ignore parameters with no gradient

Is this the right approach?, perhaps it would be better to show trainable vs nontrainable parameters. Or sill use nontrainable parameters to estimate macs

torchsummaryX/torchsummaryX.py

@@ -34,7 +34,7 @@ def summary(model, x, *args, **kwargs):
             info["inner"] = OrderedDict()
             info["params"], info["macs"] = 0, 0
             for name, param in module.named_parameters():
-                info["params"] += param.nelement()
+                info["params"] += param.nelement() * param.requires_grad
 
                 if name == "weight":
                     ksize = list(param.size())