Device on which to run the computation, by default 'cpu' 34 | level : float, optional 35 | Which level set to extract, by default 0.0 36 | 37 | Returns 38 | ------- 39 | Tuple[np.ndarray, np.ndarray] 40 | Vertices and faces of extracted mesh. If there is no zero crossing, 41 | it returns a single triangle collapsing on the origin. 42 | """ 43 | volume = grid_evaluation(f, dim, res, bound, device, *f_args, **f_kwargs) 44 | verts, faces = marching_cubes(volume, (2 * bound) / (res - 1), level) 45 | if len(faces) > 1: 46 | verts -= bound 47 | return verts, faces 48 | 49 | def grid_evaluation( 50 | f: Callable, 51 | dim: int, 52 | res: int, 53 | bound: float, 54 | device: str, 55 | split: int = 100000, 56 | *f_args, **f_kwargs, 57 | ) -> np.ndarray: 58 | """Evaluates an implicit function on a regular voxel grid. Input space 59 | coordinates be given as a tensor with shape `(B, S, dim)` where 60 | `B` is the batch size and `S` is the sample size (number of points). 61 | args and kwargs are forwarded to f when it is invoked. 62 | 63 | Parameters 64 | ---------- 65 | f : Callable 66 | SDF function. If parametric (i.e. `latent is not None`), it expects 67 | two Tensors of shapes `B x S x dim` and `B x n`. Otherwise, it expecst 68 | one Tensor of shape `B x S x dim` 69 | dim : int 70 | Dimensionality of query points for `f` 71 | res : int 72 | Grid resolution for mesh extraction 73 | bound : float 74 | Maximum coordinate of voxel grid, by default 1.0 75 | device : str 76 | Device on which to run the computation, by default 'cpu' 77 | split : int, optional 78 | _description_, by default 20000 79 | 80 | Returns 81 | ------- 82 | np.ndarray 83 | Shape `res x res x res`, containing SDF values 84 | """ 85 | 86 | volume = [] 87 | with torch.no_grad(): 88 | G = utils.make_grid(res, bound, dim) 89 | split = torch.split(G, split, dim=0) 90 | for j in range(len(split)): 91 | pnts = split[j].to(device) 92 | volume.append(f(pnts.unsqueeze(0), *f_args, **f_kwargs).detach().cpu().numpy()) 93 | return np.concatenate(volume, axis=1).reshape(res, res, res).transpose([1, 0, 2]) 94 | 95 | def marching_cubes(volume: np.ndarray, voxel_size: float, level: float = 0.0) -> Tuple[np.ndarray, np.ndarray]: 96 | """Invokes Marching Cubes on a voxel grid containing a scalar function. 97 | Gracefully handles the case of functions with no level crossing. 98 | 99 | Parameters 100 | ---------- 101 | volume : np.ndarray 102 | Shape `N x N x N` 103 | voxel_size : float 104 | Size of a voxel in all dimensions 105 | level : float, optional 106 | Function level set to extract, by default 0.0 107 | 108 | Returns 109 | ------- 110 | Tuple[np.ndarray, np.ndarray] 111 | Vertices and faces of extracted mesh. If there is no zero crossing, 112 | it returns a single triangle collapsing on the origin. 113 | """ 114 | try: 115 | verts, faces, _, _ = measure.marching_cubes(volume, level=level, spacing=[voxel_size] * 3) # [(2 * bound) / (res - 1)] * 3 116 | except: 117 | verts = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) 118 | faces = np.array([[0, 1, 2]]) 119 | return verts, faces 120 | -------------------------------------------------------------------------------- /pygmi/tasks/distance_regression.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import torch 3 | from torch import Tensor 4 | from typing import List 5 | from pygmi.tasks import TaskBaseModule 6 | from pygmi.types import SDF 7 | from pygmi.utils.extract import extract_level_set 8 | from pygmi.utils.visual import plot_trisurf 9 | from pygmi.nn.encoder import Autodecoder 10 | 11 | 12 | 13 | class SupervisedDistanceRegression(TaskBaseModule): 14 | 15 | def __init__( 16 | self, 17 | sdf_functional: SDF, 18 | num_shapes: int = 1, 19 | condition_size: int = 256, 20 | sign_agnostic: bool = True, 21 | lr_sdf: float = 5e-4, 22 | lr_autodec: float = 1e-3, 23 | lr_sched_step: int = 500, 24 | lr_sched_gamma: float = 0.5, 25 | latent_loss_w: float = 1e-3, 26 | plot_resolution: int = 100, 27 | plot_max_coord: float = 1.0, 28 | ): 29 | """Instantiates a `SupervisedDistanceRegression` task. This tasks reconstructs 30 | SDFs from labeled point clouds by regression from a signal over points in space. 31 | 32 | Parameters 33 | ---------- 34 | sdf_functional : SDF 35 | Tensor functional representing a signed distance function 36 | num_shapes : int, optional 37 | Support for multi-shape optimization, by default 1 38 | condition_size : int, optional 39 | Dimension of latent vectors for multi-shape optimization, by default 256 40 | sign_agnostic : bool, optional 41 | Whether the training data is signed (False) or unsigned (True), by default True 42 | lr_sdf : float, optional 43 | Learning rate for SDF optimization, by default 5e-4 44 | lr_autodec : float, optional 45 | Learning rate for latent vectors optimization, by default 1e-3 46 | lr_sched_step : int, optional 47 | Step LR scheduler - size of steps, by default 500 48 | lr_sched_gamma : float, optional 49 | Step LR scheduler - decay factor, by default 0.5 50 | latent_loss_w : float, optional 51 | Weight of zero-mean constraint for latent vectors, by default 1e-3 52 | plot_resolution : int, optional 53 | Grid resolution of mesh extraction for plots, by default 100 54 | plot_max_coord : float, optional 55 | Maximum absolute coordinate of plot figures, by default 1.0 56 | """ 57 | super(SupervisedDistanceRegression, self).__init__(sdf_functional) 58 | self.dim = self.geometry.dim 59 | self.sal = sign_agnostic 60 | self.lr_sdf = lr_sdf 61 | self.scheduler_step = lr_sched_step 62 | self.gamma = lr_sched_gamma 63 | self.latent_loss_w = latent_loss_w 64 | self.resolution = plot_resolution 65 | self.max_coord = plot_max_coord 66 | if num_shapes > 1: 67 | self.autodecoder = Autodecoder(num_shapes, condition_size) 68 | self.is_conditioned = True 69 | self.lr_autodec = lr_autodec 70 | 71 | def configure_optimizers(self) -> List: 72 | opt_params = [{'params': self.geometry.parameters(), 'lr': self.lr_sdf}] 73 | if self.is_conditioned: 74 | opt_params.append({'params': self.autodecoder.parameters(), 'lr': self.lr_autodec}) 75 | optimizer = torch.optim.Adam(opt_params) 76 | scheduler = torch.optim.lr_scheduler.StepLR(optimizer, self.scheduler_step, self.gamma) 77 | return [optimizer], [scheduler] 78 | 79 | def training_step(self, batch, batch_idx) -> Tensor: 80 | indices, _, _, dist_sample = batch 81 | x_space, y_space = dist_sample[..., :self.dim], dist_sample[..., self.dim:] 82 | 83 | condition = None 84 | latent_loss = 0.0 85 | if self.is_conditioned: 86 | condition = self.autodecoder(indices) 87 | if self.latent_loss_w > 0: 88 | latent_loss = self.latent_loss_w * condition.norm(dim=-1).mean() 89 | 90 | sdf = self.geometry(x_space, condition) 91 | 92 | if self.sal: 93 | sdf_loss = (sdf.view_as(y_space).abs() - y_space).abs().mean() 94 | else: 95 | sdf_loss = (sdf.view_as(y_space) - y_space).abs().mean() 96 | 97 | loss = sdf_loss + latent_loss 98 | self.log("loss", loss) 99 | self.log("sdf_loss", sdf_loss) 100 | self.log("latent_loss", latent_loss) 101 | return loss 102 | 103 | def validation_step(self, batch, batch_idx) -> None: 104 | condition = None 105 | if self.is_conditioned: 106 | condition = self.autodecoder(torch.randint(0, self.autodecoder.N, ())) 107 | V, T = extract_level_set(self.geometry, self.dim, self.resolution, self.max_coord, self.device, condition=condition) 108 | fig = plot_trisurf(V, T) 109 | self.log("Reconstruction", wandb.Image(fig)) -------------------------------------------------------------------------------- /pygmi/utils/math/diffops.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import Tensor 3 | from torch.autograd import grad 4 | 5 | 6 | def gradient(inputs: Tensor, outputs: Tensor, dim: int = 3) -> Tensor: 7 | """Computes the gradient of `outputs` wrt `inputs`. `inputs` must require grad 8 | and there must be a path from `outputs` to `inputs` in the computational graph. 9 | 10 | Parameters 11 | ---------- 12 | inputs : Tensor 13 | List of input vectors. Shape `B_1 x ... x B_n x dim` 14 | outputs : Tensor 15 | List of output scalar values. Shape `B_1 x ... x B_n x 1` 16 | dim : int, optional 17 | Dimensionality of input vectors, by default 3 18 | 19 | Returns 20 | ------- 21 | Tensor 22 | Point-wise gradient 23 | """ 24 | d_points = torch.ones_like(outputs, requires_grad=False, device=outputs.device) 25 | points_grad = grad( 26 | outputs=outputs, 27 | inputs=inputs, 28 | grad_outputs=d_points, 29 | create_graph=True, 30 | retain_graph=True, 31 | only_inputs=True) 32 | return points_grad[0][..., :dim] 33 | 34 | def divergence(inputs: Tensor, outputs: Tensor, dim: int = 3) -> Tensor: 35 | """Computes the divergence of `outputs` wrt `inputs`. `inputs` must require grad 36 | and there must be a path from `outputs` to `inputs` in the computational graph. 