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- Introduced tests for the Moge3 ONNX model, including input validation, inference, and caching mechanisms. - Added tests for ONNX runtime configurations and session management. - Implemented tests for image upscaling functionality using ONNX models, ensuring proper handling of alpha channels and memory management. - Created tests for panorama generation, verifying depth and visibility metrics. - Developed tests for prediction artifacts, ensuring correct depth and normal data handling. - Added server job tests for background removal and depth plane geometry generation, validating input handling and output correctness. - Included environment tests to ensure proper dependency management and CI configurations.
26 lines
1.1 KiB
Python
26 lines
1.1 KiB
Python
from types import SimpleNamespace
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from unittest.mock import patch
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import numpy as np
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import pytest
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from PIL import Image
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from server.models import onnx_birefnet
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@pytest.mark.parametrize("size", [1024, 2048])
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def test_probability_alpha_keeps_transparency_and_source_dimensions(size):
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session = SimpleNamespace(
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get_inputs=lambda: [SimpleNamespace(name="image", shape=[1, 3, size, size])],
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get_outputs=lambda: [SimpleNamespace(name="alpha")],
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)
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mask = np.full((1, 1, size, size), 0.37, dtype=np.float32)
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with patch.object(onnx_birefnet, "run_session", return_value=[mask]) as run:
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result = onnx_birefnet.infer_alpha(session, Image.new("RGB", (31, 17), (64, 128, 192)), release_memory=False)
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assert result.shape == (17, 31)
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assert result.dtype == np.float32
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np.testing.assert_allclose(result, 0.37, atol=1e-6)
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assert run.call_args.kwargs["release_memory"] is False
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feed = run.call_args.args[2]["image"]
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np.testing.assert_allclose(feed[0, :, 0, 0], (np.array([64, 128, 192]) / 255 - [.485, .456, .406]) / [.229, .224, .225], atol=1e-6)
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