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evaluate_trained_models.sh
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evaluate_trained_models.sh
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gpu_id=0
nb_worker=24
save_dir = [SET_YOUR_PATH, ex)/result/DCASENET/]
db_tag = [SET_YOUR_PATH, ex)/data/DCASE2019_Task2/]
db_asc = [SET_YOUR_PATH, ex)/data/DCASE2020_Task1A/]
db_sed = [SET_YOUR_PATH, ex)/data/DCASE2020_Task3/]
#########
# EvalASC
#########
CUDA_VISIBLE_DEVICES=${gpu_id} python main.py -verbose 0 \
-nb_iter_per_epoch 500 \
-task ASC\
-name EvalASC \
-model_scp DcaseNet \
-model_name get_DcaseNet_v3 \
-nb_worker ${nb_worker} \
-wd 0.0000\
-epoch 160 \
-bs_ASC 32 \
-bs_SED 24 \
-bs_TAG 32 \
-optimizer adam \
-amsgrad 0 \
-do_lr_decay 1 \
-lr_decay cosine \
-lrdec_t0 80 \
-do_mixup 1 \
-mixup_start 5 \
-loss_weight_ASC 1 \
-loss_weight_SED 1 \
-loss_weight_TAG 1 \
-save_dir ${save_dir} \
-DB_TAG ${db_tag} \
-DB_ASC ${db_asc} \
-DB_SED ${db_sed}
#########
# EvalTAG
#########
CUDA_VISIBLE_DEVICES=${gpu_id} python main.py -verbose 0 \
-nb_iter_per_epoch 500 \
-task TAG\
-name EvalTAG \
-model_scp DcaseNet \
-model_name get_DcaseNet_v3 \
-nb_worker ${nb_worker} \
-wd 0.0000\
-epoch 160 \
-bs_ASC 32 \
-bs_SED 24 \
-bs_TAG 32 \
-optimizer adam \
-amsgrad 0 \
-do_lr_decay 1 \
-lr_decay cosine \
-lrdec_t0 80 \
-do_mixup 1 \
-mixup_start 5 \
-loss_weight_ASC 1 \
-loss_weight_SED 1 \
-loss_weight_TAG 1 \
-save_dir ${save_dir} \
-DB_TAG ${db_tag} \
-DB_ASC ${db_asc} \
-DB_SED ${db_sed}
#########
# EvalSED
#########
CUDA_VISIBLE_DEVICES=${gpu_id} python main.py -verbose 0 \
-nb_iter_per_epoch 500 \
-task SED\
-name EvalSED \
-model_scp DcaseNet \
-model_name get_DcaseNet_v3 \
-nb_worker ${nb_worker} \
-wd 0.0000\
-epoch 160 \
-bs_ASC 32 \
-bs_SED 24 \
-bs_TAG 32 \
-optimizer adam \
-amsgrad 0 \
-do_lr_decay 1 \
-lr_decay cosine \
-lrdec_t0 80 \
-do_mixup 1 \
-mixup_start 5 \
-loss_weight_ASC 1 \
-loss_weight_SED 1 \
-loss_weight_TAG 1 \
-save_dir ${save_dir} \
-DB_TAG ${db_tag} \
-DB_ASC ${db_asc} \
-DB_SED ${db_sed}