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.
1020 lines
35 KiB
Python
1020 lines
35 KiB
Python
import os
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import sys
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import tempfile
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import unittest
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from contextlib import nullcontext
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from pathlib import Path
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from types import ModuleType, SimpleNamespace
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from unittest.mock import patch
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import numpy as np
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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def load_server_module(name):
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addon_directory = PROJECT_ROOT / "src" / "anyimage"
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sys.path.insert(0, str(addon_directory))
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try:
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return __import__(f"server.{name}", fromlist=[name])
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finally:
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sys.path.remove(str(addon_directory))
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def _make_fake_av(frame_count):
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"""Build a fake av module yielding frame_count integer-valued frames."""
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class FakeImage:
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def __init__(self, value):
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self.value = value
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def save(self, path, format=None):
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Path(path).write_text(str(self.value), encoding="utf-8")
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class FakeFrame:
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def __init__(self, value):
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self.value = value
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def to_image(self):
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return FakeImage(self.value)
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class FakeContainer:
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def __init__(self):
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self.position = 0
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self.closed = False
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self.streams = SimpleNamespace(video=[object()])
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def decode(self, _stream):
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while self.position < frame_count:
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frame = FakeFrame(self.position)
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self.position += 1
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yield frame
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def close(self):
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self.closed = True
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class FakeAv:
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def __init__(self):
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self.containers = []
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def open(self, _path):
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container = FakeContainer()
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self.containers.append(container)
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return container
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return FakeAv()
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class FakeModels:
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def __init__(self, directory="model"):
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self.model_directory = Path(directory)
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def directory(self, _model):
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return self.model_directory
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def get_ben2(self, _model_directory, _device):
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from server.models.onnx_ben2 import create_session
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return create_session(self.model_directory, "cpu"), 0.0
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def get_da3(self, _model_directory, _device):
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return object()
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def get_upscale(self, _model_directory, _device):
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return object()
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class FakeJobContext:
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def __init__(self, directory, job_type, models=None):
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self.directory = Path(directory)
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self.job_type = job_type
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self.models = models or FakeModels()
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self.status = {}
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self.logs = []
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def resource(self, name):
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if name != "model_manager":
