def conv3d_unknown_dim(inputs,
filters,
kernel_size,
name,
activation=None,
kernel_initializer=he_initializer,
bias_initializer=zeros_initializer,
normalization=None,
is_training=False,
data_format='channels_first',
padding='same',
strides=(1, 1, 1),
debug_print=debug_print_conv):
node, padding_for_conv = pad_for_conv(inputs=inputs,
kernel_size=kernel_size,
name=name,
padding=padding,
data_format=data_format)
# outputs = tf.layers.conv3d(inputs=node,
# filters=filters,
# kernel_size=kernel_size,
# name=name,
# kernel_initializer=kernel_initializer,
# bias_initializer=bias_initializer,
# trainable=is_training,
# data_format=data_format,
# kernel_regularizer=tf.nn.l2_loss,
# padding=padding_for_conv)
inputs_shape = inputs.get_shape().as_list()
num_inputs = inputs_shape[1]
W = tf.get_variable(name + '_w', [kernel_size[0], kernel_size[1], kernel_size[2], num_inputs, filters], initializer=kernel_initializer, regularizer=tf.nn.l2_loss)
b = tf.get_variable(name + '_b', [1, filters, 1, 1, 1], initializer=bias_initializer)
outputs = tf.nn.conv3d(inputs, W, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NCDHW', name=name)
outputs += b
#outputs_shape = outputs.shape
#outputs_4d = tf.reshape(outputs, [outputs_shape[0], outputs_shape[1], outputs_shape[2] * outputs_shape[3], outputs_shape[4]])
#outputs_4d = tf.nn.bias_add(outputs_4d, b, data_format='NCHW')
#outputs = tf.reshape(outputs_4d, outputs_shape)
if normalization is not None:
outputs = normalization(outputs, is_training=is_training, data_format=data_format, name=name+'/norm')
if activation is not None:
outputs = activation(outputs)
if debug_print:
print_conv_parameters(inputs=inputs,
outputs=outputs,
kernel_size=kernel_size,
name=name,
activation=activation,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
normalization=normalization,
is_training=is_training,
data_format=data_format,
padding=padding,
strides=strides)
return outputs
def conv3d_transpose_unknown_dim(inputs,
filters,
kernel_size,
name,
activation=None,
kernel_initializer=he_initializer,
bias_initializer=zeros_initializer,
normalization=None,
is_training=False,
data_format='channels_first',
padding='same',
strides=(1, 1, 1),
debug_print=debug_print_conv):
inputs_shape = inputs.get_shape().as_list()
num_inputs = inputs_shape[1]
W = tf.get_variable(
name + '_w',
[kernel_size[0], kernel_size[1], kernel_size[2], num_inputs, filters],
initializer=kernel_initializer,
regularizer=tf.nn.l2_loss)
b = tf.get_variable(name + '_b', [1, filters, 1, 1, 1],
initializer=bias_initializer)
output_shape = [inputs_shape[0], filters] + inputs_shape[2:]
outputs = tf.nn.conv3d_transpose(inputs,
W,
strides=[1, 1, 1, 1, 1],
padding='VALID',
data_format='NCDHW',
output_shape=output_shape,
name=name)
outputs += b
if normalization is not None:
outputs = normalization(outputs,
is_training=is_training,
data_format=data_format,
name=name + '/norm')
if activation is not None:
outputs = activation(outputs)
if debug_print:
print_conv_parameters(inputs=inputs,
outputs=outputs,
kernel_size=kernel_size,
name=name,
activation=activation,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
normalization=normalization,
is_training=is_training,
data_format=data_format,
padding=padding,
strides=strides)
return outputs
def conv2d(inputs,
filters,
kernel_size,
name,
activation=None,
kernel_initializer=he_initializer,
bias_initializer=zeros_initializer,
normalization=None,
is_training=False,
data_format='channels_first',
padding='same',
strides=(1, 1),
use_bias=True,
debug_print=debug_print_conv):
node, padding_for_conv = pad_for_conv(inputs=inputs,
kernel_size=kernel_size,
name=name,
padding=padding,
data_format=data_format)
outputs = tf.layers.conv2d(inputs=node,
filters=filters,
kernel_size=kernel_size,
name=name,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
trainable=is_training,
data_format=data_format,
kernel_regularizer=tf.nn.l2_loss,
padding=padding_for_conv,
strides=strides,
use_bias=use_bias)
if normalization is not None:
outputs = normalization(outputs, is_training=is_training, data_format=data_format, name=name+'/norm')
if activation is not None:
outputs = activation(outputs, name=name+'/activation')
if debug_print:
print_conv_parameters(inputs=inputs,
outputs=outputs,
kernel_size=kernel_size,
name=name,
activation=activation,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
normalization=normalization,
is_training=is_training,
data_format=data_format,
padding=padding,
strides=strides,
use_bias=use_bias)
return outputs
