tests.transforms.test_custom_image_subclass

Complexity

Total Complexity

19

Size/Duplication

Total Lines	225
Duplicated Lines	6.22 %

Importance

Changes

0

16 Methods

Rating	Name	Duplication	Size	Complexity
A	TestCustomImageSubclass.history_image()	0	6	1
A	HistoryImage.__init__()	3	3	1
A	MetadataImage.__init__()	0	3	1
A	TestCustomImageSubclass.test_new_like_method_directly()	0	18	1
A	HistoryImage.new_like()	7	7	2
A	MetadataImage.new_like()	0	7	2
A	TestCustomImageSubclass.test_backward_compatibility_standard_images()	0	15	1
A	TestCustomImageSubclass.test_chained_transforms_preserve_attributes()	0	20	1
A	TestCustomImageSubclass.test_to_reference_space_with_custom_image()	0	18	1
A	TestCustomImageSubclass.test_label_map_subclass()	0	35	2
A	TestCustomImageSubclass.metadata_image()	0	7	1
A	TestCustomImageSubclass.test_new_like_with_default_affine()	0	14	1
A	TestCustomImageSubclass.test_crop_with_history_image()	0	13	1
A	TestCustomImageSubclass.history_subject()	0	4	1
A	TestCustomImageSubclass.metadata_subject()	0	4	1
A	TestCustomImageSubclass.test_crop_with_metadata_image()	0	13	1

"""Tests for custom Image subclasses with transforms."""

import torch
import pytest
import torchio as tio


class HistoryImage(tio.ScalarImage):

This code seems to be duplicated in your project.

    """Test custom Image with required parameter."""

    def __init__(self, tensor, affine, history, **kwargs):
        super().__init__(tensor=tensor, affine=affine, **kwargs)
        self.history = history

    def new_like(self, tensor, affine=None):
        return type(self)(
            tensor=tensor,
            affine=affine if affine is not None else self.affine,
            history=self.history,
            check_nans=self.check_nans,
            reader=self.reader,
        )


class MetadataImage(tio.ScalarImage):
    """Test custom Image with optional parameter."""

    def __init__(self, tensor, affine, metadata=None, **kwargs):
        super().__init__(tensor=tensor, affine=affine, **kwargs)
        self.metadata = metadata or {}

    def new_like(self, tensor, affine=None):
        return type(self)(
            tensor=tensor,
            affine=affine if affine is not None else self.affine,
            metadata=self.metadata,
            check_nans=self.check_nans,
            reader=self.reader,
        )


class TestCustomImageSubclass:
    """Test suite for custom Image subclasses with transforms."""

    @pytest.fixture
    def history_image(self):
        """Create a HistoryImage for testing."""
        tensor = torch.rand(1, 10, 10, 10)
        affine = torch.eye(4)
        return HistoryImage(tensor=tensor, affine=affine, history=['created'])

    @pytest.fixture
    def metadata_image(self):
        """Create a MetadataImage for testing."""
        tensor = torch.rand(1, 12, 12, 12)
        affine = torch.eye(4)
        return MetadataImage(
            tensor=tensor, affine=affine, metadata={'id': 123, 'source': 'test'}
        )

    @pytest.fixture
    def history_subject(self, history_image):
        """Create a Subject with HistoryImage."""
        return tio.Subject(image=history_image)

    @pytest.fixture
    def metadata_subject(self, metadata_image):
        """Create a Subject with MetadataImage."""
        return tio.Subject(image=metadata_image)

    def test_crop_with_history_image(self, history_subject):
        """Test that Crop transform works with custom Image requiring history parameter."""
        transform = tio.Crop(cropping=2)
        result = transform(history_subject)

        # Check that the result is still a HistoryImage
        assert isinstance(result.image, HistoryImage)

        # Check that custom attribute is preserved
        assert result.image.history == ['created']

        # Check that cropping worked correctly
        assert result.image.shape == (1, 6, 6, 6)

    def test_crop_with_metadata_image(self, metadata_subject):
        """Test that Crop transform works with custom Image with optional parameters."""
        transform = tio.Crop(cropping=1)
        result = transform(metadata_subject)

