Update detections() self.t = tuple()

models/common.py

@@ -1,4 +1,4 @@
-# This file contains modules common to various models
+# YOLOv5 common modules
 
 import math
 from pathlib import Path
@@ -248,7 +248,7 @@ def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
         self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
         self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
         self.n = len(self.pred)
-        self.t = ((times[i + 1] - times[i]) * 1000 / self.n for i in range(3))  # timestamps (ms)
+        self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3))  # timestamps (ms)
         self.s = shape  # inference BCHW shape
 
     def display(self, pprint=False, show=False, save=False, render=False, save_dir=''):
@@ -277,8 +277,7 @@ def display(self, pprint=False, show=False, save=False, render=False, save_dir='
 
     def print(self):
         self.display(pprint=True)  # print results
-        print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
-              tuple(self.t))
+        print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
 
     def show(self):
         self.display(show=True)  # show results

models/experimental.py

@@ -1,4 +1,4 @@
-# This file contains experimental modules
+# YOLOv5 experimental modules
 
 import numpy as np
 import torch

models/yolo.py

@@ -1,3 +1,5 @@
+# YOLOv5 YOLO-specific modules
+
 import argparse
 import logging
 import sys