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90 lines
3.2 KiB
90 lines
3.2 KiB
5 years ago
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains a factory for building various models.
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"""
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import functools
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import tensorflow as tf
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# from nets import inception
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# from nets import overfeat
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# from nets import resnet_v1
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# from nets import resnet_v2
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from nets import vgg
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# from nets import xception
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from nets import ssd_vgg_300
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from nets import ssd_vgg_512
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slim = tf.contrib.slim
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networks_map = {'vgg_a': vgg.vgg_a,
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'vgg_16': vgg.vgg_16,
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'vgg_19': vgg.vgg_19,
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'ssd_300_vgg': ssd_vgg_300.ssd_net,
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'ssd_300_vgg_caffe': ssd_vgg_300.ssd_net,
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'ssd_512_vgg': ssd_vgg_512.ssd_net,
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'ssd_512_vgg_caffe': ssd_vgg_512.ssd_net,
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}
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arg_scopes_map = {'vgg_a': vgg.vgg_arg_scope,
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'vgg_16': vgg.vgg_arg_scope,
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'vgg_19': vgg.vgg_arg_scope,
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'ssd_300_vgg': ssd_vgg_300.ssd_arg_scope,
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'ssd_300_vgg_caffe': ssd_vgg_300.ssd_arg_scope_caffe,
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'ssd_512_vgg': ssd_vgg_512.ssd_arg_scope,
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'ssd_512_vgg_caffe': ssd_vgg_512.ssd_arg_scope_caffe,
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}
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networks_obj = {'ssd_300_vgg': ssd_vgg_300.SSDNet,
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'ssd_512_vgg': ssd_vgg_512.SSDNet,
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}
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def get_network(name):
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"""Get a network object from a name.
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"""
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# params = networks_obj[name].default_params if params is None else params
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return networks_obj[name]
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def get_network_fn(name, num_classes, is_training=False, **kwargs):
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"""Returns a network_fn such as `logits, end_points = network_fn(images)`.
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Args:
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name: The name of the network.
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num_classes: The number of classes to use for classification.
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is_training: `True` if the model is being used for training and `False`
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otherwise.
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weight_decay: The l2 coefficient for the model weights.
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Returns:
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network_fn: A function that applies the model to a batch of images. It has
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the following signature: logits, end_points = network_fn(images)
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Raises:
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ValueError: If network `name` is not recognized.
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"""
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if name not in networks_map:
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raise ValueError('Name of network unknown %s' % name)
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arg_scope = arg_scopes_map[name](**kwargs)
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func = networks_map[name]
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@functools.wraps(func)
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def network_fn(images, **kwargs):
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with slim.arg_scope(arg_scope):
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return func(images, num_classes, is_training=is_training, **kwargs)
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if hasattr(func, 'default_image_size'):
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network_fn.default_image_size = func.default_image_size
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return network_fn
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