Expose aligned multi-view RGB for realtime final graphs
Browse files- gwam_v12_sparse_v2/README.md +38 -0
gwam_v12_sparse_v2/README.md
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@@ -2445,3 +2445,41 @@ view evidence: sparse masks / evidence per visible pair
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visual features: sparse visible-only visual_features_sparse.npz
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dynamic relations: contact and visibility_change JSONL events
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```
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visual features: sparse visible-only visual_features_sparse.npz
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dynamic relations: contact and visibility_change JSONL events
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```
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## Multi-view RGB / graph alignment contract
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For joint training, evaluation, and real-time inference, the model-facing online call is:
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```python
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snapshot = extractor.extract_final_graph(visual_backend=visual_backend)
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graph = snapshot["gnn_graph"] # x [N_real,342], edge_attr [E,8]
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rgb_frames = snapshot["rgb_frames"] # dict: view_id -> uint8[256,256,3]
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rgb_frame_cameras = snapshot["rgb_frame_cameras"] # ordered camera names for those view ids
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```
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All fields in one `snapshot` are produced from the same current simulator state before the policy/env step advances:
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```text
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current qpos/qvel/contact state
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+ current segmentation renders from the ordered camera list
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+ current RGB renders from the same ordered camera list
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+ current visible RLE masks indexed by (node slot n, view id v)
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+ SAM2 masked pooling on rgb_frames[v] using that same mask
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= final graph x [N_real,342]
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```
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The ordered multi-view camera contract is:
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```text
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v=0 robot0_agentview_right
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v=1 robot0_agentview_left
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v=2 robot0_eye_in_hand
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```
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Alignment rules for downstream integration:
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- `view_visible[n,v]`, `view_centroid[n,v]`, `view_area[n,v]`, `view_bbox[n,v]`, `rle_masks[*]["v"]`, `visual_features_sparse["v"]`, and `rgb_frames[v]` use the same view id.
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- `visual_features_sparse["n"]` uses the original padded node slot id; `gnn_graph["slot_ids"]` maps active GNN rows back to those padded slots.
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- `rgb_frames[v]` is an upright `uint8[256,256,3]` RGB frame rendered with the same vertical flip convention as the segmentation/mask path.
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- `save_realtime_graph_snapshot(snapshot, out_dir)` writes `rgb/view_<v>_<camera>.npy` plus `rgb_manifest.json`, so saved debug/eval snapshots contain graph data first and aligned RGB images alongside it.
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