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127 lines
4.4 KiB
127 lines
4.4 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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import sys
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sys.path.append("..")
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from op_test import OpTest
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from paddle.fluid import Program, program_guard
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import paddle.fluid.dygraph as dg
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import paddle.static as static
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from numpy.random import random as rand
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paddle.enable_static()
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class TestConjOp(OpTest):
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def setUp(self):
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self.op_type = "conj"
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self.init_dtype_type()
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self.init_input_output()
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self.init_grad_input_output()
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def init_dtype_type(self):
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self.dtype = np.complex64
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def init_input_output(self):
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x = (np.random.random((12, 14)) + 1j * np.random.random(
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(12, 14))).astype(self.dtype)
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out = np.conj(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def init_grad_input_output(self):
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self.grad_out = (np.ones((12, 14)) + 1j * np.ones(
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(12, 14))).astype(self.dtype)
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self.grad_in = np.conj(self.grad_out)
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(
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['X'],
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'Out',
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user_defined_grads=[self.grad_in],
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user_defined_grad_outputs=[self.grad_out])
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class TestComplexConjOp(unittest.TestCase):
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def setUp(self):
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self._dtypes = ["float32", "float64"]
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self._places = [paddle.CPUPlace()]
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if paddle.is_compiled_with_cuda():
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self._places.append(paddle.CUDAPlace(0))
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def test_conj_api(self):
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for dtype in self._dtypes:
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input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
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[2, 20, 2, 3]).astype(dtype)
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for place in self._places:
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with dg.guard(place):
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var_x = paddle.to_tensor(input)
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result = paddle.conj(var_x).numpy()
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target = np.conj(input)
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self.assertTrue(np.array_equal(result, target))
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def test_conj_operator(self):
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for dtype in self._dtypes:
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input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
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[2, 20, 2, 3]).astype(dtype)
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for place in self._places:
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with dg.guard(place):
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var_x = paddle.to_tensor(input)
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result = var_x.conj().numpy()
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target = np.conj(input)
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self.assertTrue(np.array_equal(result, target))
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def test_conj_static_mode(self):
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def init_input_output(dtype):
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input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
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[2, 20, 2, 3]).astype(dtype)
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return {'x': input}, np.conj(input)
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for dtype in self._dtypes:
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input_dict, np_res = init_input_output(dtype)
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for place in self._places:
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with static.program_guard(static.Program()):
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x_dtype = np.complex64 if dtype == "float32" else np.complex128
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x = static.data(
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name="x", shape=[2, 20, 2, 3], dtype=x_dtype)
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out = paddle.conj(x)
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exe = static.Executor(place)
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out_value = exe.run(feed=input_dict, fetch_list=[out.name])
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self.assertTrue(np.array_equal(np_res, out_value[0]))
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def test_conj_api_real_number(self):
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for dtype in self._dtypes:
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input = rand([2, 20, 2, 3]).astype(dtype)
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for place in self._places:
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with dg.guard(place):
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var_x = paddle.to_tensor(input)
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result = paddle.conj(var_x).numpy()
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target = np.conj(input)
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self.assertTrue(np.array_equal(result, target))
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if __name__ == "__main__":
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unittest.main()
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