eval with ema weight while training (#2747)

ppdet/engine/callbacks.py

@@ -26,7 +26,6 @@ import paddle
 import paddle.distributed as dist
 
 from ppdet.utils.checkpoint import save_model
-from ppdet.optimizer import ModelEMA
 
 from ppdet.utils.logger import setup_logger
 logger = setup_logger('ppdet.engine')
@@ -143,20 +142,12 @@ class Checkpointer(Callback):
         super(Checkpointer, self).__init__(model)
         cfg = self.model.cfg
         self.best_ap = 0.
-        self.use_ema = ('use_ema' in cfg and cfg['use_ema'])
         self.save_dir = os.path.join(self.model.cfg.save_dir,
                                      self.model.cfg.filename)
         if hasattr(self.model.model, 'student_model'):
             self.weight = self.model.model.student_model
         else:
             self.weight = self.model.model
-        if self.use_ema:
-            self.ema = ModelEMA(
-                cfg['ema_decay'], self.weight, use_thres_step=True)
-
-    def on_step_end(self, status):
-        if self.use_ema:
-            self.ema.update(self.weight)
 
     def on_epoch_end(self, status):
         # Checkpointer only performed during training
@@ -170,9 +161,6 @@ class Checkpointer(Callback):
                 if epoch_id % self.model.cfg.snapshot_epoch == 0 or epoch_id == end_epoch - 1:
                     save_name = str(
                         epoch_id) if epoch_id != end_epoch - 1 else "model_final"
-                    if self.use_ema:
-                        weight = self.ema.apply()
-                    else:
                     weight = self.weight
             elif mode == 'eval':
                 if 'save_best_model' in status and status['save_best_model']:
@@ -187,9 +175,6 @@ class Checkpointer(Callback):
                         if map_res[key][0] > self.best_ap:
                             self.best_ap = map_res[key][0]
                             save_name = 'best_model'
-                            if self.use_ema:
-                                weight = self.ema.apply()
-                            else:
                             weight = self.weight
                         logger.info("Best test {} ap is {:0.3f}.".format(
                             key, self.best_ap))

ppdet/engine/trainer.py

@@ -28,6 +28,7 @@ import paddle.distributed as dist
 from paddle.distributed import fleet
 from paddle import amp
 from paddle.static import InputSpec
+from ppdet.optimizer import ModelEMA
 
 from ppdet.core.workspace import create
 from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
@@ -61,6 +62,11 @@ class Trainer(object):
             self.model = self.cfg.model
             self.is_loaded_weights = True
 
+        self.use_ema = ('use_ema' in cfg and cfg['use_ema'])
+        if self.use_ema:
+            self.ema = ModelEMA(
+                cfg['ema_decay'], self.model, use_thres_step=True)
+
         # build data loader
         self.dataset = cfg['{}Dataset'.format(self.mode.capitalize())]
         if self.mode == 'train':
@@ -281,8 +287,15 @@ class Trainer(object):
 
                 self.status['batch_time'].update(time.time() - iter_tic)
                 self._compose_callback.on_step_end(self.status)
+                if self.use_ema:
+                    self.ema.update(self.model)
                 iter_tic = time.time()
 
+            # apply ema weight on model
+            if self.use_ema:
+                weight = self.model.state_dict()
+                self.model.set_dict(self.ema.apply())
+
             self._compose_callback.on_epoch_end(self.status)
 
             if validate and (self._nranks < 2 or self._local_rank == 0) \
@@ -303,6 +316,10 @@ class Trainer(object):
                     self.status['save_best_model'] = True
                     self._eval_with_loader(self._eval_loader)
 
+            # restore origin weight on model
+            if self.use_ema:
+                self.model.set_dict(weight)
+
     def _eval_with_loader(self, loader):
         sample_num = 0
         tic = time.time()