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体验新版 GitCode,发现更多精彩内容 >>
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2ce1f04d
编写于
12月 08, 2022
作者:
C
ccrrong
提交者:
GitHub
12月 08, 2022
浏览文件
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电子邮件补丁
差异文件
remove pool2d from fluid (#48512)
* remove pool2d
上级
73bff10f
变更
17
隐藏空白更改
内联
并排
Showing
17 changed file
with
216 addition
and
796 deletion
+216
-796
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
...paddle/fluid/contrib/slim/tests/test_quantization_pass.py
+16
-16
python/paddle/fluid/contrib/tests/test_image_classification_fp16.py
...dle/fluid/contrib/tests/test_image_classification_fp16.py
+1
-3
python/paddle/fluid/contrib/tests/test_multi_precision_fp16_train.py
...le/fluid/contrib/tests/test_multi_precision_fp16_train.py
+1
-3
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+0
-244
python/paddle/fluid/nets.py
python/paddle/fluid/nets.py
+26
-17
python/paddle/fluid/tests/book/test_image_classification.py
python/paddle/fluid/tests/book/test_image_classification.py
+1
-3
python/paddle/fluid/tests/unittests/dist_se_resnext.py
python/paddle/fluid/tests/unittests/dist_se_resnext.py
+17
-24
python/paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py
.../paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py
+13
-21
python/paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py
.../paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py
+12
-27
python/paddle/fluid/tests/unittests/ir/inference/test_trt_pool_op.py
...le/fluid/tests/unittests/ir/inference/test_trt_pool_op.py
+19
-10
python/paddle/fluid/tests/unittests/mlu/test_pool2d_op_mlu.py
...on/paddle/fluid/tests/unittests/mlu/test_pool2d_op_mlu.py
+56
-70
python/paddle/fluid/tests/unittests/seresnext_net.py
python/paddle/fluid/tests/unittests/seresnext_net.py
+2
-2
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+4
-4
python/paddle/fluid/tests/unittests/test_nn_grad.py
python/paddle/fluid/tests/unittests/test_nn_grad.py
+1
-2
python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py
...luid/tests/unittests/test_parallel_executor_fetch_feed.py
+2
-2
python/paddle/fluid/tests/unittests/test_pool2d_op.py
python/paddle/fluid/tests/unittests/test_pool2d_op.py
+0
-327
python/paddle/nn/functional/pooling.py
python/paddle/nn/functional/pooling.py
+45
-21
未找到文件。
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @
2ce1f04d
...
@@ -81,12 +81,12 @@ def residual_block(num, quant_skip_pattern=None):
...
@@ -81,12 +81,12 @@ def residual_block(num, quant_skip_pattern=None):
hidden
=
paddle
.
matmul
(
hidden
,
matmul_weight
,
True
,
True
)
hidden
=
paddle
.
matmul
(
hidden
,
matmul_weight
,
True
,
True
)
if
quant_skip_pattern
:
if
quant_skip_pattern
:
with
fluid
.
name_scope
(
quant_skip_pattern
):
with
fluid
.
name_scope
(
quant_skip_pattern
):
pool
=
fluid
.
layers
.
pool2d
(
pool
=
paddle
.
nn
.
functional
.
avg_
pool2d
(
input
=
hidden
,
pool_size
=
2
,
pool_type
=
'avg'
,
pool_
stride
=
2
x
=
hidden
,
kernel_size
=
2
,
stride
=
2
)
)
else
:
else
:
pool
=
fluid
.
layers
.
pool2d
(
pool
=
paddle
.
nn
.
functional
.
avg_
pool2d
(
input
=
hidden
,
pool_size
=
2
,
pool_type
=
'avg'
,
pool_
stride
=
2
x
=
hidden
,
kernel_size
=
2
,
stride
=
2
)
)
fc
=
fluid
.
layers
.
fc
(
input
=
pool
,
size
=
10
)
fc
=
fluid
.
layers
.
fc
(
input
=
pool
,
size
=
10
)
loss
=
paddle
.
nn
.
functional
.
cross_entropy
(
loss
=
paddle
.
nn
.
functional
.
cross_entropy
(
...
@@ -733,11 +733,11 @@ def quant_dequant_residual_block(num, quant_skip_pattern=None):
...
@@ -733,11 +733,11 @@ def quant_dequant_residual_block(num, quant_skip_pattern=None):
hidden
=
paddle
.
matmul
(
hidden
,
data2
,
True
,
True
)
hidden
=
paddle
.
matmul
(
hidden
,
data2
,
True
,
True
)
if
isinstance
(
quant_skip_pattern
,
str
):
if
isinstance
(
quant_skip_pattern
,
str
):
with
fluid
.
name_scope
(
quant_skip_pattern
):
with
fluid
.
name_scope
(
quant_skip_pattern
):
pool1
=
fluid
.
layers
.
pool2d
(
pool1
=
paddle
.
nn
.
functional
.
avg_
pool2d
(
input
=
hidden
,
pool_size
=
2
,
pool_type
=
'avg'
,
pool_
stride
=
2
x
=
hidden
,
kernel_size
=
2
,
stride
=
2
)
)
pool2
=
fluid
.
layers
.
pool2d
(
pool2
=
paddle
.
nn
.
functional
.
max_
pool2d
(
input
=
hidden
,
pool_size
=
2
,
pool_type
=
'max'
,
pool_
stride
=
2
x
=
hidden
,
kernel_size
=
2
,
stride
=
2
)
)
pool_add
=
paddle
.
nn
.
functional
.
relu
(
paddle
.
add
(
x
=
pool1
,
y
=
pool2
))
pool_add
=
paddle
.
nn
.
functional
.
relu
(
paddle
.
add
(
x
=
pool1
,
y
=
pool2
))
elif
isinstance
(
quant_skip_pattern
,
list
):
elif
isinstance
(
quant_skip_pattern
,
list
):
...
@@ -745,20 +745,20 @@ def quant_dequant_residual_block(num, quant_skip_pattern=None):
...
@@ -745,20 +745,20 @@ def quant_dequant_residual_block(num, quant_skip_pattern=None):
len
(
quant_skip_pattern
)
>
1
len
(
quant_skip_pattern
)
>
1
),
'test config error: the len of quant_skip_pattern list should be greater than 1.'
),
'test config error: the len of quant_skip_pattern list should be greater than 1.'
with
fluid
.
name_scope
(
quant_skip_pattern
[
0
]):
with
fluid
.
name_scope
(
quant_skip_pattern
[
0
]):
pool1
=
fluid
.
layers
.
pool2d
(
pool1
=
paddle
.
nn
.
functional
.
avg_
pool2d
(
input
=
hidden
,
pool_size
=
2
,
pool_type
=
'avg'
,
pool_
stride
=
2
x
=
hidden
,
kernel_size
=
2
,
stride
=
2
)
)
pool2
=
fluid
.
layers
.
pool2d
(
pool2
=
paddle
.
nn
.
functional
.
max_
pool2d
(
input
=
hidden
,
pool_size
=
2
,
pool_type
=
'max'
,
pool_
stride
=
2
x
=
hidden
,
kernel_size
=
2
,
stride
=
2
)
)
with
fluid
.
name_scope
(
quant_skip_pattern
[
1
]):
with
fluid
.
name_scope
(
quant_skip_pattern
[
1
]):
pool_add
=
paddle
.
nn
.
functional
.
relu
(
paddle
.
add
(
x
=
pool1
,
y
=
pool2
))
pool_add
=
paddle
.
nn
.
functional
.
relu
(
paddle
.
add
(
x
=
pool1
,
y
=
pool2
))
else
:
else
:
pool1
=
fluid
.
layers
.
pool2d
(
pool1
=
paddle
.
nn
.
functional
.
avg_
pool2d
(
input
=
hidden
,
pool_size
=
2
,
pool_type
=
'avg'
,
pool_
stride
=
2
x
=
hidden
,
kernel_size
=
2
,
stride
=
2
)
)
pool2
=
fluid
.
layers
.
pool2d
(
pool2
=
paddle
.
nn
.
functional
.
max_
pool2d
(
input
=
hidden
,
pool_size
=
2
,
pool_type
=
'max'
,
pool_
stride
=
2
x
=
hidden
,
kernel_size
=
2
,
stride
=
2
)
)
pool_add
=
paddle
.
nn
.
functional
.
relu
(
paddle
.
add
(
x
=
pool1
,
y
=
pool2
))
pool_add
=
paddle
.
nn
.
functional
.
relu
(
paddle
.
add
(
x
=
pool1
,
y
=
pool2
))
fc
=
fluid
.
layers
.
fc
(
input
=
pool_add
,
size
=
10
)
fc
=
fluid
.
layers
.
fc
(
input
=
pool_add
,
size
=
10
)
...
