Video-depth PointDiT-B, update 32500, EMA2

video-depth-pointdit/README.md

@@ -0,0 +1,173 @@
+# Video-depth PointDiT-B (update 32,500, EMA2)
+
+A temporal PointDiT-B that predicts, for every frame of a short video, that frame's depth in its own camera. It is
+the video-depth expert of the vgg-forecast project (`github.com/NIRVANALAN/vgg_forecast_4d`), initialized from the
+released PointDiT-B 512 checkpoint and trained for 32,500 updates on a mixture of 22 synthetic and reconstructed
+depth sources. This folder holds the EMA weights of update 32,500, the exact training config, the recorded
+evaluation rows and everything needed to rebuild the model and score it with the repository's evaluator.
+
+Status of the recipe: it has plateaued. Between update 30,000 and update 32,500 no benchmark moved by more than
+0.002 AbsRel. The model is clearly behind VGGT-Omega on Sintel and KITTI (table below). More updates of this recipe
+are not expected to close that gap.
+
+## Contents
+
+| Path | What it is |
+|---|---|
+| `train/ckpt_32500/ema2/model.safetensors` | the weights: EMA with decay 0.9996; 216 tensors (213 parameters, 140,204,928 values, plus 3 fixed position buffers), fp32 |
+| `train/ckpt_32500/ema2/model.json` | the artifact record: architecture, tensor manifest, model profile, parent checkpoint hashes, DINO identity |
+| `train/ckpt_32500/point_training.json` | the training progress and config hashes at update 32,500 |
+| `train/ckpt_32500/vggforecast_checkpoint.json` | the checkpoint format marker |
+| `train/resolved_resume_29500.yaml` | the resolved training config of the last training start (resumed from update 29,500, ran to 32,500) |
+| `train/launch_resume_29500.json` | that start's launch record: source commit `321afca216fc13eaf6e8264d1f966f065d61ce4b`, resume ancestry, data census |
+| `rows/*.jsonl` | the recorded evaluation rows (raw and EMA2 weights; video and single-frame calls) |
+| `manifest.json`, `manifest.sha256` | file names, sizes, sha256 (`manifest.sha256` is `sha256sum -c` input), code commit, update count |
+| `infer_video_depth.py` | depth for your own video or frames: npy, colour-mapped PNGs, MP4 (see "Run inference on your own video") |
+| `reproduce_evaluation.sh` | the exact evaluation commands, with the folder as the run directory |
+| `load_point.py` | builds the model through the repository's builder and loads the EMA weights |
+
+There is no optimizer state and no raw (non-EMA) weight file here. The evaluator needs only the EMA2 artifact.
+The frozen DINOv3 encoder is not included (see Licence).
+
+## What the model predicts
+
+- **Architecture:** PointDiT-B/16 (12 blocks, width 768, 12 heads, bottleneck patch embedding), made temporal:
+  even blocks attend within a frame, odd blocks attend over all frames; the private 3D rotary embedding is
+  sectioned (time | row | column); one residual adaptive-LayerNorm branch carries the physical frame time
+  (`t_j - t_0` in frames). One generated channel: the released three-channel (XYZ) head is sliced to its Z column.
+- **Conditioning:** frozen DINOv3 ViT-B/16 features of every frame (layers 2, 5, 8, 11, concatenated: 3,072
+  channels per 16 x 16 patch). No raw RGB enters the Point network.
+- **Input:** a window of T RGB frames (trained on T = 1 to 25, evaluated with T = 21), height and width multiples
+  of 16 at about 512 x 512 pixels of area (ten aspect buckets, never squashed; for example 336 x 784 for Sintel,
+  288 x 928 for KITTI), and strictly increasing integer frame indices starting at 0 (the frames' true indices; they
+  set the rotary time and the physical-time branch).
+- **Output:** `[1, T, 1, H, W]`: each frame's own-camera depth Z. The depth is in the units of the window's
+  gauge: a single scale shared by every frame of the window, the mean valid depth of the window's frames. The model
+  predicts a contracted state (symmetric mip-NeRF 360 contraction); the repository decodes it, capping the state at
+  1.984375 (a depth of at most 64 gauge units).
+- **Metric-free depth.** The output is depth up to one unknown scale per window (and, in the benchmark, one fitted
+  shift): it is not metric. To get depth in metres, multiply by a known scale. The evaluation recovers it the way
+  Video-Depth-Anything does: one least-squares scale and shift per sequence, fitted in depth against the ground
+  truth. Nothing inside the model predicts the scale.
+- **Sampling for evaluation:** one Euler step from Gaussian noise (the x0 head makes one step a one-shot
+  prediction), `--base-steps 1`, on the logit-normal quantile grid the model was trained with (its native sampler is
+  four steps, noise scale 2.0).
+
+## How to load and evaluate (Civo cluster)
+
+The repository is `git@github.com:NIRVANALAN/vgg_forecast_4d.git`. The recorded rows came from commit `f7b6a03b`.
+A full re-score of the three benchmarks with `reproduce_evaluation.sh` from a downloaded copy of this folder is recorded in
+the repository's hand-off note (`doc/handoff/video_depth_pointdit_checkpoint_and_mot_integration.md`); what was checked
+before this upload is stated under "Run inference on your own video" below.
+The commands need the Civo paths: the released PointDiT-B 512 checkpoint (`model.point.checkpoint` in the config), the
+frozen DINOv3 weights (`model.dino.weights`) and the Sintel / Bonn / KITTI data. Outside Civo, edit those paths in
+`train/resolved_resume_29500.yaml`; the evaluator checks their sha256 against the config.
+
+```bash
+git clone git@github.com:NIRVANALAN/vgg_forecast_4d.git && cd vgg_forecast_4d && git checkout 2310eec5
+source /vgg/yushi/miniconda3/etc/profile.d/conda.sh && conda activate th29py312
+export PYTHONPATH=$PWD/src
+FOLDER=<this folder>            # the downloaded video-depth-pointdit/
+python $FOLDER/load_point.py $FOLDER                   # builds the model, loads EMA2 (CPU, about 15 s)
+bash $FOLDER/reproduce_evaluation.sh $FOLDER $PWD/out  # one GPU; checks manifest.sha256 first
+```
+
+`reproduce_evaluation.sh` runs `eval/temporal_point/checkpoint_evaluation.py --task video_depth` per dataset with
+the aspect-preserving depth-fit protocol, EMA2 weights, seed 0, `--base-steps 1`, `--depth-state-cap 1.984375`.
+
+## Run inference on your own video
+
+`infer_video_depth.py` takes a video file or a folder of frames and writes per-frame depth. It builds the model through
+the repository's builder (`build_point_model`), loads the EMA2 weights, computes the frozen DINOv3 ViT-B/16 features of the
+frames and samples with the repository's sampler exactly as the evaluation does: one Euler step from Gaussian noise,
+the contracted depth state capped at 1.984375, bf16 autocast. Run it from a clone of the repository, in the
+`th29py312` environment, on one GPU (a CPU works but takes minutes per frame set):
+
+```bash
+cd vgg_forecast_4d && git checkout 2310eec5 && export PYTHONPATH=$PWD/src
+source /vgg/yushi/miniconda3/etc/profile.d/conda.sh && conda activate th29py312