37 | 38 | Parameters 39 | ---------- 40 | inputs : Tensor 41 | List of input vectors. Shape `B_1 x ... x B_n x dim` 42 | outputs : Tensor 43 | List of output vectors. Shape `B_1 x ... x B_n x dim` 44 | dim : int, optional 45 | Dimensionality of input and output vectors, by default 3 46 | 47 | Returns 48 | ------- 49 | Tensor 50 | Point-wise divergence 51 | """ 52 | 53 | gradients = torch.zeros_like(outputs, dtype=torch.float, device=outputs.device) 54 | d_points = torch.ones((*outputs.shape[:-1], 1), requires_grad=False, device=outputs.device) 55 | for d in range(dim): 56 | gradients[..., d] = grad( 57 | outputs=outputs[..., d:d+1], 58 | inputs=inputs, 59 | grad_outputs=d_points, 60 | retain_graph=True, 61 | create_graph=True, 62 | only_inputs=True)[0][..., d] 63 | return gradients.sum(dim=-1, keepdim=True) 64 | 65 | def jacobian(inputs: Tensor, outputs: Tensor) -> Tensor: 66 | """Computes the Jacobian of `outputs` wrt `inputs`. `inputs` must require grad 67 | and there must be a path from `outputs` to `inputs` in the computational graph. 68 | 69 | Parameters 70 | ---------- 71 | inputs : Tensor 72 | List of input vectors. Shape `B_1 x ... x B_n x dim` 73 | outputs : Tensor 74 | List of output vectors. Shape `B_1 x ... x B_n x dim` 75 | 76 | Returns 77 | ------- 78 | Tensor 79 | Point-wise Jacobian 80 | """ 81 | in_d = inputs.shape[-1] 82 | out_d = outputs.shape[-1] 83 | J = torch.zeros((*outputs.shape[:-1], in_d, out_d), dtype=torch.float, device=outputs.device) 84 | d_points = torch.ones((*outputs.shape[:-1], 1), requires_grad=False, device=outputs.device) 85 | for d in range(out_d): 86 | J[..., :, d] = grad( 87 | outputs=outputs[..., d:d+1], 88 | inputs=inputs, 89 | grad_outputs=d_points, 90 | retain_graph=True, 91 | create_graph=True, 92 | only_inputs=True)[0] 93 | return J 94 | 95 | def hessian(inputs: Tensor, outputs: Tensor, dim: int = 3, diff: bool = True) -> Tensor: 96 | """Computes the Hessian of `outputs` wrt `inputs` as the Jacobian of the gradient. 97 | `inputs` must require grad and there must be a path from `outputs` to `inputs` 98 | in the computational graph. 99 | 100 | Parameters 101 | ---------- 102 | inputs : Tensor 103 | List of input vectors. Shape `B_1 x ... x B_n x dim` 104 | outputs : Tensor 105 | List of output scalar values. Shape `B_1 x ... x B_n x 1` 106 | dim : int, optional 107 | Dimensionality of input points, by default 3 108 | diff: bool, optional 109 | Whether the return value should be differentiable, by default True 110 | 111 | Returns 112 | ------- 113 | Tensor 114 | Point-wise Hessian 115 | """ 116 | H = torch.zeros((*outputs.shape[:-1], dim, dim), dtype=torch.float, device=outputs.device) 117 | G = gradient(inputs, outputs, dim=dim).view_as(inputs) # .squeeze() 118 | d_points = torch.ones_like(outputs, requires_grad=False) 119 | for i in range(dim): # Gradient of gradient in each dimension 120 | H[..., i, :] = torch.autograd.grad( 121 | outputs=G[..., i:i+1], 122 | inputs=inputs, 123 | create_graph=diff, 124 | retain_graph=True, 125 | grad_outputs=d_points, 126 | only_inputs=True 127 | )[0] 128 | return H 129 | -------------------------------------------------------------------------------- /pygmi/nn/encoder.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch_geometric.nn as gnn 4 | from torch import Tensor 5 | from typing import List, Tuple 6 | 7 | 8 | 9 | class Autodecoder(nn.Module): 10 | 11 | def __init__(self, num_data_pts: int, latent_dim: int, sigma: float = 0.0): 12 | """Creates an Autodecoder, as proposed in https://arxiv.org/abs/1901.05103. 13 | 14 | Parameters 15 | ---------- 16 | num_data_pts : int 17 | Number of data points to represent (= # latent vectors) 18 | latent_dim : int 19 | Dimensionality of optimized latent vectors 20 | sigma : float 21 | stddev of initial distribution 22 | """ 23 | super(Autodecoder, self).__init__() 24 | self.num_vectors = num_data_pts 25 | self.latent_dim = latent_dim 26 | self.init_sigma = sigma 27 | self.vectors = nn.parameter.Parameter( 28 | torch.randn((num_data_pts, latent_dim)) * sigma) 29 | 30 | def forward(self, idx: Tensor) -> Tensor: 31 | return self.vectors[idx, :] 32 | 33 | 34 | ### PointNet++ ### 35 | 36 | class PointNet2Layer(nn.Module): 37 | 38 | def __init__(self, point_filter: nn.Module, radius: float, density: float, set_filter: nn.Module = None): 39 | """A PointNet++ layer, performing radius aggregation over a 40 | percentage of input points. 41 | 42 | Parameters 43 | ---------- 44 | point_filter : nn.Module 45 | Point-wise MLP processing point features and coordinates 46 | radius : float 47 | Maximum distance of neighbors of each pivot point 48 | density : float 49 | Fraction of pivot points over given input point samples 50 | set_filter : nn.Module, optional 51 | Point-wise MLP processing point features and coordinates after aggregation, by default None 52 | """ 53 | super(PointNet2Layer, self).__init__() 54 | 55 | self.rad = radius 56 | self.density = density 57 | self.conv = gnn.PointNetConv(point_filter, set_filter, False) 58 | self.do_set_filter = set_filter is not None 59 | 60 | def forward(self, pos: Tensor, batch: Tensor, x: Tensor = None) -> Tuple[Tensor]: 61 | """Performs PointNet++ convolution over input. 62 | 63 | Parameters 64 | ---------- 65 | pos : Tensor 66 | Point coordinates, shape `N x D` 67 | batch : Tensor 68 | Batch tensor, shape `N x 1`, batch size = `max(batch)` 69 | x : Tensor, optional 70 | Point features, shape `N x F` 71 | 72 | Returns 73 | ------- 74 | Tuple[Tensor] 75 | Pivot points coordinates, processed features, and batch tensor 76 | """ 77 | centr_idx = gnn.fps(pos, batch, ratio=self.density) 78 | centroids, centr_batch = pos[centr_idx], batch[centr_idx] 79 | row, col = gnn.radius(pos, centroids, self.rad, batch, centr_batch, max_num_neighbors=64) 80 | centr_x = None if x is None else x[centr_idx] 81 | out = self.conv((x, centr_x), (pos, centroids), torch.stack([col, row])) 82 | return centroids, out, centr_batch 83 | 84 | 85 | class PointNet2Encoder(nn.Module): 86 | 87 | def __init__( 88 | self, 89 | density: List[float], 90 | radius: List[float], 91 | pf_size: List[List[int]], 92 | sf_size: List[List[int]] = None 93 | ): 94 | """Creates a PointNet++ encoder, mapping point clouds to n-dimensional vectors. 95 | 96 | Parameters 97 | ---------- 98 | density : List[float] 99 | Fraction of pivot points over input sample for each layer 100 | radius : List[float] 101 | Maximum distance of neighbors of each pivot point for each layer 102 | pf_size : List[List[int]] 103 | Layer dimensions of MLP point filters for each layers 104 | sf_size : List[List[int]], optional 105 | Layer dimensions of MLP set filters for each layers, by default None 106 | """ 107 | super(PointNet2Encoder, self).__init__() 108 | 109 | num_layers = len(density) 110 | self.layers = nn.ModuleList() 111 | do_set_filtering = sf_size is not None 112 | for l in range(num_layers): 113 | pf = gnn.MLP(pf_size[l]) 114 | sf = gnn.MLP(sf_size[l]) if do_set_filtering else None 115 | layer = PointNet2Layer(pf, radius[l], density[l], sf) 116 | self.layers.append(layer) 117 | 118 | def forward(self, pos: Tensor, batch: Tensor, x: Tensor = None) -> Tensor: 119 | """Encodes a batch of point clouds. 120 | 121 | Parameters 122 | ---------- 123 | pos : Tensor 124 | Point coordinates, shape `N x D` 125 | batch : Tensor 126 | Batch tensor, shape `N x 1`, batch size = `max(batch)` 127 | x : Tensor, optional 128 | Point features, shape `N x F` 129 | 130 | Returns 131 | ------- 132 | Tensor 133 | Latent vectors for each input point cloud, shape `N x L` 134 | """ 135 | p, b, x = pos, batch, x 136 | for layer in self.layers: 137 | p, x, b = layer(p, b, x) 138 | z = gnn.global_max_pool(x, b) 139 | return z -------------------------------------------------------------------------------- /pygmi/nn/siren.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import numpy as np 4 | from torch import Tensor 5 | 6 | 7 | ### Adapted from official Siren implementation https://github.com/vsitzmann/siren ### 8 | 9 | def first_layer_sine_init(m: nn.Module) -> None: 10 | """Special first layer initialization for Sine-activate MLPs. 11 | 12 | Parameters 13 | ---------- 14 | m : nn.Module 15 | Linear layer to initialize 16 | """ 17 | with torch.no_grad(): 18 | if hasattr(m, 'weight'): 19 | num_input = m.weight.size(-1) 20 | nn.init.uniform_(m.weight, -1 / num_input, 1 / num_input) 21 | 22 | 23 | def sine_init(m: nn.Module, w0: float = None) -> None: 24 | """Special initialization for layers of Sine-activated MLPs. 25 | 26 | Parameters 27 | ---------- 28 | m : nn.Module 29 | Linear layer to initialize 30 | w0 : float, optional 31 | Phase factor which can be leveraged in initialization, by default None 32 | """ 33 | with torch.no_grad(): 34 | if hasattr(m, 'weight'): 35 | num_input = m.weight.size(-1) 36 | if w0 is None: 37 | nn.init.uniform_(m.weight, -np.sqrt(6 / num_input), np.sqrt(6 / num_input)) 38 | else: 39 | nn.init.uniform_(m.weight, -np.sqrt(6 / (num_input * (w0 ** 2))), np.sqrt(6 / (num_input * (w0 ** 2)))) 40 | 41 | 42 | class Sine(nn.Module): 43 | 44 | def __init__(self, w0: float): 45 | """Initializes a Sine activation function with phase factor `w0` 46 | 47 | Parameters 48 | ---------- 49 | w0 : float 50 | Phase factor 51 | """ 52 | super(Sine, self).