forked from mindspore-Ecosystem/mindspore
73 lines
2.5 KiB
Python
73 lines
2.5 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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from mindspore.common import dtype as mstype
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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 UpperBound
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class NetUpperBound(nn.Cell):
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def __init__(self, out_type):
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super(NetUpperBound, self).__init__()
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self.upperbound = UpperBound(out_type=out_type)
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def construct(self, x, y):
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return self.upperbound(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_upperbound_2d_input_int32_output_int32():
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"""
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Feature: UpperBound gpu TEST.
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Description: 2d test case for UpperBound
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Expectation: The value and shape of output are the expected values.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x_ms = Tensor(np.array([[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]]).astype(np.int32))
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y_ms = Tensor(np.array([[2, 4, 9], [0, 2, 6]]).astype(np.int32))
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net = NetUpperBound(out_type=mstype.int32)
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z_ms = net(x_ms, y_ms)
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expect = np.array([[1, 2, 4], [0, 2, 5]]).astype(np.int32)
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assert (z_ms.asnumpy() == expect).all()
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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_upperbound_2d_input_float16_output_int64():
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"""
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Feature: UpperBound gpu TEST.
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Description: 2d test case for UpperBound
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Expectation: The value and shape of output are the expected values.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x_ms = Tensor(np.array([[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]]).astype(np.float16))
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y_ms = Tensor(np.array([[2, 4, 9], [0, 2, 6]]).astype(np.float16))
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net = NetUpperBound(out_type=mstype.int64)
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z_ms = net(x_ms, y_ms)
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expect = np.array([[1, 2, 4], [0, 2, 5]]).astype(np.int64)
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assert (z_ms.asnumpy() == expect).all()
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