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script_test_sourceonly.sh
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script_test_sourceonly.sh
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#!/bin/bash
#====== parameters ======#
dataset="epic" # hmdb_ucf | hmdb_ucf_small | ucf_olympic
num_class='97,300'
training=false # true | false
testing=true # true | false
modality=ALL
frame_type=feature # frame | feature
num_segments=5 # sample frame # of each video for training
test_segments=5
baseline_type=video
frame_aggregation=trn-m # method to integrate the frame-level features (avgpool | trn | trn-m | rnn | temconv)
add_fc=1
fc_dim=512
arch=TBN # resnet50
use_target=none #uSv # Sv # uSv # none | Sv | uSv
share_params=Y # Y | N
pred_normalize="N"
weighted_class_loss_DA="N"
weighted_class_loss="N"
if [ "$use_target" == "none" ]
then
exp_DA_name=baseline
else
exp_DA_name=DA
fi
#====== select dataset ======#
path_data_root="data/" # depend on users
path_labels_root="annotations" #"/jmain01/home/JAD026/dxd01/jjm50-dxd01/DA_Features/train_test/train/" # depend on users
path_exp_root="model/action-model/" # depend on users
train_metric="all"
if [ "$dataset" == "epic" ]
then
dataset_source="source_train" # depend on users
dataset_target="target_train" # depend on users
dataset_val="target_test" # _noun" # depend on users
num_source=16115 # number of training data (source)
num_target=26115 # number of training data (target)
path_data_source=$path_data_root'/'$dataset_source
path_data_target=$path_data_root'/'$dataset_target
path_data_val=$path_data_root'/'$dataset_val
train_source_list=$path_labels_root'/EPIC_100_uda_source_train.pkl' # '/domain_adaptation_source_train_pre-release_v3.pkl'
train_target_list=$path_labels_root'/EPIC_100_uda_target_train_timestamps.pkl' # '/domain_adaptation_target_train_pre-release_v6.pkl'
val_list=$path_labels_root'/EPIC_100_uda_target_test_timestamps.pkl' # '/domain_adaptation_target_test_pre-release_v3.pkl' # 'domain_adaptation_validation_pre-release_v3.pkl'
path_exp=$path_exp_root'Testexp'
fi
pretrained=none
#====== parameters for algorithms ======#
# parameters for DA approaches
dis_DA=none # none | DAN | JAN
alpha=0 # depend on users
adv_pos_0=Y # Y | N (discriminator for relation features)
adv_DA=RevGrad # none | RevGrad
beta_0=0.75 # 0.75 #0.75 # U->H: 0.75 | H->U: 1
beta_1=0.75 #0.75 # U->H: 0.75 | H->U: 0.75
beta_2=0.5 #0.5 # U->H: 0.5 | H->U: 0.5
use_attn=TransAttn # none | TransAttn | general
n_attn=1
use_attn_frame=none # none | TransAttn | general
use_bn=none # none | AdaBN | AutoDIAL
add_loss_DA=attentive_entropy # none | target_entropy | attentive_entropy
gamma=0.003 # U->H: 0.003 | H->U: 0.3
ens_DA=none # none | MCD
mu=0
# parameters for architectures
bS=128 # batch size
bS_2=$((bS * num_target / num_source ))
echo '('$bS', '$bS_2')'
lr=3e-3
optimizer=SGD
if [ "$use_target" == "none" ]
then
dis_DA=none
alpha=0
adv_pos_0=N
adv_DA=none
beta_0=0
beta_1=0
beta_2=0
use_attn=none
use_attn_frame=none
use_bn=none
add_loss_DA=none
gamma=0
ens_DA=none
mu=0
j=0
exp_path=$path_exp'-'$optimizer'-share_params_'$share_params'/'$dataset'-'$num_segments'seg_'$j'/'
else
exp_path=$path_exp'-'$optimizer'-share_params_'$share_params'-lr_'$lr'-bS_'$bS'_'$bS_2'/'$dataset'-'$num_segments'seg-disDA_'$dis_DA'-alpha_'$alpha'-advDA_'$adv_DA'-beta_'$beta_0'_'$beta_1'_'$beta_2'-useBN_'$use_bn'-addlossDA_'$add_loss_DA'-gamma_'$gamma'-ensDA_'$ens_DA'-mu_'$mu'-useAttn_'$use_attn'-n_attn_'$n_attn'/'
fi
echo 'exp_path: '$exp_path
#====== select mode ======#
if ($training)
then
val_segments=$test_segments
# parameters for optimization
lr_decay=10
lr_adaptive=none # dann # none | loss | dann
lr_steps_1=10
lr_steps_2=20
epochs=30
gd=20
#------ main command ------#
echo $modality
python main.py $num_class $modality $train_source_list $train_target_list $val_list $path_data_val $path_data_source $path_data_target --exp_path $exp_path \
--train_metric $train_metric --dann_warmup --arch $arch --pretrained $pretrained --baseline_type $baseline_type --frame_aggregation $frame_aggregation \
--num_segments $num_segments --val_segments $val_segments --add_fc $add_fc --fc_dim $fc_dim --dropout_i 0.5 --dropout_v 0.5 \
--use_target $use_target --share_params $share_params \
--dis_DA $dis_DA --alpha $alpha --place_dis N Y N \
--adv_DA $adv_DA --beta $beta_0 $beta_1 $beta_2 --place_adv $adv_pos_0 Y Y \
--use_bn $use_bn --add_loss_DA $add_loss_DA --gamma $gamma \
--ens_DA $ens_DA --mu $mu \
--use_attn $use_attn --n_attn $n_attn --use_attn_frame $use_attn_frame \
--pred_normalize $pred_normalize --weighted_class_loss_DA $weighted_class_loss_DA --weighted_class_loss $weighted_class_loss \
--gd $gd --lr $lr --lr_decay $lr_decay --lr_adaptive $lr_adaptive --lr_steps $lr_steps_1 $lr_steps_2 --epochs $epochs --optimizer $optimizer \
--n_rnn 1 --rnn_cell LSTM --n_directions 1 --n_ts 5 --tensorboard \
-b $bS $bS_2 $bS -j 4 -ef 1 -pf 50 -sf 50 --copy_list N N --save_model \
fi
if ($testing)
then
model=checkpoint # checkpoint | model_best
echo $model
# testing on the validation set
echo 'testing on the test set'
python test_models.py $num_class $modality $val_list \
$exp_path$modality'/'$model'.pth.tar' $path_data_val 'test.json'\
--arch $arch --test_segments $test_segments \
--save_scores $exp_path$modality'/scores_'$dataset_target'-'$model'-'$test_segments'seg' --save_confusion $exp_path$modality'/confusion_matrix_'$dataset_target'-'$model'-'$test_segments'seg' \
--n_rnn 1 --rnn_cell LSTM --n_directions 1 --n_ts 5 \
--use_attn $use_attn --n_attn $n_attn --use_attn_frame $use_attn_frame --use_bn $use_bn --share_params $share_params \
-j 4 --bS 512 --top 1 3 5 --add_fc 1 --fc_dim $fc_dim --baseline_type $baseline_type --frame_aggregation $frame_aggregation
fi
# ----------------------------------------------------------------------------------
exit 0