Fix val.py Ensemble() (ultralytics#7490)

models/experimental.py

@@ -115,7 +115,8 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
         return model[-1]  # return model
     else:
         print(f'Ensemble created with {weights}\n')
-        for k in ['names']:
-            setattr(model, k, getattr(model[-1], k))
+        for k in 'names', 'nc', 'yaml':
+            setattr(model, k, getattr(model[0], k))
         model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
+        assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
         return model  # return ensemble

val.py

@@ -163,7 +163,7 @@ def run(
     # Dataloader
     if not training:
         if pt and not single_cls:  # check --weights are trained on --data
-            ncm = model.model.yaml['nc']
+            ncm = model.model.nc
             assert ncm == nc, f'{weights[0]} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
                               f'classes). Pass correct combination of --weights and --data that are trained together.'
         model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz))  # warmup