pi: keep per-sample intrinsics so overlays use the episode camera by RyanPCo · Pull Request #690 · GaTech-RL2/EgoVerse

RyanPCo · 2026-09-24T03:19:35Z

PI overlay videos on non-Aria data drew GT and predictions with the wrong camera.

Problem. PI.process_batch_for_training keeps only keymap keys plus episode_hash. intrinsics is not a keymap key, so it was dropped, viz_gt_preds got no per-sample camera, and Human.viz fell back to ARIA_INTRINSICS. On Mecka that puts every overlay point about 58 px too low (cy 240 vs 182) and pushes points outward (fx about 7% larger). HPT and RDT keep unknown keys (zarr_key_to_keyname(...) or key), so their overlays were already right.

Verified by rebuilding one episode's GT: with the episode's intrinsics it matches the RDT video; with the Aria intrinsics it matches the pi05 video.

Fix. Carry intrinsics forward alongside episode_hash.

Scope. Only the videos were wrong. Neither training nor validation metrics read intrinsics: actions, state and loss are all 3D.

Test. test_pi_process_batch_keeps_the_per_sample_intrinsics.

🤖 Generated with Claude Code

https://claude.ai/code/session_01NxUsbpS65QBRpBiauTEtTs

Folded in (to keep the stack under Graphite's 50-branch cap):

eval: replay maps chunk steps to real frames for resampled chunks #691 — eval: replay maps chunk steps to real frames for resampled chunks

