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2c01c221
编写于
9月 27, 2018
作者:
X
Xin Pan
提交者:
GitHub
9月 27, 2018
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差异文件
Merge pull request #13531 from gongweibao/generator2
Hide kwargs
上级
0be1582d
91bc80d6
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
341 addition
and
129 deletion
+341
-129
paddle/fluid/API.spec
paddle/fluid/API.spec
+7
-7
paddle/fluid/operators/sampling_id_op.cc
paddle/fluid/operators/sampling_id_op.cc
+8
-7
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+3
-3
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+263
-104
python/paddle/fluid/layers/ops.py
python/paddle/fluid/layers/ops.py
+0
-7
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+60
-1
未找到文件。
paddle/fluid/API.spec
浏览文件 @
2c01c221
...
...
@@ -153,6 +153,13 @@ paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32', False))
paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.sum ArgSpec(args=['x', 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
...
...
@@ -224,13 +231,6 @@ paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwarg
paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sampling_id ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sum ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.slice ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.shape ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
...
...
paddle/fluid/operators/sampling_id_op.cc
浏览文件 @
2c01c221
...
...
@@ -53,15 +53,16 @@ class SamplingIdOpMaker : public framework::OpProtoAndCheckerMaker {
SamplingId Operator.
A layer for sampling id from multinomial distribution from the
input. Sampling one id for one sample.)DOC"
);
AddAttr
<
float
>
(
"min"
,
"Minimum value of random.
[default 0.0]
."
)
AddAttr
<
float
>
(
"min"
,
"Minimum value of random.
(float, default 0.0)
."
)
.
SetDefault
(
0.0
f
);
AddAttr
<
float
>
(
"max"
,
"Maximun value of random.
[default 1.0]
."
)
AddAttr
<
float
>
(
"max"
,
"Maximun value of random.
(float, default 1.0)
."
)
.
SetDefault
(
1.0
f
);
AddAttr
<
int
>
(
"seed"
,
"Random seed used for the random number engine. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random numbers every time. [default 0]."
)
AddAttr
<
int
>
(
"seed"
,
"Random seed used for the random number engine. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random numbers every time. (int, default 0)."
)
.
SetDefault
(
0
);
}
};
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
2c01c221
...
...
@@ -284,7 +284,7 @@ def detection_output(loc,
target_box
=
loc
,
code_type
=
'decode_center_size'
)
compile_shape
=
scores
.
shape
run_shape
=
ops
.
shape
(
scores
)
run_shape
=
nn
.
shape
(
scores
)
scores
=
nn
.
flatten
(
x
=
scores
,
axis
=
2
)
scores
=
nn
.
softmax
(
input
=
scores
)
scores
=
nn
.
reshape
(
x
=
scores
,
shape
=
compile_shape
,
actual_shape
=
run_shape
)
...
...
@@ -697,7 +697,7 @@ def ssd_loss(location,
raise
ValueError
(
"Only support mining_type == max_negative now."
)
num
,
num_prior
,
num_class
=
confidence
.
shape
conf_shape
=
ops
.
shape
(
confidence
)
conf_shape
=
nn
.
shape
(
confidence
)
def
__reshape_to_2d
(
var
):
return
nn
.
flatten
(
x
=
var
,
axis
=
2
)
...
...
@@ -724,7 +724,7 @@ def ssd_loss(location,
target_label
.
stop_gradient
=
True
conf_loss
=
nn
.
softmax_with_cross_entropy
(
confidence
,
target_label
)
# 3. Mining hard examples
actual_shape
=
ops
.
slice
(
conf_shape
,
axes
=
[
0
],
starts
=
[
0
],
ends
=
[
2
])
actual_shape
=
nn
.
slice
(
conf_shape
,
axes
=
[
0
],
starts
=
[
0
],
ends
=
[
2
])
actual_shape
.
stop_gradient
=
True
conf_loss
=
nn
.
reshape
(
x
=
conf_loss
,
shape
=
(
num
,
num_prior
),
actual_shape
=
actual_shape
)
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
2c01c221
...
