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87 lines
3.2 KiB
87 lines
3.2 KiB
# 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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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class TestSignOp(OpTest):
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def setUp(self):
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self.op_type = "sign"
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self.inputs = {
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'X': np.random.uniform(-10, 10, (10, 10)).astype("float64")
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}
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self.outputs = {'Out': np.sign(self.inputs['X'])}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestSignOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The input type of sign_op must be Variable or numpy.ndarray.
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input1 = 12
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self.assertRaises(TypeError, fluid.layers.sign, input1)
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# The input dtype of sign_op must be float16, float32, float64.
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input2 = fluid.layers.data(
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name='input2', shape=[12, 10], dtype="int32")
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input3 = fluid.layers.data(
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name='input3', shape=[12, 10], dtype="int64")
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self.assertRaises(TypeError, fluid.layers.sign, input2)
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self.assertRaises(TypeError, fluid.layers.sign, input3)
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input4 = fluid.layers.data(
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name='input4', shape=[4], dtype="float16")
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fluid.layers.sign(input4)
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class TestSignAPI(unittest.TestCase):
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def test_dygraph(self):
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with fluid.dygraph.guard():
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np_x = np.array([-1., 0., -0., 1.2, 1.5], dtype='float64')
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x = paddle.to_tensor(np_x)
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z = paddle.sign(x)
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np_z = z.numpy()
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z_expected = np.sign(np_x)
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self.assertEqual((np_z == z_expected).all(), True)
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def test_static(self):
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with program_guard(Program(), Program()):
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# The input type of sign_op must be Variable or numpy.ndarray.
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input1 = 12
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self.assertRaises(TypeError, paddle.tensor.math.sign, input1)
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# The input dtype of sign_op must be float16, float32, float64.
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input2 = fluid.layers.data(
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name='input2', shape=[12, 10], dtype="int32")
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input3 = fluid.layers.data(
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name='input3', shape=[12, 10], dtype="int64")
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self.assertRaises(TypeError, paddle.tensor.math.sign, input2)
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self.assertRaises(TypeError, paddle.tensor.math.sign, input3)
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input4 = fluid.layers.data(
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name='input4', shape=[4], dtype="float16")
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paddle.sign(input4)
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if __name__ == "__main__":
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unittest.main()
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