RyanPCo · 2026-09-24T03:20:00Z

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pi: keep per-sample intrinsics so overlays use the episode camera #690 : 3 dependent PRs (#691 , #707 , #716 ) 👈 (View in Graphite)
rdt: long-range visual memory; image memory window and history stride in seconds #689
norm stats: cache sampled rows per episode so splits share them #687 : 1 other dependent PR (#688 )
data: exclude episodes by hash; scan Mecka egomotion for failed head tracking #686
eval: log validation videos to wandb #684 : 1 other dependent PR (#685 )
slurm: stop cancelled jobs from leaking whole GPUs #682 : 1 other dependent PR (#683 )
algo: RDT (Robotics Diffusion Transformer) on the HPT chassis #680 : 1 other dependent PR (#681 )
pi: initialize from PaliGemma with a scratch action expert #677 : 1 other dependent PR (#678 )
perf: vectorized image augs and a torch.compile knob for HPT and Pi; Pi clips gradients like openpi #676
loader: one zarr read per key, not one per keymap entry #674
eval: build the pinned-episode video batches only where they are used #673
checkpoints: last.ckpt as a symlink, not a second full copy #672
trainHydra: metric_frames_per_episode: auto, and warn on empty prompts #671
transforms: per-step wrist framing for proprio history #670
viz: one keypoint renderer, the horizon trace #669
configs: history and backbone-LR knobs on the keypoint wrist HPT model #668
trainer: ddp_bf16_compress, DDP with a bf16 gradient all-reduce #667
eval: overlay videos play at the source rate under DDP #666
hpt: build the policy on CPU and let Lightning place it #664 : 1 other dependent PR (#665 )
hpt: never re-initialise pretrained stems; hash-check them; mask text padding #645 : 1 other dependent PR (#646 )
obs: proprio history (K proprio steps, one token each, learned time embedding) #650
loss: mask the repeat-last action padding out of the HPT loss #649
resnet: keep a frozen backbone in eval mode; give backbones their own LR group #652
augs: per-sample image augmentation for HPT train-mode augs #651
eval: deterministic validation (seeded prompts + flow sampling); log-only grad-norm MAD #644
data: mecka_fold_flagship_top3_hpt_6d, the 3-operator flagship train split #643
val: subsampled metric loaders + pinned contiguous video loaders #642
infra: mecka 6D / keypoint recipes, 300M models, defaults, and the rollout revert #640
eval: layout-aware frame-invariant metrics, and a train-split viz head #659
data: stop letting one bad episode or a rotation channel take a run down #658
norm: guard degenerate channels, horizon pooling, and no rot6d normalization #657
keypoints: the 144-D wrist-first hand-keypoint action becomes the human default #656
mecka: fix the double-mirrored LEFT wrist frame, and correct existing zarrs #655
6d: PI goes 6D-only; Eva and Human gain the continuous-rotation modes #654
6d: continuous rotation representation, bimanual layouts, shared norm helpers #653
train: give each Slurm slurm job gets its own cache dir to avoid conflict #638 : 1 other dependent PR (#639 )
write git version into the run dir for reproducibility #637
train: don't load PI base weights on resume and verify that checkpoint path exists #636
configs: declare proprio and actions dims once per HPT model config #634 : 1 other dependent PR (#635 )
cache norm stats for data configs to minimize recomputation #633
import cloud packages lazily to support local training #631 : 1 other dependent PR (#632 )
cache dataset resolver for faster training initialization #630
validate episode hashes in data config before training #628
modify pi camera map to match data #629
add tests to ensure training works between every model and data vendor #626
run CI tests on every PR #625
add env variables for user-specific paths #624
build: set pyproject and uv as the dependency managers #623
fix(configs): fix paths for data hydra yaml configs #622
refactor(algo): delete ACT #627 : 1 other dependent PR (#621 )
tests: create dedicated test folder with unit and integration tests #620
add pyright type checker #619
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…an default Human data trains on hand keypoints instead of a palm-origin cartesian pose. The action is 144-D wrist-first, per hand [wrist xyz (3) | wrist rot6d (6) | 21 MANO keypoints (63)] x {left, right}, built by the keypoints_{wrist,head}frame_6d modes: the existing ypr keypoint builders produce the 138-D layout and KeypointsYPRToRot6D re-encodes just the wrist rotation, leaving the keypoint blocks untouched. Both the action and the observations.state.keypoints proprio are converted, so neither carries a wrapping euler angle. MANO keypoint 0 IS the wrist, so per hand the wrist position rides at both ch 0-2 and ch 9-11; that is kept deliberately (21 keypoints is the MANO interface every consumer reshapes to) and written down in the bimanual_keypoint_layout docstring. include_ee_pose additionally builds the cartesian head-frame ee_pose next to the keypoint action (keymap + transform list), so a keypoint pi0.5 run discretizes the same State: prompt block as the cartesian runs and differs from them only in the action space. The cartesian builder runs with delete_target_world=False to keep obs_head_pose alive for the keypoint builder, and its actions_cartesian by-product is dropped. 144 does not fit openpi's action vector, whose PyTorch port hard-codes 32-wide action projections regardless of Pi0Config.action_dim. PI now reads action_dim and, when it is not 32, swaps in action_dim-wide action_in_proj / action_out_proj AFTER the strict base-weight load: only those two layers start fresh, everything else comes from the checkpoint, and sample_actions already draws noise at config.action_dim. pad_to_width zero-pads narrower embodiments (eva's 20-D) into the same vector and raises rather than truncating a wider one. _reduce_loss then restricts the mean to the slots the converter actually packs, so eva does not average its loss over 112 zero-padded dims and contribute a fraction of the gradient signal it had at width 32. HumanBimanualKeypoints is identity-in, slice-out. HPT human configs move to a 144-D state_keypoints stem. hpt_bc_flow_human holds the body -- there is no vendor notion at the embodiment level, so the canonical config is the embodiment one -- and hpt_bc_flow_{mecka,aria,scale} are two-line aliases of it. HPTModel.add_stem drops null stem entries so a child config can delete a stem its base declares, which hydra merges cannot express, and hpt_cotrain_flow_seperate_head uses it to drop state_ee_pose. With the keypoints_*_6d modes in place, the guard from the previous commit widens to the keypoints ypr and quat modes, so every remaining discontinuous rotation target needs an explicit allow_legacy_rotation=True (the aria->MANO and mecka keypoint viz tutorials, which read pre-6D data). The cartesian configs that were still pinned to the ypr widths move with it: hpt_bc_flow_human_cartesian and the cotrain base to human 20/18, the shared-head cotrains to a 20-wide shared action. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VBmhpVy7ZcMtsBJWh38cJp

