# 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. import contextlib import re import sys import unittest import numpy as np import paddle import scipy.fft DEVICES = [paddle.CPUPlace()] if paddle.is_compiled_with_cuda(): DEVICES.append(paddle.CUDAPlace(0)) TEST_CASE_NAME = 'suffix' # All test case will use float64 for compare percision, refs: # https://github.com/PaddlePaddle/Paddle/wiki/Upgrade-OP-Precision-to-Float64 RTOL = { 'float32': 1e-03, 'complex64': 1e-3, 'float64': 1e-7, 'complex128': 1e-7 } ATOL = {'float32': 0.0, 'complex64': 0, 'float64': 0.0, 'complex128': 0} def rand_x(dims=1, dtype='float64', min_dim_len=1, max_dim_len=10, complex=False): shape = [np.random.randint(min_dim_len, max_dim_len) for i in range(dims)] if complex: return np.random.randn(*shape).astype(dtype) + 1.j * np.random.randn( *shape).astype(dtype) else: return np.random.randn(*shape).astype(dtype) def place(devices, key='place'): def decorate(cls): module = sys.modules[cls.__module__].__dict__ raw_classes = { k: v for k, v in module.items() if k.startswith(cls.__name__) } for raw_name, raw_cls in raw_classes.items(): for d in devices: test_cls = dict(raw_cls.__dict__) test_cls.update({key: d}) new_name = raw_name + '.' + d.__class__.__name__ module[new_name] = type(new_name, (raw_cls, ), test_cls) del module[raw_name] return cls return decorate def parameterize(fields, values=None): fields = [fields] if isinstance(fields, str) else fields params = [dict(zip(fields, vals)) for vals in values] def decorate(cls): test_cls_module = sys.modules[cls.__module__].__dict__ for k, v in enumerate(params): test_cls = dict(cls.__dict__) test_cls.update(v) name = cls.__name__ + str(k) name = name + '.' + v.get('suffix') if v.get('suffix') else name test_cls_module[name] = type(name, (cls, ), test_cls) for m in list(cls.__dict__): if m.startswith("test"): delattr(cls, m) return cls return decorate @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [('test_x_float64', rand_x(5, np.float64), None, -1, 'backward'), ('test_x_complex', rand_x( 5, complex=True), None, -1, 'backward'), ('test_n_grater_input_length', rand_x( 5, max_dim_len=5), 11, -1, 'backward'), ('test_n_smaller_than_input_length', rand_x( 5, min_dim_len=5, complex=True), 3, -1, 'backward'), ('test_axis_not_last', rand_x(5), None, 3, 'backward'), ('test_norm_forward', rand_x(5), None, 3, 'forward'), ('test_norm_ortho', rand_x(5), None, 3, 'ortho')]) class TestFft(unittest.TestCase): def test_fft(self): with paddle.fluid.dygraph.guard(self.place): self.assertTrue( np.allclose( scipy.fft.fft(self.x, self.n, self.axis, self.norm), paddle.fft.fft( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype)))) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [ ('test_n_nagative', rand_x(2), -1, -1, 'backward', ValueError), ('test_n_zero', rand_x(2), 0, -1, 'backward', ValueError), ('test_axis_out_of_range', rand_x(1), None, 10, 'backward', ValueError), ('test_axis_with_array', rand_x(1), None, (0, 1), 'backward', ValueError), ('test_norm_not_in_enum_value', rand_x(2), None, -1, 'random', ValueError) ]) class TestFftException(unittest.TestCase): def test_Fft(self): with self.assertRaises(self.expect_exception): paddle.fft.fft( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [ ('test_x_float64', rand_x(5), None, (0, 1), 'backward'), ('test_x_complex128', rand_x( 5, complex=True), None, (0, 1), 'backward'), ('test_n_grater_input_length', rand_x( 5, max_dim_len=5), (6, 6), (0, 1), 'backward'), ('test_n_smaller_than_input_length', rand_x( 5, min_dim_len=5, complex=True), (4, 4), (0, 1), 'backward'), ('test_axis_random', rand_x(5), None, (1, 2), 'backward'), ('test_axis_none', rand_x(5), None, None, 'backward'), ('test_norm_forward', rand_x(5), None, (0, 1), 'forward'), ('test_norm_ortho', rand_x(5), None, (0, 1), 'ortho'), ]) class TestFft2(unittest.TestCase): def test_Fft2(self): with paddle.fluid.dygraph.guard(self.place): self.assertTrue( np.allclose( scipy.fft.fft2(self.x, self.n, self.axis, self.norm), paddle.fft.fft2( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype)))) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [('test_x_complex_input', rand_x( 