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raise KeyError(name)
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return self.models
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def progress(self, progress, message, **details):
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self.status = {
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"progress": progress,
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"message": message,
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**details,
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}
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def log(self, message, *, level="INFO"):
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self.logs.append((level, message))
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def check_cancelled(self):
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return None
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class ServerJobsTest(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.media = load_server_module("media.input")
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cls.resolution = load_server_module("media.resolution")
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cls.geometry = load_server_module("geometry")
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cls.background = load_server_module("jobs.remove_background")
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cls.ben2 = load_server_module("models.ben2")
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cls.da3_model = load_server_module("models.da3")
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cls.moge2_model = load_server_module("models.moge2")
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cls.upscale_model = load_server_module("models.upscale")
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cls.onnx_upscale = load_server_module("models.onnx_upscale")
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cls.debug_da3_outputs = load_server_module("outputs.debug_da3")
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cls.debug_moge2_outputs = load_server_module("outputs.debug_moge2")
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cls.debug_jobs = load_server_module("jobs.debug_predictions")
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cls.upscale_job = load_server_module("jobs.upscale")
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cls.download = load_server_module("model_download")
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def test_upscale_progress_omits_single_frame_count(self):
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self.assertEqual(
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self.upscale_job.frame_progress_message("Upscaling image", 0, 1),
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"Upscaling image",
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)
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self.assertEqual(
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self.upscale_job.frame_progress_message("Upscaling image", 1, 3),
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"Upscaling image (2/3)",
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)
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def test_collect_images_is_sorted_and_filters_suffixes(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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(root / "b.PNG").touch()
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(root / "a.jpg").touch()
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(root / "notes.txt").touch()
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images = self.media.collect_images(root)
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self.assertEqual([path.name for path in images], ["a.jpg", "b.PNG"])
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def test_ai_tasks_support_cooperative_cancellation(self):
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with self.assertRaises(self.media.TaskCancelled):
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self.media.check_cancelled(lambda: True)
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self.media.check_cancelled(lambda: False)
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def test_prepares_a_single_image_file(self):
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with tempfile.TemporaryDirectory() as directory:
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image_path = Path(directory) / "input.png"
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image_path.touch()
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images, temporary_directory = self.media.prepare_input(
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image_path,
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16,
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directory,
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)
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self.assertEqual(images, [image_path])
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self.assertIsNone(temporary_directory)
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def test_prepares_a_frame_directory(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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(root / "frame-0001.jpg").touch()
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(root / "frame-0002.png").touch()
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(root / "notes.txt").touch()
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images, temporary_directory = self.media.prepare_input(
