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))
imagenet_devkit_path = '/media/lealex262/OS/Users/Alex Thien An Le/Documents/GeorgiaTech/ClubsnProjects/Hackaton/HomeDepot_DeepLearning_Fall2017/active_window/tf-faster-rcnn/data'
for split in ['train', 'test']:
name = 'imagenet_{}_{}'.format('2017', split)
__sets[name] = (
lambda split=split: imagenet(split, "2017", imagenet_devkit_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]()
def list_imdbs():
"""List all registered imdbs."""
return list(__sets.keys())
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def evaluate_detections(self, all_boxes, output_dir):
self._write_ILSVRC_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_ILSVRC_results_file_template().format(cls)
os.remove(filename)
if __name__ == '__main__':
from datasets.imagenet import imagenet
d = imagenet('train', '2015', '/disk2/data/ILSVRC2015')
res = d.roidb
from IPython import embed; embed()
from datasets.caltech import caltech
from datasets.sun import sun
from datasets.cam2 import cam2
from datasets.pascal_voc import pascal_voc
from datasets.imagenet import imagenet
from datasets.coco import coco
import numpy as np
# Set up imagenet_<year>_<split>
for year in ['2014']:
for split in [
'train', 'val', 'val1', 'val2', 'test', 'short_train',
'very_short_train', 'val1_short'
]:
name = 'imagenet_{}'.format(split)
__sets[name] = (lambda split=split: imagenet(split))
# Set up voc_<year>_<split> using selective search "fast" mode
for year in ['2007', '2012']:
for split in ['train', 'val', 'trainval', 'test', 'val_small']:
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']:
name = 'coco_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: coco(split, year))
# Set up coco_2015_<split>
for year in ['2015']:
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
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 datasets.imagenet import imagenet
d = imagenet('train', '2014')
res = d.roidb
from IPython import embed
embed()
]:
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 [
'DET_train_30classes', 'VID_train_15frames', 'VID_val_frames',
'VID_val_videos'
]:
name = 'imagenet_{}'.format(split)
data_path = 'data/ILSVRC2015'
__sets[name] = (
lambda split=split, data_path=data_path: imagenet(split, 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]()
def list_imdbs():
"""List all registered imdbs."""
return list(__sets.keys())
# 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]()
def list_imdbs():
"""List all registered imdbs."""
return list(__sets.keys())
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_imagenet_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_imagenet_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 datasets.imagenet import imagenet
d = imagenet('train', '2016')
res = d.roidb
from IPython import embed
embed()
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']:
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))
__sets['imagenet'] = lambda: imagenet("train")
def get_imdb(name):
"""Get an imdb (image database) by name."""
if not __sets.has_key(name):
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
def list_imdbs():
"""List all registered imdbs."""
return __sets.keys()
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_imagenet_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_imagenet_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 datasets.imagenet import imagenet
d = imagenet('train', 'VID')
res = d.roidb
from IPython import embed; embed()
# Set up coco_2014_<split>
for year in ['2014']:
for split in ['train', 'val', 'minival', 'valminusminival']:
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))
# Set up imagenet_2015_<split>
for year in ['2015']:
for split in ['train', 'val']:
name = 'imagenet_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: imagenet(split, year))
def get_imdb(name):
"""Get an imdb (image database) by name."""
if not __sets.has_key(name):
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
def list_imdbs():
"""List all registered imdbs."""
return __sets.keys()
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_imagenet_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._wnid:
if cls == '__background__':
continue
filename = self._get_imagenet_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 datasets.imagenet import imagenet
d = imagenet('trainval')
res = d.roidb
from IPython import embed
embed()
class imagenet(imdb):
def __init__(self, image_set, year, devkit_path):
imdb.__init__(self, 'imagenet_' + year + '_' + image_set)
self._year = year
self._image_set = image_set
self._devkit_path = devkit_path
self._data_path = os.path.join(self._devkit_path, "data")
self._classes = (
'__background__', # always index 0
'active_window')
self._class_to_ind = dict(
list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = ['.jpg', '.png', '.jpeg', '.JPEG']
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
self._salt = str(uuid.uuid4())
self._comp_id = 'comp4'
# Specific config options
self.config = {'cleanup': True, 'use_salt': True, 'top_k': 2000}
assert os.path.exists(self._devkit_path), \
'Devkit path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_index[i])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
image_path = os.path.join(self._data_path, 'Images', index + ext)
if os.path.exists(image_path):
break
assert os.path.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._data_path + /ImageSets/val.txt
image_set_file = os.path.join(self._data_path, 'ImageSets',
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
return image_index
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [
self._load_imagenet_annotation(index) for index in self.image_index
]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def _load_imagenet_annotation(self, index):
