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!3691 Change distribution import

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Merge pull request !3691 from XunDeng/pp_poc_v3
pull/3691/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent b331e62400 e94d91ba95
commit 65c6755fab
13 changed files with 144 additions and 137 deletions
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3
mindspore/nn/__init__.py
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@ -24,7 +24,6 @@ from .loss import *
from .optim import *
from .metrics import *
from .wrap import *
from .probability import *
__all__ = ["Cell", "GraphKernel"]
@ -33,7 +32,7 @@ __all__.extend(loss.__all__)
__all__.extend(optim.__all__)
__all__.extend(metrics.__all__)
__all__.extend(wrap.__all__)
__all__.extend(probability.__all__)
__all__.sort()

7
mindspore/nn/probability/__init__.py
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@ -15,10 +15,7 @@
"""
Probability.
The high-level components(Distributions) used to construct the probabilistic network.
The high-level components used to construct the probabilistic network.
"""
from .distribution import *
__all__ = []
__all__.extend(distribution.__all__)
from . import distribution

25
tests/st/ops/ascend/test_distribution/test_bernoulli.py
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@ -12,11 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""test cases for bernoulli distribution"""
"""test cases for Bernoulli distribution"""
import numpy as np
from scipy import stats
import mindspore.context as context
import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore import dtype
@ -29,7 +30,7 @@ class Prob(nn.Cell):
"""
def __init__(self):
super(Prob, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -54,7 +55,7 @@ class LogProb(nn.Cell):
"""
def __init__(self):
super(LogProb, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -78,7 +79,7 @@ class KL(nn.Cell):
"""
def __init__(self):
super(KL, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -104,7 +105,7 @@ class Basics(nn.Cell):
"""
def __init__(self):
super(Basics, self).__init__()
self.b = nn.Bernoulli([0.3, 0.5, 0.7], dtype=dtype.int32)
self.b = msd.Bernoulli([0.3, 0.5, 0.7], dtype=dtype.int32)
@ms_function
def construct(self):
@ -130,7 +131,7 @@ class Sampling(nn.Cell):
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.b = nn.Bernoulli([0.7, 0.5], seed=seed, dtype=dtype.int32)
self.b = msd.Bernoulli([0.7, 0.5], seed=seed, dtype=dtype.int32)
self.shape = shape
@ms_function
@ -152,7 +153,7 @@ class CDF(nn.Cell):
"""
def __init__(self):
super(CDF, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -177,7 +178,7 @@ class LogCDF(nn.Cell):
"""
def __init__(self):
super(LogCDF, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -202,7 +203,7 @@ class SF(nn.Cell):
"""
def __init__(self):
super(SF, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -227,7 +228,7 @@ class LogSF(nn.Cell):
"""
def __init__(self):
super(LogSF, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -251,7 +252,7 @@ class EntropyH(nn.Cell):
"""
def __init__(self):
super(EntropyH, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self):
@ -274,7 +275,7 @@ class CrossEntropy(nn.Cell):
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):

25
tests/st/ops/ascend/test_distribution/test_exponential.py
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@ -12,11 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""test cases for exponential distribution"""
"""test cases for Exponential distribution"""
import numpy as np
from scipy import stats
import mindspore.context as context
import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore import dtype
@ -29,7 +30,7 @@ class Prob(nn.Cell):
"""
def __init__(self):
super(Prob, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -53,7 +54,7 @@ class LogProb(nn.Cell):
"""
def __init__(self):
super(LogProb, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -77,7 +78,7 @@ class KL(nn.Cell):
"""
def __init__(self):
super(KL, self).__init__()
self.e = nn.Exponential([1.5], dtype=dtype.float32)
self.e = msd.Exponential([1.5], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -101,7 +102,7 @@ class Basics(nn.Cell):
"""
def __init__(self):
super(Basics, self).__init__()
self.e = nn.Exponential([0.5], dtype=dtype.float32)
self.e = msd.Exponential([0.5], dtype=dtype.float32)
@ms_function
def construct(self):
@ -127,7 +128,7 @@ class Sampling(nn.Cell):
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], seed=seed, dtype=dtype.float32)
self.e = msd.Exponential([[1.0], [0.5]], seed=seed, dtype=dtype.float32)
self.shape = shape
@ms_function
@ -151,7 +152,7 @@ class CDF(nn.Cell):
"""
def __init__(self):
super(CDF, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -175,7 +176,7 @@ class LogCDF(nn.Cell):
"""
def __init__(self):
super(LogCDF, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -199,7 +200,7 @@ class SF(nn.Cell):
"""
def __init__(self):
super(SF, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -223,7 +224,7 @@ class LogSF(nn.Cell):
"""
def __init__(self):
super(LogSF, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -247,7 +248,7 @@ class EntropyH(nn.Cell):
"""
def __init__(self):
super(EntropyH, self).__init__()
self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)
self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
@ms_function
def construct(self):
@ -270,7 +271,7 @@ class CrossEntropy(nn.Cell):
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.e = nn.Exponential([1.0], dtype=dtype.float32)
self.e = msd.Exponential([1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):