...
@@ -29,110 +29,29 @@ from .. import unique_name
from
functools
import
reduce
__all__
=
[
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'dynamic_lstmp'
,
'dynamic_gru'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'conv3d'
,
'sequence_pool'
,
'sequence_softmax'
,
'softmax'
,
'pool2d'
,
'pool3d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'conv3d_transpose'
,
'sequence_expand'
,
'sequence_expand_as'
,
'sequence_pad'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'reduce_prod'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'ctc_greedy_decoder'
,
'edit_distance'
,
'l2_normalize'
,
'matmul'
,
'topk'
,
'warpctc'
,
'sequence_reshape'
,
'transpose'
,
'im2sequence'
,
'nce'
,
'hsigmoid'
,
'beam_search'
,
'row_conv'
,
'multiplex'
,
'layer_norm'
,
'softmax_with_cross_entropy'
,
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'reshape'
,
'squeeze'
,
'unsqueeze'
,
'lod_reset'
,
'lrn'
,
'pad'
,
'pad_constant_like'
,
'label_smooth'
,
'roi_pool'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'gather'
,
'scatter'
,
'sequence_scatter'
,
'random_crop'
,
'mean_iou'
,
'relu'
,
'log'
,
'crop'
,
'rank_loss'
,
'elu'
,
'relu6'
,
'pow'
,
'stanh'
,
'hard_sigmoid'
,
'swish'
,
'prelu'
,
'brelu'
,
'leaky_relu'
,
'soft_relu'
,
'flatten'
,
'sequence_mask'
,
'stack'
,
'pad2d'
,
'unstack'
,
'sequence_enumerate'
,
'expand'
,
'sequence_concat'
,
'scale'
,
'elementwise_add'
,
'elementwise_div'
,
'elementwise_sub'
,
'elementwise_mul'
,
'elementwise_max'
,
'elementwise_min'
,
'elementwise_pow'
,
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'dynamic_lstmp'
,
'dynamic_gru'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'conv3d'
,
'sequence_pool'
,
'sequence_softmax'
,
'softmax'
,
'pool2d'
,
'pool3d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'conv3d_transpose'
,
'sequence_expand'
,
'sequence_expand_as'
,
'sequence_pad'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'reduce_prod'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'ctc_greedy_decoder'
,
'edit_distance'
,
'l2_normalize'
,
'matmul'
,
'topk'
,
'warpctc'
,
'sequence_reshape'
,
'transpose'
,
'im2sequence'
,
'nce'
,
'hsigmoid'
,
'beam_search'
,
'row_conv'
,
'multiplex'
,
'layer_norm'
,
'softmax_with_cross_entropy'
,
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'reshape'
,
'squeeze'
,
'unsqueeze'
,
'lod_reset'
,
'lrn'
,
'pad'
,
'pad_constant_like'
,
'label_smooth'
,
'roi_pool'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'gather'
,
'scatter'
,
'sequence_scatter'
,
'random_crop'
,
'mean_iou'
,
'relu'
,
'log'
,
'crop'
,
'rank_loss'
,
'elu'
,
'relu6'
,
'pow'
,
'stanh'
,
'hard_sigmoid'
,
'swish'
,
'prelu'
,
'brelu'
,
'leaky_relu'
,
'soft_relu'
,
'flatten'
,
'sequence_mask'
,
'stack'
,
'pad2d'
,
'unstack'
,
'sequence_enumerate'
,
'expand'
,
'sequence_concat'
,
'scale'
,
'elementwise_add'
,
'elementwise_div'
,
'elementwise_sub'
,
'elementwise_mul'
,
'elementwise_max'
,
'elementwise_min'
,
'elementwise_pow'
,
'uniform_random_batch_size_like'
,
'gaussian_random'
,
'sampling_id'
,
'gaussian_random_batch_size_like'
,
'sum'
,
'slice'
,
'shape'
]
...
...