2, complex=True), None, (0, 1), None, ValueError), ('test_x_1dim_tensor', rand_x(1), None, (0, 1), None, ValueError), ('test_n_nagative', rand_x(2), -1, (0, 1), 'backward', ValueError), ('test_n_len_not_equal_axis', rand_x( 5, max_dim_len=5), 11, (0, 1), 'backward', ValueError), ('test_n_zero', rand_x(2), (0, 0), (0, 1), 'backward', ValueError), ('test_axis_out_of_range', rand_x(2), None, (0, 1, 2), 'backward', ValueError), ('test_axis_with_array', rand_x(1), None, (0, 1), 'backward', ValueError), ('test_axis_not_sequence', rand_x(5), None, -10, 'backward', ValueError), ('test_norm_not_enum', rand_x(2), None, -1, 'random', ValueError)]) class TestFft2Exception(unittest.TestCase): def test_fft2(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.fft2( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [('test_x_float64', rand_x(5, np.float64), None, None, 'backward'), ('test_x_complex128', rand_x( 5, complex=True), None, None, 'backward'), ('test_n_grater_input_length', rand_x( 5, max_dim_len=5), (6, 6), (1, 2), 'backward'), ( 'test_n_smaller_input_length', rand_x( 5, min_dim_len=5, complex=True), (3, 3), (1, 2), 'backward'), ('test_axis_not_default', rand_x(5), None, (1, 2), 'backward'), ('test_norm_forward', rand_x(5), None, None, 'forward'), ('test_norm_ortho', rand_x(5), None, None, 'ortho')]) class TestFftn(unittest.TestCase): def test_Fftn(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.fftn(self.x, self.n, self.axis, self.norm), paddle.fft.fftn( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype))) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [ ('test_x_complex128', (np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4) ).astype(np.complex128), None, -1, "backward"), ('test_n_grater_than_input_length', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), 4, -1, "backward"), ('test_n_smaller_than_input_length', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), 2, -1, "backward"), ('test_axis_not_last', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, 1, "backward"), ('test_norm_forward', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, 1, "forward"), ('test_norm_ortho', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, -1, "ortho"), ]) class TestHfft(unittest.TestCase): """Test hfft with norm condition """ def test_hfft(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.hfft(self.x, self.n, self.axis, self.norm), paddle.fft.hfft( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=1e-5, atol=0) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [ ('test_x_complex128', (np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4) ).astype(np.complex128), None, -1, "backward"), ('test_n_grater_than_input_length', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), 4, -1, "backward"), ('test_n_smaller_than_input_length', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), 2, -1, "backward"), ('test_axis_not_last', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, -1, "backward"), ('test_norm_forward', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, -1, "forward"), ('test_norm_ortho', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, -1, "ortho"), ]) class TestIrfft(unittest.TestCase): """Test irfft with norm condition """ def test_irfft(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.irfft(self.x, self.n, self.axis, self.norm), paddle.fft.irfft( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=1e-5, atol=0) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [ ('test_x_complex128', (np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4) ).astype(np.complex128), None, None, "backward"), ('test_n_grater_than_input_length', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), [4], None, "backward"), ('test_n_smaller_than_input_length', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), [2], None, "backward"), ('test_axis_not_last', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, None, "backward"), ('test_norm_forward', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, None, "forward"), ('test_norm_ortho', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, None, "ortho"), ]) class