# Path: piecewisecrf/slim/variables.py
# MODEL_VARIABLES = '_model_variables_'
# VARIABLES_TO_RESTORE = '_variables_to_restore_'
# def add_variable(var, restore=True):
# def get_variables(scope=None, suffix=None):
# def get_variables_to_restore():
# def get_variables_by_name(given_name, scope=None):
# def get_unique_variable(name):
#   def __init__(self,
#                num_parameter_servers=0,
#                ps_device='/job:ps',
#                placement='CPU:0'):
#   def __call__(self, op):
# def variable_device(device, name):
# def global_step(device=''):
# def variable(name, shape=None, dtype=tf.float32, initializer=None,
#              regularizer=None, trainable=True, collections=None, device='',
#              restore=True):
# class VariableDeviceChooser(object):


import tensorflow as tf
from tensorflow.python.training import moving_averages
from piecewisecrf.slim import losses
from piecewisecrf.slim import scopes
from piecewisecrf.slim import variables

  Args:
    inputs: a tensor of size [batch_size, height, width, channels]
            or [batch_size, channels].
    decay: decay for the moving average.
    center: If True, subtract beta. If False, beta is not created and ignored.
    scale: If True, multiply by gamma. If False, gamma is
      not used. When the next layer is linear (also e.g. ReLU), this can be
      disabled since the scaling can be done by the next layer.
    epsilon: small float added to variance to avoid dividing by zero.
    moving_vars: collection to store the moving_mean and moving_variance.
    activation: activation function.
    is_training: whether or not the model is in training mode.
    trainable: whether or not the variables should be trainable or not.
    restore: whether or not the variables should be marked for restore.
    scope: Optional scope for variable_op_scope.
    reuse: whether or not the layer and its variables should be reused. To be
      able to reuse the layer scope must be given.

  Returns:
    a tensor representing the output of the operation.

  """
  inputs_shape = inputs.get_shape()
  with tf.variable_op_scope([inputs], scope, 'BatchNorm', reuse=reuse):
    axis = list(range(len(inputs_shape) - 1))
    params_shape = inputs_shape[-1:]
    # Allocate parameters for the beta and gamma of the normalization.
    beta, gamma = None, None
    if center:

# Path: piecewisecrf/slim/variables.py
# MODEL_VARIABLES = '_model_variables_'
# VARIABLES_TO_RESTORE = '_variables_to_restore_'
# def add_variable(var, restore=True):
# def get_variables(scope=None, suffix=None):
# def get_variables_to_restore():
# def get_variables_by_name(given_name, scope=None):
# def get_unique_variable(name):
#   def __init__(self,
#                num_parameter_servers=0,
#                ps_device='/job:ps',
#                placement='CPU:0'):
#   def __call__(self, op):
# def variable_device(device, name):
# def global_step(device=''):
# def variable(name, shape=None, dtype=tf.float32, initializer=None,
#              regularizer=None, trainable=True, collections=None, device='',
#              restore=True):
# class VariableDeviceChooser(object):


# Path: piecewisecrf/slim/ops.py
import tensorflow as tf
from tensorflow.python.training import moving_averages
from piecewisecrf.slim import losses
from piecewisecrf.slim import scopes
from piecewisecrf.slim import variables


  Args:
    inputs: a tensor of size [batch_size, height, width, channels]
            or [batch_size, channels].
    decay: decay for the moving average.
    center: If True, subtract beta. If False, beta is not created and ignored.
    scale: If True, multiply by gamma. If False, gamma is
      not used. When the next layer is linear (also e.g. ReLU), this can be
      disabled since the scaling can be done by the next layer.
    epsilon: small float added to variance to avoid dividing by zero.
    moving_vars: collection to store the moving_mean and moving_variance.
    activation: activation function.
    is_training: whether or not the model is in training mode.
    trainable: whether or not the variables should be trainable or not.
    restore: whether or not the variables should be marked for restore.
    scope: Optional scope for variable_op_scope.
    reuse: whether or not the layer and its variables should be reused. To be
      able to reuse the layer scope must be given.

  Returns:
    a tensor representing the output of the operation.

  """
  inputs_shape = inputs.get_shape()
  with tf.variable_op_scope([inputs], scope, 'BatchNorm', reuse=reuse):
    axis = list(range(len(inputs_shape) - 1))
    params_shape = inputs_shape[-1:]
    # Allocate parameters for the beta and gamma of the normalization.
    beta, gamma = None, None
    if center:

      beta = variables.variable('beta',