def get_coco(split, img_split, data_dir=None):
url = img_urls[img_split]
if data_dir is None:
data_dir = download.get_dataset_directory(root)
img_root = os.path.join(data_dir, 'images')
created_img_root = os.path.join(img_root, img_split)
if 'test' in split:
annos_root = data_dir
else:
annos_root = os.path.join(data_dir, 'annotations')
if 'test' in split:
anno_prefix = 'image_info'
else:
anno_prefix = 'instances'
anno_fn = os.path.join(annos_root,
'{0}_{1}.json'.format(anno_prefix, split))
if not os.path.exists(created_img_root):
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, img_root, ext)
if not os.path.exists(anno_fn):
anno_url = anno_urls[split]
download_file_path = utils.cached_download(anno_url)
ext = os.path.splitext(anno_url)[1]
utils.extractall(download_file_path, annos_root, ext)
return data_dir
def get_coco(split, year, mode='person_keypoints'):
if year not in ['2017']:
raise ValueError
if split not in ['train', 'val']:
raise ValueError
data_dir = download.get_dataset_directory(root)
annos_root = os.path.join(data_dir, 'annotations')
img_root = os.path.join(data_dir, 'images')
created_img_root = os.path.join(img_root, '{}{}'.format(split, year))
img_url = img_urls[year][split]
if mode == 'person_keypoints':
anno_url = person_keypoints_anno_urls[year][split]
anno_path = os.path.join(
annos_root, 'person_keypoints_{}{}.json'.format(split, year))
else:
raise ValueError('invalid mode {}'.format(mode))
if not os.path.exists(created_img_root):
download_file_path = utils.cached_download(img_url)
ext = os.path.splitext(img_url)[1]
utils.extractall(download_file_path, img_root, ext)
if not os.path.exists(anno_path):
download_file_path = utils.cached_download(anno_url)
ext = os.path.splitext(anno_url)[1]
if split in ['train', 'val']:
utils.extractall(download_file_path, data_dir, ext)
elif split in ['valminusminival', 'minival']:
utils.extractall(download_file_path, annos_root, ext)
return data_dir
def get_coco(split, img_split, year, mode):
data_dir = download.get_dataset_directory(root)
annos_root = os.path.join(data_dir, 'annotations')
img_root = os.path.join(data_dir, 'images')
created_img_root = os.path.join(img_root, '{}{}'.format(img_split, year))
img_url = img_urls[year][img_split]
if mode == 'instances':
anno_url = instances_anno_urls[year][split]
anno_path = os.path.join(annos_root,
'instances_{}{}.json'.format(split, year))
elif mode == 'panoptic':
anno_url = panoptic_anno_url
anno_path = os.path.join(annos_root,
'panoptic_{}{}.json'.format(split, year))
if not os.path.exists(created_img_root):
download_file_path = utils.cached_download(img_url)
ext = os.path.splitext(img_url)[1]
utils.extractall(download_file_path, img_root, ext)
if not os.path.exists(anno_path):
download_file_path = utils.cached_download(anno_url)
ext = os.path.splitext(anno_url)[1]
if split in ['train', 'val']:
utils.extractall(download_file_path, data_dir, ext)
elif split in ['valminusminival', 'minival']:
utils.extractall(download_file_path, annos_root, ext)
if mode == 'panoptic':
pixelmap_path = os.path.join(annos_root,
'panoptic_{}{}'.format(split, year))
if not os.path.exists(pixelmap_path):
utils.extractall(pixelmap_path + '.zip', annos_root, '.zip')
return data_dir
def get_mot(year, split):
if year not in urls:
raise ValueError
data_root = download.get_dataset_directory(root)
if year == '2015':
mot_dirname = '2DMOT{}'.format(year)
else:
mot_dirname = 'MOT{}'.format(year[2:])
base_path = os.path.join(data_root, mot_dirname)
anno_path = os.path.join(base_path, 'annotations')
anno_txt_path = os.path.join(anno_path, '{}.txt'.format(split))
if not os.path.exists(base_path):
download_file_path = utils.cached_download(urls[year])
ext = os.path.splitext(urls[year])[1]
utils.extractall(download_file_path, data_root, ext)
if not os.path.exists(os.path.join(data_root, 'motchallenge-devkit')):
download_devfile_path = utils.cached_download(dev_urls)
dev_ext = os.path.splitext(dev_urls)[1]