Testirfftn(unittest.TestCase): """Test irfftn with norm condition """ def test_irfftn(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.irfftn(self.x, self.n, self.axis, self.norm), paddle.fft.irfftn( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=1e-5, atol=0) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [ ('test_x_complex128', (np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4) ).astype(np.complex128), None, None, "backward"), ('test_n_grater_than_input_length', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), [4], None, "backward"), ('test_n_smaller_than_input_length', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), [2], None, "backward"), ('test_axis_not_last', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, None, "backward"), ('test_norm_forward', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, None, "forward"), ('test_norm_ortho', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, None, "ortho"), ]) class Testhfftn(unittest.TestCase): """Test hfftn with norm condition """ def test_hfftn(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.hfftn(self.x, self.n, self.axis, self.norm), paddle.fft.hfftn( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=1e-5, atol=0) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 's', 'axis', 'norm'), [ ('test_x_complex128', (np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4) ).astype(np.complex128), None, (-2, -1), "backward"), ('test_with_s', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), [2, 2], (-2, -1), "backward", ValueError), ('test_axis_not_last', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, (-2, -1), "backward"), ('test_norm_forward', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, (-2, -1), "forward"), ('test_norm_ortho', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, (-2, -1), "ortho"), ]) class Testhfft2(unittest.TestCase): """Test hfft2 with norm condition """ def test_hfft2(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.hfft2(self.x, self.s, self.axis, self.norm), paddle.fft.hfft2( paddle.to_tensor(self.x), self.s, self.axis, self.norm), rtol=1e-5, atol=0) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 's', 'axis', 'norm'), [ ('test_x_complex128', (np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4) ).astype(np.complex128), None, (-2, -1), "backward"), ('test_n_equal_input_length', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (4, 6), (-2, -1), "backward"), ('test_axis_not_last', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, (-2, -1), "backward"), ('test_norm_forward', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, (-2, -1), "forward"), ('test_norm_ortho', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), None, (-2, -1), "ortho"), ]) class TestIrfft2(unittest.TestCase): """Test irfft2 with norm condition """ def test_irfft2(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.irfft2(self.x, self.s, self.axis, self.norm), paddle.fft.irfft2( paddle.to_tensor(self.x), self.s, self.axis, self.norm), rtol=1e-5, atol=0) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [( 'test_bool_input', (np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4)).astype(np.bool8), None, -1, 'backward', NotImplementedError), ( 'test_n_nagative', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), -1, -1, 'backward', ValueError), ( 'test_n_zero', np.random.randn(4, 4) + 1j * np.random.randn(4, 4), 0, -1, 'backward', ValueError), ( 'test_n_type', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (1, 2, 3), -1, 'backward', ValueError), ( 'test_axis_out_of_range', np.random.randn(4) + 1j * np.random.randn(4), None, 10, 'backward', ValueError), ( 'test_axis_with_array', np.random.randn(4) + 1j * np.random.randn(4), None, (0, 1), 'backward', ValueError), ( 'test_norm_not_in_enum_value', np.random.randn(4, 4) + 1j * np.random.randn(4, 4), None, -1, 'random', ValueError)]) class TestHfftException(unittest.TestCase): '''Test