| { |
| "model": "vjepa2-ac-vitg", |
| "source": "/home/overseer2/workdir/jepa.cpp/models/vjepa2_ac/vjepa2-ac-vitg.pt", |
| "dtype": "float32", |
| "npy_dtype_note": "all tensors float32 C-order except frames_u8 (uint8) and top5_idx (int64)", |
| "framework": { |
| "torch": "2.13.0+cpu", |
| "transformers": "5.16.1", |
| "python": "3.12.13", |
| "threads": 16, |
| "cpu": "x86_64" |
| }, |
| "created": "2026-09-01T21:43:50+0300", |
| "source_url": "https://dl.fbaipublicfiles.com/vjepa2/vjepa2-ac-vitg.pt", |
| "code": "https://github.com/facebookresearch/vjepa2 (hubconf vjepa2_ac_vit_giant, src/models/ac_predictor.py, notebooks/utils/mpc_utils.py + world_model_wrapper.py), local clone at /home/overseer2/workdir/jepa.cpp/tmp/vjepa2-src", |
| "checkpoint": { |
| "top_level_keys": [ |
| "encoder", |
| "predictor", |
| "opt", |
| "scaler", |
| "target_encoder", |
| "epoch", |
| "loss", |
| "batch_size", |
| "world_size", |
| "lr" |
| ], |
| "encoder_key_used": "encoder", |
| "key_cleanup": "strip 'module.' and 'backbone.'", |
| "epoch": 315, |
| "encoder_equals_target_encoder": true |
| }, |
| "trajectory": { |
| "file": "notebooks/franka_example_traj.npz", |
| "frames": 2, |
| "frame_size_hw": [ |
| 256, |
| 256 |
| ], |
| "states": [ |
| [ |
| 0.5804390907287598, |
| -0.0020212342496961355, |
| 0.24755726754665375, |
| -3.066615581512451, |
| 0.030529797077178955, |
| -1.9127511978149414, |
| 0.9969713687896729 |
| ], |
| [ |
| 0.6728084683418274, |
| 0.028965970501303673, |
| 0.33183684945106506, |
| -3.0514373779296875, |
| 0.017709508538246155, |
| -1.9124950170516968, |
| 0.9969713687896729 |
| ] |
| ] |
| }, |
| "hparams": { |
| "embed_dim": 1408, |
| "n_layer": 40, |
| "n_head": 22, |
| "ffn_dim": 6144, |
| "patch_size": 16, |
| "tubelet_size": 2, |
| "img_size": 256, |
| "n_frames": 64, |
| "ln_eps": 1e-06, |
| "act": "gelu_erf", |
| "cls_token": false, |
| "pos_type": "rope3d", |
| "rope_theta": 10000.0, |
| "tokens_per_frame": 256, |
| "pred": { |
| "kind": "ac", |
| "embed_dim": 1024, |
| "n_layer": 24, |
| "n_head": 16, |
| "head_dim": 64, |
| "ffn_dim": 4096, |
| "ln_eps": 1e-06, |
| "action_dim": 7, |
| "state_dim": 7, |
| "out_dim": 1408, |
| "grid_size": 16, |
| "n_cond_tokens": 2, |
| "cond_order": "action,state", |
| "frame_causal": true, |
| "use_extrinsics": false, |
| "attn_mask_shape": [ |
| 8256, |
| 8256 |
| ], |
| "rope_dims_per_axis": [ |
| 20, |
| 20, |
| 20 |
| ], |
| "rope_grid_size": 16, |
| "rope_freq_layout": "tiled" |
| } |
| }, |
| "preprocessing": { |
| "description": "app/vjepa_droid/transforms.py::make_transforms(random_horizontal_flip=False, random_resize_scale=(1,1), random_resize_aspect_ratio=(1,1), crop_size=256): random_resized_crop degenerates to a centre crop of the largest square followed by torch F.interpolate(bilinear, align_corners=False, no antialias) to 256x256, then (x - 255*mean) / (255*std) on the CTHW float tensor. On the 256x256 Franka renders both the crop and the resize are the identity.", |
| "resize": { |
| "shortest_edge": 256, |
| "resample": "bilinear", |
| "antialias": false, |
| "on_dtype": "float32", |
| "reference_order": "centre-crop to the largest square, THEN resize to 256" |
| }, |
| "center_crop": 256, |
| "rescale": 0.00392156862745098, |
| "mean": [ |
| 0.485, |
| 0.456, |
| 0.406 |
| ], |
| "std": [ |
| 0.229, |
| 0.224, |
| 0.225 |
| ] |
| }, |
| "world_model": { |
| "encode": "one observation frame -> a 2-frame clip (the frame repeated along T, tubelet 2) -> 256 tokens after the encoder's final LayerNorm; then a NON-AFFINE LayerNorm over the feature dim (torch F.layer_norm default eps 1e-5). world_model_wrapper.py:42-52.", |
