281 lines
9.5 KiB
Python
281 lines
9.5 KiB
Python
import torch
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import pytest
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from pina.label_tensor import LabelTensor
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data = torch.rand((20, 3))
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labels_column = {1: {"name": "space", "dof": ['x', 'y', 'z']}}
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labels_row = {0: {"name": "samples", "dof": range(20)}}
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labels_list = ['x', 'y', 'z']
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labels_all = labels_column | labels_row
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@pytest.mark.parametrize("labels",
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[labels_column, labels_row, labels_all, labels_list])
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def test_constructor(labels):
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print(LabelTensor(data, labels))
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def test_wrong_constructor():
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with pytest.raises(ValueError):
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LabelTensor(data, ['a', 'b'])
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@pytest.mark.parametrize("labels", [labels_column, labels_all])
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@pytest.mark.parametrize("labels_te", ['z', ['z'], {'space': ['z']}])
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def test_extract_column(labels, labels_te):
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tensor = LabelTensor(data, labels)
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new = tensor.extract(labels_te)
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assert new.ndim == tensor.ndim
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assert new.shape[1] == 1
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assert new.shape[0] == 20
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assert torch.all(torch.isclose(data[:, 2].reshape(-1, 1), new))
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@pytest.mark.parametrize("labels", [labels_row, labels_all])
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@pytest.mark.parametrize("labels_te", [{'samples': [2]}])
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def test_extract_row(labels, labels_te):
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tensor = LabelTensor(data, labels)
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new = tensor.extract(labels_te)
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assert new.ndim == tensor.ndim
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assert new.shape[1] == 3
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assert new.shape[0] == 1
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assert torch.all(torch.isclose(data[2].reshape(1, -1), new))
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@pytest.mark.parametrize("labels_te", [{
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'samples': [2],
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'space': ['z']
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}, {
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'space': 'z',
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'samples': 2
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}])
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def test_extract_2D(labels_te):
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labels = labels_all
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tensor = LabelTensor(data, labels)
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new = tensor.extract(labels_te)
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assert new.ndim == tensor.ndim
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assert new.shape[1] == 1
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assert new.shape[0] == 1
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assert torch.all(torch.isclose(data[2, 2].reshape(1, 1), new))
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def test_extract_3D():
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data = torch.rand(20, 3, 4)
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labels = {
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1: {
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"name": "space",
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"dof": ['x', 'y', 'z']
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},
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2: {
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"name": "time",
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"dof": range(4)
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},
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}
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labels_te = {'space': ['x', 'z'], 'time': range(1, 4)}
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tensor = LabelTensor(data, labels)
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new = tensor.extract(labels_te)
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tensor2 = LabelTensor(data, labels)
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assert new.ndim == tensor.ndim
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assert new.shape[0] == 20
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assert new.shape[1] == 2
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assert new.shape[2] == 3
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assert torch.all(torch.isclose(data[:, 0::2, 1:4].reshape(20, 2, 3), new))
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assert tensor2.ndim == tensor.ndim
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assert tensor2.shape == tensor.shape
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assert tensor.full_labels == tensor2.full_labels
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assert new.shape != tensor.shape
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def test_concatenation_3D():
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data_1 = torch.rand(20, 3, 4)
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labels_1 = ['x', 'y', 'z', 'w']
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lt1 = LabelTensor(data_1, labels_1)
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data_2 = torch.rand(50, 3, 4)
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labels_2 = ['x', 'y', 'z', 'w']
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lt2 = LabelTensor(data_2, labels_2)
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lt_cat = LabelTensor.cat([lt1, lt2])
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assert lt_cat.shape == (70, 3, 4)
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assert lt_cat.full_labels[0]['dof'] == range(70)
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assert lt_cat.full_labels[1]['dof'] == range(3)
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assert lt_cat.full_labels[2]['dof'] == ['x', 'y', 'z', 'w']
