--img-size stride-multiple verification

detect.py

@@ -156,6 +156,7 @@ if __name__ == '__main__':
     parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
     parser.add_argument('--augment', action='store_true', help='augmented inference')
     opt = parser.parse_args()
+    opt.img_size = check_img_size(opt.img_size)
     print(opt)
 
     with torch.no_grad():

test.py

@@ -245,6 +245,7 @@ if __name__ == '__main__':
     parser.add_argument('--augment', action='store_true', help='augmented inference')
     parser.add_argument('--verbose', action='store_true', help='report mAP by class')
     opt = parser.parse_args()
+    opt.img_size = check_img_size(opt.img_size)
     opt.save_json = opt.save_json or opt.data.endswith('coco.yaml')
     opt.data = glob.glob('./**/' + opt.data, recursive=True)[0]  # find file
     print(opt)

train.py

@@ -80,9 +80,7 @@ def train(hyp):
 
     # Image sizes
     gs = int(max(model.stride))  # grid size (max stride)
-    if any(x % gs != 0 for x in opt.img_size):
-        print('WARNING: --img-size %g,%g must be multiple of %s max stride %g' % (*opt.img_size, opt.cfg, gs))
-    imgsz, imgsz_test = [make_divisible(x, gs) for x in opt.img_size]  # image sizes (train, test)
+    imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples
 
     # Optimizer
     nbs = 64  # nominal batch size

utils/utils.py

@@ -37,13 +37,20 @@ def init_seeds(seed=0):
 
 
 def check_git_status():
+    # Suggest 'git pull' if repo is out of date
     if platform in ['linux', 'darwin']:
-        # Suggest 'git pull' if repo is out of date
         s = subprocess.check_output('if [ -d .git ]; then git fetch && git status -uno; fi', shell=True).decode('utf-8')
         if 'Your branch is behind' in s:
             print(s[s.find('Your branch is behind'):s.find('\n\n')] + '\n')
 
 
+def check_img_size(img_size, s=32):
+    # Verify img_size is a multiple of stride s
+    if img_size % s != 0:
+        print('WARNING: --img-size %g must be multiple of max stride %g' % (img_size, s))
+    return make_divisible(img_size, s)  # nearest gs-multiple
+
+
 def make_divisible(x, divisor):
     # Returns x evenly divisble by divisor
     return math.ceil(x / divisor) * divisor