__init__() 53 | self.w = w0 54 | 55 | def forward(self, input: Tensor) -> Tensor: 56 | """Runs Sine activation function over `input` 57 | 58 | Parameters 59 | ---------- 60 | input : Tensor 61 | A (partially processed) batch of data points 62 | 63 | Returns 64 | ------- 65 | Tensor 66 | Point-wise sine of the scalar product of `self.w` (phase factor) and `input` 67 | """ 68 | return torch.sin(self.w * input) 69 | 70 | 71 | class SirenMLP(nn.Module): 72 | 73 | def __init__( 74 | self, 75 | in_dim: int, 76 | out_dim: int, 77 | hidden_dim: int, 78 | hidden_layers: int, 79 | w0: float, 80 | init_w0: float, 81 | use_first_layer_init: bool, 82 | w0_in_layer_init: bool 83 | ): 84 | """Creates as fully-configurable Siren MLP as presented in https://arxiv.org/abs/2006.09661. 85 | 86 | 87 | Parameters 88 | ---------- 89 | in_dim : int 90 | Number of input features 91 | out_dim : int 92 | Number of output features 93 | hidden_dim : int 94 | Hidden dimension, same for all layers 95 | hidden_layers : int 96 | Number of hidden layers 97 | w0 : float 98 | Sine phase amplification factor (layers 2+) 99 | init_w0 : float 100 | Sine phase amplification factor (layer 1) 101 | use_first_layer_init : bool 102 | Use a special initialization for first layer weights 103 | w0_in_layer_init : bool 104 | Leverage w0 in layer initialization, as proposed in Siren supmat 105 | """ 106 | super(SirenMLP, self).__init__() 107 | 108 | self.init_w0, self.w0 = init_w0, w0 109 | self.init_actvn, self.actvn = Sine(self.init_w0), Sine(self.w0) 110 | 111 | self.net = nn.ModuleList() 112 | self.net.append(nn.Linear(in_dim, hidden_dim)) 113 | 114 | if use_first_layer_init: 115 | first_layer_sine_init(self.net[-1]) 116 | else: 117 | w = self.init_w0 if w0_in_layer_init else None 118 | sine_init(self.net[-1], w) 119 | 120 | w = self.w0 if w0_in_layer_init else None 121 | for i in range(hidden_layers): 122 | self.net.append(nn.Linear(hidden_dim, hidden_dim)) 123 | sine_init(self.net[-1], w) 124 | self.net.append(nn.Linear(hidden_dim, out_dim)) 125 | sine_init(self.net[-1], w) 126 | 127 | def forward(self, x_in: Tensor) -> Tensor: 128 | """Runs the model over a sample of points. 129 | 130 | Parameters 131 | ---------- 132 | x_in : Tensor 133 | Sample of points, shape `B_1 x ... x B_n x I` 134 | 135 | Returns 136 | ------- 137 | Tensor 138 | Output for each point, shape `B_1 x ... x B_n x O` 139 | """ 140 | h = self.init_actvn(self.net[0](x_in)) 141 | for layer in self.net[1:-1]: 142 | h = self.actvn(layer(h)) 143 | h = self.net[-1](h) 144 | return h 145 | 146 | 147 | class SirenSDF(SirenMLP): 148 | 149 | def __init__( 150 | self, 151 | in_dim: int = 3, 152 | hidden_dim: int = 256, 153 | hidden_layers: int = 5, 154 | use_first_layer_init: bool = False, 155 | w0_in_layer_init: bool = True 156 | ): 157 | """Preconfigured Siren MLP for SDF tasks. w0 is fixed to 30 as motivated 158 | in original paper. Parameter defaults are as in original implementation. 159 | 160 | Parameters 161 | ---------- 162 | in_dim : int, optional 163 | Number of input features, by default 3 164 | hidden_dim : int, optional 165 | Hidden dimension, same for all layers, by default 256 166 | hidden_layers : int, optional 167 | Number of hidden layers, by default 5 168 | use_first_layer_init : bool, optional 169 | Use a special initialization for first layer weights, by default False 170 | w0_in_layer_init : bool, optional 171 | Leverage w0 in layer initialization, by default True 172 | """ 173 | super(SirenSDF, self).__init__( 174 | in_dim, 1, hidden_dim, hidden_layers, 30.0, 30.0, use_first_layer_init, w0_in_layer_init 175 | ) -------------------------------------------------------------------------------- /pygmi/tasks/distance_eikonal_ivp.py: -------------------------------------------------------------------------------- 1 | import wandb 2 | import torch 3 | import torch.nn.functional as F 4 | import pygmi.utils.math.diffops as diffops 5 | from torch import Tensor 6 | from typing import List 7 | from pygmi.tasks import TaskBaseModule 8 | from pygmi.types import SDF 9 | from pygmi.utils.extract import extract_level_set 10 | from pygmi.utils.visual import plot_trisurf 11 | from pygmi.nn.encoder import Autodecoder 12 | 13 | 14 | 15 | class EikonalIVPOptimization(TaskBaseModule): 16 | 17 | def __init__( 18 | self, 19 | sdf_functional: SDF, 20 | num_shapes: int = 1, 21 | condition_size: int = 256, 22 | lr_sdf: float = 5e-4, 23 | lr_autodec: float = 1e-3, 24 | lr_sched_step: int = 2000, 25 | lr_sched_gamma: float = 0.5, 26 | surf_loss_w: float = 3e3, 27 | eikonal_loss_w: float = 5e1, 28 | norm_loss_w: float = 1e2, 29 | zero_penalty_w: float = 1e2, 30 | zero_penalty_a: float = -1e2, 31 | latent_loss_w: float = 1e-3, 32 | plot_resolution: int = 100, 33 | plot_max_coord: float = 1.0 34 | ): 35 | """Instantiates an `EikonalIVPOptimization` task. This task reconstructs geometry 36 | from a point cloud by requiring the SDF to vanish on zero-level set points and to 37 | have unitary norm of gradient (the SDF needs to support 2nd order derivatives). 38 | Optionally, normal constraint (gradient at surface points equals normals) and 39 | zero-value penalty (no small function values far away from surface) can be optimized for. 40 | 41 | Parameters 42 | ---------- 43 | sdf_functional : SDF 44 | Tensor functional representing a signed distance function 45 | num_shapes : int, optional 46 | Support for multi-shape optimization, by default 1 47 | condition_size : int, optional 48 | Dimension of latent vectors for multi-shape optimization, by default 256 49 | lr_sdf : float, optional 50 | Learning rate for SDF optimization, by default 5e-4 51 | lr_autodec : float, optional 52 | Learning rate for latent vectors optimization, by default 1e-3 53 | lr_sched_step : int, optional 54 | Step LR scheduler - size of steps, by default 2000 55 | lr_sched_gamma : float, optional 56 | Step LR scheduler - decay factor, by default 0.5 57 | surf_loss_w : float, optional 58 | Weight of zero level set loss, by default 1.0 59 | eikonal_loss_w : float, optional 60 | Weight of eikonal loss, by default 1e-2 61 | norm_loss_w : float, optional 62 | Weight of normal loss, by default 1.0 63 | zero_penalty_w : float, optional 64 | Weight of zero value penalty, by default 1e-1 65 | zero_penalty_a : float, optional 66 | Alpha of zero value penalty, by default 1e2 67 | latent_loss_w : float, optional 68 | Weight of zero-mean constraint for latent vectors, by default 1e-3 69 | plot_resolution : int, optional 70 | Grid resolution of mesh extraction for plots, by default 100 71 | plot_max_coord : float, optional 72 | Maximum absolute coordinate of plot figures, by default 1.0 73 | """ 74 | super(EikonalIVPOptimization, self).__init__(sdf_functional) 75 | self.dim = self.geometry.dim 76 | self.lr_sdf = lr_sdf 77 | self.scheduler_step = lr_sched_step 78 | self.gamma = lr_sched_gamma 79 | self.surf_loss_w = surf_loss_w 80 | self.eikonal_loss_w = eikonal_loss_w 81 | self.norm_loss_w = norm_loss_w 82 | self.zero_penalty_w = zero_penalty_w 83 | self.zero_penalty_a = zero_penalty_a 84 | self.latent_loss_w = latent_loss_w 85 | self.resolution = plot_resolution 86 | self.max_coord = plot_max_coord 87 | self.is_conditioned = False 88 | if num_shapes > 1: 89 | self.autodecoder = Autodecoder(num_shapes, condition_size) 90 | self.is_conditioned = True 91 | self.lr_autodec = lr_autodec 92 | 93 | def configure_optimizers(self) -> List: 94 | opt_params = [{'params': self.geometry.parameters(), 'lr': self.lr_sdf}] 95 | if self.is_conditioned: 96 | opt_params.append({'params': self.autodecoder.parameters(), 'lr': self.lr_autodec}) 97 | optimizer = torch.optim.Adam(opt_params) 98 | if self.scheduler_step is None or self.gamma is None: 99 | return optimizer 100 | scheduler = torch.optim.lr_scheduler.StepLR(optimizer, self.scheduler_step, self.gamma) 101 | return [optimizer], [scheduler] 102 | 103 | def training_step(self, batch, batch_idx) -> Tensor: 104 | indices, surf_sample, norm_sample, space_sample = batch 105 | 106 | condition = None 107 | latent_loss = 0.0 108 | if self.is_conditioned: 109 | condition = self.autodecoder(indices) 110 | if self.latent_loss_w > 0: 111 | latent_loss = self.latent_loss_w * condition.norm(dim=-1).mean() 112 | 113 | x_surf = surf_sample.requires_grad_() 114 | x_space = space_sample.requires_grad_() 115 | 116 | surf_dist = self.geometry(x_surf, condition).view(*(x_surf.shape[:-1]), 1) 117 | space_dist = self.geometry(x_space, condition) 118 | 119 | surf_loss = 0.0 120 | if self.surf_loss_w > 0: 121 | surf_loss = self.surf_loss_w * surf_dist.abs().mean() 122 | 123 | eikonal_loss = 0.0 124 | if self.eikonal_loss_w > 0: 125 | grad = diffops.gradient(x_space, space_dist, self.dim) 126 | eikonal_loss = self.eikonal_loss_w * ((grad.norm(dim=-1) - 1).abs()).mean() 127 | 128 | norm_loss = 0.0 129 | if self.norm_loss_w > 0: 130 | grad = diffops.gradient(x_surf, surf_dist, self.dim) 131 | norm_loss = self.norm_loss_w * (1 - F.cosine_similarity(grad, norm_sample, dim=-1)).mean() 132 | 133 | zero_penalty = 0.0 134 | if self.zero_penalty_w > 0: 135 | zero_penalty = self.zero_penalty_w * torch.exp(self.zero_penalty_a * torch.abs(space_dist)).mean() 136 | 137 | loss = surf_loss + eikonal_loss + norm_loss + zero_penalty + latent_loss 138 | self.log("loss", loss) 139 | self.log("surf_loss", surf_loss) 140 | self.log("eikonal_loss", eikonal_loss) 141 | self.log("norm_loss", norm_loss) 142 | self.log("zero_penalty", zero_penalty) 143 | self.log("latent_loss", latent_loss) 144 | return loss 145 | 146 | def validation_step(self, batch, batch_idx) -> None: 147 | condition = None 148 | if self.is_conditioned: 149 | condition = self.autodecoder(torch.randint(0, self.autodecoder.N, ())) 150 | V, T = extract_level_set(self.geometry, self.dim, self.resolution, self.max_coord, self.device, condition=condition) 151 | fig = plot_trisurf(V, T) 152 | self.log("Reconstruction", wandb.Image(fig)) -------------------------------------------------------------------------------- /pygmi/utils/visual/core.py: -------------------------------------------------------------------------------- 1 | import functools 2 | import plotly.graph_objects as go 3 | import pygmi.utils.extract 4 | from torch import Tensor 5 | from numpy import ndarray 6 | from plotly.subplots import make_subplots 7 | from typing import Callable, Dict, Tuple, Union 8 | from pygmi.utils import label_to_interval 9 | 10 | 11 | def validate_figure(func: Callable): 12 | """Decorator allowing to call plotting functions without 13 | passing a Figure - it is automatically created, passed to 14 | the plotting function and returned by the decorator. 15 | 16 | Parameters 17 | ---------- 18 | func : Callable 19 | A plotting function from ngt.utils.visual 20 | """ 21 | @functools.wraps(func) 22 | def wrapper(*args, **kwargs): 23 | newargs = args 24 | if 'fig' not in kwargs.keys(): 25 | ffig = None 26 | newargs = [] 27 | for arg in args: 28 | if type(arg) == go.Figure(): 29 | ffig = arg 30 | else: 31 | newargs.append(arg) 32 | kwargs['fig'] = go.Figure() if ffig is None else ffig 33 | func(*newargs, **kwargs) 34 | return kwargs['fig'] 35 | return wrapper 36 | 37 | def make_3d_subplots(rows: int = 1, cols: int = 1) -> go.Figure: 38 | """Creates a plotly Figure with 3D subplots. 39 | 40 | Parameters 41 | ---------- 42 | rows : int, optional 43 | Number of rows in subplot grid, by default 1 44 | cols : int, optional 45 | Number of columns in subplot grid, by default 1 46 | 47 | Returns 48 | ------- 49 | go.Figure 50 | Plotly Figure containing the subplots 51 | """ 52 | return make_subplots( 53 | rows, cols, specs=[[{'type': 'surface'}] * cols] * rows 54 | ) 55 | 56 | @validate_figure 57 | def plot_trisurf( 58 | vert: Union[Tensor, ndarray], 59 | triv: Union[Tensor, ndarray], 60 | fig: go.Figure = None 61 | ) -> go.Figure: 62 | """Plots a 3D mesh. 63 | 64 | Parameters 65 | ---------- 66 | vert : Union[Tensor, ndarray] 67 | Vertices of the mesh 68 | triv : Union[Tensor, ndarray] 69 | Triangles of the mesh 70 | fig : go.Figure, optional 71 | Figure to append plot to, by default None 72 | 73 | Returns 74 | ------- 75 | go.Figure 76 | Either `fig` or a new `go.Figure` containing the plot 77 | """ 78 | fig.add_trace( 79 | go.Mesh3d( 80 | x=vert[:, 0], y=vert[:, 1], z=vert[:, 2], 81 | i=triv[:, 0], j=triv[:, 1], k=triv[:, 2] 82 | ) 83 | ) 84 | 85 | @validate_figure 86 | def plot_isosurfaces( 87 | grid_coords: Union[Tensor, ndarray], 88 | F: Union[Tensor, ndarray], 89 | min_level: float = -0.5, 90 | max_level: float = 0.5, 91 | num_surfs: int = 3, 92 | fig: go.Figure = None 93 | ) -> go.Figure: 94 | """Plots multiple isosurfaces extracted from an implicit function. 95 | 96 | Parameters 97 | ---------- 98 | grid_coords : Union[Tensor, ndarray] 99 | Point coordinates of the grid 100 | F : Union[Tensor, ndarray] 101 | Implicit function evaluated on `grid_coords` 102 | min_level : float, optional 103 | Minimum function level, by default -0.5 104 | max_level : float, optional 105 | Maximum function level, by default 0.5 106 | num_surfs : int, optional 107 | Number of isosurfaces to extract, with linearly spaced levels 108 | between `min_level` and `max_level`, by default 3 109 | fig : go.Figure, optional 110 | Figure to append plot to, by default None 111 | 112 | Returns 113 | ------- 114 | go.Figure 115 | Either `fig` or a new `go.Figure` containing the plot 116 | """ 117 | fig.add_trace(go.Volume( 118 | x=grid_coords[:, 0], 119 | y=grid_coords[:, 1], 120 | z=grid_coords[:, 2], 121 | value=F, 122 | isomin=min_level, 123 | max_level=max_level, 124 | surface_count=num_surfs, 125 | opacity=0.1 126 | )) 127 | 128 | @validate_figure 129 | def isosurf_animation( 130 | F_volume: Union[Tensor, ndarray], 131 | min_level: float = -0.5, 132 | max_level: float = 0.5, 133 | axes: Tuple[float] = (-1.0, 1.0), 134 | steps: int = 3, 135 | fig: go.Figure = None 136 | ) -> go.Figure: 137 | """Plots an animated figure allowing to singularly inspect level surfaces 138 | of any given implicit function. 139 | 140 | Parameters 141 | ---------- 142 | F_volume : Union[Tensor, ndarray] 143 | `N x N x N` tensor containing function values 144 | min_level : float, optional 145 | Minimum surface level, by default -0.5 146 | max_level : float, optional 147 | Maximum surface level, by default 0.5 148 | steps : int, optional 149 | Number of linear steps between `min_level` and `max_level`, by default 3 150 | fig : go.Figure, optional 151 | Figure to append plot to, by default None 152 | 153 | Returns 154 | ------- 155 | go.Figure 156 | Either `fig` or a new `go.Figure` containing the plot 157 | """ 158 | frames = [] 159 | space_size = axes[1] - axes[0] 160 | voxel_size = (space_size) / (F_volume.shape[0] - 1) 161 | for s in range(steps): 162 | t = label_to_interval(s, min_level, max_level, steps) 163 | V, T = pygmi.utils.extract.marching_cubes(F_volume, voxel_size, t) 164 | V -= (space_size) / 2 165 | if s == 0: 166 | fig.add_trace(go.Mesh3d(x=V[:, 0], y=V[:, 1], z=V[:, 2], i=T[:, 0], j=T[:, 1], k=T[:, 2])) 167 | frames.append(go.Frame( 168 | data=go.Mesh3d(x=V[:, 0], y=V[:, 1], z=V[:, 2], i=T[:, 0], j=T[:, 1], k=T[:, 2]), 169 | name='{:1.3f}-lvl set'.format(label_to_interval(s, min_level, max_level, steps)), traces=[0])) 170 | fig.update(frames=frames) 171 | 172 | def frame_args(duration: float) -> Dict: 173 | return { 174 | "frame": {"duration": duration}, 175 | "mode": "immediate", 176 | "fromcurrent": True, 177 | "transition": {"duration": duration, "easing": "linear"}, 178 | } 179 | sliders = [ 180 | dict( 181 | pad={"b": 10, "t": 60}, 182 | len=0.9, 183 | x=0.1, 184 | y=0, 185 | steps=[ 186 | dict( 187 | method='animate', 188 | label='{:1.3f}-lvl set'.format(label_to_interval(k, min_level, max_level, steps)), 189 | args=[[f.name], frame_args(0)] 190 | ) for k, f in enumerate(fig.frames) 191 | ], 192 | ) 193 | ] 194 | fig.update_layout( 195 | title='Level sets animation', 196 | width=600, 197 | height=600, 198 | scene=dict( 199 | zaxis=dict(range=[axes[0], axes[1]], autorange=False), 200 | yaxis=dict(range=[axes[0], axes[1]], autorange=False), 201 | xaxis=dict(range=[axes[0], axes[1]], autorange=False) 202 | ), 203 | updatemenus = [ 204 | { 205 | "buttons": [ 206 | { 207 | "args": [None, frame_args(50)], 208 | "label": "▶", # play symbol 209 | "method": "animate", 210 | }, 211 | { 212 | "args": [[None], frame_args(0)], 213 | "label": "◼", # pause symbol 214 | "method": "animate", 215 | }, 216 | ], 217 | "direction": "left", 218 | "pad": {"r": 10, "t": 70}, 219 | "type": "buttons", 220 | "x": 0.1, 221 | "y": 0, 222 | } 223 | ], 224 | sliders=sliders 225 | ) 226 | -------------------------------------------------------------------------------- /pygmi/data/dataset/core.py: -------------------------------------------------------------------------------- 1 | import re 2 | import random 3 | import pytorch_lightning as pl 4 | import pygmi.data.sources 5 | import pygmi.data.preprocess 6 | from torch.utils.data import DataLoader 7 | from typing import Optional, List, Dict, Tuple, Any 8 | from pygmi.data.preprocess import gather_fnames, process_source 9 | from pygmi.utils.files import validate_fnames, mkdir_ifnotexists 10 | 11 | 12 | 13 | 14 | class _ListWithIndices(list): 15 | 16 | def __init__(self): 17 | """Initializes a subclass of list which returns objects and their indices. 