Update SnapForm to version 0.10.10 and add technical documentation
- Bump SnapForm version in uv.lock from 0.9.30 to 0.10.10. - Introduce comprehensive technical documentation for SnapForm, detailing features, usage instructions, and development guidelines.
This commit is contained in:
@@ -1,5 +1,4 @@
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import importlib.util
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import inspect
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import json
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import os
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import sys
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@@ -42,27 +41,11 @@ class WorkerTest(unittest.TestCase):
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self.assertEqual([path.name for path in images], ["a.jpg", "b.PNG"])
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def test_reconstruct_reports_status_before_loading_torch(self):
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source = inspect.getsource(self.worker.reconstruct)
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self.assertLess(
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source.index('"Starting AI runtime"'),
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source.index("import torch"),
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)
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def test_ai_tasks_support_cooperative_cancellation(self):
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with self.assertRaises(self.worker.TaskCancelled):
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self.worker.check_cancelled(lambda: True)
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self.worker.check_cancelled(lambda: False)
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self.assertIn(
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"cancel_check=cancel_check",
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inspect.getsource(self.worker.reconstruct),
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)
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self.assertIn(
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"cancel_check=cancel_check",
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inspect.getsource(self.worker.remove_background),
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)
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def test_select_device_supports_mps_and_auto_fallback(self):
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mps_torch = SimpleNamespace(
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@@ -115,13 +98,7 @@ class WorkerTest(unittest.TestCase):
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with self.assertRaisesRegex(RuntimeError, "MPS was requested"):
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self.worker.select_device("mps", cpu_only_torch)
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def test_optional_background_removal_runs_before_da3_model_load(self):
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source = inspect.getsource(self.worker.reconstruct)
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self.assertLess(
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source.index("infer_foreground("),
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source.index("DepthAnything3.from_pretrained"),
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)
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def test_reconstruct_parser_accepts_background_model_directory(self):
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args = self.worker.parser().parse_args(
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[
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"reconstruct",
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@@ -175,6 +152,199 @@ class WorkerTest(unittest.TestCase):
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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.worker.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.worker.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.worker.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_extract_video_frames_reads_frames_sequentially(self):
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class FakeCapture:
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def __init__(self):
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self.position = 0
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def isOpened(self):
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return True
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def read(self):
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if self.position >= 100:
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return False, None
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frame = self.position
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self.position += 1
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return True, frame
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def release(self):
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return None
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class FakeCv2:
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def __init__(self):
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self.captures = []
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def VideoCapture(self, _path):
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capture = FakeCapture()
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self.captures.append(capture)
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return capture
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def imwrite(self, path, frame):
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Path(path).write_text(str(frame), encoding="utf-8")
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return True
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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fake_cv2 = FakeCv2()
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image_paths = self.worker.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_cv2,
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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_cv2.captures), 1)
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self.assertEqual(fake_cv2.captures[0].position, 10)
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def test_extract_video_frames_starts_after_offset(self):
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class FakeCapture:
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def __init__(self):
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self.position = 0
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def isOpened(self):
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return True
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def read(self):
