Add train.py `--img-size floor (ultralytics#4099)

train.py

@@ -207,7 +207,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     # Image sizes
     gs = max(int(model.stride.max()), 32)  # grid size (max stride)
     nl = model.model[-1].nl  # number of detection layers (used for scaling hyp['obj'])
-    imgsz = check_img_size(opt.imgsz, gs)  # verify imgsz is gs-multiple
+    imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2)  # verify imgsz is gs-multiple
 
     # DP mode
     if cuda and RANK == -1 and torch.cuda.device_count() > 1:

utils/general.py

@@ -181,11 +181,11 @@ def check_requirements(requirements='requirements.txt', exclude=()):
         print(emojis(s))  # emoji-safe
 
 
-def check_img_size(img_size, s=32):
+def check_img_size(img_size, s=32, floor=0):
     # Verify img_size is a multiple of stride s
-    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
+    new_size = max(make_divisible(img_size, int(s)), floor)  # ceil gs-multiple
     if new_size != img_size:
-        print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
+        print(f'WARNING: --img-size {img_size} must be multiple of max stride {s}, updating to {new_size}')
     return new_size