18 | """ 19 | super(_ListWithIndices, self).__init__() 20 | 21 | def __getitem__(self, idx: int) -> Tuple[Any, int]: 22 | """Overrides the List __getitem__ to return the index along with the object. 23 | 24 | Parameters 25 | ---------- 26 | idx : int 27 | Index of objects to retrieve 28 | 29 | Returns 30 | ------- 31 | Tuple[Any, int] 32 | Retrieved object and index 33 | """ 34 | return super(_ListWithIndices, self).__getitem__(idx), idx 35 | 36 | 37 | 38 | class MultiSourceData(pl.LightningDataModule): 39 | 40 | def __init__( 41 | self, 42 | train_source_conf: List[Dict] = [], 43 | test_source_conf: List[Dict] = [], 44 | preprocessing_conf: Dict = {}, 45 | batch_size: Dict[str, int] = {'train': 1, 'val': 1, 'test': 1}, 46 | val_split: float = 0.0 47 | ): 48 | """Generic multi-source data module. Should be subclassed to define 49 | custom behaviour for collecting preprocessed data into batches. 50 | 51 | Parameters 52 | ---------- 53 | train_source_conf : List[Dict], optional 54 | List of configurations for multiple data sources. Each should specify a type 55 | (i.e. a subclass of ngt.data.sources.core.DataSource), and a configuration in 56 | dict format depending on the source type (see ngt.data.sources), by default [] 57 | test_source_conf : List[Dict], optional 58 | List of configurations for multiple data sources, by default [] 59 | preprocessing_conf : Dict, optional 60 | Configuration for preprocessing procedure for the selected data sources, by default {} 61 | batch_size : Dict[str, int], optional 62 | Batch size for train, test, val. Expects keys: {"train", "val", "test"}, 63 | by default {'train': 1, 'val': 1, 'test': 1} 64 | val_split : float, optional 65 | Fraction of training data serving for validation, by default 0.0 66 | """ 67 | super(MultiSourceData, self).__init__() 68 | self.train = train_source_conf 69 | self.test = test_source_conf 70 | self.preproc_conf = preprocessing_conf 71 | if 'script' in self.preproc_conf.keys(): 72 | self.preproc_fn = getattr(pygmi.data.preprocess, self.preproc_conf['script']) 73 | else: 74 | self.preproc_fn = None 75 | self.train_paths = _ListWithIndices() 76 | self.val_paths = _ListWithIndices() 77 | self.test_paths = _ListWithIndices() 78 | self.val_split = val_split 79 | self.batch_size = batch_size 80 | 81 | def setup(self, stage: Optional[str] = None) -> None: 82 | 83 | if stage == 'fit' or stage is None: 84 | for conf in self.train: 85 | source = getattr(pygmi.data.sources, conf['type'])(**conf['source_conf']) 86 | sname = '_'.join(re.split('/|\\\\', conf['source_conf']['source'])) 87 | fnames = sorted(gather_fnames(self.preproc_conf['out_dir'], sname, len(source))) 88 | mkdir_ifnotexists(self.preproc_conf['out_dir']) 89 | if self.preproc_conf['do_preprocessing']: 90 | process_source(source, fnames, self.preproc_fn, self.preproc_conf['conf']) 91 | else: 92 | if not validate_fnames(fnames) or len(fnames) == 0: 93 | raise RuntimeError('Processed files missing with preprocessing disabled.') 94 | self.train_paths += fnames 95 | 96 | n_val = int(len(self.train_paths) * self.val_split) 97 | val_idxs = random.sample(range(len(self.train_paths)), n_val) 98 | self.val_paths += [self.train_paths[i] for i in val_idxs] 99 | for i in val_idxs: 100 | self.train_paths.pop(i) 101 | 102 | if stage == 'test' or stage is None: 103 | for conf in self.test: 104 | source = getattr(pygmi.data.sources, conf['type'])(**conf['source_conf']) 105 | sname = '_'.join(re.split('/|\\', conf['source_conf']['source'].replace('/', '_'))) 106 | fnames = sorted(gather_fnames(self.preproc_conf['out_dir'], sname, len(source))) 107 | if self.preproc_conf['do_preprocessing']: 108 | process_source(source, self.preproc_fn, fnames, self.preproc_conf['conf']) 109 | else: 110 | if not validate_fnames(fnames): 111 | raise RuntimeError('Processed files missing with preprocessing disabled.') 112 | self.train_paths += fnames 113 | 114 | def collate(self, data: List[Any], idxs: List[int]) -> Any: 115 | """Users should override this method to define how to put 116 | several data points in batch form. 117 | 118 | Parameters 119 | ---------- 120 | data : List[Any] 121 | List of data points and their indices in the dataset 122 | idxs : List[int] 123 | Indices of `data` in the dataset 124 | 125 | Returns 126 | ------- 127 | Any 128 | Data points in batch form, ready for usage in train/val/test loops 129 | (will be returned when iterating on the DataLoader) 130 | 131 | Raises 132 | ------ 133 | NotImplementedError 134 | Has to be implemented in subclasses 135 | """ 136 | raise NotImplementedError() 137 | 138 | def load_data_point(self, path: str) -> Any: 139 | """Users should override this method to define how data points 140 | are loaded from disk into Python objects. 141 | 142 | Parameters 143 | ---------- 144 | path : str 145 | A path to a data point stored on disk. 146 | 147 | Returns 148 | ------- 149 | Any 150 | The Python object storing the loaded data point 151 | 152 | Raises 153 | ------ 154 | NotImplementedError 155 | Has to be implemented in subclasses 156 | """ 157 | raise NotImplementedError() 158 | 159 | def load_and_collate(self, paths: List[Tuple[str, int]]) -> Any: 160 | """Collate function for DataLoaders, useful for datasets stored on 161 | disk and accessed on demand. Loads each given path and returns the 162 | batch of data points. 163 | 164 | Parameters 165 | ---------- 166 | paths : List[Tuple[str, int]] 167 | A list of filepaths and the indices in the dataset of their corresponding 168 | data points 169 | 170 | Returns 171 | ------- 172 | Any 173 | Data points in batch form, ready for usage in train/val/test loops 174 | (will be returned when iterating on the DataLoader) 175 | 176 | """ 177 | loaded = [self.load_data_point(p[0]) for p in paths] 178 | collated = self.collate(loaded, [p[1] for p in paths]) 179 | return collated 180 | 181 | def train_dataloader(self) -> DataLoader: 182 | return DataLoader(self.train_paths, self.batch_size['train'], shuffle=True, collate_fn=self.load_and_collate) 183 | 184 | def val_dataloader(self) -> DataLoader: 185 | return DataLoader(self.val_paths, self.batch_size['val'], collate_fn=self.load_and_collate) 186 | 187 | def test_dataloader(self) -> DataLoader: 188 | return DataLoader(self.test_paths, self.batch_size['test'], collate_fn=self.load_and_collate) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PyGMI 2 | 3 | Following the recent trends in geometric deep learning, we release PyTorch Geometric Implicit (PyGMI): a toolbox to facilitate operating with neural implicit geometric representations. 4 | 5 | # Installation 6 | 7 | **We recommend to install in a Python environment with a working PyTorch (>= 1.8.0) installation**. If PyTorch is not found, the installer will automatically pick a minimal CPU installation. To install PyGMI, download this repository, cd to top level folder, and run: 8 | ``` 9 | pip install --extra-index-url=https://test.pypi.org/simple/ . 10 | ``` 11 | This will install PyGMI, along with several other dependencies, to the current shell's Python directory. We advise to do this in a virtual environment. 12 | 13 | ## Additional dependencies 14 | 15 | Our setup script will run additional silent `pip install`s for PyTorch Geometric and its dependencies, if it is not found in the installation environment. If you do not wish to run these code lines, set the environment variable `PYGMI_NO_ADD_DEPS` to `1`. Then, to install PyTorch Geometric, we refer to [the library's documentation](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html), but we advise to run the simple install procedure with conda: 16 | ``` 17 | conda install pyg -c pyg 18 | ``` 19 | which will auto-detect your torch and cuda versions. 20 | 21 | 22 | 23 | # Usage 24 | 25 | Besides a vast utility library spanning from volume rendering to batched differential operations, PyGMI features the following modules: 26 | 27 | * `pygmi.nn`: a torch-based collection of popularly used neural network models in the field 28 | * `pygmi.types`: an object-oriented interface to represent implicit functions 29 | * `pygmi.data`: a data utility interface which allows to load data from heterogenous sources and run preprocessing algorithms 30 | * `pygmi.tasks`: a collection of out-of-the-box PyTorch Lightning Modules allowing to quickly solve popular implicit geometry tasks 31 | 32 | We document each submodule individually. 33 | 34 | ## Neural Networks 35 | 36 | Just a collection of `torch.nn.Module`s. It features popular neural architectures used in neural implicit geometry. Most objects in this module can be initialized without constructor parameters: they will take as defaults the values shown in the original paper/implementation. SDF nets have fixed output dimension 1 and default input dimension 3. A comprehensive list: 37 | 38 | * `DeepReLUSDFNet`: An 8-layer MLP with 512 hidden units, ReLU activation, spherical weight initialization and skip connection at layer 4. 