+python $FOLDER/infer_video_depth.py $FOLDER --input clip.mp4 --out out/            # first 21 frames
+python $FOLDER/infer_video_depth.py $FOLDER --input frames_dir/ --out out/ --max-frames 25 --stride 2
+```
+
+- **Frames:** the first `--max-frames` (default 21, at most 25) frames at `--stride`, in one call. Each is resized, whole
+  and without a crop, to the raster that keeps its aspect ratio at about 512 x 512 pixels in 16-pixel patches
+  (for example 480 x 640 becomes 448 x 592; 436 x 1024 becomes 336 x 784), with `cv2.INTER_AREA`, as the benchmark does.
+  The frame indices given to the model are `0, stride, 2 stride, ...`; they set its rotary time and physical-time input,
+  so give the stride the clip really has between frames (training used strides 1 to 8). For a longer video, call it on
+  successive chunks; the chunks are then separate gauges (one unknown scale each).
+- **Outputs** in `--out`: `depth.npy` ([T,H,W] float32 on the model raster, in units of the window's mean depth: one
+  unknown scale for the whole window, not metric); `rgb.npy` (the resized input); `depth_NNN.png` (colour-mapped
+  disparity, 1 / depth, inferno, normalized over the whole clip, as Video-Depth-Anything draws it); `depth.mp4` (input and
+  colour map side by side); `info.json` (raster, frame indices, sampler settings, seed).
+- **Scale.** `depth.npy` is the model's output unchanged. With `--hfov-degrees <fov>` the script also writes
+  `depth_frame0_gauge.npy`: the same depth times the evaluator's frame-0 gauge factor (frame 0's mean point norm
+  becomes 1; it needs a field of view). Either way the depth is known only up to one scale: to get metres multiply by one
+  known scale, or fit one scale and shift per clip against a reference, as the benchmark does.
+- **Seed.** The sampler starts from Gaussian noise: `--seed` fixes it. `--seed-key 0:<dataset>:<sequence>` uses the
+  benchmark's noise key, which reproduces a benchmark row's call exactly.
+- **Weights it needs:** the DINOv3 ViT-B/16 file named under Licence (`--dino-weights` and `--dinov3-repo` override the
+  config's Civo paths), and by default the released PointDiT-B 512 checkpoint named in the config; add
+  `--random-init-point` to skip that file (every tensor then comes from the EMA2 file).
+
+**Checked:** on one H100 (2026-10-07, repository commit `2310eec5`) the script was run on the first 21 frames of Bonn
+`balloon2` and Sintel `alley_2` with the benchmark noise key (`--seed-key 0:<dataset>:<sequence>`). After one scale and
+shift per sequence in depth against the ground truth (the registered protocol), its depth scores AbsRel 0.0970 on
+`balloon2` (recorded row: 0.0970) and 0.2377 on `alley_2` (recorded: 0.2368); delta<1.25 0.9480 and 0.6414 (recorded
+0.9480 and 0.6420). The recorded rows came from an A100; the small differences are GPU and kernel numerics. The
+`--hfov-degrees` output and the MP4 ran on the same job. `--random-init-point` was checked on a CPU: a model built without
+the released checkpoint holds exactly the 216 EMA2 tensors.
+
+## Recorded results
+
+Mean AbsRel over sequences (median over sequences in brackets); the first 21 frames of each sequence; the whole
+frame at the dataset's native aspect, scored on the ground-truth grid; one least-squares scale and shift per
+sequence, fitted in depth (`video_depth_anything_aspect_preserving_depth_fit`). EMA2, one step from noise.
+
+| | Sintel (13 sequences) | Bonn (5) | KITTI (13) |
+|---|---|---|---|
+| This model, 21-frame call | 0.381 (0.262) | 0.060 (0.054) | 0.090 (0.091) |
+| This model, raw weights (not shipped) | 0.383 | 0.060 | 0.100 |
+| This model, each frame its own one-frame call (per-frame fit) | 0.379 (0.254) | 0.061 (0.054) | 0.092 (0.090) |
+| VGGT-Omega (1B), 21-frame call | 0.145 (0.114) | 0.053 (0.042) | 0.038 (0.031) |
+| Per-frame PointDiT-B 512 (released), 21 frames | 0.394 (0.415) | 0.071 (0.053) | 0.118 (0.107) |
+
+Learning curve of the same recipe (EMA2, same protocol): update 30,000: 0.379 / 0.062 / 0.089; update 32,500:
+0.381 / 0.060 / 0.090. Sintel's mean is carried by two sequences (`ambush_5` 1.85, `market_6` 0.57) whose single
+depth-space scale and shift is pulled by far pixels; the rank correlation of the raw prediction with the ground truth
+is high (0.84 and 0.97 on those two). KITTI's near third (closer than about 10 m) is under-predicted.
+
+## Training summary
+
+- **Parent:** released PointDiT-B 512 mixdata EMA branch (`pointditb-512-mixdata-nodinov3-1d42aacf.pth`, sha256
+  `1d42aacfd6aaf97c8bd96b0ce5e23bd880cd01b2efbb2ac80ccf8876fde3034b`), Z channel sliced.
+- **Updates:** 32,500 in four segments; global batch 64 (4 A100-80GB x 16), AdamW (0.9, 0.95), constant learning
+  rate 5e-5, gradient clip 1.0, bf16 autocast, EMA decays 0.9999 and 0.9996.
+- **Objective:** PointDiT's velocity loss with noise scale 2.0 plus 0.1 x a bounded relative depth term, per-sample
+  mean over valid pixels.
+- **Data (last segments):** BEDLAM, Spring, vKITTI 2, OmniWorld-Game (depth within 20 m), Syn4D v1 / v2, TartanAir,
+  MVS-Synth, DL3DV (reconstructed depth), Dynamic Replica, PointOdyssey, Kubric49 / 81, Waymo (LiDAR), Hypersim,
+  ScanNet++, ScanNet, Mid-Air, WildRGB-D, SynthVerse and BlendedMVS; 65 % video windows, 15 % single frames, 20 %
+  unordered multi-view sets; 1 to 25 frames per sample.
+- **Evaluation data is held out:** Sintel, Bonn and KITTI (video-depth benchmark sets) are not training sources.
+
+## Limits
+
+- The error that remains is not fixable by more steps of this recipe: it plateaus at update ~30,000.
+- Output is scale-free; the gauge is the window's own mean depth, not frame 0's depth (the camera expert of the
+  MoT normalizes translation by frame 0's depth: the two gauges must be reconciled before they are combined).
+- One step from noise is the evaluated sampler. The segment-4 offset diagnosis (update 27,000) found four and
+  sixteen steps give the same depth offset as one step.
+
+## Licence and third-party notes
+
+- The repository code is the project's; the PointDiT trunk code and the released PointDiT checkpoint come from
+  `github.com/google-research/pointdit` (Apache-2.0 code; read the checkpoint's own terms before redistribution).
+- The weights were trained on datasets with their own licences, several research-only (for example Hypersim, ScanNet,
+  ScanNet++, Waymo, DL3DV). Treat the weights as research-only until the owner chooses a licence.
+- DINOv3 (Meta) is under the DINOv3 licence and is NOT redistributed here. Request it at
+  `https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/`. The model needs the ViT-B/16 LVD-1689M
+  weights (`dinov3_vitb16_pretrain_lvd1689m-73cec8be.pth`, sha256
+  `73cec8be7427c8655ceced13ce62f6e20a1fa90d1b4d4a550df17a1144081a7c`). On Civo they are at
+  `/vgg/yushi/Repo/baselines/dinov3/weights/dinov3_vitb16.pth` (the config names a symlink to it under
+  `/scratch/shared/engs2907/pointdit_official/pretrained/dinov3/`).

