#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer.layers.core import LayersConfig
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
__all__ = [
'GroupConv2d',
]
[docs]class GroupConv2d(Layer):
"""The :class:`GroupConv2d` class is 2D grouped convolution, see `here <https://blog.yani.io/filter-group-tutorial/>`__.
Parameters
--------------
prev_layer : :class:`Layer`
Previous layer.
n_filter : int
The number of filters.
filter_size : int
The filter size.
stride : 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".
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip biases.
W_init_args : dictionary
The arguments for the weight matrix initializer.
b_init_args : dictionary
The arguments for the bias vector initializer.
name : str
A unique layer name.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
n_filter=32,
filter_size=(3, 3),
strides=(2, 2),
n_group=2,
act=None,
padding='SAME',
W_init=tf.truncated_normal_initializer(stddev=0.02),
b_init=tf.constant_initializer(value=0.0),
W_init_args=None, # TODO: Remove when TF <1.3 not supported
b_init_args=None, # TODO: Remove when TF <1.3 not supported
name='groupconv',
): # Windaway
super(GroupConv2d, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
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'
)
)
groupConv = lambda i, k: tf.nn.conv2d(i, k, strides=[1, strides[0], strides[1], 1], padding=padding)
channels = int(self.inputs.get_shape()[-1])
with tf.variable_scope(name):
We = tf.get_variable(
name='W', shape=[filter_size[0], filter_size[1], channels / n_group, n_filter], initializer=W_init,
dtype=LayersConfig.tf_dtype, trainable=True, **self.W_init_args
)
if n_group == 1:
self.outputs = groupConv(self.inputs, We)
else:
inputGroups = tf.split(axis=3, num_or_size_splits=n_group, value=self.inputs)
weightsGroups = tf.split(axis=3, num_or_size_splits=n_group, value=We)
convGroups = [groupConv(i, k) for i, k in zip(inputGroups, weightsGroups)]
self.outputs = tf.concat(axis=3, values=convGroups)
if b_init:
b = tf.get_variable(
name='b', shape=n_filter, initializer=b_init, dtype=LayersConfig.tf_dtype, trainable=True,
**self.b_init_args
)
self.outputs = tf.nn.bias_add(self.outputs, b, name='bias_add')
self.outputs = self._apply_activation(self.outputs)
self._add_layers(self.outputs)
if b_init:
self._add_params([We, b])
else:
self._add_params(We)