Wrist-relative actions made a latent division safe-looking: the t=0 cell of a wrist-frame chunk is exactly the identity pose, so its quantile range is 0 and the historical `(q99 - q1) + 1e-6` denominator scaled any off-convention value by 1e6. A channel whose range is below NORM_MIN_RANGE (1e-4) is now treated as constant -- it normalizes to 0 and unnormalizes to its centre rather than dividing by epsilon. Diffusion Policy / robomimic ignore ranges under 1e-4 and GR00T maps min == max to 0; this is the same rule, applied in the shared _apply_norm_one / _apply_unnorm_one so the dataset and the tests agree. norm_stats.pool_horizon (default false, so nothing changes unless asked) computes one set of stats per channel over every timestep of the chunk, the way openpi does, instead of one per (timestep, channel) cell. The pooled stats are tiled back to the (T, D) shape, so normalize, the bounds check and checkpoints see the same layout either way. It is part of the norm-stat cache key, so flipping it cannot silently reuse stats computed the other way. precompute_norm_stats.py computes and caches the stats for a recipe outside a training run, keyed by the same file + content hash. Which of the two is better is unsettled: the literature is split (LBM per-timestep with a stated rationale, openpi / RDT / GR00T / Diffusion Policy / UMI / lerobot / Octo pooled) and nothing here has compared them on a real recipe, so the default stays the inherited per-timestep one and the config comment says what would make it worth flipping. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VBmhpVy7ZcMtsBJWh38cJp

A 6D rotation is two columns of a rotation matrix: already bounded in [-1, 1], and per-channel centre-and-scale pulls the pair off the manifold without buying any resolution. The two projects that anchor an action chunk at near-identity the way this repo does -- TRI's LBM and UMI -- both exclude rotation from normalization for exactly that reason (LBM: "We avoid this normalization procedure for the 6D rotation in the poses to avoid corrupting the rotation matrix"; UMI sends its rotation channels through an identity normalizer). Per timestep AND normalized was the one combination with no precedent, and it is what manufactures the t=0 zero-range rotation cells NORM_MIN_RANGE exists to catch -- this removes them at the source. rot6d_channels() reads the indices off the existing layout tables, so the rule follows the layout rather than a second hardcoded list: the cartesian 18/20 widths and the 144-D keypoint layout, action and proprio alike. The ypr widths keep the old behaviour; an Euler angle is not a matrix column and those modes are legacy-only now. _apply_norm_one / _apply_unnorm_one take the channels and pass them through untouched, so the dataset path, the evaluator revert and the rollout revert all follow from one place, and the degenerate-channel guard is left to do what it is actually good at: the structural kp0 / kp9 position zeros. Norm-stat files stay valid -- what changes is how they are applied -- but a run before this commit and a run after it are not comparable, and the loss is now weighted differently between translation (unit scale) and rotation (raw). Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VBmhpVy7ZcMtsBJWh38cJp

…e-rank trainer/ddp.yaml and ddp_pi.yaml set devices to gpus_per_node * nodes next to num_nodes, but Lightning reads devices per node and multiplies by num_nodes itself, so a 2-node run asked each node for twice the GPUs it has. The submitit launchers made the same mistake in gres, which Slurm also reads per node. Both now use gpus_per_node; single-node runs are unchanged. trainHydra scales each val head's frames-per-episode K by devices * num_nodes, but eval mode forces devices=1 only after the loaders are built. An eval run of a multi-GPU recipe therefore kept W times the frames per episode that its single rank's limit_val_batches window holds, and the window ran out before the last episodes. _trainer_world_size now returns 1 in eval mode. Both predate this stack (the ddp devices expression is from 2025-01). Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UkAZwERXcFKjueZQKfRF6J

Each mp4 EvalVideo writes is also logged to any WandbLogger under <video dir>/<embodiment> (videos/, videos_train_viz/, videos_unseen_op_valid/), captioned with the epoch, so runs can be reviewed without copying files off the cluster. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01415567AQYskZLeSpSMsgwL

viz_mode=replay plays each action chunk twice over the frames it spans: first the ground-truth hand at each step, then the predicted one. Each step is drawn on the frame it belongs to, with the chunk mapped into that frame's camera by a rigid fit of the GT keypoints (the same hand at the same instant in both frames), so head motion needs no head pose. The 126-D keypoint layout draws both hands' 21-keypoint skeletons; other layouts fall back to the embodiment's own viz over a short trail. Chunks don't overlap and span horizon * action_stride frames; frames of an incomplete chunk carry over to the next flush. viz_max_batches must cover at least one chunk (a 100-step horizon needs ~13 batches of 8). _visualize_preds moves from HPTEvalVideo / PIEvalVideo (identical copies) into EvalVideo. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01516MaELbiPNmJzkjPGebhu