39 | * `SmoothDeepSDFNet`: An 8-layer MLP with 512 hidden units, SoftPlus activation, spherical weight initialization and skip connection at layer 4. 40 | * `SirenMLP`: Siren (sine-activated MLP) networks base class. __This is general purpose and has no defaults__. The `w_0` constant is set to 30 as motivated in original paper. 41 | * `SirenSDF`: A 5-layer MLP with 256 hidden units, Sine activation with phase `w_0=30`, and sine weight initialization. 42 | * `NeRFMLP` (and others): **To be released!** 43 | 44 | As an additional features, we include implementations of simple shape encoding architectures. 45 | 46 | * `Autodecoder`: a collection of trainable latent vectors, one for each data point. 47 | * `PointNet2Encoder`: a convenient implementation of PointNet++. Uses farthest point sampling for choosing pivots and radius clustering for convolution. 48 | 49 | 50 | ## Data Pipeline 51 | 52 | The data interface is completely optional to basic PyGMI usage, but it is applied in our pre-implemented Tasks. We mainly developed it for the common need of integrating data from multiple homogenous data sources (e.g. multiple 3D shape datasets). At the moment, our data pipeline only supports in-memory datasets (i.e. data which is preprocessed, stored on disk and loaded on-demand). 53 | 54 | While we suggest to follow the usage showed in `examples/`, a simple `pygmi.data` use case can be described as follows: 55 | 56 | 1. Select any number of data sources, each with its type (must be a class name in `pyg.data.sources`), configuration and index selection (to cherry-pick dataset elements) 57 | 2. Define a preprocessing function or select one from `pygmi.data.preprocess`; this function should take raw data as input and save processed data to disk * 58 | 3. Subclass `pygmi.data.dataset.MultiSourceData` to override: 59 | 1. The `collate` method, defining how a list of data points is aggregated to form a batch * 60 | 2. The `load_data_point` method, defining how a preprocessed data point is loaded from disk to main memory * 61 | 5. Instantiate your subclass by passing the data sources (see below) and the preprocessing information (see below) as `dict`s 62 | 63 | 64 | \* This behaviour may change in the future. 65 | 66 | 67 | Data source configuration example: 68 | ``` 69 | ### Create a dataset using both FAUST splits (train and test) as training dataset. ### 70 | 71 | train_source_conf=[ 72 | dict( 73 | type='PyGDataSource', 74 | source_conf=dict( 75 | source='FAUST', 76 | idx_select=None, 77 | root='/path/to/FAUST/dir', # kwargs for PyG FAUST constructor 78 | train=True # kwargs for PyG FAUST constructor 79 | ) 80 | ), 81 | dict( 82 | type='PyGDataSource', 83 | source_conf=dict( 84 | source='FAUST', 85 | idx_select=None, 86 | root='/path/to/FAUST/dir', # kwargs for PyG FAUST constructor 87 | train=False # kwargs for PyG FAUST constructor 88 | ) 89 | ) 90 | ] 91 | ``` 92 | 93 | Preprocessing configuration example: 94 | ``` 95 | ### Require computation of 500.000 ground truth distance values for each shape ### 96 | 97 | preprocessing_conf=dict( 98 | do_preprocessing=True, 99 | out_dir='path/to/data/output/', 100 | script='get_distance_values', 101 | conf=dict(sample=500000) 102 | ) 103 | ``` 104 | You should always supply at least `out_dir` and `do_preprocessing`; if preprocessing is not required, PyGMI will look for saved preprocessed files in `out_dir`. An error will be raised if preprocessing is disabled and there are no files to load. 105 | 106 | 107 | ## Tasks 108 | 109 | You may regard the `tasks` submodule as a collection of algorithms to solve popular implicit geometry tasks (e.g. surface reconstruction, view synthesis). 110 | As for `nn`, we tried to define as many default parameter values as possible, following the values found in original publications/code releases. 111 | 112 | In general, all tasks take as input a `pygmi.types.ImplicitFunction` object and run some optimization task on it. Once the process is completed, you may access 113 | `task.geometry` to recover the optimized implicit function along with its interface methods. 114 | 115 | Since tasks inherit from PyTorch Lightning Modules, you may execute them by creating a Trainer and a LightningDataModule (`pygmi.data.dataset` objects comply with this standard) 116 | and calling `trainer.fit(task, data)`. Take a look at `examples/` for a more concrete demonstration! 117 | 118 | 119 | ## Implicit Functions 120 | 121 | `pygmi.types` defines abstract object-oriented interfaces to work with `ImplicitFunction` subclasses. The real computation occurs in the `approximator` object which is required at creation. This could be a neural network or an analytic function, such as `pygmi.utils.sphere_sdf`. 122 | 123 | 124 | 125 | ## Utilities 126 | 127 | Our `utils` submodule is structured as follows: 128 | 129 | * `utils` 130 | * `files`: filesystem operations (useful for, e.g., preprocessing functions) 131 | * `misc`: various generic functions with no precise application 132 | * `extract`: extraction of explicit geometry from implicit representations 133 | * `core`: functions that can be applied to most implicit representations (generic level set operations) 134 | * `math`: predefined math operations 135 | * `diffops`: batched differential operations such as gradient, Jacobian, Hessian, etc. All are computed using `autograd`, optionally allowing higher-order differentiation 136 | * `visual`: visualization utilities 137 | * `core`: generic figure handling functions, common 3D plots use-cases (isosurfaces) 138 | 139 | All functions can be accessed directly from `pygmi.utils`, without further nesting. 140 | -------------------------------------------------------------------------------- /pygmi/data/preprocess/sdf.py: -------------------------------------------------------------------------------- 1 | import trimesh 2 | import torch 3 | import numpy as np 4 | from typing import Tuple 5 | from torch import Tensor 6 | from torch_geometric.data import Data as PyGData 7 | from scipy.spatial import cKDTree 8 | from CGAL.CGAL_Kernel import Triangle_3, Point_3 9 | from CGAL.CGAL_AABB_tree import AABB_tree_Triangle_3_soup 10 | 11 | 12 | 13 | def _upsample_and_normalize( 14 | S: PyGData, sample: int 15 | ) -> Tuple[trimesh.Trimesh, Tensor, Tensor, Tensor, np.ndarray, float]: 16 | """Upsamples, centers and normalizes mesh to unitary area. 17 | 18 | Parameters 19 | ---------- 20 | S : PyGData 21 | A mesh stored as a `torch_geometric.data.Data` object 22 | sample : int 23 | Number of points to sample from mesh surface 24 | 25 | Returns 26 | ------- 27 | Tuple[trimesh.Trimesh, Tensor, Tensor, Tensor, np.ndarray, float] 28 | Trimesh representation of given mesh, mesh vertices, upsampled 29 | point cloud, normals for each point in point cloud, mesh center point, 30 | mesh area. 31 | """ 32 | 33 | # Create trimesh # 34 | F = S.face 35 | V = S.pos 36 | mesh = trimesh.Trimesh(vertices=V, faces=F.T) 37 | 38 | # Sample points and normals # 39 | pnts, face_index = trimesh.sample.sample_surface(mesh, sample) 40 | center = np.mean(pnts, axis=0) 41 | pnts = pnts - np.expand_dims(center, axis=0) 42 | normals = torch.from_numpy(mesh.face_normals[face_index])[:, [0, 2, 1]].float() 43 | 44 | # Normalize registered surface points and upsample # 45 | V = V[:, [0, 2, 1]] - torch.from_numpy(center[[0, 2, 1]]).unsqueeze(0).float() 46 | area = np.sqrt(mesh.area) 47 | pnts /= area 48 | V /= area 49 | 50 | return mesh, V, torch.from_numpy(pnts)[:, [0, 2, 1]].float(), normals, center, area 51 | 52 | def _compute_sigmas(pnts: Tensor) -> np.ndarray: 53 | """Computes point-wise standard deviations for informed spatial sampling 54 | around a shape, given a Tensor of surface points. Applies the 50-th nearest 55 | neighbor heuristic. 56 | 57 | Parameters 58 | ---------- 59 | pnts : Tensor 60 | A surface sample of the shape of interest 61 | 62 | Returns 63 | ------- 64 | np.ndarray 65 | For each point in `pnts`, the distance from the 50-th nearest neighbor 66 | """ 67 | 68 | query = pnts.numpy() 69 | sigmas = [] 70 | ptree = cKDTree(query) 71 | for p in np.array_split(query,100,axis=0): 72 | d = ptree.query(p, 51) # sigma = dist from 50-th NN (heuristic) 73 | sigmas.append(d[0][:,-1]) 74 | 75 | return np.concatenate(sigmas) 76 | 77 | 78 | def get_distance_values(S: PyGData, out_path: str, sample: int, global_sigma: float = 0.2) -> None: 79 | """Preprocess a mesh for SDF tasks: get ground truth distance values 80 | without sign. Useful for learning SDFs using (e.g.) sign agnostic regression. 