def conv2D(x,
name,
kernel_size=(3, 3),
num_filters=32,
strides=(1, 1),
activation=STANDARD_NONLINEARITY,
normalisation=tf.identity,
normalise_post_activation=False,
dropout_p=None,
padding="SAME",
weight_init='he_normal',
add_bias=True,
**kwargs):
'''
Standard nets2D convolutional layer
kwargs can have training, and potentially other normalisation paramters
'''
bottom_num_filters = x.get_shape().as_list()[-1]
weight_shape = [
kernel_size[0], kernel_size[1], bottom_num_filters, num_filters
]
bias_shape = [num_filters]
strides_augm = [1, strides[0], strides[1], 1]
with tf.variable_scope(name):
weights = utils.get_weight_variable(weight_shape,
name='W',
type=weight_init,
regularize=True)
op = tf.nn.conv2d(x,
filter=weights,
strides=strides_augm,
padding=padding)
biases = None # so there is always something for summary
if add_bias and normalisation is tfnorm.batch_norm:
logging.info('Turning of bias because using batch norm.')
add_bias = False
if add_bias:
biases = utils.get_bias_variable(bias_shape, name='b')
op = tf.nn.bias_add(op, biases)
if not normalise_post_activation:
op = activation(normalisation(op, **kwargs))
else:
op = normalisation(activation(op), **kwargs)
if dropout_p is not None:
op = dropout(op, keep_prob=dropout_p, **kwargs)
# Add Tensorboard summaries
_add_summaries(op, weights, biases)
return op
def dilated_conv2D(bottom,
name,
kernel_size=(3, 3),
num_filters=32,
rate=2,
activation=STANDARD_NONLINEARITY,
normalisation=tf.identity,
normalise_post_activation=False,
dropout_p=None,
padding="SAME",
weight_init='he_normal',
add_bias=True,
**kwargs):
'''
nets2D dilated convolution layer. This layer can be used to increase the receptive field of a network.
It is described in detail in this paper: Yu et al, Multi-Scale Context Aggregation by Dilated Convolutions,
2015 (https://arxiv.org/pdf/1511.07122.pdf)
'''
bottom_num_filters = bottom.get_shape().as_list()[3]
weight_shape = [
kernel_size[0], kernel_size[1], bottom_num_filters, num_filters
]
bias_shape = [num_filters]
with tf.variable_scope(name):
weights = utils.get_weight_variable(weight_shape,
name='W',
type=weight_init,
regularize=True)
op = tf.nn.atrous_conv2d(bottom,
filters=weights,
rate=rate,
padding=padding)
biases = None
if add_bias:
biases = utils.get_bias_variable(bias_shape, name='b')
op = tf.nn.bias_add(op, biases)
if not normalise_post_activation:
op = activation(normalisation(op, **kwargs))
else:
op = normalisation(activation(op), **kwargs)
if dropout_p is not None:
op = dropout(op, keep_prob=dropout_p, **kwargs)
# Add Tensorboard summaries
_add_summaries(op, weights, biases)
return op
def conv3D(x,
name,
kernel_size=(3, 3, 3),
num_filters=32,
strides=(1, 1, 1),
activation=STANDARD_NONLINEARITY,
normalisation=tf.identity,
normalise_post_activation=False,
dropout_p=None,
padding="SAME",
weight_init='he_normal',
add_bias=True,
**kwargs):
'''
Standard nets3D convolutional layer
'''
bottom_num_filters = x.get_shape().as_list()[-1]
weight_shape = [
kernel_size[0], kernel_size[1], kernel_size[2], bottom_num_filters,
num_filters
]
bias_shape = [num_filters]
strides_augm = [1, strides[0], strides[1], strides[2], 1]
with tf.variable_scope(name):
weights = utils.get_weight_variable(weight_shape,
name='W',
type=weight_init,
regularize=True)
op = tf.nn.conv3d(x,
filter=weights,
strides=strides_augm,
padding=padding)
biases = None
if add_bias:
biases = utils.get_bias_variable(bias_shape, name='b')
op = tf.nn.bias_add(op, biases)
if not normalise_post_activation:
op = activation(normalisation(op, **kwargs))
else:
op = normalisation(activation(op), **kwargs)
if dropout_p is not None:
op = dropout(op, keep_prob=dropout_p, **kwargs)
# Add Tensorboard summaries
_add_summaries(op, weights, biases)
return op
def dense_layer(bottom,
name,
hidden_units=512,
activation=STANDARD_NONLINEARITY,
normalisation=tfnorm.batch_norm,
normalise_post_activation=False,
dropout_p=None,
weight_init='he_normal',
add_bias=True,
**kwargs):
'''
Dense a.k.a. fully connected layer
'''
bottom_flat = utils.flatten(bottom)
bottom_rhs_dim = utils.get_rhs_dim(bottom_flat)
weight_shape = [bottom_rhs_dim, hidden_units]
bias_shape = [hidden_units]
with tf.variable_scope(name):
weights = utils.get_weight_variable(weight_shape,
name='W',
type=weight_init,
regularize=True)
op = tf.matmul(bottom_flat, weights)
biases = None
if add_bias:
biases = utils.get_bias_variable(bias_shape, name='b')
op = tf.nn.bias_add(op, biases)
if not normalise_post_activation:
op = activation(normalisation(op, **kwargs))
else:
op = normalisation(activation(op), **kwargs)
if dropout_p is not None:
op = dropout(op, keep_prob=dropout_p, **kwargs)
# Add Tensorboard summaries
_add_summaries(op, weights, biases)
return op
def transposed_conv3D(bottom,
name,
kernel_size=(4, 4, 4),
num_filters=32,
strides=(2, 2, 2),
output_shape=None,
activation=STANDARD_NONLINEARITY,
normalisation=tf.identity,
normalise_post_activation=False,
dropout_p=None,
padding="SAME",
weight_init='he_normal',
add_bias=True,
**kwargs):
'''
Standard nets2D transpose (also known as deconvolution) layer. Default behaviour upsamples the input by a
factor of 2.
'''
bottom_shape = bottom.get_shape().as_list()
if output_shape is None:
batch_size = tf.shape(bottom)[0]
output_shape = tf.stack([
batch_size, bottom_shape[1] * strides[0],
bottom_shape[2] * strides[1], bottom_shape[3] * strides[2],
num_filters
])
bottom_num_filters = bottom_shape[4]
weight_shape = [
kernel_size[0], kernel_size[1], kernel_size[2], num_filters,
bottom_num_filters
]
bias_shape = [num_filters]
strides_augm = [1, strides[0], strides[1], strides[2], 1]
with tf.variable_scope(name):
weights = utils.get_weight_variable(weight_shape,
name='W',
type=weight_init,
regularize=True)
op = tf.nn.conv3d_transpose(bottom,
filter=weights,
output_shape=output_shape,
strides=strides_augm,
padding=padding)
# op = tf.reshape(op, output_shape)
biases = None
if add_bias:
biases = utils.get_bias_variable(bias_shape, name='b')
op = tf.nn.bias_add(op, biases)
if not normalise_post_activation:
op = activation(normalisation(op, **kwargs))
else:
op = normalisation(activation(op), **kwargs)
if dropout_p is not None:
op = dropout(op, keep_prob=dropout_p, **kwargs)
# Add Tensorboard summaries
_add_summaries(op, weights, biases)
return op