add check of executor

python/paddle/fluid/io.py

@@ -25,6 +25,7 @@
 from paddle.fluid.executor import Executor
 from paddle.fluid.evaluator import Evaluator
 from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable, program_guard
+from paddle.fluid.compiler import CompiledProgram
 from paddle.fluid.log_helper import get_logger
 from . import reader
 from .reader import *
@@ -177,6 +178,7 @@ def name_has_fc(var):
             # saved in the same file named 'var_file' in the path "./my_paddle_vars".
     """
     save_dirname = os.path.normpath(dirname)
+
     if vars is None:
         if main_program is None:
             main_program = default_main_program()
@@ -425,7 +427,7 @@ def is_valid(var):
         return is_valid
 
     if not isinstance(main_program, Program):
-        raise ValueError("'main_program' should be an instance of Program.")
+        raise TypeError("'main_program' should be an instance of Program.")
 
     if not main_program._is_distributed:
         raise ValueError(
@@ -594,6 +596,7 @@ def name_has_fc(var):
             # been saved in the same file named 'var_file' in the path "./my_paddle_vars".
     """
     load_dirname = os.path.normpath(dirname)
+
     if vars is None:
         if main_program is None:
             main_program = default_main_program()
@@ -612,6 +615,7 @@ def name_has_fc(var):
 
         if main_program is None:
             main_program = default_main_program()
+
         if not isinstance(main_program, Program):
             raise TypeError("program should be as Program type or None")
 
@@ -845,7 +849,7 @@ def __load_persistable_vars(executor, dirname, need_load_vars):
         executor.run(load_prog)
 
     if not isinstance(main_program, Program):
-        raise ValueError("'main_program' should be an instance of Program.")
+        raise TypeError("'main_program' should be an instance of Program.")
 
     if not main_program._is_distributed:
         raise ValueError(
@@ -1009,6 +1013,9 @@ def save_inference_model(dirname,
                                             we save the original program as inference model.",
                 RuntimeWarning)
 
+    elif not isinstance(main_program, Program):
+        raise TypeError("program should be as Program type or None")
+
     # fix the bug that the activation op's output as target will be pruned.
     # will affect the inference performance.
     # TODO(Superjomn) add an IR pass to remove 1-scale op.

python/paddle/fluid/tests/unittests/test_inference_model_io.py

@@ -23,6 +23,7 @@
 import paddle.fluid.executor as executor
 import paddle.fluid.layers as layers
 import paddle.fluid.optimizer as optimizer
+from paddle.fluid.compiler import CompiledProgram
 from paddle.fluid.framework import Program, program_guard
 from paddle.fluid.io import save_inference_model, load_inference_model
 from paddle.fluid.transpiler import memory_optimize
@@ -114,5 +115,36 @@ def test_save_inference_model(self):
         save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
 
 
+class TestInstance(unittest.TestCase):
+    def test_save_inference_model(self):
+        MODEL_DIR = "./tmp/inference_model3"
+        init_program = Program()
+        program = Program()
+
+        # fake program without feed/fetch
+        with program_guard(program, init_program):
+            x = layers.data(name='x', shape=[2], dtype='float32')
+            y = layers.data(name='y', shape=[1], dtype='float32')
+
+            y_predict = layers.fc(input=x, size=1, act=None)
+
+            cost = layers.square_error_cost(input=y_predict, label=y)
+            avg_cost = layers.mean(cost)
+
+        place = core.CPUPlace()
+        exe = executor.Executor(place)
+        exe.run(init_program, feed={}, fetch_list=[])
+
+        # will print warning message
+
+        cp_prog = CompiledProgram(program).with_data_parallel(
+            loss_name=avg_cost.name)
+
+        self.assertRaises(TypeError, save_inference_model,
+                          [MODEL_DIR, ["x", "y"], [avg_cost], exe, cp_prog])
+        self.assertRaises(TypeError, save_inference_model,
+                          [MODEL_DIR, ["x", "y"], [avg_cost], [], cp_prog])
+
+
 if __name__ == '__main__':
     unittest.main()