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89 lines
2.6 KiB
89 lines
2.6 KiB
# 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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""" Test L1Regularizer """
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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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import mindspore.context as context
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from mindspore import Tensor, ms_function
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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class Net_l1_regularizer(nn.Cell):
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def __init__(self, scale):
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super(Net_l1_regularizer, self).__init__()
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self.l1_regularizer = nn.L1Regularizer(scale)
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@ms_function
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def construct(self, weights):
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return self.l1_regularizer(weights)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_l1_regularizer01():
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scale = 0.5
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weights = Tensor(np.array([[1.0, -2.0], [-3.0, 4.0]]).astype(np.float32))
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l1_regularizer = Net_l1_regularizer(scale)
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output = l1_regularizer(weights)
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print("After l1_regularizer01 is: ", output.asnumpy())
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print("output.shape: ", output.shape)
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print("output.dtype: ", output.dtype)
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expect = 5.0
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assert np.all(output.asnumpy() == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_l1_regularizer08():
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scale = 0.5
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net = nn.L1Regularizer(scale)
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weights = Tensor(np.array([[1.0, -2.0], [-3.0, 4.0]]).astype(np.float32))
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output = net(weights)
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expect = 5.0
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print("output : ", output.asnumpy())
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assert np.all(output.asnumpy() == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_l1_regularizer_input_int():
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scale = 0.5
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net = nn.L1Regularizer(scale)
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weights = 2
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try:
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output = net(weights)
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print("output : ", output.asnumpy())
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except TypeError:
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assert True
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_l1_regularizer_input_tuple():
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scale = 0.5
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net = nn.L1Regularizer(scale)
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weights = (1, 2, 3, 4)
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try:
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output = net(weights)
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print("output : ", output.asnumpy())
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except TypeError:
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assert True
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