experiments/tasks/train_signal_forecast.py

#!/usr/bin/env python3
"""Train + evaluate frame-level future-signal forecasting (T8 v2).

Predicts the raw future signal of one target modality (IMU, EMG, or MoCap)
from past T_obs of input modalities. Reports skill score against persistence
baseline, broken down by 4 contact-event types.

Three configurations supported (driven by --modalities):
  A. Target-only      e.g. --modalities imu                        (target IMU)
  B. Target + Pressure  e.g. --modalities imu,pressure              (target IMU)
  C. Target + Pressure (zeroed)  set --modalities imu,pressure --zero_pressure_at_eval
       This loads the same checkpoint trained as B and re-evaluates with the
       pressure channel forced to zero at test time, isolating pressure's
       causal contribution net of model capacity.

Skill score = 1 - MSE(pred, true) / MSE(persistence, true)
where persistence = repeat last observed target frame T_fut times.
"""
from __future__ import annotations
import argparse
import json
import random
import sys
import time
from pathlib import Path

import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader

THIS = Path(__file__).resolve()
sys.path.insert(0, str(THIS.parent))
sys.path.insert(0, str(THIS.parents[1]))
sys.path.insert(0, str(THIS.parents[1] / "table8" / "code"))

try:
    from experiments.dataset_signal_forecast import (
        SignalForecastDataset, collate_signal_forecast,
        build_signal_train_test, EVENT_NAMES,
    )
except ModuleNotFoundError:
    from dataset_signal_forecast import (
        SignalForecastDataset, collate_signal_forecast,
        build_signal_train_test, EVENT_NAMES,
    )
from nets.models_forecast import build_forecast_model       # type: ignore


def set_seed(seed: int):
    random.seed(seed); np.random.seed(seed)
    torch.manual_seed(seed); torch.cuda.manual_seed_all(seed)


def train_epoch(model, loader, optimizer, device):
    """Model predicts residual to persistence: target = y - y_last."""
    model.train()
    total, n = 0.0, 0
    for x, y, y_last, _et, _ in loader:
        x = {m: v.to(device) for m, v in x.items()}
        y = y.to(device)
        y_last = y_last.to(device).unsqueeze(1)          # (B, 1, target_dim)
        residual_target = y - y_last                     # (B, T_fut, target_dim)
        optimizer.zero_grad()
        pred = model(x)                                  # (B, T_fut, target_dim) — residual
        loss = ((pred - residual_target) ** 2).mean()
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
        optimizer.step()
        total += loss.item() * y.numel()
        n += y.numel()
    return total / max(n, 1)


@torch.no_grad()
def evaluate(model, loader, device, t_fut: int, target_dim: int,
             zero_pressure: bool = False):
    """Return per-event-type and overall: MSE_model, MSE_persist, skill_score,
    plus per-horizon skill_score."""
    model.eval()
    # Accumulators: (4 event types + 1 overall) x ...
    sse_m = np.zeros((5, t_fut), dtype=np.float64)
    sse_p = np.zeros((5, t_fut), dtype=np.float64)
    n_pairs = np.zeros((5, t_fut), dtype=np.int64)

    for x, y, y_last, et, _ in loader:
        x = {m: v.to(device) for m, v in x.items()}
        if zero_pressure and "pressure" in x:
            x["pressure"] = torch.zeros_like(x["pressure"])
        y = y.to(device)                                 # (B, T_fut, D)
        y_last = y_last.to(device).unsqueeze(1)          # (B, 1, D)
        pred = model(x)                                  # (B, T_fut, D) — residual
        pred_full = pred + y_last                        # back to y-space
        persist = y_last.expand_as(y)                    # (B, T_fut, D)
        m_err = ((pred_full - y) ** 2).mean(dim=-1)      # (B, T_fut)
        p_err = ((persist - y) ** 2).mean(dim=-1)        # (B, T_fut)
        et_np = et.numpy()
        m_np, p_np = m_err.cpu().numpy(), p_err.cpu().numpy()
        for k in range(m_np.shape[0]):
            e = int(et_np[k])
            sse_m[e]   += m_np[k]; sse_p[e]   += p_np[k]; n_pairs[e]   += 1
            sse_m[4]   += m_np[k]; sse_p[4]   += p_np[k]; n_pairs[4]   += 1

