nan
2ad30bf4a3
- Added `ben2_onnx.py` for BEN2 ONNX inference with preprocessing. - Introduced `da3_onnx.py` for Depth Anything 3 inference, including camera projection calculations. - Created `moge_onnx.py` for MoGe-2 ONNX inference and post-processing. - Implemented shared ONNX Runtime session helpers in `onnx_runtime.py`. - Added model synchronization script `sync_models.py` for downloading and verifying ONNX models. - Developed unit tests for BEN2, Depth Anything 3, and MoGe ONNX models to ensure functionality. - Updated `vendor.lock.toml` to remove unused artifacts.
54 lines
1.5 KiB
Python
54 lines
1.5 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 = Path(__file__).resolve().parents[1] / "src" / "snapform"
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sys.path.insert(0, str(SOURCE_DIR))
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import ben2_onnx
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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 = ben2_onnx.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 = ben2_onnx.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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