update

python/paddle/hapi/model.py

@@ -737,7 +737,7 @@ class DynamicGraphAdapter(object):
             if update:
                 self.model._optimizer.minimize(final_loss)
                 self.model.network.clear_gradients()
-            
+
         metrics = []
         for metric in self.model._metrics:
             metric_outs = metric.compute(*(to_list(outputs) + labels))
@@ -1538,7 +1538,7 @@ class Model(object):
             drop_last=False,
             shuffle=True,
             num_workers=0,
-            callbacks=None, 
+            callbacks=None,
             accumulate=1, ):
         """
         Trains the model for a fixed number of epochs. If `eval_data` is set,
@@ -1703,8 +1703,9 @@ class Model(object):
 
         do_eval = eval_loader is not None
         self._test_dataloader = eval_loader
+
         self._accumulate = accumulate
-        
+
         steps = self._len_data_loader(train_loader)
         cbks = config_callbacks(
             callbacks,
@@ -1742,7 +1743,7 @@ class Model(object):
 
         cbks.on_end('train', logs)
         self._test_dataloader = None
-        
+
     def evaluate(
             self,
             eval_data,
@@ -2009,7 +2010,12 @@ class Model(object):
                 model_filename=model_filename,
                 params_filename=params_filename)
 
-    def _run_one_epoch(self, data_loader, callbacks, mode, logs={},):
+    def _run_one_epoch(
+            self,
+            data_loader,
+            callbacks,
+            mode,
+            logs={}, ):
         outputs = []
         for step, data in enumerate(data_loader):
             # data might come from different types of data_loader and have
@@ -2033,13 +2039,13 @@ class Model(object):
             callbacks.on_batch_begin(mode, step, logs)
 
             if mode != 'predict':
-                
+
                 _inputs = [data[:len(self._inputs)], data[len(self._inputs):]]
                 if mode == 'train':
                     _inputs.append((step + 1) % self._accumulate == 0)
-                    
+
                 outs = getattr(self, mode + '_batch')(*_inputs)
-                
+
                 # outs = getattr(self, mode + '_batch')(data[:len(self._inputs)],
                 #                                       data[len(self._inputs):])