Hark
5591adb39a
- B-1 运行时投影护栏恢复 5 mm；老师轨迹按认证最小间隙 < 3 mm 剔除（stats/build_dataset，gapDrop） - B-2 APG 端点+中点 hinge 取均值；B-3 check_r2 空匹配保护、5000×50000、分块 256，pass=false 时 prep 停下 - B-4 R7 按全部 B/C 题统计；B-5 r8 起点贴柱；B-7 pipeline r4（export_onnx --random，训练前测延迟） - B-8 起步失败 ≤0.2%、column_gap 分块、pipefail、门禁结果先写 .tmp - 训练控制台：ml/console.bat|sh → ml/monitor.py（开始/停止/进度/日志接口）+ monitor.html； pipeline.sh all 支持 FROM/TO，阶段状态写 ml/runs/pipeline.json（ml/pstate.py）；训练/APG 日志加 total、t - 文档：README 一键运行与训练控制台；审查文档追加第三轮处置；方案余量决策与 R4 时点
150 lines
9.3 KiB
Python
150 lines
9.3 KiB
Python
"""APG 可微微调（方案 §7.1）：按部署节奏可微展开策略，直接对"走得到、不碰、平滑"求梯度；不需要老师。
|
||
python -m ml.apg.apg_finetune --init ml/runs/base/best.pt --samples ml/data/samples --out ml/runs/apg [--pool ml/runs/mine/pool.jsonl]
|
||
每条展开固定一个噪声 z（确定性可微映射）；每次推理提交 3 步（已提交前缀 3 步，与部署一致）；
|
||
截断反传：每 seg_plans 次推理（=3·seg_plans 步）反传一次并截断状态梯度。
|
||
损失：段末到达项 + Σ碰撞铰链 ReLU(margin_train − 间隙)（逐胶囊对，端点+中点）+ 平滑（速度²+加速度²）+ 行程 + BC 锚定（MeanFlow 损失）。
|
||
2026-10-18：展开长度不再定长 60 次推理（3.6 s，会截掉老师时长 > 3.6 s 的绕远题），改为覆盖本批 exec_s p90×1.2，上限 plans_max；
|
||
"到达"仍是每 seg_plans 段末的距离项（无到达即停），属有意设计：段末项给出持续的接近梯度。
|
||
margin_train 3 mm：与训练数据过滤线一致（认证最小间隙 < 3 mm 的老师轨迹不进训练集），碰撞项不与 BC 标签对抗；
|
||
运行时投影护栏独立保持 5 mm。端点与中点两个铰链取均值，与只查端点同量纲，w_col 无需重标。"""
|
||
import argparse, json, math, os, random, time
|
||
import torch
|
||
|
||
from ml.common.kin_torch import Kin
|
||
from ml.common.features import features, STEP, H, wrap180
|
||
from ml.data import Samples, load_tasks
|
||
from ml.model.policy import build, wta_loss
|
||
from ml.model.meanflow import meanflow_loss
|
||
from ml.sim.closed_loop import evaluate, tasks_to_tensors, goal_err
|
||
|
||
|
||
def load_pool(paths):
|
||
out = []
|
||
for p in paths or []:
|
||
if os.path.exists(p):
|
||
out += [json.loads(l) for l in open(p, encoding='utf-8') if l.strip()]
|
||
return out
|
||
|
||
|
||
def main():
|
||
ap = argparse.ArgumentParser()
|
||
ap.add_argument('--init', required=True); ap.add_argument('--samples', nargs='+', default=['ml/data/samples'])
|
||
ap.add_argument('--pool', nargs='*', default=[]); ap.add_argument('--out', default='ml/runs/apg')
|
||
ap.add_argument('--config', default='ml/configs/apg.json'); ap.add_argument('--steps', type=int, default=None)
|
||
ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu'); ap.add_argument('--seed', type=int, default=0)
|
||
a = ap.parse_args()
|
||
cfg = json.load(open(a.config)); C = cfg['apg']
|
||
if a.steps: C['steps'] = a.steps
|
||
dev = a.device; torch.manual_seed(a.seed); random.seed(a.seed)
|
||
os.makedirs(a.out, exist_ok=True)
|
||
ck = torch.load(a.init, map_location=dev)
|
||
mcfg = ck['cfg']
|
||
model = build(mcfg).to(dev); model.load_state_dict(ck['model'])
|
||
kin = Kin(device=dev)
|
||
data = Samples(a.samples, device=dev, split='train')
|
||
all_tasks = load_tasks(a.samples)
|
||
