Update comments

torch example input now shown resized to size=640 and also now a multiple of P6 stride 64 (see #2722 (comment))

models/common.py

@@ -238,13 +238,13 @@ def autoshape(self):
         return self
 
     def forward(self, imgs, size=640, augment=False, profile=False):
-        # Inference from various sources. For height=720, width=1280, RGB images example inputs are:
+        # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
         #   filename:   imgs = 'data/samples/zidane.jpg'
         #   URI:             = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
-        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(720,1280,3)
-        #   PIL:             = Image.open('image.jpg')  # HWC x(720,1280,3)
-        #   numpy:           = np.zeros((720,1280,3))  # HWC
-        #   torch:           = torch.zeros(16,3,720,1280)  # BCHW
+        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
+        #   PIL:             = Image.open('image.jpg')  # HWC x(640,1280,3)
+        #   numpy:           = np.zeros((640,1280,3))  # HWC
+        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640)
         #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
 
         t = [time_synchronized()]