81 | `out_path` will contain a dict with keys {'surface', 'dists', 'vertices', 'faces', 'normals'}, 82 | respectively mapping to: a point cloud obtained by upsamping the mesh (`N x 3` Tensor), 83 | a cloud of random points labeled with distances from the surface (`M x 4` Tensor), vertices 84 | and faces of the original mesh, surface normals for points in 'surface' (`N x 3` Tensor). 85 | 86 | Parameters 87 | ---------- 88 | S : PyGData 89 | A torch_geometric.data.Data object, representing a mesh 90 | sample : int 91 | Surface sample size and half distance sample size 92 | out_path : str 93 | Memory location to save preprocessed data 94 | global_sigma : float, optional 95 | Standard deviation for sampling points around shape, by default 0.2 96 | """ 97 | 98 | mesh, V, pnts, normals, center, area = _upsample_and_normalize(S, sample) 99 | 100 | # Instantiate CGAL AABB tree # 101 | triangles = [] 102 | for tri in mesh.triangles: 103 | T = (tri - center) / area 104 | a = Point_3(T[0][0], T[0][1], T[0][2]) # (tri[0][0] - center[0]), (tri[0][1] - center[1]), (tri[0][2] - center[2])) 105 | b = Point_3(T[1][0], T[1][1], T[1][2]) # (tri[1][0] - center[0]), (tri[1][1] - center[1]), (tri[1][2] - center[2])) 106 | c = Point_3(T[2][0], T[2][1], T[2][2]) # (tri[2][0] - center[0]), (tri[2][1] - center[1]), (tri[2][2] - center[2])) 107 | triangles.append(Triangle_3(a, b, c)) 108 | tree = AABB_tree_Triangle_3_soup(triangles) 109 | 110 | # Sample points with 50-th NN heuristic # 111 | sigmas = _compute_sigmas(pnts) 112 | sigmas_big = global_sigma * np.ones_like(sigmas) 113 | 114 | sample = np.concatenate([ 115 | pnts + np.expand_dims(sigmas,-1) * np.random.normal(0.0, 1.0, size=pnts.shape), 116 | pnts + np.expand_dims(sigmas_big,-1) * np.random.normal(0.0, 1.0, size=pnts.shape)], axis=0) 117 | 118 | # Compute distances # 119 | dists = [] 120 | for np_query in sample: 121 | cgal_query = Point_3(np_query[0].astype(np.double), np_query[1].astype(np.double), np_query[2].astype(np.double)) 122 | 123 | cp = tree.closest_point(cgal_query) 124 | cp = np.array([cp.x(), cp.y(), cp.z()]) 125 | dist = np.sqrt(((cp - np_query)**2).sum(axis=0)) 126 | 127 | dists.append(dist) 128 | dists = np.array(dists).reshape(-1, 1) 129 | 130 | sample_dists = torch.from_numpy(np.concatenate([sample, dists], axis=-1))[:, [0, 2, 1, 3]].float() 131 | 132 | # Save everything to pth # 133 | torch.save( 134 | { 135 | 'surface': pnts, 136 | 'dists': sample_dists, 137 | 'vertices': V, 138 | 'faces': S.face, 139 | 'normals': normals 140 | }, 141 | out_path 142 | ) 143 | 144 | def upsample_with_normals( 145 | S: PyGData, 146 | out_path: str, 147 | sample: int, 148 | mnfld_sigma: bool = False 149 | ) -> None: 150 | """Preprocess a mesh for SDF tasks: upsample mesh vertices and normals, optionally 151 | compute spatial sampling sigmas. `out_path` will contain a dict with keys {'surface', 152 | 'vertices', 'faces', 'normals', 'mnfld_sigma'}, respectively mapping to: a point cloud 153 | obtained by upsamping the mesh (`N x 3` Tensor), vertices and faces of the original mesh, 154 | surface normals for points in 'surface' (`N x 3` Tensor), and (optionally) point-wise 155 | standard deviations for informed spatial sampling around the shape, for points in 156 | 'surface' (`N x 1` Tensor). 157 | 158 | Parameters 159 | ---------- 160 | S : PyGData 161 | A torch_geometric.data.Data object, representing a mesh 162 | sample : int 163 | Surface (and normals) sample size 164 | out_path : str 165 | Memory location to save preprocessed data 166 | mnfld_sigma: bool, optional 167 | Specifies whether to compute space sampling std for each point, by default False 168 | """ 169 | 170 | _, V, pnts, normals, _, _ = _upsample_and_normalize(S, sample) 171 | 172 | sigmas = None 173 | if mnfld_sigma: 174 | sigmas = torch.from_numpy(_compute_sigmas(pnts)).float().unsqueeze(-1) 175 | 176 | # Save everything to pth # 177 | torch.save( 178 | { 179 | 'surface': pnts, 180 | 'vertices': V, 181 | 'faces': S.face, 182 | 'normals': normals, 183 | 'mnfld_sigma': sigmas 184 | }, 185 | out_path 186 | ) 187 | 188 | def center_point_cloud(S: PyGData, out_path: str, mnfld_sigma: bool = False) -> None: 189 | """Save a point cloud to disk, after centering in the origin. `out_path` will 190 | contain a dict with keys {'surface', 'normals'}, respectively mapping to: the point cloud 191 | (`N x 3` Tensor), (optionally) surface normals for points in 'surface' (`N x 3` Tensor), 192 | and (optionally) point-wise standard deviations for informed spatial sampling around the 193 | shape, for points in 'surface' (`N x 1` Tensor). 194 | 195 | Parameters 196 | ---------- 197 | S : PyGData 198 | A torch_geometric.data.Data object, representing a point cloud (all 199 | attributes are ignored except for S.pos). If it contains normals, they 200 | are expected to be stored in `S.normal`. 201 | mnfld_sigma: bool, optional 202 | Specifies whether to compute space sampling std for each point, by default False 203 | """ 204 | 205 | # Center in origin 206 | V = S.pos - S.pos.mean(dim=0, keepdim=True) 207 | 208 | # Compute sigmas 209 | sigmas = None 210 | if mnfld_sigma: 211 | sigmas = torch.from_numpy(_compute_sigmas(V)).float().unsqueeze(-1) 212 | 213 | # Save everything to pth # 214 | torch.save( 215 | { 216 | 'surface': V, 217 | 'normals': getattr(S, 'normal', None), 218 | 'mnfld_sigma': sigmas 219 | }, 220 | out_path 221 | ) -------------------------------------------------------------------------------- /pygmi/data/dataset/sdf.py: -------------------------------------------------------------------------------- 1 | import random 2 | import torch 3 | from torch import Tensor 4 | from typing import List, Dict, Tuple, Union 5 | from pygmi.data.dataset import MultiSourceData 6 | 7 | 8 | 9 | class SDFUnsupervisedData(MultiSourceData): 10 | 11 | def __init__( 12 | self, 13 | train_source_conf: List[Dict] = [], 14 | test_source_conf: List[Dict] = [], 15 | preprocessing_conf: Dict = {}, 16 | val_split: float = 0.0, 17 | batch_size: Dict[str, int] = {'train': 1, 'val': 1, 'test': 1}, 18 | surf_sample: int = 16384, 19 | global_space_sample: int = 2048, 20 | global_sigma: float = 1.8, 21 | local_sigma: float = None, 22 | use_normals: bool = True 23 | ): 24 | """3D data for unsupervised (i.e. without ground truth distances) 25 | SDF tasks. Uses supersampled meshes with normals, and by default 26 | uses point-wise standard deviations for spatial sampling. 27 | 28 | Parameters 29 | ---------- 30 | train_source_conf : List[Dict], optional 31 | List of configurations for multiple data sources. Each should specify a type 32 | (i.e. a subclass of ngt.data.sources.core.DataSource), and a configuration in 33 | dict format depending on the source type (see ngt.data.sources), by default [] 34 | test_source_conf : List[Dict], optional 35 | List of configurations for multiple data sources, by default [] 36 | preprocessing_conf : Dict, optional 37 | Configuration for preprocessing procedure for the selected data sources, by default {} 38 | val_split : float, optional 39 | Fraction of training data serving for validation, by default 0.0 40 | batch_size : _type_, optional 41 | Batch size for train, test, val. Expects keys: {"train", "val", "test"}, 42 | by default {'train': 1, 'val': 1, 'test': 1} 43 | surf_sample : int, optional 44 | Size of point sample representing a shape's surface, by default 16384 45 | global_space_sample : int, optional 46 | Size of global point samples in spatial sampling. Usually set equal to 47 | `surf_sample // 8`. The final size of spatial samples is `surf_sample + global_space_sample`, 48 | by default 2048 49 | global_sigma : float, optional 50 | Maximum coordinate of space for global spatial point sampling, by default 1.8 51 | local_sigma : float, optional 52 | std. dev. for local spatial point sampling; if None, preprocessed shapes 53 | are expected to have key "mnfld_sigma", by default None 54 | use_normals : bool, optional 55 | Whether to sample normals together with surface points, by default True 56 | """ 57 | super(SDFUnsupervisedData, self).__init__( 58 | train_source_conf, test_source_conf, preprocessing_conf, batch_size, val_split) 59 | self.surf_sample = surf_sample 60 | self.space_sample = global_space_sample 61 | self.global_sigma = global_sigma 62 | self.use_normals = use_normals 63 | if local_sigma is None: 64 | self.fixed_local_sigma = False 65 | else: 66 | self.fixed_local_sigma = True 67 | self.local_sigma = local_sigma 68 | 69 | def sample_shape_space(self, point_cloud: Tensor, local_sigma: Union[Tensor, float]) -> Tensor: 70 | """Samples points from embedding space, concatenating a small uniformly sampled 71 | (global) sample with a large Gaussian local sample, computed either with point-wise 72 | standard deviations (if `type(local_sigma) == Tensor`) or fixed standard deviation 73 | (if `type(local_sigma) == float`) 74 | 75 | Parameters 76 | ---------- 77 | point_cloud : Tensor 78 | Surface samples of shapes for which to perform spatial sampling. Shape: `B x S x 3` 79 | local_sigma : Union[Tensor, float] 80 | Standard deviation for local sampling. Either fixed (if type is float) or point-wise 81 | (if type is Tensor) 82 | 83 | Returns 84 | ------- 85 | Tensor 86 | A random sample of points around each given point cloud 87 | """ 88 | sample_local = point_cloud + (torch.randn_like(point_cloud) * local_sigma) 89 | sample_global = ( 90 | 2 * self.global_sigma * torch.rand( 91 | point_cloud.shape[0], self.space_sample, point_cloud.shape[2] 92 | )) - self.global_sigma 93 | return torch.cat([sample_local, sample_global], dim=1) 94 | 95 | def sample_surface(self, shape: Dict[str, Tensor]) -> Tuple[Tensor, Tensor, Tensor]: 96 | """Samples a surface, optionally with normals and point-wise 97 | local sampling standard deviations. 