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89 lines
3.2 KiB
89 lines
3.2 KiB
# 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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