Notebook source code: notebooks/how_to/05_descriptors_with_feat_extractors.ipynb
Run it yourself on binder

How to compute descriptors from Features extractors?#

 In [1]:

from geomfum.dataset import NotebooksDataset
from geomfum.descriptor.learned import FeatureExtractor, LearnedDescriptor
from geomfum.shape import TriangleMesh
import torch

 In [2]:

dataset = NotebooksDataset()
mesh = TriangleMesh.from_file(dataset.get_filename("cat-00"))

INFO: Data has already been downloaded... using cached file ('/home/ubuntu/.geomfum/data/cat-00.off').
/home/ubuntu/giulio_vigano/geomfum_proj/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Set Laplace eigenbasis.

 In [3]:

mesh.laplacian.find_spectrum(spectrum_size=10, set_as_basis=True)

mesh.basis

 Out [3]:

<geomfum.basis.LaplaceEigenBasis at 0x79da45628080>

DiffusionNet#

 In [4]:

descr = LearnedDescriptor(
    feature_extractor=FeatureExtractor.from_registry(which="diffusionnet")
)
with torch.no_grad():
    hsign = descr(mesh)

hsign = hsign

hsign.shape

 Out [4]:

(128, 7207)

PointNet#

 In [5]:

descr = LearnedDescriptor(
    feature_extractor=FeatureExtractor.from_registry(which="pointnet")
)
with torch.no_grad():
    hsign = descr(mesh)

hsign = hsign

hsign.shape

 Out [5]:

(128, 7207)

Descriptors as input#

 In [6]:

from geomfum.descriptor.spectral import HeatKernelSignature

descr = LearnedDescriptor(
    feature_extractor=FeatureExtractor.from_registry(
        which="diffusionnet",
        descriptor=HeatKernelSignature(n_domain=128),
        in_channels=128,
    )
)
with torch.no_grad():
    hsign = descr(mesh)

hsign = hsign

hsign.shape

 Out [6]:

(128, 7207)

Pipeline#

We can use learned features also concatenated with other descriptors in the pipeline

 In [7]:

from geomfum.descriptor.pipeline import (
    DescriptorPipeline,
)
from geomfum.descriptor.spectral import HeatKernelSignature
from geomfum.descriptor.spectral import HeatKernelSignature
from geomfum.shape import TriangleMesh

 In [8]:

steps = [
    HeatKernelSignature(n_domain=4),
    descr,
]

pipeline = DescriptorPipeline(steps)

hsign = pipeline.apply(mesh)
hsign.shape

 Out [8]:

(132, 7207)

Saving and loading#

 In [9]:

descr = LearnedDescriptor()

descr.feature_extractor.save("./saved_model_test.pth")

 In [10]:

descr2 = LearnedDescriptor()
descr2.feature_extractor.load_from_path("./saved_model_test.pth")

Further reading#