Replay becomes EvalVideo's default. It needs consecutive frames, so it runs only on a pinned video loader; a head without one keeps the overlay video, and EvalLatent stays on overlay. The replay video loader is capped at replay_chunks (5) chunks instead of viz_max_batches, which at 8 frames a batch could not cover one 100-step chunk; viz_max_batches still bounds a loader that yields no clips. action_stride defaults to None and is inferred from the GT keypoints (step k of a chunk rigidly matches frame k*stride's own step 0): data configs use 1 or 3 and cotrain mixes them under one embodiment. TrainVizEvalVideo passes the replay settings through and tells its base which mode each step is. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01516MaELbiPNmJzkjPGebhu

2,866 of 41,607 local Mecka episodes carry one head pose for the whole episode (no tracking) and 142 have single-frame head jumps of 0.3-3.7 m. Both corrupt the head-frame action chunks: with a frozen pose, head motion leaks into the hand trajectory. The vendor's egomotion is passed through unchanged by mecka_to_zarr, so the fault is upstream. - DatasetFilter(exclude_hashes=...): a list, a file, or a blocklist name under egomimic/resources/blocklists/. Excluded episodes never match; an excluded pin is dropped with a warning (an all-excluded pin list raises, since no pins means no filter). - scan_egomotion.py classifies every local episode's obs_head_pose and writes the blocklist; mecka_bad_egomotion.txt holds its 3,008 hashes. - mecka_all_6d applies it (this reshuffles its seeded train/valid split). - mecka_20pct_6d: 393 of 4,471 train pins drop out. Its valid and unseen_op_valid video episodes were two of the frozen-pose episodes; both are repinned to packaging_food episodes with tracked egomotion (69b5691e... seen operator, 69b525ff... held-out operator). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01516MaELbiPNmJzkjPGebhu

Norm stats were cached per split, so a new episode set (20% -> full, an operator hold-out, newly collected data) resampled every episode. Each episode's sampled proprio/action rows are now cached on their own under <cache_dir>/episodes/, keyed by the resolver recipe and reader code but not by the split, and a split's stats are computed from the union of its episodes' rows. Frames sit on a power-of-two stride with a per-episode phase, so a file cached at stride s serves every coarser stride. Adds norm_stats.max_samples (now part of the split key; KEY_VERSION 3). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0118RhoJmzcWC75B3vtc3zkF

…andas Startup on the 41.6k-episode full-mecka split spent ~20 min before training: populate_from_datasets read a full sample from every episode to learn the post-transform keys, though a resolver hands every episode the same key_map and transform objects. It now probes one episode per (embodiment, key_map, transform) group, trying up to 8 before falling back to the raw key_map. Resolving pinned filters was ~50 s each: a row-wise df.apply(row.to_dict()) over the ~458k-row episode table plus a per-pin .loc in _check_pins. Pinned filters now narrow the table to their hashes first, rows are filtered as records, and _check_pins builds one dict of pinned rows. Same paths on the real table; 41.6k pins 2.5 s, val pins ~0 s, unpinned lab filter 32 s -> 13 s. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0118RhoJmzcWC75B3vtc3zkF

…ide in seconds Human.get_keymap(image_memory=N, image_memory_stride_s=s) adds <front>_mem: N front frames s seconds apart, the newest s seconds back, plus a (N,) <front>_mem_mask of the steps inside the episode. history_stride_s spaces proprio history in seconds, so windows span the same time at any fps. History windows are now point reads instead of a contiguous span thinned afterwards: on JPEG keys (one frame per chunk) a span would fetch every frame between steps. plan_reads still merges them on numeric keys. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UL4UaiqSDNbhpatNwdvJbe

