Create check_active.py

check_active.py

@@ -0,0 +1,46 @@
+"""
+Quick check: how many clips actually contain contact (nonzero) frames?
+If most clips are all-zero, the VAE will collapse to predicting 0.
+
+Usage:
+  python check_clip_activity.py --clips ... --stats ... --source_root ... --modality contact --n 200
+"""
+import argparse, sys, os
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from physical_dataset import PhysicalClipDataset
+import numpy as np
+
+ap = argparse.ArgumentParser()
+ap.add_argument("--clips", required=True)
+ap.add_argument("--stats", required=True)
+ap.add_argument("--source_root", required=True)
+ap.add_argument("--modality", default="contact")
+ap.add_argument("--n", type=int, default=200, help="how many clips to scan")
+args = ap.parse_args()
+
+ds = PhysicalClipDataset(args.clips, args.stats, args.source_root, args.modality)
+n = min(args.n, len(ds))
+print(f"scanning {n}/{len(ds)} clips...")
+
+all_zero = 0
+active_fracs = []
+contact_frame_counts = []   # how many of the 17 frames have any contact
+for i in range(n):
+    item = ds[i]
+    m = item["active_mask"]            # (1,T,H,W)
+    af = float(m.mean())
+    active_fracs.append(af)
+    if af == 0.0:
+        all_zero += 1
+    # frames with any contact
+    per_frame = m[0].reshape(m.shape[1], -1).sum(1)   # (T,)
+    contact_frame_counts.append(int((per_frame > 0).sum()))
+
+active_fracs = np.array(active_fracs)
+cfc = np.array(contact_frame_counts)
+print(f"\nall-zero clips: {all_zero}/{n} ({100*all_zero/n:.1f}%)")
+print(f"active frac: mean={active_fracs.mean():.4f} max={active_fracs.max():.4f}")
+print(f"contact frames per clip (of 17): mean={cfc.mean():.1f} "
+      f"min={cfc.min()} max={cfc.max()}")
+print(f"clips with >=1 contact frame: {(cfc>0).sum()}/{n} ({100*(cfc>0).sum()/n:.1f}%)")
+print(f"clips with >=8 contact frames: {(cfc>=8).sum()}/{n}")