# 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 unittest import numpy as np from paddle.fluid.tests.unittests.op_test import OpTest import paddle import paddle.fluid as fluid import paddle.fluid.core as core paddle.enable_static() SEED = 2049 np.random.seed(SEED) def matrix_rank_wraper(x, tol=None, use_default_tol=True, hermitian=False): return paddle.linalg.matrix_rank(x, tol, hermitian) class TestMatrixRankOP(OpTest): def setUp(self): self.python_api = matrix_rank_wraper self.op_type = "matrix_rank" self.init_data() self.inputs = {'X': self.x} self.attrs = {'hermitian': self.hermitian} if self.tol_tensor is not None: self.inputs["TolTensor"] = self.tol_tensor if self.tol is not None: self.attrs["tol"] = self.tol self.attrs["use_default_tol"] = self.use_default_tol self.outputs = {'Out': self.out} def test_check_output(self): self.check_output(check_eager=True) def init_data(self): self.x = np.eye(3, dtype=np.float32) self.tol_tensor = None self.tol = 0.1 self.use_default_tol = False self.hermitian = True self.out = np.linalg.matrix_rank(self.x, self.tol, self.hermitian) class TestMatrixRankOP1(TestMatrixRankOP): def init_data(self): self.x = np.eye(3, k=1, dtype=np.float64) self.tol_tensor = None self.tol = None self.use_default_tol = True self.hermitian = False self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, self.hermitian) class TestMatrixRankOP2(TestMatrixRankOP): def init_data(self): self.x = np.random.rand(3, 4, 5, 6).astype(np.float32) self.tol_tensor = np.random.random([3, 4]).astype(self.x.dtype) self.tol = None self.use_default_tol = False self.hermitian = False self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, self.hermitian) class TestMatrixRankOP3(TestMatrixRankOP): def init_data(self): self.x = np.eye(200, dtype=np.float64) self.tol_tensor = None self.tol = None self.use_default_tol = True self.hermitian = True self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, self.hermitian) class TestMatrixRankOP4(TestMatrixRankOP): def init_data(self): self.x = np.random.rand(1, 10).astype(np.float32) self.tol_tensor = None self.tol = None self.use_default_tol = True self.hermitian = False self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, self.hermitian) class TestMatrixRankOP5(TestMatrixRankOP): def init_data(self): self.x = np.random.rand(5, 1).astype(np.float64) self.tol_tensor = np.random.random([1, 4]).astype(self.x.dtype) self.tol = None self.use_default_tol = False self.hermitian = False self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, self.hermitian) class TestMatrixRankOP6(TestMatrixRankOP): def init_data(self): self.x = np.random.rand(3, 4, 5, 6).astype(np.float32) self.tol_tensor = None self.tol = None self.use_default_tol = False self.hermitian = False self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, self.hermitian) class TestMatrixRankOP7(TestMatrixRankOP): def init_data(self): self.x = np.eye(200, dtype=np.float64) self.tol_tensor = np.random.random([200, 200]).astype(self.x.dtype) self.tol = None self.use_default_tol = True self.hermitian = True self.out = np.linalg.matrix_rank(self.x, self.tol_tensor, self.hermitian) class TestMatrixRankAPI(unittest.TestCase): def test_dygraph(self): paddle.disable_static() x_np = np.eye(10, dtype=np.float32) x_pd = paddle.to_tensor(x_np) rank_np = np.linalg.matrix_rank(x_np, hermitian=True) rank_pd = paddle.linalg.matrix_rank(x_pd, hermitian=True) np.testing.assert_allclose(rank_np, rank_pd, rtol=1e-05) x_np = np.random.rand(3, 4, 7, 8).astype(np.float64) tol_np = np.random.random([3, 4]).astype(np.float32) x_pd = paddle.to_tensor(x_np) tol_pd = paddle.to_tensor(tol_np) rank_np = np.linalg.matrix_rank(x_np, tol_np, hermitian=False) rank_pd = paddle.linalg.matrix_rank(x_pd, tol_pd, hermitian=False) np.testing.assert_allclose(rank_np, rank_pd, rtol=1e-05) x_np = np.random.rand(3, 4, 7, 8).astype(np.float64) x_pd = paddle.to_tensor(x_np) tol = 0.1 rank_np = np.linalg.matrix_rank(x_np, tol, hermitian=False) rank_pd = paddle.linalg.matrix_rank(x_pd, tol, hermitian=False) np.testing.assert_allclose(rank_np, rank_pd, rtol=1e-05) def test_static(self): paddle.enable_static() places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for place in places: with fluid.program_guard(fluid.Program(), fluid.Program()): x_np = np.random.rand(3, 4, 7, 7).astype(np.float64) tol_np = np.random.random([3, 4]).astype(np.float32) x_pd = paddle.fluid.data(name="X", shape=[3, 4, 7, 7], dtype='float64') tol_pd = paddle.fluid.data(name="TolTensor", shape=[3, 4], dtype='float32') rank_np = np.linalg.matrix_rank(x_np, tol_np, hermitian=False) rank_pd = paddle.linalg.matrix_rank(x_pd, tol_pd, hermitian=False) exe = fluid.Executor(place) fetches = exe.run(fluid.default_main_program(), feed={ "X": x_np, "TolTensor": tol_np }, fetch_list=[rank_pd]) np.testing.assert_allclose(fetches[0], rank_np, rtol=1e-05) for place in places: with fluid.program_guard(fluid.Program(), fluid.Program()): x_np = np.random.rand(3, 4, 7, 7).astype(np.float64) x_pd = paddle.fluid.data(name="X", shape=[3, 4, 7, 7], dtype='float64') rank_np = np.linalg.matrix_rank(x_np, hermitian=True) rank_pd = paddle.linalg.matrix_rank(x_pd, hermitian=True) exe = fluid.Executor(place) fetches = exe.run(fluid.default_main_program(), feed={"X": x_np}, fetch_list=[rank_pd]) np.testing.assert_allclose(fetches[0], rank_np, rtol=1e-05) for place in places: with fluid.program_guard(fluid.Program(), fluid.Program()): x_np = np.random.rand(3, 4, 7, 7).astype(np.float64) x_pd = paddle.fluid.data(name="X", shape=[3, 4, 7, 7], dtype='float64') rank_np = np.linalg.matrix_rank(x_np, 0.1, hermitian=False) rank_pd = paddle.linalg.matrix_rank(x_pd, 0.1, hermitian=False) exe = fluid.Executor(place) fetches = exe.run(fluid.default_main_program(), feed={"X": x_np}, fetch_list=[rank_pd]) np.testing.assert_allclose(fetches[0], rank_np, rtol=1e-05) if __name__ == '__main__': unittest.main()