78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
import torch
|
|
import pytest
|
|
|
|
from pina import LabelTensor
|
|
from pina.operators import grad, div, laplacian
|
|
|
|
|
|
def func_vec(x):
|
|
return x**2
|
|
|
|
|
|
def func_scalar(x):
|
|
print('X')
|
|
x_ = x.extract(['x'])
|
|
y_ = x.extract(['y'])
|
|
mu_ = x.extract(['mu'])
|
|
return x_**2 + y_**2 + mu_**3
|
|
|
|
|
|
data = torch.rand((20, 3), requires_grad=True)
|
|
inp = LabelTensor(data, ['x', 'y', 'mu'])
|
|
labels = ['a', 'b', 'c']
|
|
tensor_v = LabelTensor(func_vec(inp), labels)
|
|
tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), labels[0])
|
|
|
|
|
|
def test_grad_scalar_output():
|
|
grad_tensor_s = grad(tensor_s, inp)
|
|
assert grad_tensor_s.shape == inp.shape
|
|
assert grad_tensor_s.labels == [
|
|
f'd{tensor_s.labels[0]}d{i}' for i in inp.labels
|
|
]
|
|
grad_tensor_s = grad(tensor_s, inp, d=['x', 'y'])
|
|
assert grad_tensor_s.shape == (inp.shape[0], 2)
|
|
assert grad_tensor_s.labels == [
|
|
f'd{tensor_s.labels[0]}d{i}' for i in ['x', 'y']
|
|
]
|
|
|
|
|
|
def test_grad_vector_output():
|
|
grad_tensor_v = grad(tensor_v, inp)
|
|
assert grad_tensor_v.shape == (20, 9)
|
|
grad_tensor_v = grad(tensor_v, inp, d=['x', 'mu'])
|
|
assert grad_tensor_v.shape == (inp.shape[0], 6)
|
|
|
|
|
|
def test_div_vector_output():
|
|
grad_tensor_v = div(tensor_v, inp)
|
|
assert grad_tensor_v.shape == (20, 1)
|
|
grad_tensor_v = div(tensor_v, inp, components=['a', 'b'], d=['x', 'mu'])
|
|
assert grad_tensor_v.shape == (inp.shape[0], 1)
|
|
|
|
|
|
def test_laplacian_scalar_output():
|
|
laplace_tensor_s = laplacian(tensor_s, inp, components=['a'], d=['x', 'y'])
|
|
assert laplace_tensor_s.shape == tensor_s.shape
|
|
assert laplace_tensor_s.labels == [f"dd{tensor_s.labels[0]}"]
|
|
true_val = 4*torch.ones_like(laplace_tensor_s)
|
|
assert all((laplace_tensor_s - true_val == 0).flatten())
|
|
|
|
|
|
def test_laplacian_vector_output():
|
|
laplace_tensor_v = laplacian(tensor_v, inp)
|
|
assert laplace_tensor_v.shape == tensor_v.shape
|
|
assert laplace_tensor_v.labels == [
|
|
f'dd{i}' for i in tensor_v.labels
|
|
]
|
|
laplace_tensor_v = laplacian(tensor_v,
|
|
inp,
|
|
components=['a', 'b'],
|
|
d=['x', 'y'])
|
|
assert laplace_tensor_v.shape == tensor_v.extract(['a', 'b']).shape
|
|
assert laplace_tensor_v.labels == [
|
|
f'dd{i}' for i in ['a', 'b']
|
|
]
|
|
true_val = 2*torch.ones_like(tensor_v.extract(['a', 'b']))
|
|
assert all((laplace_tensor_v - true_val == 0).flatten())
|