mindspore2022/tests/ut/python/nn/probability/distribution/test_beta.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Test nn.probability.distribution.Gamma.
"""
import numpy as np
import pytest

import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import dtype
from mindspore import Tensor
from mindspore import context

skip_flag = context.get_context("device_target") != "Ascend"


def test_gamma_shape_errpr():
    """
    Invalid shapes.
    """
    with pytest.raises(ValueError):
        msd.Gamma([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)


def test_type():
    with pytest.raises(TypeError):
        msd.Gamma([0.], [1.], dtype=dtype.int32)


def test_name():
    with pytest.raises(TypeError):
        msd.Gamma([0.], [1.], name=1.0)


def test_seed():
    with pytest.raises(TypeError):
        msd.Gamma([0.], [1.], seed='seed')


def test_concentration1():
    with pytest.raises(ValueError):
        msd.Gamma([0.], [1.])
    with pytest.raises(ValueError):
        msd.Gamma([-1.], [1.])


def test_concentration0():
    with pytest.raises(ValueError):
        msd.Gamma([1.], [0.])
    with pytest.raises(ValueError):
        msd.Gamma([1.], [-1.])


def test_scalar():
    with pytest.raises(TypeError):
        msd.Gamma(3., [4.])
    with pytest.raises(TypeError):
        msd.Gamma([3.], -4.)


def test_arguments():
    """
    args passing during initialization.
    """
    g = msd.Gamma()
    assert isinstance(g, msd.Distribution)
    g = msd.Gamma([3.0], [4.0], dtype=dtype.float32)
    assert isinstance(g, msd.Distribution)


class GammaProb(nn.Cell):
    """
    Gamma distribution: initialize with concentration1/concentration0.
    """
    def __init__(self):
        super(GammaProb, self).__init__()
        self.gamma = msd.Gamma([3.0, 4.0], [1.0, 1.0], dtype=dtype.float32)

    def construct(self, value):
        prob = self.gamma.prob(value)
        log_prob = self.gamma.log_prob(value)
        return prob + log_prob


@pytest.mark.skipif(skip_flag, reason="not support running in CPU and GPU")
def test_gamma_prob():
    """
    Test probability functions: passing value through construct.
    """
    net = GammaProb()
    value = Tensor([0.5, 1.0], dtype=dtype.float32)
    ans = net(value)
    assert isinstance(ans, Tensor)


class GammaProb1(nn.Cell):
    """
    Gamma distribution: initialize without concentration1/concentration0.
    """
    def __init__(self):
        super(GammaProb1, self).__init__()
        self.gamma = msd.Gamma()

    def construct(self, value, concentration1, concentration0):
        prob = self.gamma.prob(value, concentration1, concentration0)
        log_prob = self.gamma.log_prob(value, concentration1, concentration0)
        return prob + log_prob


@pytest.mark.skipif(skip_flag, reason="not support running in CPU and GPU")
def test_gamma_prob1():
    """
    Test probability functions: passing concentration1/concentration0, value through construct.
    """
    net = GammaProb1()
    value = Tensor([0.5, 1.0], dtype=dtype.float32)
    concentration1 = Tensor([2.0, 3.0], dtype=dtype.float32)
    concentration0 = Tensor([1.0], dtype=dtype.float32)
    ans = net(value, concentration1, concentration0)
    assert isinstance(ans, Tensor)


class GammaKl(nn.Cell):
    """
    Test class: kl_loss of Gamma distribution.
    """
    def __init__(self):
        super(GammaKl, self).__init__()
        self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
        self.g2 = msd.Gamma(dtype=dtype.float32)

    def construct(self, concentration1_b, concentration0_b, concentration1_a, concentration0_a):
        kl1 = self.g1.kl_loss('Gamma', concentration1_b, concentration0_b)
        kl2 = self.g2.kl_loss('Gamma', concentration1_b, concentration0_b, concentration1_a, concentration0_a)
        return kl1 + kl2


@pytest.mark.skipif(skip_flag, reason="not support running in CPU and GPU")
def test_kl():
    """
    Test kl_loss.
    """
    net = GammaKl()
    concentration1_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
    concentration0_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
    concentration1_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
    concentration0_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
    ans = net(concentration1_b, concentration0_b, concentration1_a, concentration0_a)
    assert isinstance(ans, Tensor)


class GammaCrossEntropy(nn.Cell):
    """
    Test class: cross_entropy of Gamma distribution.
    """
    def __init__(self):
        super(GammaCrossEntropy, self).__init__()
        self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
        self.g2 = msd.Gamma(dtype=dtype.float32)

    def construct(self, concentration1_b, concentration0_b, concentration1_a, concentration0_a):
        h1 = self.g1.cross_entropy('Gamma', concentration1_b, concentration0_b)
        h2 = self.g2.cross_entropy('Gamma', concentration1_b, concentration0_b, concentration1_a, concentration0_a)
        return h1 + h2


@pytest.mark.skipif(skip_flag, reason="not support running in CPU and GPU")
def test_cross_entropy():
    """
    Test cross entropy between Gamma distributions.
    """
    net = GammaCrossEntropy()
    concentration1_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
    concentration0_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
    concentration1_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
    concentration0_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
    ans = net(concentration1_b, concentration0_b, concentration1_a, concentration0_a)
    assert isinstance(ans, Tensor)


class GammaBasics(nn.Cell):
    """
    Test class: basic mean/sd function.
    """
    def __init__(self):
        super(GammaBasics, self).__init__()
        self.g = msd.Gamma(np.array([3.0, 4.0]), np.array([4.0, 6.0]), dtype=dtype.float32)

    def construct(self):
        mean = self.g.mean()
        sd = self.g.sd()
        mode = self.g.mode()
        return mean + sd + mode


@pytest.mark.skipif(skip_flag, reason="not support running in CPU and GPU")
def test_bascis():
    """
    Test mean/sd/mode/entropy functionality of Gamma.
    """
    net = GammaBasics()
    ans = net()
    assert isinstance(ans, Tensor)


class GammaConstruct(nn.Cell):
    """
    Gamma distribution: going through construct.
    """
    def __init__(self):
        super(GammaConstruct, self).__init__()
        self.gamma = msd.Gamma([3.0], [4.0])
        self.gamma1 = msd.Gamma()

    def construct(self, value, concentration1, concentration0):
        prob = self.gamma('prob', value)
        prob1 = self.gamma('prob', value, concentration1, concentration0)
        prob2 = self.gamma1('prob', value, concentration1, concentration0)
        return prob + prob1 + prob2


@pytest.mark.skipif(skip_flag, reason="not support running in CPU and GPU")
def test_gamma_construct():
    """
    Test probability function going through construct.
    """
    net = GammaConstruct()
    value = Tensor([0.5, 1.0], dtype=dtype.float32)
    concentration1 = Tensor([0.0], dtype=dtype.float32)
    concentration0 = Tensor([1.0], dtype=dtype.float32)
    ans = net(value, concentration1, concentration0)
    assert isinstance(ans, Tensor)