fix predict

examples/predict/run.py

@@ -43,7 +43,7 @@ if __name__ == '__main__':
     trainer.build_predict_forward(pred_ernie, cls_pred_head)
  
     # step 6: load pretrained model
-    pred_model = trainer.load_pretrain(pre_params)
+    pred_model = trainer.load_predict_model(pre_params)
 
     # step 7: fit prepared reader and data
     trainer.fit_reader(predict_cls_reader, phase='predict')

paddlepalm/trainer.py

@@ -359,7 +359,10 @@ class Trainer(object):
         # load data
 
         self._check_phase(phase)
-        assert self._shape_and_dtypes is not None or self._pred_shape_and_dtypes is not None, "You need to build_forward or build_predict_head first to prepare input features."
+        if phase=='train':
+            assert self._shape_and_dtypes is not None, "You need to build_forward or build_predict_head first to prepare input features."
+        else:
+            assert self._pred_shape_and_dtypes is not None, "You need to build_forward     or build_predict_head first to prepare input features."
 
         # 这里不确定是否要向上取整，需确认
         # tail = self._num_examples % batch_size > 0
@@ -463,8 +466,22 @@ class Trainer(object):
         else:
             raise Exception("model not found. You should at least build_forward or build_predict_forward to load its checkpoint.")
             
-    def load_predict_model(self, model_path):
-        raise NotImplementedError()
+    def load_predict_model(self, model_path, convert=False):
+        """
+        load pretrain models(backbone) for training.
+
+        Args:
+            model_path: the path of saved pretrained parameters.
+        """
+
+        assert self._pred_prog is not None, "training graph not found. You should at least build_forward to load its pretrained parameters."
+
+        saver.init_pretraining_params(
+            self._exe,
+            model_path,
+            convert=convert,
+            main_program=self._pred_prog)
+        # raise NotImplementedError()
 
     def load_pretrain(self, model_path, convert=False):
         """
@@ -717,7 +734,7 @@ class Trainer(object):
         if gpu_dev_count > 1:
             feed, mask = batch
             rt_outputs = self._exe.run(self._distribute_pred_prog, feed=feed, fetch_list=self._pred_fetch_list)
-            num_fakes = decode_fake(len(rt_outputs[0]), mask, self._batch_size)
+            num_fakes = decode_fake(len(rt_outputs[0]), mask, self._predict_batch_size)
             for _ in range(num_fakes):
                 for item in rt_outputs:
                     item.pop()