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#!/usr/bin/env
# The port for communication. Note that if you want to run multiple tasks on the same machine,
# you need to specify different port numbers.
export MASTER_PORT=6061
det_weight=0.1
cls_weight=0.0005
num_bins=64
log_dir=./polyformer_l_logs
save_dir=./polyformer_l_checkpoints
mkdir -p $log_dir $save_dir
bpe_dir=../../utils/BPE
user_dir=../../polyformer_module
data_dir=../../datasets/finetune
data=${data_dir}/refcoco+g_train_shuffled.tsv,${data_dir}/refcoco/refcoco_val.tsv
selected_cols=0,5,6,2,4,3,7
restore_file=../../weights/polyformer_l_pretrain.pt
task=refcoco
arch=polyformer_l
criterion=adjust_label_smoothed_cross_entropy
label_smoothing=0.1
lr=3e-5
max_epoch=5
warmup_ratio=0.06
batch_size=8
update_freq=8
resnet_drop_path_rate=0.0
encoder_drop_path_rate=0.1
decoder_drop_path_rate=0.1
dropout=0.1
attention_dropout=0.0
max_src_length=80
max_tgt_length=420
patch_image_size=512
for max_epoch in 100; do
echo "max_epoch "${max_epoch}
for lr in 5e-5; do
echo "lr "${lr}
for patch_image_size in 512; do
echo "patch_image_size "${patch_image_size}
log_file=${log_dir}/${max_epoch}"_"${lr}"_"${patch_image_size}".log"
save_path=${save_dir}/${max_epoch}"_"${lr}"_"${patch_image_size}
mkdir -p $save_path
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch --nproc_per_node=8 --master_port=${MASTER_PORT} ../../train.py \
$data \
--selected-cols=${selected_cols} \
--bpe-dir=${bpe_dir} \
--user-dir=${user_dir} \
--reset-optimizer --reset-dataloader --reset-meters \
--save-dir=${save_path} \
--task=${task} \
--arch=${arch} \
--criterion=${criterion} \
--label-smoothing=${label_smoothing} \
--batch-size=${batch_size} \
--update-freq=${update_freq} \
--encoder-normalize-before \
--restore-file=${restore_file} \
--decoder-normalize-before \
--share-decoder-input-output-embed \
--share-all-embeddings \
--layernorm-embedding \
--patch-layernorm-embedding \
--code-layernorm-embedding \
--resnet-drop-path-rate=${resnet_drop_path_rate} \
--encoder-drop-path-rate=${encoder_drop_path_rate} \
--decoder-drop-path-rate=${decoder_drop_path_rate} \
--dropout=${dropout} \
--attention-dropout=${attention_dropout} \
--weight-decay=0.01 --optimizer=adam --adam-betas="(0.9,0.999)" --adam-eps=1e-08 --clip-norm=1.0 \
--lr-scheduler=polynomial_decay --lr=${lr} \
--max-epoch=${max_epoch} --warmup-ratio=${warmup_ratio} \
--log-format=simple --log-interval=10 \
--fixed-validation-seed=7 \
--no-epoch-checkpoints --keep-best-checkpoints=1 \
--save-interval=1 --validate-interval=1 \
--save-interval-updates=500 --validate-interval-updates=500 \
--eval-acc \
--eval-args='{"beam":5,"min_len":2,"max_len_a":0,"max_len_b":2}' \
--best-checkpoint-metric=score --maximize-best-checkpoint-metric \
--max-src-length=${max_src_length} \
--max-tgt-length=${max_tgt_length} \
--find-unused-parameters \
--add-type-embedding \
--scale-attn \
--scale-fc \
--scale-heads \
--disable-entangle \
--num-bins=${num_bins} \
--patch-image-size=${patch_image_size} \
--fp16 \
--fp16-scale-window=512 \
--det_weight=${det_weight} \
--cls_weight=${cls_weight} \
--num-workers=0 > ${log_file} 2>&1
done
done
done |