forked from mindspore-Ecosystem/mindspore
71 lines
2.6 KiB
Python
71 lines
2.6 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 as ms
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import mindspore.nn as nn
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class Net(nn.Cell):
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# pylint: disable=redefined-builtin
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def construct(self, input_x, k, dim=None, largest=True, sorted=True):
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output = input_x.topk(k, dim=dim, largest=largest, sorted=sorted)
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return output
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_topk_normal(mode):
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"""
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Feature: top_k
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Description: Verify the result of topk
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Expectation: success
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"""
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ms.set_context(mode=mode)
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net = Net()
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x = ms.Tensor([[0.5368, 0.2447, 0.4302, 0.9673],
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[0.4388, 0.6525, 0.4685, 0.1868],
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[0.3563, 0.5152, 0.9675, 0.8230]], dtype=ms.float32)
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output = net(x, 2, dim=1)
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output0 = output[0]
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output1 = output[1]
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expect_output0 = np.array([[0.9673, 0.5368],
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[0.6525, 0.4685],
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[0.9675, 0.823]])
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expect_output1 = np.array([[3, 0],
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[1, 2],
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[2, 3]])
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output2 = net(x, 2, dim=1, largest=False)
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output2_0 = output2[0]
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output2_1 = output2[1]
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expect_output2_0 = np.array([[2.44700000e-01, 4.30200011e-01],
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[1.86800003e-01, 4.38800007e-01],
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[3.56299996e-01, 5.15200019e-01]])
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expect_output2_1 = np.array([[1, 2],
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[3, 0],
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[0, 1]])
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assert np.allclose(output0.asnumpy(), expect_output0)
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assert np.allclose(output1.asnumpy(), expect_output1)
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assert np.allclose(output2_0.asnumpy(), expect_output2_0)
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assert np.allclose(output2_1.asnumpy(), expect_output2_1)
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