hfft with buoudary condition Test case include: - n out of range - axis out of range - norm out of range ''' def test_hfft(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.hfft( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [('test_n_nagative', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), -1, -1, 'backward', ValueError), ('test_n_zero', np.random.randn(4, 4) + 1j * np.random.randn(4, 4), 0, -1, 'backward', ValueError), ('test_n_type', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (1, 2), -1, 'backward', ValueError), ('test_axis_out_of_range', np.random.randn(4) + 1j * np.random.randn(4), None, 10, 'backward', ValueError), ('test_axis_with_array', np.random.randn(4) + 1j * np.random.randn(4), None, (0, 1), 'backward', ValueError), ('test_norm_not_in_enum_value', np.random.randn(4, 4) + 1j * np.random.randn(4, 4), None, None, 'random', ValueError)]) class TestIrfftException(unittest.TestCase): '''Test Irfft with buoudary condition Test case include: - n out of range - axis out of range - norm out of range - the dimensions of n and axis are different ''' def test_irfft(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.irfft( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [('test_bool_input', (np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4) ).astype(np.bool8), None, (-2, -1), 'backward', NotImplementedError), ('test_n_nagative', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (-1, -2), (-2, -1), 'backward', ValueError), ('test_n_zero', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (0, 0), (-2, -1), 'backward', ValueError), ('test_n_type', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), 3, None, 'backward', ValueError), ('test_n_axis_dim', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (1, 2), (-1), 'backward', ValueError), ('test_axis_out_of_range', np.random.randn(4) + 1j * np.random.randn(4), None, (1, 2), 'backward', ValueError), ('test_axis_type', np.random.randn(4) + 1j * np.random.randn(4), None, -1, 'backward', ValueError), ('test_norm_not_in_enum_value', np.random.randn(4, 4) + 1j * np.random.randn(4, 4), None, None, 'random', ValueError)]) class TestHfft2Exception(unittest.TestCase): '''Test hfft2 with buoudary condition Test case include: - n out of range - axis out of range - the dimensions of n and axis are different - norm out of range ''' def test_hfft2(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.hfft2( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [('test_n_nagative', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (-1, -2), (-2, -1), 'backward', ValueError), ('test_zero_point', np.random.randn(4, 4, 1) + 1j * np.random.randn(4, 4, 1), None, (-2, -1), "backward", ValueError), ('test_n_zero', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (0, 0), (-2, -1), 'backward', ValueError), ('test_n_type', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), 3, -1, 'backward', ValueError), ('test_n_axis_dim', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (1, 2), (-3, -2, -1), 'backward', ValueError), ('test_axis_out_of_range', np.random.randn(4) + 1j * np.random.randn(4), None, (1, 2), 'backward', ValueError), ( 'test_axis_type', np.random.randn(4) + 1j * np.random.randn(4), None, 1, 'backward', ValueError), ('test_norm_not_in_enum_value', np.random.randn(4, 4) + 1j * np.random.randn(4, 4), None, None, 'random', ValueError)]) class TestIrfft2Exception(unittest.TestCase): '''Test irfft2 with buoudary condition Test case include: - n out of range - axis out of range - norm out of range - the dimensions of n and axis are different ''' def test_irfft2(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.irfft2( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [('test_bool_input', (np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4) ).astype(np.bool8), None, (-2, -1), 'backward', NotImplementedError), ('test_n_nagative', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (-1, -2), (-2, -1), 'backward', ValueError), ('test_n_zero', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (0, 0), (-2, -1), 