...
python/paddle/fluid/contrib/tests/test_image_classification_fp16.py
浏览文件 @
2ce1f04d
...
@@ -69,9 +69,7 @@ def resnet_cifar10(input, depth=32):
...
@@ -69,9 +69,7 @@ def resnet_cifar10(input, depth=32):
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
16
,
n
,
1
)
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
16
,
n
,
1
)
res2
=
layer_warp
(
basicblock
,
res1
,
16
,
32
,
n
,
2
)
res2
=
layer_warp
(
basicblock
,
res1
,
16
,
32
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
32
,
64
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
32
,
64
,
n
,
2
)
pool
=
fluid
.
layers
.
pool2d
(
pool
=
paddle
.
nn
.
functional
.
avg_pool2d
(
x
=
res3
,
kernel_size
=
8
,
stride
=
1
)
input
=
res3
,
pool_size
=
8
,
pool_type
=
'avg'
,
pool_stride
=
1
)
return
pool
return
pool
...
...
python/paddle/fluid/contrib/tests/test_multi_precision_fp16_train.py
浏览文件 @
2ce1f04d
...
@@ -88,9 +88,7 @@ def resnet_cifar10(input, depth=32):
...
@@ -88,9 +88,7 @@ def resnet_cifar10(input, depth=32):
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
16
,
n
,
1
)
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
16
,
n
,
1
)
res2
=
layer_warp
(
basicblock
,
res1
,
16
,
32
,
n
,
2
)
res2
=
layer_warp
(
basicblock
,
res1
,
16
,
32
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
32
,
64
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
32
,
64
,
n
,
2
)
pool
=
fluid
.
layers
.
pool2d
(
pool
=
paddle
.
nn
.
functional
.
avg_pool2d
(
x
=
res3
,
kernel_size
=
8
,
stride
=
1
)
input
=
res3
,
pool_size
=
8
,
pool_type
=
'avg'
,
pool_stride
=
1
)
return
pool
return
pool
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
2ce1f04d
...
@@ -68,7 +68,6 @@ __all__ = [
...
@@ -68,7 +68,6 @@ __all__ = [
'linear_chain_crf'
,
'linear_chain_crf'
,
'crf_decoding'
,
'crf_decoding'
,
'conv2d'
,
'conv2d'
,
'pool2d'
,
'dropout'
,
'dropout'
,
'split'
,
'split'
,
'l2_normalize'
,
'l2_normalize'
,
...
@@ -1428,249 +1427,6 @@ def conv2d(
...
@@ -1428,249 +1427,6 @@ def conv2d(
return
helper
.
append_activation
(
pre_act
)
return
helper
.
append_activation
(
pre_act
)
@
templatedoc
()
def
pool2d
(
input
,
pool_size
=-
1
,
pool_type
=
"max"
,
pool_stride
=
1
,
pool_padding
=
0
,
global_pooling
=
False
,
use_cudnn
=
True
,
ceil_mode
=
False
,
name
=
None
,
exclusive
=
True
,
data_format
=
"NCHW"
,
):
"""
${comment}
Args:
input (Variable): The input tensor of pooling operator which is a 4-D tensor with
shape [N, C, H, W]. The format of input tensor is `"NCHW"` or
`"NHWC"`, where `N` is batch size, `C` is the number of channels,
`H` is the height of the feature, and `W` is the width of the
feature. The data type if float32 or float64.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (pool_size_Height, pool_size_Width).
Otherwise, the pool kernel size will be a square of an int.
pool_type: ${pooling_type_comment}
pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
it must contain two integers, (pool_stride_Height, pool_stride_Width).
Otherwise, the pool stride size will be a square of an int.
pool_padding (string|int|list|tuple): The pool padding. If `pool_padding` is a string, either 'VALID' or
'SAME' which is the padding algorithm. If pool padding size is a tuple or list,
it could be in three forms: `[pad_height, pad_width]` or
`[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`,
`pool_padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
when `data_format` is `"NHWC"`, `pool_padding` can be in the form
`[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Otherwise, the pool padding size will be a square of an int.
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is `true`.
data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
Returns:
Variable: The output tensor of pooling result. The data type is same as input tensor.
Raises:
ValueError: If `pool_type` is not "max" nor "avg".
ValueError: If `global_pooling` is False and `pool_size` is -1.
TypeError: If `use_cudnn` is not a bool value.
ValueError: If `data_format` is not "NCHW" or "NHWC".
ValueError: If `pool_padding` is a string, but not "SAME" or "VALID".
ValueError: If `pool_padding` is "VALID", but `ceil_mode` is True.
ValueError: If `pool_padding` is a list or tuple, but the elements in the batch or channel dimensions are non-zero.
ShapeError: If the input is not a 4-D or 5-D Tensor.
ShapeError: If the dimension of input minus the size of `pool_stride` is not 2.
ShapeError: If the size of `pool_size` and `pool_stride` is not equal.
ShapeError: If the output's shape calculated is not greater than 0.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32')
# max pool2d
pool2d = fluid.layers.pool2d(
input = data,
pool_size = 2,
pool_type = "max",
pool_stride = 1,
global_pooling=False)
# average pool2d
pool2d = fluid.layers.pool2d(
input = data,
pool_size = 2,
pool_type = "avg",
pool_stride = 1,
global_pooling=False)
# global average pool2d
pool2d = fluid.layers.pool2d(
input = data,
pool_size = 2,
pool_type = "avg",
pool_stride = 1,
global_pooling=True)
# Attr(pool_padding) is a list with 4 elements, Attr(data_format) is "NCHW".
out_1 = fluid.layers.pool2d(
input = data,
pool_size = 3,
pool_type = "avg",
pool_stride = 1,
pool_padding = [1, 2, 1, 0],
data_format = "NCHW")
# Attr(pool_padding) is a string, Attr(data_format) is "NCHW".
out_2 = fluid.layers.pool2d(
input = data,
pool_size = 3,
pool_type = "avg",
pool_stride = 1,
pool_padding = "VALID",
data_format = "NCHW")
"""
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
"Unknown Attr(pool_type): '%s'. It can only be 'max' or 'avg'."
,
str
(
pool_type
),
)
if
global_pooling
is
False
and
pool_size
==
-
1
:
raise
ValueError
(
"When Attr(global_pooling) is False, Attr(pool_size) must be passed "
"and be a valid value. Received pool_size: %s."
%
str
(
pool_size
)
)
if
not
isinstance
(
use_cudnn
,
bool
):
raise
TypeError
(
"Attr(use_cudnn) should be True or False. Received "
"Attr(use_cudnn): %s."
%
str
(
use_cudnn
)
)
if
data_format
not
in
[
"NCHW"
,
"NHWC"
]:
raise
ValueError
(
"Attr(data_format) should be 'NCHW' or 'NHWC'. Received "
"Attr(data_format): %s."
%
str
(
data_format
)
)
pool_size
=
utils
.
convert_to_list
(
pool_size
,
2
,
'pool_size'
)
pool_stride
=
utils
.
convert_to_list
(
pool_stride
,
2
,
'pool_stride'
)
def
update_padding
(
padding
,
data_format
):
def
is_list_or_tuple
(
ele
):
if
isinstance
(
ele
,
list
)
or
isinstance
(
ele
,
tuple
):
return
True
return
False
if
is_list_or_tuple
(
padding
)
and
len
(
padding
)
==
4
:
if
is_list_or_tuple
(
padding
[
0
])
and
(
data_format
==
"NCHW"
):
if
not
(
padding
[
0
]
==
[
0
,
0
]
and
padding
[
1
]
==
[
0
,
0
]):
raise
ValueError
(
"Non-zero pool_padding(%s) in the batch or channel dimensions "
"is not supported."
%
str
(
padding
)
)
padding
=
padding
[
2
:
4
]
padding
=
[
ele
for
a_list
in
padding
for
ele
in
a_list
]
elif
is_list_or_tuple
(
padding
[
0
])
and
(
data_format
==
"NHWC"
):
if
not
(
padding
[
0
]
==
[
0
,
0
]
and
padding
[
3
]
==
[
0
,
0
]):
raise
ValueError
(
"Non-zero pool_padding(%s) in the batch or channel dimensions "
"is not supported."
%
str
(
padding
)
)
padding
=
padding
[
1
:
3
]
padding
=
[
ele
for
a_list
in
padding
for
ele
in
a_list
]
padding
=
utils
.
convert_to_list
(
padding
,
4
,
'padding'
)
if
utils
.
