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209 lines
6.6 KiB
209 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.Uniform.
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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_uniform_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.Uniform([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
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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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u = msd.Uniform()
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assert isinstance(u, msd.Distribution)
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u = msd.Uniform([3.0], [4.0], dtype=dtype.float32)
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assert isinstance(u, msd.Distribution)
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def test_invalid_range():
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"""
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Test range of uniform distribution.
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"""
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with pytest.raises(ValueError):
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msd.Uniform(0.0, 0.0, dtype=dtype.float32)
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with pytest.raises(ValueError):
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msd.Uniform(1.0, 0.0, dtype=dtype.float32)
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class UniformProb(nn.Cell):
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"""
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Uniform distribution: initialize with low/high.
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"""
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def __init__(self):
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super(UniformProb, self).__init__()
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self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
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def construct(self, value):
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prob = self.u.prob(value)
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log_prob = self.u.log_prob(value)
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cdf = self.u.cdf(value)
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log_cdf = self.u.log_cdf(value)
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sf = self.u.survival_function(value)
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log_sf = self.u.log_survival(value)
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return prob + log_prob + cdf + log_cdf + sf + log_sf
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def test_uniform_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 = UniformProb()
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value = Tensor([3.1, 3.2, 3.3, 3.4], dtype=dtype.float32)
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ans = net(value)
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assert isinstance(ans, Tensor)
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class UniformProb1(nn.Cell):
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"""
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Uniform distribution: initialize without low/high.
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"""
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def __init__(self):
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super(UniformProb1, self).__init__()
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self.u = msd.Uniform(dtype=dtype.float32)
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def construct(self, value, low, high):
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prob = self.u.prob(value, low, high)
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log_prob = self.u.log_prob(value, low, high)
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cdf = self.u.cdf(value, low, high)
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log_cdf = self.u.log_cdf(value, low, high)
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sf = self.u.survival_function(value, low, high)
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log_sf = self.u.log_survival(value, low, high)
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return prob + log_prob + cdf + log_cdf + sf + log_sf
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def test_uniform_prob1():
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"""
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Test probability functions: passing low/high, value through construct.
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"""
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net = UniformProb1()
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value = Tensor([0.1, 0.2, 0.3, 0.9], dtype=dtype.float32)
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low = Tensor([0.0], dtype=dtype.float32)
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high = Tensor([1.0], dtype=dtype.float32)
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ans = net(value, low, high)
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assert isinstance(ans, Tensor)
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class UniformKl(nn.Cell):
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"""
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Test class: kl_loss of Uniform distribution.
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"""
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def __init__(self):
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super(UniformKl, self).__init__()
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self.u1 = msd.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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self.u2 = msd.Uniform(dtype=dtype.float32)
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def construct(self, low_b, high_b, low_a, high_a):
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kl1 = self.u1.kl_loss('Uniform', low_b, high_b)
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kl2 = self.u2.kl_loss('Uniform', low_b, high_b, low_a, high_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 = UniformKl()
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low_b = Tensor(np.array([0.0]).astype(np.float32), dtype=dtype.float32)
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high_b = Tensor(np.array([5.0]).astype(np.float32), dtype=dtype.float32)
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low_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
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high_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
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ans = net(low_b, high_b, low_a, high_a)
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assert isinstance(ans, Tensor)
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class UniformCrossEntropy(nn.Cell):
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"""
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Test class: cross_entropy of Uniform distribution.
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"""
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def __init__(self):
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super(UniformCrossEntropy, self).__init__()
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self.u1 = msd.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
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self.u2 = msd.Uniform(dtype=dtype.float32)
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def construct(self, low_b, high_b, low_a, high_a):
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h1 = self.u1.cross_entropy('Uniform', low_b, high_b)
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h2 = self.u2.cross_entropy('Uniform', low_b, high_b, low_a, high_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 Unifrom distributions.
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"""
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net = UniformCrossEntropy()
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low_b = Tensor(np.array([0.0]).astype(np.float32), dtype=dtype.float32)
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high_b = Tensor(np.array([5.0]).astype(np.float32), dtype=dtype.float32)
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low_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
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high_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
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ans = net(low_b, high_b, low_a, high_a)
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assert isinstance(ans, Tensor)
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class UniformBasics(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(UniformBasics, self).__init__()
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self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
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def construct(self):
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mean = self.u.mean()
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sd = self.u.sd()
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var = self.u.var()
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entropy = self.u.entropy()
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return mean + sd + var + entropy
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def test_bascis():
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"""
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Test mean/sd/var/mode/entropy functionality of Uniform.
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"""
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net = UniformBasics()
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ans = net()
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assert isinstance(ans, Tensor)
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class UniConstruct(nn.Cell):
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"""
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Unifrom distribution: going through construct.
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"""
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def __init__(self):
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super(UniConstruct, self).__init__()
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self.u = msd.Uniform(-4.0, 4.0)
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self.u1 = msd.Uniform()
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def construct(self, value, low, high):
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prob = self.u('prob', value)
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prob1 = self.u('prob', value, low, high)
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prob2 = self.u1('prob', value, low, high)
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return prob + prob1 + prob2
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def test_uniform_construct():
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"""
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Test probability function going through construct.
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"""
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net = UniConstruct()
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value = Tensor([-5.0, 0.0, 1.0, 5.0], dtype=dtype.float32)
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low = Tensor([-1.0], dtype=dtype.float32)
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high = Tensor([1.0], dtype=dtype.float32)
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ans = net(value, low, high)
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assert isinstance(ans, Tensor)
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