Solve the conflict of ops with the same name, test for CI. (#23573)
* solve the conflict of ops with the same name. test=developrevert-23830-2.0-beta
Zhen Wang
5 years ago
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GitHub
parent
795a0a9ab4
commit
84cd45f674
@ -0,0 +1,99 @@
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# Copyright (c) 2018 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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import paddle.fluid as fluid
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import numpy as np
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import unittest
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class TestOpNameConflict(unittest.TestCase):
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def test_conflict(self):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, startup):
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x = fluid.data(name="x", shape=[1], dtype='float32')
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y = fluid.data(name="y", shape=[1], dtype='float32')
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z = fluid.data(name="z", shape=[1], dtype='float32')
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m = fluid.layers.elementwise_add(x, y, name="add")
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n = fluid.layers.elementwise_add(y, z, name="add")
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p = m + n
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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m_v, n_v, p_v = exe.run(feed={
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"x": np.ones((1), "float32") * 2,
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"y": np.ones((1), "float32") * 3,
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"z": np.ones((1), "float32") * 5
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},
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fetch_list=[m, n, p])
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self.assertEqual(m_v[0], 5.0)
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self.assertEqual(n_v[0], 8.0)
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self.assertEqual(p_v[0], 13.0)
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def test_layers(self):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, startup):
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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data = fluid.data(
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name='data', shape=[None, 1, 2, 2], dtype='float32')
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tensor = fluid.data(
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name='tensor', shape=[None, 32, 64], dtype='float32')
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x = fluid.data(
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name='x', shape=[None, 1], dtype='float32', lod_level=1)
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input_scale = fluid.layers.create_parameter(
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shape=[1],
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dtype="float32",
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default_initializer=fluid.initializer.Constant(2.0))
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input_bias = fluid.layers.create_parameter(
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shape=[1],
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dtype="float32",
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default_initializer=fluid.initializer.Constant(0.5))
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out_affine = fluid.layers.affine_channel(
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data, scale=input_scale, bias=input_bias)
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out_similarity = fluid.layers.similarity_focus(
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input=data, axis=1, indexes=[0])
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position_tensor = fluid.layers.add_position_encoding(
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input=tensor, alpha=1.0, beta=1.0)
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x_reversed = fluid.layers.sequence_reverse(x)
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exe.run(fluid.default_startup_program())
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test_program = fluid.default_main_program().clone(for_test=True)
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x_d = fluid.create_lod_tensor(
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np.array([[1.1], [2.2], [3.3], [4.4]]).astype('float32'),
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[[1, 3]], place)
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outs = exe.run(
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test_program,
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fetch_list=[
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out_affine, out_similarity, position_tensor, x_reversed
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],
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feed={
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data.name: np.ones([1, 1, 2, 2]).astype('float32'),
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tensor.name: np.ones([1, 32, 64]).astype('float32'),
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x.name: x_d
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},
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return_numpy=False)
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if __name__ == '__main__':
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unittest.main()
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