        # Check that the result is still a MetadataImage
        assert isinstance(result.image, MetadataImage)

        # Check that custom attribute is preserved
        assert result.image.metadata == {'id': 123, 'source': 'test'}

        # Check that cropping worked correctly
        assert result.image.shape == (1, 10, 10, 10)

    def test_chained_transforms_preserve_attributes(self, history_subject):
        """Test that chained transforms preserve custom attributes."""
        # Chain multiple transforms
        transform = tio.Compose(
            [
                tio.Crop(cropping=1),
                tio.Crop(cropping=1),
            ]
        )

        result = transform(history_subject)

        # Check that the result is still a HistoryImage after multiple transforms
        assert isinstance(result.image, HistoryImage)

        # Check that custom attribute is preserved through the chain
        assert result.image.history == ['created']

        # Check that both crops were applied
        assert result.image.shape == (1, 6, 6, 6)

    def test_backward_compatibility_standard_images(self):
        """Test that standard Images still work with transforms."""
        # Create a standard ScalarImage
        tensor = torch.rand(1, 10, 10, 10)
        affine = torch.eye(4)
        image = tio.ScalarImage(tensor=tensor, affine=affine)
        subject = tio.Subject(image=image)

        # Apply transform
        transform = tio.Crop(cropping=2)
        result = transform(subject)

        # Check that it still works
        assert isinstance(result.image, tio.ScalarImage)
        assert result.image.shape == (1, 6, 6, 6)

    def test_to_reference_space_with_custom_image(self, history_image):
        """Test that ToReferenceSpace works with custom images."""
        # Create a reference image
        reference_tensor = torch.rand(1, 20, 20, 20)
        reference_affine = torch.eye(4)
        reference = tio.ScalarImage(tensor=reference_tensor, affine=reference_affine)

        # Create embedding tensor (smaller than reference)
        embedding_tensor = torch.rand(1, 10, 10, 10)

        # Use ToReferenceSpace.from_tensor
        result = tio.ToReferenceSpace.from_tensor(embedding_tensor, history_image)

        # Check that the result preserves the custom class type
        assert isinstance(result, HistoryImage)

        # Check that custom attribute is preserved
        assert result.history == ['created']

    def test_new_like_method_directly(self, history_image):
        """Test the new_like method directly."""
        new_tensor = torch.rand(1, 5, 5, 5)
        new_affine = torch.eye(4) * 2

        # Create new image using new_like
        new_image = history_image.new_like(tensor=new_tensor, affine=new_affine)

        # Check type preservation
        assert isinstance(new_image, HistoryImage)

        # Check attribute preservation
        assert new_image.history == ['created']

        # Check new data
        assert torch.equal(new_image.data, new_tensor)
        assert torch.allclose(
            torch.tensor(new_image.affine).float(), new_affine.float()
        )

    def test_new_like_with_default_affine(self, metadata_image):
        """Test new_like method with default affine (None)."""
        new_tensor = torch.rand(1, 8, 8, 8)

        # Create new image using new_like with default affine
        new_image = metadata_image.new_like(tensor=new_tensor)

        # Check that original affine is used
        assert torch.allclose(
            torch.tensor(new_image.affine), torch.tensor(metadata_image.affine)
        )

        # Check attribute preservation
        assert new_image.metadata == {'id': 123, 'source': 'test'}

    def test_label_map_subclass(self):
        """Test that custom LabelMap subclasses also work."""

        class CustomLabelMap(tio.LabelMap):
            def __init__(self, tensor, affine, labels_info, **kwargs):
                super().__init__(tensor=tensor, affine=affine, **kwargs)
                self.labels_info = labels_info

            def new_like(self, tensor, affine=None):
                return type(self)(
                    tensor=tensor,
                    affine=affine if affine is not None else self.affine,
                    labels_info=self.labels_info,
                    check_nans=self.check_nans,
                    reader=self.reader,
                )

        # Create custom label map
        tensor = torch.randint(0, 3, (1, 8, 8, 8))
        affine = torch.eye(4)
        labels_info = {0: 'background', 1: 'tissue1', 2: 'tissue2'}

        custom_label = CustomLabelMap(
            tensor=tensor, affine=affine, labels_info=labels_info
        )
        subject = tio.Subject(labels=custom_label)

        # Apply transform
        transform = tio.Crop(cropping=1)
        result = transform(subject)

        # Check preservation
        assert isinstance(result.labels, CustomLabelMap)
        assert result.labels.labels_info == labels_info
        assert result.labels.shape == (1, 6, 6, 6)