# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function, division import unittest import numpy as np import paddle from paddle.fluid.framework import _test_eager_guard class TestComplexCastOp(unittest.TestCase): def test_complex_to_real(self): r = np.random.random(size=[10, 10]) * 10 i = np.random.random(size=[10, 10]) c_t = paddle.to_tensor(r + i * 1J, dtype='complex64') self.assertEqual(c_t.cast('int64').dtype, paddle.int64) self.assertEqual(c_t.cast('int32').dtype, paddle.int32) self.assertEqual(c_t.cast('float32').dtype, paddle.float32) self.assertEqual(c_t.cast('float64').dtype, paddle.float64) self.assertEqual(c_t.cast('bool').dtype, paddle.bool) self.assertTrue( np.allclose(c_t.cast('int64').numpy(), r.astype('int64'))) self.assertTrue( np.allclose(c_t.cast('int32').numpy(), r.astype('int32'))) self.assertTrue( np.allclose(c_t.cast('float32').numpy(), r.astype('float32'))) self.assertTrue( np.allclose(c_t.cast('float64').numpy(), r.astype('float64'))) self.assertTrue(np.allclose(c_t.cast('bool').numpy(), r.astype('bool'))) def test_real_to_complex(self): r = np.random.random(size=[10, 10]) * 10 r_t = paddle.to_tensor(r) self.assertEqual(r_t.cast('complex64').dtype, paddle.complex64) self.assertEqual(r_t.cast('complex128').dtype, paddle.complex128) self.assertTrue(np.allclose(r_t.cast('complex64').real().numpy(), r)) self.assertTrue(np.allclose(r_t.cast('complex128').real().numpy(), r)) def test_complex64_complex128(self): r = np.random.random(size=[10, 10]) i = np.random.random(size=[10, 10]) c = r + i * 1J c_64 = paddle.to_tensor(c, dtype='complex64') c_128 = paddle.to_tensor(c, dtype='complex128') self.assertTrue(c_64.cast('complex128').dtype, paddle.complex128) self.assertTrue(c_128.cast('complex128').dtype, paddle.complex64) self.assertTrue( np.allclose(c_64.cast('complex128').numpy(), c_128.numpy())) self.assertTrue( np.allclose(c_128.cast('complex128').numpy(), c_64.numpy())) def test_eager(self): with _test_eager_guard(): self.test_complex64_complex128() self.test_real_to_complex() self.test_complex_to_real() if __name__ == '__main__': unittest.main()