23
tests/st/ops/ascend/test_distribution/test_geometric.py
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@ -17,6 +17,7 @@ import numpy as np
from scipy import stats
import mindspore.context as context
import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore import dtype
@ -29,7 +30,7 @@ class Prob(nn.Cell):
"""
def __init__(self):
super(Prob, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
self.g = msd.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -53,7 +54,7 @@ class LogProb(nn.Cell):
"""
def __init__(self):
super(LogProb, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
self.g = msd.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -77,7 +78,7 @@ class KL(nn.Cell):
"""
def __init__(self):
super(KL, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
self.g = msd.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -103,7 +104,7 @@ class Basics(nn.Cell):
"""
def __init__(self):
super(Basics, self).__init__()
self.g = nn.Geometric([0.5, 0.5], dtype=dtype.int32)
self.g = msd.Geometric([0.5, 0.5], dtype=dtype.int32)
@ms_function
def construct(self):
@ -129,7 +130,7 @@ class Sampling(nn.Cell):
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.g = nn.Geometric([0.7, 0.5], seed=seed, dtype=dtype.int32)
self.g = msd.Geometric([0.7, 0.5], seed=seed, dtype=dtype.int32)
self.shape = shape
@ms_function
@ -151,7 +152,7 @@ class CDF(nn.Cell):
"""
def __init__(self):
super(CDF, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
self.g = msd.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -175,7 +176,7 @@ class LogCDF(nn.Cell):
"""
def __init__(self):
super(LogCDF, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
self.g = msd.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -199,7 +200,7 @@ class SF(nn.Cell):
"""
def __init__(self):
super(SF, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
self.g = msd.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -223,7 +224,7 @@ class LogSF(nn.Cell):
"""
def __init__(self):
super(LogSF, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
self.g = msd.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):
@ -247,7 +248,7 @@ class EntropyH(nn.Cell):
"""
def __init__(self):
super(EntropyH, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
self.g = msd.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self):
@ -270,7 +271,7 @@ class CrossEntropy(nn.Cell):
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.g = nn.Geometric(0.7, dtype=dtype.int32)
self.g = msd.Geometric(0.7, dtype=dtype.int32)
@ms_function
def construct(self, x_):

25
tests/st/ops/ascend/test_distribution/test_normal.py
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@ -12,11 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""test cases for normal distribution"""
"""test cases for Normal distribution"""
import numpy as np
from scipy import stats
import mindspore.context as context
import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore import dtype
@ -29,7 +30,7 @@ class Prob(nn.Cell):
"""
def __init__(self):
super(Prob, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -52,7 +53,7 @@ class LogProb(nn.Cell):
"""
def __init__(self):
super(LogProb, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -76,7 +77,7 @@ class KL(nn.Cell):
"""
def __init__(self):
super(KL, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
@ms_function
def construct(self, x_, y_):
@ -110,7 +111,7 @@ class Basics(nn.Cell):
"""
def __init__(self):
super(Basics, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
@ms_function
def construct(self):
@ -135,7 +136,7 @@ class Sampling(nn.Cell):
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
self.shape = shape
@ms_function
@ -160,7 +161,7 @@ class CDF(nn.Cell):
"""
def __init__(self):
super(CDF, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -184,7 +185,7 @@ class LogCDF(nn.Cell):
"""
def __init__(self):
super(LogCDF, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -207,7 +208,7 @@ class SF(nn.Cell):
"""
def __init__(self):
super(SF, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -230,7 +231,7 @@ class LogSF(nn.Cell):
"""
def __init__(self):
super(LogSF, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -253,7 +254,7 @@ class EntropyH(nn.Cell):
"""
def __init__(self):
super(EntropyH, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
@ms_function
def construct(self):
@ -276,7 +277,7 @@ class CrossEntropy(nn.Cell):
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.n = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.n = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
@ms_function
def construct(self, x_, y_):