video-depth-pointdit/infer_video_depth.py

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+#!/usr/bin/env python3
+"""Per-frame depth of a video (or a folder of frames) with the video-depth PointDiT-B of this folder.
+
+    python infer_video_depth.py <this folder> --input clip.mp4 --out out_dir
+    python infer_video_depth.py <this folder> --input frames_dir/ --out out_dir --max-frames 21 --stride 2
+
+Run it from the root of a clone of NIRVANALAN/vgg_forecast_4d (commit 2310eec5 or later) with PYTHONPATH=<clone>/src,
+in the th29py312 environment, on one GPU (a CPU works, slowly). It builds the model through the repository's builder
+(`build_point_model`), loads the EMA2 weights (`_load_weights`), computes the frozen DINOv3 ViT-B/16 features of the
+frames, and samples with the repository's sampler (`sample_window_self_maps`: the shared Euler driver, one step from
+Gaussian noise, the contracted depth state capped at 1.984375, bf16 autocast) exactly as the evaluator does.
+
+Frames. The first `--max-frames` frames (default 21, at most 25) at `--stride` are taken in one call. Each frame is
+resized, whole and without a crop, to the raster that keeps its aspect ratio at about 512 x 512 pixels in 16-pixel
+patches (`monocular_depth.aspect_matched_hw`, cv2.INTER_AREA), as the benchmark does. The call's frame indices are
+0, stride, 2 stride, ...: they set the model's rotary time and physical-time input, so use the stride the clip really
+has between frames (the training strides were 1 to 8).
+
+Outputs in `--out`:
+    depth.npy               [T,H,W] float32, the model's depth on its raster, in units of the window's mean depth
+                            (the gauge: one unknown scale for the whole window; not metric)
+    depth_frame0_gauge.npy  [T,H,W] float32, only with --hfov-degrees: depth.npy times the evaluator's frame-0
+                            gauge factor (frame 0's mean point norm becomes 1; needs a field of view)
+    rgb.npy                 [T,H,W,3] uint8, the resized input frames
+    depth_NNN.png           colour-mapped disparity (1 / depth), inferno, normalized over the whole clip
+    depth.mp4               input | colour-mapped disparity side by side (H.264)
+    info.json               raster, frame indices, sampler settings, noise seed
+
+The depth is up to one unknown scale (and, if you compare with a measured depth, a shift): to get metres, multiply
+by one known scale, or fit one scale and shift per clip against a reference as the benchmark does.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+
+IMAGE_SUFFIXES = {".png", ".jpg", ".jpeg", ".bmp", ".webp"}
+SIDE = 512                    # the raster's area is that of a SIDE x SIDE square
+MAX_FRAMES = 25               # the longest window the model was trained on
+STATE_CAP = 1.984375          # the evaluator's cap on the contracted depth state
+BASE_STEPS = 1                # the evaluator's sampler: one Euler step from noise
+
+
+def read_frames(path: Path, count: int, stride: int) -> list[np.ndarray]:
+    """The first ``count`` frames at ``stride`` of a video file or an image folder, BGR uint8."""
+    if path.is_dir():
+        files = sorted(p for p in path.iterdir() if p.suffix.lower() in IMAGE_SUFFIXES)
+        frames = [cv2.imread(str(p), cv2.IMREAD_COLOR) for p in files[: (count - 1) * stride + 1 : stride]]
+    else:
+        capture = cv2.VideoCapture(str(path))
+        frames, index = [], 0
+        while len(frames) < count:
+            ok, frame = capture.read()
+            if not ok:
+                break
+            if index % stride == 0:
+                frames.append(frame)
+            index += 1
+        capture.release()
+    if not frames or any(f is None for f in frames):
+        raise ValueError(f"no readable frames in {path}")
+    if len({f.shape for f in frames}) != 1:
+        raise ValueError("all frames must have the same size")
+    return frames
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("folder", type=Path, help="this downloaded video-depth-pointdit folder")
+    parser.add_argument("--input", type=Path, required=True, help="a video file or a folder of frames")
+    parser.add_argument("--out", type=Path, required=True)
+    parser.add_argument("--max-frames", type=int, default=21)
+    parser.add_argument("--stride", type=int, default=1, help="source frames between the frames taken")
+    parser.add_argument("--seed", type=int, default=0, help="seed of the Gaussian noise the sampler starts from")
+    parser.add_argument("--seed-key", help="the evaluator's noise key '<seed>:<dataset>:<sequence>' (reproduction)")
+    parser.add_argument("--hfov-degrees", type=float, help="horizontal field of view; writes depth_frame0_gauge.npy")
+    parser.add_argument("--dino-weights", type=Path, help="DINOv3 ViT-B/16 weights (default: the path in the config)")
+    parser.add_argument("--dinov3-repo", type=Path, help="DINOv3 source checkout (default: the path in the config)")
+    parser.add_argument("--attention-backend", help="Point attention (default: the recorded fa2 on a GPU, sdpa on a CPU)")
+    parser.add_argument("--random-init-point", action="store_true",
+                        help="do not read the released PointDiT-B 512 checkpoint: every tensor comes from the EMA2 file")
+    parser.add_argument("--no-mp4", action="store_true")
+    args = parser.parse_args()
+    if not 1 <= args.max_frames <= MAX_FRAMES or args.stride < 1:
+        parser.error(f"--max-frames must be 1..{MAX_FRAMES} and --stride at least 1")
+
+    from vggforecast.data.adapters.source_sampling import keyed_uint64
+    from vggforecast.evaluation.checkpoint_loading import _load_weights, resolve_run_config
+    from vggforecast.evaluation.monocular_depth import aspect_matched_hw
+    from vggforecast.evaluation.point_metrics import point_field_depth
+    from vggforecast.evaluation.sampling_overrides import resolve_base_steps
+    from vggforecast.evaluation.stream3r_keyframe_sampler import sample_window_self_maps
+    from vggforecast.models.point.target_centering import frame0_gauge_depth_factor
+    from vggforecast.training.microbatches import frozen_dino_provider
+    from vggforecast.training.model_loading import bind_config_runtime, build_point_model
+    from vggforecast.training.run_config import require_config
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    run, ckpt = args.folder / "train", args.folder / "train" / "ckpt_32500"
+    cfg = resolve_run_config(run, ckpt)["cfg"]
+    cfg.model.point.attention_backend = args.attention_backend or ("fa2" if device.type == "cuda" else "sdpa")
+    if args.random_init_point:
+        cfg.model.point.weights = "random"
+    if args.dino_weights:
+        cfg.model.dino.weights = str(args.dino_weights)
+    if args.dinov3_repo:
+        cfg.model.dino.repo = str(args.dinov3_repo)
+
+    bgr = read_frames(args.input, args.max_frames, args.stride)
+    height, width = bgr[0].shape[:2]
+    raster = aspect_matched_hw((height, width), SIDE)
+    rgb = np.stack([cv2.cvtColor(cv2.resize(f, (raster[1], raster[0]), interpolation=cv2.INTER_AREA), cv2.COLOR_BGR2RGB)
+                    for f in bgr])                                        # [T,H,W,3] uint8
+    frames = len(rgb)
+    ids = torch.arange(frames, dtype=torch.long) * args.stride           # the frames' true index offsets
+    print(f"{frames} frames {height}x{width} -> raster {raster[0]}x{raster[1]}, frame ids {ids.tolist()}", flush=True)
+
+    model, core, _ = build_point_model(cfg)
+    base_hw = tuple(int(v) * core.trunk.patch for v in core.trunk.position_source_grid)
+    _load_weights(model, core, ckpt, "ema2", base_hw)
+    runtime = bind_config_runtime(model, cfg, injected=False)
+    model = model.to(device).eval()
+    dino = frozen_dino_provider(cfg, device)
+    steps = int(resolve_base_steps(cfg, BASE_STEPS)["base_steps"])
+    seed = keyed_uint64(args.seed_key) % 2**63 if args.seed_key else args.seed
+    video = torch.from_numpy(rgb).permute(0, 3, 1, 2).contiguous()[None]      # [1,T,3,H,W] uint8
+    field, trace = sample_window_self_maps(
+        model, dino, runtime, video, ids[None], condition_dim=int(require_config(cfg, "model.point.core.condition_dim")),
+        base_steps=steps, seed=seed, device=device, autocast_dtype=torch.bfloat16, coordinate_frame="own_camera",
+        normalized_centred_output=True, depth_state_cap=STATE_CAP)
+    depth = point_field_depth(field)[0].double().cpu()                         # [T,H,W], window-mean-depth units
+    print("joint denoiser evaluations", int(trace.joint_denoiser_evaluations), "state decode", dict(trace.depth_decode))
+
+    args.out.mkdir(parents=True, exist_ok=True)
+    np.save(args.out / "depth.npy", depth.numpy().astype(np.float32))
+    np.save(args.out / "rgb.npy", rgb)
+    info = {"frames": frames, "source_hw": [height, width], "raster_hw": list(raster), "frame_ids": ids.tolist(),
+            "stride": args.stride, "base_steps": steps, "depth_state_cap": STATE_CAP, "seed": seed,
+            "seed_key": args.seed_key, "weights": "ema2 of update 32500", "unit": "window mean depth (not metric)"}
+    if args.hfov_degrees:
+        fx = (raster[1] / 2) / np.tan(np.radians(args.hfov_degrees) / 2)
+        k = torch.tensor([[fx, 0, (raster[1] - 1) / 2], [0, fx, (raster[0] - 1) / 2], [0, 0, 1]], dtype=torch.float64)
+        factor = frame0_gauge_depth_factor(depth[0], k)
+        if factor is None:
+            raise ValueError("the predicted frame 0 has no positive depth")
+        np.save(args.out / "depth_frame0_gauge.npy", (depth * factor).numpy().astype(np.float32))
+        info["frame0_gauge_factor"] = factor
+    (args.out / "info.json").write_text(json.dumps(info, indent=2) + "\n")
+
+    # Video-Depth-Anything style: inferno on the normalized disparity of the whole clip.
+    disparity = 1.0 / np.clip(depth.numpy(), 1e-3, None)
+    lo, hi = float(disparity.min()), float(disparity.max())
+    gray = ((disparity - lo) / max(hi - lo, 1e-8) * 255.0).astype(np.uint8)
+    colour = [cv2.cvtColor(cv2.applyColorMap(g, cv2.COLORMAP_INFERNO), cv2.COLOR_BGR2RGB) for g in gray]
+    for index, image in enumerate(colour):
+        cv2.imwrite(str(args.out / f"depth_{index:03d}.png"), cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
+    if not args.no_mp4:
+        from vggforecast.visualization.mp4 import write_h264_mp4
+
+        write_h264_mp4(args.out / "depth.mp4", [np.concatenate([a, b], axis=1) for a, b in zip(rgb, colour)], fps=10)
+    print("wrote", args.out)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

video-depth-pointdit/load_point.py

@@ -0,0 +1,22 @@
+"""Build the video-depth PointDiT-B from the Hugging Face folder and load its EMA weights (CPU check)."""
+import sys
+from pathlib import Path
+
+import torch
+
+from vggforecast.evaluation.checkpoint_loading import _load_weights, resolve_run_config
+from vggforecast.training.model_loading import build_point_model
+
+FOLDER = Path(sys.argv[1])                    # .../video-depth-pointdit
+RUN, CKPT = FOLDER / "train", FOLDER / "train" / "ckpt_32500"
+
+cfg = resolve_run_config(RUN, CKPT)["cfg"]   # resolved_resume_29500.yaml + launch_resume_29500.json
+cfg.model.point.attention_backend = "sdpa"   # the recorded run used fa2 (GPU); sdpa runs anywhere
+model, core, provenance = build_point_model(cfg)          # PointDiT-B over the released 512 checkpoint
+base_hw = tuple(int(v) * core.trunk.patch for v in core.trunk.position_source_grid)
+loaded = _load_weights(model, core, CKPT, "ema2", base_hw)  # every tensor of the model comes from ema2
+model.eval()
+print("architecture", model.architecture, "stage", core.stage if hasattr(core, "stage") else None)
+print("target_centering", core.target_centering, "parameters", sum(p.numel() for p in model.parameters()))
+print("loaded", {k: v for k, v in loaded.items() if k in ("weights", "completed_updates", "ema_decay")})
+print("point expert", type(model.mot.point).__name__, "blocks", len(core.trunk.blocks))

