forked from mindspore-Ecosystem/mindspore
123 lines
3.6 KiB
Python
123 lines
3.6 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import pytest
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import numpy as onp
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import scipy as osp
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore._checkparam import Validator as validator
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from mindspore.common import dtype as mstype
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from mindspore.ops import PrimitiveWithInfer
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from mindspore.ops import prim_attr_register
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class QR(PrimitiveWithInfer):
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"""
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QR decomposition
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A = Q.R
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"""
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@prim_attr_register
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def __init__(self, mode: str = "full"):
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super().__init__(name="QR")
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self.mode = validator.check_value_type("mode", mode, [str], self.name)
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self.init_prim_io_names(inputs=['x'], outputs=['q', 'r'])
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def __infer__(self, x):
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x_shape = x['shape']
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x_dtype = x['dtype']
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m, n = x_shape
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if self.mode == "economic":
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q_shape = (m, min(m, n))
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r_shape = (min(m, n), n)
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else:
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q_shape = (m, m)
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r_shape = (m, n)
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output = {
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'shape': (q_shape, r_shape),
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'dtype': (x_dtype, x_dtype),
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'value': None
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}
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return output
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def infer_dtype(self, x_dtype):
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validator.check_tensor_dtype_valid(x_dtype, [mstype.float32, mstype.float64], self.name, True)
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return x_dtype
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def _match_array(actual, expected, error=0):
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if isinstance(actual, int):
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actual = onp.asarray(actual)
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if isinstance(actual, tuple):
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actual = onp.asarray(actual)
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if error > 0:
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onp.testing.assert_almost_equal(actual, expected, decimal=error)
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else:
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onp.testing.assert_equal(actual, expected)
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class QRNet(nn.Cell):
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def __init__(self, mode: str = "full"):
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super(QRNet, self).__init__()
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self.mode = mode
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self.qr = QR(mode=self.mode)
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def construct(self, a):
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q, r = self.qr(a)
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if self.mode == 'r':
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return (r,)
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return q, r
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@pytest.mark.platform_x86_cpu
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@pytest.mark.parametrize('a_shape', [(9, 6), (6, 9)])
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@pytest.mark.parametrize('dtype', [onp.float32, onp.float64])
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@pytest.mark.parametrize('mode', ['full', 'r', 'economic'])
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def test_lu_net(a_shape, dtype, mode):
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"""
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Feature: ALL To ALL
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Description: test cases for qr decomposition test cases for A = Q.R
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Expectation: the result match to scipy
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"""
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onp.random.seed(0)
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if mode == 'r':
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m, n = a_shape
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a = onp.random.random((m, n)).astype(dtype)
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osp_r = osp.linalg.qr(a, mode=mode)
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msp_qr = QRNet(mode=mode)
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tensor_a = Tensor(a)
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msp_r = msp_qr(tensor_a)
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_match_array(msp_r[0].asnumpy(), osp_r[0], error=5)
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else:
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m, n = a_shape
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a = onp.random.random((m, n)).astype(dtype)
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osp_q, osp_r = osp.linalg.qr(a, mode=mode)
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msp_qr = QRNet(mode=mode)
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tensor_a = Tensor(a)
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msp_q, msp_r = msp_qr(tensor_a)
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_match_array(msp_q.asnumpy(), osp_q, error=5)
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_match_array(msp_r.asnumpy(), osp_r, error=5)
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