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!7741 [ME] format code 

Merge pull request !7741 from chenzhongming/zomi_master
This commit is contained in:
mindspore-ci-bot 2020-10-26 14:20:44 +08:00 committed by Gitee
parent e90415fcaa 44bf4c3e37
commit 7f91a3faf2
24 changed files with 78 additions and 109 deletions
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4
mindspore/_extends/graph_kernel/model/model.py
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@ -257,10 +257,10 @@ class Value:
self.data_format = data_format
def __str__(self):
return self.name + str(list(self.shape)) + str(self.value)
return self.name + str(list(self.shape))
def __repr__(self):
return "%s.%s%s%s" % (self.name, self.dtype, str(list(self.shape)), str(self.value))
return "%s.%s%s" % (self.name, self.dtype, str(list(self.shape)))
def get_size(self):
return 1

10
mindspore/common/parameter.py
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@ -158,16 +158,10 @@ class Parameter(MetaTensor_):
return (Tensor, data)
def __str__(self):
value_str = MetaTensor.__str__(self)
if isinstance(self, Tensor):
value_str = Tensor.__str__(self)
return f'Parameter (name={self._param_info.name}, value={value_str})'
return f'Parameter (name={self._param_info.name})'
def __repr__(self):
value_str = MetaTensor.__repr__(self)
if isinstance(self, Tensor):
value_str = Tensor.__repr__(self)
return f'Parameter (name={self._param_info.name}, value={value_str})'
return f'Parameter (name={self._param_info.name})'
def __parameter__(self):
"""For parse check."""

6
mindspore/nn/layer/basic.py
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@ -121,8 +121,7 @@ class Dropout(Cell):
return self.dropout_do_mask(x, output, keep_prob)
def extend_repr(self):
str_info = 'keep_prob={}, dtype={}'.format(self.keep_prob, self.dtype)
return str_info
return 'keep_prob={}, dtype={}'.format(self.keep_prob, self.dtype)
class Flatten(Cell):
@ -365,8 +364,7 @@ class Norm(Cell):
return x
def extend_repr(self):
str_info = 'axis={}, keep_dims={}'.format(self.axis, self.keep_dims)
return str_info
return 'axis={}, keep_dims={}'.format(self.axis, self.keep_dims)
class OneHot(Cell):

6
mindspore/nn/layer/normalization.py
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@ -547,9 +547,8 @@ class LayerNorm(Cell):
def extend_repr(self):
"""Display instance object as string."""
s = 'normalized_shape={}, begin_norm_axis={}, begin_params_axis={}, gamma{}, beta={}'.format(
return 'normalized_shape={}, begin_norm_axis={}, begin_params_axis={}, gamma{}, beta={}'.format(
self.normalized_shape, self.begin_norm_axis, self.begin_params_axis, self.gamma, self.beta)
return s
class GroupNorm(Cell):
@ -642,5 +641,4 @@ class GroupNorm(Cell):
def extend_repr(self):
"""Display instance object as string."""
s = 'num_groups={}, num_channels={}'.format(self.num_groups, self.num_channels)
return s
return 'num_groups={}, num_channels={}'.format(self.num_groups, self.num_channels)

27
mindspore/nn/layer/quant.py
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@ -1008,14 +1008,13 @@ class DenseQuant(Cell):
def extend_repr(self):
"""A pretty print for Dense layer."""
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}'.format(
s = 'in_channels={}, out_channels={}, weight={}, has_bias={}'.format(
self.in_channels, self.out_channels, self.weight, self.has_bias)
if self.has_bias:
str_info = str_info + ', bias={}'.format(self.bias)
s += ', bias={}'.format(self.bias)
if self.activation_flag:
str_info = str_info + ', activation={}'.format(self.activation)
return str_info
s += ', activation={}'.format(self.activation)
return s
class _QuantActivation(Cell):
@ -1387,13 +1386,13 @@ class QuantBlock(Cell):
return x
def extend_repr(self):
str_info = f'quant={self.quant}, core_op={type(self.core_op)}, weight=shape[{self.weight.shape}]'
s = f'quant={self.quant}, core_op={type(self.core_op)}, weight=shape[{self.weight.shape}]'
if self.has_bias:
str_info = str_info + f', bias=shape[{self.bias.shape}]'
s += f', bias=shape[{self.bias.shape}]'
if self.has_act:
str_info = str_info + f', activation={self.activation}'
str_info = str_info + f', dequant={self.dequant}'
return str_info
s += f', activation={self.activation}'
s += f', dequant={self.dequant}'
return s
class QuantMindirBlock(Cell):
@ -1454,9 +1453,9 @@ class QuantMindirBlock(Cell):
return x
def extend_repr(self):
str_info = f'core_op={type(self.core_op)}, weight=shape[{self.weight.shape}]'
s = f'core_op={type(self.core_op)}, weight=shape[{self.weight.shape}]'
if self.has_bias:
str_info = str_info + f', bias=shape[{self.bias.shape}]'
s += f', bias=shape[{self.bias.shape}]'
if self.has_act:
str_info = str_info + f', activation={self.activation}'
return str_info
s += f', activation={self.activation}'
return s