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root,
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16,
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directory,
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)
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self.assertEqual(
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[path.name for path in images],
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["frame-0001.jpg", "frame-0002.png"],
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)
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self.assertIsNone(temporary_directory)
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def test_prepares_first_frames_of_a_frame_directory(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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for index in range(1, 6):
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(root / f"frame-{index:04d}.jpg").touch()
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images, temporary_directory = self.media.prepare_input(
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root,
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3,
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directory,
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)
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self.assertEqual(
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[path.name for path in images],
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["frame-0001.jpg", "frame-0002.jpg", "frame-0003.jpg"],
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)
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self.assertIsNone(temporary_directory)
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def test_prepares_first_frames_after_offset_in_a_frame_directory(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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for index in range(1, 6):
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(root / f"frame-{index:04d}.jpg").touch()
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images, temporary_directory = self.media.prepare_input(
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root,
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3,
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directory,
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start_frame=2,
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)
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self.assertEqual(
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[path.name for path in images],
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["frame-0002.jpg", "frame-0003.jpg", "frame-0004.jpg"],
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)
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self.assertIsNone(temporary_directory)
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def test_copy_color_images_numbers_a_still_image(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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source = root / "source.png"
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source.write_bytes(b"frame")
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output = root / "result"
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output.mkdir()
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self.media.copy_color_images([source], output)
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self.assertEqual(
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(output / "image.0001.png").read_bytes(),
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b"frame",
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)
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def test_copy_frames_skips_a_still_image_by_default(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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source = root / "source.png"
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source.touch()
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output = root / "result"
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output.mkdir()
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self.media.copy_frames([source], output)
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self.assertEqual(list(output.iterdir()), [])
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def test_extract_video_frames_reads_frames_sequentially(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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fake_av = _make_fake_av(100)
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image_paths = self.media.extract_video_frames(
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root / "clip.mkv",
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root / "out",
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10,
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fake_av,
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)
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self.assertEqual(
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[path.name for path in image_paths],
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[f"frame.{index:04d}.jpg" for index in range(1, 11)],
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)
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self.assertEqual(len(fake_av.containers), 1)
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self.assertEqual(fake_av.containers[0].position, 10)
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def test_extract_video_frames_starts_after_offset(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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fake_av = _make_fake_av(100)
