forked from mindspore-Ecosystem/mindspore
102 lines
3.8 KiB
Python
102 lines
3.8 KiB
Python
# Copyright 2019 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 Normal distribution"""
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.nn.probability.distribution as msd
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from mindspore import Tensor
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from mindspore import dtype
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Net1(nn.Cell):
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"""
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Test class: Normal distribution. `dist_spec_args` are `mean`, `sd`.
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"""
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def __init__(self):
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super(Net1, self).__init__()
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self.normal = msd.Normal(dtype=dtype.float32)
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self.normal1 = msd.Normal(0.0, 1.0, dtype=dtype.float32)
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self.normal2 = msd.Normal(3.0, 4.0, dtype=dtype.float32)
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def construct(self, value, mean, sd, mean_a, sd_a):
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args_list = self.normal.get_dist_args(mean, sd)
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prob = self.normal1.prob(value, *args_list)
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args_list1 = self.normal.get_dist_args()
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prob1 = self.normal2.prob(value, *args_list1)
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args_list2 = self.normal1.get_dist_args()
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dist_type = self.normal1.get_dist_type()
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kl_loss = self.normal2.kl_loss(dist_type, *args_list2)
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args_list3 = self.normal.get_dist_args(mean_a, sd_a)
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dist_type = self.normal1.get_dist_type()
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kl_loss1 = self.normal2.kl_loss(dist_type, *args_list3)
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return prob, prob1, kl_loss, kl_loss1
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def test1():
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"""
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Test Normal with two `dist_spec_args`.
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"""
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net = Net1()
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mean = Tensor(3.0, dtype=dtype.float32)
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sd = Tensor(4.0, dtype=dtype.float32)
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mean_a = Tensor(0.0, dtype=dtype.float32)
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sd_a = Tensor(1.0, dtype=dtype.float32)
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value = Tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
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ans, expected, ans1, expected1 = net(value, mean, sd, mean_a, sd_a)
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected.asnumpy()) < tol).all()
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assert (np.abs(ans1.asnumpy() - expected1.asnumpy()) < tol).all()
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class Net2(nn.Cell):
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"""
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Test class: Exponential distribution. `dist_spec_args` is `rate`.
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"""
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def __init__(self):
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super(Net2, self).__init__()
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self.expon = msd.Exponential(dtype=dtype.float32)
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self.expon1 = msd.Exponential(1.0, dtype=dtype.float32)
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self.expon2 = msd.Exponential(2.0, dtype=dtype.float32)
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def construct(self, value, rate, rate1):
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args_list = self.expon.get_dist_args(rate)
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prob = self.expon1.prob(value, *args_list)
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args_list1 = self.expon.get_dist_args()
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prob1 = self.expon2.prob(value, *args_list1)
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args_list2 = self.expon1.get_dist_args()
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dist_type = self.expon1.get_dist_type()
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kl_loss = self.expon2.kl_loss(dist_type, *args_list2)
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args_list3 = self.expon.get_dist_args(rate1)
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dist_type = self.expon.get_dist_type()
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kl_loss1 = self.expon2.kl_loss(dist_type, *args_list3)
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return prob, prob1, kl_loss, kl_loss1
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def test2():
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"""
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Test Expomential with single `dist_spec_args`.
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"""
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net = Net2()
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rate = Tensor(2.0, dtype=dtype.float32)
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rate1 = Tensor(1.0, dtype=dtype.float32)
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value = Tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
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ans, expected, ans1, expected1 = net(value, rate, rate1)
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected.asnumpy()) < tol).all()
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assert (np.abs(ans1.asnumpy() - expected1.asnumpy()) < tol).all()
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