# Path: piecewisecrf/slim/ops.py
# UPDATE_OPS_COLLECTION = '_update_ops_'
# def batch_norm(inputs,
#                decay=0.999,
#                center=True,
#                scale=False,
#                epsilon=0.001,
#                moving_vars='moving_vars',
#                activation=None,
#                is_training=True,
#                trainable=True,
#                restore=True,
#                scope=None,
#                reuse=None):
# def _two_element_tuple(int_or_tuple):
# def conv2d(inputs,
#            num_filters_out,
#            kernel_size,
#            stride=1,
#            padding='SAME',
#            activation=tf.nn.relu,
#            stddev=0.01,
#            bias=0.0,
#            weight_decay=0,
#            batch_norm_params=None,
#            is_training=True,
#            trainable=True,
#            restore=True,
#            scope=None,
#            reuse=None,
#            init=None):
# def fc(inputs,
#        num_units_out,
#        activation=tf.nn.relu,
#        stddev=0.01,
#        bias=0.0,
#        weight_decay=0,
#        batch_norm_params=None,
#        is_training=True,
#        trainable=True,
#        restore=True,
#        scope=None,
#        reuse=None):
# def one_hot_encoding(labels, num_classes, scope=None):
# def max_pool(inputs, kernel_size, stride=2, padding='VALID', scope=None):
# def avg_pool(inputs, kernel_size, stride=2, padding='VALID', scope=None):
# def dropout(inputs, keep_prob=0.5, is_training=True, scope=None):
# def flatten(inputs, scope=None):
# def repeat_op(repetitions, inputs, op, *args, **kwargs):
# 
# 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 numpy as np
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
from piecewisecrf.slim import ops
from piecewisecrf.slim import scopes
from piecewisecrf.slim import variables
# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for slim.ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function






class ConvTest(tf.test.TestCase):

  def testCreateConv(self):
    height, width = 3, 3
    with self.test_session():
      images = tf.random_uniform((5, height, width, 3), seed=1)

# Path: piecewisecrf/slim/ops.py
# UPDATE_OPS_COLLECTION = '_update_ops_'
# def batch_norm(inputs,
#                decay=0.999,
#                center=True,
#                scale=False,
#                epsilon=0.001,
#                moving_vars='moving_vars',
#                activation=None,
#                is_training=True,
#                trainable=True,
#                restore=True,
#                scope=None,
#                reuse=None):
# def _two_element_tuple(int_or_tuple):
# def conv2d(inputs,
#            num_filters_out,
#            kernel_size,
#            stride=1,
#            padding='SAME',
#            activation=tf.nn.relu,
#            stddev=0.01,
#            bias=0.0,
#            weight_decay=0,
#            batch_norm_params=None,
#            is_training=True,
#            trainable=True,
#            restore=True,
#            scope=None,
#            reuse=None,
#            init=None):
# def fc(inputs,
#        num_units_out,
#        activation=tf.nn.relu,
#        stddev=0.01,
#        bias=0.0,
#        weight_decay=0,
#        batch_norm_params=None,
#        is_training=True,
#        trainable=True,
#        restore=True,
#        scope=None,
#        reuse=None):
# def one_hot_encoding(labels, num_classes, scope=None):
# def max_pool(inputs, kernel_size, stride=2, padding='VALID', scope=None):
# def avg_pool(inputs, kernel_size, stride=2, padding='VALID', scope=None):
# def dropout(inputs, keep_prob=0.5, is_training=True, scope=None):
# def flatten(inputs, scope=None):
# def repeat_op(repetitions, inputs, op, *args, **kwargs):
# 
# 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_test.py
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
from piecewisecrf.slim import ops
from piecewisecrf.slim import scopes
from piecewisecrf.slim import variables

# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for slim.ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function






class ConvTest(tf.test.TestCase):

  def testCreateConv(self):
    height, width = 3, 3
    with self.test_session():
      images = tf.random_uniform((5, height, width, 3), seed=1)

      output = ops.conv2d(images, 32, [3, 3])