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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.
181 lines
8.7 KiB
Python
181 lines
8.7 KiB
Python
"""Test Numpy Video 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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from anomalib.data.validators.numpy.video import NumpyVideoBatchValidator, NumpyVideoValidator
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class TestNumpyVideoValidator:
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"""Test NumpyVideoValidator."""
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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 = NumpyVideoValidator()
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def test_validate_image_valid(self) -> None:
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"""Test validation of a valid image."""
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image = np.zeros((10, 224, 224, 3), dtype=np.uint8)
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validated_image = self.validator.validate_image(image)
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assert isinstance(validated_image, np.ndarray)
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assert validated_image.shape == (10, 224, 224, 3)
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assert validated_image.dtype == np.float32
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np.testing.assert_array_equal(validated_image, image.astype(np.float32))
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def test_validate_image_invalid_type(self) -> None:
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"""Test validation of an image with invalid type."""
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with pytest.raises(TypeError, match="Video must be a numpy.ndarray, got <class 'list'>"):
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self.validator.validate_image([1, 2, 3])
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def test_validate_image_adds_time_dimension(self) -> None:
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"""Test validation of an image without time dimension."""
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# Create a 3D image without time dimension
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input_image = np.zeros((224, 224, 3))
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# Validate the image
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validated_image = self.validator.validate_image(input_image)
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# Check if time dimension is added
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assert validated_image.shape == (1, 224, 224, 3), "Time dimension should be added"
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# Ensure the dtype is converted to float32
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assert validated_image.dtype == np.float32, "Image should be converted to float32"
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# Verify that the image content is preserved
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assert pytest.approx(validated_image[0]) == input_image.astype(np.float32), "Image content should be preserved"
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def test_validate_image_invalid_channels(self) -> None:
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"""Test validation of an image with invalid number of channels."""
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with pytest.raises(ValueError, match="Video must have 1 or 3 channels"):
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self.validator.validate_image(np.zeros((10, 224, 224, 2)))
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def test_validate_image_valid_single_channel(self) -> None:
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"""Test validation of a valid single-channel image."""
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image = np.zeros((10, 224, 224, 1), dtype=np.uint8)
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validated_image = self.validator.validate_image(image)
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assert isinstance(validated_image, np.ndarray)
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assert validated_image.shape == (10, 224, 224, 1)
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assert validated_image.dtype == np.float32
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def test_validate_target_frame_valid(self) -> None:
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"""Test validation of a valid target frame."""
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target_frame = 5
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assert self.validator.validate_target_frame(target_frame) == target_frame
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def test_validate_target_frame_none(self) -> None:
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"""Test validation of a None target frame."""
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assert self.validator.validate_target_frame(None) is None
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def test_validate_target_frame_invalid_type(self) -> None:
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"""Test validation of a target frame with invalid type."""
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with pytest.raises(TypeError, match="Target frame must be an integer"):
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self.validator.validate_target_frame("5")
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def test_validate_target_frame_negative(self) -> None:
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"""Test validation of a negative target frame."""
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with pytest.raises(ValueError, match="Target frame index must be non-negative"):
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self.validator.validate_target_frame(-1)
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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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# Test with binary label (0: normal, 1: anomaly)
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label = 1
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validated_label = self.validator.validate_gt_label(label)
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assert isinstance(validated_label, np.ndarray)
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assert validated_label.dtype == bool
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assert validated_label.item() is True
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class TestNumpyVideoBatchValidator:
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"""Test NumpyVideoBatchValidator."""
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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 = NumpyVideoBatchValidator()
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def test_validate_image_valid(self) -> None:
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"""Test validation of a valid image batch."""
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image_batch = np.zeros((2, 10, 224, 224, 3), dtype=np.uint8)
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validated_batch = self.validator.validate_image(image_batch)
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assert isinstance(validated_batch, np.ndarray)
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assert validated_batch.shape == (2, 10, 224, 224, 3)
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assert validated_batch.dtype == np.float32
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def test_validate_image_adds_time_dimension(self) -> None:
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"""Test validation of an image batch without time dimension."""
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# Create a 4D image batch without time dimension
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input_batch = np.zeros((2, 224, 224, 3))
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# Validate the image batch
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validated_batch = self.validator.validate_image(input_batch)
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# Check if time dimension is added
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assert validated_batch.shape == (2, 224, 224, 3), "Time dimension should not be added for batch input"
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# Ensure the dtype is converted to float32
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assert validated_batch.dtype == np.float32, "Image batch should be converted to float32"
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# Verify that the image content is preserved
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assert pytest.approx(validated_batch) == input_batch.astype(np.float32), "Image content should be preserved"
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def test_validate_image_invalid_type(self) -> None:
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"""Test validation of an image batch with invalid type."""
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with pytest.raises(TypeError, match="Video batch must be a numpy.ndarray, got <class 'list'>"):
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self.validator.validate_image([1, 2, 3])
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def test_validate_image_invalid_dimensions(self) -> None:
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"""Test validation of an image batch with invalid dimensions."""
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with pytest.raises(ValueError, match="Video batch must have 4 or 5 dimensions, got shape \\(224, 224, 3\\)"):
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self.validator.validate_image(np.zeros((224, 224, 3)))
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def test_validate_image_invalid_channels(self) -> None:
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"""Test validation of an image batch with invalid number of channels."""
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with pytest.raises(ValueError, match="Video batch must have 1 or 3 channels, got 2"):
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self.validator.validate_image(np.zeros((2, 10, 224, 224, 2)))
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def test_validate_image_valid_single_channel(self) -> None:
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"""Test validation of a valid single-channel image batch."""
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image_batch = np.zeros((2, 10, 224, 224, 1), dtype=np.uint8)
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validated_batch = self.validator.validate_image(image_batch)
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assert isinstance(validated_batch, np.ndarray)
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assert validated_batch.shape == (2, 10, 224, 224, 1)
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assert validated_batch.dtype == np.float32
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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 = np.array([0, 1])
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validated_labels = self.validator.validate_gt_label(labels)
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assert isinstance(validated_labels, np.ndarray)
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assert validated_labels.dtype == bool
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assert np.array_equal(validated_labels, np.array([False, True]))
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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_valid_sequence(self) -> None:
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"""Test validation of ground truth labels with sequence input."""
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# Test with binary labels (0: normal, 1: anomaly)
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labels = [0, 1]
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validated_labels = self.validator.validate_gt_label(labels)
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assert isinstance(validated_labels, np.ndarray)
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assert validated_labels.dtype == bool
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assert np.array_equal(validated_labels, np.array([False, True]))
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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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# Test with a non-sequence, non-array type
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with pytest.raises(TypeError, match="Ground truth label batch must be a numpy.ndarray"):
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self.validator.validate_gt_label(3.14)
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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 batch must be 1-dimensional, got shape \\(2, 2\\)"):
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self.validator.validate_gt_label(np.array([[0, 1], [1, 0]]))
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def test_validate_gt_label_invalid_dtype(self) -> None:
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"""Test validation of ground truth labels with invalid dtype."""
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# Test that float labels are converted to boolean
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labels = np.array([0.5, 1.5])
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validated_labels = self.validator.validate_gt_label(labels)
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assert isinstance(validated_labels, np.ndarray)
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assert validated_labels.dtype == bool
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assert np.array_equal(validated_labels, np.array([True, True]))
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