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data.py defines the synthetic benchmark data pipeline for the spectral world model project.
Specifies vocabulary and action labels for text-conditioned dynamics
Generates moving shape sequences with image frames, text tokens, actions, and rewards
Saves train/val/test splits to a compressed .npz benchmark file
Provides TransitionDataset for frame-to-frame transition learning
Provides SequenceDataset for full-sequence rollout evaluation
'''
# Andrew Kiruluta, UC Berkeley, June 2026
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
import json
import math
import numpy as np
import torch
from torch.utils.data import Dataset
VOCAB = [
"<pad>",
"square", "circle",
"stay", "up", "down", "left", "right",
"slow", "fast",
"x0", "x1", "x2", "x3",
"y0", "y1", "y2", "y3",
]
TOKEN_TO_ID = {tok: i for i, tok in enumerate(VOCAB)}
ACTION_NAMES = ["stay", "up", "down", "left", "right"]
@dataclass
class DatasetConfig:
image_size: int = 32
seq_len: int = 8
max_text_len: int = 5
train_sequences: int = 256
val_sequences: int = 64
test_sequences: int = 64
seed: int = 7
def _draw_square(canvas: np.ndarray, x: int, y: int, size: int) -> None:
h, w = canvas.shape
x0 = max(0, x - size // 2); x1 = min(w, x + size // 2 + 1)
y0 = max(0, y - size // 2); y1 = min(h, y + size // 2 + 1)
canvas[y0:y1, x0:x1] = 1.0
def _draw_circle(canvas: np.ndarray, x: int, y: int, radius: int) -> None:
h, w = canvas.shape
yy, xx = np.ogrid[:h, :w]
mask = (xx - x) ** 2 + (yy - y) ** 2 <= radius**2
canvas[mask] = 1.0
def _text_tokens(shape: str, action: int, speed: int, x: int, y: int, image_size: int, max_len: int) -> np.ndarray:
xb = min(3, max(0, int(4 * x / image_size)))
yb = min(3, max(0, int(4 * y / image_size)))
words = [shape, ACTION_NAMES[action], "fast" if speed == 2 else "slow", f"x{xb}", f"y{yb}"]
ids = [TOKEN_TO_ID[w] for w in words][:max_len]
ids += [0] * (max_len - len(ids))
return np.asarray(ids, dtype=np.int64)
def _apply_action(x: int, y: int, action: int, speed: int, image_size: int, radius: int) -> tuple[int, int]:
dx, dy = 0, 0
if action == 1: dy = -speed
elif action == 2: dy = speed
elif action == 3: dx = -speed
elif action == 4: dx = speed
x = int(np.clip(x + dx, radius + 1, image_size - radius - 2))
y = int(np.clip(y + dy, radius + 1, image_size - radius - 2))
return x, y
def generate_sequences(n_sequences: int, cfg: DatasetConfig, rng: np.random.Generator) -> dict[str, np.ndarray]:
images = np.zeros((n_sequences, cfg.seq_len, 1, cfg.image_size, cfg.image_size), dtype=np.float32)
tokens = np.zeros((n_sequences, cfg.seq_len, cfg.max_text_len), dtype=np.int64)
actions = np.zeros((n_sequences, cfg.seq_len - 1), dtype=np.int64)
rewards = np.zeros((n_sequences, cfg.seq_len - 1), dtype=np.float32)
for n in range(n_sequences):
shape = "square" if rng.random() < 0.5 else "circle"
speed = int(rng.choice([1, 2]))
radius = 3 if shape == "circle" else 4
x = int(rng.integers(radius + 2, cfg.image_size - radius - 2))
y = int(rng.integers(radius + 2, cfg.image_size - radius - 2))
last_action = 0
for t in range(cfg.seq_len):
canvas = np.zeros((cfg.image_size, cfg.image_size), dtype=np.float32)
if shape == "square":
_draw_square(canvas, x, y, 6)
else:
_draw_circle(canvas, x, y, radius)
# Add tiny structured sensor noise to make denoising useful.
canvas += rng.normal(0.0, 0.01, size=canvas.shape).astype(np.float32)
canvas = np.clip(canvas, 0.0, 1.0)
images[n, t, 0] = canvas
tokens[n, t] = _text_tokens(shape, last_action, speed, x, y, cfg.image_size, cfg.max_text_len)
if t < cfg.seq_len - 1:
action = int(rng.integers(0, len(ACTION_NAMES)))
actions[n, t] = action
x, y = _apply_action(x, y, action, speed, cfg.image_size, radius)
last_action = action
# Reward favors reaching the upper-right quadrant.
rewards[n, t] = float((x > cfg.image_size * 0.65) and (y < cfg.image_size * 0.35))
return {"images": images, "tokens": tokens, "actions": actions, "rewards": rewards}
def generate_benchmark_npz(path: str | Path, cfg: DatasetConfig = DatasetConfig()) -> None:
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
rng = np.random.default_rng(cfg.seed)
train = generate_sequences(cfg.train_sequences, cfg, rng)
val = generate_sequences(cfg.val_sequences, cfg, rng)
test = generate_sequences(cfg.test_sequences, cfg, rng)
np.savez_compressed(
path,
train_images=train["images"], train_tokens=train["tokens"], train_actions=train["actions"], train_rewards=train["rewards"],
val_images=val["images"], val_tokens=val["tokens"], val_actions=val["actions"], val_rewards=val["rewards"],
test_images=test["images"], test_tokens=test["tokens"], test_actions=test["actions"], test_rewards=test["rewards"],
vocab=np.asarray(VOCAB), action_names=np.asarray(ACTION_NAMES),
config=json.dumps(cfg.__dict__),
)
class TransitionDataset(Dataset):
def __init__(self, npz_path: str | Path, split: str):
data = np.load(npz_path, allow_pickle=True)
self.images = data[f"{split}_images"]
self.tokens = data[f"{split}_tokens"]
self.actions = data[f"{split}_actions"]
self.seq_len = self.images.shape[1]
self.index = [(i, t) for i in range(self.images.shape[0]) for t in range(self.seq_len - 1)]
def __len__(self) -> int:
return len(self.index)
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
i, t = self.index[idx]
return {
"image": torch.from_numpy(self.images[i, t]),
"tokens": torch.from_numpy(self.tokens[i, t]).long(),
"action": torch.tensor(self.actions[i, t]).long(),
"next_image": torch.from_numpy(self.images[i, t + 1]),
"next_tokens": torch.from_numpy(self.tokens[i, t + 1]).long(),
}
class SequenceDataset(Dataset):
def __init__(self, npz_path: str | Path, split: str):
data = np.load(npz_path, allow_pickle=True)
self.images = data[f"{split}_images"]
self.tokens = data[f"{split}_tokens"]
self.actions = data[f"{split}_actions"]
self.rewards = data[f"{split}_rewards"]
def __len__(self) -> int:
return self.images.shape[0]
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
return {
"images": torch.from_numpy(self.images[idx]),
"tokens": torch.from_numpy(self.tokens[idx]).long(),
"actions": torch.from_numpy(self.actions[idx]).long(),
"rewards": torch.from_numpy(self.rewards[idx]),
}
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