@@ -6463,6 +6382,246 @@ def expand(x, expand_times, name=None):
return
out
from
paddle.fluid.framework
import
convert_np_dtype_to_dtype_
@
templatedoc
()
def
uniform_random_batch_size_like
(
input
,
shape
,
dtype
=
'float32'
,
input_dim_idx
=
0
,
output_dim_idx
=
0
,
min
=-
1.0
,
max
=
1.0
,
seed
=
0
):
"""
${comment}
Args:
input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment}
min (Float): ${min_comment}
max (Float): ${max_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper
=
LayerHelper
(
'uniform_random_batch_size_like'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
)
c_dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
helper
.
append_op
(
type
=
'uniform_random_batch_size_like'
,
inputs
=
{
'Input'
:
input
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'shape'
:
shape
,
'input_dim_idx'
:
input_dim_idx
,
'output_dim_idx'
:
output_dim_idx
,
'min'
:
min
,
'max'
:
max
,
'seed'
:
seed
,
'dtype'
:
c_dtype
})
return
out
@
templatedoc
()
def
gaussian_random
(
shape
,
mean
=
0.0
,
std
=
1.0
,
seed
=
0
,
dtype
=
'float32'
,
use_mkldnn
=
False
):
"""
${comment}
Args:
shape (tuple|list): ${shape_comment}
mean (Float): ${mean_comment}
std (Float): ${std_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): Output data type.
use_mkldnn (Bool): Only used in mkldnn kernel.
Returns:
out (Variable): ${out_comment}
"""
helper
=
LayerHelper
(
'gaussian_random'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
)
c_dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
helper
.
append_op
(
type
=
'gaussian_random'
,
outputs
=
{
'Out'
:
out
},
attrs
=
{
'shape'
:
shape
,
'mean'
:
mean
,
'std'
:
std
,
'seed'
:
seed
,
'dtype'
:
c_dtype
,
'use_mkldnn'
:
use_mkldnn
})
return
out
@
templatedoc
()
def
sampling_id
(
x
,
min
=
0.0
,
max
=
1.0
,
seed
=
0
,
dtype
=
'float32'
):
"""
${comment}
Args:
x (Variable): ${x_comment}
min (Float): ${min_comment}
max (Float): ${max_comment}
seed (Float): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper
=
LayerHelper
(
'sampling_id'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
'sampling_id'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'min'
:
min
,
'max'
:
max
,
'seed'
:
seed
})
return
out
@
templatedoc
()
def
gaussian_random_batch_size_like
(
input
,
shape
,
input_dim_idx
=
0
,
output_dim_idx
=
0
,
mean
=
0.0
,
std
=
1.0
,
seed
=
0
,
dtype
=
'float32'
):
"""
${comment}
Args:
input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment}
mean (Float): ${mean_comment}
std (Float): ${std_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
Returns:
out (Variable): ${out_comment}
"""
helper
=
LayerHelper
(
'gaussian_random_batch_size_like'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
)
c_dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
helper
.
append_op
(
type
=
'gaussian_random_batch_size_like'
,
inputs
=
{
'Input'
:
input
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'shape'
:
shape
,
'input_dim_idx'
:
input_dim_idx
,
'output_dim_idx'
:
output_dim_idx
,
'mean'
:
mean
,
'std'
:
std
,
'seed'
:
seed
,
'dtype'
:
c_dtype
})
return
out
@
templatedoc
()
def
sum
(
x
,
use_mkldnn
=
False
):
"""
${comment}
Args:
x (Variable): ${x_comment}
use_mkldnn (Bool): ${use_mkldnn_comment}
Returns:
out (Variable): ${out_comment}
"""
helper
=
LayerHelper
(
'sum'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
(
'x'
))
helper
.
append_op
(
type
=
'sum'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'use_mkldnn'
:
use_mkldnn
})
return
out
@
templatedoc
()
def
slice
(
input
,
axes
,
starts
,
ends
):
"""
${comment}
Args:
input (Variable): ${input_comment}.
