# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function
import numpy as np
import tensorflow as tf
from niftynet.layer import layer_util
from niftynet.layer.activation import ActiLayer
from niftynet.layer.base_layer import TrainableLayer
from niftynet.layer.bn import BNLayer
from niftynet.layer.gn import GNLayer
from niftynet.utilities.util_common import look_up_operations
SUPPORTED_PADDING = set(['SAME', 'VALID'])
[docs]def default_w_initializer():
def _initializer(shape, dtype, partition_info):
stddev = np.sqrt(2.0 / np.prod(shape[:-1]))
from tensorflow.python.ops import random_ops
return random_ops.truncated_normal(shape, 0.0, stddev, dtype=tf.float32)
# return tf.truncated_normal_initializer(
# mean=0.0, stddev=stddev, dtype=tf.float32)
return _initializer
[docs]def default_b_initializer():
return tf.constant_initializer(0.0)
[docs]class ConvLayer(TrainableLayer):
"""
This class defines a simple convolution with an optional bias term.
Please consider ``ConvolutionalLayer`` if batch_norm and activation
are also used.
"""
def __init__(self,
n_output_chns,
kernel_size=3,
stride=1,
dilation=1,
padding='SAME',
with_bias=False,
w_initializer=None,
w_regularizer=None,
b_initializer=None,
b_regularizer=None,
name='conv'):
super(ConvLayer, self).__init__(name=name)
self.padding = look_up_operations(padding.upper(), SUPPORTED_PADDING)
self.n_output_chns = int(n_output_chns)
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.with_bias = with_bias
self.initializers = {
'w': w_initializer if w_initializer else default_w_initializer(),
'b': b_initializer if b_initializer else default_b_initializer()}
self.regularizers = {'w': w_regularizer, 'b': b_regularizer}
[docs] def layer_op(self, input_tensor):
input_shape = input_tensor.shape.as_list()
n_input_chns = input_shape[-1]
spatial_rank = layer_util.infer_spatial_rank(input_tensor)
# initialize conv kernels/strides and then apply
w_full_size = layer_util.expand_spatial_params(
self.kernel_size, spatial_rank)
# expand kernel size to include number of features
w_full_size = w_full_size + (n_input_chns, self.n_output_chns)
full_stride = layer_util.expand_spatial_params(
self.stride, spatial_rank)
full_dilation = layer_util.expand_spatial_params(
self.dilation, spatial_rank)
conv_kernel = tf.get_variable(
'w', shape=w_full_size,
initializer=self.initializers['w'],
regularizer=self.regularizers['w'])
output_tensor = tf.nn.convolution(input=input_tensor,
filter=conv_kernel,
strides=full_stride,
dilation_rate=full_dilation,
padding=self.padding,
name='conv')
if not self.with_bias:
return output_tensor
# adding the bias term
bias_term = tf.get_variable(
'b', shape=self.n_output_chns,
initializer=self.initializers['b'],
regularizer=self.regularizers['b'])
output_tensor = tf.nn.bias_add(output_tensor,
bias_term,
name='add_bias')
return output_tensor
[docs]class ConvolutionalLayer(TrainableLayer):
"""
This class defines a composite layer with optional components::
convolution -> batch_norm -> activation -> dropout
The b_initializer and b_regularizer are applied to the ConvLayer
The w_initializer and w_regularizer are applied to the ConvLayer,
the batch normalisation layer, and the activation layer (for 'prelu')
"""
def __init__(self,
n_output_chns,
kernel_size=3,
stride=1,
dilation=1,
padding='SAME',
with_bias=False,
with_bn=True,
group_size=-1,
acti_func=None,
preactivation=False,
w_initializer=None,
w_regularizer=None,
b_initializer=None,
b_regularizer=None,
moving_decay=0.9,
eps=1e-5,
name="conv"):
self.acti_func = acti_func
self.with_bn = with_bn
self.group_size = group_size
self.preactivation = preactivation
self.layer_name = '{}'.format(name)
if self.with_bn and group_size > 0:
raise ValueError('only choose either batchnorm or groupnorm')
if self.with_bn:
self.layer_name += '_bn'
if self.group_size > 0:
self.layer_name += '_gn'
if self.acti_func is not None:
self.layer_name += '_{}'.format(self.acti_func)
super(ConvolutionalLayer, self).__init__(name=self.layer_name)
# for ConvLayer
self.n_output_chns = n_output_chns
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.padding = padding
self.with_bias = with_bias
# for BNLayer
self.moving_decay = moving_decay
self.eps = eps
self.initializers = {
'w': w_initializer if w_initializer else default_w_initializer(),
'b': b_initializer if b_initializer else default_b_initializer()}
self.regularizers = {'w': w_regularizer, 'b': b_regularizer}
[docs] def layer_op(self, input_tensor, is_training=None, keep_prob=None):
conv_layer = ConvLayer(n_output_chns=self.n_output_chns,
kernel_size=self.kernel_size,
stride=self.stride,
dilation=self.dilation,
padding=self.padding,
with_bias=self.with_bias,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
name='conv_')
if self.with_bn:
if is_training is None:
raise ValueError('is_training argument should be '
'True or False unless with_bn is False')
bn_layer = BNLayer(
regularizer=self.regularizers['w'],
moving_decay=self.moving_decay,
eps=self.eps,
name='bn_')
if self.group_size > 0:
gn_layer = GNLayer(
regularizer=self.regularizers['w'],
group_size=self.group_size,
eps=self.eps,
name='gn_')
if self.acti_func is not None:
acti_layer = ActiLayer(
func=self.acti_func,
regularizer=self.regularizers['w'],
name='acti_')
if keep_prob is not None:
dropout_layer = ActiLayer(func='dropout', name='dropout_')
def activation(output_tensor):
if self.with_bn:
output_tensor = bn_layer(output_tensor, is_training)
if self.group_size > 0:
output_tensor = gn_layer(output_tensor)
if self.acti_func is not None:
output_tensor = acti_layer(output_tensor)
if keep_prob is not None:
output_tensor = dropout_layer(output_tensor,
keep_prob=keep_prob)
return output_tensor
if self.preactivation:
output_tensor = conv_layer(activation(input_tensor))
else:
output_tensor = activation(conv_layer(input_tensor))
return output_tensor