| "predict": "predictor(context, actions, states) returns T*tpf rows; the world model keeps the LAST tpf (the next frame) and normalises them again before feeding them back. world_model_wrapper.py:56-64.", |
| "state": "the pose of frame t; the pose after a step is compute_new_pose(pose, action) = (xyz + d_xyz, euler_xyz(R(d) @ R(pose)), clip(gripper + d_gripper, 0, 1)). mpc_utils.py:166-190.", |
| "energy": "l1(a, b) = mean |a - b| over the flattened [tpf * D] final state and goal. mpc_utils.py:17-18 and the loss_fn of energy_landscape_example.ipynb.", |
| "candidates": "4 fixed action sequences of 2 steps: the trajectory's ground-truth first action poses_to_diff(states[0], states[1]) and that action shifted by +0.05 along x, y and z, each repeated over the horizon." |
| }, |
| "outputs": { |
| "frames_u8": "the uint8 observation frame, repeated to the 2-frame clip, THWC", |
| "input": "NCTHW float32 (the 2-frame clip of one observation frame)", |
| "last_hidden_state": "encoder(input)[0]: [256, 1408] tokens after the final LN, h-major then w", |
| "context": "F.layer_norm(last_hidden_state) -- the predictor's input", |
| "goal": "context of the LAST trajectory frame", |
| "action/state": "[7] the action driving the step and the pose at the frame", |
| "pred_next": "predictor(context, action, state)[-256:] -- the next frame's latents, un-normalised", |
| "pred_seq": "predictor over T=2 frames, ALL T*tpf rows (row block t predicts frame t+1)", |
| "rollout": "[K, H, 256, 1408] normalised latents per candidate per step", |
| "rollout_energy": "[K] l1(rollout[:, -1], goal)", |
| "actions/states_seq": "[K, H, 7] the candidate actions and the poses compute_new_pose produced", |
| "next_state_ref": "[K, 7] compute_new_pose(state0, actions[:, 0]) -- the jepa_ac_next_state check" |
| }, |
| "timing_s": { |
| "model_load": 8.863, |
| "forward_total": 21.136, |
| "forward_mean": 2.642, |
| "wall_total": 21.193 |
| }, |
| "samples": [ |
| { |
| "name": "frame0", |
| "media": "franka_example_traj.npz[0]", |
| "timing_s": { |
| "preprocess_s": 0.0, |
| "forward_s": 0.7271 |
| }, |
| "tensors": { |
| "frames_u8": { |
| "file": "frame0.frames_u8.npy", |
| "shape": [ |
| 2, |
| 256, |
| 256, |
| 3 |
| ], |
| "dtype": "uint8", |
| "layout": "THWC uint8 (the observation frame, repeated to the 2-frame clip)" |
| }, |
| "input": { |
| "file": "frame0.input.npy", |
| "shape": [ |
| 1, |
| 3, |
| 2, |
| 256, |
| 256 |
| ], |
| "dtype": "float32", |
| "layout": "NCTHW (T=2, the frame repeated)" |
| }, |
| "last_hidden_state": { |
| "file": "frame0.last_hidden_state.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D] h-major then w" |
| }, |
| "pooled_mean": { |
| "file": "frame0.pooled_mean.npy", |
| "shape": [ |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[D]" |
| }, |
| "context": { |
| "file": "frame0.context.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D] after the non-affine LayerNorm" |
| } |
| }, |
| "frame_index": 0, |
| "frames": 2 |
| }, |
| { |
| "name": "frame1", |
| "media": "franka_example_traj.npz[1]", |
| "timing_s": { |
| "preprocess_s": 0.0, |
| "forward_s": 0.6842 |
| }, |
| "tensors": { |
| "frames_u8": { |
| "file": "frame1.frames_u8.npy", |
| "shape": [ |
| 2, |
| 256, |
| 256, |
| 3 |
| ], |
| "dtype": "uint8", |
| "layout": "THWC uint8 (the observation frame, repeated to the 2-frame clip)" |
| }, |
| "input": { |
| "file": "frame1.input.npy", |
| "shape": [ |
| 1, |
| 3, |
| 2, |
