nan
0cf04a13c6
- Added upscale operator to handle image upscaling by 4x using AI models. - Introduced new jobs for upscale processing in the server, including debug and standard upscale jobs. - Created a new ONNX model handler for upscale operations, supporting tiled inference and resource management. - Updated tests to cover new upscale functionality, including input validation and output verification. - Added documentation for the upscale feature, detailing usage and model options.
141 lines
4.3 KiB
Python
141 lines
4.3 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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/ "anyimage"
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/ "server"
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/ "models"
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)
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sys.path.insert(0, str(SOURCE_DIR))
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import onnx_upscale
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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="output")]
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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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source = feeds["input"]
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return [np.repeat(np.repeat(source, 4, axis=2), 4, axis=3)]
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class UpscaleOnnxTest(unittest.TestCase):
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def test_preprocess_matches_dynamic_rgb_layout(self):
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image = Image.new("RGB", (7, 5), (64, 128, 192))
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result = onnx_upscale.preprocess(image)
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self.assertEqual(result.shape, (1, 3, 5, 7))
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self.assertEqual(result.dtype, np.float32)
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self.assertTrue(result.flags.c_contiguous)
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np.testing.assert_allclose(
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result[0, :, 0, 0],
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np.array((64, 128, 192), dtype=np.float32) / 255.0,
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)
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def test_infer_returns_direct_four_x_rgb_prediction(self):
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session = FakeSession()
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image = Image.new("RGB", (7, 5), (10, 20, 30))
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result = onnx_upscale.infer(session, image)
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self.assertEqual(result.mode, "RGB")
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self.assertEqual(result.size, (28, 20))
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self.assertEqual(session.output_names, ["output"])
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self.assertEqual(session.feeds["input"].shape, (1, 3, 256, 256))
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self.assertEqual(result.getpixel((0, 0)), (10, 20, 30))
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def test_infer_returns_raw_rgb_for_rgba_input(self):
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session = FakeSession()
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pixels = np.zeros((5, 7, 4), dtype=np.uint8)
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pixels[..., :3] = (10, 20, 30)
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pixels[..., 3] = np.arange(7, dtype=np.uint8) * 40
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image = Image.fromarray(pixels, mode="RGBA")
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result = onnx_upscale.infer(session, image)
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self.assertEqual(result.mode, "RGB")
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self.assertEqual(result.size, (28, 20))
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self.assertEqual(result.getpixel((0, 0)), (10, 20, 30))
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def test_tiles_use_reflected_context_at_outer_image_edges(self):
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class RecordingSession(FakeSession):
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def run(self, output_names, feeds):
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self.input = feeds["input"].copy()
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return super().run(output_names, feeds)
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pixels = np.zeros((5, 7, 3), dtype=np.uint8)
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pixels[..., 0] = np.arange(7, dtype=np.uint8) * 20
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session = RecordingSession()
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onnx_upscale.infer(
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session,
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Image.fromarray(pixels, mode="RGB"),
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)
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red = session.input[0, 0]
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self.assertAlmostEqual(red[16, 22], 120 / 255.0)
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self.assertAlmostEqual(red[16, 23], 100 / 255.0)
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def test_infer_rejects_non_four_x_output(self):
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class InvalidSession(FakeSession):
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def run(self, _output_names, feeds):
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return [feeds["input"]]
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with self.assertRaisesRegex(RuntimeError, "unexpected output shape"):
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onnx_upscale.infer(
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InvalidSession(),
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Image.new("RGB", (7, 5)),
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)
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def test_all_models_split_large_inputs_into_fixed_tiles(self):
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class CountingSession(FakeSession):
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def __init__(self):
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self.calls = 0
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def run(self, output_names, feeds):
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self.calls += 1
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return super().run(output_names, feeds)
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session = CountingSession()
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result = onnx_upscale.infer(
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session,
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Image.new("RGB", (449, 225), (1, 2, 3)),
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)
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self.assertEqual(result.size, (1796, 900))
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self.assertEqual(session.calls, 6)
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self.assertEqual(session.feeds["input"].shape, (1, 3, 256, 256))
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def test_provider_unicode_error_is_presented_as_resource_error(self):
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class InvalidProviderSession(FakeSession):
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def run(self, _output_names, _feeds):
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raise UnicodeDecodeError("utf-8", b"\xc4", 0, 1, "invalid")
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with self.assertRaisesRegex(
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onnx_upscale.UpscaleResourceError,
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"provider failed",
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):
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onnx_upscale.infer(
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InvalidProviderSession(),
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Image.new("RGB", (7, 5)),
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)
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if __name__ == "__main__":
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unittest.main()
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