    out = {}
    for e in range(5):
        n = max(int(n_pairs[e].max()), 1)
        mse_m = (sse_m[e] / np.maximum(n_pairs[e], 1)).mean()
        mse_p = (sse_p[e] / np.maximum(n_pairs[e], 1)).mean()
        skill = 1.0 - (mse_m / mse_p) if mse_p > 1e-9 else 0.0
        # per-horizon skill
        per_h_m = sse_m[e] / np.maximum(n_pairs[e], 1)
        per_h_p = sse_p[e] / np.maximum(n_pairs[e], 1)
        per_h_skill = (1.0 - per_h_m / np.maximum(per_h_p, 1e-9)).tolist()
        name = EVENT_NAMES.get(e, "overall") if e < 4 else "overall"
        out[name] = {
            "n_anchors":  int(n),
            "mse_model":  float(mse_m),
            "mse_persist": float(mse_p),
            "skill_score": float(skill),
            "per_h_skill": [float(s) for s in per_h_skill],
        }
    return out


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--model", required=True, choices=["daf", "futr", "deepconvlstm"])
    ap.add_argument("--input_modalities", required=True,
                    help="e.g. 'imu' or 'imu,pressure'")
    ap.add_argument("--target_modality", required=True, choices=["imu", "emg", "mocap"])
    ap.add_argument("--t_obs", type=float, default=1.5)
    ap.add_argument("--t_fut", type=float, default=0.5)
    ap.add_argument("--anchor_stride", type=float, default=0.25)
    ap.add_argument("--per_event_max", type=int, default=8000,
                    help="Cap each event-type pool to this many anchors (per split). "
                         "Use a large number to keep all anchors.")
    ap.add_argument("--epochs", type=int, default=25)
    ap.add_argument("--batch_size", type=int, default=64)
    ap.add_argument("--lr", type=float, default=3e-4)
    ap.add_argument("--weight_decay", type=float, default=1e-4)
    ap.add_argument("--d_model", type=int, default=128)
    ap.add_argument("--dropout", type=float, default=0.1)
    ap.add_argument("--num_workers", type=int, default=2)
    ap.add_argument("--seed", type=int, default=42)
    ap.add_argument("--patience", type=int, default=5)
    ap.add_argument("--zero_pressure_at_eval", action="store_true",
                    help="Eval-only: zero out the pressure input (causal-ablation control).")
    ap.add_argument("--load_checkpoint", type=str, default=None,
                    help="Skip training, load checkpoint and run only eval (for control C).")
    ap.add_argument("--output_dir", required=True)
    args = ap.parse_args()

    set_seed(args.seed)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    inputs = args.input_modalities.split(",")
    print(f"device={device} seed={args.seed} model={args.model} "
          f"inputs={inputs} target={args.target_modality} "
          f"t_obs={args.t_obs} t_fut={args.t_fut} "
          f"zero_pressure_at_eval={args.zero_pressure_at_eval}", flush=True)

    train_ds, test_ds = build_signal_train_test(
        input_modalities=inputs,
        target_modality=args.target_modality,
        t_obs_sec=args.t_obs, t_fut_sec=args.t_fut,
        anchor_stride_sec=args.anchor_stride,
        per_event_max=args.per_event_max,
        rng_seed=args.seed,
    )
    target_dim = train_ds.target_dim
    print(f"train={len(train_ds)} test={len(test_ds)} target_dim={target_dim}",
          flush=True)

    tr_loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True,
                           num_workers=args.num_workers, collate_fn=collate_signal_forecast,
                           drop_last=False)
    te_loader = DataLoader(test_ds, batch_size=args.batch_size, shuffle=False,
                           num_workers=args.num_workers, collate_fn=collate_signal_forecast)

