v9.1 release (ultralytics#1658)

Dockerfile

@@ -17,12 +17,6 @@ WORKDIR /usr/src/app
 # Copy contents
 COPY . /usr/src/app
 
-# Copy weights
-#RUN python3 -c "from models import *; \
-#attempt_download('weights/yolov3.pt'); \
-#attempt_download('weights/yolov3-spp.pt'); \
-#attempt_download('weights/yolov3-tiny.pt')"
-
 
 # --------------------------------------------------- Extras Below ---------------------------------------------------
 
@@ -31,7 +25,7 @@ COPY . /usr/src/app
 # for v in {300..303}; do t=ultralytics/coco:v$v && sudo docker build -t $t . && sudo docker push $t; done
 
 # Pull and Run
-# t=ultralytics/yolov3:latest && sudo docker pull $t && sudo docker run -it --ipc=host $t
+# t=ultralytics/yolov3:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
 
 # Pull and Run with local directory access
 # t=ultralytics/yolov3:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t

detect.py

@@ -9,8 +9,8 @@
 
 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, \
- strip_optimizer, set_logging, increment_path
+from utils.general import check_img_size, check_requirements, non_max_suppression, apply_classifier, scale_coords, \
+ xyxy2xywh, strip_optimizer, set_logging, increment_path
 from utils.plots import plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
@@ -81,12 +81,13 @@ def detect(save_img=False):
  # Process detections
  for i, det in enumerate(pred): # detections per image
  if webcam: # batch_size >= 1
- p, s, im0 = Path(path[i]), '%g: ' % i, im0s[i].copy()
+ p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
  else:
- p, s, im0 = Path(path), '', im0s
+ p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
 
- save_path = str(save_dir / p.name)
- txt_path = str(save_dir / 'labels' / p.stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
+ p = Path(p) # to Path
+ save_path = str(save_dir / p.name) # img.jpg
+ txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
  s += '%gx%g ' % img.shape[2:] # print string
  gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
  if len(det):
@@ -96,7 +97,7 @@ def detect(save_img=False):
  # Print results
  for c in det[:, -1].unique():
  n = (det[:, -1] == c).sum() # detections per class
- s += '%g %ss, ' % (n, names[int(c)]) # add to string
+ s += f'{n} {names[int(c)]}s, ' # add to string
 
  # Write results
  for *xyxy, conf, cls in reversed(det):
@@ -107,23 +108,21 @@ def detect(save_img=False):
  f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
  if save_img or view_img: # Add bbox to image
- label = '%s %.2f' % (names[int(cls)], conf)
+ label = f'{names[int(cls)]} {conf:.2f}'
  plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
 
  # Print time (inference + NMS)
- print('%sDone. (%.3fs)' % (s, t2 - t1))
+ print(f'{s}Done. ({t2 - t1:.3f}s)')
 
  # Stream results
  if view_img:
  cv2.imshow(str(p), im0)
- if cv2.waitKey(1) == ord('q'): # q to quit
- raise StopIteration
 
  # Save results (image with detections)
  if save_img:
- if dataset.mode == 'images':
+ if dataset.mode == 'image':
  cv2.imwrite(save_path, im0)
- else:
+ else: # 'video'
  if vid_path != save_path: # new video
  vid_path = save_path
  if isinstance(vid_writer, cv2.VideoWriter):
@@ -140,7 +139,7 @@ def detect(save_img=False):
  s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
  print(f"Results saved to {save_dir}{s}")
 
- print('Done. (%.3fs)' % (time.time() - t0))
+ print(f'Done. ({time.time() - t0:.3f}s)')
 
 
 if __name__ == '__main__':
@@ -163,6 +162,7 @@ def detect(save_img=False):
  parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
  opt = parser.parse_args()
  print(opt)
+ check_requirements()
 
  with torch.no_grad():
  if opt.update: # update all models (to fix SourceChangeWarning)

hubconf.py

@@ -17,7 +17,7 @@
 set_logging()
 
 
-def create(name, pretrained, channels, classes):
+def create(name, pretrained, channels, classes, autoshape):
  """Creates a specified YOLOv3 model
 
  Arguments:
@@ -41,7 +41,8 @@ def create(name, pretrained, channels, classes):
  model.load_state_dict(state_dict, strict=False) # load
  if len(ckpt['model'].names) == classes:
  model.names = ckpt['model'].names # set class names attribute
- # model = model.autoshape() # for PIL/cv2/np inputs and NMS
+ if autoshape:
+ model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
  return model
 
  except Exception as e:
@@ -50,7 +51,7 @@ def create(name, pretrained, channels, classes):
  raise Exception(s) from e
 
 
-def yolov3(pretrained=False, channels=3, classes=80):
+def yolov3(pretrained=False, channels=3, classes=80, autoshape=True):
  """YOLOv3 model from https://github.com/ultralytics/yolov3
 
  Arguments:
@@ -61,10 +62,10 @@ def yolov3(pretrained=False, channels=3, classes=80):
  Returns:
  pytorch model
  """
- return create('yolov3', pretrained, channels, classes)
+ return create('yolov3', pretrained, channels, classes, autoshape)
 
 
-def yolov3_spp(pretrained=False, channels=3, classes=80):
+def yolov3_spp(pretrained=False, channels=3, classes=80, autoshape=True):
  """YOLOv3-SPP model from https://github.com/ultralytics/yolov3
 
  Arguments:
@@ -75,10 +76,10 @@ def yolov3_spp(pretrained=False, channels=3, classes=80):
  Returns:
  pytorch model
  """
- return create('yolov3-spp', pretrained, channels, classes)
+ return create('yolov3-spp', pretrained, channels, classes, autoshape)
 
 
-def yolov3_tiny(pretrained=False, channels=3, classes=80):
+def yolov3_tiny(pretrained=False, channels=3, classes=80, autoshape=True):
  """YOLOv3-tiny model from https://github.com/ultralytics/yolov3
 
  Arguments:
@@ -89,16 +90,17 @@ def yolov3_tiny(pretrained=False, channels=3, classes=80):
  Returns:
  pytorch model
  """
- return create('yolov3-tiny', pretrained, channels, classes)
+ return create('yolov3-tiny', pretrained, channels, classes, autoshape)
 
 
-def custom(path_or_model='path/to/model.pt'):
+def custom(path_or_model='path/to/model.pt', autoshape=True):
  """YOLOv3-custom model from https://github.com/ultralytics/yolov3
- 
+
  Arguments (3 options):
  path_or_model (str): 'path/to/model.pt'
  path_or_model (dict): torch.load('path/to/model.pt')
  path_or_model (nn.Module): torch.load('path/to/model.pt')['model']
+
  Returns:
  pytorch model
  """
@@ -109,13 +111,12 @@ def custom(path_or_model='path/to/model.pt'):
  hub_model = Model(model.yaml).to(next(model.parameters()).device) # create
  hub_model.load_state_dict(model.float().state_dict()) # load state_dict
  hub_model.names = model.names # class names
- return hub_model
+ return hub_model.autoshape() if autoshape else hub_model
 
 
 if __name__ == '__main__':
- model = create(name='yolov3', pretrained=True, channels=3, classes=80) # pretrained example
+ model = create(name='yolov3', pretrained=True, channels=3, classes=80, autoshape=True) # pretrained example
  # model = custom(path_or_model='path/to/model.pt') # custom example
- model = model.autoshape() # for PIL/cv2/np inputs and NMS
 
  # Verify inference
  from PIL import Image