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VideoBenchEval-results / code /scripts /run_eval.sh

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#!/usr/bin/env bash
# Evaluate one checkpoint on Video-MME / MLVU / LongVideoBench, one process per GPU.
#
#   bash scripts/run_eval.sh                                   # defaults below
#   BUDGETS="4096 16384" GPUS=0,1 BENCHMARKS="videomme mlvu" bash scripts/run_eval.sh
#
# All budgets run in ONE pass: each video is decoded and encoded once, then planned /
# compressed / answered per budget. Results: results/<name>_b<B>/<benchmark>/.
# Re-running the same command resumes: answered (question, budget) pairs are skipped.
set -euo pipefail
cd "$(dirname "$0")/.."

MODEL=${MODEL:-SamChen567/phase1_qbase_internvid}
RUN_NAME=${RUN_NAME:-$(basename "${MODEL}")}   # results/${RUN_NAME}_b<B>
GPUS=${GPUS:-$(nvidia-smi --query-gpu=index --format=csv,noheader | paste -sd,)}
BENCHMARKS=${BENCHMARKS:-"videomme mlvu longvideobench"}
DATA_ROOT=${DATA_ROOT:-/workspace/chicheng/datasets}
BUDGETS=${BUDGETS:-"1024 2048 4096 8192 16384"}   # LLM-side vision tokens per video (vbe/budget.py)
NUM_WORKERS=${NUM_WORKERS:-6}
VL3_BASE=${VL3_BASE:-/workspace/chicheng/OnlineLong/pretrained_models/videollama3_7b_local}
ORIG_PROJ=${ORIG_PROJ:-checkpoints/vl3_orig_projector.pt}
# orig: Phase-1 protocol (raw frames through the original VL3 projector);
# phase3: raw frames as Phase-3 training sends them (<= RAW_MAX_TOKENS rows, checkpoint projector)
RAW_MODE=${RAW_MODE:-orig}
RAW_MAX_TOKENS=${RAW_MAX_TOKENS:-256}
# model code; Phase-3 checkpoints need the tree they were trained with (hybrid_raw.py)
OL_SRC=${OL_SRC:-third_party/OnlineLong}
PY=${PY:-.venv/bin/python}
export PATH="$(dirname "${PY}"):${PATH}"   # ffprobe/ffmpeg from setup_env.sh
export PYTHONPATH="${OL_SRC}:.${PYTHONPATH:+:${PYTHONPATH}}"

# A hub id is downloaded once into checkpoints/<name>; a local directory is used as-is.
if [ ! -d "${MODEL}" ]; then
    LOCAL=checkpoints/$(basename "${MODEL}")
    "${PY}" -c "from huggingface_hub import snapshot_download as d; d('${MODEL}', local_dir='${LOCAL}')"
    MODEL=${LOCAL}
fi

# raw frames are projected with the original VideoLLaMA3 projector
if [ "${RAW_MODE}" = orig ]; then
    [ -f "${ORIG_PROJ}" ] || "${PY}" scripts/extract_orig_projector.py "${VL3_BASE}" "${ORIG_PROJ}"
fi

OUT=results/${RUN_NAME}_b{budget}
IFS=, read -ra GPU_LIST <<< "${GPUS}"
N=${#GPU_LIST[@]}
echo "[run_eval] model=${MODEL} gpus=${GPUS} benchmarks=${BENCHMARKS} budgets=${BUDGETS} raw_mode=${RAW_MODE} -> ${OUT}"

LOGS=results/${RUN_NAME}_logs
mkdir -p "${LOGS}"
for BENCH in ${BENCHMARKS}; do
    pids=()
    for i in "${!GPU_LIST[@]}"; do
        CUDA_VISIBLE_DEVICES=${GPU_LIST[$i]} "${PY}" -m vbe.infer \
            --model_path "${MODEL}" --benchmark "${BENCH}" --data_root "${DATA_ROOT}" \
            --output "${OUT}/${BENCH}/shard${i}of${N}.jsonl" --shard_id "${i}" --num_shards "${N}" \
            --orig_projector "${ORIG_PROJ}" --raw_mode "${RAW_MODE}" --raw_max_tokens "${RAW_MAX_TOKENS}" \
            --budgets "${BUDGETS// /,}" --num_workers "${NUM_WORKERS}" \
            > "${LOGS}/${BENCH}_shard${i}of${N}.log" 2>&1 &
        pids+=($!)
    done
    fail=0
    for p in "${pids[@]}"; do wait "${p}" || fail=1; done
    if [ "${fail}" -ne 0 ]; then
        echo "[run_eval] ${BENCH}: a shard failed, see ${LOGS}/${BENCH}_*.log" >&2
        exit 1
    fi
    echo "[run_eval] ${BENCH} done"
done

for B in ${BUDGETS}; do
    "${PY}" -m vbe.score "results/${RUN_NAME}_b${B}" "${DATA_ROOT}"
done

	#!/usr/bin/env bash
	# Evaluate one checkpoint on Video-MME / MLVU / LongVideoBench, one process per GPU.
	#
	# bash scripts/run_eval.sh # defaults below
	# BUDGETS="4096 16384" GPUS=0,1 BENCHMARKS="videomme mlvu" bash scripts/run_eval.sh
	#
	# All budgets run in ONE pass: each video is decoded and encoded once, then planned /
	# compressed / answered per budget. Results: results/<name>_b<B>/<benchmark>/.
	# Re-running the same command resumes: answered (question, budget) pairs are skipped.
	set -euo pipefail
	cd "$(dirname "$0")/.."