3
mindspore/nn/probability/bijector/gumbel_cdf.py
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@ -100,8 +100,7 @@ class GumbelCDF(Bijector):
return self._parameter_type
def extend_repr(self):
str_info = f'loc = {self.loc}, scale = {self.scale}'
return str_info
return f'loc = {self.loc}, scale = {self.scale}'
def shape_mapping(self, shape):
return shape

3
mindspore/nn/probability/bijector/power_transform.py
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@ -81,8 +81,7 @@ class PowerTransform(Bijector):
return self._power
def extend_repr(self):
str_info = f'power = {self.power}'
return str_info
return f'power = {self.power}'
def shape_mapping(self, shape):
return shape

3
mindspore/nn/probability/bijector/scalar_affine.py
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@ -90,8 +90,7 @@ class ScalarAffine(Bijector):
return self._shift
def extend_repr(self):
str_info = f'scale = {self.scale}, shift = {self.shift}'
return str_info
return f'scale = {self.scale}, shift = {self.shift}'
def shape_mapping(self, shape):
return shape

3
mindspore/nn/probability/bijector/softplus.py
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@ -118,8 +118,7 @@ class Softplus(Bijector):
return self._sharpness
def extend_repr(self):
str_info = f'sharpness = {self.sharpness}'
return str_info
return f'sharpness = {self.sharpness}'
def shape_mapping(self, shape):
return shape

8
mindspore/nn/probability/bnn_layers/conv_variational.py
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@ -141,15 +141,15 @@ class _ConvVariational(_Conv):
return outputs
def extend_repr(self):
str_info = 'in_channels={}, out_channels={}, kernel_size={}, stride={}, pad_mode={}, ' \
'padding={}, dilation={}, group={}, weight_mean={}, weight_std={}, has_bias={}'\
s = 'in_channels={}, out_channels={}, kernel_size={}, stride={}, pad_mode={}, ' \
'padding={}, dilation={}, group={}, weight_mean={}, weight_std={}, has_bias={}'\
.format(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.pad_mode, self.padding,
self.dilation, self.group, self.weight_posterior.mean, self.weight_posterior.untransformed_std,
self.has_bias)
if self.has_bias:
str_info = str_info + ', bias_mean={}, bias_std={}'\
s += ', bias_mean={}, bias_std={}'\
.format(self.bias_posterior.mean, self.bias_posterior.untransformed_std)
return str_info
return s
def _apply_variational_bias(self, inputs):
bias_posterior_tensor = self.bias_posterior("sample")

9
mindspore/nn/probability/bnn_layers/dense_variational.py
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@ -107,16 +107,15 @@ class _DenseVariational(Cell):
return outputs
def extend_repr(self):
str_info = 'in_channels={}, out_channels={}, weight_mean={}, weight_std={}, has_bias={}' \
s = 'in_channels={}, out_channels={}, weight_mean={}, weight_std={}, has_bias={}' \
.format(self.in_channels, self.out_channels, self.weight_posterior.mean,
self.weight_posterior.untransformed_std, self.has_bias)
if self.has_bias:
str_info = str_info + ', bias_mean={}, bias_std={}' \
s += ', bias_mean={}, bias_std={}' \
.format(self.bias_posterior.mean, self.bias_posterior.untransformed_std)
if self.activation_flag:
str_info = str_info + ', activation={}'.format(self.activation)
return str_info
s += ', activation={}'.format(self.activation)
return s
def _apply_variational_bias(self, inputs):
bias_posterior_tensor = self.bias_posterior("sample")

6
mindspore/nn/probability/distribution/bernoulli.py
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@ -141,10 +141,10 @@ class Bernoulli(Distribution):
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'probs = {self.probs}'
s = f'probs = {self.probs}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
s = f'batch_shape = {self._broadcast_shape}'
return s
@property
def probs(self):

6
mindspore/nn/probability/distribution/categorical.py
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@ -157,10 +157,10 @@ class Categorical(Distribution):
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'probs = {self.probs}'
s = f'probs = {self.probs}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
s = f'batch_shape = {self._broadcast_shape}'
return s
@property
def probs(self):

6
mindspore/nn/probability/distribution/exponential.py
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@ -145,10 +145,10 @@ class Exponential(Distribution):
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'rate = {self.rate}'
s = f'rate = {self.rate}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
s = f'batch_shape = {self._broadcast_shape}'
return s
@property
def rate(self):

6
mindspore/nn/probability/distribution/geometric.py
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@ -150,10 +150,10 @@ class Geometric(Distribution):
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'probs = {self.probs}'
s = f'probs = {self.probs}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
s = f'batch_shape = {self._broadcast_shape}'
return s
@property
def probs(self):

6
mindspore/nn/probability/distribution/log_normal.py
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@ -163,10 +163,10 @@ class LogNormal(msd.TransformedDistribution):
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'loc = {self._mean_value}, scale = {self._sd_value}'
s = f'loc = {self._mean_value}, scale = {self._sd_value}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
s = f'batch_shape = {self._broadcast_shape}'
return s
def _mean(self, loc=None, scale=None):
"""