3
tests/st/ops/ascend/test_distribution/test_normal_new_api.py
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@ -16,6 +16,7 @@
import numpy as np
from scipy import stats
import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import dtype
from mindspore import Tensor
import mindspore.context as context
@ -30,7 +31,7 @@ class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.normal = nn.Normal(0., 1., dtype=dtype.float32)
self.normal = msd.Normal(0., 1., dtype=dtype.float32)
def construct(self, x_, y_):
kl = self.normal.kl_loss('kl_loss', 'Normal', x_, y_)

25
tests/st/ops/ascend/test_distribution/test_uniform.py
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@ -12,11 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""test cases for uniform distribution"""
"""test cases for Uniform distribution"""
import numpy as np
from scipy import stats
import mindspore.context as context
import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore import dtype
@ -29,7 +30,7 @@ class Prob(nn.Cell):
"""
def __init__(self):
super(Prob, self).__init__()
self.u = nn.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -53,7 +54,7 @@ class LogProb(nn.Cell):
"""
def __init__(self):
super(LogProb, self).__init__()
self.u = nn.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -77,7 +78,7 @@ class KL(nn.Cell):
"""
def __init__(self):
super(KL, self).__init__()
self.u = nn.Uniform([0.0], [1.5], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [1.5], dtype=dtype.float32)
@ms_function
def construct(self, x_, y_):
@ -103,7 +104,7 @@ class Basics(nn.Cell):
"""
def __init__(self):
super(Basics, self).__init__()
self.u = nn.Uniform([0.0], [3.0], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [3.0], dtype=dtype.float32)
@ms_function
def construct(self):
@ -127,7 +128,7 @@ class Sampling(nn.Cell):
"""
def __init__(self, shape, seed=0):
super(Sampling, self).__init__()
self.u = nn.Uniform([0.0], [[1.0], [2.0]], seed=seed, dtype=dtype.float32)
self.u = msd.Uniform([0.0], [[1.0], [2.0]], seed=seed, dtype=dtype.float32)
self.shape = shape
@ms_function
@ -152,7 +153,7 @@ class CDF(nn.Cell):
"""
def __init__(self):
super(CDF, self).__init__()
self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -176,7 +177,7 @@ class LogCDF(nn.Cell):
"""
def __init__(self):
super(LogCDF, self).__init__()
self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -188,7 +189,7 @@ class SF(nn.Cell):
"""
def __init__(self):
super(SF, self).__init__()
self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -200,7 +201,7 @@ class LogSF(nn.Cell):
"""
def __init__(self):
super(LogSF, self).__init__()
self.u = nn.Uniform([0.0], [1.0], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
@ms_function
def construct(self, x_):
@ -212,7 +213,7 @@ class EntropyH(nn.Cell):
"""
def __init__(self):
super(EntropyH, self).__init__()
self.u = nn.Uniform([0.0], [1.0, 2.0], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [1.0, 2.0], dtype=dtype.float32)
@ms_function
def construct(self):
@ -235,7 +236,7 @@ class CrossEntropy(nn.Cell):
"""
def __init__(self):
super(CrossEntropy, self).__init__()
self.u = nn.Uniform([0.0], [1.5], dtype=dtype.float32)
self.u = msd.Uniform([0.0], [1.5], dtype=dtype.float32)
@ms_function
def construct(self, x_, y_):

