提交 1feca960 编写于 作者: P peixu_ren

Rollback to Normal on D

上级 8300802b
...@@ -14,7 +14,6 @@ ...@@ -14,7 +14,6 @@
# ============================================================================ # ============================================================================
"""Bernoulli Distribution""" """Bernoulli Distribution"""
from mindspore.ops import operations as P from mindspore.ops import operations as P
from mindspore.ops import composite as C
from .distribution import Distribution from .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_prob from ._utils.utils import cast_to_tensor, check_prob
from ...common import dtype as mstype from ...common import dtype as mstype
...@@ -54,7 +53,6 @@ class Bernoulli(Distribution): ...@@ -54,7 +53,6 @@ class Bernoulli(Distribution):
check_prob(self._probs) check_prob(self._probs)
else: else:
self._probs = probs self._probs = probs
self.seed = seed
# ops needed for the class # ops needed for the class
self.log = P.Log() self.log = P.Log()
...@@ -66,6 +64,7 @@ class Bernoulli(Distribution): ...@@ -66,6 +64,7 @@ class Bernoulli(Distribution):
self.const = P.ScalarToArray() self.const = P.ScalarToArray()
self.less = P.Less() self.less = P.Less()
self.cast = P.Cast() self.cast = P.Cast()
self.normal = P.Normal(seed=seed)
self.erf = P.Erf() self.erf = P.Erf()
self.sqrt = P.Sqrt() self.sqrt = P.Sqrt()
...@@ -160,7 +159,7 @@ class Bernoulli(Distribution): ...@@ -160,7 +159,7 @@ class Bernoulli(Distribution):
mean_zero = self.const(0.0) mean_zero = self.const(0.0)
sd_one = self.const(1.0) sd_one = self.const(1.0)
sqrt_two = self.sqrt(self.const(2.0)) sqrt_two = self.sqrt(self.const(2.0))
sample_norm = C.normal(sample_shape, mean_zero, sd_one, self.seed) sample_norm = self.normal(sample_shape, mean_zero, sd_one)
sample_uniform = 0.5 * (1 + self.erf(self.realdiv(sample_norm, sqrt_two))) sample_uniform = 0.5 * (1 + self.erf(self.realdiv(sample_norm, sqrt_two)))
sample = self.less(sample_uniform, probs1) sample = self.less(sample_uniform, probs1)
sample = self.cast(sample, self._dtype) sample = self.cast(sample, self._dtype)
......
...@@ -15,7 +15,6 @@ ...@@ -15,7 +15,6 @@
"""Normal Distribution""" """Normal Distribution"""
import numpy as np import numpy as np
from mindspore.ops import operations as P from mindspore.ops import operations as P
from mindspore.ops import composite as C
from .distribution import Distribution from .distribution import Distribution
from ._utils.utils import convert_to_batch, check_greater_equal_zero from ._utils.utils import convert_to_batch, check_greater_equal_zero
from ...common import dtype as mstype from ...common import dtype as mstype
...@@ -61,7 +60,6 @@ class Normal(Distribution): ...@@ -61,7 +60,6 @@ class Normal(Distribution):
else: else:
self._mean_value = mean self._mean_value = mean
self._sd_value = sd self._sd_value = sd
self.seed = seed
#ops needed for the class #ops needed for the class
self.exp = P.Exp() self.exp = P.Exp()
...@@ -72,6 +70,7 @@ class Normal(Distribution): ...@@ -72,6 +70,7 @@ class Normal(Distribution):
self.sqrt = P.Sqrt() self.sqrt = P.Sqrt()
self.realdiv = P.RealDiv() self.realdiv = P.RealDiv()
self.expm1 = P.Expm1() if get_context('device_target') == 'Ascend' else self._expm1_by_step self.expm1 = P.Expm1() if get_context('device_target') == 'Ascend' else self._expm1_by_step
self.normal = P.Normal(seed=seed)
self.shape = P.Shape() self.shape = P.Shape()
self.zeroslike = P.ZerosLike() self.zeroslike = P.ZerosLike()
self.const = P.ScalarToArray() self.const = P.ScalarToArray()
...@@ -164,7 +163,7 @@ class Normal(Distribution): ...@@ -164,7 +163,7 @@ class Normal(Distribution):
sample_shape = shape + batch_shape sample_shape = shape + batch_shape
mean_zero = self.const(0.0) mean_zero = self.const(0.0)
sd_one = self.const(1.0) sd_one = self.const(1.0)
sample_norm = C.normal(sample_shape, mean_zero, sd_one, self.seed) sample_norm = self.normal(sample_shape, mean_zero, sd_one)
sample = self.add(mean, self.mul(sample_norm, sd)) sample = self.add(mean, self.mul(sample_norm, sd))
return sample return sample
return None return None
...@@ -27,7 +27,6 @@ from .clip_ops import clip_by_value ...@@ -27,7 +27,6 @@ from .clip_ops import clip_by_value
from .multitype_ops.add_impl import hyper_add from .multitype_ops.add_impl import hyper_add
from .multitype_ops.ones_like_impl import ones_like from .multitype_ops.ones_like_impl import ones_like
from .multitype_ops.zeros_like_impl import zeros_like from .multitype_ops.zeros_like_impl import zeros_like
from .random_ops import normal
__all__ = [ __all__ = [
...@@ -48,5 +47,4 @@ __all__ = [ ...@@ -48,5 +47,4 @@ __all__ = [
'zeros_like', 'zeros_like',
'ones_like', 'ones_like',
'zip_operation', 'zip_operation',
'normal',
'clip_by_value',] 'clip_by_value',]
