Format with isort

bnn_models/__init__.py

@@ -1,10 +1,11 @@
 from copy import deepcopy
 
+from sacred import Ingredient
+import torch.nn as nn
+
 from bnn_models.lenet import BLeNet
 from bnn_models.resnet import BClassifier
 from datasets import datasets
-from sacred import Ingredient
-import torch.nn as nn
 from utils import load_config_from_checkpoint
 
 bnn_models = Ingredient("bnn_model", ingredients=(datasets,))

bnn_models/lenet.py

@@ -1,10 +1,11 @@
 from typing import Tuple
 
 import bnn
-from cls_models.base import BaseClassifier
 import torch
 import torch.nn as nn
 
+from cls_models.base import BaseClassifier
+
 
 class BLeNet(BaseClassifier):
     def __init__(

bnn_models/resnet.py

@@ -1,9 +1,10 @@
 from math import ceil, floor, log2
 
 import bnn
-from cls_models.base import BaseClassifier
 import torch
 import torch.nn as nn
+
+from cls_models.base import BaseClassifier
 from utils import init_weights
 
 N_FEATUREMAPS = 32

cae_models/__init__.py

@@ -1,6 +1,7 @@
-from datasets import datasets
 from sacred import Ingredient
 import torch.nn as nn
+
+from datasets import datasets
 from utils import load_config_from_checkpoint
 
 from .medium import MediumCAE

cae_models/base.py

@@ -7,6 +7,7 @@
 import torch.nn.functional as tf
 from torch.optim import Adam
 from torch.optim.lr_scheduler import MultiStepLR
+
 from utils import save_sample_images
 
 

cae_models/resnet.py

@@ -9,6 +9,7 @@
 import torch
 from torch import Tensor
 import torch.nn as nn
+
 from utils import init_weights
 
 from .base import BaseCAE

cls_models/__init__.py

@@ -1,6 +1,8 @@
 from copy import deepcopy
 import re
 
+from sacred import Ingredient
+
 from bnn_models.lenet import BLeNet
 from bnn_models.resnet import BClassifier
 from cls_models.lenet import LeNet
@@ -9,7 +11,6 @@
 from cls_models.small import SmallClassifier
 from cls_models.toy import ToyClassifier
 from datasets import datasets
-from sacred import Ingredient
 from utils import load_config_from_checkpoint
 
 from .base import BaseClassifier

cls_models/base.py

@@ -8,6 +8,7 @@
 from torch import Tensor, optim
 import torch.nn.functional as tf
 from torch.utils.data import DataLoader
+
 from utils import entropy
 
 from .utils import set_model_to_mode

cls_models/resnet.py

@@ -1,10 +1,11 @@
 from math import floor, log2
 
-from gan_models.resnet import ResidualBlock
 import torch.nn as nn
 from torch.nn.modules.batchnorm import _BatchNorm
 from torch.nn.modules.conv import _ConvNd
 
+from gan_models.resnet import ResidualBlock
+
 from .base import BaseClassifier
 
 N_FEATUREMAPS = 32

cls_models/toy.py

@@ -1,6 +1,7 @@
-from datasets.toy import ToyDataset
 import torch.nn as nn
 
+from datasets.toy import ToyDataset
+
 from .base import BaseClassifier
 
 

confident_classifier.py

@@ -2,17 +2,18 @@
 import logging
 from typing import Any, Dict
 
-from cls_models import set_model_to_mode
-from cls_models.base import BaseClassifier
-from datasets import load_data
-from eval_ood_detection import eval_classifier
 from pytorch_lightning import LightningModule
 from pytorch_lightning.utilities.types import STEP_OUTPUT
 import torch
 from torch import optim
 import torch.nn as nn
 import torch.nn.functional as tf
 
