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164 lines
5.8 KiB
164 lines
5.8 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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import unittest
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import numpy as np
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from numpy.random import random as rand
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.dygraph as dg
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from paddle import complex as cpx
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layers = {
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"add": cpx.elementwise_add,
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"sub": cpx.elementwise_sub,
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"mul": cpx.elementwise_mul,
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"div": cpx.elementwise_div,
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}
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paddle_apis = {
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"add": paddle.add,
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"sub": paddle.subtract,
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"mul": paddle.multiply,
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"div": paddle.divide,
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}
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class TestComplexElementwiseLayers(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 fluid.core.is_compiled_with_cuda():
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self._places.append(paddle.CUDAPlace(0))
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def calc(self, x, y, op, place):
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with dg.guard(place):
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var_x = dg.to_variable(x)
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var_y = dg.to_variable(y)
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return layers[op](var_x, var_y).numpy()
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def paddle_calc(self, x, y, op, place):
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with dg.guard(place):
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x_t = paddle.Tensor(
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value=x,
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place=place,
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persistable=False,
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zero_copy=False,
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stop_gradient=True)
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y_t = paddle.Tensor(
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value=y,
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place=place,
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persistable=False,
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zero_copy=False,
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stop_gradient=True)
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return paddle_apis[op](x_t, y_t).numpy()
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def assert_check(self, pd_result, np_result, place):
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self.assertTrue(
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np.allclose(pd_result, np_result),
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"\nplace: {}\npaddle diff result:\n {}\nnumpy diff result:\n {}\n".
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format(place, pd_result[~np.isclose(pd_result, np_result)],
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np_result[~np.isclose(pd_result, np_result)]))
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def compare_by_complex_api(self, x, y):
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for place in self._places:
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self.assert_check(self.calc(x, y, "add", place), x + y, place)
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self.assert_check(self.calc(x, y, "sub", place), x - y, place)
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self.assert_check(self.calc(x, y, "mul", place), x * y, place)
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self.assert_check(self.calc(x, y, "div", place), x / y, place)
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def compare_by_basic_api(self, x, y):
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for place in self._places:
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self.assert_check(
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self.paddle_calc(x, y, "add", place), x + y, place)
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self.assert_check(
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self.paddle_calc(x, y, "sub", place), x - y, place)
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self.assert_check(
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self.paddle_calc(x, y, "mul", place), x * y, place)
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self.assert_check(
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self.paddle_calc(x, y, "div", place), x / y, place)
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def compare_op_by_complex_api(self, x, y):
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for place in self._places:
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with dg.guard(place):
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var_x = dg.to_variable(x)
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var_y = dg.to_variable(y)
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self.assert_check((var_x + var_y).numpy(), x + y, place)
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self.assert_check((var_x - var_y).numpy(), x - y, place)
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self.assert_check((var_x * var_y).numpy(), x * y, place)
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self.assert_check((var_x / var_y).numpy(), x / y, place)
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def compare_op_by_basic_api(self, x, y):
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for place in self._places:
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with dg.guard(place):
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x_t = paddle.Tensor(
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value=x,
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place=place,
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persistable=False,
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zero_copy=False,
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stop_gradient=True)
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y_t = paddle.Tensor(
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value=y,
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place=place,
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persistable=False,
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zero_copy=False,
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stop_gradient=True)
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self.assert_check((x_t + y_t).numpy(), x + y, place)
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self.assert_check((x_t - y_t).numpy(), x - y, place)
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self.assert_check((x_t * y_t).numpy(), x * y, place)
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self.assert_check((x_t / y_t).numpy(), x / y, place)
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def test_complex_xy(self):
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for dtype in self._dtypes:
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x = rand([2, 3, 4, 5]).astype(dtype) + 1j * rand(
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[2, 3, 4, 5]).astype(dtype)
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y = rand([2, 3, 4, 5]).astype(dtype) + 1j * rand(
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[2, 3, 4, 5]).astype(dtype)
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self.compare_by_complex_api(x, y)
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self.compare_op_by_complex_api(x, y)
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self.compare_op_by_complex_api(x, y)
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self.compare_op_by_basic_api(x, y)
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def test_complex_x_real_y(self):
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for dtype in self._dtypes:
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x = rand([2, 3, 4, 5]).astype(dtype) + 1j * rand(
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[2, 3, 4, 5]).astype(dtype)
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y = rand([4, 5]).astype(dtype)
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self.compare_by_complex_api(x, y)
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self.compare_op_by_complex_api(x, y)
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# promote types cases
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self.compare_by_basic_api(x, y)
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self.compare_op_by_basic_api(x, y)
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def test_real_x_complex_y(self):
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for dtype in self._dtypes:
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x = rand([2, 3, 4, 5]).astype(dtype)
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y = rand([5]).astype(dtype) + 1j * rand([5]).astype(dtype)
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self.compare_by_complex_api(x, y)
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self.compare_op_by_complex_api(x, y)
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# promote types cases
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self.compare_by_basic_api(x, y)
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self.compare_op_by_basic_api(x, y)
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if __name__ == '__main__':
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unittest.main()
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