0e3625de80
* equation class * difference domain * dummy dataloader * writer class * refactoring and minor fix
54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
import torch
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import pytest
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from pina import LabelTensor
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from pina.operators import grad, div, nabla
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def func_vec(x):
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return x**2
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def func_scalar(x):
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print('X')
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x_ = x.extract(['x'])
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y_ = x.extract(['y'])
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mu_ = x.extract(['mu'])
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return x_**2 + y_**2 + mu_**3
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data = torch.rand((20, 3), requires_grad=True)
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inp = LabelTensor(data, ['x', 'y', 'mu'])
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labels = ['a', 'b', 'c']
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tensor_v = LabelTensor(func_vec(inp), labels)
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tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), labels[0])
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def test_grad_scalar_output():
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grad_tensor_s = grad(tensor_s, inp)
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assert grad_tensor_s.shape == inp.shape
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assert grad_tensor_s.labels == [f'd{tensor_s.labels[0]}d{i}' for i in inp.labels]
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grad_tensor_s = grad(tensor_s, inp, d=['x', 'y'])
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assert grad_tensor_s.shape == (inp.shape[0], 2)
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assert grad_tensor_s.labels == [f'd{tensor_s.labels[0]}d{i}' for i in ['x', 'y']]
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def test_grad_vector_output():
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grad_tensor_v = grad(tensor_v, inp)
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assert grad_tensor_v.shape == (20, 9)
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grad_tensor_v = grad(tensor_v, inp, d=['x', 'mu'])
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assert grad_tensor_v.shape == (inp.shape[0], 6)
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def test_div_vector_output():
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grad_tensor_v = div(tensor_v, inp)
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assert grad_tensor_v.shape == (20, 1)
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grad_tensor_v = div(tensor_v, inp, components=['a', 'b'], d=['x', 'mu'])
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assert grad_tensor_v.shape == (inp.shape[0], 1)
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def test_nabla_scalar_output():
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laplace_tensor_v = nabla(tensor_s, inp, components=['a'], d=['x', 'y'])
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assert laplace_tensor_v.shape == tensor_s.shape
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def test_nabla_vector_output():
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laplace_tensor_v = nabla(tensor_v, inp)
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assert laplace_tensor_v.shape == tensor_v.shape
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laplace_tensor_v = nabla(tensor_v, inp, components=['a', 'b'], d=['x', 'y'])
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assert laplace_tensor_v.shape == tensor_v.extract(['a', 'b']).shape
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