Remove unused lib in .py files

src/demo.py

@@ -2,17 +2,15 @@
 from __future__ import division
 from __future__ import print_function
 
-import _init_paths
-
 import logging
 import os
 import os.path as osp
-from opts import opts
-from tracking_utils.utils import mkdir_if_missing
-from tracking_utils.log import logger
+
 import datasets.dataset.jde as datasets
+from opts import opts
 from track import eval_seq
-
+from tracking_utils.log import logger
+from tracking_utils.utils import mkdir_if_missing
 
 logger.setLevel(logging.INFO)
 

src/gen_data_path.py

@@ -1,6 +1,5 @@
-import os
 import glob
-import _init_paths
+import os
 
 
 def gen_caltech_path(root_path):
@@ -19,4 +18,4 @@ def gen_caltech_path(root_path):
 
 if __name__ == '__main__':
     root = '/data/yfzhang/MOT/JDE'
-    gen_caltech_path(root)
+    gen_caltech_path(root)

src/lib/datasets/dataset/jde.py

@@ -7,16 +7,10 @@
 from collections import OrderedDict
 
 import cv2
-import json
 import numpy as np
 import torch
-
-from torch.utils.data import Dataset
-from torchvision.transforms import transforms as T
-from cython_bbox import bbox_overlaps as bbox_ious
-from opts import opts
 from utils.image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian
-from utils.utils import xyxy2xywh, generate_anchors, xywh2xyxy, encode_delta
+from utils.utils import xyxy2xywh
 
 
 class LoadImages:  # for inference
@@ -535,5 +529,3 @@ def __getitem__(self, files_index):
                 labels[i, 1] += self.tid_start_index[ds]
 
         return imgs, labels0, img_path, (h, w)
-
-

src/lib/models/networks/DCNv2/setup.py

@@ -2,7 +2,6 @@
 
 import os
 import glob
-
 import torch
 
 from torch.utils.cpp_extension import CUDA_HOME
@@ -14,6 +13,7 @@
 
 requirements = ["torch", "torchvision"]
 
+
 def get_extensions():
     this_dir = os.path.dirname(os.path.abspath(__file__))
     extensions_dir = os.path.join(this_dir, "src")
@@ -53,6 +53,7 @@ def get_extensions():
     ]
     return ext_modules
 
+
 setup(
     name="DCNv2",
     version="0.1",

src/test_det.py

@@ -2,24 +2,19 @@
 from __future__ import division
 from __future__ import print_function
 
-import _init_paths
-import argparse
-import torch
 import json
-import time
 import os
-import cv2
+import time
 
-from sklearn import metrics
-from scipy import interpolate
 import numpy as np
-from torchvision.transforms import transforms as T
-from models.model import create_model, load_model
+import torch
 from datasets.dataset.jde import DetDataset, collate_fn
-from utils.utils import xywh2xyxy, ap_per_class, bbox_iou
-from opts import opts
 from models.decode import mot_decode
+from models.model import create_model, load_model
+from opts import opts
+from torchvision.transforms import transforms as T
 from utils.post_process import ctdet_post_process
+from utils.utils import xywh2xyxy, ap_per_class, bbox_iou
 
 
 def post_process(opt, dets, meta):
@@ -208,6 +203,7 @@ def test_det(
     # Return mAP
     return mean_mAP, mean_R, mean_P
 
+
 if __name__ == '__main__':
     os.environ['CUDA_VISIBLE_DEVICES'] = '1'
     opt = opts().init()

src/test_emb.py

@@ -2,27 +2,21 @@
 from __future__ import division
 from __future__ import print_function
 
-import _init_paths
-import argparse
-import torch
 import json
-import time
-import os
-import cv2
 import math
+import os
+import time
 
-from sklearn import metrics
-from scipy import interpolate
 import numpy as np
-from torchvision.transforms import transforms as T
+import torch
 import torch.nn.functional as F
+from datasets.dataset.jde import JointDataset
 from models.model import create_model, load_model
-from datasets.dataset.jde import JointDataset, collate_fn
 from models.utils import _tranpose_and_gather_feat
-from utils.utils import xywh2xyxy, ap_per_class, bbox_iou
 from opts import opts
-from models.decode import mot_decode
-from utils.post_process import ctdet_post_process
+from scipy import interpolate
+from sklearn import metrics
+from torchvision.transforms import transforms as T
 
 
 def test_emb(
@@ -104,6 +98,7 @@ def test_emb(
         print('TPR@FAR={:.7f}: {:.4f}'.format(fa, tar_at_far[f]))
     return tar_at_far
 
+
 if __name__ == '__main__':
     os.environ['CUDA_VISIBLE_DEVICES'] = '1'
     opt = opts().init()