train_tasks = [t for t in all_tasks if t['split'] == 'train'] + load_pool(a.pool)
|
||
val_tasks = [t for t in all_tasks if t['split'] == 'val'][:C['eval_tasks']]
|
||
w = [C['cat_weights'].get(t.get('cat', 'A'), 1.0) * (2.0 if t.get('hard') else 1.0) for t in train_tasks]
|
||
opt = torch.optim.AdamW(model.parameters(), lr=C['lr'], betas=(0.9, 0.95), weight_decay=0.0)
|
||
amp = dev.startswith('cuda') and C.get('amp') == 'bf16'
|
||
step = torch.tensor(STEP, device=dev)
|
||
log = open(os.path.join(a.out, 'log.jsonl'), 'w')
|
||
base, _ = evaluate(model.eval(), kin, val_tasks, dev)
|
||
print('EVAL0', json.dumps(base), flush=True); log.write(json.dumps({'eval': base, 'step': 0}) + '\n')
|
||
best = base['success']
|
||
torch.save({'model': model.state_dict(), 'cfg': mcfg, 'step': 0, 'fp': kin.fp, 'eval': base}, os.path.join(a.out, 'best.pt'))
|
||
M = C['margin_train_mm']; t0 = time.time()
|
||
for it in range(C['steps']):
|
||
model.train()
|
||
batch = random.choices(train_tasks, weights=w, k=C['batch'])
|
||
q0, T, R, ex, tr = tasks_to_tensors(batch, dev)
|
||
B = q0.shape[0]
|
||
z = torch.randn(B, H, 6, device=dev)
|
||
q, qp = q0.clone(), q0.clone()
|
||
queue = torch.zeros(B, 0, 6, device=dev)
|
||
subs = torch.zeros(B, 2, 6, device=dev); nsub = torch.zeros(B, dtype=torch.long, device=dev); sub_t = torch.zeros(B, device=dev)
|
||
opt.zero_grad(set_to_none=True)
|
||
stats = {'col': 0.0, 'tgt': 0.0, 'smooth': 0.0}
|
||
# 展开长度随本批任务时长：覆盖本批 exec_s 的 p90 × 1.2（无老师参考题按 6 s），夹在 [plans, plans_max]
|
||
exb = torch.nan_to_num(ex, nan=6.0)
|
||
n_plans = int(min(C['plans_max'], max(C['plans'], math.ceil(1.2 * torch.quantile(exb, 0.9).item() / (3 * 0.02)))))
|
||
nseg = math.ceil(n_plans / C['seg_plans'])
|
||
seg_loss = torch.zeros((), device=dev); prev_d = torch.zeros(B, 6, device=dev)
|
||
for p in range(n_plans):
|
||
tnow = p * 3 * 0.02
|
||
r = queue.shape[1]
|
||
pre = torch.zeros(B, 3, 6, device=dev); pre[:, :r] = queue[:, :r]
|
||
qe = q + queue.sum(1)
|
||
with torch.no_grad():
|
||
if (nsub > 0).any():
|
||
cur = subs[:, 0]; dd = torch.where(kin.cont, wrap180(cur - qe), cur - qe).abs().max(-1).values
|
||
adv = (nsub > 0) & ((dd < 5) | (tnow - sub_t > 1.5))
|
||
subs = torch.where(adv[:, None, None], torch.cat([subs[:, 1:], subs[:, 1:]], 1), subs)
|
||
nsub = torch.where(adv, nsub - 1, nsub); sub_t = torch.where(adv, torch.full_like(sub_t, tnow), sub_t)
|
||
feat = features(kin, q, qp, T, R, pre, torch.full((B,), r, device=dev), subs[:, 0], (nsub > 0).float())
|
||
with torch.autocast('cuda', dtype=torch.bfloat16, enabled=amp):
|
||
act, val, sub = model.sample_k(feat, 1, z)
|
||
act = act[:, 0].float()
|
||
if p == 0:
|
||
with torch.no_grad():
|
||
need = sub[:, 12].float() > 0.5
|
||
subs = q.detach()[:, None] + sub[:, :12].float().view(B, 2, 6) * 180
|
||
nsub = torch.where(need, torch.full_like(nsub, 2), torch.zeros_like(nsub))
|
||
inc = act.clamp(-1, 1) * step # [B,16,6]