"""
Load image and bounding boxes info from txt files
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.txt')
with open(filename) as f:
data = f.read()
num_objs = len(data.splitlines())
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, line in enumerate(data.splitlines()):
object = line.split(" ")
# Make pixel indexes 0-based
x1 = float(object[1]) - 1
y1 = float(object[2]) - 1
x2 = float(object[3]) - 1
y2 = float(object[4]) - 1
cls = 1
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {
'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas
}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' +
self._salt if self.config['use_salt'] else self._comp_id)
return comp_id
def _get_imagenet_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = self._get_comp_id(
) + '_det_' + self._image_set + '_{:s}.txt'
path = os.path.join(self._devkit_path, 'results', filename)
return path
def _write_imagenet_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} Imagenet results file'.format(cls))
filename = self._get_imagenet_results_file_template().format(cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write(
'{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(
index, dets[k, -1], dets[k, 0] + 1,
dets[k, 1] + 1, dets[k, 2] + 1,
dets[k, 3] + 1))
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(self._devkit_path, 'data', 'Annotations',
'{:s}.txt')
imagesetfile = os.path.join(self._devkit_path, 'data', 'ImageSets',
self._image_set + '.txt')
cachedir = os.path.join(self._devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(self._year) < 2010 else False
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__':
continue
filename = self._get_imagenet_results_file_template().format(cls)
rec, prec, ap = imagenet_eval(filename,
annopath,
imagesetfile,
cls,
cachedir,
ovthresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
print(('AP for {} = {:.4f}'.format(cls, ap)))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_imagenet_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_imagenet_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 datasets.imagenet import imagenet
d = imagenet('train', '2017', ' ')
res = d.roidb
from IPython import embed
embed()
def __init__(self, image_set, year, devkit_path=None, shuffled=None):
imdb.__init__(self, 'merged_' + image_set)
self._year = year
self._image_set = image_set
self._anno_set_dir = image_set
if "val" in image_set:
self._image_set_dir = "val"
if "val1" in image_set:
self._anno_set_dir = "val1"
if "val2" in image_set:
self._anno_set_dir = "val2"
elif "train" in image_set:
self._anno_set_dir = "train"
elif "test" in image_set:
self._anno_set_dir = "test"
if image_set == "train":
self.imdbs = [imagenet(image_set),\
coco(image_set, '2015'),\
cam2(image_set,'2017'),\
sun(image_set,'2012'),\
caltech(image_set,'2009'),\
kitti(image_set,'2013'),\
inria(image_set,'2005'),\
pascal_voc(image_set,'2007'),\
pascal_voc(image_set,'2012')]
elif image_set == "test":
self.imdbs = [imagenet('val'),\
coco('test-dev', '2015'),\
cam2('all','2017'),\
sun('test','2012'),\
caltech('test','2009'),\
kitti('val','2013'),\
inria('all','2005'),\
pascal_voc('test','2007')]
self.roidbs = [None for _ in range(len(self.datasets))]
for idx,imdb in enumerate(self.imdbs):
self.roidbs[idx] = get_training_roidb(imdb)
self._devkit_path = self._get_default_path() if devkit_path is None \
else devkit_path
self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
self._classes = ('__background__', # always index 0
'voc',
'imagenet',
'caltech',
'coco',
'sun',
'kitti',
'inria',
'cam2')
self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
self._image_ext = '.jpg'
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.selective_search_roidb
self._salt = str(uuid.uuid4())
self._comp_id = 'comp4'
# PASCAL specific config options
self.config = {'cleanup' : True,
'use_salt' : True,
'use_diff' : False,
'matlab_eval' : False,
'rpn_file' : None,
'min_size' : 2}
assert os.path.exists(self._devkit_path), \
'VOCdevkit path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
from datasets.imagenet import imagenet
from datasets.vg import vg
import numpy as np
# Set up voc_<year>_<split> using selective search "fast" mode
for year in ['2007', '2012', '0712']:
for split in ['train', 'val', 'trainval', 'test']:
name = 'voc_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: pascal_voc(split, year))
for split in ['train', 'val']:
name = 'imagenet_{}'.format(split)
devkit_path = '/scratch0/ILSVRC/devkit/'
data_path = '/scratch0/ILSVRC2015/'
__sets[name] = (lambda split=split, devkit_path=devkit_path, data_path=
data_path: imagenet(split, devkit_path, data_path))
print(name)
print(__sets[name])
# Set up coco_2014_<split>
for year in ['2014']:
for split in ['train', 'val', 'minival', 'valminusminival']:
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))
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))
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())
# 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))
for split in ['train', 'val', 'test']:
name = 'wider_face_{}'.format(split)
__sets[name] = (lambda split=split: wider_face(split))
for split in ['train', 'val', 'test']:
name = 'MI3_{}'.format(split)
__sets[name] = (lambda split=split: mi3(split))
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 voc_<year>_<split> using selective search "fast" mode
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']:
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))
__sets['imagenet'] = lambda :imagenet("train")
def get_imdb(name):
"""Get an imdb (image database) by name."""
if not __sets.has_key(name):
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
def list_imdbs():
"""List all registered imdbs."""
return __sets.keys()
