Fix Logging (#719)

* Add logging setup

* Fix fusing layers message

* Fix logging does not have end

* Add logging

* Change logging to use logger

* Update yolo.py

I tried this in a cloned branch, and everything seems to work fine

* Update yolo.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

detect.py

 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)
+    check_img_size, non_max_suppression, apply_classifier, scale_coords, 
+    xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
     webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
 
     # Initialize
+    set_logging()
     device = select_device(opt.device)
     if os.path.exists(out):
         shutil.rmtree(out)  # delete output folder

models/export.py

 import torch
 
 from utils.google_utils import attempt_download
+from utils.general import set_logging
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     opt = parser.parse_args()
     opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
     print(opt)
+    set_logging()
 
     # Input
     img = torch.zeros((opt.batch_size, 3, *opt.img_size))  # image size(1,3,320,192) iDetection

models/yolo.py

 
 from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat
 from models.experimental import MixConv2d, CrossConv, C3
-from utils.general import check_anchor_order, make_divisible, check_file
+from utils.general import check_anchor_order, make_divisible, check_file, set_logging
 from utils.torch_utils import (
     time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device)
 
     #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
 
     def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        print('Fusing layers... ', end='')
+        print('Fusing layers... ')
         for m in self.model.modules():
             if type(m) is Conv:
                 m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatability
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     opt = parser.parse_args()
     opt.cfg = check_file(opt.cfg)  # check file
+    set_logging()
     device = select_device(opt.device)
 
     # Create model

test.py

 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
 from utils.general import (
-    coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression,
-    scale_coords, xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class)
+    coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression, scale_coords, 
+    xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging)
 from utils.torch_utils import select_device, time_synchronized
 
 
         device = next(model.parameters()).device  # get model device
 
     else:  # called directly
+        set_logging()
         device = select_device(opt.device, batch_size=batch_size)
         merge, save_txt = opt.merge, opt.save_txt  # use Merge NMS, save *.txt labels
         if save_txt:

train.py

         state_dict = ckpt['model'].float().state_dict()  # to FP32
         state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude)  # intersect
         model.load_state_dict(state_dict, strict=False)  # load
-        logging.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # report
+        logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # report
     else:
         model = Model(opt.cfg, ch=3, nc=nc).to(device)  # create
 
         if rank != -1:
             dataloader.sampler.set_epoch(epoch)
         pbar = enumerate(dataloader)
-        logging.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
+        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
         if rank in [-1, 0]:
             pbar = tqdm(pbar, total=nb)  # progress bar
         optimizer.zero_grad()