'backward', ValueError), ('test_n_type', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), 3, -1, 'backward', ValueError), ('test_n_axis_dim', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (1, 2), (-3, -2, -1), 'backward', ValueError), ('test_axis_out_of_range', np.random.randn(4) + 1j * np.random.randn(4), None, (10, 20), 'backward', ValueError), ('test_axis_type', np.random.randn(4) + 1j * np.random.randn(4), None, 1, 'backward', ValueError), ('test_norm_not_in_enum_value', np.random.randn(4, 4) + 1j * np.random.randn(4, 4), None, None, 'random', ValueError)]) class TestHfftnException(unittest.TestCase): '''Test hfftn with buoudary condition Test case include: - n out of range - axis out of range - norm out of range - the dimensions of n and axis are different ''' def test_hfftn(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.hfftn( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [('test_n_nagative', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (-1, -2), (-2, -1), 'backward', ValueError), ('test_n_zero', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (0, 0), (-2, -1), 'backward', ValueError), ('test_n_type', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), 3, -1, 'backward', ValueError), ('test_n_axis_dim', np.random.randn(4, 4, 4) + 1j * np.random.randn(4, 4, 4), (1, 2), (-3, -2, -1), 'backward', ValueError), ('test_axis_out_of_range', np.random.randn(4) + 1j * np.random.randn(4), None, (10, 20), 'backward', ValueError), ('test_axis_type', np.random.randn(4) + 1j * np.random.randn(4), None, 1, 'backward', ValueError), ('test_norm_not_in_enum_value', np.random.randn(4, 4) + 1j * np.random.randn(4, 4), None, None, 'random', ValueError)]) class TestIrfftnException(unittest.TestCase): '''Test irfftn with buoudary condition Test case include: - n out of range - axis out of range - norm out of range - the dimensions of n and axis are different ''' def test_irfftn(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.irfftn( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [('test_x_float64', rand_x(5, np.float64), None, -1, 'backward'), ( 'test_n_grater_than_input_length', rand_x( 5, max_dim_len=5), 11, -1, 'backward'), ('test_n_smaller_than_input_length', rand_x( 5, min_dim_len=5), 3, -1, 'backward'), ('test_axis_not_last', rand_x(5), None, 3, 'backward'), ('test_norm_forward', rand_x(5), None, 3, 'forward'), ('test_norm_ortho', rand_x(5), None, 3, 'ortho')]) class TestRfft(unittest.TestCase): def test_rfft(self): with paddle.fluid.dygraph.guard(self.place): self.assertTrue( np.allclose( scipy.fft.rfft(self.x, self.n, self.axis, self.norm), paddle.fft.rfft( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype)))) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [ ('test_n_nagative', rand_x(2), -1, -1, 'backward', ValueError), ('test_n_zero', rand_x(2), 0, -1, 'backward', ValueError), ('test_axis_out_of_range', rand_x(1), None, 10, 'backward', ValueError), ('test_axis_with_array', rand_x(1), None, (0, 1), 'backward', ValueError), ('test_norm_not_in_enum_value', rand_x(2), None, -1, 'random', ValueError) ]) class TestRfftException(unittest.TestCase): def test_rfft(self): with self.assertRaises(self.expect_exception): paddle.fft.rfft( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [ ('test_x_float64', rand_x(5), None, (0, 1), 'backward'), ('test_n_grater_input_length', rand_x( 5, max_dim_len=5), (6, 6), (0, 1), 'backward'), ('test_n_smaller_than_input_length', rand_x( 5, min_dim_len=5), (4, 4), (0, 1), 'backward'), ('test_axis_random', rand_x(5), None, (1, 2), 'backward'), ('test_axis_none', rand_x(5), None, None, 'backward'), ('test_norm_forward', rand_x(5), None, (0, 1), 'forward'), ('test_norm_ortho', rand_x(5), None, (0, 1), 'ortho'), ]) class TestRfft2(unittest.TestCase): def test_rfft2(self): with paddle.fluid.dygraph.guard(self.place): self.assertTrue( np.allclose( scipy.fft.rfft2(self.x, self.n, self.axis, self.norm), paddle.fft.rfft2( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype)))) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [ ('test_x_complex_input', rand_x( 