| |
| """Check release integrity, cohorts, camera arrays, and reference statistics.""" |
| import argparse |
| from collections import Counter |
| import hashlib |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
|
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|
|
| def check(condition, message): |
| if not condition: |
| raise ValueError(message) |
|
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|
|
| def main(): |
| ap = argparse.ArgumentParser(description=__doc__) |
| ap.add_argument('--root', type=Path, default=Path(__file__).resolve().parents[1]) |
| ap.add_argument('--require-images', action='store_true') |
| args = ap.parse_args() |
| root = args.root |
| for line in (root / 'checksums.sha256').read_text().splitlines(): |
| expected, relative = line.split(' ', 1) |
| path = root / relative |
| check(path.is_file(), f'Missing release file: {relative}') |
| check(hashlib.sha256(path.read_bytes()).hexdigest() == expected, f'Checksum mismatch: {relative}') |
|
|
| def rows(name): |
| return [json.loads(s) for s in (root / name).read_text().splitlines()] |
|
|
| t2v, i2v = rows('data/t2v/test.jsonl'), rows('data/i2v/test.jsonl') |
| sources = rows('sources/i2v_images.jsonl') |
| check(len(t2v) == len({r['scene_id'] for r in t2v}) == 100, 'T2V scene count or uniqueness mismatch') |
| check(len(i2v) == len({r['sample_id'] for r in i2v}) == 750, 'I2V sample count or uniqueness mismatch') |
| check(len(sources) == len({r['scene_id'] for r in i2v}) == 150, 'I2V scene count mismatch') |
| check(Counter(r['scene_type'] for r in sources) == {'indoor': 50, 'outdoor': 100}, 'Incorrect scene mix') |
| for row in t2v + i2v: |
| check(isinstance(row['seed'], str) and row['seed'].isdigit(), 'Seed must be a decimal string') |
| check(0 <= int(row['seed']) < 2**63, 'Seed out of range') |
| for row in t2v: |
| check(len(row['prompts']) == 4 and all(row['prompts']), 'Incomplete T2V prompt schedule') |
| check(row['prompt_start_seconds'] == [0.0, 3.6, 6.2, 11.2], 'T2V prompt timing mismatch') |
| cohorts = {} |
| for row in i2v: |
| cohorts.setdefault(row['setting'], set()).add(row['scene_id']) |
| with np.load(root / row['trajectory_path'], allow_pickle=False) as data: |
| p, k = data['poses'], data['intrinsics'] |
| check(p.shape == (row['pose_samples'], 4, 4), 'Pose shape mismatch') |
| check(k.shape == (row['intrinsics_samples'], 4), 'Intrinsics shape mismatch') |
| check(np.isfinite(p).all() and np.isfinite(k).all(), 'Non-finite camera values') |
| check(np.allclose(k, k[0]), 'Expected constant camera intrinsics') |
| check(np.allclose(p[0], p[-1], atol=1e-5), 'Trajectory does not return to its origin') |
| check(np.allclose(p[:, 3], [0, 0, 0, 1]), 'Invalid homogeneous pose') |
| r = p[:, :3, :3] |
| check(np.allclose(r.transpose(0, 2, 1) @ r, np.eye(3), atol=1e-5), 'Invalid rotation matrix') |
| check(np.allclose(np.linalg.det(r), 1, atol=1e-5), 'Invalid rotation determinant') |
| moving = np.max(np.linalg.norm(p[:, :3, 3] - p[0, :3, 3], axis=1)) > 1e-5 |
| check(bool(moving) == row['translation'], 'Translation flag disagrees with camera path') |
| all_scenes = {r['scene_id'] for r in sources} |
| outdoors = {r['scene_id'] for r in sources if r['scene_type'] == 'outdoor'} |
| for angle in [90, 180, 360]: |
| check(cohorts[f'rotation_{angle}'] == all_scenes, 'Rotation cohort mismatch') |
| check(cohorts[f'translation_{angle}'] == outdoors, 'Translation cohort mismatch') |
| pairs = rows('evaluation/t2v_reference_pairs.jsonl') |
| check(len(pairs) == 600, 'Missing reference annotations') |
| check(len({(p['scene_id'], p['method'], p['comparison_budget_chunks']) for p in pairs}) == 600, |
| 'Duplicate reference annotations') |
| for p in pairs: |
| check(0 <= p['departure_frame'] < p['revisit_frame'] < 379, 'Reference frame out of range') |
| summary = json.loads((root / 'evaluation/t2v_reference_summary.json').read_text()) |
| for s in summary: |
| subset = [p for p in pairs if p['method'] == s['method'] and p['comparison_budget_chunks'] == s['comparison_budget_chunks']] |
| check(len(subset) == s['n'] == 100, 'Reference cohort mismatch') |
| for key in ['clip', 'lpips_alex']: |
| check(np.isclose(np.median([p[key] for p in subset]), s[f'median_{key}']), 'Reference summary mismatch') |
| prepared = 0 |
| from PIL import Image |
| for row in sources: |
| path = root / row['image_path'] |
| if args.require_images: |
| check(path.is_file(), f'Missing conditioning image: {path}') |
| if path.is_file(): |
| image = Image.open(path).convert('RGB') |
| check(image.size == (832, 480), 'Conditioning image dimensions differ') |
| check(hashlib.sha256(image.tobytes()).hexdigest() == row['rgb_sha256'], 'Conditioning image differs') |
| prepared += 1 |
| print(json.dumps(dict(status='passed', t2v_samples=100, i2v_scenes=150, i2v_samples=750, |
| trajectory_files=750, t2v_reference_pairs=600, prepared_images=prepared), indent=2)) |
|
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|
|
| if __name__ == '__main__': |
| main() |
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|
