# Set up vg_<split>
# for version in ['1600-400-20']:
# for split in ['minitrain', 'train', 'minival', 'val', 'test']:
# name = 'vg_{}_{}'.format(version,split)
# __sets[name] = (lambda split=split, version=version: vg(version, split))
for version in [
'150-50-20', '150-50-50', '500-150-80', '750-250-150', '1750-700-450',
'1600-400-20'
]:
for split in [
'minitrain', 'smalltrain', 'train', 'minival', 'smallval', 'val',
'test'
]:
name = 'vg_{}_{}'.format(version, split)
__sets[name] = (
lambda split=split, version=version: vg(version, split))
# set up image net.
for split in ['train', 'val', 'val1', 'val2', 'test']:
name = 'imagenet_{}'.format(split)
devkit_path = 'data/imagenet/ILSVRC/devkit'
data_path = 'data/imagenet/ILSVRC'
__sets[name] = (lambda split=split, devkit_path=devkit_path, data_path=
data_path: imagenet(split, devkit_path, data_path))
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]()
# 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))
# Set up vg_<split>
# for version in ['1600-400-20']:
# for split in ['minitrain', 'train', 'minival', 'val', 'test']:
# name = 'vg_{}_{}'.format(version,split)
# __sets[name] = (lambda split=split, version=version: vg(version, split))
for version in ['150-50-20', '150-50-50', '500-150-80', '750-250-150', '1750-700-450', '1600-400-20']:
for split in ['minitrain', 'smalltrain', 'train', 'minival', 'smallval', 'val', 'test']:
name = 'vg_{}_{}'.format(version,split)
__sets[name] = (lambda split=split, version=version: vg(version, split))
# set up image net.
for split in ['train', 'val', 'val1', 'val2', 'test']:
name = 'imagenet_{}'.format(split)
devkit_path = 'data/imagenet/ILSVRC/devkit'
data_path = 'data/imagenet/ILSVRC'
__sets[name] = (lambda split=split, devkit_path=devkit_path, data_path=data_path: imagenet(split,devkit_path,data_path))
# Set up pdfpages
for split in ['train', 'validation']:
name = 'pdfpages_{}'.format(split)
# path = '/home/ubuntu/persist/data_gen/data/pageseg/20190226'
path = f'/opt/ml/input/data/training'
__sets[name] = (lambda split=split: PDFPages(split, path=path))