Hark
2a0a2e55ad
- M1 数据生成器：采样/老师/距离下界认证/时间律/统计/一致性门禁；确定性老师（碰撞次数计时 + resetSession）、pos 任务冻结 J6、--consensus 邻域共识过滤、Dr 类。 - 全流程代码与运行手册：数据打包/可微运动学与对拍/MeanFlow+WTA 模型/闭环仿真/APG/挖错循环/ONNX 导出；webapp 策略运行时 ik-policy.js、?policy=1 接入、tests/policy_bench.cjs；ik-solver 增 pairList/clusterExport/criticalPairs/pairGap/leverTable。
56 lines
2.6 KiB
Python
56 lines
2.6 KiB
Python
"""样本加载：ml/data/samples/*.{X,Y,V,SG,TID}.npy + *.tasks.jsonl（ml/prep/build_dataset.cjs 产出）
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python -m ml.data --index ml/data/samples # 汇总样本/任务数，写 index.json
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全部载入内存（200 万样本约 1.5 GB），按任务 split 字段划分 train / val。"""
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import argparse, glob, json, os
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import numpy as np
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import torch
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def load_tasks(dirs):
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tasks = []
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for d in dirs:
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for f in sorted(glob.glob(os.path.join(d, '*.tasks.jsonl'))):
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for line in open(f, encoding='utf-8'):
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if line.strip():
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tasks.append(json.loads(line))
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return tasks
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class Samples:
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def __init__(self, dirs, device='cpu', split=None):
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if isinstance(dirs, str): dirs = [dirs]
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xs, ys, vs, ss, ts = [], [], [], [], []
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for d in dirs:
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for fx in sorted(glob.glob(os.path.join(d, '*.X.npy'))):
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b = fx[:-6]
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xs.append(np.load(fx)); ys.append(np.load(b + '.Y.npy')); vs.append(np.load(b + '.V.npy'))
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ss.append(np.load(b + '.SG.npy')); ts.append(np.load(b + '.TID.npy'))
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if not xs: raise SystemExit(f'没有样本：{dirs}（先跑 ml/prep/run_prep.sh）')
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self.tasks = load_tasks(dirs)
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val_tid = {t['tid'] for t in self.tasks if t['split'] == 'val'}
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X, Y, V, SG, TID = map(np.concatenate, (xs, ys, vs, ss, ts))
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is_val = np.isin(TID, np.fromiter(val_tid, dtype=np.int64)) if val_tid else np.zeros(len(TID), bool)
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sel = {'train': ~is_val, 'val': is_val, None: np.ones(len(TID), bool)}[split]
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t = lambda a: torch.from_numpy(np.ascontiguousarray(a[sel])).to(device)
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self.X, self.Y, self.V, self.SG, self.TID = t(X), t(Y), t(V), t(SG), t(TID)
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self.n = self.X.shape[0]
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def batch(self, bs, gen=None):
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i = torch.randint(0, self.n, (bs,), device=self.X.device, generator=gen)
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return self.X[i], self.Y[i], self.V[i], self.SG[i]
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def main():
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ap = argparse.ArgumentParser(); ap.add_argument('--index', default='ml/data/samples'); a = ap.parse_args()
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tr, va = Samples(a.index, split='train'), Samples(a.index, split='val')
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tasks = load_tasks([a.index])
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cats = {}
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for t in tasks: cats[t['cat'] + '/' + t['mode']] = cats.get(t['cat'] + '/' + t['mode'], 0) + 1
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info = {'train_samples': tr.n, 'val_samples': va.n, 'tasks': len(tasks), 'val_tasks': sum(t['split'] == 'val' for t in tasks), 'by_cat_mode': cats}
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json.dump(info, open(os.path.join(a.index, 'index.json'), 'w'), indent=1)
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print(json.dumps(info, indent=1))
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if __name__ == '__main__':
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main()
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