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image_paths = self.media.extract_video_frames(
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root / "clip.mkv",
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root / "out",
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10,
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fake_av,
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start_frame=71,
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)
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extracted = [
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Path(path).read_text(encoding="utf-8")
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for path in image_paths
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]
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self.assertEqual(extracted, [str(frame) for frame in range(70, 80)])
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self.assertEqual(fake_av.containers[0].position, 80)
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def test_extract_video_frames_tolerates_short_video_tail(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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image_paths = self.media.extract_video_frames(
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root / "clip.mkv",
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root / "out",
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10,
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_make_fake_av(99),
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)
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self.assertEqual(
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[path.name for path in image_paths],
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[f"frame.{index:04d}.jpg" for index in range(1, 11)],
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)
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def test_rejects_unsupported_input_file(self):
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with tempfile.TemporaryDirectory() as directory:
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input_path = Path(directory) / "input.txt"
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input_path.touch()
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with self.assertRaisesRegex(RuntimeError, "Unsupported input type"):
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self.media.prepare_input(input_path, 16, directory)
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def test_download_model_uses_actual_request_without_preflight(self):
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calls = []
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fake_hugging_face = ModuleType("huggingface_hub")
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fake_hugging_face.snapshot_download = lambda **options: calls.append(
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options
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)
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args = SimpleNamespace(
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repository="depth-anything/DA3-SMALL",
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model_dir="model",
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progress=lambda *_args, **_kwargs: None,
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allow_pattern=[],
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)
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with tempfile.TemporaryDirectory() as directory, patch.dict(
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sys.modules,
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{"huggingface_hub": fake_hugging_face},
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), patch.object(
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self.download,
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"create_download_progress_class",
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return_value=object,
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), patch.dict(os.environ, {"HF_ENDPOINT": ""}), patch.object(
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self.download,
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"_verify_model_complete",
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return_value=None,
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):
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args.model_dir = str(Path(directory) / "models" / "DA3")
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self.download.download_model(args)
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self.assertEqual(len(calls), 1)
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def test_download_model_refuses_success_when_files_missing(self):
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fake_hugging_face = ModuleType("huggingface_hub")
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fake_hugging_face.snapshot_download = lambda **options: None
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args = SimpleNamespace(
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repository="PramaLLC/BEN2",
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model_dir="model",
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progress=lambda *_args, **_kwargs: None,
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allow_pattern=[],
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)
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with tempfile.TemporaryDirectory() as directory, patch.dict(
