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208 lines
6.3 KiB
208 lines
6.3 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.Bernoulli.
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"""
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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_arguments():
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"""
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Args passing during initialization.
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"""
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b = msd.Bernoulli()
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assert isinstance(b, msd.Distribution)
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b = msd.Bernoulli([0.1, 0.3, 0.5, 0.9], dtype=dtype.int32)
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assert isinstance(b, msd.Distribution)
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def test_type():
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with pytest.raises(TypeError):
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msd.Bernoulli([0.1], dtype=dtype.float32)
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def test_name():
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with pytest.raises(TypeError):
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msd.Bernoulli([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.Bernoulli([0.1], seed='seed')
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def test_prob():
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"""
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Invalid probability.
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"""
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with pytest.raises(ValueError):
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msd.Bernoulli([-0.1], dtype=dtype.int32)
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with pytest.raises(ValueError):
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msd.Bernoulli([1.1], dtype=dtype.int32)
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with pytest.raises(ValueError):
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msd.Bernoulli([0.0], dtype=dtype.int32)
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with pytest.raises(ValueError):
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msd.Bernoulli([1.0], dtype=dtype.int32)
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class BernoulliProb(nn.Cell):
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"""
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Bernoulli distribution: initialize with probs.
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"""
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def __init__(self):
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super(BernoulliProb, self).__init__()
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self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
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def construct(self, value):
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prob = self.b.prob(value)
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log_prob = self.b.log_prob(value)
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cdf = self.b.cdf(value)
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log_cdf = self.b.log_cdf(value)
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sf = self.b.survival_function(value)
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log_sf = self.b.log_survival(value)
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return prob + log_prob + cdf + log_cdf + sf + log_sf
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def test_bernoulli_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 = BernoulliProb()
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value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
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ans = net(value)
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assert isinstance(ans, Tensor)
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class BernoulliProb1(nn.Cell):
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"""
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Bernoulli distribution: initialize without probs.
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"""
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def __init__(self):
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super(BernoulliProb1, self).__init__()
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self.b = msd.Bernoulli(dtype=dtype.int32)
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def construct(self, value, probs):
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prob = self.b.prob(value, probs)
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log_prob = self.b.log_prob(value, probs)
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cdf = self.b.cdf(value, probs)
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log_cdf = self.b.log_cdf(value, probs)
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sf = self.b.survival_function(value, probs)
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log_sf = self.b.log_survival(value, probs)
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return prob + log_prob + cdf + log_cdf + sf + log_sf
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def test_bernoulli_prob1():
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"""
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Test probability functions: passing value/probs through construct.
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"""
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net = BernoulliProb1()
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value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
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probs = Tensor([0.5], dtype=dtype.float32)
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ans = net(value, probs)
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assert isinstance(ans, Tensor)
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class BernoulliKl(nn.Cell):
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"""
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Test class: kl_loss between Bernoulli distributions.
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"""
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def __init__(self):
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super(BernoulliKl, self).__init__()
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self.b1 = msd.Bernoulli(0.7, dtype=dtype.int32)
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self.b2 = msd.Bernoulli(dtype=dtype.int32)
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def construct(self, probs_b, probs_a):
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kl1 = self.b1.kl_loss('Bernoulli', probs_b)
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kl2 = self.b2.kl_loss('Bernoulli', probs_b, probs_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 function.
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"""
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ber_net = BernoulliKl()
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probs_b = Tensor([0.3], dtype=dtype.float32)
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probs_a = Tensor([0.7], dtype=dtype.float32)
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ans = ber_net(probs_b, probs_a)
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assert isinstance(ans, Tensor)
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class BernoulliCrossEntropy(nn.Cell):
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"""
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Test class: cross_entropy of Bernoulli distribution.
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"""
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def __init__(self):
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super(BernoulliCrossEntropy, self).__init__()
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self.b1 = msd.Bernoulli(0.7, dtype=dtype.int32)
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self.b2 = msd.Bernoulli(dtype=dtype.int32)
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def construct(self, probs_b, probs_a):
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h1 = self.b1.cross_entropy('Bernoulli', probs_b)
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h2 = self.b2.cross_entropy('Bernoulli', probs_b, probs_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 Bernoulli distributions.
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"""
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net = BernoulliCrossEntropy()
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probs_b = Tensor([0.3], dtype=dtype.float32)
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probs_a = Tensor([0.7], dtype=dtype.float32)
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ans = net(probs_b, probs_a)
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assert isinstance(ans, Tensor)
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class BernoulliBasics(nn.Cell):
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"""
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Test class: basic mean/sd/var/mode/entropy function.
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"""
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def __init__(self):
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super(BernoulliBasics, self).__init__()
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self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
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def construct(self):
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mean = self.b.mean()
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sd = self.b.sd()
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var = self.b.var()
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mode = self.b.mode()
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entropy = self.b.entropy()
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return mean + sd + var + mode + entropy
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def test_bascis():
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"""
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Test mean/sd/var/mode/entropy functionality of Bernoulli distribution.
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"""
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net = BernoulliBasics()
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ans = net()
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assert isinstance(ans, Tensor)
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class BernoulliConstruct(nn.Cell):
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"""
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Bernoulli distribution: going through construct.
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"""
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def __init__(self):
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super(BernoulliConstruct, self).__init__()
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self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
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self.b1 = msd.Bernoulli(dtype=dtype.int32)
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def construct(self, value, probs):
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prob = self.b('prob', value)
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prob1 = self.b('prob', value, probs)
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prob2 = self.b1('prob', value, probs)
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return prob + prob1 + prob2
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def test_bernoulli_construct():
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"""
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Test probability function going through construct.
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"""
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net = BernoulliConstruct()
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value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
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probs = Tensor([0.5], dtype=dtype.float32)
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ans = net(value, probs)
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assert isinstance(ans, Tensor)
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