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* refactor: Standardize naming conventions * chore: Update the version and re-run experiments * chore: Improve code quality tooling and documentation - Add pre-commit job to CI workflow for automated linting on PRs - Update uniface/__init__.py with copyright header, module docstring, and logically grouped exports - Revise CONTRIBUTING.md to reflect pre-commit handles all formatting - Remove redundant ruff check from CI (now handled by pre-commit) - Update build job Python version to 3.11 (matches requires-python)
123 lines
3.3 KiB
Python
123 lines
3.3 KiB
Python
# Copyright 2025 Yakhyokhuja Valikhujaev
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# Author: Yakhyokhuja Valikhujaev
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# GitHub: https://github.com/yakhyo
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"""Tests for BiSeNet face parsing model."""
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from __future__ import annotations
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import numpy as np
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import pytest
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from uniface.constants import ParsingWeights
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from uniface.parsing import BiSeNet, create_face_parser
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def test_bisenet_initialization():
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"""Test BiSeNet initialization."""
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parser = BiSeNet()
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assert parser is not None
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assert parser.input_size == (512, 512)
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def test_bisenet_with_different_models():
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"""Test BiSeNet with different model weights."""
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parser_resnet18 = BiSeNet(model_name=ParsingWeights.RESNET18)
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parser_resnet34 = BiSeNet(model_name=ParsingWeights.RESNET34)
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assert parser_resnet18 is not None
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assert parser_resnet34 is not None
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def test_bisenet_preprocess():
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"""Test preprocessing."""
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parser = BiSeNet()
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# Create a dummy face image
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face_image = np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8)
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# Preprocess
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preprocessed = parser.preprocess(face_image)
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assert preprocessed.shape == (1, 3, 512, 512)
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assert preprocessed.dtype == np.float32
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def test_bisenet_postprocess():
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"""Test postprocessing."""
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parser = BiSeNet()
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# Create dummy model output (batch_size=1, num_classes=19, H=512, W=512)
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dummy_output = np.random.randn(1, 19, 512, 512).astype(np.float32)
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# Postprocess
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mask = parser.postprocess(dummy_output, original_size=(256, 256))
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assert mask.shape == (256, 256)
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assert mask.dtype == np.uint8
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assert mask.min() >= 0
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assert mask.max() < 19 # 19 classes (0-18)
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def test_bisenet_parse():
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"""Test end-to-end parsing."""
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parser = BiSeNet()
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# Create a dummy face image
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face_image = np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8)
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# Parse
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mask = parser.parse(face_image)
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assert mask.shape == (256, 256)
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assert mask.dtype == np.uint8
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assert mask.min() >= 0
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assert mask.max() < 19
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def test_bisenet_callable():
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"""Test that BiSeNet is callable."""
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parser = BiSeNet()
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face_image = np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8)
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# Should work as callable
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mask = parser(face_image)
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assert mask.shape == (256, 256)
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assert mask.dtype == np.uint8
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def test_create_face_parser_with_enum():
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"""Test factory function with enum."""
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parser = create_face_parser(ParsingWeights.RESNET18)
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assert parser is not None
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assert isinstance(parser, BiSeNet)
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def test_create_face_parser_with_string():
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"""Test factory function with string."""
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parser = create_face_parser('parsing_resnet18')
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assert parser is not None
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assert isinstance(parser, BiSeNet)
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def test_create_face_parser_invalid_model():
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"""Test factory function with invalid model name."""
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with pytest.raises(ValueError, match='Unknown face parsing model'):
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create_face_parser('invalid_model')
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def test_bisenet_different_input_sizes():
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"""Test parsing with different input image sizes."""
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parser = BiSeNet()
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# Test with different sizes
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sizes = [(128, 128), (256, 256), (512, 512), (640, 480)]
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for h, w in sizes:
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face_image = np.random.randint(0, 255, (h, w, 3), dtype=np.uint8)
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mask = parser.parse(face_image)
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assert mask.shape == (h, w), f'Failed for size {h}x{w}'
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assert mask.dtype == np.uint8
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