#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer
# from tensorlayer.layers.core import LayersConfig
__all__ = [
'GroupConv2d',
]
[docs]class GroupConv2d(Layer):
"""The :class:`GroupConv2d` class is 2D grouped convolution, see `here <https://blog.yani.io/filter-group-tutorial/>`__.
Parameters
--------------
n_filter : int
The number of filters.
filter_size : tuple of int
The filter size.
strides : tuple of int
The stride step.
n_group : int
The number of groups.
act : activation function
The activation function of this layer.
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
"channels_last" (NHWC, default) or "channels_first" (NCHW).
dilation_rate : tuple of int
Specifying the dilation rate to use for dilated convolution.
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip biases.
in_channels : int
The number of in channels.
name : None or str
A unique layer name.
Examples
---------
With TensorLayer
>>> net = tl.layers.Input([8, 24, 24, 32], name='input')
>>> groupconv2d = tl.layers.QuanConv2d(
... n_filter=64, filter_size=(3, 3), strides=(2, 2), n_group=2, name='group'
... )(net)
>>> print(groupconv2d)
>>> output shape : (8, 12, 12, 64)
"""
def __init__(
self,
n_filter=32,
filter_size=(3, 3),
strides=(2, 2),
n_group=2,
act=None,
padding='SAME',
data_format='channels_last',
dilation_rate=(1, 1),
W_init=tl.initializers.truncated_normal(stddev=0.02),
b_init=tl.initializers.constant(value=0.0),
in_channels=None,
name=None # 'groupconv',
): # Windaway
super().__init__(name, act=act)
self.n_filter = n_filter
self.filter_size = filter_size
self.strides = self._strides = strides
self.n_group = n_group
self.padding = padding
self.data_format = data_format
self.dilation_rate = self._dilation_rate = dilation_rate
self.W_init = W_init
self.b_init = b_init
self.in_channels = in_channels
if self.in_channels:
self.build(None)
self._built = True
logging.info(
"GroupConv2d %s: n_filter: %d size: %s strides: %s n_group: %d pad: %s act: %s" % (
self.name, n_filter, str(filter_size), str(strides), n_group, padding,
self.act.__name__ if self.act is not None else 'No Activation'
)
)
def __repr__(self):
actstr = self.act.__name__ if self.act is not None else 'No Activation'
s = (
'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
', strides={strides}, padding={padding}'
)
if self.dilation_rate != (1, ) * len(self.dilation_rate):
s += ', dilation={dilation_rate}'
if self.b_init is None:
s += ', bias=False'
s += (', ' + actstr)
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape):
if self.data_format == 'channels_last':
self.data_format = 'NHWC'
if self.in_channels is None:
self.in_channels = inputs_shape[-1]
self._strides = [1, self._strides[0], self._strides[1], 1]
self._dilation_rate = [1, self._dilation_rate[0], self._dilation_rate[1], 1]
elif self.data_format == 'channels_first':
self.data_format = 'NCHW'
if self.in_channels is None:
self.in_channels = inputs_shape[1]
self._strides = [1, 1, self._strides[0], self._strides[1]]
self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1]]
else:
raise Exception("data_format should be either channels_last or channels_first")
self.groupConv = lambda i, k: tf.nn.conv2d(
i, k, strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self.
_dilation_rate, name=self.name
)
self.filter_shape = (
self.filter_size[0], self.filter_size[1], int(self.in_channels / self.n_group), self.n_filter
)
self.We = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
if self.b_init:
self.b = self._get_weights("biases", shape=self.n_filter, init=self.b_init)
def forward(self, inputs):
if self.n_group == 1:
outputs = self.groupConv(inputs, self.We)
else:
inputGroups = tf.split(axis=3, num_or_size_splits=self.n_group, value=inputs)
weightsGroups = tf.split(axis=3, num_or_size_splits=self.n_group, value=self.We)
convGroups = [self.groupConv(i, k) for i, k in zip(inputGroups, weightsGroups)]
outputs = tf.concat(axis=3, values=convGroups)
if self.b_init:
outputs = tf.nn.bias_add(outputs, self.b, data_format=self.data_format, name='bias_add')
if self.act:
outputs = self.act(outputs)
return outputs