video-depth-pointdit/manifest.json

@@ -0,0 +1,103 @@
+{
+  "model": "video-depth PointDiT-B (temporal, own-camera depth), EMA2 (decay 0.9996) of update 32500",
+  "update_count": 32500,
+  "training_run": "temporal_pointdit_b_video_depth_expanded_mixture_rebalanced_sample_layouts_variable_frames_window_mean_depth_scale_per_sample_bounded_relative_loss_no_time_shift_21_frames_512_a100x4_rebalanced_sample_layouts_window_mean_depth_from_ckpt_23000_20261004",
+  "training_code_commit_of_last_start": "321afca216fc13eaf6e8264d1f966f065d61ce4b",
+  "training_code_commits_by_segment_start": {
+    "update_23000": "331077424d3929e7b815b0d8313bca45c10c2fbe",
+    "update_23500_to_32500": "321afca216fc13eaf6e8264d1f966f065d61ce4b"
+  },
+  "repository": "git@github.com:NIRVANALAN/vgg_forecast_4d.git",
+  "evaluated_at_commits": {
+    "recorded_rows": "f7b6a03b",
+    "inference_script_checked": "2310eec5"
+  },
+  "training_config_sha256": "cba8b57ed304a187dc484cdcf734f30407c1aa0e42344c42bca54d730b9c7d8e",
+  "parent_checkpoint_sha256": "1d42aacfd6aaf97c8bd96b0ce5e23bd880cd01b2efbb2ac80ccf8876fde3034b",
+  "dino_weights_not_included": {
+    "file": "dinov3_vitb16_pretrain_lvd1689m-73cec8be.pth",
+    "sha256": "73cec8be7427c8655ceced13ce62f6e20a1fa90d1b4d4a550df17a1144081a7c"
+  },
+  "files": [
+    {
+      "path": "README.md",
+      "bytes": 13715,
+      "sha256": "30a3803b864b20b96c6bbc1ec8f4c3bfccd78370c944f4951dd149004f262b8a"
+    },
+    {
+      "path": "infer_video_depth.py",
+      "bytes": 10596,
+      "sha256": "d8f4e2ee11f66b9a1b6d69a5eb1dbf8821241b340170e6c1d9d54bdc42877305"
+    },
+    {
+      "path": "load_point.py",
+      "bytes": 1351,
+      "sha256": "2e12b1461a95d28c7fb6326d0549c59865c3cab24772efdd416d496ec46d870a"
+    },
+    {
+      "path": "reproduce_evaluation.sh",
+      "bytes": 1784,
+      "sha256": "726305ffb62dc37a75dfc15c1709e1b1646423acd269156e096c3b9da421d1f0"
+    },
+    {
+      "path": "rows/single_frame_test_video_depth_anything_aspect_preserving_depth_fit_bonn.jsonl",
+      "bytes": 156936,
+      "sha256": "2ef4877a1486026e9c8904b5e481a6f953b3fbf2694c5b441bbbb54bf41d1c58"
+    },
+    {
+      "path": "rows/single_frame_test_video_depth_anything_aspect_preserving_depth_fit_kitti.jsonl",
+      "bytes": 413767,
+      "sha256": "ebd65468808940716fb759f5b7f6f8c8892b1c65947d759eccd4e3a0544c5b5a"
+    },
+    {
+      "path": "rows/single_frame_test_video_depth_anything_aspect_preserving_depth_fit_sintel.jsonl",
+      "bytes": 390068,
+      "sha256": "1940d1b65e64dc545002c415a139aff185f2afcf990a7f29fd41d5ecf3e2323d"
+    },
+    {
+      "path": "rows/test_video_depth_anything_aspect_preserving_depth_fit_bonn.jsonl",
+      "bytes": 22880,
+      "sha256": "60299bf229f348324a97813fba5de2669d244b0eccc00ac8bf886d1cda4c95fa"
+    },
+    {
+      "path": "rows/test_video_depth_anything_aspect_preserving_depth_fit_kitti.jsonl",
+      "bytes": 57813,
+      "sha256": "2f15eaa3e31a1acfd30f035788aac103b78ce1f4d7bba7e7e695718ed3fa120c"
+    },
+    {
+      "path": "rows/test_video_depth_anything_aspect_preserving_depth_fit_sintel.jsonl",
+      "bytes": 44683,
+      "sha256": "f247cde4d93c03cab5ec1f9d600f703e5e8ce52c06c750d09720a11587c0a6c3"
+    },
+    {
+      "path": "train/ckpt_32500/ema2/model.json",
+      "bytes": 40734,
+      "sha256": "ba2d405ec51e6518dd86d3417eea4f9c90cea2953d297f29853ae88fee36dcae"
+    },
+    {
+      "path": "train/ckpt_32500/ema2/model.safetensors",
+      "bytes": 576572953,
+      "sha256": "77f015b7a66eb65e846ac61015da36694266c44e7e1a2aa8dfebe49d9f687c6a"
+    },
+    {
+      "path": "train/ckpt_32500/point_training.json",
+      "bytes": 1152,
+      "sha256": "70e1ef03b85575c0c073657cc3e5d1c39f9cc0176f217ae5ffc1b8e0c8851bec"
+    },
+    {
+      "path": "train/ckpt_32500/vggforecast_checkpoint.json",
+      "bytes": 121,
+      "sha256": "321e8743882f31132c10751520d1b4672f9459e386fe2c77616c9fb3e1392a2a"
+    },
+    {
+      "path": "train/launch_resume_29500.json",
+      "bytes": 30164,
+      "sha256": "afe1a44772bcccd4945123847e8ea1efe66ce8b156f23fc611799d4a6c8e0d33"
+    },
+    {
+      "path": "train/resolved_resume_29500.yaml",
+      "bytes": 23474,
+      "sha256": "cba8b57ed304a187dc484cdcf734f30407c1aa0e42344c42bca54d730b9c7d8e"
+    }
+  ]
+}

video-depth-pointdit/manifest.sha256

@@ -0,0 +1,16 @@
+30a3803b864b20b96c6bbc1ec8f4c3bfccd78370c944f4951dd149004f262b8a  README.md
+d8f4e2ee11f66b9a1b6d69a5eb1dbf8821241b340170e6c1d9d54bdc42877305  infer_video_depth.py
+2e12b1461a95d28c7fb6326d0549c59865c3cab24772efdd416d496ec46d870a  load_point.py
+726305ffb62dc37a75dfc15c1709e1b1646423acd269156e096c3b9da421d1f0  reproduce_evaluation.sh
+2ef4877a1486026e9c8904b5e481a6f953b3fbf2694c5b441bbbb54bf41d1c58  rows/single_frame_test_video_depth_anything_aspect_preserving_depth_fit_bonn.jsonl
+ebd65468808940716fb759f5b7f6f8c8892b1c65947d759eccd4e3a0544c5b5a  rows/single_frame_test_video_depth_anything_aspect_preserving_depth_fit_kitti.jsonl
+1940d1b65e64dc545002c415a139aff185f2afcf990a7f29fd41d5ecf3e2323d  rows/single_frame_test_video_depth_anything_aspect_preserving_depth_fit_sintel.jsonl
+60299bf229f348324a97813fba5de2669d244b0eccc00ac8bf886d1cda4c95fa  rows/test_video_depth_anything_aspect_preserving_depth_fit_bonn.jsonl
+2f15eaa3e31a1acfd30f035788aac103b78ce1f4d7bba7e7e695718ed3fa120c  rows/test_video_depth_anything_aspect_preserving_depth_fit_kitti.jsonl
+f247cde4d93c03cab5ec1f9d600f703e5e8ce52c06c750d09720a11587c0a6c3  rows/test_video_depth_anything_aspect_preserving_depth_fit_sintel.jsonl
+ba2d405ec51e6518dd86d3417eea4f9c90cea2953d297f29853ae88fee36dcae  train/ckpt_32500/ema2/model.json
+77f015b7a66eb65e846ac61015da36694266c44e7e1a2aa8dfebe49d9f687c6a  train/ckpt_32500/ema2/model.safetensors
+70e1ef03b85575c0c073657cc3e5d1c39f9cc0176f217ae5ffc1b8e0c8851bec  train/ckpt_32500/point_training.json
+321e8743882f31132c10751520d1b4672f9459e386fe2c77616c9fb3e1392a2a  train/ckpt_32500/vggforecast_checkpoint.json
+afe1a44772bcccd4945123847e8ea1efe66ce8b156f23fc611799d4a6c8e0d33  train/launch_resume_29500.json
+cba8b57ed304a187dc484cdcf734f30407c1aa0e42344c42bca54d730b9c7d8e  train/resolved_resume_29500.yaml