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sys.modules,
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{"huggingface_hub": fake_hugging_face},
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), patch.object(
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self.download,
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"create_download_progress_class",
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return_value=object,
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), patch.dict(os.environ, {"HF_ENDPOINT": ""}):
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args.model_dir = str(Path(directory) / "model")
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with self.assertRaisesRegex(
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RuntimeError,
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"All download sources failed",
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):
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self.download.download_model(args)
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def test_download_model_reports_all_failed_sources(self):
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args = SimpleNamespace(
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repository="depth-anything/DA3-SMALL",
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model_dir="model",
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progress=lambda *_args, **_kwargs: None,
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allow_pattern=[],
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r2_directory="DA3Small",
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r2_file=["config.json|100|abc"],
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)
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with tempfile.TemporaryDirectory() as directory, patch.object(
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self.download,
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"download_from_hf",
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side_effect=OSError("direct down"),
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), patch.object(
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self.download,
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"download_from_r2",
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side_effect=OSError("mirror down"),
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):
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args.model_dir = str(Path(directory) / "model")
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with self.assertRaisesRegex(RuntimeError, "models.omoolab.xyz"):
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self.download.download_model(args)
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def test_download_model_prefers_direct_hf_then_r2(self):
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args = SimpleNamespace(
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repository="depth-anything/DA3-SMALL",
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model_dir="model",
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progress=lambda *_args, **_kwargs: None,
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allow_pattern=["config.json"],
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r2_directory="DA3Small",
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r2_file=["config.json|100|abc"],
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)
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order = []
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|
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def fake_download_from_hf(*_args, **_kwargs):
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order.append("hf")
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raise OSError("hf down")
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|
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def fake_download_from_r2(*_args, **_kwargs):
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order.append("r2")
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|
|
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with tempfile.TemporaryDirectory() as directory, patch.object(
|
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self.download,
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"download_from_hf",
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side_effect=fake_download_from_hf,
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), patch.object(
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self.download,
|
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"download_from_r2",
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side_effect=fake_download_from_r2,
|
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), patch.object(
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self.download,
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"_verify_model_complete",
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return_value=None,
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):
|
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args.model_dir = str(Path(directory) / "models" / "DA3")
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self.download.download_model(args)
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|
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self.assertEqual(order, ["hf", "r2"])
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|
|
|
def test_download_from_r2_downloads_declared_files(self):
|
|
args = SimpleNamespace(
|
|
