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#!/bin/bash
DATA_DIR=$1
TASK=$2
DISP=False
echo "Training dataset... Folder: $DATA_DIR Task $TASK"
# You can parallelize these depending on how much resources you have
#############################
## Language-Conditioned Tasks
trap "kill 0" SIGINT
LANG_TASKS=$2
for task in $LANG_TASKS
do
# Generate data
bash scripts/generate_gpt_datasets.sh data $task
# TRAIN
python cliport/train.py train.task=$task \
train.agent=cliport \
train.attn_stream_fusion_type=add \
train.trans_stream_fusion_type=conv \
train.lang_fusion_type=mult \
train.n_demos=200 \
train.n_steps=10000 \
train.exp_folder=exps/exps-singletask \
dataset.cache=True \
train.batch_size=1 \
train.log=True
# EVAL
# python cliport/eval.py eval_task=$task \
# agent=cliport \
# mode=val \
# n_demos=100 \
# train_demos=100 \
# checkpoint_type=val_missing \
# exp_folder=exps
# TEST
python cliport/eval.py eval_task=$task \
agent=cliport \
mode=test \
n_demos=100 \
train_demos=200 \
checkpoint_type=test_best \
exp_folder=exps/exps-singletask \
update_results=True \
disp=True
done
python notebooks/print_results.py -r=exps-singletask
echo "Finished Training."
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