video-depth-pointdit/reproduce_evaluation.sh

@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+# Re-scores this checkpoint (EMA2, update 32,500) on Sintel / Bonn / KITTI with the registered native-aspect depth-fit
+# protocol, one 21-frame call per sequence, one step from Gaussian noise. Run from the root of a clone of
+# NIRVANALAN/vgg_forecast_4d (commit 2310eec5 or later) on a Civo GPU node, in the th29py312 environment:
+#
+#   bash reproduce_evaluation.sh <downloaded video-depth-pointdit folder> <output dir>
+#
+# Needs the Civo data and the frozen weights named in train/resolved_resume_29500.yaml (model.point.checkpoint,
+# model.dino.weights). Rows are written to <output dir>/test_<protocol>_<dataset>.jsonl and compare with rows/.
+set -uo pipefail
+(( $# == 2 )) || { echo "usage: $0 <folder> <out_dir>" >&2; exit 2; }
+FOLDER=$(realpath "$1"); OUT=$(realpath -m "$2"); mkdir -p "$OUT"
+export PYTHONPATH="$PWD/src" WANDB_MODE=disabled
+export TRACK4D_ROOT=/scratch/shared/engs2907/logs/vgg_forecast_4d/tools/track4d_dev_93b3649d
+export STREAM3R_ROOT=/vgg/yushi/Repo/baselines/STream3R VGGT_OMEGA_ROOT=/vgg/yushi/Repo/baselines/vggt-omega
+(cd "$FOLDER" && sha256sum -c manifest.sha256) || exit 3     # the files are the ones in the manifest
+RUN=$FOLDER/train; CK=$RUN/ckpt_32500; PROTO=video_depth_anything_aspect_preserving_depth_fit
+DECODE=$(python -B eval/temporal_point/video_depth_decode_arguments.py --run-dir "$RUN" --checkpoint "$CK" \
+  --centred-decode normalized_centred_depth) || exit 4
+for ds in sintel bonn kitti; do
+  python -B eval/temporal_point/checkpoint_evaluation.py --task video_depth --dataset $ds --run-dir "$RUN" \
+    --checkpoint "$CK" --sequence-frames window $DECODE --video-depth-protocol $PROTO --weights ema2 --seeds 0 \
+    --base-steps 1 --depth-state-cap 1.984375 --out "$OUT/test_${PROTO}_${ds}.jsonl" || exit 5
+done

video-depth-pointdit/rows/test_video_depth_anything_aspect_preserving_depth_fit_bonn.jsonl

@@ -0,0 +1,2 @@
+{"base_steps": 1, "base_steps_override": true, "checkpoint": "/scratch/shared/engs2907/logs/vgg_forecast_4d/eval/video_depth_views_1_25_benchmark_20261004/weights/temporal_pointdit_b_video_depth_expanded_mixture_rebalanced_sample_layouts_variable_frames_window_mean_depth_scale_per_sample_bounded_relative_loss_no_time_shift_21_frames_512_a100x4_rebalanced_sample_layouts_window_mean_depth_from_ckpt_23000_20261004/ckpt_32500", "dataset": "bonn", "depth_decode": "frame0_gauge_rescaled_depth", "depth_decode_status_counts": {"frame0_gauge_rescaled_depth": 5}, "depth_state_decode": {"capped_state_elements": 0, "nonfinite_state_elements": 0, "requested_state_cap": 1.984375, "state_elements": 27847680}, "excluded_sequences": {}, "frame_calls": "video", "frames": 21, "frames_per_sequence": {"balloon2": ["balloon2/1548266531.51903.png", "balloon2/1548266531.55255.png", "balloon2/1548266531.58562.png", "balloon2/1548266531.61894.png", "balloon2/1548266531.65246.png", "balloon2/1548266531.68616.png", "balloon2/1548266531.71985.png", "balloon2/1548266531.75319.png", "balloon2/1548266531.78674.png", "balloon2/1548266531.82026.png", "balloon2/1548266531.85392.png", "balloon2/1548266531.88745.png", "balloon2/1548266531.92095.png", "balloon2/1548266531.95432.png", "balloon2/1548266531.98783.png", "balloon2/1548266532.02132.png", "balloon2/1548266532.05479.png", "balloon2/1548266532.08834.png", "balloon2/1548266532.12186.png", "balloon2/1548266532.15534.png", "balloon2/1548266532.18884.png"], "crowd2": ["crowd2/1548339893.24032.png", "crowd2/1548339893.27383.png", "crowd2/1548339893.30735.png", "crowd2/1548339893.34087.png", "crowd2/1548339893.37426.png", "crowd2/1548339893.40778.png", "crowd2/1548339893.44055.png", "crowd2/1548339893.47407.png", "crowd2/1548339893.50759.png", "crowd2/1548339893.54130.png", "crowd2/1548339893.57482.png", "crowd2/1548339893.60833.png", "crowd2/1548339893.64182.png", "crowd2/1548339893.67534.png", "crowd2/1548339893.70886.png", "crowd2/1548339893.74237.png", "crowd2/1548339893.77589.png", "crowd2/1548339893.80940.png", "crowd2/1548339893.84292.png", "crowd2/1548339893.87644.png", "crowd2/1548339893.90995.png"], "crowd3": ["crowd3/1548339960.10108.png", "crowd3/1548339960.13460.png", "crowd3/1548339960.16812.png", "crowd3/1548339960.20168.png", "crowd3/1548339960.23526.png", "crowd3/1548339960.26902.png", "crowd3/1548339960.30268.png", "crowd3/1548339960.33626.png", "crowd3/1548339960.36979.png", "crowd3/1548339960.40330.png", "crowd3/1548339960.43682.png", "crowd3/1548339960.47035.png", "crowd3/1548339960.50390.png", "crowd3/1548339960.53746.png", "crowd3/1548339960.57098.png", "crowd3/1548339960.60450.png", "crowd3/1548339960.63816.png", "crowd3/1548339960.67173.png", "crowd3/1548339960.70524.png", "crowd3/1548339960.73876.png", "crowd3/1548339960.77228.png"], "person_tracking2": ["person_tracking2/1548265920.50431.png", "person_tracking2/1548265920.53787.png", "person_tracking2/1548265920.57139.png", "person_tracking2/1548265920.60490.png", "person_tracking2/1548265920.63842.png", "person_tracking2/1548265920.67193.png", "person_tracking2/1548265920.70551.png", "person_tracking2/1548265920.73902.png", "person_tracking2/1548265920.77248.png", "person_tracking2/1548265920.80600.png", "person_tracking2/1548265920.83951.png", "person_tracking2/1548265920.87298.png", "person_tracking2/1548265920.90648.png", "person_tracking2/1548265920.93986.png", "person_tracking2/1548265920.97337.png", "person_tracking2/1548265921.00689.png", "person_tracking2/1548265921.04040.png", "person_tracking2/1548265921.07378.png", "person_tracking2/1548265921.10729.png", "person_tracking2/1548265921.14081.png", "person_tracking2/1548265921.17433.png"], "synchronous": ["synchronous/1548340302.92359.png", "synchronous/1548340302.95711.png", "synchronous/1548340302.99061.png", "synchronous/1548340303.02413.png", "synchronous/1548340303.05762.png", "synchronous/1548340303.09113.png", "synchronous/1548340303.12460.png", "synchronous/1548340303.15811.png", "synchronous/1548340303.19161.png", "synchronous/1548340303.22511.png", "synchronous/1548340303.25858.png", "synchronous/1548340303.29197.png", "synchronous/1548340303.32540.png", "synchronous/1548340303.35889.png", "synchronous/1548340303.39238.png", "synchronous/1548340303.42590.png", "synchronous/1548340303.45942.png", "synchronous/1548340303.49296.png", "synchronous/1548340303.52647.png", "synchronous/1548340303.55999.png", "synchronous/1548340303.59353.png"]}, "framing": "aspect_preserving_resize", "headline": {"block": "video_depth_anything", "coverage": {"depth_decode_status_counts": {"frame0_gauge_rescaled_depth": 5}, "scored": 5, "status_counts": {"scored": 5}, "total": 5, "units": "sequences"}, "metrics": {"abs_rel": 0.05993674781642198, "delta_1_25": 0.9712193232208902}, "rule": "Video-Depth-Anything's benchmark: the whole frame, prediction resized to the GT grid, one least-squares scale and shift per sequence in depth, frame-averaged AbsRel and delta<1.25"}, "max_depth": 70.0, "min_frames": 21, "native_base_steps": 4, "point_sampling": {"inference_time_grid": "logit_normal_quantile", "inference_time_grid_mu": -3.0, "inference_time_grid_reference_noise_scale": null, "inference_time_grid_sigma": 1.0, "overrides": {}, "sampler_initialization": "gaussian", "time_shift": 1.0}, "point_target_centering": "window_valid_mean_depth", "protocol": "video_depth_anything_aspect_preserving_depth_fit", "raster_hw": [448, 592], "run_config": {"config_file": "/scratch/shared/engs2907/logs/vgg_forecast_4d/train/point/temporal_pointdit_b_video_depth_expanded_mixture_rebalanced_sample_layouts_variable_frames_window_mean_depth_scale_per_sample_bounded_relative_loss_no_time_shift_21_frames_512_a100x4_rebalanced_sample_layouts_window_mean_depth_from_ckpt_23000_20261004/train/resolved_resume_29500.yaml", "resumed_from": "/scratch/shared/engs2907/logs/vgg_forecast_4d/train/point/temporal_pointdit_b_video_depth_expanded_mixture_rebalanced_sample_layouts_variable_frames_window_mean_depth_scale_per_sample_bounded_relative_loss_no_time_shift_21_frames_512_a100x4_rebalanced_sample_layouts_window_mean_depth_from_ckpt_23000_20261004/train/ckpt_29500", "start_update": 29500}, "seed": 0, "sequence_lengths": {"balloon2": 21, "crowd2": 21, "crowd3": 21, "person_tracking2": 21, "synchronous": 21}, "sequences": {"balloon2": {"abs_rel": 0.09801595404197518, "delta_1_25": 0.9482854512547347, "depth_decode": {"call_frame0_factors": [0.9183548802558696], "decode": "frame0_gauge_rescaled_depth"}, "depth_state_decode": {"capped_state_elements": 0, "nonfinite_state_elements": 0, "requested_state_cap": 1.984375, "state_cap": 1.984375, "state_elements": 5569536, "transform": "symmetric_mip_nerf_360_contraction"}, "fit_space": "depth", "frames": 21, "nfe": 1, "nonfinite_prediction_pixels": 0, "prediction_kind": "depth", 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video-depth-pointdit/rows/test_video_depth_anything_aspect_preserving_depth_fit_kitti.jsonl

