Utils reorganization (#1392)

* Utils reorganization

* Add new utils files

* cleanup

* simplify

* reduce datasets.py

* remove evolve.sh

* loadWebcam cleanup

detect.py

@@ -10,14 +10,15 @@ from numpy import random
 from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
 from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \
-    plot_one_box, strip_optimizer, set_logging, increment_path
+    strip_optimizer, set_logging, increment_path
+from utils.plots import plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
 def detect(save_img=False):
     source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
-    webcam = source.isnumeric() or source.endswith('.txt') or \
-             source.lower().startswith(('rtsp://', 'rtmp://', 'http://'))
+    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+        ('rtsp://', 'rtmp://', 'http://'))
 
     # Directories
     save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
@@ -38,8 +39,7 @@ def detect(save_img=False):
     classify = False
     if classify:
         modelc = load_classifier(name='resnet101', n=2)  # initialize
-        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
-        modelc.to(device).eval()
+        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
 
     # Set Dataloader
     vid_path, vid_writer = None, None
@@ -53,7 +53,7 @@ def detect(save_img=False):
 
     # Get names and colors
     names = model.module.names if hasattr(model, 'module') else model.names
-    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
+    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
 
     # Run inference
     t0 = time.time()