repository="depth-anything/DA3-SMALL",
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model_dir="model",
|
|
progress=lambda *_args, **_kwargs: None,
|
|
allow_pattern=["config.json"],
|
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r2_directory="DA3Small",
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r2_file=["config.json|100|abc"],
|
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)
|
|
downloaded = []
|
|
|
|
with tempfile.TemporaryDirectory() as directory, patch.object(
|
|
self.download,
|
|
"_download_file_from_url",
|
|
side_effect=lambda *args, **kwargs: downloaded.append(args[1].name),
|
|
):
|
|
args.model_dir = str(Path(directory) / "model")
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self.download.download_from_r2(args, args.model_dir)
|
|
|
|
self.assertEqual(downloaded, ["config.json"])
|
|
|
|
def test_ben2_background_removal_writes_fast_transparent_png(self):
|
|
inference_options = []
|
|
save_options = []
|
|
|
|
class FakeImage:
|
|
size = (1, 1)
|
|
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, *_args):
|
|
return None
|
|
|
|
def convert(self, mode):
|
|
self.mode = mode
|
|
return self
|
|
|
|
def getchannel(self, _name):
|
|
return self
|
|
|
|
def putalpha(self, _alpha):
|
|
return None
|
|
|
|
def paste(self, *_args):
|
|
return None
|
|
|
|
def save(self, path, **options):
|
|
save_options.append(options)
|
|
Path(path).touch()
|
|
|
|
class FakeModel:
|
|
@classmethod
|
|
def from_pretrained(cls, model_dir):
|
|
self.assertEqual(model_dir, "model")
|
|
return cls()
|
|
|
|
def to(model_self, device):
|
|
self.assertEqual(device.type, "cpu")
|
|
return model_self
|
|
|
|
def eval(self):
|
|
return None
|
|
|
|
def inference(self, image, **options):
|
|
inference_options.append((image, options))
|
|
return FakeImage()
|
|
|
|
fake_torch = ModuleType("torch")
|
|
fake_torch.cuda = SimpleNamespace(
|
|
is_available=lambda: False,
|
|
synchronize=lambda: None,
|
|
)
|
|
fake_torch.device = lambda device_type: SimpleNamespace(type=device_type)
|
|
fake_torch.inference_mode = nullcontext
|
|
fake_ben2 = ModuleType("server.models.onnx_ben2")
|
|
fake_ben2.create_session = lambda _model_dir, _device: object()
|
|
fake_ben2.infer = lambda _model, image: (
|
|
inference_options.append((image, {})) or FakeImage()
|
|
)
|
|
fake_pil = ModuleType("PIL")
|
|
fake_pil.Image = SimpleNamespace(
|
|
open=lambda _path: FakeImage(),
|
|
new=lambda *_args, **_kwargs: FakeImage(),
|
|
)
|
|
fake_pil.ImageChops = SimpleNamespace(
|
|
multiply=lambda _source, _foreground: FakeImage()
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as directory, patch.dict(
|
|
sys.modules,
|
|
{
|
|
"torch": fake_torch,
|
|
"server.models.onnx_ben2": fake_ben2,
|
|
"PIL": fake_pil,
|
|
},
|
|
):
|
|
root = Path(directory)
|
|
input_path = root / "input.png"
|
|
input_path.touch()
|
|
context = FakeJobContext(root / "result", "remove-background")
|
|
self.background.run(
|
|
context,
|
|
{"input": str(input_path), "model": "BEN2", "device": "cpu"},
|
|
)
|
|
|
|
self.assertTrue((root / "result" / "image.0001.png").is_file())
|
|
|
|
self.assertEqual(
|
|
inference_options[0][1],
|
|
{},
|
|
)
|
|
self.assertEqual(
|
|
save_options[0],
|
|
{"format": "PNG", "compress_level": 1},
|
|
)
|
|
self.assertIn("Background removed in", context.status["message"])
|
|
|
|
def test_ben2_preserves_input_alpha_and_hides_transparent_rgb(self):
|
|
from PIL import Image
|
|
|
|
onnx_ben2 = load_server_module("models.onnx_ben2")
|
|
source_pixels = np.asarray(
|
|
(((255, 0, 0, 0), (0, 255, 0, 128)),),
|
|
dtype=np.uint8,
|
|
)
|
|
model_result = Image.new("RGBA", (2, 1), (10, 20, 30, 128))
|
|
received = []
|
|
|
|
def infer(_model, image):
|
|
received.append(np.asarray(image.convert("RGB"), dtype=np.uint8))
|
|
return model_result
|
|
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
input_path = Path(directory) / "input.png"
|
|
Image.fromarray(source_pixels, mode="RGBA").save(input_path)
|
|
with (
|
|
patch.object(onnx_ben2, "create_session", return_value=object()),
|
|
patch.object(onnx_ben2, "infer", side_effect=infer),
|
|
):
|
|
result, _load_ms, _inference_ms = self.ben2.infer_foreground(
|
|
input_path,
|
|
"model",
|
|
"cpu",
|
|
preserve_input_alpha=True,
|
|
)
|
|
raw_result, _load_ms, _inference_ms = self.ben2.infer_foreground(
|
|
input_path,
|
|
"model",
|
|
"cpu",
|
|
)
|
|
|
|
inference_pixels = received[0]
|
|
result_pixels = np.asarray(result.convert("RGBA"), dtype=np.uint8)
|
|
self.assertEqual(tuple(inference_pixels[0, 0]), (0, 0, 0))
|
|
self.assertEqual(tuple(inference_pixels[0, 1]), (0, 128, 0))
|
|
self.assertEqual(tuple(received[1][0, 0]), (0, 0, 0))
|
|
self.assertEqual(tuple(received[1][0, 1]), (0, 128, 0))
|
|
self.assertEqual(tuple(result_pixels[0, 0]), (255, 0, 0, 0))
|
|
self.assertEqual(tuple(result_pixels[0, 1]), (0, 255, 0, 64))
|
|
self.assertEqual(
|
|
tuple(np.asarray(raw_result.convert("RGBA"))[0, 0]),
|
|
(10, 20, 30, 128),
|
|
)
|
|
|
|
def test_ben2_background_removal_processes_each_frame(self):
|
|
inference_options = []
|
|
|
|
class FakeImage:
|
|
size = (1, 1)
|
|
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, *_args):
|
|
return None
|
|
|
|
def convert(self, mode):
|
|
self.mode = mode
|
|
return self
|
|
|
|
def getchannel(self, _name):
|
|
return self
|
|
|
|
def putalpha(self, _alpha):
|
|
return None
|
|
|
|
def paste(self, *_args):
|
|
return None
|
|
|
|
def save(self, path, **options):
|
|
Path(path).touch()
|
|
|
|
class FakeModel:
|
|
@classmethod
|
|
def from_pretrained(cls, model_dir):
|
|
return cls()
|
|
|
|
def to(model_self, device):
|
|
return model_self
|
|
|
|
def eval(self):
|
|
return None
|
|
|
|