@@ -0,0 +1,2 @@
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video-depth-pointdit/rows/test_video_depth_anything_aspect_preserving_depth_fit_sintel.jsonl

@@ -0,0 +1,2 @@
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video-depth-pointdit/train/ckpt_32500/ema2/model.json

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+        "visibility_capability": "none"
+      },
+      "synthverse_depth": {
+        "annotation_provenance": "synthverse_embodied_rendered_depth_pose_no_tracks_read",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "tartanair": {
+        "annotation_provenance": "tartanair_analytic_static_world_correspondence_owner_declared_depth_consistency_mask",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "vkitti": {
+        "annotation_provenance": "processed_depth_pose_pointmap_no_registered_persistent_labels",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "waymo": {
+        "annotation_provenance": "waymo_lidar_static_world_and_tracked_box_object_correspondence",
+        "capability": "dynamic_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "wildrgbd": {
+        "annotation_provenance": "processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      }
+    },
+    "source_contract": "capability_v1",
+    "source_manifest_sha256": "8588a195931075e7e89c5a2addace1e71af56d462b9bab1f58dd9aae4fb89fa0",
+    "source_names": [
+      "bedlam",
+      "spring",
+      "vkitti",
+      "omniworld_game",
+      "syn4d_v1",
+      "syn4d_v2_mixed",
+      "syn4d_v2_mixed_no_sim",
+      "tartanair",
+      "mvs_synth",
+      "dl3dv",
+      "dynamic_replica",
+      "pointodyssey_track4d",
+      "kubric49",
+      "kubric81",
+      "waymo",
+      "hypersim",
+      "scannetpp",
+      "scannet",
+      "midair",
+      "wildrgbd",
+      "synthverse_depth",
+      "blendedmvs"
+    ],
+    "source_sampling": "random_legal",
+    "split": "train",
+    "start_cycle": 4214,
+    "supervision": "final_output_valid_mask; missing_visibility_unsupervised",
+    "task_source_mixture": null,
+    "tensor_sharing_strategy": "file_descriptor",
+    "window_indices": {},
+    "worker_batch_size": 1,
+    "world_size": 4
+  },
+  "stream_packet_cache": null,
+  "stream_packet_precision": null,
+  "supervision_contract": {
+    "empty_cross_time_support": "empty_track_support",
+    "source_contract": "capability_v1",
+    "sources": {
+      "bedlam": {
+        "annotation_provenance": "processed_depth_pose_pointmap_no_registered_persistent_labels",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "blendedmvs": {
+        "annotation_provenance": "blendedmvs_mesh_rendered_depth_pose_static_scene_views",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "dl3dv": {
+        "annotation_provenance": "dl3dv_reconstructed_depth_pose_no_tracks_read",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "dynamic_replica": {
+        "annotation_provenance": "dynamic_replica_dense_persistent_3d_annotation",
+        "capability": "dynamic_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "native"
+      },
+      "hypersim": {
+        "annotation_provenance": "processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "kubric49": {
+        "annotation_provenance": "kubric49_dense_object_and_static_world_correspondence",
+        "capability": "dynamic_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "kubric81": {
+        "annotation_provenance": "kubric81_dense_object_and_static_world_correspondence",
+        "capability": "dynamic_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "midair": {
+        "annotation_provenance": "processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "mvs_synth": {
+        "annotation_provenance": "mvs_synth_analytic_static_world_correspondence_owner_declared_depth_consistency_mask",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "omniworld_game": {
+        "annotation_provenance": "processed_depth_pose_pointmap_no_registered_persistent_labels",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "pointodyssey_track4d": {
+        "annotation_provenance": "track4d_sparse_persistent_3d_annotation",
+        "capability": "dynamic_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "native"
+      },
+      "scannet": {
+        "annotation_provenance": "processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "scannetpp": {
+        "annotation_provenance": "processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "spring": {
+        "annotation_provenance": "processed_depth_pose_pointmap_no_registered_persistent_labels",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "syn4d_v1": {
+        "annotation_provenance": "syn4d_rendered_depth_pose_no_tracks_read",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "syn4d_v2_mixed": {
+        "annotation_provenance": "syn4d_rendered_depth_pose_no_tracks_read",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "syn4d_v2_mixed_no_sim": {
+        "annotation_provenance": "syn4d_rendered_depth_pose_no_tracks_read",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "synthverse_depth": {
+        "annotation_provenance": "synthverse_embodied_rendered_depth_pose_no_tracks_read",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "tartanair": {
+        "annotation_provenance": "tartanair_analytic_static_world_correspondence_owner_declared_depth_consistency_mask",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "vkitti": {
+        "annotation_provenance": "processed_depth_pose_pointmap_no_registered_persistent_labels",
+        "capability": "self_geometry",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "waymo": {
+        "annotation_provenance": "waymo_lidar_static_world_and_tracked_box_object_correspondence",
+        "capability": "dynamic_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      },
+      "wildrgbd": {
+        "annotation_provenance": "processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels",
+        "capability": "static_cross_time",
+        "supervision": [
+          "pointmap"
+        ],
+        "visibility_capability": "none"
+      }
+    }
+  },
+  "trainable_parameter_groups": null,
+  "world_size": 4
+}

