nan
f48bbb3d05
- Implement tests for image layer functionality including alpha curve and depth layer behavior. - Create tests for image plane evaluation and modifier input handling. - Add tests to verify the saved asset inventory and its persistence. - Introduce tests for the documentation command to ensure correct project version deployment. - Validate the extension manifest structure and required permissions. - Develop tests for the packaging process, ensuring correct platform handling and dependency management. - Enhance image depth plane group functionality with new split threshold input and surface processing.
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
import unittest
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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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from PIL import Image
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from server.models import onnx_ben2
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class FakeSession:
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def get_providers(self):
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return ["CPUExecutionProvider"]
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def get_inputs(self):
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return [SimpleNamespace(name="input")]
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def get_outputs(self):
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return [SimpleNamespace(name="alpha")]
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def run(self, output_names, feeds):
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self.output_names = output_names
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self.feeds = feeds
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gradient = np.linspace(0.0, 1.0, 1024, dtype=np.float32)
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return [np.tile(gradient, (1024, 1))[None, None]]
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class Ben2OnnxTest(unittest.TestCase):
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def test_infer_forwards_memory_release_boundary(self):
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session = FakeSession()
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with patch.object(
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onnx_ben2,
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"run_session",
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side_effect=lambda model, names, feeds, **_options: model.run(
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names, feeds
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),
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) as run:
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result = onnx_ben2.infer(
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session,
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Image.new("RGB", (40, 20)),
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release_memory=False,
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)
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self.assertEqual(result.size, (40, 20))
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self.assertFalse(run.call_args.kwargs["release_memory"])
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def test_preprocess_matches_model_layout(self):
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image = Image.new("RGB", (40, 20), (64, 128, 192))
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result = onnx_ben2.preprocess(image)
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self.assertEqual(result.shape, (1, 3, 1024, 1024))
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self.assertEqual(result.dtype, np.float32)
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self.assertTrue(result.flags.c_contiguous)
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def test_infer_restores_original_size_and_alpha(self):
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session = FakeSession()
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image = Image.new("RGB", (40, 20), (10, 20, 30))
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result = onnx_ben2.infer(session, image)
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self.assertEqual(result.mode, "RGBA")
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self.assertEqual(result.size, image.size)
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self.assertEqual(session.feeds["input"].shape, (1, 3, 1024, 1024))
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alpha = np.asarray(result.getchannel("A"))
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self.assertLess(int(alpha[:, 0].max()), int(alpha[:, -1].min()))
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if __name__ == "__main__":
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unittest.main()
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