output-validation

# 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 ```python 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.