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221 lines
7.2 KiB
221 lines
7.2 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.Gamma.
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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_gamma_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.Gamma([[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.Gamma([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.Gamma([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.Gamma([0.], [1.], seed='seed')
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def test_rate():
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with pytest.raises(ValueError):
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msd.Gamma([0.], [0.])
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with pytest.raises(ValueError):
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msd.Gamma([0.], [-1.])
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def test_scalar():
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with pytest.raises(TypeError):
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msd.Gamma(3., [4.])
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with pytest.raises(TypeError):
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msd.Gamma([3.], -4.)
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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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g = msd.Gamma()
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assert isinstance(g, msd.Distribution)
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g = msd.Gamma([3.0], [4.0], dtype=dtype.float32)
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assert isinstance(g, msd.Distribution)
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class GammaProb(nn.Cell):
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"""
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Gamma distribution: initialize with concentration/rate.
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"""
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def __init__(self):
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super(GammaProb, self).__init__()
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self.gamma = msd.Gamma([3.0, 4.0], [1.0, 1.0], dtype=dtype.float32)
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def construct(self, value):
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prob = self.gamma.prob(value)
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log_prob = self.gamma.log_prob(value)
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cdf = self.gamma.cdf(value)
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log_cdf = self.gamma.log_cdf(value)
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sf = self.gamma.survival_function(value)
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log_sf = self.gamma.log_survival(value)
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return prob + log_prob + cdf + log_cdf + sf + log_sf
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def test_gamma_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 = GammaProb()
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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 GammaProb1(nn.Cell):
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"""
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Gamma distribution: initialize without concentration/rate.
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"""
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def __init__(self):
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super(GammaProb1, self).__init__()
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self.gamma = msd.Gamma()
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def construct(self, value, concentration, rate):
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prob = self.gamma.prob(value, concentration, rate)
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log_prob = self.gamma.log_prob(value, concentration, rate)
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cdf = self.gamma.cdf(value, concentration, rate)
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log_cdf = self.gamma.log_cdf(value, concentration, rate)
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sf = self.gamma.survival_function(value, concentration, rate)
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log_sf = self.gamma.log_survival(value, concentration, rate)
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return prob + log_prob + cdf + log_cdf + sf + log_sf
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def test_gamma_prob1():
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"""
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Test probability functions: passing concentration/rate, value through construct.
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"""
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net = GammaProb1()
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value = Tensor([0.5, 1.0], dtype=dtype.float32)
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concentration = Tensor([2.0, 3.0], dtype=dtype.float32)
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rate = Tensor([1.0], dtype=dtype.float32)
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ans = net(value, concentration, rate)
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assert isinstance(ans, Tensor)
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class GammaKl(nn.Cell):
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"""
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Test class: kl_loss of Gamma distribution.
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"""
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def __init__(self):
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super(GammaKl, self).__init__()
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self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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self.g2 = msd.Gamma(dtype=dtype.float32)
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def construct(self, concentration_b, rate_b, concentration_a, rate_a):
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kl1 = self.g1.kl_loss('Gamma', concentration_b, rate_b)
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kl2 = self.g2.kl_loss('Gamma', concentration_b, rate_b, concentration_a, rate_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 = GammaKl()
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concentration_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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rate_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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concentration_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
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rate_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
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ans = net(concentration_b, rate_b, concentration_a, rate_a)
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assert isinstance(ans, Tensor)
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class GammaCrossEntropy(nn.Cell):
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"""
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Test class: cross_entropy of Gamma distribution.
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"""
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def __init__(self):
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super(GammaCrossEntropy, self).__init__()
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self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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self.g2 = msd.Gamma(dtype=dtype.float32)
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def construct(self, concentration_b, rate_b, concentration_a, rate_a):
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h1 = self.g1.cross_entropy('Gamma', concentration_b, rate_b)
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h2 = self.g2.cross_entropy('Gamma', concentration_b, rate_b, concentration_a, rate_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 Gamma distributions.
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"""
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net = GammaCrossEntropy()
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concentration_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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rate_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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concentration_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
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rate_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
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ans = net(concentration_b, rate_b, concentration_a, rate_a)
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assert isinstance(ans, Tensor)
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class GammaBasics(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(GammaBasics, self).__init__()
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self.g = msd.Gamma(np.array([3.0, 4.0]), np.array([4.0, 6.0]), dtype=dtype.float32)
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def construct(self):
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mean = self.g.mean()
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sd = self.g.sd()
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mode = self.g.mode()
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return mean + sd + mode
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def test_bascis():
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"""
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Test mean/sd/mode/entropy functionality of Gamma.
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"""
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net = GammaBasics()
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ans = net()
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assert isinstance(ans, Tensor)
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class GammaConstruct(nn.Cell):
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"""
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Gamma distribution: going through construct.
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"""
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def __init__(self):
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super(GammaConstruct, self).__init__()
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self.gamma = msd.Gamma([3.0], [4.0])
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self.gamma1 = msd.Gamma()
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def construct(self, value, concentration, rate):
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prob = self.gamma('prob', value)
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prob1 = self.gamma('prob', value, concentration, rate)
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prob2 = self.gamma1('prob', value, concentration, rate)
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return prob + prob1 + prob2
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def test_gamma_construct():
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"""
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Test probability function going through construct.
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"""
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net = GammaConstruct()
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value = Tensor([0.5, 1.0], dtype=dtype.float32)
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concentration = Tensor([0.0], dtype=dtype.float32)
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rate = Tensor([1.0], dtype=dtype.float32)
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ans = net(value, concentration, rate)
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assert isinstance(ans, Tensor)
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