98 | 99 | Parameters 100 | ---------- 101 | shape : Dict[str, Tensor] 102 | Preprocessed shape data. Expects keys {'surface', 'normals'} 103 | and optionally 'mnfld_sigma' 104 | 105 | Returns 106 | ------- 107 | Tuple[Tensor, Tensor, Tensor] 108 | Surface sample, normals sample, sigmas sample. Normals and sigmas can be 109 | None if they are not required by configuration 110 | """ 111 | surf = shape['surface'] 112 | indices = random.sample(range(surf.shape[0]), self.surf_sample) 113 | surf_sample = surf[indices, :] 114 | norm_sample = shape['normals'][indices, :] if self.use_normals else None 115 | sigmas_sample = shape['mnfld_sigma'][indices, :] if not self.fixed_local_sigma else None 116 | return surf_sample, norm_sample, sigmas_sample 117 | 118 | def collate(self, data: List[Dict], idxs: List[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor]: 119 | """Implementation of collate method. Loads a list of dictionaries with keys 120 | {'surface', 'normals', 'mnfld_sigma'} to a tuple of 4 Tensors (some of which may be 121 | None, depending on configuration). 122 | 123 | Parameters 124 | ---------- 125 | data : List[Dict] 126 | A list of dictionaries with keys {'surface', 'normals', 'mnfld_sigma'} and Tensor values 127 | idxs : List[int] 128 | Indices of `data` in the dataset 129 | 130 | Returns 131 | ------- 132 | Tuple[Tensor, Tensor, Tensor, Tensor] 133 | `B x 1` LongTensor of indices of each shape in the batch, 134 | `B x S x 3` FloatTensor of surface samples for each shape in the batch, 135 | `B x S x 3` FloatTensor of normals for each sampled surface point (may be None), 136 | `B x T x 3` FloatTensor of space point samples for each shape in the batch 137 | """ 138 | shape_ids = torch.tensor(idxs, dtype=torch.long) 139 | samples = [self.sample_surface(x) for x in data] 140 | surf_sample = torch.stack([x[0] for x in samples]) 141 | norm_sample = torch.stack([x[1] for x in samples]) if self.use_normals else None 142 | sigma = self.local_sigma if self.fixed_local_sigma else torch.stack([x[2] for x in samples]) 143 | space_sample = self.sample_shape_space(surf_sample, sigma) 144 | return shape_ids, surf_sample, norm_sample, space_sample 145 | 146 | def load_data_point(self, path: str) -> Dict[str, Tensor]: 147 | """Implementation of load_data_point method. Loads a dictionary from `path` using 148 | `torch.load`. 149 | 150 | Parameters 151 | ---------- 152 | path : str 153 | Path to data point stored on disk 154 | 155 | Returns 156 | ------- 157 | Dict[str, Tensor] 158 | Loaded data point, must have keys {'surface', 'normals'} and optionally 'mnfld_sigma' 159 | """ 160 | return torch.load(path) 161 | 162 | 163 | 164 | class SDFSupervisedData(MultiSourceData): 165 | 166 | def __init__( 167 | self, 168 | train_source_conf: List[Dict] = [], 169 | test_source_conf: List[Dict] = [], 170 | preprocessing_conf: Dict = {}, 171 | val_split: float = 0.0, 172 | batch_size: Dict[str, int] = {'train': 1, 'val': 1, 'test': 1}, 173 | surf_sample: int = 16384, 174 | space_sample: int = 16384, 175 | use_normals: bool = True 176 | ): 177 | """3D data for supervised (i.e. with ground truth distances) SDF tasks. 178 | 179 | Parameters 180 | ---------- 181 | train_source_conf : List[Dict], optional 182 | List of configurations for multiple data sources. Each should specify a type 183 | (i.e. a subclass of ngt.data.sources.core.DataSource), and a configuration in 184 | dict format depending on the source type (see ngt.data.sources), by default [] 185 | test_source_conf : List[Dict], optional 186 | List of configurations for multiple data sources, by default [] 187 | preprocessing_conf : Dict, optional 188 | Configuration for preprocessing procedure for the selected data sources, by default {} 189 | val_split : float, optional 190 | Fraction of training data serving for validation, by default 0.0 191 | batch_size : Dict[str, int], optional 192 | Batch size for train, test, val. Expects keys: {"train", "val", "test"}, 193 | by default {'train': 1, 'val': 1, 'test': 1} 194 | surf_sample : int, optional 195 | Size of point sample representing a shape's surface, by default 16384 196 | space_sample : int, optional 197 | Size of point samples for a shape's embedding space, with distances, by default 16384 198 | use_normals : bool, optional 199 | Whether to sample normals together with surface points, by default True 200 | """ 201 | super(SDFSupervisedData, self).__init__( 202 | train_source_conf, test_source_conf, preprocessing_conf, batch_size, val_split) 203 | self.surf_sample = surf_sample 204 | self.space_sample = space_sample 205 | self.use_normals = use_normals 206 | 207 | def sample_distances(self, shape: Dict[str, Tensor]) -> Tensor: 208 | """Samples indices from a Tensor containing coordinates and 209 | distance values, expected to be the value of `shape['dists']` 210 | 211 | Parameters 212 | ---------- 213 | shape : Dict[str, Tensor] 214 | A dict with key 'dists' mapping to a `N x 4` Tensor 215 | 216 | Returns 217 | ------- 218 | Tensor 219 | A sample of points and distances, with shape `self.space_sample x 4` 220 | """ 221 | dist = shape['dists'] 222 | indices = random.sample(range(dist.shape[0]), self.space_sample) 223 | return dist[indices, :] 224 | 225 | def sample_surface(self, shape: Dict[str, Tensor]) -> Tuple[Tensor, Tensor]: 226 | """Samples indices from a Tensor containing surface points of a shape, 227 | expected to be the value of `shape['surface']`. If required by configuration, 228 | normals are sampled as well (from `shape['normals']`) 229 | 230 | Parameters 231 | ---------- 232 | shape : Dict[str, Tensor] 233 | A dict with keys 'surface' mapping to a `N x 3` Tensor and 'normals' mapping 234 | to a `N x 3` Tensor (optional) 235 | 236 | Returns 237 | ------- 238 | Tuple[Tensor, Tensor] 239 | Surface sample, normals sample. Normals can be 240 | None if they are not required by configuration 241 | """ 242 | surf = shape['surface'] 243 | indices = random.sample(range(surf.shape[0]), self.surf_sample) 244 | surf_sample = surf[indices, :] 245 | norm_sample = shape['normals'][indices, :] if self.use_normals else None 246 | return surf_sample, norm_sample 247 | 248 | def collate(self, data: List[Dict], idxs: List[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor]: 249 | """Implementation of collate method. Loads a list of dictionaries with keys 250 | {'surface', 'normals', 'dists'} to a tuple of 4 Tensors (some of which may be 251 | None, depending on configuration). 252 | 253 | Parameters 254 | ---------- 255 | data : List[Dict] 256 | A list of dictionaries with keys {'surface', 'normals', 'dists'} and Tensor values 257 | idxs : List[int] 258 | Indices of `data` in the dataset 259 | 260 | Returns 261 | ------- 262 | Tuple[Tensor, Tensor, Tensor, Tensor] 263 | `B x 1` LongTensor of indices of each shape in the batch, 264 | `B x S x 3` FloatTensor of surface samples for each shape in the batch, 265 | `B x S x 3` FloatTensor of normals for each sampled surface point (may be None), 266 | `B x T x 3` FloatTensor of space point samples for each shape in the batch 267 | """ 268 | shape_ids = torch.tensor(idxs, dtype=torch.long) 269 | samples = [self.sample_surface(x) for x in data] 270 | surf_sample = torch.stack([x[0] for x in samples]) 271 | norm_sample = torch.stack([x[1] for x in samples]) if self.use_normals else None 272 | dist_sample = torch.stack([self.sample_distances(x) for x in data]) 273 | return shape_ids, surf_sample, norm_sample, dist_sample 274 | 275 | def load_data_point(self, path: str) -> Dict[str, Tensor]: 276 | """Implementation of load_data_point method. Loads a dictionary from `path` using 277 | `torch.load`. 278 | 279 | Parameters 280 | ---------- 281 | path : str 282 | Path to data point stored on disk 283 | 284 | Returns 285 | ------- 286 | Dict[str, Tensor] 287 | Loaded data point, must have keys {'surface', 'normals', 'dists'} 288 | """ 289 | return torch.load(path) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------