MMSRL

Our Multimodal Semantic Role Labeling model for CONSTRAINT 2022
git clone https://esimon.eu/repos/MMSRL.git
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generic_features_attention.sh (2323B)

      1 #!/bin/bash
      2 #SBATCH -A lco@gpu
      3 #SBATCH --job-name=mmsrl             # job name
      4 #SBATCH --ntasks=1                   # number of MP tasks
      5 #SBATCH --ntasks-per-node=1          # number of MPI tasks per node
      6 #SBATCH --gres=gpu:1                 # number of GPUs per node
      7 #SBATCH --cpus-per-task=10           # number of cores per tasks
      8 #SBATCH --hint=nomultithread         # we get physical cores not logical
      9 #SBATCH --distribution=block:block   # we pin the tasks on contiguous cores
     10 #SBATCH --time=10:00:00               # maximum execution time (HH:MM:SS)
     11 #SBATCH --output=/gpfswork/rech/lco/url46ht/outputs/generic_features_attention/generic_features_attention_%j_%x_%A_%a.out  # output file name
     12 #SBATCH --error=/gpfswork/rech/lco/url46ht/outputs/generic_features_attention/generic_features_attention_%j_%x_%A_%a.err   # error file name
     13 #SBATCH --array=0-39
     14 
     15 
     16 cd $WORK/mmsrl
     17 
     18 module purge
     19 source ~/.bashrc
     20 conda activate pytorch
     21 export DATA_PATH=$WORK/data
     22 export TRANSFORMERS_CACHE=$HOME/.cache/huggingface/transformers
     23 export TRANSFORMERS_OFFLINE=1
     24 
     25 base_config=("--features_path=features_b7 --use_sbert=True --use_caption=True --use_entities=True --use_visualbert=True --use_clip=True"
     26              "--use_clip=True"
     27              "--features_path=features_b7 --use_clip=True"
     28              "--features_path=features_vgg --use_clip=True"
     29              "--features_path=features_frcnn --use_clip=True"
     30              "--features_path='all' --use_caption=True --use_clip=True"
     31              "--features_path='all' --use_sbert=True --use_entities=True --use_clip=True"
     32              "--features_path='all' --use_visualbert=True --use_clip=True")
     33 
     34 base_name=("all"
     35            "clip"
     36            "b7"
     37            "vgg"
     38            "frcnn"
     39            "caption"
     40            "entities"
     41            "visualbert")
     42 
     43 DIRNAME=generic_features_attention
     44 mkdir $WORK/outputs/$DIRNAME
     45 
     46 iconfig=$(($SLURM_ARRAY_TASK_ID%8))
     47 config=${base_config[$iconfig]}
     48 name=${base_name[$iconfig]}}
     49 
     50 python -m mmsrl.train configs/generic.py --pooling=attention $config \
     51     --patience=26 --batch_per_epoch=1000 \
     52     --cyclic_subsample=None --subsample_labels='interpolate_micro_to_macro' \
     53     --output_val=\"$WORK/outputs/$DIRNAME/val_${name}_${SLURM_ARRAY_TASK_ID}.pkl\" \
     54     --output_test=\"$WORK/outputs/$DIRNAME/test_${name}_${SLURM_ARRAY_TASK_ID}.pkl\"