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b4b2877 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 | """Frame-level future motor-primitive forecasting dataset.
Task definition
---------------
At a sampled anchor time t in a recording:
past = sensor frames over [t - T_obs, t] ← input
future = per-frame verb_fine labels over (t, t + T_fut] ← target
We use NUM_VERB_FINE (= 17) as a sentinel "idle / no segment" class for
frames not covered by any annotated segment, so every future frame has a
valid label (output cardinality = NUM_VERB_FINE + 1 = 18).
Anchors are sampled at fixed stride within each recording so the model
sees both intra-segment future (mostly stationary) and across-boundary
future (where the next-action label changes — the interesting cases).
"""
from __future__ import annotations
import os
import sys
from pathlib import Path
from typing import Dict, List, Optional, Sequence, Tuple
import numpy as np
import torch
from torch.utils.data import Dataset
THIS = Path(__file__).resolve()
sys.path.insert(0, str(THIS.parent))
sys.path.insert(0, str(THIS.parents[1]))
try:
from experiments.dataset_seqpred import (
SAMPLING_RATE_HZ, _load_recording_sensors, _load_annotations,
parse_ts_range, TRAIN_VOLS_V3, TEST_VOLS_V3,
DEFAULT_DATASET_DIR, DEFAULT_ANNOT_DIR,
)
from experiments.taxonomy import (
classify_segment, NUM_VERB_FINE,
)
except ModuleNotFoundError:
from dataset_seqpred import (
SAMPLING_RATE_HZ, _load_recording_sensors, _load_annotations,
parse_ts_range, TRAIN_VOLS_V3, TEST_VOLS_V3,
DEFAULT_DATASET_DIR, DEFAULT_ANNOT_DIR,
)
from taxonomy import classify_segment, NUM_VERB_FINE
IDLE_LABEL = NUM_VERB_FINE # = 17, sentinel for "no segment covers this frame"
NUM_FORECAST_CLASSES = NUM_VERB_FINE + 1 # = 18
class ForecastDataset(Dataset):
"""Forecast next T_fut seconds of per-frame verb_fine given past T_obs."""
def __init__(
self,
volunteers: Sequence[str],
modalities: Sequence[str],
t_obs_sec: float = 1.5,
t_fut_sec: float = 0.5,
anchor_stride_sec: float = 0.25,
downsample: int = 5,
dataset_dir: Path = DEFAULT_DATASET_DIR,
annot_dir: Path = DEFAULT_ANNOT_DIR,
stats: Optional[Dict[str, Tuple[np.ndarray, np.ndarray]]] = None,
expected_dims: Optional[Dict[str, int]] = None,
contact_only: bool = False,
contact_threshold_g: float = 5.0,
log: bool = True,
):
super().__init__()
self.modalities = list(modalities)
self.t_obs_sec = float(t_obs_sec)
self.t_fut_sec = float(t_fut_sec)
self.anchor_stride_sec = float(anchor_stride_sec)
self.downsample = int(downsample)
self.sr = SAMPLING_RATE_HZ // self.downsample
self.dataset_dir = Path(dataset_dir)
self.annot_dir = Path(annot_dir)
self.contact_only = bool(contact_only)
self.contact_threshold_g = float(contact_threshold_g)
# Output time-step counts (after downsample)
self.T_obs = int(round(self.t_obs_sec * self.sr))
self.T_fut = int(round(self.t_fut_sec * self.sr))
self._items: List[dict] = []
# Pre-seed modality dims if caller (e.g. test set) provides them
self._modality_dims: Dict[str, int] = dict(expected_dims) if expected_dims else {}
for vol in volunteers:
vol_dir = self.dataset_dir / vol
if not vol_dir.is_dir():
continue
for scenario_dir in sorted(vol_dir.glob("s*")):
if not scenario_dir.is_dir():
continue
scene = scenario_dir.name
annot_path = self.annot_dir / vol / f"{scene}.json"
if not annot_path.exists():