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if self.position >= 100:
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return False, None
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frame = self.position
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self.position += 1
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return True, frame
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def release(self):
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return None
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class FakeCv2:
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def __init__(self):
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self.captures = []
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def VideoCapture(self, _path):
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capture = FakeCapture()
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self.captures.append(capture)
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return capture
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def imwrite(self, path, frame):
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Path(path).write_text(str(frame), encoding="utf-8")
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return True
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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fake_cv2 = FakeCv2()
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image_paths = self.worker.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_cv2,
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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_cv2.captures[0].position, 80)
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def test_extract_video_frames_tolerates_short_video_tail(self):
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class ShortCapture:
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def __init__(self):
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self.position = 0
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def isOpened(self):
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return True
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def read(self):
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if self.position >= 99:
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return False, None
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frame = self.position
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self.position += 1
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return True, frame
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def release(self):
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return None
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class FakeCv2:
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def VideoCapture(self, _path):
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return ShortCapture()
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def imwrite(self, path, frame):
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Path(path).write_text(str(frame), encoding="utf-8")
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return True
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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image_paths = self.worker.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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FakeCv2(),
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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_npz_is_complete_requires_readable_archive(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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@@ -235,10 +405,6 @@ class WorkerTest(unittest.TestCase):
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self.worker,
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"create_download_progress_class",
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return_value=object,
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), patch.object(
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self.worker,
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"resolve_hf_endpoint",
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return_value=(self.worker.HF_ENDPOINT, False),
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):
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args.model_dir = str(Path(directory) / "model")
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args.status = str(Path(directory) / "status.json")
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@@ -248,13 +414,17 @@ class WorkerTest(unittest.TestCase):
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calls[0]["allow_patterns"],
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["config.json", "model.safetensors"],
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)
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self.assertEqual(calls[0]["endpoint"], self.worker.HF_ENDPOINT)
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def test_download_model_switches_to_mirror_before_hf_import(self):
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def test_download_model_falls_back_to_modelscope_when_direct_fails(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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def fake_snapshot_download(**options):
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calls.append(options)
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raise OSError("Connection reset by peer")
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fake_hugging_face.snapshot_download = fake_snapshot_download
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args = SimpleNamespace(
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repository="PramaLLC/BEN2",
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model_dir="model",
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@@ -269,23 +439,151 @@ class WorkerTest(unittest.TestCase):
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self.worker,
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"create_download_progress_class",
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return_value=object,
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), patch.object(
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self.worker,
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"resolve_hf_endpoint",
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return_value=("https://hf-mirror.com", True),
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), patch.dict(os.environ, {"HF_ENDPOINT": ""}), patch.object(
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self.worker,
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"write_status",
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) as mocked_write:
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"download_from_modelscope",