axes (List): ${axes_comment}
starts (List): ${starts_comment}
ends (List): ${ends_comment}
Returns:
out (Variable): ${out_comment}
"""
helper
=
LayerHelper
(
'slice'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
(
'input'
))
helper
.
append_op
(
type
=
'slice'
,
inputs
=
{
'Input'
:
input
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'axes'
:
axes
,
'starts'
:
starts
,
'ends'
:
ends
})
return
out
@
templatedoc
()
def
shape
(
input
):
"""
${comment}
Args:
input (Variable): ${input_comment}
Returns:
out (Variable): ${out_comment}
"""
helper
=
LayerHelper
(
'shape'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
(
'input'
))
helper
.
append_op
(
type
=
'shape'
,
inputs
=
{
'Input'
:
input
},
outputs
=
{
'Out'
:
out
})
return
out
def
_elementwise_op
(
helper
):
op_type
=
helper
.
layer_type
x
=
helper
.
kwargs
.
get
(
'x'
,
None
)
...
...
python/paddle/fluid/layers/ops.py
浏览文件 @
2c01c221
...
...
@@ -45,13 +45,6 @@ __all__ = [
'logical_or'
,
'logical_xor'
,
'logical_not'
,
'uniform_random_batch_size_like'
,
'gaussian_random'
,
'sampling_id'
,
'gaussian_random_batch_size_like'
,
'sum'
,
'slice'
,
'shape'
,
'maxout'
,
]
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
2c01c221
...
...
@@ -541,7 +541,7 @@ class TestBook(unittest.TestCase):
with
program_guard
(
program
):
input
=
layers
.
data
(
name
=
"input"
,
shape
=
[
3
,
100
,
100
],
dtype
=
"float32"
)
out
=
layers
.
shape
(
input
,
name
=
"shape"
)
out
=
layers
.
shape
(
input
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
...
...
@@ -758,6 +758,65 @@ class TestBook(unittest.TestCase):
out
=
layers
.
expand
(
x
,
[
1
,
2
])
print
(
str
(
program
))
def
test_uniform_random_batch_size_like
(
self
):
program
=
Program
()
with
program_guard
(
program
):
input
=
layers
.
data
(
name
=
"input"
,
shape
=
[
13
,
11
],
dtype
=
'float32'
)
out
=
layers
.
uniform_random_batch_size_like
(
input
,
[
-
1
,
11
])
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_gaussian_random
(
self
):
program
=
Program
()
with
program_guard
(
program
):
out
=
layers
.
gaussian_random
(
shape
=
[
20
,
30
])
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_sampling_id
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
"X"
,
shape
=
[
13
,
11
],
dtype
=
'float32'
,
append_batch_size
=
False
)
out
=
layers
.
sampling_id
(
x
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_gaussian_random_batch_size_like
(
self
):
program
=
Program
()
with
program_guard
(
program
):
input
=
layers
.
data
(
name
=
"input"
,
shape
=
[
13
,
11
],
dtype
=
'float32'
)
out
=
layers
.
gaussian_random_batch_size_like
(
input
,
shape
=
[
-
1
,
11
],
mean
=
1.0
,
std
=
2.0
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_sum
(
self
):
program
=
Program
()
with
program_guard
(
program
):
input
=
layers
.
data
(
name
=
"input"
,
shape
=
[
13
,
11
],
dtype
=
'float32'
)
out
=
layers
.
sum
(
input
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_slice
(
self
):
starts
=
[
1
,
0
,
2
]
ends
=
[
3
,
3
,
4
]
axes
=
[
0
,
1
,
2
]
program
=
Program
()
with
program_guard
(
program
):
input
=
layers
.
data
(
name
=
"input"
,
shape
=
[
3
,
4
,
5
,
6
],
dtype
=
'float32'
)
out
=
layers
.
slice
(
input
,
axes
=
axes
,
starts
=
starts
,
ends
=
ends
)
def
test_softshrink
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
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