initial commit

Author: YuanGongND

.gitignore

@@ -3,6 +3,7 @@
 *.pyc
 *.DS_Store
 pretrained_models/ensemble/*
+pretrained_models/load_pretrained_model_internal.py
 pretrained_models/legal_pretrained_mdl/*
 src/convert_model.py
 src/convert_model2.py

README.md

@@ -131,10 +131,10 @@ cd ast/egs/audioset
 (local user) ./run.sh
 ```  
 You should get a model achieves `0.448 mAP` (without weight averaging) and `0.459` (with weight averaging). This is the best **single** model reported in the paper. 
-The result of each epoch is saved in `ast/egs/audioset/exp/yourexpname/result.csv` in format `[mAP, mAUC, average_precision, average_recall, d_prime, train_loss, valid_loss, cum_mAP, cum_mAUC, lr]`
-, where `cum_` results are the checkpoint ensemble results (i.e., averaging the prediction of checkpoint models of each epoch, please check our [PSLA paper](https://arxiv.org/abs/2102.01243) for details).
+The result of each epoch is saved in `ast/egs/audioset/exp/yourexpname/result.csv` in format `[mAP, mAUC, precision, recall, d_prime, train_loss, valid_loss, cum_mAP, cum_mAUC, lr]`
+, where `cum_` results are the checkpoint ensemble results (i.e., averaging the prediction of checkpoint models of each epoch, please check our [PSLA paper](https://arxiv.org/abs/2102.01243) for details). The result of weighted averaged model is saved in `wa_result.csv` in format `[mAP, AUC, precision, recall, d-prime]`. We attached our log file in `ast/egs/audioset/test-full-f10-t10-pTrue-b12-lr1e-5/`, the model achieves `0.459` mAP.
 
-In order to reproduce ensembe results of `0.475 mAP` and `0.485 mAP`, please train 3 models use the same setting (i.e., repeat above three times) and train 6 models with different `tstride` and `fstride`, and average the output of the models. Please refer to `ast/egs/audioset/ensemble.py`. We attached our ensemble log in `/ast/egs/audioset/exp/ensemble-s.log` and `ensemble-m.log`.
+In order to reproduce ensembe results of `0.475 mAP` and `0.485 mAP`, please train 3 models use the same setting (i.e., repeat above three times) and train 6 models with different `tstride` and `fstride`, and average the output of the models. Please refer to `ast/egs/audioset/ensemble.py`. We attached our ensemble log in `/ast/egs/audioset/exp/ensemble-s.log` and `ensemble-m.log`. You can use our pretrained models (see below) to test ensemble result.
 
 ## Pretrained Models
 We provide full AudioSet pretrained models.

egs/audioset/run.sh

@@ -1,8 +1,8 @@
 #!/bin/bash
-##SBATCH -p sm
-##SBATCH -x sls-sm-1,sls-2080-[1,3],sls-1080-3,sls-sm-5
-#SBATCH -p gpu
-#SBATCH -x sls-titan-[0-2]
+#SBATCH -p sm
+#SBATCH -x sls-sm-1,sls-2080-[1,3],sls-1080-3,sls-sm-5
+##SBATCH -p gpu
+##SBATCH -x sls-titan-[0-2]
 #SBATCH --gres=gpu:4
 #SBATCH -c 4
 #SBATCH -n 1
@@ -41,7 +41,7 @@ mixup=0.5
 fstride=10
 tstride=10
 batch_size=12
-exp_dir=./exp/test-${set}-f$fstride-t$tstride-p$imagenetpretrain-b$batch_size-lr${lr}
+exp_dir=./exp/test-${set}-f$fstride-t$tstride-p$imagenetpretrain-b$batch_size-lr${lr}-demo
 if [ -d $exp_dir ]; then
   echo 'exp exist'
   exit