_is_symmetric_padding
(
padding
,
2
):
padding
=
[
padding
[
0
],
padding
[
2
]]
else
:
padding
=
utils
.
convert_to_list
(
padding
,
2
,
'padding'
)
return
padding
padding_algorithm
=
"EXPLICIT"
if
isinstance
(
pool_padding
,
str
):
pool_padding
=
pool_padding
.
upper
()
if
pool_padding
not
in
[
"SAME"
,
"VALID"
]:
raise
ValueError
(
"Unknown Attr(pool_padding): '%s'. It can only be 'SAME' or 'VALID'."
%
str
(
pool_padding
)
)
if
pool_padding
==
"VALID"
:
padding_algorithm
=
"VALID"
pool_padding
=
[
0
,
0
]
if
ceil_mode
is
not
False
:
raise
ValueError
(
"When Attr(pool_padding) is
\"
VALID
\"
, Attr(ceil_mode) must be False. "
"Received ceil_mode: True."
)
elif
pool_padding
==
"SAME"
:
padding_algorithm
=
"SAME"
pool_padding
=
[
0
,
0
]
pool_padding
=
update_padding
(
pool_padding
,
data_format
)
if
in_dygraph_mode
():
input
=
input
.
_use_gpudnn
(
use_cudnn
)
return
_C_ops
.
pool2d
(
input
,
pool_size
,
pool_stride
,
pool_padding
,
ceil_mode
,
exclusive
,
data_format
,
pool_type
,
global_pooling
,
False
,
padding_algorithm
,
)
op_type
=
'pool2d'
helper
=
LayerHelper
(
op_type
,
**
locals
())
dtype
=
helper
.
input_dtype
()
pool_out
=
helper
.
create_variable_for_type_inference
(
dtype
)
helper
.
append_op
(
type
=
op_type
,
inputs
=
{
"X"
:
input
},
outputs
=
{
"Out"
:
pool_out
},
attrs
=
{
"pooling_type"
:
pool_type
,
"ksize"
:
pool_size
,
"global_pooling"
:
global_pooling
,
"strides"
:
pool_stride
,
"paddings"
:
pool_padding
,
"padding_algorithm"
:
padding_algorithm
,
"use_cudnn"
:
use_cudnn
,
"ceil_mode"
:
ceil_mode
,
"use_mkldnn"
:
False
,
"exclusive"
:
exclusive
,
"data_format"
:
data_format
,
},
)
return
pool_out
@
templatedoc
()
@
templatedoc
()
def
layer_norm
(
def
layer_norm
(
input
,
input
,
...
...
python/paddle/fluid/nets.py
浏览文件 @
2ce1f04d
...
@@ -132,16 +132,20 @@ def simple_img_conv_pool(
...
@@ -132,16 +132,20 @@ def simple_img_conv_pool(
act
=
act
,
act
=
act
,
use_cudnn
=
use_cudnn
,
use_cudnn
=
use_cudnn
,
)
)
if
pool_type
==
'max'
:
pool_out
=
layers
.
pool2d
(
pool_out
=
paddle
.
nn
.
functional
.
max_pool2d
(
input
=
conv_out
,
x
=
conv_out
,
pool_size
=
pool_size
,
kernel_size
=
pool_size
,
pool_type
=
pool_type
,
stride
=
pool_stride
,
pool_stride
=
pool_stride
,
padding
=
pool_padding
,
pool_padding
=
pool_padding
,
)
global_pooling
=
global_pooling
,
else
:
use_cudnn
=
use_cudnn
,
pool_out
=
paddle
.
nn
.
functional
.
avg_pool2d
(
)
x
=
conv_out
,
kernel_size
=
pool_size
,
stride
=
pool_stride
,
padding
=
pool_padding
,
)
return
pool_out
return
pool_out
...
@@ -258,13 +262,18 @@ def img_conv_group(
...
@@ -258,13 +262,18 @@ def img_conv_group(
if
abs
(
drop_rate
)
>
1e-5
:
if
abs
(
drop_rate
)
>
1e-5
:
tmp
=
layers
.
dropout
(
x
=
tmp
,
dropout_prob
=
drop_rate
)
tmp
=
layers
.
dropout
(
x
=
tmp
,
dropout_prob
=
drop_rate
)
pool_out
=
layers
.
pool2d
(
if
pool_type
==
'max'
:
input
=
tmp
,
pool_out
=
paddle
.
nn
.
functional
.
max_pool2d
(
pool_size
=
pool_size
,
x
=
tmp
,
pool_type
=
pool_type
,
kernel_size
=
pool_size
,
pool_stride
=
pool_stride
,
stride
=
pool_stride
,
use_cudnn
=
use_cudnn
,
)
)
else
:
pool_out
=
paddle
.
nn
.
functional
.
avg_pool2d
(
x
=
tmp
,
kernel_size
=
pool_size
,
stride
=
pool_stride
,
)
return
pool_out
return
pool_out
...
...
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @
2ce1f04d
...
@@ -68,9 +68,7 @@ def resnet_cifar10(input, depth=32):
...
@@ -68,9 +68,7 @@ def resnet_cifar10(input, depth=32):
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
16
,
n
,
1
)
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
16
,
n
,
1
)
res2
=
layer_warp
(
basicblock
,
res1
,
16
,
32
,
n
,
2
)
res2
=
layer_warp
(
basicblock
,
res1
,
16
,
32
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
32
,
64
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
32
,
64
,
n
,
2
)
pool
=
fluid
.
layers
.
pool2d
(
pool
=
paddle
.
nn
.
functional
.
avg_pool2d
(
x
=
res3
,
kernel_size
=
8
,
stride
=
1
)
input
=
res3
,
pool_size
=
8
,
pool_type
=
'avg'
,
pool_stride
=
1
)
return
pool
return
pool
...
...
python/paddle/fluid/tests/unittests/dist_se_resnext.py
浏览文件 @
2ce1f04d
...
@@ -59,12 +59,11 @@ class SE_ResNeXt:
...
@@ -59,12 +59,11 @@ class SE_ResNeXt:
conv
=
self
.
conv_bn_layer
(
conv
=
self
.
conv_bn_layer
(
input
=
input
,
num_filters
=
64
,
filter_size
=
7
,
stride
=
2
,
act
=
'relu'
input
=
input
,
num_filters
=
64
,
filter_size
=
7
,
stride
=
2
,
act
=
'relu'
)
)
conv
=
fluid
.
layers
.
pool2d
(
conv
=
paddle
.
nn
.
functional
.
max_pool2d
(
input
=
conv
,
x
=
conv
,
pool_size
=
3
,
kernel_size
=
3
,
pool_stride
=
2
,
stride
=
2
,
pool_padding
=
1
,
padding
=
1
,
pool_type
=
'max'
,
)
)
elif
layers
==
101
:
elif
layers
==
101
:
cardinality
=
32
cardinality
=
32
...
@@ -75,12 +74,11 @@ class SE_ResNeXt:
...
@@ -75,12 +74,11 @@ class SE_ResNeXt:
conv
=
self
.
conv_bn_layer
(
conv
=
self
.
conv_bn_layer
(
input
=
input
,
num_filters
=
64
,
filter_size
=
7
,
stride
=
2
,
act
=
'relu'
input
=
input
,
num_filters
=
64
,
filter_size
=
7
,
stride
=
2
,
act
=
'relu'
)
)
conv
=
fluid
.
layers
.
pool2d
(
conv
=
paddle
.
nn
.
functional
.
max_pool2d
(
input
=
conv
,
x
=
conv
,
pool_size
=
3
,
kernel_size
=
3
,
pool_stride
=
2
,
stride
=
2
,
pool_padding
=
1
,
padding
=
1
,
pool_type
=
'max'
,
)
)
elif
layers
==
152
:
elif
layers
==
152
:
cardinality
=
64
cardinality
=
64
...
@@ -97,12 +95,11 @@ class SE_ResNeXt:
...
@@ -97,12 +95,11 @@ class SE_ResNeXt:
conv
=
self
.
conv_bn_layer
(
conv
=
self
.
conv_bn_layer
(
input
=
conv
,
num_filters
=
128
,
filter_size
=
3
,
stride
=
1
,
act
=
'relu'
input
=
conv
,
num_filters
=
128
,
filter_size
=
3
,
stride
=
1
,
act
=
'relu'
)
)
conv
=
fluid
.
layers
.
pool2d
(
conv
=
paddle
.
nn
.
functional
.
max_pool2d
(
input
=
conv
,
x
=
conv
,
pool_size
=
3
,
kernel_size
=
3
,
pool_stride
=
2
,
stride
=
2
,
pool_padding
=
1
,
padding
=
1
,
pool_type
=
'max'
,
)
)
for
block
in
range
(
len
(
depth
)):
for
block
in
range
(
len
(
depth
)):
...
@@ -115,9 +112,7 @@ class SE_ResNeXt:
...
@@ -115,9 +112,7 @@ class SE_ResNeXt:
reduction_ratio
=
reduction_ratio
,
reduction_ratio
=
reduction_ratio
,
)
)
pool
=
fluid
.
layers
.
pool2d
(
pool
=
paddle
.
nn
.
functional
.
adaptive_avg_pool2d
(
x
=
conv
,
output_size
=
1
)
input
=
conv
,
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
drop
=
fluid
.
layers
.
dropout
(
x
=
pool
,
dropout_prob
=
0.2
)
drop
=
fluid
.
layers
.
dropout
(
x
=
pool
,
dropout_prob
=
0.2
)
stdv
=
1.0
/
math
.
sqrt
(
drop
.
shape
[
1
]
*
1.0
)
stdv
=
1.0
/
math
.
sqrt
(
drop
.
shape
[
1
]
*
1.0
)
out
=
fluid
.
layers
.
fc
(
out
=
fluid
.
layers
.
fc
(
...
@@ -185,9 +180,7 @@ class SE_ResNeXt:
...
@@ -185,9 +180,7 @@ class SE_ResNeXt:
return
paddle
.
static
.
nn
.
batch_norm
(
input
=
conv
,
act
=
act
)
return
paddle
.
static
.
nn
.
batch_norm
(
input
=
conv
,
act
=
act
)
def
squeeze_excitation
(
self
,
input
,
num_channels
,
reduction_ratio
):
def
squeeze_excitation
(
self
,
input
,
num_channels
,
reduction_ratio
):
pool
=
fluid
.
layers
.
pool2d
(
pool
=
paddle
.
nn
.
functional
.
adaptive_avg_pool2d
(
x
=
input
,
output_size
=
1
)
input
=
input
,
pool_size
=
0
,
pool_type
=
'avg'
,
global_pooling
=
True
)
stdv
=
1.0
/
math
.
sqrt
(
pool
.
shape
[
1
]
*
1.0
)
stdv
=
1.0
/
math
.
sqrt
(
pool
.
shape
[
1
]
*
1.0
)
squeeze
=
fluid
.
layers
.
fc
(
squeeze
=
fluid
.
layers
.
fc
(
input
=
pool
,
input
=
pool
,
...
...
python/paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py
浏览文件 @
2ce1f04d
...
@@ -41,11 +41,9 @@ class TestBase(IPUOpTest):
...
@@ -41,11 +41,9 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
self
.
attrs
=
{
"pool_size"
:
3
,
"kernel_size"
:
3
,
"pool_type"
:
'avg'
,
"stride"
:
1
,
"pool_stride"
:
1
,
"padding"
:
0
,
"pool_padding"
:
0
,
"global_pooling"
:
False
,
"ceil_mode"
:
False
,
"ceil_mode"
:
False
,
"exclusive"
:
True
,
"exclusive"
:
True
,
"data_format"
:
'NCHW'
,
"data_format"
:
'NCHW'
,
...
@@ -56,7 +54,7 @@ class TestBase(IPUOpTest):
...
@@ -56,7 +54,7 @@ class TestBase(IPUOpTest):
x
=
paddle
.
static
.
data
(
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
)
out
=
paddle
.
fluid
.
layers
.
pool2d
(
x
,
**
self
.
attrs
)
out
=
paddle
.
nn
.
functional
.
avg_
pool2d
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
def
run_model
(
self
,
exec_mode
):
...
@@ -73,58 +71,52 @@ class TestBase(IPUOpTest):
...
@@ -73,58 +71,52 @@ class TestBase(IPUOpTest):
class
TestCase1
(
TestBase
):
class
TestCase1
(
TestBase
):
def
set_attrs
(
self
):
def
set_attrs
(
self
):
super
().
set_attrs
()
super
().
set_attrs
()
self
.
attrs
[
'
poo
l_size'
]
=
3
self
.
attrs
[
'
kerne
l_size'
]
=
3
class
TestCase1_2
(
TestBase
):
class
TestCase1_2
(
TestBase
):
def
set_attrs
(
self
):
def
set_attrs
(
self
):
super
().
set_attrs
()
super
().
set_attrs
()
self
.
attrs
[
'
poo
l_size'
]
=
[
3
,
1
]
self
.
attrs
[
'
kerne
l_size'
]
=
[
3
,
1
]
class
TestCase2
(
TestBase
):
class
TestCase2
(
TestBase
):
def
set_attrs
(
self
):
def
set_attrs
(
self
):
super
().
set_attrs
()
super
().
set_attrs
()
self
.
attrs
[
'
pool_
stride'
]
=
2
self
.
attrs
[
'stride'
]
=
2
class
TestCase2_2
(
TestBase
):
class
TestCase2_2
(
TestBase
):
def
set_attrs
(
self
):
def
set_attrs
(
self
):
super
().
set_attrs
()
super
().
set_attrs
()
self
.
attrs
[
'
pool_
stride'
]
=
[
2
,
1
]
self
.
attrs
[
'stride'
]
=
[
2
,
1
]
class
TestCase3
(
TestBase
):
class
TestCase3
(
TestBase
):
def
set_attrs
(
self
):
def
set_attrs
(
self
):
super
().
set_attrs
()
super
().
set_attrs
()
self
.
attrs
[
'p
ool_p
adding'
]
=
[
1
,
1
]
self
.
attrs
[
'padding'
]
=
[
1
,
1
]
class
TestCase3_2
(
TestBase
):
class
TestCase3_2
(
TestBase
):
def
set_attrs
(
self
):
def
set_attrs
(
self
):
super
().
set_attrs
()
super
().
set_attrs
()
self
.
attrs
[
'p
ool_p
adding'
]
=
[
1
,
1
,
2
,
2
]
self
.
attrs
[
'padding'
]
=
[
1
,
1
,
2
,
2
]
@
unittest
.
skip
(
'the results has a positional offset'
)
@
unittest
.
skip
(
'the results has a positional offset'
)
class
TestCase3_3
(
TestBase
):
class
TestCase3_3
(
TestBase
):
def
set_attrs
(
self
):
def
set_attrs
(
self
):
super
().
set_attrs
()
super
().
set_attrs
()
self
.
attrs
[
'p
ool_p
adding'
]
=
[
1
,
2
,
1
,
1
]
self
.
attrs
[
'padding'
]
=
[
1
,
2
,
1
,
1
]
@
unittest
.
skip
(
'paddle output has nan'
)
@
unittest
.
skip
(
'paddle output has nan'
)
class
TestCase3_4
(
TestBase
):
class
TestCase3_4
(
TestBase
):
def
set_attrs
(
self
):
def
set_attrs
(
self
):
super
().
set_attrs
()
super
().
set_attrs
()
self
.
attrs
[
'pool_size'
]
=
1
self
.
attrs
[
'size'
]
=
1
self
.
attrs
[
'pool_padding'
]
=
1
self
.
attrs
[
'padding'
]
=
1
class
TestCase4
(
TestBase
):
def
set_attrs
(
self
):
super
().
set_attrs
()
self
.
attrs
[
'global_pooling'
]
=
True
class
TestCase5
(
TestBase
):
class
TestCase5
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py
浏览文件 @
2ce1f04d
...
@@ -41,13 +41,10 @@ class TestBase(IPUOpTest):
...
@@ -41,13 +41,10 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
self
.
attrs
=
{
"pool_size"
:
3
,
"kernel_size"
:
3
,
"pool_type"
:
'max'
,
"stride"
:
1
,
"pool_stride"
:
1
,
"padding"
:
0
,
"pool_padding"
:
0
,
"global_pooling"
:
False
,
"ceil_mode"
:
False
,
"ceil_mode"
:
False
,
"exclusive"
:
True
,
"data_format"
:
'NCHW'
,
"data_format"
:
'NCHW'
,
}
}
...
@@ -56,7 +53,7 @@ class TestBase(IPUOpTest):
...
@@ -56,7 +53,7 @@ class TestBase(IPUOpTest):
x
=
paddle
.
static
.
data
(
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
)
out
=
paddle
.
fluid
.
layers
.
pool2d
(
x
,
**
self
.
attrs
)
out
=
paddle
.
nn
.
functional
.
max_
pool2d
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
def
run_model
(
self
,
exec_mode
):
...
