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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=1051
export CUDA_VISIBLE_DEVICES=0
export GPUS_PER_NODE=1
bpe_dir=../../utils/BPE
user_dir=../../ofa_module
restore_file=../../checkpoints/ofa_large.pt
data_dir=../../dataset/pretrain_data
neg_sample_dir=${data_dir}/negative_sample
data=${data_dir}/vision_language_examples.tsv
text_data=${data_dir}/text_examples.tsv
image_data=${data_dir}/image_examples.tsv
detection_data=${data_dir}/detection_examples.tsv
selected_cols=0,1,2,3,4,5,6,7
text_selected_cols=0,1
image_selected_cols=0,1,2
detection_selected_cols=0,1,2
task=unify_task
arch=ofa_large
criterion=adjust_label_smoothed_cross_entropy
label_smoothing=0.0
lr=1e-4
max_epoch=50
warmup_ratio=0.01
batch_size=4
update_freq=1
resnet_drop_path_rate=0.0
encoder_drop_path_rate=0.1
decoder_drop_path_rate=0.1
dropout=0.1
attention_dropout=0.0
max_src_length=80
max_tgt_length=30
num_bins=1000
patch_image_size=384
sample_patch_num=196
max_image_size=512
save_path=./checkpoints
python3 -m torch.distributed.launch --nproc_per_node=${GPUS_PER_NODE} --master_port=${MASTER_PORT} ../../train.py \
$data \
--text-data=${text_data} \
--image-data=${image_data} \
--detection-data=${detection_data} \
--selected-cols=${selected_cols} \
--text-selected-cols=${text_selected_cols} \
--image-selected-cols=${image_selected_cols} \
--detection-selected-cols=${detection_selected_cols} \
--bpe-dir=${bpe_dir} \
--user-dir=${user_dir} \
--restore-file=${restore_file} \
--reset-optimizer --reset-dataloader --reset-meters \
--save-dir=${save_path} \
--neg-sample-dir=${neg_sample_dir} \
--task=${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=5.0 \
--lr-scheduler=polynomial_decay --lr=${lr} \
--max-epoch=${max_epoch} --warmup-ratio=${warmup_ratio} \
--log-format=simple --log-interval=10 \
--fixed-validation-seed=7 \
--keep-last-epochs=15 \
--save-interval=1 \
--save-interval-updates=6000 \
--disable-validation \
--max-src-length=${max_src_length} \
--max-tgt-length=${max_tgt_length} \
--add-type-embedding \
--scale-attn \
--scale-fc \
--scale-heads \
--disable-entangle \
--num-bins=${num_bins} \
--patch-image-size=${patch_image_size} \
--sample-patch-num=${sample_patch_num} \
--max-image-size=${max_image_size} \
--fp16 \
--fp16-scale-window=128 \
--num-workers=0 |