+from cls_models import set_model_to_mode
+from cls_models.base import BaseClassifier
+from datasets import load_data
+from eval_ood_detection import eval_classifier
+
 
 class ConfidentClassifier(LightningModule):
     def __init__(

cvae_models/__init__.py

@@ -1,8 +1,9 @@
+from sacred import Ingredient
+import torch.nn as nn
+
 from cvae_models.medium import MediumCVAE
 from cvae_models.small import SmallCVAE
 from datasets import datasets
-from sacred import Ingredient
-import torch.nn as nn
 from utils import load_config_from_checkpoint
 
 cvae_models = Ingredient("cvae_model", ingredients=(datasets,))

datasets/__init__.py

@@ -3,6 +3,14 @@
 import logging
 from typing import Dict, Optional, Tuple
 
+import numpy as np
+from sacred import Ingredient
+import torch
+from torch.utils.data import Dataset, WeightedRandomSampler
+import torchvision.transforms as trans
+import torchvision.transforms.functional as ftrans
+from typing_extensions import TypedDict
+
 from datasets.celeba import CelebA
 from datasets.cifar10 import CIFAR10
 from datasets.cifar100 import CIFAR100
@@ -18,13 +26,6 @@
 from datasets.svhn import SVHN
 from datasets.tinyimagenet import TinyImageNet
 from datasets.toy import ToyDataset, ToyDataset2, ToyDataset3, ToyDataset4, ToyDataset5
-import numpy as np
-from sacred import Ingredient
-import torch
-from torch.utils.data import Dataset, WeightedRandomSampler
-import torchvision.transforms as trans
-import torchvision.transforms.functional as ftrans
-from typing_extensions import TypedDict
 from utils import IncompatibleRange, get_range
 
 datasets = Ingredient("dataset")

datasets/clawa2.py

@@ -1,11 +1,12 @@
 import logging
 from typing import Callable, Optional
 
-from datasets.datahandlers.cl import AwA2
 import numpy as np
 import torch
 from torch.utils.data import Dataset, random_split
 
+from datasets.datahandlers.cl import AwA2
+
 EVAL_RATIO = 0.2
 
 

datasets/datahandlers/cl/awa2.py

@@ -1,13 +1,14 @@
 # PyTorch Dataloader based on 'https://github.com/dfan/awa2-zero-shot-learning'
 
-import numpy as np
-import torch
+import logging
 import os
-from os.path import exists, join, splitext, isfile
+from os.path import exists, isfile, join, splitext
+from typing import Callable, Optional, Sequence, Union
+
 from PIL import Image
+import numpy as np
+import torch
 from torch.utils.data import Dataset
-import logging
-from typing import Callable, Optional, Union, Sequence
 
 
 class AwA2(Dataset):

datasets/datahandlers/cl/cub.py

@@ -26,8 +26,8 @@ def __init__(
         Args:
             root: Root path of the dataset.
             split: Split to use. Valid options: ('train', 'test', 'all')
-            minimum_attribute_certainty: Minimum certainty of the annotated attribute as provided by the human
-                annotator. Valid options: (1, 2, 3, 4)
+            minimum_attribute_certainty: Minimum certainty of the annotated attribute
+                as provided by the human annotator. Valid options: (1, 2, 3, 4)
             transform: Image transforms.
             target_transform: Target transforms.
             target_type: Target types to return. Valid options: ('attr', 'class')

datasets/tinyimagenet.py

@@ -1,10 +1,11 @@
 import logging
 from typing import Callable, Optional
 
-from datasets.datahandlers.cl import TinyImageNet as myTinyImageNet
 import torch
 from torch.utils.data import Dataset, random_split
 
+from datasets.datahandlers.cl import TinyImageNet as myTinyImageNet
+
 EVAL_RATIO = 0.2
 
 

datasets/toy.py

@@ -279,7 +279,7 @@ def __len__(self) -> int:
 
 
 if __name__ == "__main__":
-    import matplotlib
+    import matplotlib  # noqa: F401
 
     # matplotlib.use("Qt5Agg")
     import matplotlib.pyplot as plt

eval_ood_detection.py

@@ -3,9 +3,6 @@
 import os
 from os.path import abspath, exists, expanduser, join
 
-from cls_models import cls_models, load_cls_model, set_model_to_mode
-from cls_models.base import BaseClassifier
-from datasets import datasets, load_data
 from eval.binary import aupr, auroc, ece, fprxtpr
 from logging_utils import log_config
 import numpy as np
@@ -15,6 +12,10 @@
 from tabulate import tabulate
 import torch
 from torch.utils.data import ConcatDataset, DataLoader
+
+from cls_models import cls_models, load_cls_model, set_model_to_mode
+from cls_models.base import BaseClassifier
+from datasets import datasets, load_data
 from utils import extract_exp_id_from_path, format_int_list, get_range, init_experiment
 
 ex = Experiment("evaluate OOD detection", ingredients=[datasets, cls_models])

eval_ood_detection_deep_ensembles.py

@@ -1,8 +1,6 @@
 import os
 from os.path import abspath, exists, expanduser, join
 
