update logic for cache clean up code add test cases for new api update include of code specify the resolve attr to ClassMember avoid resolve attr of selfpull/3453/head
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# 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 cases for new api of normal distribution"""
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import numpy as np
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from scipy import stats
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import mindspore.nn as nn
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from mindspore import dtype
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from mindspore import Tensor
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import mindspore.context as context
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Net(nn.Cell):
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"""
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Test class: new api of normal distribution.
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"""
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def __init__(self):
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super(Net, self).__init__()
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self.normal = nn.Normal(0., 1., dtype=dtype.float32)
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def construct(self, x_, y_):
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kl = self.normal.kl_loss('kl_loss', 'Normal', x_, y_)
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prob = self.normal.prob('prob', kl)
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return prob
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def test_new_api():
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"""
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Test new api of normal distribution.
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"""
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prob = Net()
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mean_a = np.array([0.0]).astype(np.float32)
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sd_a = np.array([1.0]).astype(np.float32)
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mean_b = np.array([1.0]).astype(np.float32)
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sd_b = np.array([1.0]).astype(np.float32)
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ans = prob(Tensor(mean_b), Tensor(sd_b))
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diff_log_scale = np.log(sd_a) - np.log(sd_b)
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squared_diff = np.square(mean_a / sd_b - mean_b / sd_b)
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expect_kl_loss = 0.5 * squared_diff + 0.5 * \
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np.expm1(2 * diff_log_scale) - diff_log_scale
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norm_benchmark = stats.norm(np.array([0.0]), np.array([1.0]))
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expect_prob = norm_benchmark.pdf(expect_kl_loss).astype(np.float32)
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expect_prob) < tol).all()
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