"ignoring input and redirecting stderr to stdout" when fine tuning image captioning

[teenaxta]

I'm trying to follow the fine-tuning steps for captioning as listed in readme.md. However my output is just blank and once i hit enter, it exits. Pretraining worked fine, it's fine-tuning thats not working at all. Any idea on what might be causing this issue My GPU has 8GB vram

[logicwong]

@teenaxta You can check the two files train_stage1.out and stage1_logs/5_0.06_6000.log to see logs.

[teenaxta]

here's what my logs are saying

train_stage1.out:

max_epoch {2,} warmup_ratio {0.06,} drop_worst_after {2500,}

train_stage2.out:

lr {1e-5,} max_epoch {3,}

[logicwong]

@teenaxta What about the files under stage1_logs? There should be more detailed logs.

[teenaxta]

I am sharing these detailed log files

2_0.06_2500.log {5,}{0.06,}{6000,}.log {2,}{0.06,}{2500,}el0.75.log {2,}{0.06,}{2500,}.log

[logicwong]

@teenaxta It seems that the specified GPU number is wrong, try the following script?

#!/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=1061

log_dir=./stage1_logs
save_dir=./stage1_checkpoints
mkdir -p $log_dir $save_dir

bpe_dir=../../utils/BPE
user_dir=../../ofa_module

data_dir=../../dataset/caption_data
data=${data_dir}/caption_stage1_train.tsv,${data_dir}/caption_val.tsv
restore_file=../../checkpoints/ofa_base.pt
selected_cols=0,4,2

task=caption
arch=ofa_base
criterion=adjust_label_smoothed_cross_entropy
label_smoothing=0.1
lr=1e-5
max_epoch=5
warmup_ratio=0.06
batch_size=8
update_freq=4
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=20
num_bins=1000
patch_image_size=480
drop_worst_after=6000
eval_cider_cached=${data_dir}/cider_cached_tokens/coco-valid-words.p
drop_worst_ratio=0.2


log_file=${log_dir}/${max_epoch}"_"${warmup_ratio}"_"${drop_worst_after}".log"
save_path=${save_dir}/${max_epoch}"_"${warmup_ratio}"_"${drop_worst_after}
mkdir -p $save_path

CUDA_VISIBLE_DEVICES=0 python3 ../../train.py \
  $data \
  --selected-cols=${selected_cols} \
  --bpe-dir=${bpe_dir} \
  --user-dir=${user_dir} \
  --restore-file=${restore_file} \
  --reset-optimizer --reset-dataloader --reset-meters \
  --save-dir=${save_path} \
  --task=${task} \
  --arch=${arch} \
  --criterion=${criterion} \
  --label-smoothing=${label_smoothing} \
  --batch-size=${batch_size} \
  --update-freq=${update_freq} \
  --encoder-normalize-before \
  --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-cider \
  --eval-cider-cached-tokens=${eval_cider_cached} \
  --eval-args='{"beam":5,"max_len_b":16,"no_repeat_ngram_size":3}' \
  --best-checkpoint-metric=cider --maximize-best-checkpoint-metric \
  --max-src-length=${max_src_length} \
  --max-tgt-length=${max_tgt_length} \
  --find-unused-parameters \
  --freeze-encoder-embedding \
  --freeze-decoder-embedding \
  --add-type-embedding \
  --scale-attn \
  --scale-fc \
  --scale-heads \
  --disable-entangle \
  --num-bins=${num_bins} \
  --patch-image-size=${patch_image_size} \
  --drop-worst-ratio=${drop_worst_ratio} \
  --drop-worst-after=${drop_worst_after} \
  --fp16 \
  --fp16-scale-window=512 \
  --num-workers=0

[teenaxta]

@logicwong i was able to run train_caption_stage1.sh with this script but after an hour or so I got the following output

[logicwong]

@teenaxta That means the GPU memory is not enough, you should decrease the batch_size or patch_image_size

[teenaxta]

@logicwong so i now changed the batch size to 2 and patch size to 200. Here's the output.

[logicwong]

@teenaxta Try adding --freeze-resnet , increasing --validate-interval-updates and --save-interval-updates. In addition, setting patch_image_size to 200 may be too small, you can try setting it to 384 and training with more GPUs.