6
mindspore/nn/probability/distribution/logistic.py
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@ -156,10 +156,10 @@ class Logistic(Distribution):
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'location = {self._loc}, scale = {self._scale}'
s = f'location = {self._loc}, scale = {self._scale}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
s = f'batch_shape = {self._broadcast_shape}'
return s
@property
def loc(self):

6
mindspore/nn/probability/distribution/normal.py
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@ -149,10 +149,10 @@ class Normal(Distribution):
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'mean = {self._mean_value}, standard deviation = {self._sd_value}'
s = f'mean = {self._mean_value}, standard deviation = {self._sd_value}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
s = f'batch_shape = {self._broadcast_shape}'
return s
def _mean(self, mean=None, sd=None):
"""

6
mindspore/nn/probability/distribution/uniform.py
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@ -154,10 +154,10 @@ class Uniform(Distribution):
def extend_repr(self):
if self.is_scalar_batch:
str_info = f'low = {self.low}, high = {self.high}'
s = f'low = {self.low}, high = {self.high}'
else:
str_info = f'batch_shape = {self._broadcast_shape}'
return str_info
s = f'batch_shape = {self._broadcast_shape}'
return s
@property
def low(self):

22
model_zoo/official/cv/resnet_thor/src/thor_layer.py
View File

@ -392,15 +392,12 @@ class Dense_Thor_GPU(Cell):
def extend_repr(self):
"""extend_repr"""
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
.format(self.in_channels, self.out_channels, self.weight, self.has_bias)
s = 'in_channels={}, out_channels={}'.format(self.in_channels, self.out_channels)
if self.has_bias:
str_info = str_info + ', bias={}'.format(self.bias)
s += ', has_bias={}'.format(self.has_bias)
if self.activation_flag:
str_info = str_info + ', activation={}'.format(self.activation)
return str_info
s += ', activation={}'.format(self.activation)
return s
class Conv2d_Thor(_Conv):
@ -775,12 +772,9 @@ class Dense_Thor(Cell):
def extend_repr(self):
"""extend_repr"""
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
.format(self.in_channels, self.out_channels, self.weight, self.has_bias)
s = 'in_channels={}, out_channels={}'.format(self.in_channels, self.out_channels)
if self.has_bias:
str_info = str_info + ', bias={}'.format(self.bias)
s += ', has_bias={}'.format(self.has_bias)
if self.activation_flag:
str_info = str_info + ', activation={}'.format(self.activation)
return str_info
s += ', activation={}'.format(self.activation)
return s

8
model_zoo/official/gnn/gat/src/gat.py
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@ -102,12 +102,10 @@ class GNNFeatureTransform(nn.Cell):
return output
def extend_repr(self):
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
.format(self.in_channels, self.out_channels, self.weight, self.has_bias)
s = 'in_channels={}, out_channels={}'.format(self.in_channels, self.out_channels)
if self.has_bias:
str_info = str_info + ', bias={}'.format(self.bias)
return str_info
s += ', has_bias={}'.format(self.has_bias)
return s
class _BaseAggregator(nn.Cell):

11
model_zoo/official/nlp/bert_thor/src/thor_layer.py
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@ -270,12 +270,9 @@ class Dense_Thor(Cell):
def extend_repr(self):
"""extend_repr"""
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
.format(self.in_channels, self.out_channels, self.weight, self.has_bias)
s = 'in_channels={}, out_channels={}'.format(self.in_channels, self.out_channels)
if self.has_bias:
str_info = str_info + ', bias={}'.format(self.bias)
s += ', bias={}'.format(self.bias)
if self.activation_flag:
str_info = str_info + ', activation={}'.format(self.activation)
return str_info
s += ', activation={}'.format(self.activation)
return s

8
tests/st/gnn/aggregator.py
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@ -104,12 +104,10 @@ class GNNFeatureTransform(nn.Cell):
return output
def extend_repr(self):
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
.format(self.in_channels, self.out_channels, self.weight, self.has_bias)
s = 'in_channels={}, out_channels={}'.format(self.in_channels, self.out_channels)
if self.has_bias:
str_info = str_info + ', bias={}'.format(self.bias)
return str_info
s += ', has_bias={}'.format(self.has_bias)
return s
class _BaseAggregator(nn.Cell):

8
tests/st/networks/models/resnet50/src_thor/thor_layer.py
View File

@ -470,12 +470,10 @@ class Dense_Thor(Cell):
def extend_repr(self):
"""extend_repr"""
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
.format(self.in_channels, self.out_channels, self.weight, self.has_bias)
s = 'in_channels={}, out_channels={}'.format(self.in_channels, self.out_channels)
if self.has_bias:
str_info = str_info + ', bias={}'.format(self.bias)
s += ', has_bias={}'.format(self.has_bias)
if self.activation_flag:
str_info = str_info + ', activation={}'.format(self.activation)
s += ', activation={}'.format(self.activation)
return str_info