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#!/bin/bash
DATA_DIR=$1
TRAINTASK=${2-'[rainbow-stack,bowl-ball-placement]'}
STEPS=${3-'61000'}
DISP=False
echo "Training single-task dataset... Folder: $DATA_DIR Task $TRAINTASK"
trap "kill 0" SIGINT
# You can parallelize these depending on how much resources you have
#############################
## Language-Conditioned Tasks
# [align-rope,assembling-kits-seq-seen-colors,assembling-kits-seq-unseen-colors,packing-shapes]
# TRAIN
python cliport/train.py train.task=$TRAINTASK \
train.agent=cliport \
train.model_task=$TRAINTASK \
train.attn_stream_fusion_type=add \
train.trans_stream_fusion_type=conv \
train.lang_fusion_type=mult \
train.n_demos=200 \
train.n_steps=${STEPS} \
dataset.cache=True \
train.exp_folder=exps/exp-$TRAINTASK \
dataset.type=single \
train.load_from_last_ckpt=False
# Convert Python list to Bash array
bash_array=$(python3 -c "import sys; print(' '.join((sys.argv[1])[1:-1].split(',')))" "$TRAINTASK")
# # Convert the space-separated string to a bash array
# echo "Testing single-task dataset... Folder: $DATA_DIR Task $TASK"
# echo "Testing $TASK"
# # TEST
# # bash scripts/generate_gpt_datasets.sh $DATA_DIR $task
# python cliport/eval.py model_task=$TRAINTASK \
# eval_task=$TRAINTASK \
# agent=cliport \
# mode=test \
# n_demos=100 \
# train_demos=200 \
# checkpoint_type=test_best \
# type=single \
# exp_folder=exps/exp-$TRAINTASK \
# update_results=True
# # wait
# python notebooks/print_results.py -r=exps/exp-$TRAINTASK/ --single
# echo "Finished Training." |