forked from mindspore-Ecosystem/mindspore
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
# Copyright 2022 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 numpy as np
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import pytest
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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.ops.operations.array_ops import RightShift
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class NetRightShift(nn.Cell):
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def __init__(self):
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super(NetRightShift, self).__init__()
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self.rightshift = RightShift()
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def construct(self, x, y):
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return self.rightshift(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_rightshift_1d():
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"""
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Feature: RightShift gpu TEST.
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Description: 1d test case for RightShift
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Expectation: the result match to numpy
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x_np = (np.array([-1, -5, -3, -14, 64])).astype(np.int8)
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y_np = (np.array([5, 0, 7, 11, 66])).astype(np.int8)
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z_np = np.right_shift(x_np, y_np)
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x_ms = Tensor(x_np)
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y_ms = Tensor(y_np)
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net = NetRightShift()
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z_ms = net(x_ms, y_ms)
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assert np.allclose(z_np, z_ms.asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_rightshift_2d():
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"""
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Feature: RightShift gpu TEST.
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Description: 2d test case for RightShift
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Expectation: the result match to numpy
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x_np = (np.array([[-1, -5, -3], [-14, 64, 0]])).astype(np.int8)
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y_np = (np.array([[5, 0, 7], [11, 66, 0]])).astype(np.int8)
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z_np = np.right_shift(x_np, y_np)
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x_ms = Tensor(x_np)
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y_ms = Tensor(y_np)
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net = NetRightShift()
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z_ms = net(x_ms, y_ms)
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assert np.allclose(z_np, z_ms.asnumpy())
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