nets_factory.py

# Copyright 2017 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
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# ==============================================================================
"""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
from nets.mobilenet import mobilenet_v2
from nets.nasnet import nasnet
from nets.nasnet import pnasnet

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,
                'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_075,
                'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_050,
                'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_025,
                'mobilenet_v2': mobilenet_v2.mobilenet,
                'mobilenet_v2_140': mobilenet_v2.mobilenet_v2_140,
                'mobilenet_v2_035': mobilenet_v2.mobilenet_v2_035,
                'nasnet_cifar': nasnet.build_nasnet_cifar,
                'nasnet_mobile': nasnet.build_nasnet_mobile,
                'nasnet_large': nasnet.build_nasnet_large,
                'pnasnet_large': pnasnet.build_pnasnet_large,
                'pnasnet_mobile': pnasnet.build_pnasnet_mobile,
               }

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,
                  'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_arg_scope,
                  'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_arg_scope,
                  'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_arg_scope,
                  'mobilenet_v2': mobilenet_v2.training_scope,
                  'mobilenet_v2_035': mobilenet_v2.training_scope,
                  'mobilenet_v2_140': mobilenet_v2.training_scope,
                  'nasnet_cifar': nasnet.nasnet_cifar_arg_scope,
                  'nasnet_mobile': nasnet.nasnet_mobile_arg_scope,
                  'nasnet_large': nasnet.nasnet_large_arg_scope,
                  'pnasnet_large': pnasnet.pnasnet_large_arg_scope,
                  'pnasnet_mobile': pnasnet.pnasnet_mobile_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. If 0 or None,
      the logits layer is omitted and its input features are returned instead.
    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:
          net, end_points = network_fn(images)
      The `images` input is a tensor of shape [batch_size, height, width, 3]
      with height = width = network_fn.default_image_size. (The permissibility
      and treatment of other sizes depends on the network_fn.)
      The returned `end_points` are a dictionary of intermediate activations.
      The returned `net` is the topmost layer, depending on `num_classes`:
      If `num_classes` was a non-zero integer, `net` is a logits tensor
      of shape [batch_size, num_classes].
      If `num_classes` was 0 or `None`, `net` is a tensor with the input
      to the logits layer of shape [batch_size, 1, 1, num_features] or
      [batch_size, num_features]. Dropout has not been applied to this
      (even if the network's original classification does); it remains for
      the caller to do this or not.

  Raises:
    ValueError: If network `name` is not recognized.
  """
  if name not in networks_map:
    raise ValueError('Name of network unknown %s' % name)
  func = networks_map[name]
  @functools.wraps(func)
  def network_fn(images, **kwargs):
    arg_scope = arg_scopes_map[name](weight_decay=weight_decay)
    with slim.arg_scope(arg_scope):
      return func(images, num_classes, is_training=is_training, **kwargs)
  if hasattr(func, 'default_image_size'):
    network_fn.default_image_size = func.default_image_size

  return network_fn