diff --git a/video-depth-pointdit/README.md b/video-depth-pointdit/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c6390925005f7dc017afe1edbb897a6cb27954e1 --- /dev/null +++ b/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= # 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 ` 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::` 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::`). 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/`). diff --git a/video-depth-pointdit/infer_video_depth.py b/video-depth-pointdit/infer_video_depth.py new file mode 100644 index 0000000000000000000000000000000000000000..6bf8158d6efe51c9e2cd1f8707c23ab204be05a1 --- /dev/null +++ b/video-depth-pointdit/infer_video_depth.py @@ -0,0 +1,172 @@ +#!/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 --input clip.mp4 --out out_dir + python infer_video_depth.py --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=/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 '::' (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()) diff --git a/video-depth-pointdit/load_point.py b/video-depth-pointdit/load_point.py new file mode 100644 index 0000000000000000000000000000000000000000..a1ce4ec279de8e964cfd2c62f36b3ab6717e7e1b --- /dev/null +++ b/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)) diff --git a/video-depth-pointdit/manifest.json b/video-depth-pointdit/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..fbf72e65f7e48dfa2474ee2b7405d4cd1805803f --- /dev/null +++ b/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": 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+++ b/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 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file mode 100644 index 0000000000000000000000000000000000000000..0b2dd89258bab60bfe04ca874b9b1196b8c94554 --- /dev/null +++ b/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 +# +# 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 /test__.jsonl and compare with rows/. +set -uo pipefail +(( $# == 2 )) || { echo "usage: $0 " >&2; exit 2; } +FOLDER=$(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 diff --git a/video-depth-pointdit/rows/single_frame_test_video_depth_anything_aspect_preserving_depth_fit_bonn.jsonl b/video-depth-pointdit/rows/single_frame_test_video_depth_anything_aspect_preserving_depth_fit_bonn.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..14864d7491df3d8f1eb04c94fa8c5246b6d42b40 --- /dev/null +++ b/video-depth-pointdit/rows/single_frame_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": 105}, "depth_state_decode": {"capped_state_elements": 0, "nonfinite_state_elements": 0, "requested_state_cap": 1.984375, "state_elements": 27847680}, "excluded_sequences": {}, "frame_calls": "single_frame", "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", 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video_depth_anything_aspect_preserving_depth_fit: Video-Depth-Anything's least-squares scale and shift per frame in depth; the unweighted mean over frames"}, "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", 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"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" + } + }, + "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 +} diff --git a/video-depth-pointdit/train/resolved_resume_29500.yaml b/video-depth-pointdit/train/resolved_resume_29500.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d0a56badf2f9b93ebbdbd39f0dffffea6145615a --- /dev/null +++ b/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