@@ -73,57 +70,51 @@ class TestBase(IPUOpTest):
...
@@ -73,57 +70,51 @@ class TestBase(IPUOpTest):
class
TestCase1
(
TestBase
):
class
TestCase1
(
TestBase
):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
super
().
set_op_attrs
()
self
.
attrs
[
'
poo
l_size'
]
=
3
self
.
attrs
[
'
kerne
l_size'
]
=
3
class
TestCase1_2
(
TestBase
):
class
TestCase1_2
(
TestBase
):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
super
().
set_op_attrs
()
self
.
attrs
[
'
poo
l_size'
]
=
[
3
,
1
]
self
.
attrs
[
'
kerne
l_size'
]
=
[
3
,
1
]
class
TestCase2
(
TestBase
):
class
TestCase2
(
TestBase
):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
super
().
set_op_attrs
()
self
.
attrs
[
'
pool_
stride'
]
=
2
self
.
attrs
[
'stride'
]
=
2
class
TestCase2_2
(
TestBase
):
class
TestCase2_2
(
TestBase
):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
super
().
set_op_attrs
()
self
.
attrs
[
'
pool_
stride'
]
=
[
2
,
1
]
self
.
attrs
[
'stride'
]
=
[
2
,
1
]
class
TestCase3
(
TestBase
):
class
TestCase3
(
TestBase
):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
super
().
set_op_attrs
()
self
.
attrs
[
'p
ool_p
adding'
]
=
[
1
,
1
]
self
.
attrs
[
'padding'
]
=
[
1
,
1
]
class
TestCase3_2
(
TestBase
):
class
TestCase3_2
(
TestBase
):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
super
().
set_op_attrs
()
self
.
attrs
[
'p
ool_p
adding'
]
=
[
1
,
1
,
2
,
2
]
self
.
attrs
[
'padding'
]
=
[
1
,
1
,
2
,
2
]
@
unittest
.
skip
(
'auto_pad is not currently supported'
)
@
unittest
.
skip
(
'auto_pad is not currently supported'
)
class
TestCase3_3
(
TestBase
):
class
TestCase3_3
(
TestBase
):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
super
().
set_op_attrs
()
self
.
attrs
[
'p
ool_p
adding'
]
=
'VALID'
self
.
attrs
[
'padding'
]
=
'VALID'
@
unittest
.
skip
(
'auto_pad is not currently supported'
)
@
unittest
.
skip
(
'auto_pad is not currently supported'
)
class
TestCase3_4
(
TestBase
):
class
TestCase3_4
(
TestBase
):
def
set_op_attrs
(
self
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
super
().
set_op_attrs
()
self
.
attrs
[
'pool_padding'
]
=
'SAME'
self
.
attrs
[
'padding'
]
=
'SAME'
class
TestCase4
(
TestBase
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
self
.
attrs
[
'global_pooling'
]
=
True
class
TestCase5
(
TestBase
):
class
TestCase5
(
TestBase
):
...
@@ -132,11 +123,5 @@ class TestCase5(TestBase):
...
@@ -132,11 +123,5 @@ class TestCase5(TestBase):
self
.
attrs
[
'ceil_mode'
]
=
True
self
.
attrs
[
'ceil_mode'
]
=
True
class
TestCase6
(
TestBase
):
def
set_op_attrs
(
self
):
super
().
set_op_attrs
()
self
.
attrs
[
'exclusive'
]
=
False
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
unittest
.
main
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/ir/inference/test_trt_pool_op.py
浏览文件 @
2ce1f04d
...
@@ -20,6 +20,7 @@ import unittest
...
@@ -20,6 +20,7 @@ import unittest
import
numpy
as
np
import
numpy
as
np
from
inference_pass_test
import
InferencePassTest
from
inference_pass_test
import
InferencePassTest
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.core
as
core
import
paddle.static.nn
as
nn
import
paddle.static.nn
as
nn
...
@@ -63,17 +64,25 @@ class TensorRTPoolTest(InferencePassTest):
...
@@ -63,17 +64,25 @@ class TensorRTPoolTest(InferencePassTest):
shape
=
[
-
1
,
self
.
channel
,
self
.
height
,
self
.
width
],
shape
=
[
-
1
,
self
.
channel
,
self
.
height
,
self
.
width
],
dtype
=
'float32'
,
dtype
=
'float32'
,
)
)
pool_out
=
fluid
.
layers
.
pool2d
(
if
self
.
pool_type
==
'max'
:
input
=
data
,
pool_out
=
paddle
.
nn
.
functional
.
max_pool2d
(
pool_size
=
self
.
pool_size
,
x
=
data
,
pool_type
=
self
.
pool_type
,
kernel_size
=
self
.
pool_size
,
pool_stride
=
self
.
pool_stride
,
stride
=
self
.
pool_stride
,
pool_padding
=
self
.
pool_padding
,
padding
=
self
.
pool_padding
,
global_pooling
=
self
.
global_pooling
,
ceil_mode
=
self
.
ceil_mode
,
ceil_mode
=
self
.
ceil_mode
,
)
exclusive
=
self
.
exclusive
,
else
:
)
pool_out
=
paddle
.
nn
.
functional
.
avg_pool2d
(
x
=
data
,
kernel_size
=
self
.
pool_size
,
stride
=
self
.
pool_stride
,
padding
=
self
.
pool_padding
,
ceil_mode
=
self
.
ceil_mode
,
exclusive
=
self
.
exclusive
,
)
out
=
nn
.
batch_norm
(
pool_out
,
is_test
=
True
)
out
=
nn
.
batch_norm
(
pool_out
,
is_test
=
True
)
self
.
fetch_list
=
[
out
]
self
.
fetch_list
=
[
out
]
def
check_output
(
self
):
def
check_output
(
self
):
...
...
python/paddle/fluid/tests/unittests/mlu/test_pool2d_op_mlu.py
浏览文件 @
2ce1f04d
...
@@ -734,86 +734,76 @@ class TestPool2DAPI(unittest.TestCase):
...
@@ -734,86 +734,76 @@ class TestPool2DAPI(unittest.TestCase):
)
)
ksize
=
[
3
,
3
]
ksize
=
[
3
,
3
]
out_1
=
fluid
.
layers
.
pool2d
(
out_1
=
paddle
.
nn
.
functional
.
max_pool2d
(
input
=
input_NHWC
,
x
=
input_NHWC
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"max"
,
padding
=
[
1
,
1
],
pool_padding
=
[
1
,
1
],
data_format
=
"NHWC"
,
data_format
=
"NHWC"
,
)
)
out_2
=
fluid
.
layers
.
pool2d
(
out_2
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input
=
input_NHWC
,
x
=
input_NHWC
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"avg"
,
padding
=
[[
0
,
0
],
[
1
,
1
],
[
1
,
1
],
[
0
,
0
]],
pool_padding
=
[[
0
,
0
],
[
1
,
1
],
[
1
,
1
],
[
0
,
0
]],
data_format
=
"NHWC"
,
data_format
=
"NHWC"
,
)
)
out_3
=
fluid
.
layers
.
pool2d
(
out_3
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input
=
input_NCHW
,
x
=
input_NCHW
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"avg"
,
padding
=
[[
0
,
0
],
[
0
,
0
],
[
1
,
1
],
[
1
,
1
]],
pool_padding
=
[[
0
,
0
],
[
0
,
0
],
[
1
,
1
],
[
1
,
1
]],
data_format
=
"NCHW"
,
data_format
=
"NCHW"
,
)
)
out_4
=
fluid
.
layers
.
pool2d
(
out_4
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input
=
input_NCHW
,
x
=
input_NCHW
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"avg"
,
padding
=
[
1
,
2
,
1
,
0
],
pool_padding
=
[
1
,
2
,
1
,
0
],
data_format
=
"NCHW"
,
data_format
=
"NCHW"
,
)
)
# test VALID
# test VALID
out_5
=
fluid
.
layers
.
pool2d
(
out_5
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input
=
input_NCHW
,
x
=
input_NCHW
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"avg"
,
padding
=
"VALID"
,
pool_padding
=
"VALID"
,
data_format
=
"NCHW"
,
data_format
=
"NCHW"
,
)
)
out_6
=
fluid
.
layers
.
pool2d
(
out_6
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input
=
input_NHWC
,
x
=
input_NHWC
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"max"
,
padding
=
"VALID"
,
pool_padding
=
"VALID"
,
data_format
=
"NHWC"
,
data_format
=
"NHWC"
,
)
)
# test SAME
# test SAME
out_7
=
fluid
.
layers
.