-from cls_models import cls_models, load_cls_model
-from datasets import datasets, load_data
 from eval.binary import aupr, auroc, ece, fprxtpr
 from logging_utils import log_config
 import numpy as np
@@ -12,6 +10,9 @@
 from tabulate import tabulate
 import torch
 from torch.utils.data import ConcatDataset, DataLoader
+
+from cls_models import cls_models, load_cls_model
+from datasets import datasets, load_data
 from utils import (
     entropy,
     extract_exp_id_from_path,

gan_models/__init__.py

@@ -1,9 +1,10 @@
 from copy import deepcopy
 from typing import Tuple
 
-from datasets import datasets
 from sacred import Ingredient
 import torch.nn as nn
+
+from datasets import datasets
 from utils import load_config_from_checkpoint
 
 from .dcgan import Discriminator as DCGANDiscriminator

gan_models/dcgan.py

@@ -2,6 +2,7 @@
 import torch
 from torch.distributions.normal import Normal
 import torch.nn as nn
+
 from utils import init_weights
 
 LATENT_DIM = 128

gan_models/resnet.py

@@ -9,6 +9,7 @@
 import torch
 from torch.distributions.uniform import Uniform
 import torch.nn as nn
+
 from utils import init_weights
 
 LATENT_DIM = 128

gan_models/small.py

@@ -2,6 +2,7 @@
 import torch
 from torch.distributions.uniform import Uniform
 import torch.nn as nn
+
 from utils import init_weights
 
 LATENT_DIM = 128

gan_models/toy.py

@@ -1,8 +1,9 @@
-from datasets.toy import ToyDataset
 from pytorch_lightning import LightningModule
 import torch
 from torch.distributions.uniform import Uniform
 import torch.nn as nn
+
+from datasets.toy import ToyDataset
 from utils import init_gan_weights
 
 

gen.py

@@ -1,16 +1,17 @@
 from copy import deepcopy
 from typing import Any, Dict
 
-from cls_models.base import BaseClassifier, set_model_to_mode
-from datasets import load_data
-from eval_ood_detection import eval_classifier
 from pytorch_lightning import LightningModule, Trainer
 from pytorch_lightning.accelerators.registry import AcceleratorRegistry
 import torch
 from torch import Tensor, optim
 import torch.nn as nn
 import torch.nn.functional as tf
 
+from cls_models.base import BaseClassifier, set_model_to_mode
+from datasets import load_data
+from eval_ood_detection import eval_classifier
+
 
 class GEN(LightningModule):
     def __init__(

meta_models/__init__.py

@@ -1,5 +1,6 @@
 from sacred import Ingredient
 import torch.nn as nn
+
 from utils import load_config_from_checkpoint
 
 from .fc import MetaClassifier

meta_models/fc.py

@@ -1,4 +1,5 @@
 import torch.nn as nn
+
 from utils import init_weights
 
 

train_cae.py

@@ -11,10 +11,11 @@
 
 matplotlib.use("Agg")
 
-from cae_models import cae_models, load_cae_model
-from datasets import datasets, load_data
 from logging_utils import log_config
 from logging_utils.lightning_sacred import SacredLogger
+
+from cae_models import cae_models, load_cae_model
+from datasets import datasets, load_data
 from utils import TimeEstimator, get_experiment_folder, init_experiment
 
 ex = Experiment("train_cae", ingredients=[datasets, cae_models])

train_classifier.py

@@ -2,15 +2,16 @@
 from os.path import exists
 import shutil
 
-from cls_models import cls_models, load_cls_model
-from datasets import datasets, load_data
 from logging_utils import log_config
 from logging_utils.lightning_sacred import SacredLogger
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
 from sacred import Experiment
 import torch
 from torch.utils.data import DataLoader
+
+from cls_models import cls_models, load_cls_model
+from datasets import datasets, load_data
 from utils import TimeEstimator, get_experiment_folder, init_experiment
 
 ex = Experiment("train_classifier", ingredients=(cls_models, datasets))