	MODEL=${MODEL:-SamChen567/phase1_qbase_internvid}
	RUN_NAME=${RUN_NAME:-$(basename "${MODEL}")} # results/${RUN_NAME}_b<B>
	GPUS=${GPUS:-$(nvidia-smi --query-gpu=index --format=csv,noheader \| paste -sd,)}
	BENCHMARKS=${BENCHMARKS:-"videomme mlvu longvideobench"}
	DATA_ROOT=${DATA_ROOT:-/workspace/chicheng/datasets}
	BUDGETS=${BUDGETS:-"1024 2048 4096 8192 16384"} # LLM-side vision tokens per video (vbe/budget.py)
	NUM_WORKERS=${NUM_WORKERS:-6}
	VL3_BASE=${VL3_BASE:-/workspace/chicheng/OnlineLong/pretrained_models/videollama3_7b_local}
	ORIG_PROJ=${ORIG_PROJ:-checkpoints/vl3_orig_projector.pt}
	# orig: Phase-1 protocol (raw frames through the original VL3 projector);
	# phase3: raw frames as Phase-3 training sends them (<= RAW_MAX_TOKENS rows, checkpoint projector)
	RAW_MODE=${RAW_MODE:-orig}
	RAW_MAX_TOKENS=${RAW_MAX_TOKENS:-256}
	# model code; Phase-3 checkpoints need the tree they were trained with (hybrid_raw.py)
	OL_SRC=${OL_SRC:-third_party/OnlineLong}
	PY=${PY:-.venv/bin/python}
	export PATH="$(dirname "${PY}"):${PATH}" # ffprobe/ffmpeg from setup_env.sh
	export PYTHONPATH="${OL_SRC}:.${PYTHONPATH:+:${PYTHONPATH}}"

	# A hub id is downloaded once into checkpoints/<name>; a local directory is used as-is.
	if [ ! -d "${MODEL}" ]; then
	LOCAL=checkpoints/$(basename "${MODEL}")
	"${PY}" -c "from huggingface_hub import snapshot_download as d; d('${MODEL}', local_dir='${LOCAL}')"
	MODEL=${LOCAL}
	fi

	# raw frames are projected with the original VideoLLaMA3 projector
	if [ "${RAW_MODE}" = orig ]; then
	[ -f "${ORIG_PROJ}" ] \|\| "${PY}" scripts/extract_orig_projector.py "${VL3_BASE}" "${ORIG_PROJ}"
	fi

	OUT=results/${RUN_NAME}_b{budget}
	IFS=, read -ra GPU_LIST <<< "${GPUS}"
	N=${#GPU_LIST[@]}
	echo "[run_eval] model=${MODEL} gpus=${GPUS} benchmarks=${BENCHMARKS} budgets=${BUDGETS} raw_mode=${RAW_MODE} -> ${OUT}"

	LOGS=results/${RUN_NAME}_logs
	mkdir -p "${LOGS}"
	for BENCH in ${BENCHMARKS}; do
	pids=()
	for i in "${!GPU_LIST[@]}"; do
	CUDA_VISIBLE_DEVICES=${GPU_LIST[$i]} "${PY}" -m vbe.infer \
	--model_path "${MODEL}" --benchmark "${BENCH}" --data_root "${DATA_ROOT}" \
	--output "${OUT}/${BENCH}/shard${i}of${N}.jsonl" --shard_id "${i}" --num_shards "${N}" \
	--orig_projector "${ORIG_PROJ}" --raw_mode "${RAW_MODE}" --raw_max_tokens "${RAW_MAX_TOKENS}" \
	--budgets "${BUDGETS// /,}" --num_workers "${NUM_WORKERS}" \
	> "${LOGS}/${BENCH}_shard${i}of${N}.log" 2>&1 &
	pids+=($!)
	done
	fail=0
	for p in "${pids[@]}"; do wait "${p}" \|\| fail=1; done
	if [ "${fail}" -ne 0 ]; then
	echo "[run_eval] ${BENCH}: a shard failed, see ${LOGS}/${BENCH}_*.log" >&2
	exit 1
	fi
	echo "[run_eval] ${BENCH} done"
	done

	for B in ${BUDGETS}; do
	"${PY}" -m vbe.score "results/${RUN_NAME}_b${B}" "${DATA_ROOT}"
	done