Raise an exception when the specified module does not support evaluat…

…ion.

paddlehub/finetune/trainer.py

@@ -67,6 +67,7 @@ def __init__(self,
 
         if not isinstance(self.model, paddle.nn.Layer):
             raise TypeError('The model {} is not a `paddle.nn.Layer` object.'.format(self.model.__name__))
+
 
         if self.local_rank == 0 and not os.path.exists(self.checkpoint_dir):
             os.makedirs(self.checkpoint_dir)
@@ -178,6 +179,9 @@ def train(self,
             collate_fn(callable): function to generate mini-batch data by merging the sample list.
                 None for only stack each fields of sample in axis 0(same as :attr::`np.stack(..., axis=0)`). Default None
         '''
+        if eval_dataset is not None and not hasattr(self.model, 'validation_step'):
+            raise NotImplementedError('The specified finetuning model does not support evaluation.')
+
         batch_sampler = paddle.io.DistributedBatchSampler(
             train_dataset, batch_size=batch_size, shuffle=True, drop_last=False)
         loader = paddle.io.DataLoader(
@@ -298,6 +302,7 @@ def evaluate(self,
             with logger.processing('Evaluation on validation dataset'):
                 for batch_idx, batch in enumerate(loader):
                     result = self.validation_step(batch, batch_idx)
+
                     loss = result.get('loss', None)
                     metrics = result.get('metrics', {})
                     bs = batch[0].shape[0]
@@ -363,7 +368,7 @@ def validation_step(self, batch: Any, batch_idx: int):
             batch_idx(int) : The index of batch.
         '''
         if self.nranks > 1:
-            result = self.model._layers.validation_step(batch, batch_idx)
+            result = self.model._layers.validation_step(batch, batch_idx) 
         else:
             result = self.model.validation_step(batch, batch_idx)
         return result

paddlehub/module/cv_module.py

@@ -643,21 +643,8 @@ def training_step(self, batch: List[paddle.Tensor], batch_idx: int) -> dict:
 
         Returns:
             results(dict): The model outputs, such as loss.
+            
         '''
-
-        return self.validation_step(batch, batch_idx)
-
-    def validation_step(self, batch: List[paddle.Tensor], batch_idx: int) -> dict:
-        """
-         One step for validation, which should be called as forward computation.
-
-        Args:
-            batch(list[paddle.Tensor]): The one batch data, which contains images and labels.
-            batch_idx(int): The index of batch.
-
-        Returns:
-            results(dict) : The model outputs, such as metrics.
-        """
 
         label = batch[1].astype('int64')
         criterionCE = nn.loss.CrossEntropyLoss()
@@ -666,10 +653,12 @@ def validation_step(self, batch: List[paddle.Tensor], batch_idx: int) -> dict:
         for i in range(len(logits)):
             logit = logits[i]
             if logit.shape[-2:] != label.shape[-2:]:
-                logit = F.resize_bilinear(logit, label.shape[-2:])
+                logit = F.interpolate(logit, label.shape[-2:],  mode='bilinear')
+
             logit = logit.transpose([0,2,3,1])
             loss_ce = criterionCE(logit, label)
             loss += loss_ce / len(logits)
+
         return {"loss": loss}
 
     def predict(self, images: Union[str, np.ndarray], batch_size: int = 1, visualization: bool = True, save_path: str = 'seg_result') -> List[np.ndarray]: