- name
- output-validation
- description
- Local self-check of instructions and mask outputs (format/range/consistency) without using GT.
When to use
- After generating your outputs (interval instructions, masks, etc.), before submission/hand-off.
Checks
- Key format: every key is
"{start}->{end}", integers only, start<=end. - Coverage: max frame index ≤ video total-1; consistent with your sampling policy.
- Frame count: NPZ
f_{i}_* count equals sampled frame count; no gaps or missing components. - CSR integrity: each frame has
data/indices/indptr; len(indptr)==H+1; indptr[-1]==indices.size; indices within [0,W). - Value validity: JSON values are non-empty string lists; labels in the allowed set.
Reference snippet
import json, numpy as np, cv2
VIDEO_PATH = "<path/to/video>"
INSTRUCTIONS_PATH = "<path/to/interval_instructions.json>"
MASKS_PATH = "<path/to/masks.npz>"
cap=cv2.VideoCapture(VIDEO_PATH)
n=int(cap.get(cv2.CAP_PROP_FRAME_COUNT)); H=int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)); W=int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
j=json.load(open(INSTRUCTIONS_PATH))
npz=np.load(MASKS_PATH)
for k,v in j.items():
s,e=k.split("->"); assert s.isdigit() and e.isdigit()
s=int(s); e=int(e); assert 0<=s<=e<n
for lbl in v: assert isinstance(lbl,str)
frames=0
while f"f_{frames}_data" in npz: frames+=1
assert frames>0
assert npz["shape"][0]==H and npz["shape"][1]==W
indptr=npz["f_0_indptr"]; indices=npz["f_0_indices"]
assert indptr.shape[0]==H+1 and indptr[-1]==indices.size
assert indices.size==0 or (indices.min()>=0 and indices.max()<W)
Self-check list
- [ ] JSON keys/values pass format checks.
- [ ] Max frame index within video range and near sampled max.
- [ ] NPZ frame count matches sampling; keys consecutive.
- [ ] CSR structure and
shape validated.
