def __init__(self,
image_sets,
flip=False,
proposal_files=None,
phase='train',
result_path=''):
super(coco, self).__init__()
image_dirs = {
'train2014':
os.path.join(config.dataset.dataset_path, 'coco_train2014'),
'val2014':
os.path.join(config.dataset.dataset_path, 'coco_val2014'),
'minival2014':
os.path.join(config.dataset.dataset_path, 'coco_val2014'),
'valminusminival2014':
os.path.join(config.dataset.dataset_path, 'coco_val2014'),
'test2015':
os.path.join(config.dataset.dataset_path, 'coco_test2015'),
'test-dev2015':
os.path.join(config.dataset.dataset_path, 'coco_test2015'),
'train2017':
os.path.join(config.dataset.dataset_path, 'images', 'train2017'),
'val2017':
os.path.join(config.dataset.dataset_path, 'images', 'val2017'),
'test-dev2017':
os.path.join(config.dataset.dataset_path, 'images', 'test2017'),
}
anno_files = {
'train2014': 'instances_train2014.json',
'val2014': 'instances_val2014.json',
'minival2014': 'instances_minival2014.json',
'valminusminival2014': 'instances_valminusminival2014.json',
'test2015': 'image_info_test2015.json',
'test-dev2015': 'image_info_test-dev2015.json',
'train2017': 'instances_train2017.json',
'val2017': 'instances_val2017.json',
'test-dev2017': 'image_info_test-dev2017.json',
}
if image_sets[0] == 'test-dev2017':
self.panoptic_json_file = os.path.join(
config.dataset.dataset_path, 'annotations',
'image_info_test-dev2017.json')
else:
self.panoptic_json_file = os.path.join(
config.dataset.dataset_path, 'annotations',
'panoptic_val2017_stff.json')
self.panoptic_gt_folder = os.path.join(config.dataset.dataset_path,
'annotations',
'panoptic_val2017')
if proposal_files is None:
proposal_files = [None] * len(image_sets)
if phase == 'train' and len(image_sets) > 1:
# combine multiple datasets
roidbs = []
for image_set, proposal_file in zip(image_sets, proposal_files):
dataset = JsonDataset('coco_' + image_set,
image_dir=image_dirs[image_set],
anno_file=os.path.join(
config.dataset.dataset_path,
'annotations',
anno_files[image_set]))
roidb = dataset.get_roidb(
gt=True,
proposal_file=proposal_file,
crowd_filter_thresh=config.train.crowd_filter_thresh)
if flip:
if logger:
logger.info(
'Appending horizontally-flipped training examples...'
)
extend_with_flipped_entries(roidb, dataset)
roidbs.append(roidb)
roidb = roidbs[0]
for r in roidbs[1:]:
roidb.extend(r)
roidb = filter_for_training(roidb)
add_bbox_regression_targets(roidb)
else:
assert len(image_sets) == 1
self.dataset = JsonDataset('coco_' + image_sets[0],
image_dir=image_dirs[image_sets[0]],
anno_file=os.path.join(
config.dataset.dataset_path,
'annotations',
anno_files[image_sets[0]]))
roidb = self.dataset.get_roidb(
gt=True,
proposal_file=proposal_files[0],
crowd_filter_thresh=config.train.crowd_filter_thresh
if phase != 'test' else 0)
if flip:
if logger:
logger.info(
'Appending horizontally-flipped training examples...')
extend_with_flipped_entries(roidb, self.dataset)
if phase != 'test':
roidb = filter_for_training(roidb)
add_bbox_regression_targets(roidb)
self.roidb = roidb
self.phase = phase
self.flip = flip
self.result_path = result_path
self.num_classes = 81
def __init__(self,
image_sets,
flip=False,
proposal_files=None,
phase='train',
result_path=''):
super(Cityscapes, self).__init__()
self.image_dirs = {
'train': os.path.join(config.dataset.dataset_path, 'images'),
'val': os.path.join(config.dataset.dataset_path, 'images'),
'test': os.path.join(config.dataset.dataset_path, 'images'),
'train_extra': os.path.join(config.dataset.dataset_path, 'images'),
'debug': os.path.join(config.dataset.dataset_path, 'images'),
}
self.anno_files = {
'train': 'instancesonly_gtFine_train.json',
'val': 'instancesonly_gtFine_val.json',
'test': 'image_info_test.json',
'train_extra': 'instancesonly_gtCoarse_train_extra.json',
'debug': 'instancesonly_gtFine_debug.json',
}
self.panoptic_json_file = os.path.join(config.dataset.dataset_path,
'annotations',
'cityscapes_fine_val.json')
self.panoptic_gt_folder = 'data/cityscapes/panoptic'
self.flip = flip
self.result_path = result_path
self.num_classes = 9
self.phase = phase
self.image_sets = image_sets
if image_sets[0] == 'demoVideo':
assert len(image_sets) == 1
assert phase == 'test'
im_path = [
_.strip()
for _ in open('data/cityscapes/split/demoVideo_img.txt',
'r').readlines()
]
self.roidb = [{'image': _, 'flipped': False} for _ in im_path]
return
if proposal_files is None:
proposal_files = [None] * len(image_sets)
if phase == 'train' and len(image_sets) > 1:
# combine multiple datasets
roidbs = []
for image_set, proposal_file in zip(image_sets, proposal_files):
dataset = JsonDataset('cityscapes_' + image_set,
image_dir=self.image_dirs[image_set],
anno_file=os.path.join(
config.dataset.dataset_path,
'annotations',
self.anno_files[image_set]))
roidb = dataset.get_roidb(
gt=True,
proposal_file=proposal_file,
crowd_filter_thresh=config.train.crowd_filter_thresh)
if flip:
if logger:
logger.info(
'Appending horizontally-flipped training examples...'
)
extend_with_flipped_entries(roidb, dataset)
roidbs.append(roidb)
roidb = roidbs[0]
for r in roidbs[1:]:
roidb.extend(r)
roidb = filter_for_training(roidb)
add_bbox_regression_targets(roidb)
else:
assert len(image_sets) == 1
self.dataset = JsonDataset(
'cityscapes_' + image_sets[0],
image_dir=self.image_dirs[image_sets[0]],
anno_file=os.path.join(config.dataset.dataset_path,
'annotations',
self.anno_files[image_sets[0]]))
roidb = self.dataset.get_roidb(
gt=True,
proposal_file=proposal_files[0],
crowd_filter_thresh=config.train.crowd_filter_thresh
if phase != 'test' else 0)
if flip:
if logger:
logger.info(
'Appending horizontally-flipped training examples...')
extend_with_flipped_entries(roidb, self.dataset)
if phase != 'test':
roidb = filter_for_training(roidb)
add_bbox_regression_targets(roidb)
self.roidb = roidb