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