29
tests/ut/python/nn/distribution/test_bernoulli.py
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@ -13,11 +13,12 @@
# limitations under the License.
# ============================================================================
"""
Test nn.Distribution.Bernoulli.
Test nn.probability.distribution.Bernoulli.
"""
import pytest
import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import dtype
from mindspore import Tensor
@ -25,19 +26,19 @@ def test_arguments():
"""
Args passing during initialization.
"""
b = nn.Bernoulli()
assert isinstance(b, nn.Distribution)
b = nn.Bernoulli([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
assert isinstance(b, nn.Distribution)
b = msd.Bernoulli()
assert isinstance(b, msd.Distribution)
b = msd.Bernoulli([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
assert isinstance(b, msd.Distribution)
def test_prob():
"""
Invalid probability.
"""
with pytest.raises(ValueError):
nn.Bernoulli([-0.1], dtype=dtype.int32)
msd.Bernoulli([-0.1], dtype=dtype.int32)
with pytest.raises(ValueError):
nn.Bernoulli([1.1], dtype=dtype.int32)
msd.Bernoulli([1.1], dtype=dtype.int32)
class BernoulliProb(nn.Cell):
"""
@ -45,7 +46,7 @@ class BernoulliProb(nn.Cell):
"""
def __init__(self):
super(BernoulliProb, self).__init__()
self.b = nn.Bernoulli(0.5, dtype=dtype.int32)
self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
def construct(self, value):
prob = self.b('prob', value)
@ -71,7 +72,7 @@ class BernoulliProb1(nn.Cell):
"""
def __init__(self):
super(BernoulliProb1, self).__init__()
self.b = nn.Bernoulli(dtype=dtype.int32)
self.b = msd.Bernoulli(dtype=dtype.int32)
def construct(self, value, probs):
prob = self.b('prob', value, probs)
@ -98,8 +99,8 @@ class BernoulliKl(nn.Cell):
"""
def __init__(self):
super(BernoulliKl, self).__init__()
self.b1 = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b2 = nn.Bernoulli(dtype=dtype.int32)
self.b1 = msd.Bernoulli(0.7, dtype=dtype.int32)
self.b2 = msd.Bernoulli(dtype=dtype.int32)
def construct(self, probs_b, probs_a):
kl1 = self.b1('kl_loss', 'Bernoulli', probs_b)
@ -122,8 +123,8 @@ class BernoulliCrossEntropy(nn.Cell):
"""
def __init__(self):
super(BernoulliCrossEntropy, self).__init__()
self.b1 = nn.Bernoulli(0.7, dtype=dtype.int32)
self.b2 = nn.Bernoulli(dtype=dtype.int32)
self.b1 = msd.Bernoulli(0.7, dtype=dtype.int32)
self.b2 = msd.Bernoulli(dtype=dtype.int32)
def construct(self, probs_b, probs_a):
h1 = self.b1('cross_entropy', 'Bernoulli', probs_b)
@ -146,7 +147,7 @@ class BernoulliBasics(nn.Cell):
"""
def __init__(self):
super(BernoulliBasics, self).__init__()
self.b = nn.Bernoulli([0.3, 0.5], dtype=dtype.int32)
self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
def construct(self):
mean = self.b('mean')

