--- /dev/null
+# Copyright 2016 The TensorFlow Authors. 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.
+# ==============================================================================
+"""Contains a factory for building various models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import functools
+
+import tensorflow as tf
+
+from nets import alexnet
+from nets import cifarnet
+from nets import inception
+from nets import lenet
+from nets import mobilenet_v1
+from nets import overfeat
+from nets import resnet_v1
+from nets import resnet_v2
+from nets import vgg
+
+slim = tf.contrib.slim
+
+networks_map = {'alexnet_v2': alexnet.alexnet_v2,
+ 'cifarnet': cifarnet.cifarnet,
+ 'overfeat': overfeat.overfeat,
+ 'vgg_a': vgg.vgg_a,
+ 'vgg_16': vgg.vgg_16,
+ 'vgg_19': vgg.vgg_19,
+ 'inception_v1': inception.inception_v1,
+ 'inception_v2': inception.inception_v2,
+ 'inception_v3': inception.inception_v3,
+ 'inception_v4': inception.inception_v4,
+ 'inception_resnet_v2': inception.inception_resnet_v2,
+ 'lenet': lenet.lenet,
+ 'resnet_v1_50': resnet_v1.resnet_v1_50,
+ 'resnet_v1_101': resnet_v1.resnet_v1_101,
+ 'resnet_v1_152': resnet_v1.resnet_v1_152,
+ 'resnet_v1_200': resnet_v1.resnet_v1_200,
+ 'resnet_v2_50': resnet_v2.resnet_v2_50,
+ 'resnet_v2_101': resnet_v2.resnet_v2_101,
+ 'resnet_v2_152': resnet_v2.resnet_v2_152,
+ 'resnet_v2_200': resnet_v2.resnet_v2_200,
+ 'mobilenet_v1': mobilenet_v1.mobilenet_v1,
+ }
+
+arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope,
+ 'cifarnet': cifarnet.cifarnet_arg_scope,
+ 'overfeat': overfeat.overfeat_arg_scope,
+ 'vgg_a': vgg.vgg_arg_scope,
+ 'vgg_16': vgg.vgg_arg_scope,
+ 'vgg_19': vgg.vgg_arg_scope,
+ 'inception_v1': inception.inception_v3_arg_scope,
+ 'inception_v2': inception.inception_v3_arg_scope,
+ 'inception_v3': inception.inception_v3_arg_scope,
+ 'inception_v4': inception.inception_v4_arg_scope,
+ 'inception_resnet_v2':
+ inception.inception_resnet_v2_arg_scope,
+ 'lenet': lenet.lenet_arg_scope,
+ 'resnet_v1_50': resnet_v1.resnet_arg_scope,
+ 'resnet_v1_101': resnet_v1.resnet_arg_scope,
+ 'resnet_v1_152': resnet_v1.resnet_arg_scope,
+ 'resnet_v1_200': resnet_v1.resnet_arg_scope,
+ 'resnet_v2_50': resnet_v2.resnet_arg_scope,
+ 'resnet_v2_101': resnet_v2.resnet_arg_scope,
+ 'resnet_v2_152': resnet_v2.resnet_arg_scope,
+ 'resnet_v2_200': resnet_v2.resnet_arg_scope,
+ 'mobilenet_v1': mobilenet_v1.mobilenet_v1_arg_scope,
+ }
+
+
+def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False):
+ """Returns a network_fn such as `logits, end_points = network_fn(images)`.
+
+ Args:
+ name: The name of the network.
+ num_classes: The number of classes to use for classification.
+ weight_decay: The l2 coefficient for the model weights.
+ is_training: `True` if the model is being used for training and `False`
+ otherwise.
+
+ Returns:
+ network_fn: A function that applies the model to a batch of images. It has
+ the following signature:
+ logits, end_points = network_fn(images)
+ Raises:
+ ValueError: If network `name` is not recognized.
+ """
+ if name not in networks_map:
+ raise ValueError('Name of network unknown %s' % name)
+ arg_scope = arg_scopes_map[name](weight_decay=weight_decay)
+ func = networks_map[name]
+ @functools.wraps(func)
+ def network_fn(images):
+ with slim.arg_scope(arg_scope):
+ return func(images, num_classes, is_training=is_training)
+ if hasattr(func, 'default_image_size'):
+ network_fn.default_image_size = func.default_image_size
+
+ return network_fn