EMA bug fix 2 (#2330)

* EMA bug fix 2

* update

hubconf.py

     """
     model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model  # load checkpoint
     if isinstance(model, dict):
-        model = model['model']  # load model
+        model = model['ema' if model.get('ema') else 'model']  # load model
 
     hub_model = Model(model.yaml).to(next(model.parameters()).device)  # create
     hub_model.load_state_dict(model.float().state_dict())  # load state_dict

models/experimental.py

     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
         attempt_download(w)
-        model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval())  # load FP32 model
+        ckpt = torch.load(w, map_location=map_location)  # load
+        model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
 
     # Compatibility updates
     for m in model.modules():

train.py

 
         # EMA
         if ema and ckpt.get('ema'):
-            ema.ema.load_state_dict(ckpt['ema'][0].float().state_dict())
-            ema.updates = ckpt['ema'][1]
+            ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
+            ema.updates = ckpt['updates']
 
         # Results
         if ckpt.get('training_results') is not None:
                 ckpt = {'epoch': epoch,
                         'best_fitness': best_fitness,
                         'training_results': results_file.read_text(),
-                        'model': ema.ema if final_epoch else deepcopy(
-                            model.module if is_parallel(model) else model).half(),
-                        'ema': (deepcopy(ema.ema).half(), ema.updates),
+                        'model': deepcopy(model.module if is_parallel(model) else model).half(),
+                        'ema': deepcopy(ema.ema).half(),
+                        'updates': ema.updates,
                         'optimizer': optimizer.state_dict(),
                         'wandb_id': wandb_run.id if wandb else None}
 

utils/general.py

     return output
 
 
-def strip_optimizer(f='weights/best.pt', s=''):  # from utils.general import *; strip_optimizer()
+def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_optimizer()
     # Strip optimizer from 'f' to finalize training, optionally save as 's'
     x = torch.load(f, map_location=torch.device('cpu'))
-    for k in 'optimizer', 'training_results', 'wandb_id', 'ema':  # keys
+    if x.get('ema'):
+        x['model'] = x['ema']  # replace model with ema
+    for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates':  # keys
         x[k] = None
     x['epoch'] = -1
     x['model'].half()  # to FP16
         p.requires_grad = False
     torch.save(x, s or f)
     mb = os.path.getsize(s or f) / 1E6  # filesize
-    print('Optimizer stripped from %s,%s %.1fMB' % (f, (' saved as %s,' % s) if s else '', mb))
+    print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
 
 
 def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):