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d050c188
编写于
12月 06, 2022
作者:
W
wangzhen38
提交者:
GitHub
12月 06, 2022
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电子邮件补丁
差异文件
[remove fluid] Pool2D (#48707)
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16
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Showing
16 changed file
with
36 addition
and
309 deletion
+36
-309
python/paddle/fluid/contrib/slim/tests/test_imperative_skip_op.py
...addle/fluid/contrib/slim/tests/test_imperative_skip_op.py
+0
-1
python/paddle/fluid/dygraph/nn.py
python/paddle/fluid/dygraph/nn.py
+0
-233
python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_se_resnext.py
...unittests/collective/fleet/parallel_dygraph_se_resnext.py
+2
-6
python/paddle/fluid/tests/unittests/dygraph_to_static/test_mnist.py
...dle/fluid/tests/unittests/dygraph_to_static/test_mnist.py
+4
-7
python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py
...luid/tests/unittests/dygraph_to_static/test_mobile_net.py
+2
-6
python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py
...le/fluid/tests/unittests/dygraph_to_static/test_resnet.py
+1
-3
python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_v2.py
...fluid/tests/unittests/dygraph_to_static/test_resnet_v2.py
+1
-3
python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py
...fluid/tests/unittests/dygraph_to_static/test_se_resnet.py
+2
-6
python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py
...addle/fluid/tests/unittests/dygraph_to_static/test_tsm.py
+1
-3
python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py
...on/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py
+4
-7
python/paddle/fluid/tests/unittests/test_dygraph_mnist_fp16.py
...n/paddle/fluid/tests/unittests/test_dygraph_mnist_fp16.py
+4
-7
python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py
...addle/fluid/tests/unittests/test_dygraph_multi_forward.py
+4
-7
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
+4
-7
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
...on/paddle/fluid/tests/unittests/test_imperative_resnet.py
+1
-3
python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py
...addle/fluid/tests/unittests/test_imperative_se_resnext.py
+2
-6
python/paddle/tests/test_model.py
python/paddle/tests/test_model.py
+4
-4
未找到文件。
python/paddle/fluid/contrib/slim/tests/test_imperative_skip_op.py
浏览文件 @
d050c188
...
...
@@ -26,7 +26,6 @@ from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
from
paddle.fluid.dygraph.io
import
INFER_MODEL_SUFFIX
,
INFER_PARAMS_SUFFIX
from
paddle.nn.layer
import
ReLU
,
LeakyReLU
,
Sigmoid
,
Softmax
,
ReLU6
from
paddle.nn
import
Linear
,
Conv2D
,
Softmax
,
BatchNorm
from
paddle.fluid.dygraph.nn
import
Pool2D
from
paddle.fluid.log_helper
import
get_logger
from
imperative_test_utils
import
(
...
...
python/paddle/fluid/dygraph/nn.py
浏览文件 @
d050c188
...
...
@@ -50,7 +50,6 @@ from paddle import _C_ops, _legacy_C_ops
__all__
=
[
'Conv3D'
,
'Pool2D'
,
'Linear'
,
'BatchNorm'
,
'Embedding'
,
...
...
@@ -506,238 +505,6 @@ class Conv3DTranspose(layers.Layer):
return
self
.
_helper
.
append_activation
(
pre_act
,
act
=
self
.
_act
)
class
Pool2D
(
layers
.
Layer
):
r
"""
This interface is used to construct a callable object of the ``Pool2D`` class.
For more details, refer to code examples.
The pooling2d operation calculates the output based on the input, pool_type and pool_size, pool_stride,
pool_padding parameters.Input and output are in NCHW format, where N is batch size, C is the number of feature map,
H is the height of the feature map, and W is the width of the feature map.
