resolve the sweep issue

Author: mohamedr002

main.py

@@ -44,7 +44,7 @@
 # 1- Change the naming of the functions ---> ( Done)
 # 2- Change the algorithms following DCORAL --> (Done)
 # 3- Keep one trainer for both train and test -->(Done)
-# 4- Creat the new joint loader that consider the all possible batches --> Done
-# 4- Add the best hparams 
-# 5- Implement Lower/Upper Bound Approach
-# 4- Add pretrain based methods (ADDA, MCD)
+# 4- Create the new joint loader that consider the all possible batches --> Done
+# 5- Implement Lower/Upper Bound Approach --> Done
+# 6- Add the best hparams --> Done
+# 7- Add pretrain based methods (ADDA, MCD, MDD)

main_sweep.py

@@ -11,15 +11,16 @@
                         help='DANN, Deep_Coral, WDGRL, MMDA, VADA, DIRT, CDAN, ADDA, HoMM, CoDATS')
 
     # ========= Select the DATASET ==============
-    parser.add_argument('--data_path', default=r'../data', type=str, help='Path containing datase2t')
+    parser.add_argument('--data_path', default=r'../ADATIME_data', type=str, help='Path containing datase2t')
     parser.add_argument('--dataset', default='HAR', type=str, help='Dataset of choice: (WISDM - EEG - HAR - HHAR_SA)')
 
     # ========= Select the BACKBONE ==============
     parser.add_argument('--backbone', default='CNN', type=str, help='Backbone of choice: (CNN - RESNET18 - TCN)')
 
     # ========= Experiment settings ===============
-    parser.add_argument('--num_runs', default=3, type=int, help='Number of consecutive run with different seeds')
+    parser.add_argument('--num_runs', default=1, type=int, help='Number of consecutive run with different seeds')
     parser.add_argument('--device', default="cuda", type=str, help='cpu or cuda')
+    parser.add_argument('--exp_name',     default='sweep_EXP1',         type=str, help='experiment name')
 
     # ======== sweep settings =====================
     parser.add_argument('--num_sweeps', default=1, type=str, help='Number of sweep runs')

trainers/abstract_trainer.py

@@ -36,7 +36,7 @@ def __init__(self, args):
         # Exp Description
         self.experiment_description = args.dataset 
         self.run_description = f"{args.da_method}_{args.exp_name}"
-
+        
         # paths
         self.home_path =  os.getcwd() #os.path.dirname(os.getcwd())
         self.save_dir = args.save_dir

trainers/sweep.py

@@ -20,7 +20,7 @@
 from models.models import get_backbone_class
 from utils import AverageMeter
 
-from abstract_trainer import AbstractTrainer
+from trainers.abstract_trainer import AbstractTrainer
 
 warnings.filterwarnings("ignore", category=sklearn.exceptions.UndefinedMetricWarning)
 parser = argparse.ArgumentParser()