Faster Rcnn 代碼解讀之 factory.py

# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""Factory method for easily getting imdbs by name."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

__sets = {}
from datasets.pascal_voc import pascal_voc
from datasets.coco import coco

import numpy as np

'''
本檔案作用是把所有可能的dataset都列舉一次
然後主函數調用時看看此時的dataset在不在之前列舉都情況裡
有就傳回這個資料集類型的對象，沒有就報錯。
'''

# Set up voc_<year>_<split> 
for year in ['2007', '2012']:
    for split in ['train', 'val', 'trainval', 'test']:
        name = 'voc_{}_{}'.format(year, split)
        #
        __sets[name] = (lambda split=split, year=year: pascal_voc(split, year))

for year in ['2007', '2012']:
    for split in ['train', 'val', 'trainval', 'test']:
        name = 'voc_{}_{}_diff'.format(year, split)
        __sets[name] = (lambda split=split, year=year: pascal_voc(split, year, use_diff=True))

# Set up coco_2014_<split>
for year in ['2014']:
    for split in ['train', 'val', 'minival', 'valminusminival', 'trainval']:
        name = 'coco_{}_{}'.format(year, split)
        __sets[name] = (lambda split=split, year=year: coco(split, year))

# Set up coco_2015_<split>
for year in ['2015']:
    for split in ['test', 'test-dev']:
        name = 'coco_{}_{}'.format(year, split)
        __sets[name] = (lambda split=split, year=year: coco(split, year))


def get_imdb(name):
    """Get an imdb (image database) by name."""
    # 判斷有沒有定義此資料集
    # _set為一個dict，存了幾個資料集的名稱
    if name not in __sets:
        raise KeyError('Unknown dataset: {}'.format(name))
    return __sets[name]()


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