Increase FLOPS robustness (#1608)

utils/torch_utils.py

 # PyTorch utils
 
 import logging
-import math
 import os
 import time
 from contextlib import contextmanager
 from copy import deepcopy
 
+import math
 import torch
 import torch.backends.cudnn as cudnn
 import torch.nn as nn
@@ -152,7 +152,7 @@ def model_info(model, verbose=False, img_size=640):
 
     try:  # FLOPS
         from thop import profile
-        stride = int(model.stride.max())
+        stride = int(model.stride.max()) if hasattr(model, 'stride') else 32
         img = torch.zeros((1, 3, stride, stride), device=next(model.parameters()).device)  # input
         flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride FLOPS
         img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float