Parameters(ksize, strides, paddings) are two elements. These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
- Input:
Input shape: :math:`(N, C, H_{in}, W_{in})`
- Output:
Output shape: :math:`(N, C, H_{out}, W_{out})`
If ``ceil_mode`` = False:
.. math::
H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\\\
W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
If ``ceil_mode`` = True:
.. math::
H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1 \\\\
W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
If ``exclusive`` = False:
.. math::
hstart &= i * strides[0] - paddings[0] \\\\
hend &= hstart + ksize[0] \\\\
wstart &= j * strides[1] - paddings[1] \\\\
wend &= wstart + ksize[1] \\\\
Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
If ``exclusive`` = True:
.. math::
hstart &= max(0, i * strides[0] - paddings[0])\\\\
hend &= min(H, hstart + ksize[0]) \\\\
wstart &= max(0, j * strides[1] - paddings[1]) \\\\
wend & = min(W, wstart + ksize[1]) \\\\
Output(i ,j) & = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
Parameters:
pool_size (int or list or tuple, optional): 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. Default: -1.
pool_type(str, optional) : The pooling type, can be "max" for max-pooling and "avg" for average-pooling.
Default: max.
pool_stride (int or list or tuple, optional): 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. Default: 1.
pool_padding (int or list or tuple, optional): The padding size for pooling operation.
If ``pool_padding`` is a tuple,
it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width).
Otherwise, the padding size for pooling operation will be a square of an int. Default: 0.
global_pooling (bool, optional): Whether to use the global pooling. If global_pooling = true,
kernel size and paddings will be ignored. Default: False.
use_cudnn (bool, optional): Only used in cudnn kernel, need install cudnn. Default: True.
ceil_mode (bool, optional): Whether to use the ceil function to calculate output height and width.
False is the default. If it is set to False, the floor function will be used. Default: False.
exclusive (bool, optional): Whether to exclude padding points in average pooling mode. Default: 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]``. When it is `"NHWC"`, the data is
stored in the order of: ``[batch_size, input_height, input_width, input_channels]``
Returns:
None
Raises:
ValueError: If ``pool_type`` is not "max" nor "avg".
ValueError: If ``global_pooling`` is False and ``pool_size`` is -1.
ValueError: If ``use_cudnn`` is not a bool value.
ValueError: If ``data_format`` is not "NCHW" nor "NHWC".
Examples:
.. code-block:: python
import paddle.fluid as fluid
from paddle.fluid.dygraph.base import to_variable
import numpy as np
with fluid.dygraph.guard():
data = numpy.random.random((3, 32, 32, 5)).astype('float32')
pool2d = fluid.dygraph.Pool2D(pool_size=2,
pool_type='max',
pool_stride=1,
global_pooling=False)
pool2d_res = pool2d(to_variable(data))
"""
def
__init__
(
self
,
pool_size
=-
1
,
pool_type
=
"max"
,
pool_stride
=
1
,
pool_padding
=
0
,
global_pooling
=
False
,
use_cudnn
=
True
,
ceil_mode
=
False
,
exclusive
=
True
,
data_format
=
"NCHW"
,
):
data_format
=
data_format
.
upper
()
# supprt NHWC, nhwc, etc.
pool_type
=
pool_type
.
lower
()
# supprt max, Max, etc.
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
"Unknown 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 the global_pooling is False, pool_size must be passed "
"and be a valid value. Received pool_size: "
+
str
(
pool_size
)
)
if
not
isinstance
(
use_cudnn
,
bool
):
raise
ValueError
(
"use_cudnn should be True or False"
)
self
.
_use_mkldnn
=
_global_flags
()[
"FLAGS_use_mkldnn"
]
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
)
)
super
().
__init__
()
self
.
_pool_type
=
pool_type
self
.
_pool_size
=
utils
.
convert_to_list
(
pool_size
,
2
,
'pool_size'
)
self
.
_pool_padding
=
utils
.
convert_to_list
(
pool_padding
,
2
,
'pool_padding'
)
self
.
_pool_stride
=
utils
.
convert_to_list
(
pool_stride
,
2
,
'pool_stride'
)
self
.
_global_pooling
=
global_pooling
self
.
_use_cudnn
=
use_cudnn
self
.
_ceil_mode
=
ceil_mode
self
.
_exclusive
=
exclusive
self
.
_data_format
=
data_format
self
.
_l_type
=
'pool2d'
def
forward
(
self
,
input
):
if
_non_static_mode
():
if
not
self
.
