model_factory.py

# 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 tensorflow as tf

from tensorflow.contrib.slim import nets
from slim.nets import lenet

slim = tf.contrib.slim


def get_model(name, num_classes, weight_decay=0.0, is_training=False):
  """Returns a model_fn such as `logits, end_points = model_fn(images)`.

  Args:
    name: The name of the model.
    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:
    model_fn: A function that applies the model to a batch of images. It has
      the following signature:
        logits, end_points = model_fn(images)
  Raises:
    ValueError: If model `name` is not recognized.
  """
  if name == 'inception_v1':
    default_image_size = nets.inception.inception_v1.default_image_size
    def func(images):
      with slim.arg_scope(nets.inception.inception_v1_arg_scope(
          weight_decay=weight_decay)):
        return nets.inception.inception_v1(images,
                                           num_classes,
                                           is_training=is_training)
    model_fn = func
  elif name == 'inception_v2':
    default_image_size = nets.inception.inception_v2.default_image_size
    def func(images):
      with slim.arg_scope(nets.inception.inception_v2_arg_scope(
          weight_decay=weight_decay)):
        return nets.inception.inception_v2(images,
                                           num_classes=num_classes,
                                           is_training=is_training)
    model_fn = func
  elif name == 'inception_v3':
    default_image_size = nets.inception.inception_v3.default_image_size
    def func(images):
      with slim.arg_scope(nets.inception.inception_v3_arg_scope(
          weight_decay=weight_decay)):
        return nets.inception.inception_v3(images,
                                           num_classes=num_classes,
                                           is_training=is_training)
    model_fn = func
  elif name == 'lenet':
    default_image_size = lenet.lenet.default_image_size
    def func(images):
      with slim.arg_scope(lenet.lenet_arg_scope(weight_decay=weight_decay)):
        return lenet.lenet(images,
                           num_classes=num_classes,
                           is_training=is_training)
    model_fn = func
  elif name == 'resnet_v1_50':
    default_image_size = nets.resnet_v1.resnet_v1.default_image_size
    def func(images):
      with slim.arg_scope(nets.resnet_v1.resnet_arg_scope(
          is_training, weight_decay=weight_decay)):
        net, end_points = nets.resnet_v1.resnet_v1_50(
            images, num_classes=num_classes)
        net = tf.squeeze(net, squeeze_dims=[1, 2])
        return net, end_points
    model_fn = func
  elif name == 'resnet_v1_101':
    default_image_size = nets.resnet_v1.resnet_v1.default_image_size
    def func(images):
      with slim.arg_scope(nets.resnet_v1.resnet_arg_scope(
          is_training, weight_decay=weight_decay)):
        net, end_points = nets.resnet_v1.resnet_v1_101(
            images, num_classes=num_classes)
        net = tf.squeeze(net, squeeze_dims=[1, 2])
        return net, end_points
    model_fn = func
  elif name == 'resnet_v1_152':
    default_image_size = nets.resnet_v1.resnet_v1.default_image_size
    def func(images):
      with slim.arg_scope(nets.resnet_v1.resnet_arg_scope(
          is_training, weight_decay=weight_decay)):
        net, end_points = nets.resnet_v1.resnet_v1_152(
            images, num_classes=num_classes)
        net = tf.squeeze(net, squeeze_dims=[1, 2])
        return net, end_points
    model_fn = func
  elif name == 'vgg_a':
    default_image_size = nets.vgg.vgg_a.default_image_size
    def func(images):
      with slim.arg_scope(nets.vgg.vgg_arg_scope(weight_decay)):
        return nets.vgg.vgg_a(images,
                              num_classes=num_classes,
                              is_training=is_training)
    model_fn = func
  elif name == 'vgg_16':
    default_image_size = nets.vgg.vgg_16.default_image_size
    def func(images):
      with slim.arg_scope(nets.vgg.vgg_arg_scope(weight_decay)):
        return nets.vgg.vgg_16(images,
                               num_classes=num_classes,
                               is_training=is_training)
    model_fn = func
  elif name == 'vgg_19':
    default_image_size = nets.vgg.vgg_19.default_image_size
    def func(images):
      with slim.arg_scope(nets.vgg.vgg_arg_scope(weight_decay)):
        return nets.vgg.vgg_19(images,
                               num_classes=num_classes,
                               is_training=is_training)
    model_fn = func
  else:
    raise ValueError('Model name [%s] was not recognized' % name)

  model_fn.default_image_size = default_image_size

  return model_fn