folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Revert "Improve and fix spline (#610)"

Browse Source 
This reverts commit 0844282335.
This commit is contained in:
GiovanniCanali
2025-10-15 17:35:24 +02:00
parent 0844282335
commit 4a3748c735
7 changed files with 176 additions and 838 deletions
Download Patch File Download Diff File Expand all files Collapse all files

180
tests/test_model/test_spline_surface.py
View File

@@ -1,180 +0,0 @@
import torch
import random
import pytest
from pina.model import SplineSurface
from pina import LabelTensor
# Utility quantities for testing
orders = [random.randint(1, 8) for _ in range(2)]
n_ctrl_pts = random.randint(max(orders), max(orders) + 5)
n_knots = [orders[i] + n_ctrl_pts for i in range(2)]
# Input tensor
points = [
LabelTensor(torch.rand(100, 2), ["x", "y"]),
LabelTensor(torch.rand(2, 100, 2), ["x", "y"]),
]
@pytest.mark.parametrize(
"knots_u",
[
torch.rand(n_knots[0]),
{"n": n_knots[0], "min": 0, "max": 1, "mode": "auto"},
{"n": n_knots[0], "min": 0, "max": 1, "mode": "uniform"},
None,
],
)
@pytest.mark.parametrize(
"knots_v",
[
torch.rand(n_knots[1]),
{"n": n_knots[1], "min": 0, "max": 1, "mode": "auto"},
{"n": n_knots[1], "min": 0, "max": 1, "mode": "uniform"},
None,
],
)
@pytest.mark.parametrize(
"control_points", [torch.rand(n_ctrl_pts, n_ctrl_pts), None]
)
def test_constructor(knots_u, knots_v, control_points):
# Skip if knots_u, knots_v, and control_points are all None
if (knots_u is None or knots_v is None) and control_points is None:
return
SplineSurface(
orders=orders,
knots_u=knots_u,
knots_v=knots_v,
control_points=control_points,
)
# Should fail if orders is not list of two elements
with pytest.raises(ValueError):
SplineSurface(
orders=[orders[0]],
knots_u=knots_u,
knots_v=knots_v,
control_points=control_points,
)
# Should fail if both knots and control_points are None
with pytest.raises(ValueError):
SplineSurface(
orders=orders,
knots_u=None,
knots_v=None,
control_points=None,
)
# Should fail if control_points is not a torch.Tensor when provided
with pytest.raises(ValueError):
SplineSurface(
orders=orders,
knots_u=knots_u,
knots_v=knots_v,
control_points=[[0.0] * n_ctrl_pts] * n_ctrl_pts,
)
# Should fail if control_points is not of the correct shape when provided
# It assumes that at least one among knots_u and knots_v is not None
if knots_u is not None or knots_v is not None:
with pytest.raises(ValueError):
SplineSurface(
orders=orders,
knots_u=knots_u,
knots_v=knots_v,
control_points=torch.rand(n_ctrl_pts + 1, n_ctrl_pts + 1),
)
# Should fail if there are not enough knots_u to define the control points
with pytest.raises(ValueError):
SplineSurface(
orders=orders,
knots_u=torch.linspace(0, 1, orders[0]),
knots_v=knots_v,
control_points=None,
)
# Should fail if there are not enough knots_v to define the control points
with pytest.raises(ValueError):
SplineSurface(
orders=orders,
knots_u=knots_u,
knots_v=torch.linspace(0, 1, orders[1]),
control_points=None,
)
@pytest.mark.parametrize(
"knots_u",
[
torch.rand(n_knots[0]),
{"n": n_knots[0], "min": 0, "max": 1, "mode": "auto"},
{"n": n_knots[0], "min": 0, "max": 1, "mode": "uniform"},
],
)
@pytest.mark.parametrize(
"knots_v",
[
torch.rand(n_knots[1]),
{"n": n_knots[1], "min": 0, "max": 1, "mode": "auto"},
{"n": n_knots[1], "min": 0, "max": 1, "mode": "uniform"},
],
)
@pytest.mark.parametrize(
"control_points", [torch.rand(n_ctrl_pts, n_ctrl_pts), None]
)
@pytest.mark.parametrize("pts", points)
def test_forward(knots_u, knots_v, control_points, pts):
# Define the model
model = SplineSurface(
orders=orders,
knots_u=knots_u,
knots_v=knots_v,
control_points=control_points,
)
# Evaluate the model
output_ = model(pts)
assert output_.shape == (*pts.shape[:-1], 1)
@pytest.mark.parametrize(
"knots_u",
[
torch.rand(n_knots[0]),
{"n": n_knots[0], "min": 0, "max": 1, "mode": "auto"},
{"n": n_knots[0], "min": 0, "max": 1, "mode": "uniform"},
],
)
@pytest.mark.parametrize(
"knots_v",
[
torch.rand(n_knots[1]),
{"n": n_knots[1], "min": 0, "max": 1, "mode": "auto"},
{"n": n_knots[1], "min": 0, "max": 1, "mode": "uniform"},
],
)
@pytest.mark.parametrize(
"control_points", [torch.rand(n_ctrl_pts, n_ctrl_pts), None]
)
@pytest.mark.parametrize("pts", points)
def test_backward(knots_u, knots_v, control_points, pts):
# Define the model
model = SplineSurface(
orders=orders,
knots_u=knots_u,
knots_v=knots_v,
control_points=control_points,
)
# Evaluate the model
output_ = model(pts)
loss = torch.mean(output_)
loss.backward()
assert model.control_points.grad.shape == model.control_points.shape