2, complex=True), None, (0, 1), 'backward', RuntimeError), ('test_x_1dim_tensor', rand_x(1), None, (0, 1), 'backward', ValueError), ('test_n_nagative', rand_x(2), -1, (0, 1), 'backward', ValueError), ('test_n_zero', rand_x(2), 0, (0, 1), 'backward', ValueError), ('test_axis_out_of_range', rand_x(2), None, (0, 1, 2), 'backward', ValueError), ('test_axis_with_array', rand_x(1), None, (0, 1), 'backward', ValueError), ('test_axis_not_sequence', rand_x(5), None, -10, 'backward', ValueError), ('test_norm_not_enum', rand_x(2), None, -1, 'random', ValueError), ]) class TestRfft2Exception(unittest.TestCase): def test_rfft(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.rfft2( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [ ('test_x_float64', rand_x(5, np.float64), None, None, 'backward'), ('test_n_grater_input_length', rand_x( 5, max_dim_len=5), (6, 6), (1, 2), 'backward'), ('test_n_smaller_input_length', rand_x( 5, min_dim_len=5), (3, 3), (1, 2), 'backward'), ('test_axis_not_default', rand_x(5), None, (1, 2), 'backward'), ('test_norm_forward', rand_x(5), None, None, 'forward'), ('test_norm_ortho', rand_x(5), None, None, 'ortho'), ]) class TestRfftn(unittest.TestCase): def test_rfftn(self): with paddle.fluid.dygraph.guard(self.place): self.assertTrue( np.allclose( scipy.fft.rfftn(self.x, self.n, self.axis, self.norm), paddle.fft.rfftn( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype)))) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [('test_x_complex', rand_x( 4, complex=True), None, None, 'backward', RuntimeError), ('test_n_nagative', rand_x(4), (-1, -1), (1, 2), 'backward', ValueError), ('test_n_not_sequence', rand_x(4), -1, None, 'backward', ValueError), ('test_n_zero', rand_x(4), 0, None, 'backward', ValueError), ( 'test_axis_out_of_range', rand_x(1), None, [0, 1], 'backward', ValueError), ('test_norm_not_in_enum', rand_x(2), None, -1, 'random', ValueError)]) class TestRfftnException(unittest.TestCase): def test_rfft(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.rfftn( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [('test_x_float64', rand_x(5, np.float64), None, -1, 'backward'), ( 'test_n_grater_than_input_length', rand_x( 5, max_dim_len=5), 11, -1, 'backward'), ('test_n_smaller_than_input_length', rand_x( 5, min_dim_len=5), 3, -1, 'backward'), ('test_axis_not_last', rand_x(5), None, 3, 'backward'), ('test_norm_forward', rand_x(5), None, 3, 'forward'), ('test_norm_ortho', rand_x(5), None, 3, 'ortho')]) class TestIhfft(unittest.TestCase): def test_ihfft(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.ihfft(self.x, self.n, self.axis, self.norm), paddle.fft.ihfft( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype))) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [ ('test_n_nagative', rand_x(2), -1, -1, 'backward', ValueError), ('test_n_zero', rand_x(2), 0, -1, 'backward', ValueError), ('test_axis_out_of_range', rand_x(1), None, 10, 'backward', ValueError), ('test_axis_with_array', rand_x(1), None, (0, 1), 'backward', ValueError), ('test_norm_not_in_enum_value', rand_x(2), None, -1, 'random', ValueError) ]) class TestIhfftException(unittest.TestCase): def test_ihfft(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.ihfft( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [ ('test_x_float64', rand_x(5), None, (0, 1), 'backward'), ('test_n_grater_input_length', rand_x( 5, max_dim_len=5), (11, 11), (0, 1), 'backward'), ('test_n_smaller_than_input_length', rand_x( 5, min_dim_len=5), (1, 1), (0, 1), 'backward'), ('test_axis_random', rand_x(5), None, (1, 2), 'backward'), ('test_axis_none', rand_x(5), None, None, 'backward'), ('test_norm_forward', rand_x(5), None, (0, 1), 'forward'), ('test_norm_ortho', rand_x(5), None, (0, 1), 'ortho'), ]) class TestIhfft2(unittest.TestCase): def test_ihfft2(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.ihfft2(self.x, self.n, self.axis, self.norm), paddle.fft.ihfft2( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype))) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [('test_x_complex_input', rand_x( 