…-step eval MSE _stem_history_len called .get on the policy's stems, an nn.ModuleDict once built, so every proprio-history (K > 1) run crashed at its first batch. The unit tests used a plain dict. The last-step MSE handed torchmetrics a strided [:, -1] slice, which it .view(-1)s; on GPU .cpu() copied it contiguous, on a CPU run it raised. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UL4UaiqSDNbhpatNwdvJbe

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UL4UaiqSDNbhpatNwdvJbe

…lation arms RDTMemory: N past frames of one camera through a frozen DINOv3 tower (only its proj trains), a Perceiver resampler shared across frames pooling each to M latents, a sin-cos code of each frame's age in seconds, and optionally that step's proprio token (fuse_proprio). The N x M tokens join the DiT's self-attention prefix [t; freq; state; memory; actions] under a key mask, so steps before the episode start and dropped memories (trunk.memory.dropout) are never attended. Without trunk.memory the model is unchanged. Four train configs on mecka 20% 6D, a 2x2 over proprio history and visual memory (10 steps, 1 s apart): _base, _proprio, _vision, _both. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UL4UaiqSDNbhpatNwdvJbe

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UL4UaiqSDNbhpatNwdvJbe

Follows the dual-encoder design of diffusion-policy-experiments: - the memory tower is a second DINOv3, fine-tuned at the backbone LR scale (the param-group walk now covers every module with backbone_parameters), pooling each frame to its CLS token; the Perceiver resampler is gone - the window ends at the current frame (t-9 s .. t) - short_dropout (0.3 in the memory arms) replaces a sample's short image stream by a learned null token, replacing whole-memory dropout Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UL4UaiqSDNbhpatNwdvJbe

Backprop through 320 frames / GPU OOMed an H100; with the tower's blocks recomputed the both arm trains at 1.19 s / step on 8xH100. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UL4UaiqSDNbhpatNwdvJbe

PI.process_batch_for_training kept only keymap keys plus episode_hash, so batch['intrinsics'] never reached viz_gt_preds and Human.viz fell back to ARIA_INTRINSICS. Every PI overlay video on non-Aria data was drawn with the wrong camera (about 58 px low on Mecka). Metrics were unaffected. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NxUsbpS65QBRpBiauTEtTs

The keypoint pipeline resamples 30 raw frames to a 100-step chunk, so a mecka step is 29/99 of a frame, but the replay drew step k on frame a + k*stride with an integer stride (inferred as 1). GT and prediction played ~3.4x slow and the head-motion fit paired different hand poses: 15-50 mm between the drawn GT and the frame's own label on the pinned valid episode, 1.6 mm mean after this change. render_replay takes a fractional stride and draws each real frame with the step that falls on it; infer_stride searches D/(H-1) for whole spans D. Integer strides render as before. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015Kf5s9frd6T6cbBvapfjFy

This was referenced Sep 24, 2026

slurm: stop cancelled jobs from leaking whole GPUs #682

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trainer: DDP devices are per node; eval-mode val subsampling is single-rank #683

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eval: log validation videos to wandb #684

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aidang3019 mentioned this pull request Sep 29, 2026

eva: joint-space mode for the ABC YAM action space; robot image history #702

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aidang3019 force-pushed the ryanco/rdt-memory branch from ed3d274 to ff9dc58 Compare September 30, 2026 01:06

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aidang3019 mentioned this pull request Sep 30, 2026

ponytail(u1): drop bench/ and duplicate viz scripts, reuse gpu_orphans in the reaper #703

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RyanPCo force-pushed the ryanco/pi-overlay-intrinsics branch from 70140ee to ecf46e5 Compare October 12, 2026 20:26

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RyanPCo mentioned this pull request Oct 19, 2026

lance migration #716

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RyanPCo and others added 3 commits October 19, 2026 19:36

RyanPCo and others added 16 commits October 19, 2026 15:44

configs: mecka 20% 6D pinned split, loaner submitit launcher

4af2f47

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UL4UaiqSDNbhpatNwdvJbe

configs: record the memory arms' measured step cost

715067a

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UL4UaiqSDNbhpatNwdvJbe

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pi: keep per-sample intrinsics so overlays use the episode camera - #690

pi: keep per-sample intrinsics so overlays use the episode camera#690
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