| 256, |
| 256 |
| ], |
| "dtype": "float32", |
| "layout": "NCTHW (T=2, the frame repeated)" |
| }, |
| "last_hidden_state": { |
| "file": "frame1.last_hidden_state.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D] h-major then w" |
| }, |
| "pooled_mean": { |
| "file": "frame1.pooled_mean.npy", |
| "shape": [ |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[D]" |
| }, |
| "context": { |
| "file": "frame1.context.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D] after the non-affine LayerNorm" |
| } |
| }, |
| "frame_index": 1, |
| "frames": 2 |
| }, |
| { |
| "name": "goalframe", |
| "media": "franka_example_traj.npz[1]", |
| "timing_s": { |
| "preprocess_s": 0.0, |
| "forward_s": 0.6708 |
| }, |
| "tensors": { |
| "frames_u8": { |
| "file": "goalframe.frames_u8.npy", |
| "shape": [ |
| 2, |
| 256, |
| 256, |
| 3 |
| ], |
| "dtype": "uint8", |
| "layout": "THWC uint8 (the observation frame, repeated to the 2-frame clip)" |
| }, |
| "input": { |
| "file": "goalframe.input.npy", |
| "shape": [ |
| 1, |
| 3, |
| 2, |
| 256, |
| 256 |
| ], |
| "dtype": "float32", |
| "layout": "NCTHW (T=2, the frame repeated)" |
| }, |
| "last_hidden_state": { |
| "file": "goalframe.last_hidden_state.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D] h-major then w" |
| }, |
| "pooled_mean": { |
| "file": "goalframe.pooled_mean.npy", |
| "shape": [ |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[D]" |
| }, |
| "context": { |
| "file": "goalframe.context.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D] after the non-affine LayerNorm" |
| } |
| }, |
| "frame_index": 1, |
| "frames": 2 |
| }, |
| { |
| "name": "step1", |
| "media": "franka_example_traj.npz[0]", |
| "timing_s": { |
| "preprocess_s": 0.0, |
| "forward_s": 0.2757 |
| }, |
| "tensors": { |
| "context": { |
| "file": "step1.context.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D]" |
| }, |
| "action": { |
| "file": "step1.action.npy", |
| "shape": [ |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[7]" |
| }, |
| "state": { |
| "file": "step1.state.npy", |
| "shape": [ |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[7]" |
| }, |
| "pred_next": { |
| "file": "step1.pred_next.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D] predictor output, T=1" |
| } |
| }, |
| "frames": 1 |
| }, |
| { |
| "name": "step2", |
| "media": "franka_example_traj.npz[0]", |
| "timing_s": { |
| "preprocess_s": 0.0, |
| "forward_s": 0.4742 |
| }, |
| "tensors": { |
| "context_seq": { |
| "file": "step2.context_seq.npy", |
| "shape": [ |
| 512, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[2*256, D]" |
| }, |
| "action_seq": { |
| "file": "step2.action_seq.npy", |
| "shape": [ |
| 2, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[2, 7]" |
| }, |
| "state_seq": { |
| "file": "step2.state_seq.npy", |
| "shape": [ |
| 2, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[2, 7]" |
| }, |
| "pred_seq": { |
| "file": "step2.pred_seq.npy", |
| "shape": [ |
| 512, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[2*256, D] all rows; block t predicts frame t+1" |
| } |
| }, |
| "frames": 2 |
| }, |
| { |
| "name": "rollout", |
| "media": "franka_example_traj.npz", |
| "timing_s": { |
| "preprocess_s": 0.0, |
| "forward_s": 2.2028 |
| }, |
| "tensors": { |
| "context": { |
| "file": "rollout.context.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D] shared seed" |
| }, |
| "goal": { |
| "file": "rollout.goal.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D]" |
| }, |
| "state0": { |
| "file": "rollout.state0.npy", |