continue
# Always include pressure for the filter, even if model
# doesn't see it as input. We separate "filter sensors"
# (load_mods) from "model input sensors" (self.modalities).
load_mods = list(dict.fromkeys(list(self.modalities) + ["pressure"]))
try:
sensors_all = _load_recording_sensors(
scenario_dir, vol, scene, load_mods
)
except Exception:
continue
if sensors_all is None or any(a is None for a in sensors_all.values()):
continue
pressure_full = sensors_all.get("pressure") # (T, 50)
# Subset to model-input modalities for everything downstream
sensors = {m: sensors_all[m] for m in self.modalities}
# Track modality dim consistency
for m, arr in sensors.items():
if m in self._modality_dims:
target = self._modality_dims[m]
if arr.shape[1] != target:
if arr.shape[1] < target:
pad = np.zeros((arr.shape[0], target - arr.shape[1]),
dtype=np.float32)
sensors[m] = np.concatenate([arr, pad], axis=1)
else:
sensors[m] = arr[:, :target]
else:
self._modality_dims[m] = arr.shape[1]
T_avail = min(a.shape[0] for a in sensors.values())
if T_avail < (self.T_obs + self.T_fut) * self.downsample:
continue
# Build per-frame verb_fine timeline at full 100 Hz
timeline = np.full(T_avail, IDLE_LABEL, dtype=np.int64)
segs = _load_annotations(annot_path)
for seg in segs:
a = seg.get("action_annotation", {})
labels = classify_segment(a)
if labels is None:
continue
start_sec, end_sec = parse_ts_range(seg.get("timestamp", ""))
s = int(round(start_sec * SAMPLING_RATE_HZ))
e = int(round(end_sec * SAMPLING_RATE_HZ))
s = max(0, s); e = min(T_avail, e)
if e > s:
timeline[s:e] = labels["verb_fine"]
# Downsample timeline to 20 Hz
timeline_ds = timeline[::self.downsample]
T_ds = len(timeline_ds)
# Downsample sensors to 20 Hz (kept as full record;
# we'll slice windows below)
sensors_ds = {m: arr[::self.downsample] for m, arr in sensors.items()}
# Build contact mask at 20 Hz (per-frame): is pressure-sum > thr?
# Pressure is 50 channels; we follow the T2 contact convention
# (sum across all fingertips and threshold at 5 g).
if pressure_full is not None:
pressure_ds = pressure_full[::self.downsample]
contact_ds = pressure_ds.sum(axis=1) > self.contact_threshold_g
else:
contact_ds = np.zeros(T_ds, dtype=bool)
# Sample anchors at fixed stride (in 20 Hz frames)
stride = max(1, int(round(self.anchor_stride_sec * self.sr)))
first_anchor = self.T_obs
last_anchor = T_ds - self.T_fut
if last_anchor <= first_anchor:
continue
for anchor in range(first_anchor, last_anchor + 1, stride):
# contact-rich filter: any contact frame in past or future window?
if self.contact_only:
win = contact_ds[max(0, anchor - self.T_obs):
min(T_ds, anchor + self.T_fut)]
if not win.any():
continue
past_slice = {m: arr[anchor - self.T_obs:anchor]
for m, arr in sensors_ds.items()}
fut_labels = timeline_ds[anchor:anchor + self.T_fut].copy()
# length sanity
if any(w.shape[0] != self.T_obs for w in past_slice.values()):
continue
if fut_labels.shape[0] != self.T_fut:
continue
self._items.append({
"x": past_slice, # dict[mod] -> (T_obs, F_mod)
"y_seq": fut_labels, # (T_fut,) int in [0..17]
"meta": {"vol": vol, "scene": scene, "anchor_idx": int(anchor)},
})
if not self._items:
raise RuntimeError("ForecastDataset: collected 0 anchors. Check annot_dir / modalities.")
