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211 lines
6.6 KiB
211 lines
6.6 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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"""
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Test nn.probability.distribution.Normal.
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"""
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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.nn.probability.distribution as msd
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from mindspore import dtype
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from mindspore import Tensor
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def test_normal_shape_errpr():
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"""
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Invalid shapes.
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"""
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with pytest.raises(ValueError):
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msd.Normal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
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def test_type():
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with pytest.raises(TypeError):
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msd.Normal(0., 1., dtype=dtype.int32)
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def test_name():
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with pytest.raises(TypeError):
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msd.Normal(0., 1., name=1.0)
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def test_seed():
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with pytest.raises(TypeError):
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msd.Normal(0., 1., seed='seed')
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def test_arguments():
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"""
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args passing during initialization.
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"""
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n = msd.Normal()
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assert isinstance(n, msd.Distribution)
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n = msd.Normal([3.0], [4.0], dtype=dtype.float32)
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assert isinstance(n, msd.Distribution)
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class NormalProb(nn.Cell):
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"""
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Normal distribution: initialize with mean/sd.
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"""
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def __init__(self):
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super(NormalProb, self).__init__()
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self.normal = msd.Normal(3.0, 4.0, dtype=dtype.float32)
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def construct(self, value):
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prob = self.normal.prob(value)
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log_prob = self.normal.log_prob(value)
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cdf = self.normal.cdf(value)
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log_cdf = self.normal.log_cdf(value)
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sf = self.normal.survival_function(value)
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log_sf = self.normal.log_survival(value)
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return prob + log_prob + cdf + log_cdf + sf + log_sf
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def test_normal_prob():
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"""
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Test probability functions: passing value through construct.
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"""
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net = NormalProb()
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value = Tensor([0.5, 1.0], dtype=dtype.float32)
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ans = net(value)
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assert isinstance(ans, Tensor)
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class NormalProb1(nn.Cell):
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"""
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Normal distribution: initialize without mean/sd.
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"""
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def __init__(self):
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super(NormalProb1, self).__init__()
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self.normal = msd.Normal()
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def construct(self, value, mean, sd):
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prob = self.normal.prob(value, mean, sd)
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log_prob = self.normal.log_prob(value, mean, sd)
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cdf = self.normal.cdf(value, mean, sd)
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log_cdf = self.normal.log_cdf(value, mean, sd)
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sf = self.normal.survival_function(value, mean, sd)
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log_sf = self.normal.log_survival(value, mean, sd)
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return prob + log_prob + cdf + log_cdf + sf + log_sf
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def test_normal_prob1():
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"""
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Test probability functions: passing mean/sd, value through construct.
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"""
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net = NormalProb1()
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value = Tensor([0.5, 1.0], dtype=dtype.float32)
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mean = Tensor([0.0], dtype=dtype.float32)
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sd = Tensor([1.0], dtype=dtype.float32)
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ans = net(value, mean, sd)
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assert isinstance(ans, Tensor)
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class NormalKl(nn.Cell):
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"""
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Test class: kl_loss of Normal distribution.
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"""
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def __init__(self):
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super(NormalKl, self).__init__()
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self.n1 = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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self.n2 = msd.Normal(dtype=dtype.float32)
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def construct(self, mean_b, sd_b, mean_a, sd_a):
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kl1 = self.n1.kl_loss('Normal', mean_b, sd_b)
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kl2 = self.n2.kl_loss('Normal', mean_b, sd_b, mean_a, sd_a)
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return kl1 + kl2
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def test_kl():
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"""
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Test kl_loss.
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"""
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net = NormalKl()
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mean_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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sd_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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mean_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
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sd_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
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ans = net(mean_b, sd_b, mean_a, sd_a)
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assert isinstance(ans, Tensor)
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class NormalCrossEntropy(nn.Cell):
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"""
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Test class: cross_entropy of Normal distribution.
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"""
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def __init__(self):
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super(NormalCrossEntropy, self).__init__()
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self.n1 = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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self.n2 = msd.Normal(dtype=dtype.float32)
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def construct(self, mean_b, sd_b, mean_a, sd_a):
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h1 = self.n1.cross_entropy('Normal', mean_b, sd_b)
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h2 = self.n2.cross_entropy('Normal', mean_b, sd_b, mean_a, sd_a)
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return h1 + h2
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def test_cross_entropy():
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"""
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Test cross entropy between Normal distributions.
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"""
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net = NormalCrossEntropy()
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mean_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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sd_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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mean_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
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sd_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
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ans = net(mean_b, sd_b, mean_a, sd_a)
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assert isinstance(ans, Tensor)
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class NormalBasics(nn.Cell):
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"""
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Test class: basic mean/sd function.
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"""
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def __init__(self):
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super(NormalBasics, self).__init__()
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self.n = msd.Normal(3.0, 4.0, dtype=dtype.float32)
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def construct(self):
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mean = self.n.mean()
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sd = self.n.sd()
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mode = self.n.mode()
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entropy = self.n.entropy()
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return mean + sd + mode + entropy
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def test_bascis():
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"""
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Test mean/sd/mode/entropy functionality of Normal.
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"""
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net = NormalBasics()
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ans = net()
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assert isinstance(ans, Tensor)
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class NormalConstruct(nn.Cell):
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"""
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Normal distribution: going through construct.
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"""
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def __init__(self):
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super(NormalConstruct, self).__init__()
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self.normal = msd.Normal(3.0, 4.0)
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self.normal1 = msd.Normal()
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def construct(self, value, mean, sd):
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prob = self.normal('prob', value)
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prob1 = self.normal('prob', value, mean, sd)
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prob2 = self.normal1('prob', value, mean, sd)
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return prob + prob1 + prob2
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def test_normal_construct():
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"""
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Test probability function going through construct.
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"""
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net = NormalConstruct()
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value = Tensor([0.5, 1.0], dtype=dtype.float32)
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mean = Tensor([0.0], dtype=dtype.float32)
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sd = Tensor([1.0], dtype=dtype.float32)
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ans = net(value, mean, sd)
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assert isinstance(ans, Tensor)
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