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The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
239 lines
12 KiB
Python
239 lines
12 KiB
Python
"""Test Torch Depth Validators."""
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# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import torch
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from torchvision.tv_tensors import Image, Mask
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from anomalib.data.validators.torch.depth import DepthBatchValidator, DepthValidator
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class TestDepthValidator:
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"""Test DepthValidator."""
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def setup_method(self) -> None:
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"""Set up the validator for each test method."""
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self.validator = DepthValidator()
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def test_validate_image_valid(self) -> None:
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"""Test validation of a valid depth image."""
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image = torch.rand(3, 224, 224)
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validated_image = self.validator.validate_image(image)
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assert isinstance(validated_image, Image)
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assert validated_image.shape == (3, 224, 224)
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assert validated_image.dtype == torch.float32
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def test_validate_image_invalid_type(self) -> None:
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"""Test validation of a depth image with invalid type."""
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with pytest.raises(TypeError, match="Image must be a torch.Tensor"):
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self.validator.validate_image(np.random.default_rng().random((3, 224, 224)))
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def test_validate_image_invalid_dimensions(self) -> None:
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"""Test validation of a depth image with invalid dimensions."""
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with pytest.raises(ValueError, match="Image must have shape"):
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self.validator.validate_image(torch.rand(224, 224))
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def test_validate_image_invalid_channels(self) -> None:
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"""Test validation of a depth image with invalid number of channels."""
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with pytest.raises(ValueError, match="Image must have 3 channels"):
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self.validator.validate_image(torch.rand(1, 224, 224))
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def test_validate_gt_label_valid(self) -> None:
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"""Test validation of a valid ground truth label."""
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label = torch.tensor(1)
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validated_label = self.validator.validate_gt_label(label)
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assert isinstance(validated_label, torch.Tensor)
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assert validated_label.dtype == torch.bool
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assert validated_label.item() is True
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def test_validate_gt_label_none(self) -> None:
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"""Test validation of a None ground truth label."""
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assert self.validator.validate_gt_label(None) is None
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def test_validate_gt_label_invalid_type(self) -> None:
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"""Test validation of a ground truth label with invalid type."""
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with pytest.raises(TypeError, match="Ground truth label must be an integer or a torch.Tensor"):
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self.validator.validate_gt_label("1")
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def test_validate_gt_mask_valid(self) -> None:
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"""Test validation of a valid ground truth mask."""
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mask = torch.randint(0, 2, (1, 224, 224))
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validated_mask = self.validator.validate_gt_mask(mask)
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assert isinstance(validated_mask, Mask)
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assert validated_mask.shape == (224, 224)
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assert validated_mask.dtype == torch.bool
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def test_validate_gt_mask_none(self) -> None:
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"""Test validation of a None ground truth mask."""
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assert self.validator.validate_gt_mask(None) is None
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def test_validate_gt_mask_invalid_type(self) -> None:
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"""Test validation of a ground truth mask with invalid type."""
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with pytest.raises(TypeError, match="Mask must be a torch.Tensor"):
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self.validator.validate_gt_mask(np.random.default_rng().integers(0, 2, (224, 224)))
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def test_validate_gt_mask_invalid_shape(self) -> None:
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"""Test validation of a ground truth mask with invalid shape."""
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with pytest.raises(ValueError, match="Mask must have 1 channel, got 2."):
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self.validator.validate_gt_mask(torch.randint(0, 2, (2, 224, 224)))
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def test_validate_anomaly_map_valid(self) -> None:
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"""Test validation of a valid anomaly map."""
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anomaly_map = torch.rand(1, 224, 224)
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validated_map = self.validator.validate_anomaly_map(anomaly_map)
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assert isinstance(validated_map, Mask)
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assert validated_map.shape == (224, 224)
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assert validated_map.dtype == torch.float32
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def test_validate_anomaly_map_none(self) -> None:
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"""Test validation of a None anomaly map."""
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assert self.validator.validate_anomaly_map(None) is None
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def test_validate_anomaly_map_invalid_type(self) -> None:
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"""Test validation of an anomaly map with invalid type."""
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with pytest.raises(TypeError, match="Anomaly map must be a torch.Tensor"):
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self.validator.validate_anomaly_map(np.random.default_rng().random((224, 224)))
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def test_validate_anomaly_map_invalid_shape(self) -> None:
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"""Test validation of an anomaly map with invalid shape."""
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with pytest.raises(ValueError, match="Anomaly map with 3 dimensions must have 1 channel, got 2."):
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self.validator.validate_anomaly_map(torch.rand(2, 224, 224))
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def test_validate_pred_score_valid(self) -> None:
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"""Test validation of a valid prediction score."""
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score = torch.tensor(0.8)
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validated_score = self.validator.validate_pred_score(score)
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assert isinstance(validated_score, torch.Tensor)
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assert validated_score.dtype == torch.float32
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assert validated_score.item() == pytest.approx(0.8)
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def test_validate_pred_score_none(self) -> None:
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"""Test validation of a None prediction score."""
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assert self.validator.validate_pred_score(None) is None
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class TestDepthBatchValidator: # noqa: PLR0904
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"""Test DepthBatchValidator."""
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def setup_method(self) -> None:
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"""Set up the validator for each test method."""
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self.validator = DepthBatchValidator()
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def test_validate_image_valid(self) -> None:
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"""Test validation of a valid depth image batch."""
