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* Fourier feature embedding added, and modify typos in doc Periodic Boundary Embedding.

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* Fixing doc for Periodic Boundary Embedding.
* Creating doc for Fourier Feature Embedding.
This commit is contained in:
Monthly Tag bot
2024-06-06 11:30:40 +02:00
committed by Nicola Demo
parent 89ee010a94
commit 5785b2732c
6 changed files with 172 additions and 51 deletions
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93
tests/test_layers/test_embedding.py
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@@ -1,8 +1,7 @@
import torch
import pytest
from pina.model.layers import PeriodicBoundaryEmbedding
from pina import LabelTensor
from pina.model.layers import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
# test tolerance
tol = 1e-6
@@ -23,7 +22,7 @@ def grad(u, x):
create_graph=True, allow_unused=True,
retain_graph=True)[0]
def test_constructor():
def test_constructor_PeriodicBoundaryEmbedding():
PeriodicBoundaryEmbedding(input_dimension=1, periods=2)
PeriodicBoundaryEmbedding(input_dimension=1, periods={'x': 3, 'y' : 4})
PeriodicBoundaryEmbedding(input_dimension=1, periods={0: 3, 1 : 4})
@@ -32,14 +31,16 @@ def test_constructor():
PeriodicBoundaryEmbedding()
with pytest.raises(ValueError):
PeriodicBoundaryEmbedding(input_dimension=1., periods=1)
PeriodicBoundaryEmbedding(input_dimension=1, periods=1, output_dimension=1.)
PeriodicBoundaryEmbedding(input_dimension=1, periods=1,
output_dimension=1.)
PeriodicBoundaryEmbedding(input_dimension=1, periods={'x':'x'})
PeriodicBoundaryEmbedding(input_dimension=1, periods={0:'x'})
@pytest.mark.parametrize("period", [1, 4, 10])
@pytest.mark.parametrize("input_dimension", [1, 2, 3])
def test_forward_same_period(input_dimension, period):
def test_forward_backward_same_period_PeriodicBoundaryEmbedding(input_dimension,
period):
func = torch.nn.Sequential(
PeriodicBoundaryEmbedding(input_dimension=input_dimension,
output_dimension=60, periods=period),
@@ -58,46 +59,46 @@ def test_forward_same_period(input_dimension, period):
# output
f = func(x)
assert check_same_columns(f)
# compute backward
loss = f.mean()
loss.backward()
def test_constructor_FourierFeatureEmbedding():
FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
sigmas=1)
FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
sigmas=[0.01, 0.1, 1])
FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
sigmas=[0.01, 0.1, 1])
FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
sigmas=1, embedding_output_dimension=20)
with pytest.raises(TypeError):
FourierFeatureEmbedding()
with pytest.raises(ValueError):
FourierFeatureEmbedding(input_dimension='x', output_dimension=20,
sigmas=1)
FourierFeatureEmbedding(input_dimension=1, output_dimension='x',
sigmas=1)
FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
sigmas='x')
FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
sigmas=1, embedding_output_dimension='x')
# def test_forward_same_period_labels():
# func = torch.nn.Sequential(
# PeriodicBoundaryEmbedding(input_dimension=2,
# output_dimension=60, periods={'x':1, 'y':2}),
# torch.nn.Tanh(),
# torch.nn.Linear(60, 60),
# torch.nn.Tanh(),
# torch.nn.Linear(60, 1)
# )
# # coordinates
# tensor = torch.tensor([[0., 0.], [0., 2.], [1., 0.], [1., 2.]])
# with pytest.raises(RuntimeError):
# func(tensor)
# tensor = tensor.as_subclass(LabelTensor)
# tensor.labels = ['x', 'y']
# tensor.requires_grad = True
# # output
# f = func(tensor)
# assert check_same_columns(f)
# def test_forward_same_period_index():
# func = torch.nn.Sequential(
# PeriodicBoundaryEmbedding(input_dimension=2,
# output_dimension=60, periods={0:1, 1:2}),
# torch.nn.Tanh(),
# torch.nn.Linear(60, 60),
# torch.nn.Tanh(),
# torch.nn.Linear(60, 1)
# )
# # coordinates
# tensor = torch.tensor([[0., 0.], [0., 2.], [1., 0.], [1., 2.]])
# tensor.requires_grad = True
# # output
# f = func(tensor)
# assert check_same_columns(f)
# tensor = tensor.as_subclass(LabelTensor)
# tensor.labels = ['x', 'y']
# # output
# f = func(tensor)
# assert check_same_columns(f)
@pytest.mark.parametrize("output_dimension", [1, 2, 2])
@pytest.mark.parametrize("input_dimension", [1, 2, 3])
@pytest.mark.parametrize("sigmas", [1, [0.01, 0.1, 1]])
@pytest.mark.parametrize("embedding_output_dimension", [1, 2, 3])
def test_forward_backward_FourierFeatureEmbedding(input_dimension,
output_dimension,
sigmas,
embedding_output_dimension):
func = FourierFeatureEmbedding(input_dimension, output_dimension,
sigmas, embedding_output_dimension)
# coordinates
x = torch.rand((10, input_dimension), requires_grad=True)
# output
f = func(x)
assert f.shape[-1] == output_dimension
# compute backward
loss = f.mean()
loss.backward()