_use_mkldnn
and
in_dygraph_mode
():
input
=
input
.
_use_gpudnn
(
self
.
_use_cudnn
)
return
_C_ops
.
pool2d
(
input
,
self
.
_pool_size
,
self
.
_pool_stride
,
self
.
_pool_padding
,
self
.
_ceil_mode
,
self
.
_exclusive
,
self
.
_data_format
,
self
.
_pool_type
,
self
.
_global_pooling
,
False
,
"EXPLICIT"
,
)
attrs
=
(
'pooling_type'
,
self
.
_pool_type
,
'ksize'
,
self
.
_pool_size
,
'global_pooling'
,
self
.
_global_pooling
,
'strides'
,
self
.
_pool_stride
,
'paddings'
,
self
.
_pool_padding
,
'use_cudnn'
,
self
.
_use_cudnn
,
'ceil_mode'
,
self
.
_ceil_mode
,
'use_mkldnn'
,
self
.
_use_mkldnn
,
'exclusive'
,
self
.
_exclusive
,
'data_format'
,
self
.
_data_format
,
)
return
_legacy_C_ops
.
pool2d
(
input
,
*
attrs
)
check_variable_and_dtype
(
input
,
'input'
,
[
'int8'
,
'uint8'
,
'float16'
,
'float32'
,
'float64'
],
'Pool2D'
,
)
attrs
=
{
"pooling_type"
:
self
.
_pool_type
,
"ksize"
:
self
.
_pool_size
,
"global_pooling"
:
self
.
_global_pooling
,
"strides"
:
self
.
_pool_stride
,
"paddings"
:
self
.
_pool_padding
,
"use_cudnn"
:
self
.
_use_cudnn
,
"ceil_mode"
:
self
.
_ceil_mode
,
"use_mkldnn"
:
self
.
_use_mkldnn
,
"exclusive"
:
self
.
_exclusive
,
"data_format"
:
self
.
_data_format
,
}
inputs
=
{
"X"
:
[
input
]}
pool_out
=
self
.
_helper
.
create_variable_for_type_inference
(
self
.
_dtype
)
self
.
_helper
.
append_op
(
type
=
self
.
_l_type
,
inputs
=
{
"X"
:
input
},
outputs
=
{
"Out"
:
pool_out
},
attrs
=
attrs
,
)
return
pool_out
class
Linear
(
layers
.
Layer
):
"""
...
...
python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_se_resnext.py
浏览文件 @
d050c188
...
...
@@ -114,9 +114,7 @@ class SqueezeExcitation(fluid.dygraph.Layer):
super
().
__init__
()
self
.
_num_channels
=
num_channels
self
.
_pool
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
0
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
_pool
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
stdv
=
1.0
/
math
.
sqrt
(
num_channels
*
1.0
)
self
.
_squeeze
=
Linear
(
num_channels
,
...
...
@@ -295,9 +293,7 @@ class SeResNeXt(fluid.dygraph.Layer):
self
.
bottleneck_block_list
.
append
(
bottleneck_block
)
shortcut
=
True
self
.
pool2d_avg
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
stdv
=
1.0
/
math
.
sqrt
(
2048
*
1.0
)
self
.
pool2d_avg_output
=
num_filters
[
len
(
num_filters
)
-
1
]
*
2
*
1
*
1
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_mnist.py
浏览文件 @
d050c188
...
...
@@ -69,13 +69,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
bias_attr
=
None
,
)
self
.
_pool2d
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
pool_size
,
pool_type
=
pool_type
,
pool_stride
=
pool_stride
,
pool_padding
=
pool_padding
,
global_pooling
=
global_pooling
,
use_cudnn
=
use_cudnn
,
self
.
_pool2d
=
paddle
.
nn
.
MaxPool2D
(
kernel_size
=
pool_size
,
stride
=
pool_stride
,
padding
=
pool_padding
,
)
def
forward
(
self
,
inputs
):
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py
浏览文件 @
d050c188
...
...
@@ -256,9 +256,7 @@ class MobileNetV1(fluid.dygraph.Layer):
)
self
.
dwsl
.
append
(
dws6
)
self
.
pool2d_avg
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
self
.
out
=
Linear
(
int
(
1024
*
scale
),
...
