fix import sorting

src/augmentation.py

@@ -1,9 +1,8 @@
+import os
 import torch
 from torch import Tensor
 from torch.nn.parameter import Parameter
 from torch.utils.data import Dataset
-
-import os
 from typing import Iterator
 
 from base_experiment import BaseExperiment

src/base_experiment.py

@@ -5,7 +5,8 @@
 from abc import abstractmethod
 from torch import Tensor, optim
 from torch.nn.parameter import Parameter
-from torch.optim.lr_scheduler import ChainedScheduler, LRScheduler, ReduceLROnPlateau
+from torch.optim.lr_scheduler import (ChainedScheduler, LRScheduler,
+                                      ReduceLROnPlateau)
 from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader, Dataset
 from typing import Iterator, Optional

src/classification.py

@@ -1,9 +1,8 @@
+import os
 import torch
 from torch import Tensor
 from torch.nn.parameter import Parameter
 from torch.utils.data import Dataset
-
-import os
 from typing import Iterator, List
 
 from base_experiment import BaseExperiment

src/compiler.py

@@ -1,9 +1,10 @@
 import argparse
 import json
-import youtube_dl
 import os
 import sys
 
+import youtube_dl
+
 parser = argparse.ArgumentParser(description='Youtube dataset compiler')
 parser.add_argument(
     '--input',

src/dataset/__init__.py

@@ -2,7 +2,6 @@
 from torch import Size
 from torch.utils.data import Dataset
 from torch.utils.data.sampler import WeightedRandomSampler
-
 #import nonechucks as nc
 from typing import Optional
 

src/dataset/batch_video.py

@@ -1,6 +1,6 @@
+import os
 import torch
 
-import os
 from decord import VideoLoader, cpu
 
 

src/dataset/cq500.py

@@ -1,8 +1,8 @@
+import os
 import torch
 import torch.utils.data as data
 
 import boto3
-import os
 import pydicom
 
 CQ500_HEADER = 'name,Category,R1:ICH,R1:IPH,R1:IVH,R1:SDH,R1:EDH,R1:SAH,R1:BleedLocation-Left,R1:BleedLocation-Right,R1:ChronicBleed,R1:Fracture,R1:CalvarialFracture,R1:OtherFracture,R1:MassEffect,R1:MidlineShift,R2:ICH,R2:IPH,R2:IVH,R2:SDH,R2:EDH,R2:SAH,R2:BleedLocation-Left,R2:BleedLocation-Right,R2:ChronicBleed,R2:Fracture,R2:CalvarialFracture,R2:OtherFracture,R2:MassEffect,R2:MidlineShift,R3:ICH,R3:IPH,R3:IVH,R3:SDH,R3:EDH,R3:SAH,R3:BleedLocation-Left,R3:BleedLocation-Right,R3:ChronicBleed,R3:Fracture,R3:CalvarialFracture,R3:OtherFracture,R3:MassEffect,R3:MidlineShift\n'

src/dataset/deeplesion.py

@@ -1,13 +1,13 @@
+import os
 import torch
 import torch.utils.data as data
 from torch.nn import functional as F
+from typing import List
 
 import boto3
 import numpy as np
-import os
 from skimage.io import imread
 from skimage.transform import resize
-from typing import List
 
 
 def read_hu(x):

src/dataset/forrestgump.py

@@ -1,11 +1,11 @@
+import json
+import os
 import torch
 import torch.utils.data as data
 from torch import Tensor
+from typing import Optional
 
-import json
 import numpy as np
-import os
-from typing import Optional
 
 
 def load_scenes(path: str) -> list:

src/dataset/forrestgump_converter.py

@@ -1,9 +1,9 @@
 import json
 import os
-import numpy as np
+from math import ceil
+
 import nilearn as nl
 import nilearn.plotting
-from math import ceil
 import numpy as np
 
 

src/dataset/forrestgumpraw.py

@@ -1,14 +1,14 @@
 import os
-import numpy as np
-import nilearn as nl
-import nilearn.plotting
-from math import floor
-import numpy as np
 import torch
 import torch.utils.data as data
+from math import floor
 from torch import Tensor
 from typing import Optional
 
+import nilearn as nl
+import nilearn.plotting
+import numpy as np
+
 
 def load_scenes(path: str) -> list:
     with open(path, "r") as f:

src/dataset/grasp_and_lift_eeg.py

@@ -1,14 +1,14 @@
+import os
+import time
 import torch
 import torch.nn.functional as F
 import torch.utils.data as data
+import zipfile
 from torch import Tensor
+from typing import List, Optional
 
 import numpy as np
-import os
 import requests
-import time
-import zipfile
-from typing import List, Optional
 
 
 def recursive_listdir(path: str, suffix: str) -> list:

src/dataset/la5c.py

@@ -1,10 +1,10 @@
+import os
 import torch.utils.data as data
 from torch import Tensor
+from typing import List
 
 import nilearn as nl
 import numpy as np
-import os
-from typing import List
 
 
 class LA5cDataset(data.Dataset):

src/dataset/rsna_intracranial.py

@@ -1,11 +1,11 @@
+import gzip
+import os
 import torch
 import torch.utils.data as data
 from torch import Tensor
 from torch.nn import functional as F
 
