nan
e8129fe495
- Added validation documentation for AI model tiers. - Introduced `background.py` for background removal logic, including inference functions for alpha and foreground extraction. - Created `onnx_birefnet.py` to handle BiRefNet model sessions and alpha inference. - Developed tests for background models, ensuring proper dispatch and session reuse. - Added tests for BiRefNet ONNX model to validate alpha output and error handling for invalid values. - Implemented model export scripts for BiRefNet and HAT Sharper, ensuring proper weight compression and licensing. - Created `deform_sampling.py` for deformable convolution operations used in BiRefNet. - Added utility functions for compressing ONNX weights without altering graph arithmetic. - Introduced a reference plane node for depth field adjustments in geometry processing.
37 lines
1.6 KiB
Python
37 lines
1.6 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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@pytest.mark.parametrize("value", [np.nan, np.inf, -0.1, 1.1])
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def test_invalid_alpha_is_rejected(value):
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session = SimpleNamespace(
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get_inputs=lambda: [SimpleNamespace(name="image", shape=[1, 3, 1024, 1024])],
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get_outputs=lambda: [SimpleNamespace(name="alpha")],
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)
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with patch.object(onnx_birefnet, "run_session", return_value=[np.full((1, 1, 1024, 1024), value)]):
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with pytest.raises(RuntimeError, match="invalid alpha"):
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onnx_birefnet.infer_alpha(session, Image.new("RGB", (2, 3)))
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