video-depth-pointdit/train/resolved_resume_29500.yaml

@@ -0,0 +1,818 @@
+data:
+  backend: track4d_stream3r
+  split: train
+  full_split: true
+  num_views: 21
+  track4d:
+    provider: track4d
+    split: train
+    frames: 21
+    image_size: 256
+    resolutions_hw:
+    - - 512
+      - 512
+    - - 592
+      - 448
+    - - 448
+      - 592
+    - - 688
+      - 384
+    - - 416
+      - 640
+    - - 384
+      - 688
+    - - 368
+      - 720
+    - - 336
+      - 784
+    - - 320
+      - 832
+    - - 288
+      - 896
+    resolution_global_batch: 64
+    source_sampling: random_legal
+    sources:
+    - name: bedlam
+      loader: bedlam
+      unified_name: bedlam
+      virtual_length: 30769
+      partition: train
+      track_coordinates: world
+      annotation_provenance: processed_depth_pose_pointmap_no_registered_persistent_labels
+      allow_missing_tracks: true
+      capability: self_geometry
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /scratch/shared/engs2907/processed_bedlam
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        max_interval: 8
+    - name: spring
+      loader: spring
+      unified_name: spring
+      virtual_length: 82051
+      partition: train
+      track_coordinates: world
+      annotation_provenance: processed_depth_pose_pointmap_no_registered_persistent_labels
+      allow_missing_tracks: true
+      capability: self_geometry
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/engs2907_processed/processed_spring
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        max_interval: 8
+    - name: vkitti
+      loader: virtual_kitti2
+      unified_name: vkitti2
+      virtual_length: 61538
+      partition: train
+      track_coordinates: world
+      annotation_provenance: processed_depth_pose_pointmap_no_registered_persistent_labels
+      allow_missing_tracks: true
+      capability: self_geometry
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /scratch/shared/engs2907/processed_vkitti
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        max_interval: 8
+    - name: omniworld_game
+      loader: omniworld_game
+      unified_name: omniworld_game
+      virtual_length: 123081
+      partition: train
+      track_coordinates: world
+      annotation_provenance: processed_depth_pose_pointmap_no_registered_persistent_labels
+      allow_missing_tracks: true
+      capability: self_geometry
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/OmniWorld_Game
+        split: train
+        aug_crop: 0
+        max_interval: 8
+        metric_scales_csv: /vgg/yushi/dataset/OmniWorld_Game/metadata/omniworld_game_metadata.csv
+        max_depth_meters: 20
+        min_focal_pixels: 200
+        index_unit: window_start
+    - name: syn4d_v1
+      loader: syn4d
+      unified_name: syn4d
+      virtual_length: 51282
+      partition: train
+      track_coordinates: world
+      annotation_provenance: syn4d_rendered_depth_pose_no_tracks_read
+      allow_missing_tracks: true
+      capability: self_geometry
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        depth_only: true
+        preserve_resolution_orientation: true
+        ROOT: /vgg/chenzhao/syn4d_v1_stride_1
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        stride:
+        - 1
+        - 2
+        - 3
+        - 4
+        - 5
+        - 6
+        - 7
+        - 8
+        stride_mode: all
+        trackable_scene_suffixes: null
+        enable_sequence_disk_cache: false
+        index_cache_dir: /scratch/shared/engs2907/logs/vgg_forecast_4d/cache/syn4d_index
+        index_cache_content_signature: true
+    - name: syn4d_v2_mixed
+      loader: syn4d
+      unified_name: syn4d
+      virtual_length: 51282
+      partition: train
+      track_coordinates: world
+      annotation_provenance: syn4d_rendered_depth_pose_no_tracks_read
+      allow_missing_tracks: true
+      capability: self_geometry
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        depth_only: true
+        preserve_resolution_orientation: true
+        ROOT: /vgg/chenzhao/syn4d_v2_stride_1/mixed
+        frame_exclusion_manifest: configs/data/syn4d_v2_mixed_frame_exclusions.json
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        stride:
+        - 1
+        - 2
+        - 3
+        - 4
+        - 5
+        - 6
+        - 7
+        - 8
+        stride_mode: all
+        trackable_scene_suffixes: null
+        enable_sequence_disk_cache: false
+        index_cache_dir: /scratch/shared/engs2907/logs/vgg_forecast_4d/cache/syn4d_index
+        index_cache_content_signature: true
+    - name: syn4d_v2_mixed_no_sim
+      loader: syn4d
+      unified_name: syn4d
+      virtual_length: 25641
+      partition: train
+      track_coordinates: world
+      annotation_provenance: syn4d_rendered_depth_pose_no_tracks_read
+      allow_missing_tracks: true
+      capability: self_geometry
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        depth_only: true
+        preserve_resolution_orientation: true
+        ROOT: /vgg/chenzhao/syn4d_v2_stride_1/mixed_no_sim
+        frame_exclusion_manifest: configs/data/syn4d_v2_mixed_no_sim_frame_exclusions.json
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        stride:
+        - 1
+        - 2
+        - 3
+        - 4
+        - 5
+        - 6
+        - 7
+        - 8
+        stride_mode: all
+        trackable_scene_suffixes: null
+        enable_sequence_disk_cache: false
+        index_cache_dir: /scratch/shared/engs2907/logs/vgg_forecast_4d/cache/syn4d_index
+        index_cache_content_signature: true
+    - name: tartanair
+      loader: tartanair
+      unified_name: tartanair
+      virtual_length: 61538
+      partition: train
+      track_coordinates: world
+      annotation_provenance: tartanair_analytic_static_world_correspondence_owner_declared_depth_consistency_mask
+      allow_missing_tracks: true
+      capability: static_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /datasets/engs2907/cut3r_processed/processed_tartanair
+        split: null
+        depth_far_clip_percentile: 98
+        allow_repeat: false
+        aug_crop: 0
+        long_video_stride: 4
+        start_step: 1
+        temporal_stride_choices:
+        - 1
+        - 2
+        - 3
+        - 4
+        - 5
+        - 6
+        - 7
+        - 8
+    - name: mvs_synth
+      loader: mvs_synth
+      unified_name: mvs_synth_track
+      virtual_length: 30769
+      partition: train
+      track_coordinates: world
+      annotation_provenance: mvs_synth_analytic_static_world_correspondence_owner_declared_depth_consistency_mask
+      allow_missing_tracks: true
+      capability: static_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/engs2907_processed/processed_mvs_synth
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        return_trackvid: false
+        exclude_sequence_ids:
+        - '0000'
+    - name: dl3dv
+      loader: dl3dv
+      unified_name: dl3dv_track
+      virtual_length: 102564
+      partition: train
+      track_coordinates: world
+      annotation_provenance: dl3dv_reconstructed_depth_pose_no_tracks_read
+      allow_missing_tracks: true
+      capability: static_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /scratch/shared/engs2907/processed_dl3dv_ours_parts/processed_dl3dv_ours
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        max_interval: 8
+        camera_exclusion_manifest: configs/data/camera_screening/dl3dv.json
+        index_cache_dir: /scratch/shared/engs2907/logs/vgg_forecast_4d/cache/dl3dv_index
+    - name: dynamic_replica
+      loader: dynamic_replica
+      unified_name: dynamic_replica
+      virtual_length: 51282
+      partition: train
+      track_coordinates: world
+      annotation_provenance: dynamic_replica_dense_persistent_3d_annotation
+      allow_missing_tracks: false
+      capability: dynamic_cross_time
+      visibility_capability: native
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /datasets/engs2907/tracking_dataset/processed_dynamic_replica_track
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        min_interval: 1
+        max_interval: 1
+        long_video_stride: 4
+        start_step: 1
+        temporal_stride_choices:
+        - 1
+        - 2
+        - 3
+        - 4
+        - 5
+        - 6
+        - 7
+        - 8
+        query_random_prob: 0.0
+        sparse_track_use_target_validity: true
+    - name: pointodyssey_track4d
+      loader: pointodyssey
+      unified_name: pointodyssey
+      virtual_length: 51282
+      partition: train
+      track_coordinates: world
+      annotation_provenance: track4d_sparse_persistent_3d_annotation
+      allow_missing_tracks: false
+      capability: dynamic_cross_time
+      visibility_capability: native
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /datasets/engs2907/tracking_dataset/processed_pointodyssey_track
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        min_interval: 1
+        max_interval: 1
+        long_video_stride: 4
+        start_step: 1
+        temporal_stride_choices:
+        - 1
+        - 2
+        - 3
+        - 4
+        - 5
+        - 6
+        - 7
+        - 8
+        query_random_prob: 0.0
+        sparse_track_use_target_validity: true
+        exclude_scenes:
+        - cab_e1_ego2
+    - name: kubric49
+      loader: kubric49
+      unified_name: kubric
+      virtual_length: 10256
+      partition: train
+      track_coordinates: world
+      annotation_provenance: kubric49_dense_object_and_static_world_correspondence
+      allow_missing_tracks: false
+      capability: dynamic_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/kubric49/train_preprocessed_49frames
+        split: train
+        camera_id: null
+        num_cameras: 4
+        max_interval: 8
+        random_stride: true
+        query_frame: 0
+        reverse_prob: 0.0
+        fixed_start_offset: null
+        aug_crop: 0
+        depth_region: foreground_objects
+        min_source_foreground_fraction: 0.01
+    - name: kubric81
+      loader: kubric81
+      unified_name: kubric_81
+      virtual_length: 10256
+      partition: train
+      track_coordinates: world
+      annotation_provenance: kubric81_dense_object_and_static_world_correspondence
+      allow_missing_tracks: false
+      capability: dynamic_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/kubric81/train_preprocessed_81frames
