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[API 2.0]Fix adaptive pooling bug (#26922)

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* fix

* fix doc, test=document_fix
revert-26856-strategy_example2
ruri 5 years ago committed by GitHub
parent 3eacced950
commit 3900f66c19
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 34 additions and 10 deletions
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20
python/paddle/nn/functional/pooling.py
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@ -976,6 +976,7 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
if isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 2, 'output_size')
else:
output_size = list(output_size)
if output_size[0] == None:
output_size[0] = in_h
if output_size[1] == None:
@ -1079,6 +1080,7 @@ def adaptive_avg_pool3d(x, output_size, data_format='NCDHW', name=None):
if isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 3, 'output_size')
else:
output_size = list(output_size)
if output_size[0] == None:
output_size[0] = in_l
if output_size[1] == None:
@ -1123,8 +1125,7 @@ def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
with shape [N, C, L]. The format of input tensor is NCL,
where N is batch size, C is the number of channels, L is the
length of the feature. The data type is float32 or float64.
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain one int.
output_size (int): The pool kernel size. The value should be an integer.
return_indices (bool): If true, the index of max pooling point will be returned along
with outputs. It cannot be set in average pooling type. Default False.
name(str, optional): For detailed information, please refer
@ -1134,9 +1135,10 @@ def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
Tensor: The output tensor of adaptive pooling result. The data type is same
as input tensor.
Raises:
ValueError: 'output_size' should be a integer or list or tuple with length as 1.
ValueError: 'output_size' should be an integer.
Examples:
.. code-block:: python
# max adaptive pool1d
# suppose input data in shape of [N, C, L], `output_size` is m or [m],
# output shape is [N, C, m], adaptive pool divide L dimension
@ -1162,7 +1164,7 @@ def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
check_variable_and_dtype(x, 'x', ['float32', 'float64'],
'adaptive_max_pool1d')
_check_input(x, 3)
check_type(output_size, 'pool_size', (int), 'adaptive_max_pool1d')
check_type(output_size, 'pool_size', int, 'adaptive_max_pool1d')
check_type(return_indices, 'return_indices', bool, 'adaptive_max_pool1d')
pool_size = [1] + utils.convert_to_list(output_size, 1, 'pool_size')
@ -1201,15 +1203,19 @@ def adaptive_max_pool2d(x, output_size, return_indices=False, name=None):
"""
This operation applies a 2D adaptive max pooling on input tensor.
See more details in :ref:`api_nn_pooling_AdaptiveMaxPool2d` .
Args:
x (Tensor): The input tensor of adaptive max pool2d operator, which is a 4-D tensor. The data type can be float16, float32, float64, int32 or int64.
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two elements, (H, W). H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default.
Returns:
Tensor: The output tensor of adaptive max pool2d result. The data type is same as input tensor.
Examples:
.. code-block:: python
# max adaptive pool2d
# suppose input data in the shape of [N, C, H, W], `output_size` is [m, n]
# output shape is [N, C, m, n], adaptive pool divide H and W dimensions
@ -1247,6 +1253,7 @@ def adaptive_max_pool2d(x, output_size, return_indices=False, name=None):
if isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 2, 'output_size')
else:
output_size = list(output_size)
if output_size[0] == None:
output_size[0] = in_h
if output_size[1] == None:
@ -1283,15 +1290,19 @@ def adaptive_max_pool3d(x, output_size, return_indices=False, name=None):
"""
This operation applies a 3D adaptive max pooling on input tensor.
See more details in :ref:`api_nn_pooling_AdaptiveMaxPool3d` .
Args:
x (Tensor): The input tensor of adaptive max pool3d operator, which is a 5-D tensor. The data type can be float32, float64.
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default.
Returns:
Tensor: The output tensor of adaptive max pool3d result. The data type is same as input tensor.
Examples:
.. code-block:: python
# adaptive max pool3d
# suppose input data in the shape of [N, C, D, H, W], `output_size` is [l, m, n]
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
@ -1333,6 +1344,7 @@ def adaptive_max_pool3d(x, output_size, return_indices=False, name=None):
if isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 3, 'output_size')
else:
output_size = list(output_size)
if output_size[0] == None:
output_size[0] = in_l
if output_size[1] == None:

24
python/paddle/nn/layer/pooling.py
Unescape View File

@ -87,6 +87,7 @@ class AvgPool1d(layers.Layer):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
paddle.disable_static()
@ -176,6 +177,7 @@ class AvgPool2d(layers.Layer):
ShapeError: If the output's shape calculated is not greater than 0.
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
import numpy as np
@ -267,6 +269,7 @@ class AvgPool3d(layers.Layer):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
import numpy as np
@ -457,6 +460,7 @@ class MaxPool2d(layers.Layer):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
import numpy as np
@ -547,6 +551,7 @@ class MaxPool3d(layers.Layer):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
import numpy as np
@ -915,8 +920,11 @@ class AdaptiveMaxPool2d(layers.Layer):
"""
This operation applies 2D adaptive max pooling on input tensor. The h and w dimensions
of the output tensor are determined by the parameter output_size. The difference between adaptive pooling and pooling is adaptive one focus on the output size.
For adaptive max pool2d:
.. math::
hstart &= floor(i * H_{in} / H_{out})
hend &= ceil((i + 1) * H_{in} / H_{out})
wstart &= floor(j * W_{in} / W_{out})
@ -936,6 +944,7 @@ class AdaptiveMaxPool2d(layers.Layer):
A callable object of AdaptiveMaxPool2d.
Examples:
.. code-block:: python
# adaptive max pool2d
# suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
# output shape is [N, C, m, n], adaptive pool divide H and W dimensions
@ -976,10 +985,13 @@ class AdaptiveMaxPool2d(layers.Layer):
class AdaptiveMaxPool3d(layers.Layer):
"""
This operation applies 3D adaptive max pooling on input tensor. The h and w dimensions
This operation applies 3D adaptive max pooling on input tensor. The h and w dimensions
of the output tensor are determined by the parameter output_size. The difference between adaptive pooling and pooling is adaptive one focus on the output size.
For adaptive max pool3d:
.. math::
dstart &= floor(i * D_{in} / D_{out})
dend &= ceil((i + 1) * D_{in} / D_{out})
hstart &= floor(j * H_{in} / H_{out})
@ -987,10 +999,9 @@ class AdaptiveMaxPool3d(layers.Layer):
wstart &= floor(k * W_{in} / W_{out})
wend &= ceil((k + 1) * W_{in} / W_{out})
Output(i ,j, k) &= max(Input[dstart:dend, hstart:hend, wstart:wend])
Parameters:
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means
the size will be the same as that of the input.
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
@ -1002,6 +1013,7 @@ class AdaptiveMaxPool3d(layers.Layer):
A callable object of AdaptiveMaxPool3d.
Examples:
.. code-block:: python
# adaptive max pool3d
# suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
@ -1028,8 +1040,8 @@ class AdaptiveMaxPool3d(layers.Layer):
pool = paddle.nn.AdaptiveMaxPool3d(output_size=4)
out = pool(x)
# out shape: [2, 3, 4, 4, 4]
pool, indices = paddle.nn.AdaptiveMaxPool3d(output_size=3, return_indices=True)
out = pool(x)
pool = paddle.nn.AdaptiveMaxPool3d(output_size=3, return_indices=True)
out, indices = pool(x)
# out shape: [2, 3, 4, 4, 4], indices shape: [2, 3, 4, 4, 4]
"""

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