Faster-RCNN_TF代码解读5：networks/factory.py

一呆飞仙

于 2017-09-18 16:09:00 发布

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分类专栏： Faster-RCNN_TF 文章标签： python tensorflow faster-r-cnn 人工智能算法

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本文介绍了SubCNN_TF项目的网络配置方法，包括如何通过名称获取网络实例，并提供了训练和测试网络的选择方式。该项目遵循MIT许可，由YuXiang编写。

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# --------------------------------------------------------
# SubCNN_TF
# Copyright (c) 2016 CVGL Stanford
# Licensed under The MIT License [see LICENSE for details]
# Written by Yu Xiang
# --------------------------------------------------------

"""Factory method for easily getting imdbs by name."""

__sets = {}

import networks.VGGnet_train
import networks.VGGnet_test
import pdb
import tensorflow as tf

#__sets['VGGnet_train'] = networks.VGGnet_train()

#__sets['VGGnet_test'] = networks.VGGnet_test()


def get_network(name):
    """Get a network by name."""
    #if not __sets.has_key(name):
    #    raise KeyError('Unknown dataset: {}'.format(name))
    #return __sets[name]
    #根据给定的network_name来拆分，根据test/train位置取net的性质信息
    if name.split('_')[1] == 'test':
       return networks.VGGnet_test()
    elif name.split('_')[1] == 'train':
       return networks.VGGnet_train()
    else:
       raise KeyError('Unknown dataset: {}'.format(name))


def list_networks():
    """List all registered imdbs."""
    return __sets.keys()