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feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2)
Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization); 6 reproducibility defects documented (broken imports, corrupted dataset tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable test phase). After repairs, retraining with upstream defaults reproduces 96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs verified. Third-party code/weights/data stay out of tree (gitignored). Co-Authored-By: claude-flow <ruv@ruv.net>
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"""ADR-152 §2.2 measurement (a): reproduce WiFlow-STD (DY2434) published test metrics.
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Runs the released pretrained checkpoint (upstream/best_pose_model.pth) against the
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released Kaggle dataset (kaka2434/wiflow-dataset) using the upstream code path:
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identical dataset class, identical file-level 70/15/15 split at seed 42, identical
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PCK/MPJPE implementations (utils/metrics.py).
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Published claims (README, "Setting 1 random split"):
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PCK@20 97.25% | PCK@30 98.63% | PCK@40 99.16% | PCK@50 99.48% | MPJPE 0.007 m
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Usage:
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.venv/Scripts/python.exe eval_repro.py --data-dir <dir containing csi_windows.npy>
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"""
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import argparse
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import json
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import os
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import random
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import sys
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import time
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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UPSTREAM = os.path.join(os.path.dirname(os.path.abspath(__file__)), "upstream")
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sys.path.insert(0, UPSTREAM)
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# Upstream bug: models/__init__.py imports TemporalConvNet, which models/tcn.py
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# does not define (it defines TemporalBlock) — the package fails to import as
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# published. Register a stub package so the broken __init__ never executes;
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# submodules (models.pose_model etc.) still resolve via __path__.
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import types # noqa: E402
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_models_pkg = types.ModuleType("models")
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_models_pkg.__path__ = [os.path.join(UPSTREAM, "models")]
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sys.modules["models"] = _models_pkg
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import dataset as upstream_dataset # noqa: E402
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from dataset import PreprocessedCSIKeypointsDataset, create_preprocessed_train_val_test_loaders # noqa: E402
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from models.pose_model import WiFlowPoseModel # noqa: E402
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from utils.metrics import calculate_pck, calculate_mpjpe # noqa: E402
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# csi_windows.npy is ~13 GB; mmap large arrays instead of loading into RAM.
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_np_load = np.load
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def _np_load_mmap(path, *a, **kw):
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if (isinstance(path, str) and path.endswith(".npy")
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and os.path.getsize(path) > 1 << 30 and "mmap_mode" not in kw):
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kw["mmap_mode"] = "r"
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return _np_load(path, *a, **kw)
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upstream_dataset.np.load = _np_load_mmap
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def set_seed(seed=42):
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# mirror upstream run.py exactly
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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def find_data_dir(root):
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for dirpath, _dirnames, filenames in os.walk(root):
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if "csi_windows.npy" in filenames:
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return dirpath
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return None
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def evaluate(model, loader, device):
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model.eval()
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totals = {t: 0.0 for t in (0.1, 0.2, 0.3, 0.4, 0.5)}
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total_mpe = 0.0
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n = 0
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t0 = time.time()
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with torch.no_grad():
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for batch_idx, (batch_x, batch_y) in enumerate(loader):
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batch_x = batch_x.to(device)
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batch_y = batch_y.to(device)
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outputs = model(batch_x)
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mpe = calculate_mpjpe(outputs, batch_y)
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pck = calculate_pck(outputs, batch_y, thresholds=[0.1, 0.2, 0.3, 0.4, 0.5])
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bs = batch_y.size(0)
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total_mpe += mpe * bs
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for t in totals:
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totals[t] += pck[t] * bs
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n += bs
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if batch_idx % 50 == 0:
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print(f" batch {batch_idx}: n={n} pck20={totals[0.2]/n:.4f} "
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f"mpjpe={total_mpe/n:.4f} ({time.time()-t0:.0f}s)", flush=True)
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return {
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"samples": n,
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"mpjpe": total_mpe / n,
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**{f"pck@{int(t*100)}": totals[t] / n for t in totals},
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"wall_seconds": time.time() - t0,
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}
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--data-dir", required=True,
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help="Directory containing csi_windows.npy (searched recursively)")
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parser.add_argument("--checkpoint", default=os.path.join(UPSTREAM, "best_pose_model.pth"))
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parser.add_argument("--batch-size", type=int, default=64)
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parser.add_argument("--out", default=os.path.join(os.path.dirname(os.path.abspath(__file__)),
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"results", "repro_a.json"))
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args = parser.parse_args()
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data_dir = args.data_dir
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if not os.path.exists(os.path.join(data_dir, "csi_windows.npy")):
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located = find_data_dir(data_dir)
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if located is None:
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sys.exit(f"csi_windows.npy not found under {data_dir}")
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data_dir = located
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print(f"data dir: {data_dir}")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"device: {device}, torch {torch.__version__}")
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set_seed(42)
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dataset = PreprocessedCSIKeypointsDataset(
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data_dir=data_dir, keypoint_scale=1000.0, enable_temporal_clean=True)
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# split must match upstream: file-level shuffle at random_seed=42, 70/15/15
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_train_loader, _val_loader, test_loader = create_preprocessed_train_val_test_loaders(
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dataset=dataset, batch_size=args.batch_size, num_workers=0, random_seed=42)
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model = WiFlowPoseModel(dropout=0.5).to(device)
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state = torch.load(args.checkpoint, map_location=device, weights_only=True)
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# released checkpoint predates the published code: modules were renamed
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# att -> attention, final_conv -> decoder (param count identical, 2.23M)
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renames = {"att.": "attention.", "final_conv.": "decoder."}
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state = {next((new + k[len(old):] for old, new in renames.items()
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if k.startswith(old)), k): v
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for k, v in state.items()}
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model.load_state_dict(state, strict=True)
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n_params = sum(p.numel() for p in model.parameters())
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print(f"checkpoint: {args.checkpoint} ({n_params/1e6:.2f}M params)")
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# upstream also evaluates with drop_last=True; we report the full test set
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# (drop_last=False) and the drop_last variant for exact comparability
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results = {"published": {"pck@20": 0.9725, "pck@30": 0.9863, "pck@40": 0.9916,
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"pck@50": 0.9948, "mpjpe": 0.007},
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"params_millions": n_params / 1e6,
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"data_dir": data_dir,
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"device": str(device)}
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print("=== test set (full, drop_last=False) ===")
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results["test_full"] = evaluate(model, test_loader, device)
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print(json.dumps(results["test_full"], indent=2))
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test_loader_dl = DataLoader(test_loader.dataset, batch_size=args.batch_size,
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shuffle=False, drop_last=True)
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print("=== test set (drop_last=True, as upstream train.py) ===")
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results["test_drop_last"] = evaluate(model, test_loader_dl, device)
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print(json.dumps(results["test_drop_last"], indent=2))
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os.makedirs(os.path.dirname(args.out), exist_ok=True)
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with open(args.out, "w") as f:
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json.dump(results, f, indent=2)
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print(f"wrote {args.out}")
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if __name__ == "__main__":
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main()
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