...
@@ -424,9 +422,7 @@ class MobileNetV2(fluid.dygraph.Layer):
)
# 4. pool
self
.
_pool2d_avg
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
_pool2d_avg
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
# 5. fc
tmp_param
=
ParamAttr
(
name
=
self
.
full_name
()
+
"fc10_weights"
)
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py
浏览文件 @
d050c188
...
...
@@ -184,9 +184,7 @@ class ResNet(fluid.dygraph.Layer):
)
self
.
bottleneck_block_list
.
append
(
bottleneck_block
)
shortcut
=
True
self
.
pool2d_avg
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
self
.
pool2d_avg_output
=
num_filters
[
len
(
num_filters
)
-
1
]
*
4
*
1
*
1
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet_v2.py
浏览文件 @
d050c188
...
...
@@ -184,9 +184,7 @@ class ResNet(paddle.nn.Layer):
)
self
.
bottleneck_block_list
.
append
(
bottleneck_block
)
shortcut
=
True
self
.
pool2d_avg
=
paddle
.
fluid
.
dygraph
.
Pool2D
(
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
self
.
pool2d_avg_output
=
num_filters
[
len
(
num_filters
)
-
1
]
*
4
*
1
*
1
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py
浏览文件 @
d050c188
...
...
@@ -127,9 +127,7 @@ class SqueezeExcitation(fluid.dygraph.Layer):
super
().
__init__
()
self
.
_num_channels
=
num_channels
self
.
_pool
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
0
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
_pool
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
stdv
=
1.0
/
math
.
sqrt
(
num_channels
*
1.0
)
self
.
_fc
=
Linear
(
num_channels
,
...
...
@@ -309,9 +307,7 @@ class SeResNeXt(fluid.dygraph.Layer):
num_channels
=
bottleneck_block
.
_num_channels_out
self
.
bottleneck_block_list
.
append
(
bottleneck_block
)
shortcut
=
True
self
.
pool2d_avg
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
stdv
=
1.0
/
math
.
sqrt
(
2048
*
1.0
)
self
.
pool2d_avg_output
=
num_filters
[
len
(
num_filters
)
-
1
]
*
2
*
1
*
1
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py
浏览文件 @
d050c188
...
...
@@ -185,9 +185,7 @@ class TSM_ResNet(fluid.dygraph.Layer):
num_channels
=
int
(
bottleneck_block
.
_num_channels_out
)
self
.
bottleneck_block_list
.
append
(
bottleneck_block
)
shortcut
=
True
self
.
pool2d_avg
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
import
math
stdv
=
1.0
/
math
.
sqrt
(
2048
*
1.0
)
...
...
python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py
浏览文件 @
d050c188
...
...
@@ -54,13 +54,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
bias_attr
=
None
,
)
self
.
_pool2d
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
pool_size
,
pool_type
=
pool_type
,
pool_stride
=
pool_stride
,
pool_padding
=
pool_padding
,
global_pooling
=
global_pooling
,
use_cudnn
=
use_cudnn
,
self
.
_pool2d
=
paddle
.
nn
.
MaxPool2D
(
kernel_size
=
pool_size
,
stride
=
pool_stride
,
padding
=
pool_padding
,
)
def
forward
(
self
,
inputs
):
...
...
python/paddle/fluid/tests/unittests/test_dygraph_mnist_fp16.py
浏览文件 @
d050c188
...
...
@@ -57,13 +57,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
bias_attr
=
bias_attr
,
)
self
.
_pool2d
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
pool_size
,
pool_type
=
pool_type
,
pool_stride
=
pool_stride
,
pool_padding
=
pool_padding
,
global_pooling
=
global_pooling
,
use_cudnn
=
use_cudnn
,
self
.
_pool2d
=
paddle
.
nn
.
MaxPool2D
(
kernel_size
=
pool_size
,
stride
=
pool_stride
,
padding
=
pool_padding
,
)
def
forward
(
self
,
inputs
):
...
...
python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py
浏览文件 @
d050c188
...
