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45ac12e | 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 | VERSION="v0"
TASK="10" # spatial / object / goal / 10 / 90
VLA_PATH="checkpoints/initialized_pt_vla/initailized_openvla_with_SF_spatial_v0.4.2"
DATA_ROOT_DIR="data/libero_openvla"
RUN_ROOT_DIR="experiments/training_results"
REGULARIZATION_LORA_VECTOR_PATH="checkpoints/lora_diff/sf_150000_steps_spatial_adapter_diff.safetensors"
WANDB_ENTITY="YOUR_WANDB_ENTITY"
WANDB_PROJECT="YOUR_WANDB_PROJECT"
EVAL_LOG_PATH="experiments/eval_logs/${VERSION}_output.log"
torchrun --standalone --nnodes 1 --nproc-per-node 1 vla-scripts/finetune_regular_loss.py \
--vla_path "$VLA_PATH" \
--data_root_dir "$DATA_ROOT_DIR" \
--dataset_name libero_${TASK}_no_noops \
--run_root_dir "$RUN_ROOT_DIR" \
--use_l1_regression True \
--use_diffusion False \
--use_film False \
--num_images_in_input 2 \
--use_proprio True \
--batch_size 8 \
--learning_rate 5e-4 \
--scheduler CosineAnnealingLR \
--max_steps 150100 \
--save_freq 150000 \
--save_latest_checkpoint_only True \
--merge_lora_during_training True \
--regularization_lora_vector_path "$REGULARIZATION_LORA_VECTOR_PATH" \
--regularization_weight 1e-4 \
--image_aug True \
--lora_rank 32 \
--wandb_entity "$WANDB_ENTITY" \
--wandb_project "$WANDB_PROJECT" \
--run_id_override "$VERSION"
python experiments/robot/libero/run_libero_eval.py --pretrained_checkpoint "$RUN_ROOT_DIR/$VERSION" --task_suite_name libero_${TASK} > "$EVAL_LOG_PATH" 2>&1
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