 import boto3
-import gzip
-import os
 import pydicom
 
 from .dicom_util import normalized_dicom_pixels

src/dataset/toy_neural_graphics.py

@@ -1,14 +1,17 @@
 import os
-import numpy as np
 import torch
 import torch.utils.data as data
-from pytorch3d.io import load_objs_as_meshes, load_obj
-from pytorch3d.transforms import random_rotation, Translate, Rotate, Transform3d
-from pytorch3d.renderer import (look_at_view_transform, FoVPerspectiveCameras,
-                                PointLights, DirectionalLights, Materials,
-                                RasterizationSettings, MeshRenderer,
-                                MeshRasterizer, SoftPhongShader,
-                                HardFlatShader, TexturesUV, TexturesVertex)
+
+import numpy as np
+from pytorch3d.io import load_obj, load_objs_as_meshes
+from pytorch3d.renderer import (DirectionalLights, FoVPerspectiveCameras,
+                                HardFlatShader, Materials, MeshRasterizer,
+                                MeshRenderer, PointLights,
+                                RasterizationSettings, SoftPhongShader,
+                                TexturesUV, TexturesVertex,
+                                look_at_view_transform)
+from pytorch3d.transforms import (Rotate, Transform3d, Translate,
+                                  random_rotation)
 
 
 class ToyNeuralGraphicsDataset(data.Dataset):

src/dataset/trends_fmri.py

@@ -1,10 +1,10 @@
+import os
 import torch
 import torch.utils.data as data
 
 import h5py
 import nilearn as nl
 import numpy as np
-import os
 
 
 def load_subject(filename: str, mask_niimg):

src/dataset/video.py

@@ -1,7 +1,7 @@
+import os
 import torch
 import torch.utils.data as data
 
-import os
 from decord import VideoReader, cpu
 
 

src/dataset/video_old.py

@@ -1,12 +1,13 @@
+import json
 import os
-import numpy as np
 import torch
 import torch.utils.data as data
 from math import floor
-import youtube_dl
+
 import cv2
+import numpy as np
+import youtube_dl
 from torchvision.transforms import Resize, ToPILImage, ToTensor
-import json
 
 
 def load_links(path):
@@ -193,9 +194,10 @@ def __len__(self):
 
 
 if __name__ == '__main__':
-    import youtube_dl
-    import cv2
     import time
+
+    import cv2
+    import youtube_dl
     width = 640
     height = 480
     num_frames = 9

src/draw_boxes.py

@@ -1,7 +1,8 @@
 # Source: https://www.kaggle.com/kmader/deeplesion-overview
+import os
+
 import matplotlib.pyplot as plt
 import numpy as np
-import os
 import pandas as pd
 from matplotlib.patches import Rectangle
 from skimage.io import imread

src/load_config.py

@@ -1,7 +1,8 @@
 import os
-import yaml
 from typing import List, Union
 
+import yaml
+
 from merge_strategy import deep_merge
 
 

src/main.py

@@ -1,14 +1,14 @@
-import torch.backends.cudnn as cudnn
-
 import argparse
-import cv2
-import decord  # must be imported after torch
 import gc
-import numpy as np
 import os
 import time
+import torch.backends.cudnn as cudnn
 from typing import List, Union
 
+import cv2
+import decord  # must be imported after torch
+import numpy as np
+
 from entry import experiment_main
 from load_config import load_config
 

src/models/augmenter.py

@@ -1,7 +1,6 @@
 import torch
-from torch import Tensor, nn
-
 from abc import abstractmethod
+from torch import Tensor, nn
 from typing import List
 
 

src/models/base.py

@@ -1,8 +1,7 @@
 import torch
+from abc import abstractmethod
 from torch import Tensor, nn
 from torch.nn import functional as F
-
-from abc import abstractmethod
 from typing import Any, List
 
 from .util import reparameterize

src/models/classifier.py

@@ -1,8 +1,7 @@
 import torch
+from abc import abstractmethod
 from torch import Tensor, nn
 from torch.nn import functional as F
-
-from abc import abstractmethod
 from typing import List
 
 

src/models/conv4d.py

@@ -4,9 +4,9 @@
 
 import torch
 from torch import nn
+from typing import Callable, Tuple, Union
 
 import numpy as np
-from typing import Callable, Tuple, Union
 
 
 class Conv4d(nn.Module):

src/models/encoder_wrapper.py

@@ -1,5 +1,4 @@
 from torch import Tensor, nn
-
 from typing import List
 
 

src/models/localizer.py

@@ -4,11 +4,8 @@
 from torch.nn import functional as F
 from typing import Optional
 
-from torchvision.ops import (
-    complete_box_iou_loss,
-    distance_box_iou_loss,
-    generalized_box_iou_loss,
-)
+from torchvision.ops import (complete_box_iou_loss, distance_box_iou_loss,
+                             generalized_box_iou_loss)
 
 
 class Localizer(nn.Module):

src/models/renderer.py

@@ -1,8 +1,7 @@
 import torch
+from abc import abstractmethod
 from torch import Tensor, nn
 from torch.nn import functional as F
-
-from abc import abstractmethod
 from typing import List
 
 from .inception import InceptionV3

src/models/resnet_augmenter1d.py

@@ -1,6 +1,5 @@
 import torch
 from torch import Size, Tensor, nn
-
 from typing import List
 
 from .augmenter import Augmenter

src/models/resnet_classifier1d.py

@@ -1,7 +1,6 @@
 import torch
-from torch import Size, Tensor, nn
-
 from math import ceil
+from torch import Size, Tensor, nn
 from typing import List
 
 from .classifier import Classifier

src/models/resnet_classifier2d.py

@@ -1,7 +1,6 @@
 import torch
-from torch import Size, Tensor, nn
-
 from math import ceil
+from torch import Size, Tensor, nn
 from typing import List
 
 from .classifier import Classifier

src/models/resnet_classifier3d.py

@@ -1,7 +1,6 @@
+from math import ceil
 from torch import Size, Tensor, nn
 from torch.nn import functional as F
-
-from math import ceil
 from typing import List
 
 from .classifier import Classifier