pool2d
(
out_7
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input
=
input_NCHW
,
x
=
input_NCHW
,
pool_size
=
[
4
,
4
],
kernel_size
=
[
4
,
4
],
pool_type
=
"avg"
,
padding
=
"SAME"
,
pool_padding
=
"SAME"
,
data_format
=
"NCHW"
,
data_format
=
"NCHW"
,
)
)
out_8
=
fluid
.
layers
.
pool2d
(
out_8
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input
=
input_NHWC
,
x
=
input_NHWC
,
pool_size
=
[
4
,
4
],
kernel_size
=
[
4
,
4
],
pool_type
=
"max"
,
padding
=
"SAME"
,
pool_padding
=
"SAME"
,
data_format
=
"NHWC"
,
data_format
=
"NHWC"
,
)
)
# test negetive
# test negetive
out_9
=
fluid
.
layers
.
pool2d
(
out_9
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input
=
input_NHWC_negetive
,
x
=
input_NHWC_negetive
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"avg"
,
padding
=
[
0
,
0
],
pool_padding
=
[
0
,
0
],
data_format
=
"NHWC"
,
data_format
=
"NHWC"
,
)
)
assert
out_9
.
shape
==
(
2
,
-
1
,
3
,
3
)
assert
out_9
.
shape
==
(
2
,
-
1
,
3
,
3
)
out_10
=
fluid
.
layers
.
pool2d
(
out_10
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input
=
input_NCHW_negetive
,
x
=
input_NCHW_negetive
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"avg"
,
padding
=
[
0
,
0
],
pool_padding
=
[
0
,
0
],
data_format
=
"NCHW"
,
data_format
=
"NCHW"
,
)
)
assert
out_10
.
shape
==
(
2
,
3
,
-
1
,
-
1
)
assert
out_10
.
shape
==
(
2
,
3
,
-
1
,
-
1
)
...
@@ -950,11 +940,10 @@ class TestPool2DAPI_Error(unittest.TestCase):
...
@@ -950,11 +940,10 @@ class TestPool2DAPI_Error(unittest.TestCase):
# data_format value error
# data_format value error
def
run_2
():
def
run_2
():
out_2
=
fluid
.
layers
.
pool2d
(
out_2
=
paddle
.
nn
.
functional
.
max_pool2d
(
input
=
input_NHWC
,
x
=
input_NHWC
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"max"
,
padding
=
[
1
,
1
],
pool_padding
=
[
1
,
1
],
data_format
=
"NHWCC"
,
data_format
=
"NHWCC"
,
)
)
...
@@ -962,11 +951,10 @@ class TestPool2DAPI_Error(unittest.TestCase):
...
@@ -962,11 +951,10 @@ class TestPool2DAPI_Error(unittest.TestCase):
# padding str value error
# padding str value error
def
run_3
():
def
run_3
():
out_3
=
fluid
.
layers
.
pool2d
(
out_3
=
paddle
.
nn
.
functional
.
max_pool2d
(
input
=
input_NHWC
,
x
=
input_NHWC
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"max"
,
padding
=
"VALIDSAME"
,
pool_padding
=
"VALIDSAME"
,
data_format
=
"NHWC"
,
data_format
=
"NHWC"
,
)
)
...
@@ -974,11 +962,10 @@ class TestPool2DAPI_Error(unittest.TestCase):
...
@@ -974,11 +962,10 @@ class TestPool2DAPI_Error(unittest.TestCase):
# padding str valid and ceil_mode value error
# padding str valid and ceil_mode value error
def
run_4
():
def
run_4
():
out_4
=
fluid
.
layers
.
pool2d
(
out_4
=
paddle
.
nn
.
functional
.
max_pool2d
(
input
=
input_NHWC
,
x
=
input_NHWC
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"max"
,
padding
=
"VALID"
,
pool_padding
=
"VALID"
,
ceil_mode
=
True
,
ceil_mode
=
True
,
data_format
=
"NHWC"
,
data_format
=
"NHWC"
,
)
)
...
@@ -987,11 +974,10 @@ class TestPool2DAPI_Error(unittest.TestCase):
...
@@ -987,11 +974,10 @@ class TestPool2DAPI_Error(unittest.TestCase):
# padding with 8 ele. value error
# padding with 8 ele. value error
def
run_5
():
def
run_5
():
out_5
=
fluid
.
layers
.
pool2d
(
out_5
=
paddle
.
nn
.
functional
.
max_pool2d
(
input
=
input_NHWC
,
x
=
input_NHWC
,
pool_size
=
ksize
,
kernel_size
=
ksize
,
pool_type
=
"max"
,
padding
=
[[
1
,
1
],
[
0
,
0
],
[
0
,
0
],
[
1
,
1
]],
pool_padding
=
[[
1
,
1
],
[
0
,
0
],
[
0
,
0
],
[
1
,
1
]],
data_format
=
"NHWC"
,
data_format
=
"NHWC"
,
)
)
...
...
python/paddle/fluid/tests/unittests/seresnext_net.py
浏览文件 @
2ce1f04d
...
@@ -141,8 +141,8 @@ def SE_ResNeXt50Small(use_feed):
...
@@ -141,8 +141,8 @@ def SE_ResNeXt50Small(use_feed):
conv
=
conv_bn_layer
(
conv
=
conv_bn_layer
(
input
=
conv
,
num_filters
=
16
,
filter_size
=
3
,
stride
=
1
,
act
=
'relu'
input
=
conv
,
num_filters
=
16
,
filter_size
=
3
,
stride
=
1
,
act
=
'relu'
)
)
conv
=
fluid
.
layers
.
pool2d
(
conv
=
paddle
.
nn
.
functional
.
max_
pool2d
(
input
=
conv
,
pool_size
=
3
,
pool_stride
=
2
,
pool_padding
=
1
,
pool_type
=
'max'
x
=
conv
,
kernel_size
=
3
,
stride
=
2
,
padding
=
1
)
)
cardinality
=
32
cardinality
=
32
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
2ce1f04d
...
@@ -2121,8 +2121,8 @@ class TestBook(LayerTest):
...
@@ -2121,8 +2121,8 @@ class TestBook(LayerTest):
fluid
.
default_main_program
(),
fluid
.
default_startup_program
()
fluid
.
default_main_program
(),
fluid
.
default_startup_program
()
):
):
x
=
self
.
_get_data
(
name
=
'x'
,
shape
=
[
3
,
224
,
224
],
dtype
=
'float32'
)
x
=
self
.
_get_data
(
name
=
'x'
,
shape
=
[
3
,
224
,
224
],
dtype
=
'float32'
)
return
layers
.
pool2d
(
return
paddle
.
nn
.
functional
.
max_
pool2d
(
x
,
pool_size
=
[
5
,
3
],
pool_stride
=
[
1
,
2
],
pool_
padding
=
(
2
,
1
)
x
,
kernel_size
=
[
5
,
3
],
stride
=
[
1
,
2
],
padding
=
(
2
,
1
)
)
)
def
make_pool2d_infershape
(
self
):
def
make_pool2d_infershape
(
self
):
...
@@ -2133,8 +2133,8 @@ class TestBook(LayerTest):
...
@@ -2133,8 +2133,8 @@ class TestBook(LayerTest):
x
=
paddle
.
nn
.
functional
.
affine_grid
(
x
=
paddle
.
nn
.
functional
.
affine_grid
(
theta
,
out_shape
=
[
2
,
3
,
244
,
244
]
theta
,
out_shape
=
[
2
,
3
,
244
,
244
]
)
)
return
layers
.
pool2d
(
return
paddle
.
nn
.
functional
.
max_
pool2d
(
x
,
pool_size
=
[
5
,
3
],
pool_stride
=
[
1
,
2
],
pool_
padding
=
(
2
,
1
)
x
,
kernel_size
=
[
5
,
3
],
stride
=
[
1
,
2
],
padding
=
(
2
,
1
)
)
)
def
make_lstm_unit
(
self
):
def
make_lstm_unit
(
self
):
...
...
python/paddle/fluid/tests/unittests/test_nn_grad.py
浏览文件 @
2ce1f04d
...
@@ -431,7 +431,7 @@ class TestAvgPool2DDoubleGradCheckCase1(unittest.TestCase):
...
