Buckets:
| set -euo pipefail | |
| SOURCE_ROOT=$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd) | |
| PROJECT_ROOT=${SPATIAL_MMCOT_PROJECT_ROOT:-$(cd "$SOURCE_ROOT/../.." && pwd)} | |
| OUTPUT_DIR=${OUTPUT_DIR:?set OUTPUT_DIR to one prepared run directory} | |
| NPROC_PER_NODE=${NPROC_PER_NODE:-4} | |
| MODEL_NAME=${MODEL_NAME:-thinkmorph-7b-local} | |
| DATASET_NAMES=${DATASET_NAMES:-BLINK_Jigsaw VStarBench MMVP SAT_circular} | |
| JUDGE_NAME=${JUDGE_NAME:-deepseek-v4-flash} | |
| if [[ "$JUDGE_NAME" == deepseek-* && -z "${DEEPSEEK_API_KEY:-}" ]]; then | |
| DEEPSEEK_API_KEY_FILE=${DEEPSEEK_API_KEY_FILE:-$PROJECT_ROOT/projects/VLMEvalKit_Thinkmorph_llm/API_KEY} | |
| export DEEPSEEK_API_KEY | |
| DEEPSEEK_API_KEY=$(<"$DEEPSEEK_API_KEY_FILE") | |
| fi | |
| export LMUData=${LMU_DATA_ROOT:-$PROJECT_ROOT/data/VLMEvalKit} | |
| export THINKMORPH_SAVE_DIR=${THINKMORPH_SAVE_DIR:-$OUTPUT_DIR/generated_images} | |
| export SPLIT_THINK=1 | |
| export VLMEVAL_EVAL_ID=${VLMEVAL_EVAL_ID:-RUN} | |
| export VLMEVAL_DUMP_EVERY=${VLMEVAL_DUMP_EVERY:-1} | |
| export VLMEVAL_SCHEDULING=${VLMEVAL_SCHEDULING:-static} | |
| export PYTORCH_CUDA_ALLOC_CONF=${PYTORCH_CUDA_ALLOC_CONF:-expandable_segments:True} | |
| export PYTHONPATH="$SOURCE_ROOT${PYTHONPATH:+:$PYTHONPATH}" | |
| read -r -a DATASET_ARGS <<< "$DATASET_NAMES" | |
| exec torchrun --standalone --nproc-per-node="$NPROC_PER_NODE" "$SOURCE_ROOT/run.py" \ | |
| --data "${DATASET_ARGS[@]}" \ | |
| --model "$MODEL_NAME" \ | |
| --judge "$JUDGE_NAME" \ | |
| --reuse \ | |
| --work-dir "$OUTPUT_DIR/results" | |
Xet Storage Details
- Size:
- 1.43 kB
- Xet hash:
- 68b070421e501cbb26123b3a19d0c310ff74260067b35a63f6911ff8636f5190
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.