Source code for dltk.core.residual_unit

from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import

import tensorflow as tf
import numpy as np


[docs]def vanilla_residual_unit_3d(inputs, out_filters, kernel_size=(3, 3, 3), strides=(1, 1, 1), mode=tf.estimator.ModeKeys.EVAL, use_bias=False, kernel_initializer=tf.initializers.variance_scaling(distribution='uniform'), bias_initializer=tf.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None): """Implementation of a 3D residual unit according to [1]. This implementation supports strided convolutions and automatically handles different input and output filters. [1] K. He et al. Identity Mappings in Deep Residual Networks. ECCV 2016. Args: inputs (tf.Tensor): Input tensor to the residual unit. Is required to have a rank of 5 (i.e. [batch, x, y, z, channels]). out_filters (int): Number of convolutional filters used in the sub units. kernel_size (tuple, optional): Size of the convoltional kernels used in the sub units strides (tuple, optional): Convolution strides in (x,y,z) of sub unit 0. Allows downsampling of the input tensor via strides convolutions. mode (str, optional): One of the tf.estimator.ModeKeys: TRAIN, EVAL or PREDICT use_bias (bool, optional): Train a bias with each convolution. kernel_initializer (TYPE, optional): Initialisation of convolution kernels bias_initializer (TYPE, optional): Initialisation of bias kernel_regularizer (None, optional): Additional regularisation op bias_regularizer (None, optional): Additional regularisation op Returns: tf.Tensor: Output of the residual unit """ relu_op = tf.nn.relu6 # or tf.nn.relu pool_op = tf.layers.max_pooling3d conv_params = {'padding': 'same', 'use_bias': use_bias, 'kernel_initializer': kernel_initializer, 'bias_initializer': bias_initializer, 'kernel_regularizer': kernel_regularizer, 'bias_regularizer': bias_regularizer} in_filters = inputs.get_shape().as_list()[-1] assert in_filters == inputs.get_shape().as_list()[-1], \ 'Module was initialised for a different input shape' x = inputs orig_x = x # Handle strided convolutions if np.prod(strides) != 1: orig_x = pool_op(inputs=orig_x, pool_size=strides, strides=strides, padding='valid') # Sub unit 0 with tf.variable_scope('sub_unit0'): # Adjust the strided conv kernel size to prevent losing information k = [s * 2 if s > 1 else k for k, s in zip(kernel_size, strides)] x = tf.layers.batch_normalization( x, training=mode == tf.estimator.ModeKeys.TRAIN) x = relu_op(x) x = tf.layers.conv3d( inputs=x, filters=out_filters, kernel_size=k, strides=strides, **conv_params) # Sub unit 1 with tf.variable_scope('sub_unit1'): x = tf.layers.batch_normalization( x, training=mode == tf.estimator.ModeKeys.TRAIN) x = relu_op(x) x = tf.layers.conv3d( inputs=x, filters=out_filters, kernel_size=kernel_size, strides=(1, 1, 1), **conv_params) # Add the residual with tf.variable_scope('sub_unit_add'): # Handle differences in input and output filter sizes if in_filters < out_filters: orig_x = tf.pad( tensor=orig_x, paddings=[[0, 0]] * (len(x.get_shape().as_list()) - 1) + [[ int(np.floor((out_filters - in_filters) / 2.)), int(np.ceil((out_filters - in_filters) / 2.))]]) elif in_filters > out_filters: orig_x = tf.layers.conv3d( inputs=orig_x, filters=out_filters, kernel_size=kernel_size, strides=(1, 1, 1), **conv_params) x += orig_x return x