forked from mindspore-Ecosystem/mindspore
74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
# Copyright 2020 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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"""ut for batchnorm layer"""
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore.common.api import _executor
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def test_bn_pars_valid1():
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"""ut of BatchNorm parameters' validation"""
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with pytest.raises(ValueError):
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nn.BatchNorm2d(num_features=0)
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def test_bn_pars_valid2():
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"""ut of BatchNorm parameters' validation"""
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with pytest.raises(ValueError):
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nn.BatchNorm2d(num_features=3, momentum=-0.1)
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def test_bn_init():
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"""ut of BatchNorm parameters' validation"""
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bn = nn.BatchNorm2d(num_features=3)
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assert isinstance(bn.gamma, Parameter)
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assert isinstance(bn.beta, Parameter)
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assert isinstance(bn.moving_mean, Parameter)
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assert isinstance(bn.moving_variance, Parameter)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.bn = nn.BatchNorm2d(num_features=3)
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def construct(self, input_x):
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return self.bn(input_x)
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def test_compile():
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net = Net()
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input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32))
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_executor.compile(net, input_data)
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class GroupNet(nn.Cell):
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def __init__(self):
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super(GroupNet, self).__init__()
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self.group_bn = nn.GroupNorm()
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def construct(self, x):
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return self.group_bn(x)
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def test_compile_groupnorm():
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net = nn.GroupNorm(16, 64)
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input_data = Tensor(np.random.rand(1, 64, 256, 256).astype(np.float32))
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_executor.compile(net, input_data)
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