@@ -431,7 +431,7 @@ class TestAvgPool2DDoubleGradCheckCase1(unittest.TestCase):
)
)
input_NCHW
.
persistable
=
True
input_NCHW
.
persistable
=
True
y
=
layers
.
pool2d
(
input_NCHW
,
pool_size
=
2
,
pool_type
=
"avg"
)
y
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input_NCHW
,
kernel_size
=
2
)
x_arr
=
np
.
random
.
uniform
(
-
1
,
1
,
[
2
,
3
,
5
,
5
]).
astype
(
np
.
float32
)
x_arr
=
np
.
random
.
uniform
(
-
1
,
1
,
[
2
,
3
,
5
,
5
]).
astype
(
np
.
float32
)
gradient_checker
.
double_grad_check
(
gradient_checker
.
double_grad_check
(
...
@@ -533,7 +533,6 @@ class TestAvgPool2DDoubleGradCheckCase4(unittest.TestCase):
...
@@ -533,7 +533,6 @@ class TestAvgPool2DDoubleGradCheckCase4(unittest.TestCase):
)
)
input_NCHW
.
persistable
=
True
input_NCHW
.
persistable
=
True
y
=
layers
.
pool2d
(
input_NCHW
,
pool_size
=
[
4
,
4
],
pool_type
=
"avg"
)
y
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input_NCHW
,
kernel_size
=
[
4
,
4
])
y
=
paddle
.
nn
.
functional
.
avg_pool2d
(
input_NCHW
,
kernel_size
=
[
4
,
4
])
x_arr
=
np
.
random
.
uniform
(
-
1
,
1
,
[
2
,
3
,
5
,
5
]).
astype
(
np
.
float32
)
x_arr
=
np
.
random
.
uniform
(
-
1
,
1
,
[
2
,
3
,
5
,
5
]).
astype
(
np
.
float32
)
...
...
python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py
浏览文件 @
2ce1f04d
...
@@ -27,10 +27,10 @@ from paddle.fluid import compiler
...
@@ -27,10 +27,10 @@ from paddle.fluid import compiler
def
Lenet
(
data
,
class_dim
):
def
Lenet
(
data
,
class_dim
):
conv1
=
fluid
.
layers
.
conv2d
(
data
,
4
,
5
,
1
,
act
=
None
)
conv1
=
fluid
.
layers
.
conv2d
(
data
,
4
,
5
,
1
,
act
=
None
)
bn1
=
paddle
.
static
.
nn
.
batch_norm
(
conv1
,
act
=
'relu'
)
bn1
=
paddle
.
static
.
nn
.
batch_norm
(
conv1
,
act
=
'relu'
)
pool1
=
fluid
.
layers
.
pool2d
(
bn1
,
2
,
'max'
,
2
)
pool1
=
paddle
.
nn
.
functional
.
max_pool2d
(
bn1
,
2
,
2
)
conv2
=
fluid
.
layers
.
conv2d
(
pool1
,
16
,
5
,
1
,
act
=
None
)
conv2
=
fluid
.
layers
.
conv2d
(
pool1
,
16
,
5
,
1
,
act
=
None
)
bn2
=
paddle
.
static
.
nn
.
batch_norm
(
conv2
,
act
=
'relu'
)
bn2
=
paddle
.
static
.
nn
.
batch_norm
(
conv2
,
act
=
'relu'
)
pool2
=
fluid
.
layers
.
pool2d
(
bn2
,
2
,
'max'
,
2
)
pool2
=
paddle
.
nn
.
functional
.
max_pool2d
(
bn2
,
2
,
2
)
fc1
=
fluid
.
layers
.
fc
(
pool2
,
size
=
50
,
act
=
'relu'
)
fc1
=
fluid
.
layers
.
fc
(
pool2
,
size
=
50
,
act
=
'relu'
)
fc2
=
fluid
.
layers
.
fc
(
fc1
,
size
=
class_dim
,
act
=
'softmax'
)
fc2
=
fluid
.
layers
.
fc
(
fc1
,
size
=
class_dim
,
act
=
'softmax'
)
...
...
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @
2ce1f04d
...
@@ -16,7 +16,6 @@ import unittest
...
@@ -16,7 +16,6 @@ import unittest
import
numpy
as
np
import
numpy
as
np
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.core
as
core
from
paddle.fluid.tests.unittests.op_test
import
OpTest
from
paddle.fluid.tests.unittests.op_test
import
OpTest
...
@@ -1133,331 +1132,5 @@ create_test_padding_SAME_class(TestCase1_strides)
...
@@ -1133,331 +1132,5 @@ create_test_padding_SAME_class(TestCase1_strides)
create_test_cudnn_padding_SAME_class
(
TestCase1_strides
)
create_test_cudnn_padding_SAME_class
(
TestCase1_strides
)
# ----- test API
class
TestPool2DAPI
(
unittest
.
TestCase
):
def
test_api
(
self
):
x_NHWC
=
np
.
random
.
random
([
2
,
5
,
5
,
3
]).
astype
(
"float32"
)
x_NCHW
=
np
.
random
.
random
([
2
,
3
,
5
,
5
]).
astype
(
"float32"
)
input_NHWC
=
fluid
.
layers
.
data
(
name
=
"input_NHWC"
,
shape
=
[
2
,
5
,
5
,
3
],
append_batch_size
=
False
,
dtype
=
"float32"
,
)
input_NCHW
=
fluid
.
layers
.
data
(
name
=
"input_NCHW"
,
shape
=
[
2
,
3
,
5
,
5
],
append_batch_size
=
False
,
dtype
=
"float32"
,
)
input_NHWC_negetive
=
fluid
.
layers
.
data
(
name
=
"input_NHWC_negetive"
,
shape
=
[
2
,
-
1
,
5
,
3
],
append_batch_size
=
False
,
dtype
=
"float32"
,
)
input_NCHW_negetive
=
fluid
.
layers
.
data
(
name
=
"input_NCHW_negetive"
,
shape
=
[
2
,
3
,
-
1
,
-
1
],
append_batch_size
=
False
,
dtype
=
"float32"
,
)
ksize
=
[
3
,
3
]
out_1
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC
,
pool_size
=
ksize
,
pool_type
=
"max"
,
pool_padding
=
[
1
,
1
],
use_cudnn
=
False
,
data_format
=
"NHWC"
,
)
out_2
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC
,
pool_size
=
ksize
,
pool_type
=
"avg"
,
pool_padding
=
[[
0
,
0
],
[
1
,
1
],
[
1
,
1
],
[
0
,
0
]],
use_cudnn
=
False
,
data_format
=
"NHWC"
,
)
out_3
=
fluid
.
layers
.
pool2d
(
input
=
input_NCHW
,
pool_size
=
ksize
,
pool_type
=
"avg"
,
pool_padding
=
[[
0
,
0
],
[
0
,
0
],
[
1
,
1
],
[
1
,
1
]],
use_cudnn
=
False
,
data_format
=
"NCHW"
,
)
out_4
=
fluid
.
layers
.
pool2d
(
input
=
input_NCHW
,
pool_size
=
ksize
,
pool_type
=
"avg"
,
pool_padding
=
[
1
,
2
,
1
,
0
],
use_cudnn
=
False
,
data_format
=
"NCHW"
,
)
# test VALID
out_5
=
fluid
.
layers
.
pool2d
(
input
=
input_NCHW
,
pool_size
=
ksize
,
pool_type
=
"avg"
,
pool_padding
=
"VALID"
,
use_cudnn
=
False
,
data_format
=
"NCHW"
,
)
out_6
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC
,
pool_size
=
ksize
,
pool_type
=
"max"
,
pool_padding
=
"VALID"
,
use_cudnn
=
False
,
data_format
=
"NHWC"
,
)
# test SAME
out_7
=
fluid
.
layers
.
pool2d
(
input
=
input_NCHW
,
pool_size
=
[
4
,
4
],
pool_type
=
"avg"
,
pool_padding
=
"SAME"
,
use_cudnn
=
False
,
data_format
=
"NCHW"
,
)
out_8
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC
,
pool_size
=
[
4
,
4
],
pool_type
=
"max"
,
pool_padding
=
"SAME"
,
use_cudnn
=
False
,
data_format
=
"NHWC"
,
)
# test negetive
out_9
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC_negetive
,
pool_size
=
ksize
,
pool_type
=
"avg"
,
pool_padding
=
[
0
,
0
],
use_cudnn
=
False
,
data_format
=
"NHWC"
,
)
assert
out_9
.
shape
==
(
2
,
-
1
,
3
,
3
)
out_10
=
fluid
.
layers
.
pool2d
(
input
=
input_NCHW_negetive
,
pool_size
=
ksize
,
pool_type
=
"avg"
,
pool_padding
=
[
0
,
0
],
use_cudnn
=
False
,
data_format
=
"NCHW"
,
)
assert
out_10
.
shape
==
(
2
,
3
,
-
1
,
-
1
)
exe
=
fluid
.
Executor
(
place
=
fluid
.
