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#!/usr/bin/env bash
# The port for communication. Note that if you want to run multiple tasks on the same machine,
# you need to specify different port numbers.
# Number of GPUs per GPU worker
export GPUS_PER_NODE=8
# Number of GPU workers, for single-worker training, please set to 1
export NUM_NODES=$SLURM_NNODES
# The ip address of the rank-0 worker, for single-worker training, please set to localhost
master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
export MASTER_ADDR=$master_addr
# The port for communication
export MASTER_PORT=12350
# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
export RANK=$SLURM_NODEID
echo "MASTER_ADDR: $MASTER_ADDR"
echo "RANK :$RANK"
echo "NUM_NODES :$NUM_NODES"
echo "GPUS_PER_NODE :$GPUS_PER_NODE"
export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/
echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH"
num_workers=0
ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
base_log_dir=/work/NAT/gda2204/mshukor/logs
bpe_dir=${ofa_dir}/utils/BPE
user_dir=${ofa_dir}/ofa_module
data_dir=${base_data_dir}/ofa/caption_data
split=test
data=${data_dir}/caption_${split}.tsv # caption_val caption_test
selected_cols=1,4,2
image_encoder_name=resnet #vit_base_patch16_224
exp_name=eval_caption_base_best_caption_stage_1_caption_ratacapgroundsnlivqaofapt
path=${base_log_dir}/ofa/pretrained_models/average_models/caption_ratacapgroundsnlivqaofapt.pt
echo ${path}
result_path=${base_log_dir}/ofa/results/caption/${exp_name}_${split}
mkdir ${result_path}
python3 -m torch.distributed.launch \
--nnodes=${NUM_NODES} \
--nproc_per_node=${GPUS_PER_NODE} \
--master_port=${MASTER_PORT} \
--node_rank=${RANK} \
--master_addr=${MASTER_ADDR} \
--use_env ${ofa_dir}/evaluate.py \
${data} \
--path=${path} \
--user-dir=${user_dir} \
--task=caption \
--batch-size=16 \
--log-format=simple --log-interval=10 \
--seed=7 \
--gen-subset=${split} \
--results-path=${result_path} \
--beam=5 \
--max-len-b=16 \
--no-repeat-ngram-size=3 \
--fp16 \
--num-workers=0 \
--model-overrides="{\"data\":\"${data}\",\"bpe_dir\":\"${bpe_dir}\",\"eval_cider\":False,\"selected_cols\":\"${selected_cols}\"}" \
--strict
python ${ofa_dir}/run_scripts/caption/coco_eval.py ${result_path}/test_predict.json ${data_dir}/test_caption_coco_format.json
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