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178 lines
6.7 KiB
178 lines
6.7 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.tensor as tensor
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from paddle.static import Program, program_guard
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DYNAMIC = 1
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STATIC = 2
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def _run_power(mode, x, y):
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# dynamic mode
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if mode == DYNAMIC:
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paddle.disable_static()
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# y is scalar
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if isinstance(y, (int, float)):
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x_ = paddle.to_tensor(x)
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y_ = y
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res = paddle.pow(x_, y_)
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return res.numpy()
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# y is tensor
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else:
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x_ = paddle.to_tensor(x)
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y_ = paddle.to_tensor(y)
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res = paddle.pow(x_, y_)
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return res.numpy()
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# static mode
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elif mode == STATIC:
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paddle.enable_static()
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# y is scalar
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if isinstance(y, (int, float)):
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with program_guard(Program(), Program()):
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x_ = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
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y_ = y
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res = paddle.pow(x_, y_)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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outs = exe.run(feed={'x': x}, fetch_list=[res])
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return outs[0]
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# y is tensor
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else:
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with program_guard(Program(), Program()):
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x_ = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
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y_ = paddle.static.data(name="y", shape=y.shape, dtype=y.dtype)
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res = paddle.pow(x_, y_)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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outs = exe.run(feed={'x': x, 'y': y}, fetch_list=[res])
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return outs[0]
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class TestPowerAPI(unittest.TestCase):
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"""TestPowerAPI."""
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def test_power(self):
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"""test_power."""
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np.random.seed(7)
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# test 1-d float tensor ** float scalar
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dims = (np.random.randint(200, 300), )
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x = (np.random.rand(*dims) * 10).astype(np.float64)
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y = np.random.rand() * 10
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res = _run_power(DYNAMIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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res = _run_power(STATIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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# test 1-d float tensor ** int scalar
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dims = (np.random.randint(200, 300), )
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x = (np.random.rand(*dims) * 10).astype(np.float64)
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y = int(np.random.rand() * 10)
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res = _run_power(DYNAMIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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res = _run_power(STATIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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x = (np.random.rand(*dims) * 10).astype(np.int64)
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y = int(np.random.rand() * 10)
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res = _run_power(DYNAMIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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res = _run_power(STATIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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# test 1-d float tensor ** 1-d float tensor
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dims = (np.random.randint(200, 300), )
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x = (np.random.rand(*dims) * 10).astype(np.float64)
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y = (np.random.rand(*dims) * 10).astype(np.float64)
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res = _run_power(DYNAMIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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res = _run_power(STATIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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# test 1-d int tensor ** 1-d int tensor
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dims = (np.random.randint(200, 300), )
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x = (np.random.rand(*dims) * 10).astype(np.int64)
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y = (np.random.rand(*dims) * 10).astype(np.int64)
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res = _run_power(DYNAMIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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res = _run_power(STATIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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# test 1-d int tensor ** 1-d int tensor
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dims = (np.random.randint(200, 300), )
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x = (np.random.rand(*dims) * 10).astype(np.int32)
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y = (np.random.rand(*dims) * 10).astype(np.int32)
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res = _run_power(DYNAMIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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res = _run_power(STATIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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# test 1-d int tensor ** 1-d int tensor
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dims = (np.random.randint(200, 300), )
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x = (np.random.rand(*dims) * 10).astype(np.float32)
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y = (np.random.rand(*dims) * 10).astype(np.float32)
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res = _run_power(DYNAMIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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res = _run_power(STATIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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# test broadcast
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dims = (np.random.randint(1, 10), np.random.randint(5, 10),
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np.random.randint(5, 10))
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x = (np.random.rand(*dims) * 10).astype(np.float64)
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y = (np.random.rand(dims[-1]) * 10).astype(np.float64)
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res = _run_power(DYNAMIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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res = _run_power(STATIC, x, y)
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self.assertTrue(np.allclose(res, np.power(x, y)))
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class TestPowerError(unittest.TestCase):
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"""TestPowerError."""
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def test_errors(self):
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"""test_errors."""
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np.random.seed(7)
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# test dynamic computation graph: inputs must be broadcastable
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dims = (np.random.randint(1, 10), np.random.randint(5, 10),
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np.random.randint(5, 10))
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x = (np.random.rand(*dims) * 10).astype(np.float64)
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y = (np.random.rand(dims[-1] + 1) * 10).astype(np.float64)
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self.assertRaises(ValueError, _run_power, DYNAMIC, x, y)
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self.assertRaises(ValueError, _run_power, STATIC, x, y)
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# test dynamic computation graph: inputs must be broadcastable
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dims = (np.random.randint(1, 10), np.random.randint(5, 10),
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np.random.randint(5, 10))
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x = (np.random.rand(*dims) * 10).astype(np.float64)
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y = (np.random.rand(dims[-1] + 1) * 10).astype(np.int8)
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self.assertRaises(TypeError, paddle.pow, x, y)
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# test 1-d float tensor ** int string
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dims = (np.random.randint(200, 300), )
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x = (np.random.rand(*dims) * 10).astype(np.float64)
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y = int(np.random.rand() * 10)
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self.assertRaises(TypeError, paddle.pow, x, str(y))
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if __name__ == '__main__':
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unittest.main()
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