|
||
commit = inc[:, r:r + 3]
|
||
if r == 0: # 冷启动：先提交一块，下一轮才开始执行
|
||
queue = commit; continue
|
||
# 执行队列中的 3 步（上一轮提交的）
|
||
for k in range(3):
|
||
d = queue[:, k]
|
||
qp = q; q = kin.clamp_box(q + d)
|
||
# 碰撞铰链：端点 + 中点（与仿真/运行时的离散检查一致）
|
||
G = kin.gaps(torch.cat([q, 0.5 * (q + qp)], 0), M)
|
||
hinge = torch.relu(M - G['gap'])
|
||
col = torch.zeros(2 * B, device=dev).index_add(0, G['b'], hinge).view(2, B).mean(0) # 端点与中点取均值：与只查端点时同量纲，w_col 无需重标
|
||
sm = ((d / step) ** 2).sum(-1) + (((d - prev_d) / step) ** 2).sum(-1)
|
||
prev_d = d
|
||
seg_loss = seg_loss + C['w_col'] * col.mean() + C['w_smooth'] * sm.mean() + C['w_len'] * d.abs().sum(-1).mean()
|
||
stats['col'] += col.mean().item(); stats['smooth'] += sm.mean().item()
|
||
queue = commit
|
||
if (p + 1) % C['seg_plans'] == 0 or p == n_plans - 1:
|
||
xf = kin.cum_xf(q)
|
||
pe = kin.fk(q, xf); Rq = kin.orient(q, xf).reshape(B, 9)
|
||
pose = (R.abs().sum(-1) > 0).float()
|
||
lt = ((T - pe) / 100).pow(2).sum(-1) + C['w_rot'] * pose * (Rq - R).pow(2).sum(-1)
|
||
seg_loss = seg_loss + C['w_tgt'] * lt.mean()
|
||
stats['tgt'] += lt.mean().item()
|
||
(seg_loss / nseg).backward()
|
||
seg_loss = torch.zeros((), device=dev)
|
||
q, qp, queue, prev_d = q.detach(), qp.detach(), queue.detach(), prev_d.detach()
|
||
# BC 锚定
|
||
X, Y, V, SG = data.batch(C['batch'] * 4)
|
||
with torch.autocast('cuda', dtype=torch.bfloat16, enabled=amp):
|
||
mem = model.encode(X)
|
||
l_bc = wta_loss(model, mem, Y)[0] if model.head == 'wta' else meanflow_loss(model, mem, Y)[0]
|
||
v, s = model.heads(mem)
|
||
l_bc = l_bc + 0.1 * (v.float() - V).abs().mean() + 0.1 * (s.float() - SG).abs().mean()
|
||
(C['w_bc'] * l_bc).backward()
|
||
gn = torch.nn.utils.clip_grad_norm_(model.parameters(), C['grad_clip'])
|
||
opt.step()
|
||
with torch.no_grad():
|
||
e, oe = goal_err(kin, q, T, R)
|
||
rec = {'it': it, 'total': C['steps'], 't': time.time(), 'plans': n_plans, 'err_mm_mean': e.mean().item(), 'reach_frac': ((e < 1) & (oe < 0.5)).float().mean().item(), 'bc': l_bc.item(),
|
||
'gnorm': gn.item(), **stats, 'min': (time.time() - t0) / 60}
|
||
if it % 10 == 0: print(json.dumps(rec), flush=True)
|
||
log.write(json.dumps(rec) + '\n'); log.flush()
|
||
if (it + 1) % C['eval_every'] == 0 or it == C['steps'] - 1:
|
||
res, _ = evaluate(model.eval(), kin, val_tasks, dev)
|
||
res['it'] = it + 1; res['t'] = time.time()
|
||
print('EVAL', json.dumps(res), flush=True); log.write(json.dumps({'eval': res}) + '\n'); log.flush()
|
||
sd = {'model': model.state_dict(), 'cfg': mcfg, 'step': it + 1, 'fp': kin.fp, 'eval': res}
|
||
torch.save(sd, os.path.join(a.out, 'last.pt'))
|
||
if res['success'] > best + 1e-4:
|
||
best = res['success']; torch.save(sd, os.path.join(a.out, 'best.pt'))
|
||
print(f'done, best success {best:.4f}', flush=True)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|