nan
c5b77c5c85
- Introduced `onnx_moge2.py` for MoGe-2 ONNX inference with NumPy post-processing. - Added `onnx_ben2.py` for BEN2 ONNX inference with Pillow and NumPy preprocessing. - Created `onnx_da3.py` for Depth Anything 3 inference with ONNX Runtime and NumPy. - Implemented shared ONNX Runtime session helpers in `onnx_runtime.py`. - Developed unit tests for server functionalities in `test_server.py`, including model caching and job management. - Enhanced job server to handle frame extraction without requiring models.
59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
import sys
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import unittest
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from pathlib import Path
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from types import SimpleNamespace
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import numpy as np
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from PIL import Image
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SOURCE_DIR = (
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Path(__file__).resolve().parents[1]
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/ "src"
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/ "snapform"
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/ "server"
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)
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sys.path.insert(0, str(SOURCE_DIR))
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import onnx_ben2
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class FakeSession:
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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_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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