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体验新版 GitCode,发现更多精彩内容 >>
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dc2ff42e
编写于
12月 13, 2018
作者:
D
dengkaipeng
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add math in python examples. test=develop
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7ec3264b
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+34
-6
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+34
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python/paddle/fluid/layers/nn.py
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dc2ff42e
...
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@@ -2536,16 +2536,28 @@ def adaptive_pool2d(input,
ValueError: 'pool_size' should be a list or tuple with length as 2.
Examples:
.. code-block:: python
# suppose input data in shape of [N, C, H, W], `pool_size` is [m, n],
# output shape is [N, C, m, n], adaptive pool divide H and W dimentions
# of input data into m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive average pool performs calculations as follow:
#
# for i in range(m):
# for j in range(n):
# hstart = floor(i * H / m)
# hend = ceil((i + 1) * H / m)
# wstart = floor(i * W / n)
# wend = ceil((i + 1) * W / n)
# output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
#
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
pool_out = fluid.layers.adaptive_pool2d(
input=data,
pool_size=[3, 3],
pool_type='max',
require_index=False)
pool_type='avg')
"""
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
...
...
@@ -2632,16 +2644,32 @@ def adaptive_pool3d(input,
ValueError: 'pool_size' should be a list or tuple with length as 2.
Examples:
.. code-block:: python
# suppose input data in shape of [N, C, D, H, W], `pool_size` is [l, m, n],
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimentions
# of input data into l * m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive average pool performs calculations as follow:
#
# for i in range(l):
# for j in range(m):
# for k in range(n):
# dstart = floor(i * D / l)
# dend = ceil((i + 1) * D / l)
# hstart = floor(j * H / m)
# hend = ceil((j + 1) * H / m)
# wstart = floor(k * W / n)
# wend = ceil((k + 1) * W / n)
# output[:, :, i, j, k] =
# avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
#
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
pool_out, mask = fluid.layers.adaptive_pool3d(
input=data,
pool_size=[3, 3],
pool_type='max',
require_index=True)
pool_type='avg')
"""
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
...
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