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.

tests/ai/test_model_birefnet_onnx.py

@@ -0,0 +1,25 @@
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+from PIL import Image
+
+from server.models import onnx_birefnet
+
+
+@pytest.mark.parametrize("size", [1024, 2048])
+def test_probability_alpha_keeps_transparency_and_source_dimensions(size):
+    session = SimpleNamespace(
+        get_inputs=lambda: [SimpleNamespace(name="image", shape=[1, 3, size, size])],
+        get_outputs=lambda: [SimpleNamespace(name="alpha")],
+    )
+    mask = np.full((1, 1, size, size), 0.37, dtype=np.float32)
+    with patch.object(onnx_birefnet, "run_session", return_value=[mask]) as run:
+        result = onnx_birefnet.infer_alpha(session, Image.new("RGB", (31, 17), (64, 128, 192)), release_memory=False)
+    assert result.shape == (17, 31)
+    assert result.dtype == np.float32
+    np.testing.assert_allclose(result, 0.37, atol=1e-6)
+    assert run.call_args.kwargs["release_memory"] is False
+    feed = run.call_args.args[2]["image"]
+    np.testing.assert_allclose(feed[0, :, 0, 0], (np.array([64, 128, 192]) / 255 - [.485, .456, .406]) / [.229, .224, .225], atol=1e-6)