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1566af8a
编写于
1月 09, 2018
作者:
Z
zhangchao
提交者:
GitHub
1月 09, 2018
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Merge pull request #7301 from peterzhang2029/conv_group_fix
Fix the docstring of 'filter groups' in img_conv_layer.
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python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
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python/paddle/trainer_config_helpers/layers.py
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...
@@ -2542,14 +2542,20 @@ def img_conv_layer(input,
...
@@ -2542,14 +2542,20 @@ def img_conv_layer(input,
what-are-deconvolutional-layers/>`_ .
what-are-deconvolutional-layers/>`_ .
The num_channel means input image's channel number. It may be 1 or 3 when
The num_channel means input image's channel number. It may be 1 or 3 when
input is raw pixels of image(mono or RGB), or it may be the previous layer's
input is raw pixels of image(mono or RGB), or it may be the previous layer's
num_filters
* num_group
.
num_filters.
There are several groups of filters in PaddlePaddle implementation.
There are several groups of filters in PaddlePaddle implementation.
Each group will process some channels of the input. For example, if
If the groups attribute is greater than 1, for example groups=2,
num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create
the input will be splitted into 2 parts along the channel axis, and
32*4 = 128 filters to process the input. The channels will be split into 4
the filters will also be splitted into 2 parts. The first half of the filters
pieces. First 256/4 = 64 channels will be processed by first 32 filters. The
is only connected to the first half of the input channels, while the second
rest channels will be processed by the rest groups of filters.
half of the filters is only connected to the second half of the input. After
the computation of convolution for each part of input,
the output will be obtained by concatenating the two results.
The details of grouped convolution, please refer to:
`ImageNet Classification with Deep Convolutional Neural Networks
<http://www.cs.toronto.edu/~kriz/imagenet_classification_with_deep_convolutional.pdf>`_
The example usage is:
The example usage is:
...
@@ -2575,7 +2581,8 @@ def img_conv_layer(input,
...
@@ -2575,7 +2581,8 @@ def img_conv_layer(input,
:param filter_size_y: The dimension of the filter kernel on the y axis. If the parameter
:param filter_size_y: The dimension of the filter kernel on the y axis. If the parameter
is not set, it will be set automatically according to filter_size.
is not set, it will be set automatically according to filter_size.
:type filter_size_y: int
:type filter_size_y: int
:param num_filters: Each filter group's number of filter
:param num_filters: The number of filters. It is as same as the output image channel.
:type num_filters: int
:param act: Activation type. ReluActivation is the default activation.
:param act: Activation type. ReluActivation is the default activation.
:type act: BaseActivation
:type act: BaseActivation
:param groups: The group number. 1 is the default group number.
:param groups: The group number. 1 is the default group number.
...
@@ -7177,7 +7184,7 @@ def img_conv3d_layer(input,
...
@@ -7177,7 +7184,7 @@ def img_conv3d_layer(input,
:param filter_size: The dimensions of the filter kernel along three axises. If the parameter
:param filter_size: The dimensions of the filter kernel along three axises. If the parameter
is set to one integer, the three dimensions will be same.
is set to one integer, the three dimensions will be same.
:type filter_size: int | tuple | list
:type filter_size: int | tuple | list
:param num_filters: The number of filters
in each group
.
:param num_filters: The number of filters
. It is as same as the output image channel
.
:type num_filters: int
:type num_filters: int
:param act: Activation type. ReluActivation is the default activation.
:param act: Activation type. ReluActivation is the default activation.
:type act: BaseActivation
:type act: BaseActivation
...
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