sort python imports with isort

tools/eval_utils/eval_utils.py

-import tqdm
-import time
 import pickle
+import time
+
 import numpy as np
 import torch
-from pcdet.utils import common_utils
+import tqdm
+
 from pcdet.models import load_data_to_gpu
+from pcdet.utils import common_utils
 
 
 def statistics_info(cfg, ret_dict, metric, disp_dict):

tools/test.py

+import argparse
+import datetime
+import glob
 import os
+import re
+import time
+from pathlib import Path
+
 import numpy as np
 import torch
 from tensorboardX import SummaryWriter
-import time
-import glob
-import re
-import datetime
-import argparse
-from pathlib import Path
+
+from eval_utils import eval_utils
+from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
 from pcdet.datasets import build_dataloader
 from pcdet.models import build_network
 from pcdet.utils import common_utils
-from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
-from eval_utils import eval_utils
 
 
 def parse_config():
@@ -195,4 +197,3 @@ def main():
 
 if __name__ == '__main__':
     main()
-

tools/train.py

+import argparse
+import datetime
+import glob
 import os
+from pathlib import Path
+from test import repeat_eval_ckpt
+
 import torch
+import torch.distributed as dist
 import torch.nn as nn
 from tensorboardX import SummaryWriter
-from pcdet.config import cfg, log_config_to_file, cfg_from_list, cfg_from_yaml_file
-from pcdet.utils import common_utils
+
+from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
 from pcdet.datasets import build_dataloader
 from pcdet.models import build_network, model_fn_decorator
+from pcdet.utils import common_utils
 from train_utils.optimization import build_optimizer, build_scheduler
 from train_utils.train_utils import train_model
-import torch.distributed as dist
-from test import repeat_eval_ckpt
-from pathlib import Path
-import argparse
-import datetime
-import glob
 
 
 def parse_config():

tools/train_utils/optimization/__init__.py

+from functools import partial
+
 import torch.nn as nn
 import torch.optim as optim
 import torch.optim.lr_scheduler as lr_sched
-from functools import partial
+
 from .fastai_optim import OptimWrapper
-from .learning_schedules_fastai import OneCycle, CosineWarmupLR
+from .learning_schedules_fastai import CosineWarmupLR, OneCycle
 
 
 def build_optimizer(model, optim_cfg):

tools/train_utils/optimization/fastai_optim.py

@@ -4,8 +4,8 @@ from collections import Iterable
 
 import torch
 from torch import nn
-from torch.nn.utils import parameters_to_vector
 from torch._utils import _unflatten_dense_tensors
+from torch.nn.utils import parameters_to_vector
 
 bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)
 

tools/train_utils/optimization/learning_schedules_fastai.py

 # This file is modified from https://github.com/traveller59/second.pytorch
 
-import numpy as np
 import math
 from functools import partial
+
+import numpy as np
 import torch.optim.lr_scheduler as lr_sched
+
 from .fastai_optim import OptimWrapper
 
 

tools/train_utils/train_utils.py

-import torch
-import os
 import glob
+import os
+
+import torch
 import tqdm
 from torch.nn.utils import clip_grad_norm_
 

tools/visual_utils/visualize_utils.py

-import numpy as np
 import mayavi.mlab as mlab
+import numpy as np
 import torch
 
 box_colormap = [