29
tests/ut/python/nn/distribution/test_exponential.py
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@ -13,11 +13,12 @@
# limitations under the License.
# ============================================================================
"""
Test nn.Distribution.Exponential.
Test nn.probability.distribution.Exponential.
"""
import pytest
import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import dtype
from mindspore import Tensor
@ -26,19 +27,19 @@ def test_arguments():
"""
Args passing during initialization.
"""
e = nn.Exponential()
assert isinstance(e, nn.Distribution)
e = nn.Exponential([0.1, 0.3, 0.5, 1.0], dtype=dtype.float32)
assert isinstance(e, nn.Distribution)
e = msd.Exponential()
assert isinstance(e, msd.Distribution)
e = msd.Exponential([0.1, 0.3, 0.5, 1.0], dtype=dtype.float32)
assert isinstance(e, msd.Distribution)
def test_rate():
"""
Invalid rate.
"""
with pytest.raises(ValueError):
nn.Exponential([-0.1], dtype=dtype.float32)
msd.Exponential([-0.1], dtype=dtype.float32)
with pytest.raises(ValueError):
nn.Exponential([0.0], dtype=dtype.float32)
msd.Exponential([0.0], dtype=dtype.float32)
class ExponentialProb(nn.Cell):
"""
@ -46,7 +47,7 @@ class ExponentialProb(nn.Cell):
"""
def __init__(self):
super(ExponentialProb, self).__init__()
self.e = nn.Exponential(0.5, dtype=dtype.float32)
self.e = msd.Exponential(0.5, dtype=dtype.float32)
def construct(self, value):
prob = self.e('prob', value)
@ -72,7 +73,7 @@ class ExponentialProb1(nn.Cell):
"""
def __init__(self):
super(ExponentialProb1, self).__init__()
self.e = nn.Exponential(dtype=dtype.float32)
self.e = msd.Exponential(dtype=dtype.float32)
def construct(self, value, rate):
prob = self.e('prob', value, rate)
@ -99,8 +100,8 @@ class ExponentialKl(nn.Cell):
"""
def __init__(self):
super(ExponentialKl, self).__init__()
self.e1 = nn.Exponential(0.7, dtype=dtype.float32)
self.e2 = nn.Exponential(dtype=dtype.float32)
self.e1 = msd.Exponential(0.7, dtype=dtype.float32)
self.e2 = msd.Exponential(dtype=dtype.float32)
def construct(self, rate_b, rate_a):
kl1 = self.e1('kl_loss', 'Exponential', rate_b)
@ -123,8 +124,8 @@ class ExponentialCrossEntropy(nn.Cell):
"""
def __init__(self):
super(ExponentialCrossEntropy, self).__init__()
self.e1 = nn.Exponential(0.3, dtype=dtype.float32)
self.e2 = nn.Exponential(dtype=dtype.float32)
self.e1 = msd.Exponential(0.3, dtype=dtype.float32)
self.e2 = msd.Exponential(dtype=dtype.float32)
def construct(self, rate_b, rate_a):
h1 = self.e1('cross_entropy', 'Exponential', rate_b)
@ -147,7 +148,7 @@ class ExponentialBasics(nn.Cell):
"""
def __init__(self):
super(ExponentialBasics, self).__init__()
self.e = nn.Exponential([0.3, 0.5], dtype=dtype.float32)
self.e = msd.Exponential([0.3, 0.5], dtype=dtype.float32)
def construct(self):
mean = self.e('mean')