| "shape": [ |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[7]" |
| }, |
| "actions": { |
| "file": "rollout.actions.npy", |
| "shape": [ |
| 4, |
| 2, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[4, 2, 7]" |
| }, |
| "states_seq": { |
| "file": "rollout.states_seq.npy", |
| "shape": [ |
| 4, |
| 2, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[4, 2, 7]" |
| }, |
| "rollout": { |
| "file": "rollout.rollout.npy", |
| "shape": [ |
| 4, |
| 2, |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[4, 2, 256, D]" |
| }, |
| "rollout_energy": { |
| "file": "rollout.rollout_energy.npy", |
| "shape": [ |
| 4 |
| ], |
| "dtype": "float32", |
| "layout": "[4]" |
| }, |
| "next_state_ref": { |
| "file": "rollout.next_state_ref.npy", |
| "shape": [ |
| 4, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[4, 7]" |
| } |
| }, |
| "candidates": 4, |
| "horizon": 2 |
| }, |
| { |
| "name": "rollout_seed2", |
| "media": "franka_example_traj.npz", |
| "timing_s": { |
| "preprocess_s": 0.0, |
| "forward_s": 3.5616 |
| }, |
| "tensors": { |
| "context": { |
| "file": "rollout_seed2.context.npy", |
| "shape": [ |
| 512, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[2*256, D] two OBSERVED frames" |
| }, |
| "goal": { |
| "file": "rollout_seed2.goal.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D]" |
| }, |
| "seed_actions": { |
| "file": "rollout_seed2.seed_actions.npy", |
| "shape": [ |
| 1, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[n_seed-1, 7] the action between the observed frames" |
| }, |
| "seed_states": { |
| "file": "rollout_seed2.seed_states.npy", |
| "shape": [ |
| 2, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[2, 7]" |
| }, |
| "actions": { |
| "file": "rollout_seed2.actions.npy", |
| "shape": [ |
| 4, |
| 2, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[4, 2, 7]" |
| }, |
| "states_seq": { |
| "file": "rollout_seed2.states_seq.npy", |
| "shape": [ |
| 4, |
| 2, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[4, 2, 7]" |
| }, |
| "rollout": { |
| "file": "rollout_seed2.rollout.npy", |
| "shape": [ |
| 4, |
| 2, |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[4, 2, 256, D]" |
| }, |
| "rollout_energy": { |
| "file": "rollout_seed2.rollout_energy.npy", |
| "shape": [ |
| 4 |
| ], |
| "dtype": "float32", |
| "layout": "[4]" |
| } |
| }, |
| "candidates": 4, |
| "horizon": 2, |
| "n_seed": 2 |
| }, |
| { |
| "name": "cem", |
| "media": "franka_example_traj.npz", |
| "timing_s": { |
| "preprocess_s": 0.0, |
| "forward_s": 12.5391 |
| }, |
| "tensors": { |
| "context": { |
| "file": "cem.context.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D]" |
| }, |
| "goal": { |
| "file": "cem.goal.npy", |
| "shape": [ |
| 256, |
| 1408 |
| ], |
| "dtype": "float32", |
| "layout": "[256, D]" |
| }, |
| "state0": { |
| "file": "cem.state0.npy", |
| "shape": [ |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[7]" |
| }, |
| "cem_noise": { |
| "file": "cem.cem_noise.npy", |
| "shape": [ |
| 3, |
| 2, |
| 8, |
| 4 |
| ], |
| "dtype": "float32", |
| "layout": "[3, 2, 8, 4] every torch.randn draw, in Meta's order" |
| }, |
| "cem_action": { |
| "file": "cem.cem_action.npy", |
| "shape": [ |
| 2, |
| 7 |
| ], |
| "dtype": "float32", |
| "layout": "[2, 7] the plan cem() returned" |
| } |
| }, |
| "cem": { |
| "samples": 8, |
| "topk": 4, |
| "cem_steps": 3, |
| "rollout": 2, |
| "maxnorm": 0.05, |
| "momentum_mean": 0.15, |
| "momentum_std": 0.75, |
| "momentum_mean_gripper": 0.15, |
| "momentum_std_gripper": 0.15 |
| } |
| } |
| ] |
| } |
