fix reviews

model.py

@@ -905,7 +905,7 @@ class Model(fluid.dygraph.Layer):
             eval_freq (int): The frequency, in number of epochs, an evalutation
                 is performed.
             log_freq (int): The frequency, in number of steps, the training logs
-                is printed.
+                are printed.
             save_dir(str|None): The directory to save checkpoint during training.
                 If None, will not save checkpoint.
             save_freq (int): The frequency, in number of epochs, to save checkpoint.
@@ -987,14 +987,22 @@ class Model(fluid.dygraph.Layer):
                 loader = train_loader()
             logs = self._run_one_epoch(
                 loader, cbks, 'train', metrics_name, epoch=epoch)
-            cbks.on_epoch_end(epoch, logs)
 
             if do_eval and epoch % eval_freq == 0:
                 # FIXME: adapt to DataLoader
                 loader = eval_loader
                 if not isinstance(eval_loader, Iterable):
                     loader = eval_loader()
+
+                eval_steps = len(loader) if hasattr(loader,
+                                                    '__len__') else None
+                cbks.on_begin('eval', {
+                    'steps': eval_steps,
+                    'metrics_name': metrics_name
+                })
+
                 logs = self._run_one_epoch(loader, cbks, 'eval', metrics_name)
+
                 cbks.on_end('eval', logs)
 
         cbks.on_end('train', logs)
@@ -1017,11 +1025,11 @@ class Model(fluid.dygraph.Layer):
             batch_size (int): Integer number. The batch size of train_data and eval_data. 
                 When train_data and eval_data are both the instance of Dataloader, this 
                 parameter will be ignored.
-            log_freq (int): The frequency, in number of steps, the training logs
-                is printed.
+            log_freq (int): The frequency, in number of steps, the eval logs
+                are printed.
             verbose (int): The verbosity mode, should be 0, 1, or 2.
                 0 = silent, 1 = progress bar, 2 = one line per epoch.
-            num_workers (int): the number of subprocess to load data, 0 for no subprocess 
+            num_workers (int): The number of subprocess to load data, 0 for no subprocess 
                 used and loading data in main process. When train_data and eval_data are
                 both the instance of Dataloader, this parameter will be ignored.
             callbacks (Callback|None): A list of `Callback` instances to apply
@@ -1050,12 +1058,9 @@ class Model(fluid.dygraph.Layer):
         self._test_dataloader = eval_loader
         metrics_name = self._metrics_name()
 
-        steps = len(eval_loader)
-
         cbks = config_callbacks(
             callbacks,
             model=self,
-            steps=steps,
             log_freq=log_freq,
             verbose=verbose,
             metrics=self._metrics_name(), )
@@ -1063,7 +1068,14 @@ class Model(fluid.dygraph.Layer):
         loader = eval_loader
         if not isinstance(eval_loader, Iterable):
             loader = eval_loader()
+
+        eval_steps = len(loader) if hasattr(loader, '__len__') else None
+        cbks.on_begin('eval',
+                      {'steps': eval_steps,
+                       'metrics_name': metrics_name})
+
         logs = self._run_one_epoch(loader, cbks, 'eval', metrics_name)
+
         cbks.on_end('eval', logs)
 
         self._test_dataloader = None
@@ -1157,9 +1169,8 @@ class Model(fluid.dygraph.Layer):
             'metrics_name': metrics_name,
         }
 
-        callbacks.on_begin(mode, logs)
         if mode == 'train':
-            assert epoch is not None, 'when mode is train, '
+            assert epoch is not None, 'when mode is train, epoch must be given'
             callbacks.on_epoch_begin(epoch)
 
         for step, data in enumerate(data_loader):
@@ -1198,6 +1209,11 @@ class Model(fluid.dygraph.Layer):
 
             callbacks.on_batch_end(mode, step, logs)
         self._reset_metrics()
+
+        if mode == 'train':
+            assert epoch is not None, 'when mode is train, epoch must be given'
+            callbacks.on_epoch_end(epoch)
+
         return logs
 
     def _reset_metrics(self):