2, complex=True), None, (0, 1), None, ValueError), ('test_x_1dim_tensor', rand_x(1), None, (0, 1), None, ValueError), ('test_n_nagative', rand_x(2), -1, (0, 1), 'backward', ValueError), ('test_n_len_not_equal_axis', rand_x( 5, max_dim_len=5), 11, (0, 1), 'backward', ValueError), ('test_n_zero', rand_x(2), (0, 0), (0, 1), 'backward', ValueError), ('test_axis_out_of_range', rand_x(2), None, (0, 1, 2), 'backward', ValueError), ('test_axis_with_array', rand_x(1), None, (0, 1), 'backward', ValueError), ('test_axis_not_sequence', rand_x(5), None, -10, 'backward', ValueError), ('test_norm_not_enum', rand_x(2), None, -1, 'random', ValueError)]) class TestIhfft2Exception(unittest.TestCase): def test_rfft(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.ihfft2( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm'), [('test_x_float64', rand_x(5, np.float64), None, None, 'backward'), ('test_n_grater_input_length', rand_x( 5, max_dim_len=5), (11, 11), (0, 1), 'backward'), ('test_n_smaller_input_length', rand_x( 5, min_dim_len=5), (1, 1), (0, 1), 'backward'), ('test_axis_not_default', rand_x(5), None, (1, 2), 'backward'), ('test_norm_forward', rand_x(5), None, None, 'forward'), ('test_norm_ortho', rand_x(5), None, None, 'ortho')]) class TestIhfftn(unittest.TestCase): def test_rfftn(self): with paddle.fluid.dygraph.guard(self.place): self.assertTrue( np.allclose( scipy.fft.ihfftn(self.x, self.n, self.axis, self.norm), paddle.fft.ihfftn( paddle.to_tensor(self.x), self.n, self.axis, self.norm), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype)))) @place(DEVICES) @parameterize( (TEST_CASE_NAME, 'x', 'n', 'axis', 'norm', 'expect_exception'), [('test_x_complex', rand_x( 4, complex=True), None, None, 'backward', RuntimeError), ('test_n_nagative', rand_x(4), -1, None, 'backward', ValueError), ('test_n_zero', rand_x(4), 0, None, 'backward', ValueError), ( 'test_axis_out_of_range', rand_x(1), None, [0, 1], 'backward', ValueError), ('test_norm_not_in_enum', rand_x(2), None, -1, 'random', ValueError)]) class TestIhfftnException(unittest.TestCase): def test_rfft(self): with paddle.fluid.dygraph.guard(self.place): with self.assertRaises(self.expect_exception): paddle.fft.ihfftn( paddle.to_tensor(self.x), self.n, self.axis, self.norm) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'n', 'd', 'dtype'), [ ('test_without_d', 20, 1, 'float32'), ('test_with_d', 20, 0.5, 'float32'), ]) class TestFftFreq(unittest.TestCase): def test_fftfreq(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.fftfreq(self.n, self.d).astype(self.dtype), paddle.fft.fftfreq(self.n, self.d, self.dtype).numpy(), rtol=RTOL.get(str(self.dtype)), atol=ATOL.get(str(self.dtype))) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'n', 'd', 'dtype'), [ ('test_without_d', 20, 1, 'float32'), ('test_with_d', 20, 0.5, 'float32'), ]) class TestRfftFreq(unittest.TestCase): def test_rfftfreq(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.rfftfreq(self.n, self.d).astype(self.dtype), paddle.fft.rfftfreq(self.n, self.d, self.dtype).numpy(), rtol=RTOL.get(str(self.dtype)), atol=ATOL.get(str(self.dtype))) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'axes', 'dtype'), [ ('test_1d', np.random.randn(10), (0, ), 'float64'), ('test_2d', np.random.randn(10, 10), (0, 1), 'float64'), ]) class TestFftShift(unittest.TestCase): def test_fftshift(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.fftshift(self.x, self.axes), paddle.fft.fftshift(paddle.to_tensor(self.x), self.axes).numpy(), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype))) @place(DEVICES) @parameterize((TEST_CASE_NAME, 'x', 'axes'), [ ('test_1d', np.random.randn(10), (0, ), 'float64'), ('test_2d', np.random.randn(10, 10), (0, 1), 'float64'), ]) class TestIfftShift(unittest.TestCase): def test_ifftshift(self): with paddle.fluid.dygraph.guard(self.place): np.testing.assert_allclose( scipy.fft.ifftshift(self.x, self.axes), paddle.fft.ifftshift(paddle.to_tensor(self.x), self.axes).numpy(), rtol=RTOL.get(str(self.x.dtype)), atol=ATOL.get(str(self.x.dtype))) if __name__ == '__main__': unittest.main() # yapf: enable