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AnyImage/tests/ai/test_model_geometry.py
nan 2b3c0194a6
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Add comprehensive tests for ONNX models and server jobs
- 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.
2026-09-19 19:24:07 +08:00

47 lines
1.6 KiB
Python

from unittest.mock import patch
import numpy as np
from anyimage.server.models import moge as moge2_model
def test_moge_infer_returns_geometry_frame_with_model_validity():
mask = np.asarray(((1.0, 0.2), (0.7, 0.0)), dtype=np.float32)
prediction = {
"depth": np.ones((2, 2), dtype=np.float32),
"normal": np.zeros((2, 2, 3), dtype=np.float32),
"mask": mask,
"intrinsics": np.asarray(
((1.0, 0.0, 0.5), (0.0, 1.0, 0.5), (0.0, 0.0, 1.0)),
dtype=np.float32,
),
"points": np.ones((2, 2, 3), dtype=np.float32),
}
with patch.object(moge2_model, "infer_one", return_value=prediction):
frame = moge2_model.infer(None, {}, "image", include_points=True)
assert np.array_equal(frame.validity, mask.astype(np.float32))
assert np.array_equal(frame.normal, prediction["normal"])
assert np.array_equal(frame.points, prediction["points"])
assert np.array_equal(
frame.intrinsics,
np.asarray(((2.0, 0.0, 1.0), (0.0, 2.0, 1.0), (0.0, 0.0, 1.0))),
)
def test_moge_frame_omits_points_when_not_requested():
prediction = {
"depth": np.ones((2, 2), dtype=np.float32),
"normal": np.zeros((2, 2, 3), dtype=np.float32),
"mask": np.ones((2, 2), dtype=bool),
"intrinsics": np.eye(3, dtype=np.float32),
"points": np.ones((2, 2, 3), dtype=np.float32),
}
with patch.object(moge2_model, "infer_one", return_value=prediction):
frame = moge2_model.infer(None, {}, "image", include_points=False)
assert frame.points is None
assert frame.validity.all()