From a4e2436b065f5471ee50e0378f2632f59d21afbe Mon Sep 17 00:00:00 2001
From: Mustafa Chasmai <mustchasmai@gmail.com>
Date: Wed, 2 Jun 2021 08:39:00 +0530
Subject: [PATCH] configs | cleanup - remove/rename redundant code

---
 README.md                                     |   4 +-
 .../{test_epic_RGM.yml => test_epic_RGM.yaml} |   0
 ...train_epic_RGB.yml => train_epic_RGB.yaml} |   0
 main_lightning.py => main.py                  |   0
 models_lightning.py => model.py               |   0
 .../script_test_sourceonly.sh                 |   0
 script_test_ta3n_lightning.sh                 | 182 ------------------
 test_models_lightning.py => test.py           |   0
 utils/model_init.py                           |   2 +-
 9 files changed, 3 insertions(+), 185 deletions(-)
 rename configs/{test_epic_RGM.yml => test_epic_RGM.yaml} (100%)
 rename configs/{train_epic_RGB.yml => train_epic_RGB.yaml} (100%)
 rename main_lightning.py => main.py (100%)
 rename models_lightning.py => model.py (100%)
 rename script_test_sourceonly.sh => original/script_test_sourceonly.sh (100%)
 delete mode 100755 script_test_ta3n_lightning.sh
 rename test_models_lightning.py => test.py (100%)

diff --git a/README.md b/README.md
index cf96138..ab64779 100644
--- a/README.md
+++ b/README.md
@@ -11,8 +11,8 @@ Notebook added to show demo and verify working correctly:
 
 Instructions to run:
 ~~~
-python3 main_lightning.py --cfg configs/train_epic_RGB.yml
-python3 test_models_lightning.py --cfg configs/test_epic_RGB.yml
+python3 main.py --cfg configs/train_epic_RGB.yaml
+python3 test.py --cfg configs/test_epic_RGB.yaml --ckpt <model.pth.tar>
 ~~~
 
 
diff --git a/configs/test_epic_RGM.yml b/configs/test_epic_RGM.yaml
similarity index 100%
rename from configs/test_epic_RGM.yml
rename to configs/test_epic_RGM.yaml
diff --git a/configs/train_epic_RGB.yml b/configs/train_epic_RGB.yaml
similarity index 100%
rename from configs/train_epic_RGB.yml
rename to configs/train_epic_RGB.yaml
diff --git a/main_lightning.py b/main.py
similarity index 100%
rename from main_lightning.py
rename to main.py
diff --git a/models_lightning.py b/model.py
similarity index 100%
rename from models_lightning.py
rename to model.py
diff --git a/script_test_sourceonly.sh b/original/script_test_sourceonly.sh
similarity index 100%
rename from script_test_sourceonly.sh
rename to original/script_test_sourceonly.sh
diff --git a/script_test_ta3n_lightning.sh b/script_test_ta3n_lightning.sh
deleted file mode 100755
index caa59dd..0000000
--- a/script_test_ta3n_lightning.sh
+++ /dev/null
@@ -1,182 +0,0 @@
-#!/bin/bash
-
-#====== parameters ======#
-dataset="epic" # hmdb_ucf | hmdb_ucf_small | ucf_olympic
-num_class='97,300'
-training=true # true | false
-testing=false # true | false
-modality=RGB
-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=uSv #uSv # Sv # uSv # none | Sv | uSv
-share_params=Y # Y | N
-pred_normalize="N"
-weighted_class_loss_DA="N"
-weighted_class_loss="N"
-
-tovalidate=false
-
-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
-	if ($tovalidate)
-	then
-		val_source_data=$path_data_root"/source_val"
-		val_target_data=$path_data_root"/target_val"
-	else
-		val_source_data="none"
-		val_target_data="none"
-	fi
-
-	train_source_list=$path_labels_root'/EPIC_100_uda_source_train.pkl' # '/domain_adaptation_source_train_pre-release_v3.pkl'
-	train_target_list=$path_labels_root'/EPIC_100_uda_target_train_timestamps.pkl' # '/domain_adaptation_target_train_pre-release_v6.pkl'
-	if ($tovalidate)
-	then
-		val_source_list=$path_labels_root'/EPIC_100_uda_source_val.pkl' # '/domain_adaptation_source_train_pre-release_v3.pkl'
-		val_target_list=$path_labels_root'/EPIC_100_uda_target_val.pkl' # '/domain_adaptation_target_train_pre-release_v6.pkl'
-	else
-		val_source_list="none"
-		val_target_list="none"
-	fi
-	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_lightning.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 \
-	--val_source_data $val_source_data --val_target_data $val_target_data \
-	--val_source_list $val_source_list --val_target_list $val_target_list \
-	--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 $bS -j 2 -ef 1 -pf 50 -sf 50 --copy_list N N --save_model \
-
-fi
-
-if ($testing)
-then
-	model=checkpoint # checkpoint | model_best
-	echo $model
-
-	# testing on the test set
-	echo 'testing on the test set'
-	python test_models_lightning.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 2 --bS 512 --top 1 3 5 --add_fc 1 --fc_dim $fc_dim --baseline_type $baseline_type --frame_aggregation $frame_aggregation
-fi
-
-# ----------------------------------------------------------------------------------
-exit 0
diff --git a/test_models_lightning.py b/test.py
similarity index 100%
rename from test_models_lightning.py
rename to test.py
diff --git a/utils/model_init.py b/utils/model_init.py
index d62b5e7..d18465d 100644
--- a/utils/model_init.py
+++ b/utils/model_init.py
@@ -1,6 +1,6 @@
 import os
 
-from models_lightning import VideoModel
+from model import VideoModel
 
 import torch
 import torch.backends.cudnn as cudnn