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())
