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c3fdb817
编写于
11月 24, 2022
作者:
Z
zqw_1997
提交者:
GitHub
11月 24, 2022
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差异文件
remove paddle.fluid.layers.nn.shuffle_channel (#48226)
上级
d2f87d96
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
0 addition
and
112 deletion
+0
-112
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+0
-76
python/paddle/fluid/tests/unittests/ir/inference/test_trt_subgraph_pass.py
...id/tests/unittests/ir/inference/test_trt_subgraph_pass.py
+0
-28
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+0
-8
未找到文件。
python/paddle/fluid/layers/nn.py
浏览文件 @
c3fdb817
...
...
@@ -146,7 +146,6 @@ __all__ = [
'bilinear_tensor_product'
,
'merge_selected_rows'
,
'get_tensor_from_selected_rows'
,
'shuffle_channel'
,
'temporal_shift'
,
'py_func'
,
'psroi_pool'
,
...
...
@@ -9849,81 +9848,6 @@ def get_tensor_from_selected_rows(x, name=None):
return
out
def
shuffle_channel
(
x
,
group
,
name
=
None
):
"""
This operator shuffles the channels of input x.
It divide the input channels in each group into :attr:`group` subgroups,
and obtain a new order by selecting element from every subgroup one by one.
Please refer to the paper
https://arxiv.org/pdf/1707.01083.pdf
.. code-block:: text
Given a 4-D tensor input with the shape (N, C, H, W):
input.shape = (1, 4, 2, 2)
input.data =[[[[0.1, 0.2],
[0.2, 0.3]],
[[0.3, 0.4],
[0.4, 0.5]],
[[0.5, 0.6],
[0.6, 0.7]],
[[0.7, 0.8],
[0.8, 0.9]]]]
Given group: 2
then we get a 4-D tensor out with the same shape of input:
out.shape = (1, 4, 2, 2)
out.data = [[[[0.1, 0.2],
[0.2, 0.3]],
[[0.5, 0.6],
[0.6, 0.7]],
[[0.3, 0.4],
[0.4, 0.5]],
[[0.7, 0.8],
[0.8, 0.9]]]]
Args:
x(Variable): The input tensor variable. It should be a 4-D tensor with shape [N, C, H, W]
group(int): Indicating the counts of subgroups, It should divide the number of channels.
Returns:
out(Variable): the channels shuffling result is a tensor variable with the
same shape and same type as the input.
Raises:
ValueError: If group is not an int type variable.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
input = fluid.data(name='input', shape=[None,4,2,2], dtype='float32')
out = fluid.layers.shuffle_channel(x=input, group=2)
"""
helper
=
LayerHelper
(
"shuffle_channel"
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
if
not
isinstance
(
group
,
int
):
raise
TypeError
(
"group must be int type"
)
helper
.
append_op
(
type
=
"shuffle_channel"
,
inputs
=
{
"X"
:
x
},
outputs
=
{
"Out"
:
out
},
attrs
=
{
"group"
:
group
},
)
return
out
@
templatedoc
()
def
temporal_shift
(
x
,
seg_num
,
shift_ratio
=
0.25
,
name
=
None
,
data_format
=
"NCHW"
):
"""
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_subgraph_pass.py
浏览文件 @
c3fdb817
...
...
@@ -453,33 +453,5 @@ class TensorRTSubgraphPassElementwiseBroadcastDynamicTest(InferencePassTest):
)
class
TensorRTSubgraphPassShuffleChannelTest
(
InferencePassTest
):
def
setUp
(
self
):
with
fluid
.
program_guard
(
self
.
main_program
,
self
.
startup_program
):
data
=
fluid
.
data
(
name
=
"data"
,
shape
=
[
-
1
,
6
,
64
,
64
],
dtype
=
"float32"
)
sc_out
=
fluid
.
layers
.
shuffle_channel
(
data
,
group
=
3
)
out
=
fluid
.
layers
.
batch_norm
(
sc_out
,
is_test
=
True
)
self
.
feeds
=
{
"data"
:
np
.
random
.
random
([
1
,
6
,
64
,
64
]).
astype
(
"float32"
),
}
self
.
enable_trt
=
True
self
.
trt_parameters
=
(
TensorRTSubgraphPassShuffleChannelTest
.
TensorRTParam
(
1
<<
30
,
32
,
0
,
AnalysisConfig
.
Precision
.
Float32
,
False
,
False
)
)
self
.
fetch_list
=
[
out
]
def
test_check_output
(
self
):
if
core
.
is_compiled_with_cuda
():
use_gpu
=
True
self
.
check_output_with_option
(
use_gpu
)
self
.
assertTrue
(
PassVersionChecker
.
IsCompatible
(
'tensorrt_subgraph_pass'
)
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
c3fdb817
...
...
@@ -3744,14 +3744,6 @@ class TestBook(LayerTest):
out
=
layers
.
temporal_shift
(
x
,
seg_num
=
2
,
shift_ratio
=
0.2
)
return
out
def
make_shuffle_channel
(
self
):
with
program_guard
(
fluid
.
default_main_program
(),
fluid
.
default_startup_program
()
):
x
=
self
.
_get_data
(
name
=
"X"
,
shape
=
[
16
,
4
,
4
],
dtype
=
"float32"
)
out
=
layers
.
shuffle_channel
(
x
,
group
=
4
)
return
out
def
make_fsp_matrix
(
self
):
with
program_guard
(
fluid
.
default_main_program
(),
fluid
.
default_startup_program
()
...
...
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