Update import path

Author: thucbx99

examples/domain_adaptation/re_identification/baseline.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -18,7 +17,7 @@
 from torch.optim import Adam
 from torch.utils.data import DataLoader
 
-sys.path.append('../../..')
+import utils
 from tllib.vision.models.reid.loss import CrossEntropyLossWithLabelSmooth, SoftTripletLoss
 from tllib.vision.models.reid.identifier import ReIdentifier
 import tllib.vision.datasets.reid as datasets
@@ -32,9 +31,6 @@
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-sys.path.append('.')
-import utils
-
 
 def main(args: argparse.Namespace):
     logger = CompleteLogger(args.log, args.phase)

examples/domain_adaptation/re_identification/baseline_cluster.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -20,7 +19,7 @@
 from torch.utils.data import DataLoader
 from sklearn.cluster import KMeans, DBSCAN
 
-sys.path.append('../../..')
+import utils
 import tllib.vision.datasets.reid as datasets
 from tllib.vision.datasets.reid.convert import convert_to_pytorch_dataset
 from tllib.vision.models.reid.identifier import ReIdentifier
@@ -31,9 +30,6 @@
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_adaptation/re_identification/mmt.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import os.path as osp
 
@@ -19,7 +18,7 @@
 from torch.utils.data import DataLoader
 from sklearn.cluster import KMeans, DBSCAN
 
-sys.path.append('../../..')
+import utils
 import tllib.vision.datasets.reid as datasets
 from tllib.vision.datasets.reid.convert import convert_to_pytorch_dataset
 from tllib.vision.models.reid.identifier import ReIdentifier
@@ -32,9 +31,6 @@
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_generalization/image_classification/coral.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -18,17 +17,14 @@
 from torch.utils.data import DataLoader
 import torch.nn.functional as F
 
-sys.path.append('../../..')
+import utils
 from tllib.alignment.coral import CorrelationAlignmentLoss
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.metric import accuracy
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 from tllib.utils.analysis import tsne, a_distance
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_generalization/image_classification/erm.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -18,16 +17,13 @@
 from torch.utils.data import DataLoader
 import torch.nn.functional as F
 
-sys.path.append('../../..')
+import utils
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.metric import accuracy
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 from tllib.utils.analysis import tsne, a_distance
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_generalization/image_classification/groupdro.py

@@ -6,7 +6,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -19,17 +18,14 @@
 from torch.utils.data import DataLoader
 import torch.nn.functional as F
 
-sys.path.append('../../..')
+import utils
 from tllib.reweight.groupdro import AutomaticUpdateDomainWeightModule
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.metric import accuracy
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 from tllib.utils.analysis import tsne, a_distance
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_generalization/image_classification/irm.py

@@ -6,7 +6,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -20,16 +19,13 @@
 import torch.nn.functional as F
 import torch.autograd as autograd
 
-sys.path.append('../../..')
+import utils
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.metric import accuracy
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 from tllib.utils.analysis import tsne, a_distance
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_generalization/image_classification/mixstyle.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -18,17 +17,14 @@
 from torch.utils.data import DataLoader
 import torch.nn.functional as F
 
-sys.path.append('../../..')
+import utils
 import tllib.normalization.mixstyle.resnet as models
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.metric import accuracy
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 from tllib.utils.analysis import tsne, a_distance
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_generalization/image_classification/mldg.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -19,16 +18,13 @@
 import torch.nn.functional as F
 import higher
 
-sys.path.append('../../..')
+import utils
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.metric import accuracy
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 from tllib.utils.analysis import tsne, a_distance
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_generalization/image_classification/vrex.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -18,16 +17,13 @@
 from torch.utils.data import DataLoader
 import torch.nn.functional as F
 
-sys.path.append('../../..')
+import utils
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.metric import accuracy
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 from tllib.utils.analysis import tsne, a_distance
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_generalization/re_identification/baseline.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -18,7 +17,7 @@
 from torch.optim import Adam
 from torch.utils.data import DataLoader
 
-sys.path.append('../../..')
+import utils
 from tllib.vision.models.reid.loss import CrossEntropyLossWithLabelSmooth, SoftTripletLoss
 from tllib.vision.models.reid.identifier import ReIdentifier
 import tllib.vision.datasets.reid as datasets
@@ -30,9 +29,6 @@
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/domain_generalization/re_identification/mixstyle.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 import os.path as osp
@@ -17,7 +16,7 @@
 from torch.optim import Adam
 from torch.utils.data import DataLoader
 
-sys.path.append('../../..')
+import utils
 from tllib.normalization.mixstyle.sampler import RandomDomainMultiInstanceSampler
 import tllib.normalization.mixstyle.resnet as models
 from tllib.vision.models.reid.identifier import ReIdentifier
@@ -32,9 +31,6 @@
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.logger import CompleteLogger
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/semi_supervised_learning/image_classification/erm.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 
@@ -17,16 +16,13 @@
 from torch.optim.lr_scheduler import LambdaLR
 from torch.utils.data import DataLoader, ConcatDataset
 
-sys.path.append('../../..')
+import utils
 from tllib.vision.transforms import MultipleApply
 from tllib.utils.metric import accuracy
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.logger import CompleteLogger
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/semi_supervised_learning/image_classification/fixmatch.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 
@@ -17,17 +16,14 @@
 from torch.optim.lr_scheduler import LambdaLR
 from torch.utils.data import DataLoader
 
-sys.path.append('../../..')
+import utils
 from tllib.self_training.pseudo_label import ConfidenceRegularizedSelfTrainingLoss
 from tllib.vision.transforms import MultipleApply
 from tllib.utils.metric import accuracy
 from tllib.utils.meter import AverageMeter, ProgressMeter
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.logger import CompleteLogger
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/semi_supervised_learning/image_classification/mean_teacher.py

@@ -5,7 +5,6 @@
 import random
 import time
 import warnings
-import sys
 import argparse
 import shutil
 
@@ -17,7 +16,7 @@
 from torch.optim.lr_scheduler import LambdaLR
 from torch.utils.data import DataLoader
 
-sys.path.append('../../..')
+import utils
 from tllib.self_training.pi_model import sigmoid_warm_up, L2ConsistencyLoss
 from tllib.self_training.mean_teacher import update_bn, EMATeacher
 from tllib.vision.transforms import MultipleApply
@@ -26,9 +25,6 @@
 from tllib.utils.data import ForeverDataIterator
 from tllib.utils.logger import CompleteLogger
 
-sys.path.append('.')
-import utils
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")