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) as mocked_modelscope:
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def fake_download_from_modelscope(*args, **kwargs):
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model_dir = Path(args[1])
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model_dir.mkdir(parents=True, exist_ok=True)
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(model_dir / "config.json").write_text("{}")
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(model_dir / "model.safetensors").write_bytes(b"x")
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mocked_modelscope.side_effect = fake_download_from_modelscope
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args.model_dir = str(Path(directory) / "model")
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args.status = str(Path(directory) / "status.json")
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self.worker.download_model(args)
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self.assertEqual(
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os.environ["HF_ENDPOINT"],
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"https://hf-mirror.com",
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)
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messages = [call.args[2] for call in mocked_write.call_args_list]
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self.assertTrue(any("(mirror)" in message for message in messages))
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self.assertEqual(len(calls), 1)
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self.assertEqual(calls[0]["endpoint"], self.worker.HF_ENDPOINT)
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self.assertEqual(mocked_modelscope.call_count, 1)
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def test_download_model_skips_unreachable_endpoint(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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status="status.json",
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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.worker,
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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.worker,
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"hf_endpoint_reachable",
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return_value=False,
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), patch.object(
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self.worker,
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"download_from_modelscope",
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) as mocked_modelscope, patch.object(
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self.worker,
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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) / "model")
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args.status = str(Path(directory) / "status.json")
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self.worker.download_model(args)
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self.assertEqual(calls, [])
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self.assertEqual(mocked_modelscope.call_count, 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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status="status.json",
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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.worker,
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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.worker,
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"download_from_modelscope",
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):
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args.model_dir = str(Path(directory) / "model")
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args.status = str(Path(directory) / "status.json")
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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.worker.download_model(args)
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def test_download_model_reports_all_failed_sources(self):
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fake_hugging_face = ModuleType("huggingface_hub")
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def fake_snapshot_download(**options):
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raise OSError("direct down")
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fake_hugging_face.snapshot_download = fake_snapshot_download
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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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status="status.json",
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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.worker,
|
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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.worker,
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"download_from_modelscope",
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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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args.status = str(Path(directory) / "status.json")
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with self.assertRaisesRegex(RuntimeError, "modelscope.cn"):
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self.worker.download_model(args)
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def test_download_from_modelscope_downloads_selected_files(self):
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args = SimpleNamespace(
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repository="depth-anything/DA3-SMALL",
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||||
model_dir="model",
|
||||
status="status.json",
|
||||
allow_pattern=["config.json"],
|
||||
)
|
||||
downloaded = []
|
||||
|
||||
with tempfile.TemporaryDirectory() as directory, patch.object(
|
||||
self.worker,
|
||||
"modelscope_file_list",
|
||||
return_value=[
|
||||
("config.json", 100),
|
||||
("model.safetensors", 200),
|
||||
(".gitattributes", 10),
|
||||
],
|
||||
), patch.object(
|
||||
self.worker,
|
||||
"_download_modelscope_file",
|
||||
side_effect=lambda *args, **kwargs: downloaded.append(args[1]),
|
||||
), patch.object(self.worker, "write_status"):
|
||||
args.model_dir = str(Path(directory) / "model")
|
||||
args.status = str(Path(directory) / "status.json")
|
||||
self.worker.download_from_modelscope(args, args.model_dir)
|
||||
|
||||
self.assertEqual(downloaded, ["config.json"])
|
||||
|
||||
def test_ben2_background_removal_writes_fast_transparent_png(self):
|
||||
inference_options = []
|
||||
@@ -368,6 +666,166 @@ class WorkerTest(unittest.TestCase):
|
||||
)
|
||||
self.assertIn("Background removed in", status["message"])
|
||||
|
||||
