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generate_evaluate_iter1_example.sh
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155 lines (144 loc) · 6.17 KB
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#!/bin/zsh
# Input arguments with defaults
DATASET_NAME=${1:-"coco"}
DATA_SPLIT=${2:-"test"}
MODEL_NAME=${3:-"llama3.1:8b"}
TEMPERATURE=${4:-0.7}
TOP_P=${5:-0.9}
GEN_MODE=${6:-"deceptive-general"}
NUM_RETURN_SEQUENCES=${7:-4}
NUM_RETURN_SEQUENCES_AT_TEST_TIME=${8:-"-1"}
NUM_ITERATIONS=${9:-1}
MODEL_CHECKPOINT_FNAME=${10:-"lr2e-4_lora_r16_lora_alpha32_from-64-maxent"}
MODEL_CHECKPOINT_STEPS=${11:-31000}
CROSSMODAL_MODEL=${12:-"clip"}
SAMPLE_SFT_METHOD=${13:-"maxent"}
# Echo input arguments
echo "=== Arguments ==="
echo "Dataset Name : $DATASET_NAME"
echo "Data Split : $DATA_SPLIT"
echo "Model Name : $MODEL_NAME"
echo "Temperature : $TEMPERATURE"
echo "Top-p : $TOP_P"
echo "Generation Mode : $GEN_MODE"
echo "Num Return Sequences : $NUM_RETURN_SEQUENCES"
echo "Num Return Sequences (Test) : $NUM_RETURN_SEQUENCES_AT_TEST_TIME"
echo "Number of Iterations : $NUM_ITERATIONS"
echo "Model Checkpoint Filename : $MODEL_CHECKPOINT_FNAME"
echo "Model Checkpoint Steps : $MODEL_CHECKPOINT_STEPS"
echo "Crossmodal Model : $CROSSMODAL_MODEL"
echo "Sample SFT Method : $SAMPLE_SFT_METHOD"
echo "==================="
mkdir -p ./checkpoints/${DATASET_NAME}/${DATA_SPLIT}/${GEN_MODE}-${NUM_RETURN_SEQUENCES}_${MODEL_NAME}_${TEMPERATURE}_${TOP_P}/iter${NUM_ITERATIONS}/${CROSSMODAL_MODEL}/${MODEL_CHECKPOINT_FNAME}/checkpoint-${MODEL_CHECKPOINT_STEPS}
echo "Executing generate_candidates.py..."
python dataset_processing/generate_candidates.py \
--dataset_name "$DATASET_NAME" \
--data_split "$DATA_SPLIT" \
--model_name "$MODEL_NAME" \
--temperature "$TEMPERATURE" \
--top_p "$TOP_P" \
--gen_mode "$GEN_MODE" \
--num_return_sequences "$NUM_RETURN_SEQUENCES" \
--num_return_sequences_at_test_time "$NUM_RETURN_SEQUENCES_AT_TEST_TIME" \
--num_iterations "$NUM_ITERATIONS" \
--model_checkpoint_fname "$MODEL_CHECKPOINT_FNAME" \
--model_checkpoint_steps "$MODEL_CHECKPOINT_STEPS" \
--crossmodal_model "$CROSSMODAL_MODEL" \
--do_batch_decoding
# IMPORTANT: If you get an OOM (Out-Of-Memory) error, remove the --do_batch_decoding option
echo "Executing evaluate_scores.py with crossmodal model..."
python dataset_processing/evaluate_scores.py \
--dataset_name "$DATASET_NAME" \
--data_split "$DATA_SPLIT" \
--gen_mode "$GEN_MODE" \
--num_return_sequences "$NUM_RETURN_SEQUENCES" \
--num_return_sequences_at_test_time "$NUM_RETURN_SEQUENCES_AT_TEST_TIME" \
--generation_model "$MODEL_NAME" \
--temperature "$TEMPERATURE" \
--top_p "$TOP_P" \
--num_iterations "$NUM_ITERATIONS" \
--model_checkpoint_fname "$MODEL_CHECKPOINT_FNAME" \
--model_checkpoint_steps "$MODEL_CHECKPOINT_STEPS" \
--crossmodal_model "$CROSSMODAL_MODEL" \
--compute_crossmodal
echo "Executing evaluate_scores.py with unimodal models..."