# Per-modality z-score using training stats
if stats is None:
stats = self._compute_stats()
self._stats = stats
self._apply_stats(stats)
if log:
print(f"[ForecastDataset] vols={len(volunteers)} "
f"anchors={len(self._items)} "
f"T_obs={self.T_obs} T_fut={self.T_fut} "
f"contact_only={self.contact_only} "
f"modality_dims={self._modality_dims} "
f"sr={self.sr}Hz", flush=True)
# ----- Stats / normalization -----
def _compute_stats(self) -> Dict[str, Tuple[np.ndarray, np.ndarray]]:
accs = {m: [] for m in self._modality_dims}
for it in self._items:
for m, w in it["x"].items():
accs[m].append(w)
out = {}
for m, ws in accs.items():
cat = np.concatenate(ws, axis=0)
mu = cat.mean(axis=0)
sd = cat.std(axis=0); sd = np.where(sd < 1e-6, 1.0, sd)
out[m] = (mu.astype(np.float32), sd.astype(np.float32))
return out
def _apply_stats(self, stats):
for it in self._items:
for m, w in it["x"].items():
if m in stats:
mu, sd = stats[m]
it["x"][m] = ((w - mu) / sd).astype(np.float32)
# ----- Dataset protocol -----
def __len__(self):
return len(self._items)
def __getitem__(self, idx):
it = self._items[idx]
x = {m: torch.from_numpy(np.ascontiguousarray(w)) for m, w in it["x"].items()}
y_seq = torch.from_numpy(np.ascontiguousarray(it["y_seq"])) # (T_fut,)
return x, y_seq, it["meta"]
@property
def modality_dims(self):
return dict(self._modality_dims)
def class_freq(self) -> np.ndarray:
c = np.zeros(NUM_FORECAST_CLASSES, dtype=np.int64)
for it in self._items:
for v in it["y_seq"]:
c[int(v)] += 1
return c
def collate_forecast(batch):
"""Stack (x_dict, y_seq, meta) -> batched tensors. All samples share T_obs/T_fut."""
xs, ys, metas = zip(*batch)
B = len(batch)
mods = list(xs[0].keys())
x_out: Dict[str, torch.Tensor] = {}
for m in mods:
x_out[m] = torch.stack([x[m] for x in xs], dim=0) # (B, T_obs, F_mod)
y_out = torch.stack(ys, dim=0) # (B, T_fut)
return x_out, y_out, list(metas)
def build_train_test(
modalities: Sequence[str],
t_obs_sec: float = 1.5,
t_fut_sec: float = 0.5,
anchor_stride_sec: float = 0.25,
downsample: int = 5,
dataset_dir: Path = DEFAULT_DATASET_DIR,
annot_dir: Path = DEFAULT_ANNOT_DIR,
contact_only: bool = False,
contact_threshold_g: float = 5.0,
):
train = ForecastDataset(
TRAIN_VOLS_V3, modalities=modalities,
t_obs_sec=t_obs_sec, t_fut_sec=t_fut_sec,
anchor_stride_sec=anchor_stride_sec, downsample=downsample,
dataset_dir=dataset_dir, annot_dir=annot_dir,
contact_only=contact_only, contact_threshold_g=contact_threshold_g,
stats=None, log=True,
)
test = ForecastDataset(
TEST_VOLS_V3, modalities=modalities,
t_obs_sec=t_obs_sec, t_fut_sec=t_fut_sec,
anchor_stride_sec=anchor_stride_sec, downsample=downsample,
dataset_dir=dataset_dir, annot_dir=annot_dir,
contact_only=contact_only, contact_threshold_g=contact_threshold_g,
stats=train._stats, expected_dims=train._modality_dims, log=True,
)
return train, test
if __name__ == "__main__":
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("--modalities", type=str, default="imu,emg,eyetrack,mocap,pressure")
ap.add_argument("--t_obs", type=float, default=1.5)
ap.add_argument("--t_fut", type=float, default=0.5)
ap.add_argument("--stride", type=float, default=0.25)
args = ap.parse_args()
mods = args.modalities.split(",")
tr, te = build_train_test(
modalities=mods,
t_obs_sec=args.t_obs, t_fut_sec=args.t_fut,
anchor_stride_sec=args.stride,
)
print(f"\nTrain={len(tr)} Test={len(te)} T_obs={tr.T_obs} T_fut={tr.T_fut}")
print(f"Train class freq:\n{tr.class_freq()}")
print(f"Test class freq:\n{te.class_freq()}")
x, y, meta = tr[0]
print(f"Sample: x={ {m: tuple(v.shape) for m,v in x.items()} } y_seq={tuple(y.shape)}")
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