+        split: train
+        camera_id: null
+        num_cameras: 4
+        max_interval: 8
+        random_stride: true
+        query_frame: 0
+        reverse_prob: 0.0
+        fixed_start_offset: null
+        aug_crop: 0
+        depth_region: foreground_objects
+        min_source_foreground_fraction: 0.01
+    - name: waymo
+      loader: waymo
+      unified_name: waymo
+      virtual_length: 30769
+      partition: train
+      track_coordinates: world
+      annotation_provenance: waymo_lidar_static_world_and_tracked_box_object_correspondence
+      allow_missing_tracks: false
+      capability: dynamic_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /datasets/engs2907/tracking_dataset/processed_waymo_track
+        split: training
+        allow_repeat: false
+        aug_crop: 0
+        min_interval: 1
+        max_interval: 1
+        long_video_stride: 4
+        start_step: 1
+        temporal_stride_choices:
+        - 1
+        - 2
+        - 3
+        - 4
+        - 5
+        - 6
+        - 7
+        - 8
+        query_random_prob: 0.0
+    - name: hypersim
+      loader: hypersim
+      unified_name: hypersim
+      virtual_length: 61538
+      partition: train
+      track_coordinates: world
+      annotation_provenance: processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels
+      allow_missing_tracks: true
+      capability: static_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      - multi_view_set
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/cut3r_processed/processed_hypersim_new
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        max_interval: 4
+    - name: scannetpp
+      loader: scannetpp
+      unified_name: scannetpp
+      virtual_length: 51282
+      partition: train
+      track_coordinates: world
+      annotation_provenance: processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels
+      allow_missing_tracks: true
+      capability: static_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      - multi_view_set
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/cut3r_processed/processed_scannetpp_fix
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        max_interval: 1
+        uniform_temporal_stride: true
+        return_trackvid: false
+    - name: scannet
+      loader: scannet
+      unified_name: scannet
+      virtual_length: 30769
+      partition: train
+      track_coordinates: world
+      annotation_provenance: processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels
+      allow_missing_tracks: true
+      capability: static_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/cut3r_processed/processed_scannet
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        max_interval: 8
+        return_trackvid: false
+    - name: midair
+      loader: midair
+      unified_name: midair
+      virtual_length: 30769
+      partition: train
+      track_coordinates: world
+      annotation_provenance: processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels
+      allow_missing_tracks: true
+      capability: static_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/MidAir
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        max_interval: 8
+        max_depth_meters: 200
+    - name: wildrgbd
+      loader: wildrgbd
+      unified_name: wildrgbd
+      virtual_length: 30769
+      partition: train
+      track_coordinates: world
+      annotation_provenance: processed_depth_pose_pointmap_static_scene_no_registered_persistent_labels
+      allow_missing_tracks: true
+      capability: static_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/yushi/dataset/cut3r_processed/processed_wildrgbd_mp
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        mask_bg: false
+        max_interval: 8
+        uniform_temporal_stride: true
+    - name: synthverse_depth
+      loader: synthverse_depth
+      unified_name: synthverse_depth
+      virtual_length: 20513
+      partition: train
+      track_coordinates: world
+      annotation_provenance: synthverse_embodied_rendered_depth_pose_no_tracks_read
+      allow_missing_tracks: true
+      capability: self_geometry
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - video_window
+      - single_frame
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/chenzhao/synthverse
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+        max_interval: 8
+    - name: blendedmvs
+      loader: blendedmvs
+      unified_name: blendedmvs
+      virtual_length: 30769
+      partition: train
+      track_coordinates: world
+      annotation_provenance: blendedmvs_mesh_rendered_depth_pose_static_scene_views
+      allow_missing_tracks: true
+      capability: static_cross_time
+      visibility_capability: none
+      supervision:
+      - pointmap
+      sample_layouts:
+      - single_frame
+      - multi_view_set
+      kwargs:
+        return_tracks: false
+        ROOT: /vgg/chenzhao/BlendedMVS_processed
+        index_layout: seq_data_covisibility
+        covisibility_root: /vgg/chenzhao/blendedmvs_meta
+        split: train
+        allow_repeat: false
+        aug_crop: 0
+    geometry_gauge: source_frame_valid_mean_norm_v2
+    self_reconstruction_camera: own_camera
+    sample_packing:
+      layouts:
+        video_window: 0.65
+        single_frame: 0.15
+        multi_view_set: 0.2
+      window_frames:
+      - 1
+      - 25
+      frames_per_microbatch: 336
+  fixed_window: null
+  fixed_clips: null
+  fixed_source_trajectory_set: null
+  geometry_task_evaluation:
+    source: kubric49
+    window_indices:
+    - 0
+    - 1
+    eval_stride: 2
+    seed: 0
+    bucket: 0
+    start_offset: 0
+  frames: 21
+train:
+  worker_batch_size: 4
+  data_in_order: true
+  formal_launch_authorized: true
+  bounded_gate_authorized: false
+  bounded_gate_max_steps: 50000
+  max_steps: 32500
+  max_windows: null
+  max_wall_seconds: 155000
+  save_every: 500
+  checkpoint_format: full_state
+  max_checkpoints_to_keep: 1
+  checkpoint_milestone_steps: 5000
+  distributed_timeout_seconds: 1800
+  eval_every: null
+  eval_protocol: null
+  official_split_evaluation: null
+  output_dir: /vgg/yushi/logs/vgg_forecast_4d/train/point/temporal_pointdit_b_video_depth_expanded_mixture_rebalanced_sample_layouts_variable_frames_window_mean_depth_scale_per_sample_bounded_relative_loss_no_time_shift_21_frames_512_a100x4_rebalanced_sample_layouts_window_mean_depth_from_ckpt_23000_20261004/train
+  resume_from: /vgg/yushi/logs/vgg_forecast_4d/train/point/temporal_pointdit_b_video_depth_expanded_mixture_rebalanced_sample_layouts_variable_frames_window_mean_depth_scale_per_sample_bounded_relative_loss_no_time_shift_21_frames_512_a100x4_rebalanced_sample_layouts_window_mean_depth_from_ckpt_23000_20261004/train/ckpt_29500
+  cpu: false
+  mixed_precision: bf16
+  gradient_checkpointing: global_attention
+  gradient_accumulation_steps: 1
+  physical_batch_size: 16
+  batch_size: 64
+  ema_decays:
+  - 0.9999
+  - 0.9996
+  num_workers: 7
+  persistent_workers: true
+  prefetch_factor: 1
+  pin_memory: true
+  data_prefetch_batches: 1
+  dino_schedule: task_required_frames_per_window
+  fixed_set_dino_cache: null
+  bounded_gate_stop: null
+  optimizer_state_keep_latest: 2
+  noise_generator: cuda_keyed
+  tensor_sharing_strategy: file_descriptor
+  fit_window_evaluation: null
+  max_milestones_to_keep: 4
+model:
+  point:
+    private_rope_layout: sectioned
+    checkpoint: /scratch/shared/engs2907/pointdit_official/pretrained/pointditb-512-mixdata-nodinov3-1d42aacf.pth
+    checkpoint_sha256: 1d42aacfd6aaf97c8bd96b0ce5e23bd880cd01b2efbb2ac80ccf8876fde3034b
+    branch: model_ema1
+    input_order: official_dino_first
+    native_image_size: 512
+    fixed_position_resize: bicubic
+    attention_backend: fa2
+    physical_conditioning:
+      method: residual_adaln_source_to_target_v1
+      physical_dim: 128
+      fourier_dim: 64
+      hidden_dim: 256
+    core:
+      dim: 768
+      ffn_dim: 3072
+      heads: 12
+      layers: 12
+      condition_dim: 3072
+      frequency_dim: 256
+    stage: temporal_point_depth
+    depth_target_transform: symmetric_mip_nerf_360_contraction
+    target_centering: window_valid_mean_depth
+  dino:
+    arch: vitb16
+    repo: /vgg/yushi/Repo/baselines/dinov3
+    weights: /scratch/shared/engs2907/pointdit_official/pretrained/dinov3/dinov3_vitb16_pretrain_lvd1689m-73cec8be.pth
+    weights_sha256: 73cec8be7427c8655ceced13ce62f6e20a1fa90d1b4d4a550df17a1144081a7c
+    layers:
+    - 2
+    - 5
+    - 8
+    - 11
+    chunk: 16
+    autocast_dtype: bf16
+seed: 20260925
+stream_seed: 20260925
+joint_training:
+  task_cycle:
+  - temporal_point
+query:
+  frame_time_unit: frames
+  common_time_policy: wan_latent_group
+  empty_cross_time_support: empty_track_support
+  source_sampling: adapter
+  fixed_source: null
+schedules:
+  pointmap:
+    convention: pointdit_v
+    p_mean: -0.8
+    p_std: 0.8
+    p_zero: 0.2
+    noise_scale: 2.0
+    training_t_eps: 0.05
+    sample_t_eps: 0.0
+    sampler_initialization: gaussian
+    relative_point_loss_weight: 0.1
+    inference_time_grid: logit_normal_quantile
+    inference_time_grid_mu: -3.0
+    inference_time_grid_sigma: 1.0
+    time_shift: 1.0
+    relative_point_loss_space: decoded_depth_bounded
+    loss_reduction: per_sample_mean
+sampler:
+  base_steps: 4
+  point_match_probability: 0.0
+  forecast_independent_zero_progress_probability: null
+task:
+  modes:
+    self_reconstruction: 1.0
+    backward_alignment: 0.0
+    forward_prediction: 0.0
+    source_trajectory: 0.0
+  microbatch_homogeneous: false
+  backward_reference_frames: []
+  forward_source_frames: []
+  source_trajectory_frames:
+  - 0
+  frame0_background: generated
+loss:
+  point_supervision_support: all_pixels
+optim:
+  lr: 5.0e-05
+  betas:
+  - 0.9
+  - 0.95
+  weight_decay: 0.0
+  max_grad_norm: 1.0
+logging:
+  mode: online
+  project: vgg-forecast
+  group: pointdit_video_depth
+  name: temporal_pointdit_b_video_depth_expanded_mixture_rebalanced_sample_layouts_variable_frames_window_mean_depth_scale_per_sample_bounded_relative_loss_no_time_shift_21_frames_512
+  on_error: local_only
+  tags:
+  - video_depth
+  - own_camera
+  - 21_frames
+  - expanded_mixture
+  - multi_aspect_512_pixels
+  - pointdit_b
+  - expanded_mixture_rebalanced_sample_layouts_variable_frames_window_mean_depth_scale_per_sample_bounded_relative_loss_no_time_shift