File size: 3,590 Bytes
6461f0c | 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 | #!/usr/bin/env bash
# ======================================================================
# Vanilla Video Reward Pipeline — vLLM/OpenAI-compatible backend,
# per-dimension scoring (3 dims):
# - instruction_following
# - visual_quality
# - world_consistency
# Definitions live in ../prompts/definitions.md.
#
# For every video, the model is called once per scoring dimension and
# asked to first reason, then assign an integer score in 1..5.
#
# Input data: a point-wise benchmark JSON whose ``video_path`` field is
# resolved relative to the JSON's directory.
#
# Start a vLLM OpenAI-compatible server in another terminal first.
#
# Usage:
# bash infer.sh
# bash infer.sh 50
# bash infer.sh all 12 my_run
# bash infer.sh all 12 my_run /path/data.json
#
# $1 num_samples : number of videos or 'all' (default: all)
# $2 concurrency : default 32
# $3 output tag : default infer
# $4 data json path : default ../data/firm-video-bench.json
# $5 vLLM base URL : default http://127.0.0.1:8000/v1
# $6 served model name : default Qwen3-VL-8B-Instruct
# ======================================================================
set -euo pipefail
NUM_SAMPLES="${1:-all}"
CONCURRENCY="${2:-32}"
TAG="${3:-infer}"
DATA="${4:-}"
VLLM_BASE_URL="${5:-${VLLM_BASE_URL:-http://127.0.0.1:8000/v1}}"
VLLM_MODEL="${6:-${VLLM_MODEL:-Qwen3-VL-8B-Instruct}}"
VLLM_API_KEY="${VLLM_API_KEY:-EMPTY}"
VLLM_MAX_TOKENS="${VLLM_MAX_TOKENS:-4096}"
VLLM_TEMPERATURE="${VLLM_TEMPERATURE:-0}"
VLLM_REQUEST_INTERVAL="${VLLM_REQUEST_INTERVAL:-0.0}"
VLLM_MAX_RETRIES="${VLLM_MAX_RETRIES:-3}"
VLLM_RETRY_BASE_DELAY="${VLLM_RETRY_BASE_DELAY:-2.0}"
VLLM_REQUEST_TIMEOUT="${VLLM_REQUEST_TIMEOUT:-300}"
if [[ "${VLLM_BASE_URL}" != */v1 ]]; then
VLLM_BASE_URL="${VLLM_BASE_URL%/}/v1"
fi
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
PROJECT_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
if [ -z "${DATA}" ]; then
DATA="${PROJECT_ROOT}/data/firm-video-bench.json"
fi
RESULTS_DIR="${PROJECT_ROOT}/results"
SCORE_JSON="${RESULTS_DIR}/${TAG}_scores.json"
mkdir -p "${RESULTS_DIR}"
if command -v curl >/dev/null 2>&1; then
if ! curl -fsS "${VLLM_BASE_URL}/models" >/dev/null; then
echo "Cannot reach vLLM endpoint: ${VLLM_BASE_URL}" >&2
echo "Please start a vLLM OpenAI-compatible server first." >&2
exit 1
fi
fi
echo "============================================================"
echo " Vanilla Pipeline (vLLM/OpenAI-compatible, 3 dims, per-dim calls)"
echo "============================================================"
echo " vLLM base: ${VLLM_BASE_URL}"
echo " Model: ${VLLM_MODEL}"
echo " Data: ${DATA}"
echo " Num samples: ${NUM_SAMPLES} (videos)"
echo " Concurrency: ${CONCURRENCY}"
echo " Max tokens: ${VLLM_MAX_TOKENS}"
echo " Temperature: ${VLLM_TEMPERATURE}"
echo " Scores: ${SCORE_JSON}"
echo "============================================================"
python "${SCRIPT_DIR}/infer.py" \
--data "${DATA}" \
--score_output "${SCORE_JSON}" \
--num_samples "${NUM_SAMPLES}" \
--concurrency "${CONCURRENCY}" \
--vllm_base_url "${VLLM_BASE_URL}" \
--model "${VLLM_MODEL}" \
--api_key "${VLLM_API_KEY}" \
--max_tokens "${VLLM_MAX_TOKENS}" \
--temperature "${VLLM_TEMPERATURE}" \
--request_interval "${VLLM_REQUEST_INTERVAL}" \
--max_retries "${VLLM_MAX_RETRIES}" \
--retry_base_delay "${VLLM_RETRY_BASE_DELAY}" \
--request_timeout "${VLLM_REQUEST_TIMEOUT}"
echo ""
echo "Done!"
echo " Scores: ${SCORE_JSON}"
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