forked from mindspore-Ecosystem/mindspore
clean the warning code
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5c72a3c2bc
commit
77769f2a41
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@ -190,7 +190,7 @@ class Bijector(Cell):
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"""
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"""
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if 'param_dict' not in self.parameters.keys():
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if 'param_dict' not in self.parameters.keys():
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return None
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return None
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param_dict = self.parameters['param_dict']
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param_dict = self.parameters.get('param_dict')
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broadcast_shape_tensor = None
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broadcast_shape_tensor = None
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for value in param_dict.values():
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for value in param_dict.values():
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if value is None:
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if value is None:
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@ -208,7 +208,7 @@ class Bijector(Cell):
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"""
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"""
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if 'param_dict' not in self.parameters.keys():
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if 'param_dict' not in self.parameters.keys():
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return False
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return False
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param_dict = self.parameters['param_dict']
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param_dict = self.parameters.get('param_dict')
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for value in param_dict.values():
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for value in param_dict.values():
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if value is None:
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if value is None:
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continue
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continue
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@ -327,4 +327,4 @@ class Bijector(Cell):
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return self.forward_log_jacobian(*args, **kwargs)
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return self.forward_log_jacobian(*args, **kwargs)
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if name == 'inverse_log_jacobian':
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if name == 'inverse_log_jacobian':
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return self.inverse_log_jacobian(*args, **kwargs)
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return self.inverse_log_jacobian(*args, **kwargs)
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return None
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raise Exception('Invalid name')
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@ -218,7 +218,7 @@ class Distribution(Cell):
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"""
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"""
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Check if the parameters used during initialization are scalars.
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Check if the parameters used during initialization are scalars.
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"""
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"""
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param_dict = self.parameters['param_dict']
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param_dict = self.parameters.get('param_dict')
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for value in param_dict.values():
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for value in param_dict.values():
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if value is None:
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if value is None:
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continue
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continue
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@ -67,10 +67,10 @@ class Gumbel(TransformedDistribution):
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TypeError: When the input `dtype` is not a subclass of float.
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TypeError: When the input `dtype` is not a subclass of float.
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Examples:
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Examples:
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>>> import numpy as np
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>>> import mindspore
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>>> import mindspore
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>>> import mindspore.nn as nn
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>>> import numpy as np
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>>> import mindspore.nn.probability.distribution as msd
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>>> import mindspore.nn.probability.distribution as msd
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>>> import mindspore.nn as nn
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>>> from mindspore import Tensor
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>>> from mindspore import Tensor
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>>> class Prob(nn.Cell):
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>>> class Prob(nn.Cell):
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... def __init__(self):
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... def __init__(self):
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