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# 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 :$MIOPEN_USER_DB_PATH"
num_workers=0
ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
base_log_dir=/work/NAT/gda2204/mshukor/logs
exp_name=unival_vqa
image_dir=${base_data_dir}
data_dir=${base_data_dir}/ofa/vqa_data
# data=${data_dir}/vqa_train.tsv,${data_dir}/vqa_val.tsv
# Note: If you have shuffled the data in advance, please uncomment the line below.
data=${data_dir}/vqa_train_1.tsv,${data_dir}/vqa_train_2.tsv,${data_dir}/vqa_train_3.tsv,${data_dir}/vqa_train_4.tsv,${data_dir}/vqa_train_5.tsv,${data_dir}/vqa_train_6.tsv,${data_dir}/vqa_train_7.tsv,${data_dir}/vqa_train_8.tsv,${data_dir}/vqa_train_9.tsv,${data_dir}/vqa_train_10.tsv,${data_dir}/vqa_val.tsv
ans2label_file=${base_data_dir}/ofa/vqa_data/trainval_ans2label.pkl
selected_cols=0,5,2,3,4
save_base_log_dir=/lus/scratch/NAT/gda2204/SHARED/logs
save_dir=${save_base_log_dir}/ofa/checkpoints/vqa/${exp_name}
log_dir=${save_dir}
mkdir -p $log_dir $save_dir
restore_file=${base_log_dir}/ofa/checkpoints/pretrain/unival_s2_hs/checkpoint1.pt
lr=1e-4
bpe_dir=${ofa_dir}/utils/BPE
user_dir=${ofa_dir}/ofa_module
task=vqa_gen
arch=unival_base
criterion=adjust_label_smoothed_cross_entropy
label_smoothing=0.1
batch_size=16
update_freq=1
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_object_length=30
max_tgt_length=30
num_bins=1000
uses_ema="--uses-ema"
store_ema="--store-ema"
ema_fp32="--ema-fp32"
ema_decay=0.9999
ema_start_update=0
# Specify the inference type in validation after each fine-tuning epoch
# As mentioned in the readme, you can choose from allcand or beamsearch evaluation, default to allcand
val_inference_type=beamsearch
# Specify whether to activate unconstrained VQA finetuning, which does not use a pre-defined candidate answer set
# If --unconstrained-training is acitvated, --ans2label-file will **not be used even if it is specified**
# Meanwhile, --val-inference-type must be set to **beamsearch**
# By default, we follow the constrained finetuning as we mentioned in OFA paper, the candidate answer set shall be specified by --ans2label-file
# For more details about this option, please refer to issue #123 and PR #124
unconstrained_training_flag=""
# unconstrained_training_flag="--unconstrained-training"
save_interval_updates=0
###
image_encoder_name=timm_resnet #vit_base_patch16_224
patch_image_size=480
resnet_type=resnet101
resnet_model_path=${base_log_dir}/pretrained_models/resnet101-5d3b4d8f.pth
# video
video_encoder_name=all_resnext101
patch_frame_size=384
video_model_path=${base_log_dir}/pretrained_models/3dcnn/resnext-101-kinetics.pth #${base_log_dir}/pretrained_models/TimeSformer_divST_8x32_224_K600.pyth
num_frames=4
sample_patch_num='--sample-patch-num=784' # ''
eval_args='--eval-args={"beam":5,"unnormalized":true,"temperature":1.0,"stop_on_max_len":true}'
validate_interval_updates=2000
save_interval_updates=0
for max_epoch in {20,}; do
echo "max_epoch "${max_epoch}
for warmup_ratio in {0.04,}; do
echo "warmup_updates "${warmup_updates}
for lr in {$lr,}; do
echo "lr "${lr}
for patch_image_size in {$patch_image_size,}; do
echo "patch_image_size "${patch_image_size}
log_file=${log_dir}/${max_epoch}"_"${warmup_ratio}"_"${lr}"_"${patch_image_size}"_rank"${RANK}".log"
save_path=${save_dir}/${max_epoch}"_"${warmup_ratio}"_"${lr}"_"${patch_image_size}
mkdir -p $save_path
python3 -m torch.distributed.launch \
--nnodes=${NUM_NODES} \
--nproc_per_node=${GPUS_PER_NODE} \
--master_port=${MASTER_PORT} \
--node_rank=${RANK} \
--master_addr=${MASTER_ADDR} \
--use_env ${ofa_dir}/train.py \
${data} \
--selected-cols=${selected_cols} \
--bpe-dir=${bpe_dir} \
--user-dir=${user_dir} \
--restore-file=${restore_file} \
--save-dir=${save_path} \
--task=${task} \
--arch=${arch} \
--criterion=${criterion} \
--label-smoothing=${label_smoothing} \
--batch-size=${batch_size} \
--update-freq=${update_freq} \
--encoder-normalize-before \
--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 \
--keep-best-checkpoints=1 \
--no-epoch-checkpoints \
--save-interval=1 --validate-interval=1 \
--save-interval-updates=${save_interval_updates} --validate-interval-updates=${validate_interval_updates} \
--best-checkpoint-metric=vqa_score --maximize-best-checkpoint-metric \
--max-src-length=${max_src_length} \
--max-object-length=${max_object_length} \
--max-tgt-length=${max_tgt_length} \
--find-unused-parameters \
--freeze-encoder-embedding \
--freeze-decoder-embedding \
${unconstrained_training_flag} \
--ans2label-file=${ans2label_file} \
--valid-batch-size=20 \
--add-type-embedding \
--scale-attn \
--scale-fc \
--scale-heads \
--disable-entangle \
--num-bins=${num_bins} \
--patch-image-size=${patch_image_size} \
--prompt-type=prev_output \
--fp16 \
--fp16-scale-window=512 \
${uses_ema} \
${store_ema} \
${ema_fp32} \
--ema-decay=${ema_decay} \
--ema-start-update=${ema_start_update} \
--val-inference-type=${val_inference_type} \
--num-workers=0 \
--image-encoder-name=${image_encoder_name} \
--image-dir=${image_dir} \
--video-encoder-name=${video_encoder_name} \
--video-model-path=${video_model_path} \
--patch-frame-size=${patch_frame_size} \
${sample_patch_num} \
${eval_args} \
--no-epoch-checkpoints \
--resnet-type=${resnet_type} \
--resnet-model-path=${resnet_model_path} \
--reset-dataloader --reset-meters --reset-optimizer
done
done
done
done
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