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276 lines
10 KiB
276 lines
10 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, division
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import unittest
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from decorator_helper import prog_scope
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import paddle
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import paddle.fluid as fluid
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import numpy
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class TestMathOpPatches(unittest.TestCase):
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def setUp(self):
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paddle.enable_static()
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@prog_scope()
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def test_add_scalar(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = a + 10
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ab = fluid.layers.concat(input=[a, b], axis=1)
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c = ab + 10
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d = ab + a
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# e = a + ab
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np, c_np, d_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b, c, d])
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self.assertTrue(numpy.allclose(a_np + 10, b_np))
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ab_np = numpy.concatenate([a_np, b_np], axis=1)
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self.assertTrue(numpy.allclose(ab_np + 10, c_np))
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d_expected = ab_np + numpy.concatenate([a_np, a_np], axis=1)
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self.assertTrue(numpy.allclose(d_expected, d_np))
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@prog_scope()
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def test_radd_scalar(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = 10 + a
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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self.assertTrue(numpy.allclose(a_np + 10, b_np))
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@prog_scope()
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def test_sub_scalar(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = a - 10
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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self.assertTrue(numpy.allclose(a_np - 10, b_np))
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@prog_scope()
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def test_radd_scalar(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = 10 - a
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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self.assertTrue(numpy.allclose(10 - a_np, b_np))
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@prog_scope()
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def test_mul_scalar(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = a * 10
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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self.assertTrue(numpy.allclose(a_np * 10, b_np))
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@prog_scope()
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def test_rmul_scalar(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = 10 * a
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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self.assertTrue(numpy.allclose(10 * a_np, b_np))
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@prog_scope()
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def test_div_scalar(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = a / 10
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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self.assertTrue(numpy.allclose(a_np / 10, b_np))
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@prog_scope()
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def test_rdiv_scalar(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = 10 / a
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32') + 1e-2
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b_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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self.assertTrue(numpy.allclose(10 / a_np, b_np))
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@prog_scope()
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def test_div_two_tensor(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = fluid.layers.data(name="b", shape=[1])
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c = a / b
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = numpy.random.random(size=[10, 1]).astype('float32') + 1e-2
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c_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np,
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'b': b_np},
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fetch_list=[c])
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self.assertTrue(numpy.allclose(a_np / b_np, c_np))
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@prog_scope()
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def test_mul_two_tensor(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = fluid.layers.data(name="b", shape=[1])
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c = a * b
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = numpy.random.random(size=[10, 1]).astype('float32')
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c_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np,
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'b': b_np},
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fetch_list=[c])
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self.assertTrue(numpy.allclose(a_np * b_np, c_np))
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@prog_scope()
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def test_add_two_tensor(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = fluid.layers.data(name="b", shape=[1])
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c = a + b
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = numpy.random.random(size=[10, 1]).astype('float32')
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c_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np,
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'b': b_np},
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fetch_list=[c])
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self.assertTrue(numpy.allclose(a_np + b_np, c_np))
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@prog_scope()
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def test_sub_two_tensor(self):
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a = fluid.layers.data(name="a", shape=[1])
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b = fluid.layers.data(name="b", shape=[1])
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c = a - b
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.random(size=[10, 1]).astype('float32')
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b_np = numpy.random.random(size=[10, 1]).astype('float32')
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c_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np,
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'b': b_np},
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fetch_list=[c])
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self.assertTrue(numpy.allclose(a_np - b_np, c_np))
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@prog_scope()
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def test_integer_div(self):
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a = fluid.layers.data(name="a", shape=[1], dtype='int64')
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b = a / 7
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.array([3, 4, 10, 14, 9, 18]).astype('int64')
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b_np, = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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b_np_actual = (a_np / 7).astype('float32')
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self.assertTrue(numpy.allclose(b_np, b_np_actual))
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@prog_scope()
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def test_equal(self):
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a = fluid.layers.data(name="a", shape=[1], dtype='float32')
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b = fluid.layers.data(name="b", shape=[1], dtype='float32')
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c = (a == b)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.array([3, 4, 10, 14, 9, 18]).astype('float32')
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b_np = numpy.array([3, 4, 11, 15, 8, 18]).astype('float32')
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c_np, = exe.run(fluid.default_main_program(),
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feed={"a": a_np,
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"b": b_np},
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fetch_list=[c])
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self.assertTrue(numpy.array_equal(c_np, a_np == b_np))
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self.assertEqual(c.dtype, fluid.core.VarDesc.VarType.BOOL)
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@prog_scope()
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def test_equal_and_cond(self):
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a = fluid.layers.data(name="a", shape=[1], dtype='float32')
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b = fluid.layers.data(name="b", shape=[1], dtype='float32')
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one = fluid.layers.ones(shape=[1], dtype='int32')
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zero = fluid.layers.zeros(shape=[1], dtype='int32')
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cond = (one == zero)
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c = fluid.layers.cond(cond, lambda: a + b, lambda: a - b)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.array([3, 4, 10, 14, 9, 18]).astype('float')
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b_np = numpy.array([3, 4, 11, 15, 8, 18]).astype('float')
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c_np, = exe.run(fluid.default_main_program(),
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feed={"a": a_np,
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"b": b_np},
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fetch_list=[c])
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self.assertTrue(numpy.array_equal(c_np, a_np - b_np))
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@prog_scope()
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def test_neg(self):
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a = fluid.layers.data(name="a", shape=[10, 1])
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b = -a
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.uniform(-1, 1, size=[10, 1]).astype('float32')
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b_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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self.assertTrue(numpy.allclose(-a_np, b_np))
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@prog_scope()
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def test_astype(self):
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a = fluid.layers.data(name="a", shape=[10, 1])
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b = a.astype('float32')
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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a_np = numpy.random.uniform(-1, 1, size=[10, 1]).astype('float64')
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b_np = exe.run(fluid.default_main_program(),
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feed={"a": a_np},
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fetch_list=[b])
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self.assertTrue(numpy.allclose(a_np.astype('float32'), b_np))
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if __name__ == '__main__':
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unittest.main()
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