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Running on Zero
Running on Zero
| """Dataset index shared by all backbones. | |
| The dataset is described by one JSON-lines annotation file, one row per 81-frame clip: | |
| {"clip": "000123", # clip id = file stem of the video and of every latent file | |
| "video": "clips/000123.mp4", # path relative to the annotation file | |
| "split": "train", # "train" or "val" | |
| "kind": "plain", # "plain", or "burn" / "hardcut" for scene-transition clips | |
| "valid": true, # false if the camera track of the clip is unreliable | |
| "rt_ratio": 0.98, # real-time ratio of the recording (plain clips) | |
| "sources": ["000045", "000981"], # transition clips: the two clips they were composed from | |
| "frame_offset_px": [[0.0, 0.0], ...], # 81 x (dx, dy) camera offsets in px (see actionrope.geometry) | |
| "prompts": ["In a 2.5D top down view, the player is in ...", ...]} # 21 per-cell scene prompts (no actions) | |
| The 21 prompts follow the Wan-family latent frames (``geometry.cell_frames``); they only differ inside transition | |
| clips. Latents are pre-computed per backbone with ``scripts/encode_latents.py`` into ``<latent_dir>/<clip>.pt``. | |
| """ | |
| from __future__ import annotations | |
| import json | |
| import os | |
| from dataclasses import dataclass | |
| import numpy as np | |
| import torch | |
| TRANSITION_KINDS = ("burn", "hardcut") | |
| class Clip: | |
| clip: str | |
| split: str | |
| kind: str | |
| valid: bool | |
| rt_ratio: float | None | |
| sources: tuple[str, ...] | |
| frame_offset_px: np.ndarray # float64 [81, 2] | |
| prompts: list[str] | |
| video: str | None = None | |
| def load_annotations(path: str) -> list[Clip]: | |
| root = os.path.dirname(os.path.abspath(path)) | |
| clips = [] | |
| with open(path, encoding="utf-8") as fh: | |
| for line in fh: | |
| if not line.strip(): | |
| continue | |
| r = json.loads(line) | |
| clips.append(Clip( | |
| clip=r["clip"], split=r["split"], kind=r.get("kind", "plain"), valid=bool(r.get("valid", True)), | |
| rt_ratio=r.get("rt_ratio"), sources=tuple(r.get("sources") or ()), | |
| frame_offset_px=np.asarray(r["frame_offset_px"], np.float64), prompts=list(r["prompts"]), | |
| video=os.path.join(root, r["video"]) if r.get("video") else None)) | |
| return clips | |
| def training_clips(clips: list[Clip], min_rt_ratio: float = 0.8, exclude_val_sources: bool = True, | |
| verbose: bool = True) -> list[Clip]: | |
| """Training split after the filters of the paper: | |
| * drop clips whose camera track is invalid; | |
| * drop transition clips composed from a validation clip (their frames would leak into training); | |
| * drop plain clips recorded below ``min_rt_ratio`` of real time (the game stuttered, so motion per frame drops). | |
| """ | |
| val = {c.clip for c in clips if c.split == "val"} | |
| out, n_invalid, n_leak, n_slow = [], 0, 0, 0 | |
| for c in clips: | |
| if c.split != "train": | |
| continue | |
| if not c.valid: | |
| n_invalid += 1 | |
| elif c.kind in TRANSITION_KINDS: | |
| if exclude_val_sources and any(s in val for s in c.sources): | |
| n_leak += 1 | |
| else: | |
| out.append(c) | |
| elif min_rt_ratio > 0 and float(1.0 if c.rt_ratio is None else c.rt_ratio) < min_rt_ratio: | |
| n_slow += 1 | |
| else: | |
| out.append(c) | |
| if verbose: | |
| n_train = sum(c.split == "train" for c in clips) | |
| print(f"[data] train clips: {n_train} - invalid {n_invalid} - leaking transitions {n_leak} - " | |
| f"rt_ratio < {min_rt_ratio} {n_slow} = {len(out)}", flush=True) | |
| return out | |
| def validation_clips(clips: list[Clip], min_rt_ratio: float = 0.9, n: int | None = 16, seed: int = 0) -> list[Clip]: | |
| """Validation clips used to monitor the training loss (``n`` random clips with rt_ratio >= ``min_rt_ratio``).""" | |
| import random | |
| pool = [c for c in clips if c.split == "val" and c.valid and c.kind not in TRANSITION_KINDS | |
| and float(1.0 if c.rt_ratio is None else c.rt_ratio) >= min_rt_ratio] | |
| pool.sort(key=lambda c: c.clip) | |
| if n is not None and n < len(pool): | |
| random.Random(seed).shuffle(pool) | |
| pool = sorted(pool[:n], key=lambda c: c.clip) | |
| return pool | |
| def load_text_table(path: str) -> dict[str, torch.Tensor]: | |
| """{prompt: embedding} written by ``scripts/build_text_table.py``.""" | |
| d = torch.load(path, map_location="cpu", weights_only=False) | |
| return d["table"] if isinstance(d, dict) and "table" in d and isinstance(d["table"], dict) else d | |