Return VarBase of ProgramTranslator.get_output instead of numpy.ndarray. test=develop (#23663)

python/paddle/fluid/dygraph/dygraph_to_static/program_translator.py

@@ -24,6 +24,7 @@ from collections import defaultdict
 
 from paddle.fluid import framework
 from paddle.fluid import core, executor
+from paddle.fluid.dygraph import guard, to_variable
 from paddle.fluid.dygraph.dygraph_to_static.ast_transformer import convert_to_static
 from paddle.fluid.dygraph.dygraph_to_static.ast_transformer import DygraphToStaticAst
 from paddle.fluid.dygraph.dygraph_to_static.utils import ast_to_source_code
@@ -285,6 +286,8 @@ class ProgramTranslator(object):
                                                                 *args, **kwargs)
         if not program_cache.in_build_process:
             outputs = self.run(*args, **kwargs)
+            with guard():
+                outputs = [to_variable(x) for x in outputs]
         return outputs
 
     def get_func(self, dygraph_func):

python/paddle/fluid/tests/unittests/dygraph_to_static/test_cache_program.py

@@ -50,7 +50,7 @@ class TestCacheProgram(unittest.TestCase):
                 ])
                 if batch_id > 0:
                     self.assertTrue(
-                        np.allclose(prev_out[0], cur_out[0]),
+                        np.allclose(prev_out[0].numpy(), cur_out[0].numpy()),
                         msg='Output in previous batch is {}\n Output in current batch is \n{}'
                         .format(prev_out, cur_out))
                     self.assertEqual(prev_ops, cur_ops)
@@ -81,7 +81,7 @@ class TestCacheProgramWithOptimizer(unittest.TestCase):
 
             for batch_id in range(self.batch_num):
                 pred, avg_loss = static_net(self.data)
-                loss_data.append(np.array(avg_loss))
+                loss_data.append(np.array(avg_loss.numpy()))
 
         return loss_data
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_fetch_feed.py

@@ -82,7 +82,7 @@ class TestPool2D(unittest.TestCase):
         with fluid.program_guard(main_prog, startup_prog):
             dy_layer = self.dygraph_class()
             out = dy_layer(x=self.data)
-            return out[0]
+            return out[0].numpy()
 
     def test_static_output(self):
         dygraph_res = self.run_dygraph_mode()