def inference(self, image, **options):
|
|
inference_options.append((image, options))
|
|
return FakeImage()
|
|
|
|
fake_torch = ModuleType("torch")
|
|
fake_torch.cuda = SimpleNamespace(
|
|
is_available=lambda: False,
|
|
synchronize=lambda: None,
|
|
)
|
|
fake_torch.device = lambda device_type: SimpleNamespace(type=device_type)
|
|
fake_torch.inference_mode = nullcontext
|
|
fake_ben2 = ModuleType("server.models.onnx_ben2")
|
|
fake_ben2.create_session = lambda _model_dir, _device: object()
|
|
fake_ben2.infer = lambda _model, image: (
|
|
inference_options.append((image, {})) or FakeImage()
|
|
)
|
|
fake_pil = ModuleType("PIL")
|
|
fake_pil.Image = SimpleNamespace(
|
|
open=lambda _path: FakeImage(),
|
|
new=lambda *_args, **_kwargs: FakeImage(),
|
|
)
|
|
fake_pil.ImageChops = SimpleNamespace(
|
|
multiply=lambda _source, _foreground: FakeImage()
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as directory, patch.dict(
|
|
sys.modules,
|
|
{
|
|
"torch": fake_torch,
|
|
"server.models.onnx_ben2": fake_ben2,
|
|
"PIL": fake_pil,
|
|
},
|
|
):
|
|
root = Path(directory)
|
|
input_directory = root / "frames"
|
|
input_directory.mkdir()
|
|
(input_directory / "frame-0001.jpg").touch()
|
|
(input_directory / "frame-0002.jpg").touch()
|
|
context = FakeJobContext(root / "result", "remove-background")
|
|
self.background.run(
|
|
context,
|
|
{
|
|
"input": str(input_directory),
|
|
"model": "BEN2",
|
|
"device": "cpu",
|
|
"video_frames": 8,
|
|
},
|
|
)
|
|
|
|
result_directory = context.directory
|
|
self.assertTrue(
|
|
(result_directory / "image.0001.png").is_file()
|
|
)
|
|
self.assertTrue(
|
|
(result_directory / "image.0002.png").is_file()
|
|
)
|
|
|
|
self.assertEqual(len(inference_options), 2)
|
|
for _image, options in inference_options:
|
|
self.assertEqual(options, {})
|
|
|
|
def test_da3_debug_saves_only_direct_api_prediction(self):
|
|
prediction = SimpleNamespace(
|
|
depth=np.full((1, 2, 2), 2.0, dtype=np.float32),
|
|
conf=np.ones((1, 2, 2), dtype=np.float32),
|
|
extrinsics=np.eye(4, dtype=np.float32)[None, :3],
|
|
intrinsics=np.eye(3, dtype=np.float32)[None],
|
|
processed_images=np.zeros((1, 2, 2, 3), dtype=np.uint8),
|
|
is_metric=0,
|
|
sky=None,
|
|
scale_factor=None,
|
|
)
|
|
fake_da3 = ModuleType("server.models.onnx_da3")
|
|
calls = []
|
|
|
|
def fake_infer(_session, images, **options):
|
|
calls.append((images, options))
|
|
return prediction
|
|
|
|
fake_da3.infer = fake_infer
|
|
fake_runtime = ModuleType("server.models.onnx_runtime")
|
|
fake_runtime.provider_label = lambda _session: "CPU"
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
root = Path(directory)
|
|
input_path = root / "input.png"
|
|
input_path.touch()
|
|
with patch.dict(
|
|
sys.modules,
|
|
{
|
|
"server.models.onnx_da3": fake_da3,
|
|
"server.models.onnx_runtime": fake_runtime,
|
|
},
|
|
):
|
|
context = FakeJobContext(root / "result", "debug-da3")
|
|
self.debug_jobs.run_da3(
|
|
context,
|
|
{
|
|
"input": str(input_path),
|
|
"model": "DA3",
|
|
"video_frames": 1,
|
|
"device": "cpu",
|
|
"process_res": 518,
|
|
"process_res_method": "lower_bound_resize",
|
|
"ref_view_strategy": "middle",
|
|
},
|
|
)
|
|
with np.load(context.directory / "prediction.npz") as result:
|
|
self.assertEqual(
|
|
set(result.files),
|
|
{
|
|
"depth",
|
|
"conf",
|
|
"extrinsics",
|
|
"intrinsics",
|
|
"processed_images",
|
|
"is_metric",
|
|
},
|
|
)
|
|
np.testing.assert_array_equal(result["conf"], prediction.conf)
|
|
self.assertFalse(any(context.directory.glob("*.glb")))
|
|
self.assertFalse(any(context.directory.glob("*.png")))
|
|
self.assertEqual(calls[0][0], [str(input_path)])
|
|
self.assertEqual(calls[0][1]["process_res"], 518)
|
|
|
|
def test_moge2_debug_saves_each_direct_api_dictionary(self):
|
|
prediction = {
|
|
"points": np.ones((2, 2, 3), dtype=np.float32),
|
|
"depth": np.ones((2, 2), dtype=np.float32),
|
|
"normal": np.ones((2, 2, 3), dtype=np.float32),
|
|
"mask": np.ones((2, 2), dtype=bool),
|
|
"intrinsics": np.eye(3, dtype=np.float32),
|
|
}
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
root = Path(directory)
|
|
frames = root / "frames"
|
|
frames.mkdir()
|
|
(frames / "a.png").touch()
|
|
(frames / "b.png").touch()
|
|
context = FakeJobContext(root / "result", "debug-moge2")
|
|
with patch.object(
|
|
self.debug_jobs,
|
|
"infer_moge2",
|
|
return_value=prediction,
|
|
) as infer_one:
|
|
self.debug_jobs.run_moge2(
|
|
context,
|
|
{
|
|
"input": str(frames),
|
|
"model": "MOGE2_VITS_NORMAL",
|
|
"video_frames": 2,
|
|
"device": "cpu",
|
|
"resolution_level": 5,
|
|
},
|
|
)
|
|
self.assertEqual(infer_one.call_count, 2)
|
|
archives = sorted(context.directory.glob("prediction.*.npz"))
|
|
self.assertEqual(len(archives), 2)
|
|
with np.load(archives[0]) as result:
|
|
self.assertEqual(set(result.files), set(prediction))
|
|
self.assertFalse(any(context.directory.glob("*.glb")))
|
|
|
|
def test_ben2_debug_saves_direct_api_foreground(self):
|
|
class Foreground:
|
|
def save(self, path):
|
|
Path(path).write_bytes(b"foreground")
|
|
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
root = Path(directory)
|
|
input_path = root / "input.png"
|
|
input_path.touch()
|
|
context = FakeJobContext(root / "result", "debug-ben2")
|
|
with patch.object(
|
|
self.debug_jobs,
|
|
"infer_foreground",
|
|
return_value=(Foreground(), 1.0, 2.0),
|
|
) as infer_foreground:
|
|
self.debug_jobs.run_ben2(
|
|
context,
|
|
{
|
|
"input": str(input_path),
|
|
"model": "BEN2_BASE",
|
|
"video_frames": 1,
|
|
"device": "cpu",
|
|
},
|
|
)
|
|
infer_foreground.assert_called_once()
|
|
self.assertEqual(
|
|
(context.directory / "foreground.png").read_bytes(),
|
|
b"foreground",
|
|
)
|
|
|
|