    # Build model with output dim = target_dim (regression)
    model = build_forecast_model(
        args.model, train_ds.modality_dims,
        num_classes=target_dim,
        t_obs=train_ds.T_obs, t_fut=train_ds.T_fut,
        d_model=args.d_model, dropout=args.dropout,
    ).to(device)
    n_params = sum(p.numel() for p in model.parameters())
    print(f"params={n_params:,}", flush=True)

    out_dir = Path(args.output_dir); out_dir.mkdir(parents=True, exist_ok=True)

    # ---- Eval-only mode (config C: load checkpoint trained as B, re-eval) ----
    if args.load_checkpoint is not None:
        print(f"loading checkpoint {args.load_checkpoint}", flush=True)
        sd = torch.load(args.load_checkpoint, map_location=device)
        model.load_state_dict(sd)
        ev = evaluate(model, te_loader, device,
                      t_fut=train_ds.T_fut, target_dim=target_dim,
                      zero_pressure=args.zero_pressure_at_eval)
        out = {
            "method": args.model,
            "input_modalities": inputs,
            "target_modality": args.target_modality,
            "seed": args.seed,
            "n_params": n_params,
            "T_obs": train_ds.T_obs, "T_fut": train_ds.T_fut, "target_dim": target_dim,
            "best_epoch": -1, "mode": "eval_only",
            "zero_pressure_at_eval": bool(args.zero_pressure_at_eval),
            "loaded_from": args.load_checkpoint,
            "eval": ev,
            "args": vars(args),
        }
        with open(out_dir / "results.json", "w") as f:
            json.dump(out, f, indent=2)
        print(f"[done] overall skill_score = {ev['overall']['skill_score']:.4f}", flush=True)
        for e in ("non-contact", "pre-contact", "steady-grip", "release"):
            print(f"  {e:14s} skill={ev[e]['skill_score']:+.4f} (n={ev[e]['n_anchors']})", flush=True)
        return

    # ---- Standard training (config A or B) ----
    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
    sched = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs, eta_min=args.lr * 0.05)

    best_skill = -1e9
    best_epoch = 0
    best_eval = None
    patience_counter = 0
    for ep in range(1, args.epochs + 1):
        t0 = time.time()
        tr_loss = train_epoch(model, tr_loader, optimizer, device)
        ev = evaluate(model, te_loader, device,
                      t_fut=train_ds.T_fut, target_dim=target_dim,
                      zero_pressure=False)
        sched.step()
        skill = ev["overall"]["skill_score"]
        print(f"  E{ep:2d} | tr_mse {tr_loss:.4f} | te_skill {skill:+.4f} "
              f"| pre {ev['pre-contact']['skill_score']:+.3f} "
              f"steady {ev['steady-grip']['skill_score']:+.3f} "
              f"release {ev['release']['skill_score']:+.3f} "
              f"non {ev['non-contact']['skill_score']:+.3f} "
              f"| {time.time()-t0:.1f}s", flush=True)
        if skill > best_skill:
            best_skill = skill
            best_epoch = ep
            best_eval = ev
            torch.save({k: v.cpu() for k, v in model.state_dict().items()},
                       out_dir / "model_best.pt")
            patience_counter = 0
        else:
            patience_counter += 1
        if patience_counter >= args.patience:
            print(f"  early stop at epoch {ep} (best {best_epoch})", flush=True)
            break

    out = {
        "method": args.model,
        "input_modalities": inputs,
        "target_modality": args.target_modality,
        "seed": args.seed,
        "n_params": n_params,
        "T_obs": train_ds.T_obs, "T_fut": train_ds.T_fut, "target_dim": target_dim,
        "best_epoch": int(best_epoch),
        "best_skill": float(best_skill),
        "eval": best_eval,
        "args": vars(args),
    }
    with open(out_dir / "results.json", "w") as f:
        json.dump(out, f, indent=2)
    print(f"\n[done] best skill={best_skill:+.4f} at epoch {best_epoch}", flush=True)
    print(f"saved to {out_dir}/results.json", flush=True)


if __name__ == "__main__":
    main()