| # Universal zero-predictor for the leaderboard E2E canary. | |
| # | |
| # This is the SOURCE OF TRUTH for the toy model's predictor.py. Upload it to the HF repo | |
| # the canary submits (default MedOtter/zero-canary), at the repo ROOT as `predictor.py`: | |
| # | |
| # huggingface-cli upload MedOtter/zero-canary \ | |
| # scripts/e2e/zero-canary-predictor.py predictor.py --repo-type model | |
| # | |
| # It returns an all-background label map for ANY (C, Z, Y, X) volume, so it satisfies every | |
| # segmentation benchmark's contract (glioma MRI, abdominal CT, canary_tiny, …) with no | |
| # weights and no GPU. A full-task run therefore finishes fast. Scores are near-zero; the | |
| # canary only asserts the pipeline RAN, not that the model scored well. | |
| import numpy as np | |
| class _ZeroPredictor: | |
| def predict(self, volume): | |
| # volume: (C, Z, Y, X). The label map is spatial only -> drop the channel dim. | |
| z, y, x = volume.shape[-3:] | |
| return np.zeros((z, y, x), dtype=np.uint8) | |
| def load(): | |
| return _ZeroPredictor() | |