|
|
|
@ -2542,15 +2542,21 @@ def img_conv_layer(input,
|
|
|
|
|
what-are-deconvolutional-layers/>`_ .
|
|
|
|
|
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
|
|
|
|
|
num_filters * num_group.
|
|
|
|
|
num_filters.
|
|
|
|
|
|
|
|
|
|
There are several groups of filters in PaddlePaddle implementation.
|
|
|
|
|
Each group will process some channels of the input. For example, if
|
|
|
|
|
num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create
|
|
|
|
|
32*4 = 128 filters to process the input. The channels will be split into 4
|
|
|
|
|
pieces. First 256/4 = 64 channels will be processed by first 32 filters. The
|
|
|
|
|
rest channels will be processed by the rest groups of filters.
|
|
|
|
|
|
|
|
|
|
If the groups attribute is greater than 1, for example groups=2,
|
|
|
|
|
the input will be splitted into 2 parts along the channel axis, and
|
|
|
|
|
the filters will also be splitted into 2 parts. The first half of the filters
|
|
|
|
|
is only connected to the first half of the input channels, while the second
|
|
|
|
|
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:
|
|
|
|
|
|
|
|
|
|
.. code-block:: python
|
|
|
|
@ -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
|
|
|
|
|
is not set, it will be set automatically according to filter_size.
|
|
|
|
|
: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.
|
|
|
|
|
:type act: BaseActivation
|
|
|
|
|
:param groups: The group number. 1 is the default group number.
|
|
|
|
@ -7177,7 +7184,7 @@ def img_conv3d_layer(input,
|
|
|
|
|
: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.
|
|
|
|
|
: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
|
|
|
|
|
:param act: Activation type. ReluActivation is the default activation.
|
|
|
|
|
:type act: BaseActivation
|
|
|
|
|