def test_ben2_background_removal_processes_each_frame(self):
|
||||
inference_options = []
|
||||
|
||||
class FakeImage:
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, *_args):
|
||||
return None
|
||||
|
||||
def convert(self, mode):
|
||||
self.mode = mode
|
||||
return self
|
||||
|
||||
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("ben2")
|
||||
fake_ben2.BEN_Base = FakeModel
|
||||
fake_pil = ModuleType("PIL")
|
||||
fake_pil.Image = SimpleNamespace(open=lambda _path: FakeImage())
|
||||
|
||||
with tempfile.TemporaryDirectory() as directory, patch.dict(
|
||||
sys.modules,
|
||||
{
|
||||
"torch": fake_torch,
|
||||
"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()
|
||||
args = SimpleNamespace(
|
||||
input=str(input_directory),
|
||||
model_dir="model",
|
||||
output_dir=str(root / "result"),
|
||||
status=str(root / "status.json"),
|
||||
device="cpu",
|
||||
video_frames=8,
|
||||
)
|
||||
self.worker.remove_background(args)
|
||||
|
||||
result_directory = Path(args.output_dir)
|
||||
self.assertTrue(
|
||||
(result_directory / "foreground.0001.png").is_file()
|
||||
)
|
||||
self.assertTrue(
|
||||
(result_directory / "foreground.0002.png").is_file()
|
||||
)
|
||||
self.assertTrue(
|
||||
(result_directory / "frame.0001.jpg").is_file()
|
||||
)
|
||||
self.assertTrue(
|
||||
(result_directory / "frame.0002.jpg").is_file()
|
||||
)
|
||||
|
||||
self.assertEqual(len(inference_options), 2)
|
||||
for _image, options in inference_options:
|
||||
self.assertEqual(options, {"refine_foreground": False})
|
||||
|
||||
def test_remove_background_parser_accepts_video_frames(self):
|
||||
args = self.worker.parser().parse_args(
|
||||
[
|
||||
"remove-background",
|
||||
"--input",
|
||||
"input.mp4",
|
||||
"--model-dir",
|
||||
"ben2",
|
||||
"--output-dir",
|
||||
"result",
|
||||
"--status",
|
||||
"status.json",
|
||||
"--device",
|
||||
"auto",
|
||||
"--video-frames",
|
||||
"24",
|
||||
]
|
||||
)
|
||||
|
||||
self.assertEqual(args.command, "remove-background")
|
||||
self.assertEqual(args.video_frames, 24)
|
||||
|
||||
def test_extract_frames_parser_accepts_frame_range(self):
|
||||
args = self.worker.parser().parse_args(
|
||||
[
|
||||
"extract-frames",
|
||||
"--input",
|
||||
"frames",
|
||||
"--output-dir",
|
||||
"result",
|
||||
"--status",
|
||||
"status.json",
|
||||
"--video-frames",
|
||||
"24",
|
||||
"--start-frame",
|
||||
"70",
|
||||
]
|
||||
)
|
||||
|
||||
self.assertEqual(args.command, "extract-frames")
|
||||
self.assertEqual(args.video_frames, 24)
|
||||
self.assertEqual(args.start_frame, 70)
|
||||
|
||||
def test_extract_frames_copies_selected_frames(self):
|
||||
with tempfile.TemporaryDirectory() as directory:
|
||||
root = Path(directory)
|
||||
input_directory = root / "frames"
|
||||
input_directory.mkdir()
|
||||
for index in range(1, 6):
|
||||
(input_directory / f"frame-{index:04d}.jpg").touch()
|
||||
args = SimpleNamespace(
|
||||
input=str(input_directory),
|
||||
output_dir=str(root / "result"),
|
||||
status=str(root / "status.json"),
|
||||
video_frames=3,
|
||||
start_frame=2,
|
||||
)
|
||||
|
||||
self.worker.extract_frames(args)
|
||||
|
||||
result_directory = Path(args.output_dir)
|
||||
self.assertEqual(
|
||||
sorted(
|
||||
path.name
|
||||
for path in result_directory.iterdir()
|
||||
if path.is_file()
|
||||
),
|
||||
["frame.0001.jpg", "frame.0002.jpg", "frame.0003.jpg"],
|
||||
)
|
||||
status = json.loads(
|
||||
Path(args.status).read_text(encoding="utf-8")
|
||||
)
|
||||
self.assertEqual(status["progress"], 1.0)
|
||||
self.assertIn("3 frame", status["message"])
|
||||
|
||||
def test_uses_official_da3_export_parameters_for_all_four_formats(self):
|
||||
inference_options = []
|
||||
export_calls = []
|
||||
@@ -500,6 +958,95 @@ class WorkerTest(unittest.TestCase):
|
||||
infer_gs_values = [options["infer_gs"] for _, options in inference_options]
|
||||
self.assertEqual(infer_gs_values, [False, True, False, False])
|
||||
|
||||
def test_reconstruct_copies_frames_for_multi_frame_input(self):
|
||||
class FakeModel:
|
||||
@classmethod
|
||||
def from_pretrained(cls, model_dir):
|
||||
return cls()
|
||||
|
||||
def to(self, device):
|
||||
return self
|
||||
|
||||
def eval(self):
|
||||
return None
|
||||
|
||||
def inference(self, images, **options):
|
||||
return SimpleNamespace()
|
||||
|
||||
def fake_export(prediction, export_format, export_dir, **options):
|
||||
mini_directory = Path(export_dir) / "exports" / "mini_npz"
|
||||
mini_directory.mkdir(parents=True)
|
||||
np.savez_compressed(
|
||||
mini_directory / "results.npz",
|
||||
depth=np.zeros((2, 14, 14)),
|
||||
)
|
||||
|
||||
fake_torch = ModuleType("torch")
|
||||
fake_torch.device = lambda device_type: SimpleNamespace(
|
||||
type=device_type,
|
||||
)
|
||||
fake_torch.inference_mode = nullcontext
|
||||
fake_api = ModuleType("depth_anything_3.api")
|
||||
fake_api.DepthAnything3 = FakeModel
|
||||
fake_export_module = ModuleType("depth_anything_3.utils.export")
|
||||
fake_export_module.export = fake_export
|
||||
fake_utils = ModuleType("depth_anything_3.utils")
|
||||
fake_utils.__path__ = []
|
||||
fake_package = ModuleType("depth_anything_3")
|
||||
fake_package.__path__ = []
|
||||
|
||||
with tempfile.TemporaryDirectory() as directory:
|
||||
root = Path(directory)
|
||||
input_directory = root / "frames"
|
||||
input_directory.mkdir()
|
||||
(input_directory / "frame-0001.jpg").touch()
|
||||
(input_directory / "frame-0002.jpg").touch()
|
||||
with patch.dict(
|
||||
sys.modules,
|
||||
{
|
||||
"torch": fake_torch,
|
||||
"depth_anything_3": fake_package,
|
||||
"depth_anything_3.api": fake_api,
|
||||
"depth_anything_3.utils": fake_utils,
|
||||
"depth_anything_3.utils.export": fake_export_module,
|
||||
},
|
||||
):
|
||||
args = SimpleNamespace(
|
||||
input=str(input_directory),
|
||||
video_frames=16,
|
||||
output_dir=str(root / "result"),
|
||||
export_format="mini_npz",
|
||||
status=str(root / "status.json"),
|
||||
device="cpu",
|
||||
model_dir="model",
|
||||
process_res=518,
|
||||
process_res_method="upper_bound_resize",
|
||||
use_ray_pose=1,
|
||||
ref_view_strategy="first",
|
||||
conf_thresh_percentile=40.0,
|
||||
num_max_points=1000,
|
||||
show_cameras=0,
|
||||
gs_views_interval=1,
|
||||
remove_background_model_dir="",
|
||||
)
|
||||
self.worker.reconstruct(args)
|
||||
|
||||
result_directory = Path(args.output_dir)
|
||||
self.assertTrue(
|
||||
(result_directory / "frame.0001.jpg").is_file()
|
||||
)
|
||||
self.assertTrue(
|
||||
(result_directory / "frame.0002.jpg").is_file()
|
||||
)
|
||||
self.assertTrue(
|
||||
(result_directory / "results.npz").is_file()
|
||||
)
|
||||
self.assertFalse(
|
||||
(result_directory / "exports").exists()
|
||||
)
|
||||
status = json.loads(Path(args.status).read_text(encoding="utf-8"))
|
||||
self.assertEqual(status["progress"], 1.0)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
Reference in New Issue
Block a user