29
tests/ut/python/nn/distribution/test_geometric.py
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@ -13,11 +13,12 @@
# limitations under the License.
# ============================================================================
"""
Test nn.Distribution.Geometric.
Test nn.probability.distribution.Geometric.
"""
import pytest
import mindspore.nn as nn
import mindspore.nn.probability.distribution as msd
from mindspore import dtype
from mindspore import Tensor
@ -26,19 +27,19 @@ def test_arguments():
"""
Args passing during initialization.
"""
g = nn.Geometric()
assert isinstance(g, nn.Distribution)
g = nn.Geometric([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
assert isinstance(g, nn.Distribution)
g = msd.Geometric()
assert isinstance(g, msd.Distribution)
g = msd.Geometric([0.0, 0.3, 0.5, 1.0], dtype=dtype.int32)
assert isinstance(g, msd.Distribution)
def test_prob():
"""
Invalid probability.
"""
with pytest.raises(ValueError):
nn.Geometric([-0.1], dtype=dtype.int32)
msd.Geometric([-0.1], dtype=dtype.int32)
with pytest.raises(ValueError):
nn.Geometric([1.1], dtype=dtype.int32)
msd.Geometric([1.1], dtype=dtype.int32)
class GeometricProb(nn.Cell):
"""
@ -46,7 +47,7 @@ class GeometricProb(nn.Cell):
"""
def __init__(self):
super(GeometricProb, self).__init__()
self.g = nn.Geometric(0.5, dtype=dtype.int32)
self.g = msd.Geometric(0.5, dtype=dtype.int32)
def construct(self, value):
prob = self.g('prob', value)
@ -72,7 +73,7 @@ class GeometricProb1(nn.Cell):
"""
def __init__(self):
super(GeometricProb1, self).__init__()
self.g = nn.Geometric(dtype=dtype.int32)
self.g = msd.Geometric(dtype=dtype.int32)
def construct(self, value, probs):
prob = self.g('prob', value, probs)
@ -100,8 +101,8 @@ class GeometricKl(nn.Cell):
"""
def __init__(self):
super(GeometricKl, self).__init__()
self.g1 = nn.Geometric(0.7, dtype=dtype.int32)
self.g2 = nn.Geometric(dtype=dtype.int32)
self.g1 = msd.Geometric(0.7, dtype=dtype.int32)
self.g2 = msd.Geometric(dtype=dtype.int32)
def construct(self, probs_b, probs_a):
kl1 = self.g1('kl_loss', 'Geometric', probs_b)
@ -124,8 +125,8 @@ class GeometricCrossEntropy(nn.Cell):
"""
def __init__(self):
super(GeometricCrossEntropy, self).__init__()
self.g1 = nn.Geometric(0.3, dtype=dtype.int32)
self.g2 = nn.Geometric(dtype=dtype.int32)
self.g1 = msd.Geometric(0.3, dtype=dtype.int32)
self.g2 = msd.Geometric(dtype=dtype.int32)
def construct(self, probs_b, probs_a):
h1 = self.g1('cross_entropy', 'Geometric', probs_b)
@ -148,7 +149,7 @@ class GeometricBasics(nn.Cell):
"""
def __init__(self):
super(GeometricBasics, self).__init__()
self.g = nn.Geometric([0.3, 0.5], dtype=dtype.int32)
self.g = msd.Geometric([0.3, 0.5], dtype=dtype.int32)
def construct(self):
mean = self.g('mean')

27
tests/ut/python/nn/distribution/test_normal.py
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@ -13,12 +13,13 @@
# limitations under the License.
# ============================================================================
"""
Test nn.Distribution.Normal.
Test nn.probability.distribution.Normal.
"""
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
@ -27,17 +28,17 @@ def test_normal_shape_errpr():
Invalid shapes.
"""
with pytest.raises(ValueError):
nn.Normal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
msd.Normal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
def test_arguments():
"""
args passing during initialization.
"""
n = nn.Normal()
assert isinstance(n, nn.Distribution)
n = nn.Normal([3.0], [4.0], dtype=dtype.float32)
assert isinstance(n, nn.Distribution)
n = msd.Normal()
assert isinstance(n, msd.Distribution)
n = msd.Normal([3.0], [4.0], dtype=dtype.float32)
assert isinstance(n, msd.Distribution)
class NormalProb(nn.Cell):
@ -46,7 +47,7 @@ class NormalProb(nn.Cell):
"""
def __init__(self):
super(NormalProb, self).__init__()
self.normal = nn.Normal(3.0, 4.0, dtype=dtype.float32)
self.normal = msd.Normal(3.0, 4.0, dtype=dtype.float32)
def construct(self, value):
prob = self.normal('prob', value)
@ -73,7 +74,7 @@ class NormalProb1(nn.Cell):
"""
def __init__(self):
super(NormalProb1, self).__init__()
self.normal = nn.Normal()
self.normal = msd.Normal()
def construct(self, value, mean, sd):
prob = self.normal('prob', value, mean, sd)
@ -101,8 +102,8 @@ class NormalKl(nn.Cell):
"""
def __init__(self):
super(NormalKl, self).__init__()
self.n1 = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.n2 = nn.Normal(dtype=dtype.float32)
self.n1 = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.n2 = msd.Normal(dtype=dtype.float32)
def construct(self, mean_b, sd_b, mean_a, sd_a):
kl1 = self.n1('kl_loss', 'Normal', mean_b, sd_b)
@ -127,8 +128,8 @@ class NormalCrossEntropy(nn.Cell):
"""
def __init__(self):
super(NormalCrossEntropy, self).__init__()
self.n1 = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.n2 = nn.Normal(dtype=dtype.float32)
self.n1 = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.n2 = msd.Normal(dtype=dtype.float32)
def construct(self, mean_b, sd_b, mean_a, sd_a):
h1 = self.n1('cross_entropy', 'Normal', mean_b, sd_b)
@ -153,7 +154,7 @@ class NormalBasics(nn.Cell):
"""
def __init__(self):
super(NormalBasics, self).__init__()
self.n = nn.Normal(3.0, 4.0, dtype=dtype.float32)
self.n = msd.Normal(3.0, 4.0, dtype=dtype.float32)
def construct(self):
mean = self.n('mean')

