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!4956 Fix CheckTuple in pynative mode 

Merge pull request !4956 from XunDeng/pp_issue_branch
This commit is contained in:
mindspore-ci-bot 2020-08-22 09:37:47 +08:00 committed by Gitee
parent ab45bec828 dc11fa9f53
commit 9f19076788
8 changed files with 23 additions and 30 deletions
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19
mindspore/nn/probability/distribution/_utils/utils.py
View File

@ -22,6 +22,7 @@ from mindspore.common.parameter import Parameter
from mindspore.common import dtype as mstype
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore import context
import mindspore.nn as nn
import mindspore.nn.probability as msp
@ -273,7 +274,8 @@ def check_type(data_type, value_type, name):
@constexpr
def raise_none_error(name):
raise ValueError(f"{name} should be specified. Value cannot be None")
raise TypeError(f"the type {name} should be subclass of Tensor."
f" It should not be None since it is not specified during initialization.")
@constexpr
def raise_not_impl_error(name):
@ -298,15 +300,20 @@ class CheckTuple(PrimitiveWithInfer):
def __infer__(self, x, name):
if not isinstance(x['dtype'], tuple):
raise TypeError("Input type should be a tuple: " + name["value"])
raise TypeError(f"For {name['value']}, Input type should b a tuple.")
out = {'shape': None,
'dtype': None,
'value': None}
'value': x["value"]}
return out
def __call__(self, *args):
return
def __call__(self, x, name):
if context.get_context("mode") == 0:
return x["value"]
#Pynative mode
if isinstance(x, tuple):
return x
raise TypeError(f"For {name['value']}, Input type should b a tuple.")
class CheckTensor(PrimitiveWithInfer):
"""
@ -327,5 +334,5 @@ class CheckTensor(PrimitiveWithInfer):
'value': None}
return out
def __call__(self, *args):
def __call__(self, x, name):
return

6
mindspore/nn/probability/distribution/bernoulli.py
View File

@ -18,7 +18,6 @@ from mindspore.ops import operations as P
from mindspore.ops import composite as C
from .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name, raise_none_error
from ._utils.utils import CheckTensor, CheckTuple
from ._utils.custom_ops import log_by_step
class Bernoulli(Distribution):
@ -125,9 +124,6 @@ class Bernoulli(Distribution):
self.sqrt = P.Sqrt()
self.uniform = C.uniform
self.checktensor = CheckTensor()
self.checktuple = CheckTuple()
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'probs = {self.probs}'
@ -279,7 +275,7 @@ class Bernoulli(Distribution):
Returns:
Tensor, shape is shape + batch_shape.
"""
self.checktuple(shape, 'shape')
shape = self.checktuple(shape, 'shape')
probs1 = self._check_param(probs1)
origin_shape = shape + self.shape(probs1)
if origin_shape == ():

4
mindspore/nn/probability/distribution/distribution.py
View File

@ -17,6 +17,7 @@ from mindspore.nn.cell import Cell
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from ._utils.utils import calc_broadcast_shape_from_param, check_scalar_from_param
from ._utils.utils import CheckTuple, CheckTensor
class Distribution(Cell):
"""
@ -79,6 +80,9 @@ class Distribution(Cell):
self._set_log_survival()
self._set_cross_entropy()
self.checktuple = CheckTuple()
self.checktensor = CheckTensor()
@property
def name(self):
return self._name

5
mindspore/nn/probability/distribution/exponential.py
View File

@ -20,7 +20,6 @@ from mindspore.common import dtype as mstype
from .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_greater_zero, check_type, check_distribution_name,\
raise_none_error
from ._utils.utils import CheckTensor, CheckTuple
from ._utils.custom_ops import log_by_step
class Exponential(Distribution):
@ -127,8 +126,6 @@ class Exponential(Distribution):
self.sq = P.Square()
self.uniform = C.uniform
self.checktensor = CheckTensor()
self.checktuple = CheckTuple()
def extend_repr(self):
if self.is_scalar_batch:
@ -270,7 +267,7 @@ class Exponential(Distribution):
Returns:
Tensor, shape is shape + batch_shape.
"""
self.checktuple(shape, 'shape')
shape = self.checktuple(shape, 'shape')
rate = self._check_param(rate)
origin_shape = shape + self.shape(rate)
if origin_shape == ():

5
mindspore/nn/probability/distribution/geometric.py
View File

@ -20,7 +20,6 @@ from mindspore.common import dtype as mstype
from .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name,\
raise_none_error
from ._utils.utils import CheckTensor, CheckTuple
from ._utils.custom_ops import log_by_step
class Geometric(Distribution):
@ -131,8 +130,6 @@ class Geometric(Distribution):
self.sqrt = P.Sqrt()
self.uniform = C.uniform
self.checktensor = CheckTensor()
self.checktuple = CheckTuple()
def extend_repr(self):
if self.is_scalar_batch:
@ -278,7 +275,7 @@ class Geometric(Distribution):
Returns:
Tensor, shape is shape + batch_shape.
"""
self.checktuple(shape, 'shape')
shape = self.checktuple(shape, 'shape')
probs1 = self._check_param(probs1)
origin_shape = shape + self.shape(probs1)
if origin_shape == ():

6
mindspore/nn/probability/distribution/normal.py
View File

@ -20,7 +20,6 @@ from mindspore.common import dtype as mstype
from .distribution import Distribution
from ._utils.utils import convert_to_batch, check_greater_zero, check_type, check_distribution_name,\
raise_none_error
from ._utils.utils import CheckTensor, CheckTuple
from ._utils.custom_ops import log_by_step, expm1_by_step
class Normal(Distribution):
@ -128,9 +127,6 @@ class Normal(Distribution):
self.sqrt = P.Sqrt()
self.zeroslike = P.ZerosLike()
self.checktensor = CheckTensor()
self.checktuple = CheckTuple()
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'mean = {self._mean_value}, standard deviation = {self._sd_value}'
@ -277,7 +273,7 @@ class Normal(Distribution):
Returns:
Tensor, shape is shape + batch_shape.
"""
self.checktuple(shape, 'shape')
shape = self.checktuple(shape, 'shape')
mean, sd = self._check_param(mean, sd)
batch_shape = self.shape(mean + sd)
origin_shape = shape + batch_shape

2
mindspore/nn/probability/distribution/transformed_distribution.py
View File

@ -116,4 +116,4 @@ class TransformedDistribution(Distribution):
if not self.is_linear_transformation:
raise_not_impl_error("mean")
return self.bijector("forward", self.distribution("mean"))
return self.bijector("forward", self.distribution("mean", *args, **kwargs))

6
mindspore/nn/probability/distribution/uniform.py
View File

@ -19,7 +19,6 @@ from mindspore.common import dtype as mstype
from .distribution import Distribution
from ._utils.utils import convert_to_batch, check_greater, check_type, check_distribution_name,\
raise_none_error
from ._utils.utils import CheckTensor, CheckTuple
from ._utils.custom_ops import log_by_step
class Uniform(Distribution):
@ -131,9 +130,6 @@ class Uniform(Distribution):
self.zeroslike = P.ZerosLike()
self.uniform = C.uniform
self.checktensor = CheckTensor()
self.checktuple = CheckTuple()
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'low = {self.low}, high = {self.high}'
@ -306,7 +302,7 @@ class Uniform(Distribution):
Returns:
Tensor, shape is shape + batch_shape.
"""
self.checktuple(shape, 'shape')
shape = self.checktuple(shape, 'shape')
low, high = self._check_param(low, high)
broadcast_shape = self.shape(low + high)
origin_shape = shape + broadcast_shape