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114 lines
4.8 KiB
114 lines
4.8 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 numpy as np
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import paddle
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import paddle.fluid.core as core
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class ApiMaximumTest(unittest.TestCase):
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def setUp(self):
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if core.is_compiled_with_cuda():
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self.place = core.CUDAPlace(0)
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else:
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self.place = core.CPUPlace()
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self.input_x = np.random.rand(10, 15).astype("float32")
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self.input_y = np.random.rand(10, 15).astype("float32")
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self.input_z = np.random.rand(15).astype("float32")
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self.input_a = np.array([0, np.nan, np.nan]).astype('int64')
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self.input_b = np.array([2, np.inf, -np.inf]).astype('int64')
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self.input_c = np.array([4, 1, 3]).astype('int64')
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self.np_expected1 = np.maximum(self.input_x, self.input_y)
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self.np_expected2 = np.maximum(self.input_x, self.input_z)
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self.np_expected3 = np.maximum(self.input_a, self.input_c)
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self.np_expected4 = np.maximum(self.input_b, self.input_c)
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def test_static_api(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
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data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
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result_max = paddle.maximum(data_x, data_y)
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exe = paddle.static.Executor(self.place)
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res, = exe.run(feed={"x": self.input_x,
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"y": self.input_y},
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fetch_list=[result_max])
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self.assertTrue(np.allclose(res, self.np_expected1))
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
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data_z = paddle.static.data("z", shape=[15], dtype="float32")
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result_max = paddle.maximum(data_x, data_z)
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exe = paddle.static.Executor(self.place)
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res, = exe.run(feed={"x": self.input_x,
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"z": self.input_z},
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fetch_list=[result_max])
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self.assertTrue(np.allclose(res, self.np_expected2))
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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data_a = paddle.static.data("a", shape=[3], dtype="int64")
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data_c = paddle.static.data("c", shape=[3], dtype="int64")
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result_max = paddle.maximum(data_a, data_c)
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exe = paddle.static.Executor(self.place)
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res, = exe.run(feed={"a": self.input_a,
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"c": self.input_c},
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fetch_list=[result_max])
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self.assertTrue(np.allclose(res, self.np_expected3))
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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data_b = paddle.static.data("b", shape=[3], dtype="int64")
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data_c = paddle.static.data("c", shape=[3], dtype="int64")
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result_max = paddle.maximum(data_b, data_c)
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exe = paddle.static.Executor(self.place)
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res, = exe.run(feed={"b": self.input_b,
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"c": self.input_c},
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fetch_list=[result_max])
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self.assertTrue(np.allclose(res, self.np_expected4))
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def test_dynamic_api(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.input_x)
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y = paddle.to_tensor(self.input_y)
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z = paddle.to_tensor(self.input_z)
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a = paddle.to_tensor(self.input_a)
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b = paddle.to_tensor(self.input_b)
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c = paddle.to_tensor(self.input_c)
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res = paddle.maximum(x, y)
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res = res.numpy()
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self.assertTrue(np.allclose(res, self.np_expected1))
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# test broadcast
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res = paddle.maximum(x, z)
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res = res.numpy()
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self.assertTrue(np.allclose(res, self.np_expected2))
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res = paddle.maximum(a, c)
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res = res.numpy()
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self.assertTrue(np.allclose(res, self.np_expected3))
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res = paddle.maximum(b, c)
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res = res.numpy()
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self.assertTrue(np.allclose(res, self.np_expected4))
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