forked from mindspore-Ecosystem/mindspore
116 lines
3.3 KiB
Python
116 lines
3.3 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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"""
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test pooling api
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"""
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.common.api import _cell_graph_executor
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class MaxPoolNet(nn.Cell):
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"""MaxPool3d"""
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def __init__(self):
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super(MaxPoolNet, self).__init__()
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self.pool1 = nn.MaxPool3d(kernel_size=3, stride=1, padding=1)
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self.pool2 = nn.MaxPool3d(kernel_size=3, stride=1, padding=1, return_indices=True)
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def construct(self, x):
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output1 = self.pool1(x)
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output2 = self.pool2(x)
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return output1, output2
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def test_compile_max():
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"""
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Feature: Test MaxPool3d
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Description: Test the functionality of MaxPool3d
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Expectation: Success
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"""
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net = MaxPoolNet()
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x = ms.Tensor(np.random.randint(0, 10, [1, 2, 4, 4, 5]), ms.float32)
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_cell_graph_executor.compile(net, x)
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class AvgPoolNet(nn.Cell):
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"""AvgPool3d"""
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def __init__(self):
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super(AvgPoolNet, self).__init__()
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self.pool = nn.AvgPool3d(kernel_size=3, stride=1)
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def construct(self, x):
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return self.pool(x)
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def test_compile_avg():
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"""
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Feature: Test AvgPool3d
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Description: Test the functionality of AvgPool3d
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Expectation: Success
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"""
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net = AvgPoolNet()
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x = ms.Tensor(np.random.randint(0, 10, [1, 2, 4, 4, 5]), ms.float32)
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_cell_graph_executor.compile(net, x)
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class LPPool1d(nn.Cell):
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"""LPPool1d"""
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def __init__(self):
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super(LPPool1d, self).__init__()
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self.pool = nn.LPPool1d(norm_type=1, kernel_size=3, stride=1)
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def construct(self, x):
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output1 = self.pool(x)
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return output1
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def test_compile_lpool1d():
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"""
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Feature: Test LPPool1d
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Description: Test the functionality of LPPool1d
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Expectation: Success
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"""
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net = LPPool1d()
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x = ms.Tensor(np.arange(2 * 3 * 4).reshape((2, 3, 4)), dtype=ms.float32)
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y = ms.Tensor(np.arange(3 * 4).reshape((3, 4)), dtype=ms.float32)
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_cell_graph_executor.compile(net, x)
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_cell_graph_executor.compile(net, y)
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class LPPool2d(nn.Cell):
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def __init__(self):
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super(LPPool2d, self).__init__()
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self.pool = nn.LPPool2d(norm_type=1, kernel_size=3, stride=1)
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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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def test_compile_lppool2d():
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"""
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Feature: Test LPPool2d
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Description: Test the functionality of LPPool2d
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Expectation: Success
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"""
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net = LPPool2d()
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x = ms.Tensor(np.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5)), dtype=ms.float32)
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_cell_graph_executor.compile(net, x)
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