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data_1 = torch.rand(20, 3, 4)
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labels_1 = ['x', 'y', 'z', 'w']
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lt1 = LabelTensor(data_1, labels_1)
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data_2 = torch.rand(20, 2, 4)
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labels_2 = ['x', 'y', 'z', 'w']
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lt2 = LabelTensor(data_2, labels_2)
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lt_cat = LabelTensor.cat([lt1, lt2], dim=1)
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assert lt_cat.shape == (20, 5, 4)
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assert lt_cat.full_labels[0]['dof'] == range(20)
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assert lt_cat.full_labels[1]['dof'] == range(5)
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assert lt_cat.full_labels[2]['dof'] == ['x', 'y', 'z', 'w']
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data_1 = torch.rand(20, 3, 2)
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labels_1 = ['x', 'y']
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lt1 = LabelTensor(data_1, labels_1)
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data_2 = torch.rand(20, 3, 3)
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labels_2 = ['z', 'w', 'a']
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lt2 = LabelTensor(data_2, labels_2)
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lt_cat = LabelTensor.cat([lt1, lt2], dim=2)
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assert lt_cat.shape == (20, 3, 5)
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assert lt_cat.full_labels[2]['dof'] == ['x', 'y', 'z', 'w', 'a']
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assert lt_cat.full_labels[0]['dof'] == range(20)
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assert lt_cat.full_labels[1]['dof'] == range(3)
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data_1 = torch.rand(20, 2, 4)
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labels_1 = ['x', 'y', 'z', 'w']
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lt1 = LabelTensor(data_1, labels_1)
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data_2 = torch.rand(20, 3, 4)
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labels_2 = ['x', 'y', 'z', 'w']
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lt2 = LabelTensor(data_2, labels_2)
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with pytest.raises(ValueError):
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LabelTensor.cat([lt1, lt2], dim=2)
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data_1 = torch.rand(20, 3, 2)
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labels_1 = ['x', 'y']
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lt1 = LabelTensor(data_1, labels_1)
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data_2 = torch.rand(20, 3, 3)
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labels_2 = ['x', 'w', 'a']
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lt2 = LabelTensor(data_2, labels_2)
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lt_cat = LabelTensor.cat([lt1, lt2], dim=2)
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assert lt_cat.shape == (20, 3, 5)
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assert lt_cat.full_labels[2]['dof'] == range(5)
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assert lt_cat.full_labels[0]['dof'] == range(20)
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assert lt_cat.full_labels[1]['dof'] == range(3)
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def test_summation():
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lt1 = LabelTensor(torch.ones(20, 3), labels_all)
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lt2 = LabelTensor(torch.ones(30, 3), ['x', 'y', 'z'])
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with pytest.raises(RuntimeError):
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LabelTensor.summation([lt1, lt2])
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lt1 = LabelTensor(torch.ones(20, 3), labels_all)
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lt2 = LabelTensor(torch.ones(20, 3), labels_all)
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lt_sum = LabelTensor.summation([lt1, lt2])
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assert lt_sum.ndim == lt_sum.ndim
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assert lt_sum.shape[0] == 20
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assert lt_sum.shape[1] == 3
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assert lt_sum.full_labels == labels_all
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assert torch.eq(lt_sum.tensor, torch.ones(20, 3) * 2).all()
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lt1 = LabelTensor(torch.ones(20, 3), labels_all)
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lt2 = LabelTensor(torch.ones(20, 3), labels_all)
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lt3 = LabelTensor(torch.zeros(20, 3), labels_all)
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lt_sum = LabelTensor.summation([lt1, lt2, lt3])
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assert lt_sum.ndim == lt_sum.ndim
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assert lt_sum.shape[0] == 20
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assert lt_sum.shape[1] == 3
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assert lt_sum.full_labels == labels_all
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assert torch.eq(lt_sum.tensor, torch.ones(20, 3) * 2).all()
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def test_append_3D():
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data_1 = torch.rand(20, 3, 2)
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labels_1 = ['x', 'y']
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lt1 = LabelTensor(data_1, labels_1)
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data_2 = torch.rand(20, 3, 2)
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labels_2 = ['z', 'w']
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lt2 = LabelTensor(data_2, labels_2)
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lt1 = lt1.append(lt2)
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assert lt1.shape == (20, 3, 4)
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assert lt1.full_labels[0]['dof'] == range(20)
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assert lt1.full_labels[1]['dof'] == range(3)
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assert lt1.full_labels[2]['dof'] == ['x', 'y', 'z', 'w']
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def test_append_2D():
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data_1 = torch.rand(20, 2)
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labels_1 = ['x', 'y']
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lt1 = LabelTensor(data_1, labels_1)
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data_2 = torch.rand(20, 2)
