Source code for tensorlayer.layers.convolution.group_conv

#! /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__ = [

[docs]class GroupConv2d(Layer): """The :class:`GroupConv2d` class is 2D grouped convolution, see `here <>`__. 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) "GroupConv2d %s: n_filter: %d size: %s strides: %s n_group: %d pad: %s act: %s" % (, 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)