...
@@ -61,13 +61,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
bias_attr
=
None
,
)
self
.
_pool2d
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
pool_size
,
pool_type
=
pool_type
,
pool_stride
=
pool_stride
,
pool_padding
=
pool_padding
,
global_pooling
=
global_pooling
,
use_cudnn
=
use_cudnn
,
self
.
_pool2d
=
paddle
.
nn
.
MaxPool2D
(
kernel_size
=
pool_size
,
stride
=
pool_stride
,
padding
=
pool_padding
,
)
def
forward
(
self
,
inputs
):
...
...
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
浏览文件 @
d050c188
...
...
@@ -59,13 +59,10 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
weight_attr
=
None
,
bias_attr
=
None
,
)
self
.
_pool2d
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
pool_size
,
pool_type
=
pool_type
,
pool_stride
=
pool_stride
,
pool_padding
=
pool_padding
,
global_pooling
=
global_pooling
,
use_cudnn
=
use_cudnn
,
self
.
_pool2d
=
paddle
.
nn
.
MaxPool2D
(
kernel_size
=
pool_size
,
stride
=
pool_stride
,
padding
=
pool_padding
,
)
def
forward
(
self
,
inputs
):
...
...
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @
d050c188
...
...
@@ -215,9 +215,7 @@ class ResNet(fluid.Layer):
)
self
.
bottleneck_block_list
.
append
(
bottleneck_block
)
shortcut
=
True
self
.
pool2d_avg
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
self
.
pool2d_avg_output
=
num_filters
[
-
1
]
*
4
*
1
*
1
...
...
python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py
浏览文件 @
d050c188
...
...
@@ -104,9 +104,7 @@ class SqueezeExcitation(fluid.dygraph.Layer):
super
().
__init__
()
self
.
_num_channels
=
num_channels
self
.
_pool
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
0
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
_pool
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
self
.
_squeeze
=
paddle
.
nn
.
Linear
(
num_channels
,
num_channels
//
reduction_ratio
,
...
...
@@ -286,9 +284,7 @@ class SeResNeXt(fluid.dygraph.Layer):
num_channels
=
bottleneck_block
.
_num_channels_out
self
.
bottleneck_block_list
.
append
(
bottleneck_block
)
shortcut
=
True
self
.
pool2d_avg
=
paddle
.
fluid
.
dygraph
.
nn
.
Pool2D
(
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg
=
paddle
.
nn
.
AdaptiveAvgPool2D
(
1
)
import
math
stdv
=
1.0
/
math
.
sqrt
(
2048
*
1.0
)
...
...
python/paddle/tests/test_model.py
浏览文件 @
d050c188
...
...
@@ -41,10 +41,10 @@ class LeNetDygraph(paddle.nn.Layer):
self
.
features
=
Sequential
(
Conv2D
(
1
,
6
,
3
,
stride
=
1
,
padding
=
1
),
ReLU
(),
paddle
.
fluid
.
dygraph
.
Pool2D
(
2
,
'max'
,
2
),
paddle
.
nn
.
MaxPool2D
(
2
,
2
),
Conv2D
(
6
,
16
,
5
,
stride
=
1
,
padding
=
0
),
ReLU
(),
paddle
.
fluid
.
dygraph
.
Pool2D
(
2
,
'max'
,
2
),
paddle
.
nn
.
MaxPool2D
(
2
,
2
),
)
if
num_classes
>
0
:
...
...
@@ -93,10 +93,10 @@ class LeNetListInput(paddle.nn.Layer):
self
.
features
=
Sequential
(
self
.
cov
,
ReLU
(),
paddle
.
fluid
.
dygraph
.
Pool2D
(
2
,
'max'
,
2
),
paddle
.
nn
.
MaxPool2D
(
2
,
2
),
Conv2D
(
6
,
16
,
5
,
stride
=
1
,
padding
=
0
),
ReLU
(),
paddle
.
fluid
.
dygraph
.
Pool2D
(
2
,
'max'
,
2
),
paddle
.
nn
.
MaxPool2D
(
2
,
2
),
)
if
num_classes
>
0
:
...
...
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