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johnnynunez
f53490c15e
Isaac-GR00T crops a random crop_fraction window during training and the deterministic center window at eval, replaying the sampled window across all camera views of a sample. This contract is unchanged since the N1.5 release (gr00t/data/transform/video.py: "If mode is 'train', return a random crop transform. If mode is 'eval', return a center crop transform.") and mirrors LeRobot's own Diffusion/VQBeT crop_is_random pattern. The LeRobot N1.7 port used the eval center crop for training too, so the fine-tuned projector/DiT never sees frame borders and trains on a single fixed appearance point. Scope: crop geometry ONLY - no color jitter, no new dependencies. The random window is plain numpy slicing inside the existing cv2 eval transform: - _transform_n1_7_image_for_vlm_albumentations gains crop_position=(y, x) fractions; None keeps the center crop byte-identical to before (verified by test) - GrootN17VLMEncodeStep gains a runtime-only 'training' flag (never serialized; reloaded pipelines default to eval); training samples ONE window per sample and reuses it across (timestep, view) frames - Isaac's cross-view consistency - gated on torch.is_grad_enabled() so no_grad validation and frozen-eval paths are unaffected - wired via dataset_meta is not None in make_groot_pre_post_processors and the existing _set_groot_preprocessor_training on serialized reloads Verification: tests/policies/groot/test_groot_train_random_crop.py (8 passed: center-crop bit-exactness with crop_position=None, corner/center windows, cross-view replay, train!=eval, no_grad gating, seed reproducibility, serialization contract) + groot suite 23 passed / 5 skipped on RTX PRO 6000 / CUDA 13.3.