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image_batch = torch.rand(32, 3, 224, 224)
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validated_batch = self.validator.validate_image(image_batch)
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assert isinstance(validated_batch, Image)
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assert validated_batch.shape == (32, 3, 224, 224)
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assert validated_batch.dtype == torch.float32
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def test_validate_image_invalid_type(self) -> None:
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"""Test validation of a depth image batch with invalid type."""
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with pytest.raises(TypeError, match="Image must be a torch.Tensor"):
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self.validator.validate_image(np.random.default_rng().random((32, 3, 224, 224)))
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def test_validate_image_invalid_dimensions(self) -> None:
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"""Test validation of a depth image batch with invalid dimensions."""
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with pytest.raises(ValueError, match="Image must have shape"):
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self.validator.validate_image(torch.rand(32, 224, 224))
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def test_validate_image_invalid_channels(self) -> None:
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"""Test validation of a depth image batch with invalid number of channels."""
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with pytest.raises(ValueError, match="Image must have 3 channels"):
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self.validator.validate_image(torch.rand(32, 1, 224, 224))
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def test_validate_gt_label_valid(self) -> None:
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"""Test validation of valid ground truth labels."""
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labels = torch.tensor([0, 1, 1, 0])
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validated_labels = self.validator.validate_gt_label(labels)
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assert isinstance(validated_labels, torch.Tensor)
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assert validated_labels.dtype == torch.bool
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assert torch.equal(validated_labels, torch.tensor([False, True, True, False]))
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def test_validate_gt_label_none(self) -> None:
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"""Test validation of None ground truth labels."""
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assert self.validator.validate_gt_label(None) is None
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def test_validate_gt_label_invalid_type(self) -> None:
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"""Test validation of ground truth labels with invalid type."""
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with pytest.raises(ValueError, match="too many dimensions 'str'"):
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self.validator.validate_gt_label(["0", "1"])
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def test_validate_gt_label_invalid_dimensions(self) -> None:
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"""Test validation of ground truth labels with invalid dimensions."""
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with pytest.raises(ValueError, match="Ground truth label must be a 1-dimensional vector"):
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self.validator.validate_gt_label(torch.tensor([[0, 1], [1, 0]]))
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def test_validate_gt_mask_valid(self) -> None:
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"""Test validation of valid ground truth masks."""
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masks = torch.randint(0, 2, (4, 224, 224))
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validated_masks = self.validator.validate_gt_mask(masks)
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assert isinstance(validated_masks, Mask)
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assert validated_masks.shape == (4, 224, 224)
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assert validated_masks.dtype == torch.bool
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def test_validate_gt_mask_none(self) -> None:
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"""Test validation of None ground truth masks."""
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assert self.validator.validate_gt_mask(None) is None
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def test_validate_gt_mask_invalid_type(self) -> None:
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"""Test validation of ground truth masks with invalid type."""
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with pytest.raises(TypeError, match="Ground truth mask must be a torch.Tensor"):
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self.validator.validate_gt_mask([torch.zeros(224, 224)])
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def test_validate_gt_mask_invalid_dimensions(self) -> None:
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"""Test validation of ground truth masks with invalid dimensions."""
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with pytest.raises(ValueError, match="Ground truth mask must have 1 channel, got 2."):
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self.validator.validate_gt_mask(torch.zeros(4, 2, 224, 224))
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def test_validate_anomaly_map_valid(self) -> None:
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"""Test validation of a valid anomaly map batch."""
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anomaly_map = torch.rand(4, 224, 224)
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validated_map = self.validator.validate_anomaly_map(anomaly_map)
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assert isinstance(validated_map, Mask)
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assert validated_map.shape == (4, 224, 224)
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assert validated_map.dtype == torch.float32
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def test_validate_anomaly_map_none(self) -> None:
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"""Test validation of a None anomaly map batch."""
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assert self.validator.validate_anomaly_map(None) is None
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def test_validate_anomaly_map_invalid_shape(self) -> None:
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"""Test validation of an anomaly map batch with invalid shape."""
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with pytest.raises(ValueError, match="Anomaly map must have 1 channel, got 2."):
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self.validator.validate_anomaly_map(torch.rand(4, 2, 224, 224))
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def test_validate_pred_score_valid(self) -> None:
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"""Test validation of valid prediction scores."""
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scores = torch.tensor([0.1, 0.2, 0.3, 0.4])
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validated_scores = self.validator.validate_pred_score(scores)
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assert torch.equal(validated_scores, scores)
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def test_validate_pred_score_none_with_anomaly_map(self) -> None:
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"""Test validation of None prediction scores with anomaly map."""
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computed_scores = self.validator.validate_pred_score(None)
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assert computed_scores is None
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def test_validate_pred_label_valid(self) -> None:
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"""Test validation of valid prediction labels."""
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labels = torch.tensor([[1], [0], [1], [1]])
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validated_labels = self.validator.validate_pred_label(labels)
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assert torch.equal(validated_labels, torch.tensor([True, False, True, True]))
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def test_validate_pred_label_none(self) -> None:
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"""Test validation of None prediction labels."""
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assert self.validator.validate_pred_label(None) is None
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def test_validate_pred_label_invalid_type(self) -> None:
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"""Test validation of prediction labels with invalid type."""
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with pytest.raises(TypeError, match="Predicted label must be a torch.Tensor"):
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self.validator.validate_pred_label([1, 0, 1, 1])
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def test_validate_pred_label_invalid_shape(self) -> None:
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"""Test validation of prediction labels with invalid shape."""
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with pytest.raises(ValueError, match="Predicted label must be 1-dimensional or 2-dimensional"):
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self.validator.validate_pred_label(torch.tensor([[[1]], [[0]], [[1]], [[1]]]))
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