nan
e7aeeb21d4
- Implement sync_models.py for preparing and uploading ONNX artifacts. - Create build_node.py for building and validating Blender scripts. - Add docs.py for building and previewing documentation with Mike or MkDocs. - Introduce model_exports.py for exporting PyTorch models to ONNX format. - Develop pack.py for packaging the project with dependencies for different platforms. - Add test_sync_models.py to validate sync_models functionality. - Add test_tool_commands.py to ensure repository tools have correct command scripts. - Initialize tools package with __init__.py.
141 lines
4.3 KiB
Python
141 lines
4.3 KiB
Python
import sys
|
|
import unittest
|
|
from pathlib import Path
|
|
from types import SimpleNamespace
|
|
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
|
|
SOURCE_DIR = (
|
|
Path(__file__).resolve().parents[1]
|
|
/ "src"
|
|
/ "anyimage"
|
|
/ "server"
|
|
/ "models"
|
|
)
|
|
sys.path.insert(0, str(SOURCE_DIR))
|
|
|
|
import onnx_upscale
|
|
|
|
|
|
class FakeSession:
|
|
def get_inputs(self):
|
|
return [SimpleNamespace(name="input")]
|
|
|
|
def get_outputs(self):
|
|
return [SimpleNamespace(name="output")]
|
|
|
|
def run(self, output_names, feeds):
|
|
self.output_names = output_names
|
|
self.feeds = feeds
|
|
source = feeds["input"]
|
|
return [np.repeat(np.repeat(source, 4, axis=2), 4, axis=3)]
|
|
|
|
|
|
class UpscaleOnnxTest(unittest.TestCase):
|
|
def test_preprocess_matches_dynamic_rgb_layout(self):
|
|
image = Image.new("RGB", (7, 5), (64, 128, 192))
|
|
|
|
result = onnx_upscale.preprocess(image)
|
|
|
|
self.assertEqual(result.shape, (1, 3, 5, 7))
|
|
self.assertEqual(result.dtype, np.float32)
|
|
self.assertTrue(result.flags.c_contiguous)
|
|
np.testing.assert_allclose(
|
|
result[0, :, 0, 0],
|
|
np.array((64, 128, 192), dtype=np.float32) / 255.0,
|
|
)
|
|
|
|
def test_infer_returns_direct_four_x_rgb_prediction(self):
|
|
session = FakeSession()
|
|
image = Image.new("RGB", (7, 5), (10, 20, 30))
|
|
|
|
result = onnx_upscale.infer(session, image)
|
|
|
|
self.assertEqual(result.mode, "RGB")
|
|
self.assertEqual(result.size, (28, 20))
|
|
self.assertEqual(session.output_names, ["output"])
|
|
self.assertEqual(session.feeds["input"].shape, (1, 3, 256, 256))
|
|
self.assertEqual(result.getpixel((0, 0)), (10, 20, 30))
|
|
|
|
def test_infer_returns_raw_rgb_for_rgba_input(self):
|
|
session = FakeSession()
|
|
pixels = np.zeros((5, 7, 4), dtype=np.uint8)
|
|
pixels[..., :3] = (10, 20, 30)
|
|
pixels[..., 3] = np.arange(7, dtype=np.uint8) * 40
|
|
image = Image.fromarray(pixels, mode="RGBA")
|
|
|
|
result = onnx_upscale.infer(session, image)
|
|
|
|
self.assertEqual(result.mode, "RGB")
|
|
self.assertEqual(result.size, (28, 20))
|
|
self.assertEqual(result.getpixel((0, 0)), (10, 20, 30))
|
|
|
|
def test_tiles_use_reflected_context_at_outer_image_edges(self):
|
|
class RecordingSession(FakeSession):
|
|
def run(self, output_names, feeds):
|
|
self.input = feeds["input"].copy()
|
|
return super().run(output_names, feeds)
|
|
|
|
pixels = np.zeros((5, 7, 3), dtype=np.uint8)
|
|
pixels[..., 0] = np.arange(7, dtype=np.uint8) * 20
|
|
session = RecordingSession()
|
|
|
|
onnx_upscale.infer(
|
|
session,
|
|
Image.fromarray(pixels, mode="RGB"),
|
|
)
|
|
|
|
red = session.input[0, 0]
|
|
self.assertAlmostEqual(red[16, 22], 120 / 255.0)
|
|
self.assertAlmostEqual(red[16, 23], 100 / 255.0)
|
|
|
|
def test_infer_rejects_non_four_x_output(self):
|
|
class InvalidSession(FakeSession):
|
|
def run(self, _output_names, feeds):
|
|
return [feeds["input"]]
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "unexpected output shape"):
|
|
onnx_upscale.infer(
|
|
InvalidSession(),
|
|
Image.new("RGB", (7, 5)),
|
|
)
|
|
|
|
def test_all_models_split_large_inputs_into_fixed_tiles(self):
|
|
class CountingSession(FakeSession):
|
|
def __init__(self):
|
|
self.calls = 0
|
|
|
|
def run(self, output_names, feeds):
|
|
self.calls += 1
|
|
return super().run(output_names, feeds)
|
|
|
|
session = CountingSession()
|
|
result = onnx_upscale.infer(
|
|
session,
|
|
Image.new("RGB", (449, 225), (1, 2, 3)),
|
|
)
|
|
|
|
self.assertEqual(result.size, (1796, 900))
|
|
self.assertEqual(session.calls, 6)
|
|
self.assertEqual(session.feeds["input"].shape, (1, 3, 256, 256))
|
|
|
|
def test_provider_unicode_error_is_presented_as_resource_error(self):
|
|
class InvalidProviderSession(FakeSession):
|
|
def run(self, _output_names, _feeds):
|
|
raise UnicodeDecodeError("utf-8", b"\xc4", 0, 1, "invalid")
|
|
|
|
with self.assertRaisesRegex(
|
|
onnx_upscale.UpscaleResourceError,
|
|
"provider failed",
|
|
):
|
|
onnx_upscale.infer(
|
|
InvalidProviderSession(),
|
|
Image.new("RGB", (7, 5)),
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|