forked from mindspore-Ecosystem/mindspore
55 lines
1.6 KiB
Python
55 lines
1.6 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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"""test Maxpool3d dynamic shape"""
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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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from mindspore import ops
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from mindspore import nn
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.pool = ops.MaxPool3D(kernel_size=2, strides=1, pad_mode="valid")
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def construct(self, x):
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out = self.pool(x)
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return out
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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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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_maxpool3d_valid(mode):
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"""
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Feature: MaxPool3d
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Description: test dynamic shape of MaxPool3d
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Expectation: success
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"""
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ms.set_context(mode=mode)
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x = ms.Tensor(np.arange(1 * 2 * 2 * 2 * 3).reshape((1, 2, 2, 2, 3)), ms.float32)
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expected_shape = (1, 2, 1, 1, 2)
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net = Net()
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x_dyn = ms.Tensor(shape=[None]*len(x.shape), dtype=x.dtype)
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net.set_inputs(x_dyn)
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output = net(x)
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assert output.shape == expected_shape
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