| # Run BEAR inference on THIS task folder's JSON file(s). | |
| # A copy of this script lives inside every task folder; the shared runners and | |
| # util/ live at the repository root (one level up). | |
| # | |
| # Usage: bash run.sh <series> <model_name> | |
| # <series> = gpt | gemini | claude -> API models (run_api_model.py) | |
| # <series> = image -> local VLM (run_image_model.py, needs vlmeval) | |
| # | |
| # Examples: | |
| # bash run.sh gpt gpt-4o | |
| # bash run.sh gemini gemini-2.5-pro | |
| # bash run.sh claude claude-sonnet-4-20250514 | |
| # bash run.sh image llava_next | |
| # | |
| # Media paths inside each JSON are relative to the task folder, so we cd here first. | |
| set -euo pipefail | |
| HERE="$(cd "$(dirname "$0")" && pwd)" | |
| ROOT="$(cd "$HERE/.." && pwd)" | |
| SERIES="${1:?series: gpt|gemini|claude|image}" | |
| MODEL="${2:?model name}" | |
| cd "$HERE" | |
| shopt -s nullglob | |
| for JSON in *_official.json vqa_all_episodes.json; do | |
| echo "================= $JSON =================" | |
| TAG="$(basename "${JSON%.json}")" | |
| if [ "$SERIES" = "image" ]; then | |
| python "$ROOT/run_image_model.py" \ | |
| --model_name "$MODEL" \ | |
| --input_json_path "$JSON" \ | |
| --evaluate_output_category "$TAG" | |
| else | |
| python "$ROOT/run_api_model.py" \ | |
| --model_name "$MODEL" \ | |
| --model_series "$SERIES" \ | |
| --input_json_path "$JSON" \ | |
| --evaluate_output_category "$TAG" | |
| fi | |
| done | |
| echo "Done. Outputs: final_${MODEL}_evaluate_*.json in this folder." | |