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544e392 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | #!/usr/bin/env python3
"""Append offset-0 RGB frames to an existing strict-causal RGB cache.
This is faster than rebuilding the full cache with [-8,-4,-2,-1,0],
because the existing strict cache already stores the negative-offset history.
The output is intended only for current-observation ablations.
"""
from __future__ import annotations
import argparse
import copy
import json
import os
import sys
from collections import Counter, defaultdict
from typing import Any, Dict, List, Tuple
import cv2
import torch
sys.path.insert(0, os.path.dirname(__file__))
from build_rgb_frame_cache import parse_cell_frames, video_t_to_cell # noqa: E402
def video_path(processed_root: str, boss: str, fight: int) -> str:
return os.path.join(processed_root, boss, f"video_fight{int(fight)}.mp4")
def read_decord_group(
path: str,
frame_indices: List[List[int]],
height: int,
width: int,
chunk_size: int,
) -> torch.Tensor:
from decord import VideoReader, cpu
vr = VideoReader(path, ctx=cpu(0), width=width, height=height, num_threads=2)
n_frames = len(vr)
flat = [min(max(0, int(x)), n_frames - 1) for row in frame_indices for x in row]
out = torch.empty((len(flat), 3, height, width), dtype=torch.uint8)
for start in range(0, len(flat), chunk_size):
idx = flat[start:start + chunk_size]
batch = vr.get_batch(idx).asnumpy()
out[start:start + len(idx)] = torch.from_numpy(batch).permute(0, 3, 1, 2).contiguous()
return out.reshape(len(frame_indices), len(frame_indices[0]), 3, height, width)
def read_opencv_group(
path: str,
frame_indices: List[List[int]],
height: int,
width: int,
) -> torch.Tensor:
cv2.setNumThreads(1)
cap = cv2.VideoCapture(path)
if not cap.isOpened():
raise RuntimeError(f"cannot open video: {path}")
n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
rows = []
for row in frame_indices:
frames = []
for frame_idx in row:
idx = min(max(0, int(frame_idx)), max(0, n_frames - 1))
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ok, bgr = cap.read()
if not ok or bgr is None:
cap.release()
raise RuntimeError(f"cannot read frame {idx} from {path}")
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
if rgb.shape[0] != height or rgb.shape[1] != width:
rgb = cv2.resize(rgb, (width, height), interpolation=cv2.INTER_AREA)
frames.append(torch.from_numpy(rgb).permute(2, 0, 1).contiguous())
rows.append(torch.stack(frames, dim=0))
cap.release()
return torch.stack(rows, dim=0).to(torch.uint8)
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--base_cache", required=True)
ap.add_argument("--processed_root", default="data/processed")
ap.add_argument("--frame_in_cell", default="2")
ap.add_argument("--backend", choices=["decord", "opencv"], default="decord")
ap.add_argument("--chunk_size", type=int, default=512)
ap.add_argument("--out", required=True)
args = ap.parse_args()
data = torch.load(args.base_cache, map_location="cpu", weights_only=False)
base_rgb = data["rgb"].contiguous()
if base_rgb.dtype != torch.uint8:
raise TypeError(f"expected uint8 rgb cache, got {base_rgb.dtype}")
if any(int(x) >= 0 for x in data.get("history_offsets", [])):
raise ValueError("base_cache must be strict-causal; use build_rgb_frame_cache for custom current caches")
samples: List[Dict[str, Any]] = data["samples"]
frame_in_cell = parse_cell_frames(args.frame_in_cell)
n, t, c, h, w = base_rgb.shape
if c != 3:
raise ValueError(f"expected RGB channel dimension 3, got {c}")
groups: Dict[Tuple[str, int], List[Tuple[int, List[int]]]] = defaultdict(list)
all_first_indices = []
for i, sample in enumerate(samples):
boss = sample["boss"]
fight = int(sample["fight"])
target_cell = video_t_to_cell(sample["belief"]["time"])
frame_indices = [4 * target_cell + int(in_cell) for in_cell in frame_in_cell]
all_first_indices.append(frame_indices[0])
groups[(boss, fight)].append((i, frame_indices))
current = torch.empty((n, len(frame_in_cell), 3, h, w), dtype=torch.uint8)
missing = 0
missing_by_reason = Counter()
for group_id, ((boss, fight), entries) in enumerate(sorted(groups.items()), start=1):
path = video_path(args.processed_root, boss, fight)
if not os.path.exists(path):
missing += len(entries)
missing_by_reason["missing_video"] += len(entries)
continue
order = [i for i, _ in entries]
frame_indices = [idxs for _, idxs in entries]
try:
if args.backend == "decord":
frames = read_decord_group(path, frame_indices, h, w, args.chunk_size)
else:
frames = read_opencv_group(path, frame_indices, h, w)
except Exception as exc:
missing += len(entries)
missing_by_reason[f"read_error:{type(exc).__name__}"] += len(entries)
print(f"warning: failed {boss}/fight{fight}: {type(exc).__name__}: {exc}", flush=True)
continue
current[torch.tensor(order, dtype=torch.long)] = frames
print(
f"processed_groups {group_id}/{len(groups)} "
f"group={boss}/fight{fight} samples={len(entries)} filled={n - missing} missing={missing}",
flush=True,
)
if missing:
raise RuntimeError(f"missing current frames: {missing} {dict(missing_by_reason)}")
out_rgb = torch.empty((n, t + len(frame_in_cell), 3, h, w), dtype=torch.uint8)
out_rgb[:, :t] = base_rgb
out_rgb[:, t:] = current
payload = dict(data)
payload["rgb"] = out_rgb.contiguous()
payload["history_offsets"] = list(data.get("history_offsets", [])) + [0]
payload["frame_in_cell"] = list(data.get("frame_in_cell", frame_in_cell))
payload["allow_current_frame"] = True
payload["current_frame_source"] = "appended_from_raw_processed_fight_video"
payload["base_cache"] = args.base_cache
payload["video_backend"] = args.backend
payload["audit"] = copy.deepcopy(data.get("audit", {}))
payload["audit"]["append_current_frame"] = {
"base_cache": args.base_cache,
"requested": int(n),
"kept": int(n),
"missing": int(missing),
"missing_by_reason": dict(missing_by_reason),
"groups": int(len(groups)),
"frame_in_cell": list(frame_in_cell),
"current_frame_min": int(min(all_first_indices)) if all_first_indices else None,
"current_frame_max": int(max(all_first_indices)) if all_first_indices else None,
}
os.makedirs(os.path.dirname(args.out), exist_ok=True)
torch.save(payload, args.out)
print(json.dumps({
"out": args.out,
"base_cache": args.base_cache,
"shape": list(out_rgb.shape),
"history_offsets": payload["history_offsets"],
"allow_current_frame": True,
"audit": payload["audit"]["append_current_frame"],
}, ensure_ascii=False, indent=2), flush=True)
if __name__ == "__main__":
main()
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