# 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.
# ============================================================================
"""Operations for random number generatos."""
from mindspore.ops.primitive import constexpr
from .. import operations as P
# set graph-level RNG seed
_GRAPH_SEED = 0
@constexpr
def set_seed(seed):
global _GRAPH_SEED
_GRAPH_SEED = seed
@constexpr
def get_seed():
return _GRAPH_SEED
def normal(shape, mean, stddev, seed):
"""
Generates random numbers according to the Normal (or Gaussian) random number distribution.
It is defined as:
Args:
- **shape** (tuple) - The shape of random tensor to be generated.
- **mean** (Tensor) - The mean μ distribution parameter, which specifies the location of the peak.
With float32 data type.
- **stddev** (Tensor) - The deviation σ distribution parameter. With float32 data type.
- **seed** (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
Default: 0.
Returns:
Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of mean and stddev.
The dtype is float32.
Examples:
>>> shape = (4, 16)
>>> mean = Tensor(1.0, mstype.float32)
>>> stddev = Tensor(1.0, mstype.float32)
>>> output = C.normal(shape, mean, stddev, seed=5)
"""
set_seed(10)
seed1 = get_seed()
seed2 = seed
stdnormal = P.StandardNormal(seed1, seed2)
rnd = stdnormal(shape)
value = rnd * stddev + mean
return value
...@@ -55,7 +55,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A ...@@ -55,7 +55,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e,
Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps) Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps)
from .random_ops import (RandomChoiceWithMask, StandardNormal) from .random_ops import (RandomChoiceWithMask, Normal)
from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, ApplyMomentum, BatchNorm, from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, ApplyMomentum, BatchNorm,
BiasAdd, Conv2D, BiasAdd, Conv2D,
DepthwiseConv2dNative, DepthwiseConv2dNative,
...@@ -170,7 +170,7 @@ __all__ = [ ...@@ -170,7 +170,7 @@ __all__ = [
'HSigmoid', 'HSigmoid',
'Tanh', 'Tanh',
'RandomChoiceWithMask', 'RandomChoiceWithMask',
'StandardNormal', 'Normal',
'ResizeBilinear', 'ResizeBilinear',
'ScalarSummary', 'ScalarSummary',
'ImageSummary', 'ImageSummary',
......
...@@ -21,48 +21,6 @@ from ...common import dtype as mstype ...@@ -21,48 +21,6 @@ from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, prim_attr_register from ..primitive import PrimitiveWithInfer, prim_attr_register
class StandardNormal(PrimitiveWithInfer):
r"""
Generates random numbers according to the standard Normal (or Gaussian) random number distribution.
Args:
seed (int): Random seed. Default: 0.
seed2 (int): Random seed2. Default: 0.
Inputs:
- **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
Outputs:
Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of mean and stddev.
The dtype is float32.