31
tests/ut/python/nn/distribution/test_uniform.py
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@ -13,12 +13,13 @@
# limitations under the License.
# ============================================================================
"""
Test nn.Distribution.Uniform.
Test nn.probability.distribution.Uniform.
"""
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
@ -27,17 +28,17 @@ def test_uniform_shape_errpr():
Invalid shapes.
"""
with pytest.raises(ValueError):
nn.Uniform([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
msd.Uniform([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
def test_arguments():
"""
Args passing during initialization.
"""
u = nn.Uniform()
assert isinstance(u, nn.Distribution)
u = nn.Uniform([3.0], [4.0], dtype=dtype.float32)
assert isinstance(u, nn.Distribution)
u = msd.Uniform()
assert isinstance(u, msd.Distribution)
u = msd.Uniform([3.0], [4.0], dtype=dtype.float32)
assert isinstance(u, msd.Distribution)
def test_invalid_range():
@ -45,9 +46,9 @@ def test_invalid_range():
Test range of uniform distribution.
"""
with pytest.raises(ValueError):
nn.Uniform(0.0, 0.0, dtype=dtype.float32)
msd.Uniform(0.0, 0.0, dtype=dtype.float32)
with pytest.raises(ValueError):
nn.Uniform(1.0, 0.0, dtype=dtype.float32)
msd.Uniform(1.0, 0.0, dtype=dtype.float32)
class UniformProb(nn.Cell):
@ -56,7 +57,7 @@ class UniformProb(nn.Cell):
"""
def __init__(self):
super(UniformProb, self).__init__()
self.u = nn.Uniform(3.0, 4.0, dtype=dtype.float32)
self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
def construct(self, value):
prob = self.u('prob', value)
@ -82,7 +83,7 @@ class UniformProb1(nn.Cell):
"""
def __init__(self):
super(UniformProb1, self).__init__()
self.u = nn.Uniform(dtype=dtype.float32)
self.u = msd.Uniform(dtype=dtype.float32)
def construct(self, value, low, high):
prob = self.u('prob', value, low, high)
@ -110,8 +111,8 @@ class UniformKl(nn.Cell):
"""
def __init__(self):
super(UniformKl, self).__init__()
self.u1 = nn.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.u2 = nn.Uniform(dtype=dtype.float32)
self.u1 = msd.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.u2 = msd.Uniform(dtype=dtype.float32)
def construct(self, low_b, high_b, low_a, high_a):
kl1 = self.u1('kl_loss', 'Uniform', low_b, high_b)
@ -136,8 +137,8 @@ class UniformCrossEntropy(nn.Cell):
"""
def __init__(self):
super(UniformCrossEntropy, self).__init__()
self.u1 = nn.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.u2 = nn.Uniform(dtype=dtype.float32)
self.u1 = msd.Uniform(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
self.u2 = msd.Uniform(dtype=dtype.float32)
def construct(self, low_b, high_b, low_a, high_a):
h1 = self.u1('cross_entropy', 'Uniform', low_b, high_b)
@ -162,7 +163,7 @@ class UniformBasics(nn.Cell):
"""
def __init__(self):
super(UniformBasics, self).__init__()
self.u = nn.Uniform(3.0, 4.0, dtype=dtype.float32)
self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
def construct(self):
mean = self.u('mean')

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