Update detect.py

detect.py

@@ -46,7 +46,7 @@ def detect(save_img=False):
         dataset = LoadImages(source, img_size=imgsz)
 
     # Get names and colors
-    names = model.names if hasattr(model, 'names') else model.modules.names
+    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))]
 
     # Run inference
@@ -80,6 +80,7 @@ def detect(save_img=False):
                 p, s, im0 = path, '', im0s
 
             save_path = str(Path(out) / Path(p).name)
+            txt_path = save_path[:save_path.rfind('.')] + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
             s += '%gx%g ' % img.shape[2:]  # print string
             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  #  normalization gain whwh
             if det is not None and len(det):
@@ -95,12 +96,8 @@ def detect(save_img=False):
                 for *xyxy, conf, cls in det:
                     if save_txt:  # Write to file
                         xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-                        if dataset.frame == 0:
-                            with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as f:
-                                f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
-                        else:
-                            with open(save_path[:save_path.rfind('.')] + '_' + str(dataset.frame) + '.txt', 'a') as f:
-                                f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
+                        with open(txt_path + '.txt', 'a') as f:
+                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
 
                     if save_img or view_img:  # Add bbox to image
                         label = '%s %.2f' % (names[int(cls)], conf)
@@ -160,3 +157,8 @@ if __name__ == '__main__':
 
     with torch.no_grad():
         detect()
+
+        # Update all models
+        # for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov3-spp.pt']:
+        #    detect()
+        #    create_pretrained(opt.weights, opt.weights)