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labels_2 = ['z', 'w']
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lt2 = LabelTensor(data_2, labels_2)
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lt1 = lt1.append(lt2, mode='cross')
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assert lt1.shape == (400, 4)
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assert lt1.full_labels[0]['dof'] == range(400)
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assert lt1.full_labels[1]['dof'] == ['x', 'y', 'z', 'w']
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def test_vstack_3D():
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data_1 = torch.rand(20, 3, 2)
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labels_1 = {
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1: {
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'dof': ['a', 'b', 'c'],
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'name': 'first'
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},
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2: {
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'dof': ['x', 'y'],
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'name': 'second'
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}
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}
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lt1 = LabelTensor(data_1, labels_1)
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data_2 = torch.rand(20, 3, 2)
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labels_1 = {
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1: {
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'dof': ['a', 'b', 'c'],
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'name': 'first'
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},
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2: {
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'dof': ['x', 'y'],
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'name': 'second'
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}
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}
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lt2 = LabelTensor(data_2, labels_1)
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lt_stacked = LabelTensor.vstack([lt1, lt2])
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assert lt_stacked.shape == (40, 3, 2)
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assert lt_stacked.full_labels[0]['dof'] == range(40)
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assert lt_stacked.full_labels[1]['dof'] == ['a', 'b', 'c']
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assert lt_stacked.full_labels[2]['dof'] == ['x', 'y']
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assert lt_stacked.full_labels[1]['name'] == 'first'
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assert lt_stacked.full_labels[2]['name'] == 'second'
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def test_vstack_2D():
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data_1 = torch.rand(20, 2)
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labels_1 = {1: {'dof': ['x', 'y'], 'name': 'second'}}
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lt1 = LabelTensor(data_1, labels_1)
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data_2 = torch.rand(20, 2)
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labels_1 = {1: {'dof': ['x', 'y'], 'name': 'second'}}
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lt2 = LabelTensor(data_2, labels_1)
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lt_stacked = LabelTensor.vstack([lt1, lt2])
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assert lt_stacked.shape == (40, 2)
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assert lt_stacked.full_labels[0]['dof'] == range(40)
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assert lt_stacked.full_labels[1]['dof'] == ['x', 'y']
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assert lt_stacked.full_labels[0]['name'] == 0
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assert lt_stacked.full_labels[1]['name'] == 'second'
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def test_sorting():
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data = torch.ones(20, 5)
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data[:, 0] = data[:, 0] * 4
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data[:, 1] = data[:, 1] * 2
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data[:, 2] = data[:, 2]
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data[:, 3] = data[:, 3] * 5
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data[:, 4] = data[:, 4] * 3
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labels = ['d', 'b', 'a', 'e', 'c']
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lt_data = LabelTensor(data, labels)
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lt_sorted = LabelTensor.sort_labels(lt_data)
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assert lt_sorted.shape == (20, 5)
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assert lt_sorted.labels == ['a', 'b', 'c', 'd', 'e']
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assert torch.eq(lt_sorted.tensor[:, 0], torch.ones(20) * 1).all()
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assert torch.eq(lt_sorted.tensor[:, 1], torch.ones(20) * 2).all()
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assert torch.eq(lt_sorted.tensor[:, 2], torch.ones(20) * 3).all()
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assert torch.eq(lt_sorted.tensor[:, 3], torch.ones(20) * 4).all()
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assert torch.eq(lt_sorted.tensor[:, 4], torch.ones(20) * 5).all()
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data = torch.ones(20, 4, 5)
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data[:, 0, :] = data[:, 0] * 4
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data[:, 1, :] = data[:, 1] * 2
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data[:, 2, :] = data[:, 2]
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data[:, 3, :] = data[:, 3] * 3
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labels = {1: {'dof': ['d', 'b', 'a', 'c'], 'name': 1}}
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lt_data = LabelTensor(data, labels)
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lt_sorted = LabelTensor.sort_labels(lt_data, dim=1)
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assert lt_sorted.shape == (20, 4, 5)
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assert lt_sorted.full_labels[1]['dof'] == ['a', 'b', 'c', 'd']
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assert torch.eq(lt_sorted.tensor[:, 0, :], torch.ones(20, 5) * 1).all()
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assert torch.eq(lt_sorted.tensor[:, 1, :], torch.ones(20, 5) * 2).all()
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assert torch.eq(lt_sorted.tensor[:, 2, :], torch.ones(20, 5) * 3).all()
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assert torch.eq(lt_sorted.tensor[:, 3, :], torch.ones(20, 5) * 4).all()
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