def test_upscale_debug_saves_each_direct_api_prediction(self):
|
|
from PIL import Image
|
|
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
root = Path(directory)
|
|
frames = root / "frames"
|
|
frames.mkdir()
|
|
(frames / "a.png").touch()
|
|
(frames / "b.png").touch()
|
|
context = FakeJobContext(root / "result", "debug-upscale")
|
|
with patch.object(
|
|
self.debug_jobs,
|
|
"infer_upscale",
|
|
return_value=Image.new("RGB", (8, 8), (10, 20, 30)),
|
|
) as infer_one:
|
|
self.debug_jobs.run_upscale(
|
|
context,
|
|
{
|
|
"input": str(frames),
|
|
"model": "REALESRGAN_GENERAL_WDN_X4V3",
|
|
"video_frames": 2,
|
|
"device": "cpu",
|
|
},
|
|
)
|
|
|
|
self.assertEqual(infer_one.call_count, 2)
|
|
predictions = sorted(context.directory.glob("prediction.*.png"))
|
|
self.assertEqual(len(predictions), 2)
|
|
with Image.open(predictions[0]) as prediction:
|
|
self.assertEqual(prediction.size, (8, 8))
|
|
self.assertEqual(prediction.mode, "RGB")
|
|
|
|
def test_upscale_job_saves_importable_upscale_sequence(self):
|
|
from PIL import Image
|
|
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
root = Path(directory)
|
|
frames = root / "frames"
|
|
frames.mkdir()
|
|
(frames / "a.png").touch()
|
|
(frames / "b.png").touch()
|
|
context = FakeJobContext(root / "result", "upscale-image")
|
|
with patch.object(
|
|
self.upscale_job,
|
|
"infer_one",
|
|
return_value=Image.new("RGB", (8, 8), (10, 20, 30)),
|
|
) as infer_one:
|
|
result = self.upscale_job.run(
|
|
context,
|
|
{
|
|
"input": str(frames),
|
|
"model": "REALESRGAN_GENERAL_WDN_X4V3",
|
|
"video_frames": 2,
|
|
"device": "cpu",
|
|
},
|
|
)
|
|
|
|
self.assertEqual(infer_one.call_count, 2)
|
|
self.assertEqual(result, {"upscale": "."})
|
|
colors = sorted(context.directory.glob("upscale.*.png"))
|
|
self.assertEqual(
|
|
[path.name for path in colors],
|
|
["upscale.0001.png", "upscale.0002.png"],
|
|
)
|
|
self.assertIn(
|
|
(
|
|
"INFO",
|
|
"Upscale model: REALESRGAN_GENERAL_WDN_X4V3; "
|
|
"maximum AI input size: 2048 px",
|
|
),
|
|
context.logs,
|
|
)
|
|
|
|
def test_upscale_job_preserves_source_alpha(self):
|
|
from PIL import Image
|
|
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
root = Path(directory)
|
|
source_path = root / "source.png"
|
|
pixels = np.zeros((3, 5, 4), dtype=np.uint8)
|
|
pixels[..., :3] = (10, 20, 30)
|
|
pixels[..., 3] = np.arange(5, dtype=np.uint8) * 60
|
|
Image.fromarray(pixels, mode="RGBA").save(source_path)
|
|
context = FakeJobContext(root / "result", "upscale-image")
|
|
prediction = Image.new("RGB", (20, 12), (10, 20, 30))
|
|
|
|
with patch.object(
|
|
self.onnx_upscale,
|
|
"infer",
|
|
return_value=prediction,
|
|
):
|
|
self.upscale_job.run(
|
|
context,
|
|
{
|
|
"input": str(source_path),
|
|
"model": "REALESRGAN_GENERAL_WDN_X4V3",
|
|
"video_frames": 1,
|
|
"device": "cpu",
|
|
},
|
|
)
|
|
|
|
with Image.open(context.directory / "upscale.0001.png") as result:
|
|
self.assertEqual(result.mode, "RGBA")
|
|
self.assertEqual(result.size, (20, 12))
|
|
alpha = np.asarray(result.getchannel("A"))
|
|
expected_alpha = np.asarray(
|
|
Image.fromarray(pixels[:, :, 3], mode="L").resize(
|
|
result.size,
|
|
Image.Resampling.LANCZOS,
|
|
)
|
|
)
|
|
np.testing.assert_array_equal(alpha, expected_alpha)
|
|
self.assertLess(
|
|
int(alpha[:, 0].max()),
|
|
int(alpha[:, -1].min()),
|
|
)
|
|
|
|
def test_upscale_input_uses_the_global_long_edge_limit(self):
|
|
from PIL import Image
|
|
|
|
source = Image.new("RGB", (3000, 1500))
|
|
limited = self.resolution.limit_image(source, 2048)
|
|
|
|
self.assertEqual(limited.size, (2048, 1024))
|
|
self.assertEqual(
|
|
self.resolution.limit_image(Image.new("RGB", (800, 600)), 2048).size,
|
|
(800, 600),
|
|
)
|
|
with self.assertRaisesRegex(ValueError, "between 1 and 8192"):
|
|
self.resolution.limit_image(source, 9000)
|
|
def test_upscale_resource_error_releases_session_and_is_readable(self):
|
|
from PIL import Image
|
|
|
|
class Models:
|
|
def __init__(self, directory):
|
|
self.model_directory = directory
|
|
self.released = False
|
|
|
|
def directory(self, _model):
|
|
return self.model_directory
|
|
|
|
def get_upscale(self, _directory, _device):
|
|
return object()
|
|
|
|
def release_upscale(self):
|
|
self.released = True
|
|
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
path = Path(directory) / "input.png"
|
|
Image.new("RGB", (8, 8)).save(path)
|
|
models = Models(Path(directory))
|
|
with patch.object(
|
|
self.onnx_upscale,
|
|
"infer",
|
|
side_effect=self.onnx_upscale.UpscaleResourceError(
|
|
"provider memory exhausted"
|
|
),
|
|
):
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"Reduce Maximum AI Input Size or select CPU",
|
|
):
|
|
self.upscale_model.infer_one(
|
|
models,
|
|
{
|
|
"model": "REALESRGAN_GENERAL_WDN_X4V3",
|
|
"device": "directml",
|
|
"max_input_size": 2048,
|
|
},
|
|
path,
|
|
)
|
|
|
|
self.assertTrue(models.released)
|
|
|
|
def test_da3_debug_writer_keeps_api_values_without_derived_attributes(self):
|
|
prediction = SimpleNamespace(
|
|
depth=np.ones((1, 2, 3), dtype=np.float32),
|
|
conf=np.arange(6, dtype=np.float32).reshape(1, 2, 3),
|
|
intrinsics=np.eye(3, dtype=np.float32)[None],
|
|
extrinsics=np.eye(4, dtype=np.float32)[None],
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as directory:
|
|
output_path = self.debug_da3_outputs.write_prediction(
|
|
prediction,
|
|
Path(directory) / "prediction.npz",
|
|
)
|
|
with np.load(output_path) as attributes:
|
|
self.assertEqual(attributes["depth"].shape, (1, 2, 3))
|
|
self.assertEqual(attributes["conf"].shape, (1, 2, 3))
|
|
self.assertNotIn("confidence_percentile", attributes.files)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|