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train_tmab.sh
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train_tmab.sh
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#!/bin/bash
# Normally, bash shell cannot support floating point arthematic, thus, here we use `bc` package
GPU_ID=7
task_type=E2E
CL=ADAPTER
bottleneck_size=900
lr=3e-3 # 6.25e-3
n_epochs=50 # 10
train_batch_size=10
gradient_accumulation_steps=8
seed=2
dataset_list=TM19,TM20 # SGD,TM19,TM20,MWOZ
retrain_epochs=3
retrain_lr_factor=0.1
retrain_gradient_accumulation_steps=1
#direction=1
meta_query_step=1
episodic_mem_size=200
sample_domain=2000
# train_adapter_expand train_wo_pytorch_lightning
CUDA_VISIBLE_DEVICES=$GPU_ID python ./train_adapter_expand.py \
--episodic_mem_size $episodic_mem_size \
--task_type $task_type \
--CL $CL \
--bottleneck_size $bottleneck_size \
--lr $lr \
--n_epochs $n_epochs \
--train_batch_size $train_batch_size \
--gradient_accumulation_steps $gradient_accumulation_steps \
--seed $seed \
--dataset_list $dataset_list \
--test_every_step \
--single \
-mi \
-e \
-nm 3 \
\
-aug 3 \
-sd $sample_domain \
-mqs $meta_query_step \
\
--mask || exit
#CUDA_VISIBLE_DEVICES=$GPU_ID python ./train_adapter_expand.py \
# --episodic_mem_size $episodic_mem_size \
# --task_type $task_type \
# --CL $CL \
# --bottleneck_size $bottleneck_size \
# --lr $lr \
# --n_epochs $n_epochs \
# --train_batch_size $train_batch_size \
# --gradient_accumulation_steps $gradient_accumulation_steps \
# --seed $seed \
# --dataset_list $dataset_list \
# --test_every_step \
# --single \
# -mi \
# --retrain \
# --retrain_lr_factor $retrain_lr_factor \
# --retrain_epochs $retrain_epochs \
# --retrain_gradient_accumulation_steps $retrain_gradient_accumulation_steps\
# -k1 \
# \
# -b \
# \
# -sd $sample_domain \
# -mqs $meta_query_step \
# --meta \
# -sm \
# -na 3 \
# \
# --mask || exit
#-u
#-aug 3
# -t 5
#-nm 3
# -md
# -l -v
# -de $direction
# -f -mc -task 1
# --test_every_step -u
# --meta --retrain -t1 -cm
# --retrain_lr_factor 1
#python ./scorer.py \
#mode=test
# scorer.py train_wo_pytorch_lightning
# --mode $mode
# CUDA_VISIBLE_DEVICES=$GPU_ID python ./scorer.py \
# --mode test\
# --episodic_mem_size $episodic_mem_size \
# --task_type $task_type \
# --CL $CL \
# --bottleneck_size $bottleneck_size \
# --lr $lr \
# --n_epochs $n_epochs \
# --train_batch_size $train_batch_size \
# --gradient_accumulation_steps $gradient_accumulation_steps \
# --seed $seed \
# --dataset_list $dataset_list \
# --test_every_step \
# --single \
# -mi \
# --retrain \
# --retrain_lr_factor $retrain_lr_factor \
# --retrain_epochs $retrain_epochs \
# --retrain_gradient_accumulation_steps $retrain_gradient_accumulation_steps\
# -k1 \
# -u \
# -b \
# -aug 3 \
# -sd $sample_domain \
# -mqs $meta_query_step \
# --meta \
# -sm \
# -na 3 \
# --mask || exit
# CUDA_VISIBLE_DEVICES=$GPU_ID python ./test_fwt.py \
# --episodic_mem_size $episodic_mem_size \
# --task_type $task_type \
# --CL $CL \
# --bottleneck_size $bottleneck_size \
# --lr $lr \
# --n_epochs $n_epochs \
# --train_batch_size $train_batch_size \
# --gradient_accumulation_steps $gradient_accumulation_steps \
# --seed $seed \
# --dataset_list $dataset_list \
# --test_every_step \
# --single \
# -mi \
# --retrain \
# --retrain_lr_factor $retrain_lr_factor \
# --retrain_epochs $retrain_epochs \
# --retrain_gradient_accumulation_steps $retrain_gradient_accumulation_steps\
# -k1 \
# -u \
# -b \
# -aug 3 \
# -sd $sample_domain \
# -mqs $meta_query_step \
# --meta \
# -sm \
# -na 3 \
# --mode test \
# -fwt \
# -notil \
# --mask || exit