for UNIMODAL_MODEL in roberta_large_mnli deberta_xlarge_mnli bart_large_mnli; do
python dataset_processing/evaluate_scores.py \
--dataset_name "$DATASET_NAME" \
--data_split "$DATA_SPLIT" \
--gen_mode "$GEN_MODE" \
--num_return_sequences "$NUM_RETURN_SEQUENCES" \
--num_return_sequences_at_test_time "$NUM_RETURN_SEQUENCES_AT_TEST_TIME" \
--generation_model "$MODEL_NAME" \
--temperature "$TEMPERATURE" \
--top_p "$TOP_P" \
--num_iterations "$NUM_ITERATIONS" \
--model_checkpoint_fname "$MODEL_CHECKPOINT_FNAME" \
--model_checkpoint_steps "$MODEL_CHECKPOINT_STEPS" \
--crossmodal_model "$CROSSMODAL_MODEL" \
--unimodal_model "$UNIMODAL_MODEL" \
--compute_unimodal
done
echo "Executing evaluate_deception.py..."
python dataset_processing/evaluate_deception.py \
--dataset_name "$DATASET_NAME" \
--data_split "$DATA_SPLIT" \
--gen_mode "$GEN_MODE" \
--num_return_sequences "$NUM_RETURN_SEQUENCES" \
--num_return_sequences_at_test_time "$NUM_RETURN_SEQUENCES_AT_TEST_TIME" \
--generation_model "$MODEL_NAME" \
--temperature "$TEMPERATURE" \
--top_p "$TOP_P" \
--num_iterations "$NUM_ITERATIONS" \
--model_checkpoint_fname "$MODEL_CHECKPOINT_FNAME" \
--model_checkpoint_steps "$MODEL_CHECKPOINT_STEPS" \
--crossmodal_model "$CROSSMODAL_MODEL"
echo "Executing evaluate_diversity.py..."
python dataset_processing/evaluate_diversity.py \
--dataset_name "$DATASET_NAME" \
--data_split "$DATA_SPLIT" \
--gen_mode "$GEN_MODE" \
--num_return_sequences "$NUM_RETURN_SEQUENCES" \
--num_return_sequences_at_test_time "$NUM_RETURN_SEQUENCES_AT_TEST_TIME" \
--generation_model "$MODEL_NAME" \
--temperature "$TEMPERATURE" \
--top_p "$TOP_P" \
--num_iterations "$NUM_ITERATIONS" \
--model_checkpoint_fname "$MODEL_CHECKPOINT_FNAME" \
--model_checkpoint_steps "$MODEL_CHECKPOINT_STEPS" \
--crossmodal_model "$CROSSMODAL_MODEL"
echo "Executing print_overall_results.py..."
python dataset_processing/print_overall_results.py \
--dataset_name "$DATASET_NAME" \
--data_split "$DATA_SPLIT" \
--gen_mode "$GEN_MODE" \
--num_return_sequences "$NUM_RETURN_SEQUENCES" \
--num_return_sequences_at_test_time "$NUM_RETURN_SEQUENCES_AT_TEST_TIME" \
--generation_model "$MODEL_NAME" \
--temperature "$TEMPERATURE" \
--top_p "$TOP_P" \
--num_iterations "$NUM_ITERATIONS" \
--model_checkpoint_fname "$MODEL_CHECKPOINT_FNAME" \
--model_checkpoint_steps "$MODEL_CHECKPOINT_STEPS" \
--crossmodal_model "$CROSSMODAL_MODEL"
# Only run this in training split
if [[ "$DATA_SPLIT" == "train" ]]; then
echo "Executing sample_deception_dataset.py..."
python dataset_processing/sample_deception_dataset.py \
--dataset_name "$DATASET_NAME" \
--data_split "$DATA_SPLIT" \
--gen_mode "$GEN_MODE" \
--num_return_sequences "$NUM_RETURN_SEQUENCES" \
--num_return_sequences_at_test_time "$NUM_RETURN_SEQUENCES_AT_TEST_TIME" \
--generation_model "$MODEL_NAME" \
--temperature "$TEMPERATURE" \
--top_p "$TOP_P" \
--num_iterations "$NUM_ITERATIONS" \
--model_checkpoint_fname "$MODEL_CHECKPOINT_FNAME" \
--model_checkpoint_steps "$MODEL_CHECKPOINT_STEPS" \
--crossmodal_model "$CROSSMODAL_MODEL" \
--sample_sft_method "$SAMPLE_SFT_METHOD"
else
echo "Skipping sample_deception_dataset.py (data_split=$DATA_SPLIT)"
fi
echo "✅ All tasks executed successfully."