Examples:
>>> shape = (4, 16)
>>> stdnormal = P.StandardNormal(seed=2)
>>> output = stdnormal(shape)
"""
@prim_attr_register
def __init__(self, seed=0, seed2=0):
"""Init StandardNormal"""
self.init_prim_io_names(inputs=['shape'], outputs=['output'])
validator.check_value_type('seed', seed, [int], self.name)
validator.check_value_type('seed2', seed2, [int], self.name)
def __infer__(self, shape):
shape_v = shape["value"]
if shape_v is None:
raise ValueError(f"For {self.name}, shape must be const.")
validator.check_value_type("shape", shape_v, [tuple], self.name)
for i, shape_i in enumerate(shape_v):
validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name)
out = {
'shape': shape_v,
'dtype': mstype.float32,
'value': None}
return out
class RandomChoiceWithMask(PrimitiveWithInfer): class RandomChoiceWithMask(PrimitiveWithInfer):
""" """
Generates a random samply as index tensor with a mask tensor from a given tensor. Generates a random samply as index tensor with a mask tensor from a given tensor.
...@@ -106,3 +64,47 @@ class RandomChoiceWithMask(PrimitiveWithInfer): ...@@ -106,3 +64,47 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
def infer_dtype(self, x_dtype): def infer_dtype(self, x_dtype):
validator.check_tensor_type_same({'x': x_dtype}, [mstype.bool_], self.name) validator.check_tensor_type_same({'x': x_dtype}, [mstype.bool_], self.name)
return (mstype.int32, mstype.bool_) return (mstype.int32, mstype.bool_)
class Normal(PrimitiveWithInfer):
"""
Generates random samples from a normal(Gaussian) distribution.
Args:
seed (int): Random seed. Default: 0.
Inputs:
- **shape** (tuple[int]) - The shape of output tensor. Only constant value is allowed.
- **mean** (Tensor) - The mean of the distribution, with float32 data type.
- **stddev** (Tensor) - The standard deviation of the distribution, with float32 data type.
Outputs:
Tensor, with the given shape from the specific distribution and float32 data type.
Examples:
>>> normal = P.Normal()
>>> mean = Tensor(0., mstype.float32)
>>> stddev = Tensor(1., mstype.float32)
>>> out = normal((32, 3, 3), mean, stddev)
"""
@prim_attr_register
def __init__(self, seed=0):
"""Init Normal"""
validator.check_value_type("seed", seed, [int], self.name)
def __infer__(self, shape, mean, stddev):
shape_value = shape["value"]
if shape_value is None:
raise ValueError(f"For {self.name}, shape must be const.")
validator.check_value_type("shape", shape_value, [tuple], self.name)
for i, shape_i in enumerate(shape_value):
validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GE, self.name)
validator.check_tensor_type_same({"mean": mean["dtype"]}, [mstype.float32], self.name)
validator.check_tensor_type_same({"stddev": stddev["dtype"]}, [mstype.float32], self.name)
out = {"shape": shape_value,
"dtype": mstype.float32,
"value": None}
return out
# 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.
# ============================================================================
import numpy as np
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.ops import composite as C
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
class Net(nn.Cell):
def __init__(self, shape, seed=0):
super(Net, self).__init__()
self.shape = shape
self.seed = seed
def construct(self, mean, stddev):
return C.normal(self.shape, mean, stddev, self.seed)
def test_net_1D():
seed = 10
shape = (3, 2, 4)
mean = 1.0
stddev = 1.0
net = Net(shape, seed)
tmean, tstddev = Tensor(mean, mstype.float32), Tensor(stddev, mstype.float32)
output = net(tmean, tstddev)
assert output.shape == (3, 2, 4)
def test_net_ND():
seed = 10
shape = (3, 1, 2)
mean = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]).astype(np.float32)
stddev = np.array([1.0]).astype(np.float32)
net = Net(shape, seed)
tmean, tstddev = Tensor(mean, mstype.float32), Tensor(stddev, mstype.float32)
output = net(tmean, tstddev)
assert output.shape == (3, 2, 2)
...@@ -533,10 +533,10 @@ class NormalNet(nn.Cell): ...@@ -533,10 +533,10 @@ class NormalNet(nn.Cell):
def __init__(self, shape=None, seed=0): def __init__(self, shape=None, seed=0):
super(NormalNet, self).__init__() super(NormalNet, self).__init__()
self.shape = shape self.shape = shape
self.seed = seed self.normal = P.Normal(seed=seed)
def construct(self, mean, stddev): def construct(self, mean, stddev):
out = C.normal(self.shape, mean, stddev, self.seed) out = self.normal(self.shape, mean, stddev)
return out return out
......
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