Fixed the issue that the model does not support training in multi-card scene.

paddlehub/finetune/trainer.py

@@ -18,8 +18,8 @@ import time
 from collections import defaultdict
 from typing import Any, Callable, Generic, List
 
-import numpy as np
 import paddle
+import numpy as np
 from visualdl import LogWriter
 
 from paddlehub.utils.log import logger
@@ -82,6 +82,7 @@ class Trainer(object):
         if self.nranks > 1:
             paddle.distributed.init_parallel_env()
             self.model = paddle.DataParallel(self.model)
+
         self.compare_metrics = self._compare_metrics if not compare_metrics else compare_metrics
         self._load_checkpoint()
 
@@ -178,8 +179,14 @@ class Trainer(object):
             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.')
+        if eval_dataset is not None:
+            if isinstance(self.model, paddle.DataParallel):
+                model = self.model._layers
+            else:
+                model = self.model
+
+            if not hasattr(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)