# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import paddle.fluid as fluid from paddle.fluid import core class TestSaveLoadAPIError(unittest.TestCase): def test_get_valid_program_error(self): # case 1: CompiledProgram no program graph = core.Graph(core.ProgramDesc()) compiled_program = fluid.CompiledProgram(graph) with self.assertRaises(TypeError): fluid.io._get_valid_program(compiled_program) # case 2: main_program type error with self.assertRaises(TypeError): fluid.io._get_valid_program("program") def test_load_vars_error(self): place = fluid.CPUPlace() exe = fluid.Executor(place) # case 1: main_program type error when vars None with self.assertRaises(TypeError): fluid.io.load_vars( executor=exe, dirname="./fake_dir", main_program="program") # case 2: main_program type error when vars not None with self.assertRaises(TypeError): fluid.io.load_vars( executor=exe, dirname="./fake_dir", main_program="program", vars="vars") class TestSaveInferenceModelAPIError(unittest.TestCase): def test_useless_feeded_var_names(self): start_prog = fluid.Program() main_prog = fluid.Program() with fluid.program_guard(main_prog, start_prog): x = fluid.data(name='x', shape=[10, 16], dtype='float32') y = fluid.data(name='y', shape=[10, 16], dtype='float32') z = fluid.layers.fc(x, 4) exe = fluid.Executor(fluid.CPUPlace()) exe.run(start_prog) with self.assertRaisesRegexp( ValueError, "not involved in the target_vars calculation"): fluid.io.save_inference_model( dirname='./model', feeded_var_names=['x', 'y'], target_vars=[z], executor=exe, main_program=main_prog) if __name__ == '__main__': unittest.main()