CPUPlace
())
[
res_1
,
res_2
,
res_3
,
res_4
,
res_5
,
res_6
,
res_7
,
res_8
]
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"input_NHWC"
:
x_NHWC
,
"input_NCHW"
:
x_NCHW
,
"input_NHWC_negetive"
:
x_NHWC
,
"input_NCHW_negetive"
:
x_NCHW
,
},
fetch_list
=
[
out_1
,
out_2
,
out_3
,
out_4
,
out_5
,
out_6
,
out_7
,
out_8
],
)
assert
np
.
allclose
(
res_1
,
pool2D_forward_naive
(
x
=
x_NHWC
,
ksize
=
ksize
,
pool_type
=
"max"
,
strides
=
[
1
,
1
],
paddings
=
[
1
,
1
],
data_format
=
"NHWC"
,
),
)
assert
np
.
allclose
(
res_2
,
pool2D_forward_naive
(
x
=
x_NHWC
,
ksize
=
ksize
,
pool_type
=
"avg"
,
strides
=
[
1
,
1
],
paddings
=
[
1
,
1
,
1
,
1
],
data_format
=
"NHWC"
,
),
)
assert
np
.
allclose
(
res_3
,
pool2D_forward_naive
(
x
=
x_NCHW
,
ksize
=
ksize
,
pool_type
=
"avg"
,
strides
=
[
1
,
1
],
paddings
=
[
1
,
1
,
1
,
1
],
data_format
=
"NCHW"
,
),
rtol
=
0.07
,
atol
=
1e-05
,
)
assert
np
.
allclose
(
res_4
,
pool2D_forward_naive
(
x
=
x_NCHW
,
ksize
=
ksize
,
pool_type
=
"avg"
,
strides
=
[
1
,
1
],
paddings
=
[
1
,
2
,
1
,
0
],
data_format
=
"NCHW"
,
),
rtol
=
0.07
,
atol
=
1e-05
,
)
# VALID
assert
np
.
allclose
(
res_5
,
pool2D_forward_naive
(
x
=
x_NCHW
,
ksize
=
ksize
,
pool_type
=
"avg"
,
strides
=
[
1
,
1
],
paddings
=
[
10
,
20
],
# any ele is ok
padding_algorithm
=
"VALID"
,
data_format
=
"NCHW"
,
),
rtol
=
0.07
,
atol
=
1e-05
,
)
assert
np
.
allclose
(
res_6
,
pool2D_forward_naive
(
x
=
x_NHWC
,
ksize
=
ksize
,
pool_type
=
"max"
,
strides
=
[
1
,
1
],
paddings
=
[
10
,
20
],
padding_algorithm
=
"VALID"
,
data_format
=
"NHWC"
,
),
)
# SAME
assert
np
.
allclose
(
res_7
,
pool2D_forward_naive
(
x
=
x_NCHW
,
ksize
=
[
4
,
4
],
pool_type
=
"avg"
,
strides
=
[
1
,
1
],
paddings
=
[
10
,
20
],
padding_algorithm
=
"SAME"
,
data_format
=
"NCHW"
,
),
rtol
=
0.07
,
atol
=
1e-05
,
)
assert
np
.
allclose
(
res_8
,
pool2D_forward_naive
(
x
=
x_NHWC
,
ksize
=
[
4
,
4
],
pool_type
=
"max"
,
strides
=
[
1
,
1
],
paddings
=
[
10
,
20
],
padding_algorithm
=
"SAME"
,
data_format
=
"NHWC"
,
),
)
class
TestPool2DAPI_Error
(
unittest
.
TestCase
):
def
test_api
(
self
):
input_NHWC
=
fluid
.
layers
.
data
(
name
=
"input_NHWC"
,
shape
=
[
2
,
5
,
5
,
3
],
append_batch_size
=
False
,
dtype
=
"float32"
,
)
ksize
=
[
3
,
3
]
# cudnn type error
def
run_1
():
out_1
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC
,
pool_size
=
ksize
,
pool_type
=
"max"
,
pool_padding
=
[
1
,
1
],
use_cudnn
=
[
0
],
data_format
=
"NHWC"
,
)
self
.
assertRaises
(
TypeError
,
run_1
)
# data_format value error
def
run_2
():
out_2
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC
,
pool_size
=
ksize
,
pool_type
=
"max"
,
pool_padding
=
[
1
,
1
],
use_cudnn
=
False
,
data_format
=
"NHWCC"
,
)
self
.
assertRaises
(
ValueError
,
run_2
)
# padding str value error
def
run_3
():
out_3
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC
,
pool_size
=
ksize
,
pool_type
=
"max"
,
pool_padding
=
"VALIDSAME"
,
use_cudnn
=
False
,
data_format
=
"NHWC"
,
)
self
.
assertRaises
(
ValueError
,
run_3
)
# padding str valid and ceil_mode value error
def
run_4
():
out_4
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC
,
pool_size
=
ksize
,
pool_type
=
"max"
,
pool_padding
=
"VALID"
,
use_cudnn
=
False
,
ceil_mode
=
True
,
data_format
=
"NHWC"
,
)
self
.
assertRaises
(
ValueError
,
run_4
)
# padding with 8 ele. value error
def
run_5
():
out_5
=
fluid
.
layers
.
pool2d
(
input
=
input_NHWC
,
pool_size
=
ksize
,
pool_type
=
"max"
,
pool_padding
=
[[
1
,
1
],
[
0
,
0
],
[
0
,
0
],
[
1
,
1
]],
use_cudnn
=
False
,
data_format
=
"NHWC"
,
)
self
.
assertRaises
(
ValueError
,
run_5
)
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
unittest
.
main
()
unittest
.
main
()
python/paddle/nn/functional/pooling.py
浏览文件 @
2ce1f04d
...
@@ -1427,29 +1427,53 @@ def max_pool2d(
...
@@ -1427,29 +1427,53 @@ def max_pool2d(
)
)
dtype
=
helper
.
input_dtype
(
input_param_name
=
'x'
)
dtype
=
helper
.
input_dtype
(
input_param_name
=
'x'
)
pool_out
=
helper
.
create_variable_for_type_inference
(
dtype
)
pool_out
=
helper
.
create_variable_for_type_inference
(
dtype
)
mask
=
helper
.
create_variable_for_type_inference
(
"int32"
)
outputs
=
{
"Out"
:
pool_out
,
"Mask"
:
mask
}
helper
.
append_op
(
if
return_mask
:
type
=
op_type
,
mask
=
helper
.
create_variable_for_type_inference
(
"int32"
)
inputs
=
{
"X"
:
x
},
outputs
=
{
"Out"
:
pool_out
,
"Mask"
:
mask
}
outputs
=
outputs
,
attrs
=
{
"pooling_type"
:
'max'
,
"ksize"
:
kernel_size
,
"global_pooling"
:
False
,
"strides"
:
stride
,
"paddings"
:
padding
,
"padding_algorithm"
:
padding_algorithm
,
"use_cudnn"
:
True
,
"ceil_mode"
:
ceil_mode
,
"use_mkldnn"
:
False
,
"exclusive"
:
True
,
"data_format"
:
data_format
,
},
)
return
(
pool_out
,
mask
)
if
return_mask
else
pool_out
helper
.
append_op
(
type
=
"max_pool2d_with_index"
,
inputs
=
{
"X"
:
x
},
outputs
=
outputs
,
attrs
=
{
"pooling_type"
:
'max'
,
"ksize"
:
kernel_size
,
"global_pooling"
:
False
,
"strides"
:
stride
,
"paddings"
:
padding
,
"padding_algorithm"
:
padding_algorithm
,
"use_cudnn"
:
True
,
"ceil_mode"
:
ceil_mode
,
"use_mkldnn"
:
False
,
"exclusive"
:
True
,
"data_format"
:
data_format
,
},
)
return
(
pool_out
,
mask
)
else
:
outputs
=
{
"Out"
:
pool_out
}
helper
.
append_op
(
type
=
"pool2d"
,
inputs
=
{
"X"
:
x
},
outputs
=
outputs
,
attrs
=
{
"pooling_type"
:
'max'
,
"ksize"
:
kernel_size
,
"global_pooling"
:
False
,
"strides"
:
stride
,
"paddings"
:
padding
,
"padding_algorithm"
:
padding_algorithm
,
"use_cudnn"
:
True
,
"ceil_mode"
:
ceil_mode
,
"use_mkldnn"
:
False
,
"exclusive"
:
True
,
"data_format"
:
data_format
,
},
)
return
pool_out
def
max_pool3d
(
def
max_pool3d
(
...
...
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