@ -10290,7 +10290,7 @@ def expand(x, expand_times, name=None):
Args :
x ( Variable ) : A tensor with rank in [ 1 , 6 ] .
expand_times ( list | tuple ) : Expand times number for each dimension .
expand_times ( list | tuple | Variable ) : Expand times number for each dimension .
Returns :
Variable : The expanded variable which is a LoDTensor . After expanding , size of each dimension of Output ( Out ) is equal to ithe size of the corresponding dimension of Input ( X ) multiplying the corresponding value given by expand_times .
@ -10300,44 +10300,70 @@ def expand(x, expand_times, name=None):
. . code - block : : python
import paddle . fluid as fluid
x = fluid . layers . fill_constant ( shape = [ 2 , 3 , 1 ] , dtype = ' int32 ' , value = 0 )
out = fluid . layers . expand ( x = x , expand_times = [ 1 , 2 , 2 ] )
# example 1:
data_1 = fluid . layers . fill_constant ( shape = [ 2 , 3 , 1 ] , dtype = ' int32 ' , value = 0 )
expanded_1 = fluid . layers . expand ( data_1 , expand_times = [ 1 , 2 , 2 ] )
# example 2:
data_2 = fluid . layers . fill_constant ( shape = [ 12 , 14 ] , dtype = " int32 " , value = 3 )
expand_times = fluid . layers . fill_constant ( shape = [ 2 ] , dtype = " int32 " , value = 4 )
expanded_2 = fluid . layers . expand ( data_2 , expand_times = expand_times )
"""
helper = LayerHelper ( ' expand ' , input = x , * * locals ( ) )
dtype = helper . input_dtype ( input_param_name = ' x ' )
out = helper . create_variable_for_type_inference ( dtype )
# check expand_times have tensor
if in_dygraph_mode ( ) :
inputs = { ' X ' : x }
attrs = { ' expand_times ' : expand_times }
else :
if not isinstance ( expand_times , ( list , tuple , Variable ) ) :
raise ValueError (
" Input expand_times must be an Variable, python list or tuple. " )
def contain_tensor ( expand_times ) :
helper = LayerHelper ( ' expand ' , input = x , * * locals ( ) )
inputs = { " X " : x }
attrs = { }
def contain_var ( expand_times ) :
for ele in expand_times :
if isinstance ( ele , Variable ) :
return True
return False
if contain_tensor ( expand_times ) :
new_expand_times = [ ]
for ele in expand_times :
def get_attr_expand_times ( list_expand_times ) :
attrs_expand_times = [ ]
for idx , times in enumerate ( list_expand_times ) :
if isinstance ( times , Variable ) :
attrs_expand_times . append ( - 1 )
else :
attrs_expand_times . append ( times )
assert times > 0 , (
" Each element given in expand_times must not be negtive. " )
return attrs_expand_times
def get_new_expand_times_tensor ( list_expand_times ) :
new_expand_times_tensor = [ ]
for ele in list_expand_times :
if isinstance ( ele , Variable ) :
ele . stop_gradient = True
new_expand_times . append ( ele )
new_expand_times _tensor . append ( ele )
else :
assert ( isinstance ( ele , int ) )
temp_out = helper . create_variable_for_type_inference (
" int32 " )
fill_constant (
[ 1 ] , ' int32 ' , ele , force_cpu = True , out = temp_out )
new_expand_times . append ( temp_out )
inputs = { ' X ' : x , ' expand_times_tensor ' : new_expand_times }
attrs = { }
else :
temp_out = helper . create_variable_for_type_inference ( ' int32 ' )
fill_constant ( [ 1 ] , ' int32 ' , ele , force_cpu = True , out = temp_out )
new_expand_times_tensor . append ( temp_out )
return new_expand_times_tensor
if in_dygraph_mode ( ) :
inputs = { ' X ' : x }
attrs = { ' expand_times ' : expand_times }
else :
if isinstance ( expand_times , Variable ) :
expand_times . stop_gradient = True
inputs [ ' ExpandTimes ' ] = expand_times
elif isinstance ( expand_times , ( list , tuple ) ) :
attrs [ ' expand_times ' ] = get_attr_expand_times ( expand_times )
if contain_var ( expand_times ) :
inputs [ ' expand_times_tensor ' ] = get_new_expand_times_tensor (
expand_times )
dtype = helper . input_dtype ( input_param_name = ' x ' )
out = helper . create_variable_for_type_inference ( dtype )
helper . append_op (
type = ' expand ' , inputs = inputs , outputs = { ' Out ' : out } , attrs = attrs )
return out
liym27
5 years ago
committed by
Aurelius84