def get_imdb(name, root_path=None): """Get an imdb (image database) by name.""" # if name not in __sets: # raise KeyError('Unknown dataset: {}'.format(name)) return pascal_voc(**__sets[name], root_path=root_path)
from __future__ import print_function __sets = {} from lib.datasets.pascal_voc import pascal_voc from lib.datasets.coco import coco from lib.datasets.imagenet import imagenet from lib.datasets.vg import vg from lib.datasets.vrd import vrd import numpy as np # 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)) # 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_2014_cap_<split> for year in ['2014']: for split in ['train', 'val', 'capval', 'valminuscapval', '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']:
"""Factory method for easily getting imdbs by name.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function __sets = {} from lib.datasets.pascal_voc import pascal_voc from lib.datasets.coco import coco import numpy as np # 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, devkit_path='/path/to/voc/')) # 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)) def get_imdb(name): """Get an imdb (image database) by name.""" if name not in __sets: raise KeyError('Unknown dataset: {}'.format(name)) return __sets[name]()
"""Factory method for easily getting imdbs by name.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function __sets = {} from lib.datasets.pascal_voc import pascal_voc from lib.datasets.coco import coco import numpy as np # 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']:
def evaluate_detections(self, all_boxes, output_dir=None): self._write_voc_results_file(all_boxes) self._do_python_eval(output_dir) if self.config['matlab_eval']: self._do_matlab_eval(output_dir) if self.config['cleanup']: for cls in self._classes: if cls == '__background__': continue filename = self._get_voc_results_file_template().format(cls) os.remove(filename) def competition_mode(self, on): if on: self.config['use_salt'] = False self.config['cleanup'] = False else: self.config['use_salt'] = True self.config['cleanup'] = True if __name__ == '__main__': from lib.datasets.pascal_voc import pascal_voc d = pascal_voc('trainval', '2007') res = d.roidb from IPython import embed embed()
self._do_python_eval(output_dir) if self.config['matlab_eval']: self._do_matlab_eval(output_dir) if self.config['cleanup']: for cls in self._classes: if cls == '__background__': continue filename = self._get_voc_results_file_template().format(cls) os.remove(filename) def competition_mode(self, on): if on: self.config['use_salt'] = False self.config['cleanup'] = False else: self.config['use_salt'] = True self.config['cleanup'] = True if __name__ == '__main__': from lib.datasets.pascal_voc import pascal_voc d = pascal_voc('trainval', '2012') res = d.roidb from IPython import embed; embed() # 如果更改了这个文件的内容就要把./data/cache下文件删除重新生成
"""Factory method for easily getting imdbs by name.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function __sets = {} from lib.datasets.pascal_voc import pascal_voc from lib.datasets.gene_pascal_voc import gene_pascal_voc import numpy as np # Set up voc_<year>_<split> for year in ['2007']: for split in ['trainval']: name = 'voc_{}_{}'.format(year, split) __sets[name] = (lambda split=split, year=year: pascal_voc(split, year)) # pascal_voc('trainval','2007') # dataset newly generated for year in ['2007']: for split in ['trainval']: name = 'gene_{}_{}'.format(year, split) __sets[name] = (lambda split=split, year=year: gene_pascal_voc(split, year)) # gene_pascal_voc('trainval','2007') def get_imdb(name): """Get an imdb (image database) by name.""" 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())