utils.extractall(download_devfile_path, data_root, dev_ext)
if not os.path.exists(anno_path):
os.mkdir(anno_path)
if split == 'train':
split_dirs = ['train']
elif split == 'val':
split_dirs = ['test']
elif split == 'trainval':
split_dirs = ['train', 'test']
else:
raise ValueError
data_ids = []
for split_d in split_dirs:
seq_dirs = sorted(os.listdir(os.path.join(base_path, split_d)))
for seq_d in seq_dirs:
img_dir = os.path.join(base_path, split_d, seq_d, 'img1')
img_names = sorted(os.listdir(img_dir))
for img_name in img_names:
data_id = '{0}_{1}_{2}'.format(
split_d, seq_d, img_name.split('.')[0])
data_ids.append(data_id)
with open(anno_txt_path, 'w') as anno_f:
anno_f.write('\n'.join(data_ids))
return base_path
def get_sbd(data_dir=None):
if data_dir is None:
data_dir = download.get_dataset_directory(root)
label_dir = osp.join(data_dir, 'fcis_label')
if not osp.exists(label_dir):
os.makedirs(label_dir)
fcn.data.cached_download(
url=val_url,
path=osp.join(label_dir, 'val.txt'),
md5='905db61182fcaaf6b981af6ae6dd7ff2'
)
fcn.data.cached_download(
url=train_url,
path=osp.join(label_dir, 'train.txt'),
md5='79bff800c5f0b1ec6b21080a3c066722'
)
base_path = osp.join(data_dir, 'benchmark_RELEASE/dataset')
if osp.exists(base_path):
return base_path
download_file_path = utils.cached_download(url)
ext = osp.splitext(url)[1]
utils.extractall(download_file_path, data_dir, ext)
return base_path
def get_voc(year, split):
if year not in urls:
raise ValueError
key = year
if split == 'test' and year == '2007':
key = '2007_test'
# To support ChainerMN, the target directory should be locked.
with filelock.FileLock(
os.path.join(
download.get_dataset_directory('pfnet/chainercv/.lock'),
'voc.lock')):
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'VOCdevkit/VOC{}'.format(year))
split_file = os.path.join(base_path,
'ImageSets/Main/{}.txt'.format(split))
if os.path.exists(split_file):
# skip downloading
return base_path
download_file_path = utils.cached_download(urls[key])
ext = os.path.splitext(urls[key])[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def get_ade20k(root, url):
data_root = download.get_dataset_directory(root)
if os.path.exists(os.path.join(data_root, 'ADEChallengeData2016')):
return data_root
cache_path = utils.cached_download(url)
utils.extractall(cache_path, data_root, os.path.splitext(url)[1])
return data_root
def get_voc(year, split):
if year not in urls:
raise ValueError
key = year
if split == 'test' and year == '2007':
key = '2007_test'
if not os.path.isdir(voc_dir):
os.makedirs(voc_dir)
filelock_path = os.path.join(voc_dir, 'voc.lock')
# To support ChainerMN, the target directory should be locked.
with filelock.FileLock(filelock_path):
base_path = os.path.join(voc_dir, 'VOCdevkit/VOC{}'.format(year))
split_file = os.path.join(base_path,
'ImageSets/Main/{}.txt'.format(split))
if os.path.exists(split_file):
# skip downloading
if os.path.isfile(filelock_path):
os.remove(filelock_path) # remove lockfile
return base_path
download_file_path = utils.cached_download(urls[key])
ext = os.path.splitext(urls[key])[1]
utils.extractall(download_file_path, voc_dir, ext)
if os.path.isfile(filelock_path):
os.remove(filelock_path) # remove lockfile
return base_path
def get_coco(split, img_split):
url = img_urls[img_split]
data_dir = download.get_dataset_directory(root)
img_root = os.path.join(data_dir, 'images')
created_img_root = os.path.join(img_root, '{}2014'.format(img_split))
annos_root = os.path.join(data_dir, 'annotations')
anno_path = os.path.join(annos_root, 'instances_{}2014.json'.format(split))
if not os.path.exists(created_img_root):
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, img_root, ext)
if not os.path.exists(anno_path):
anno_url = anno_urls[split]
download_file_path = utils.cached_download(anno_url)
ext = os.path.splitext(anno_url)[1]
utils.extractall(download_file_path, annos_root, ext)
return data_dir
def get_ade20k(root, url):
data_root = download.get_dataset_directory(root)
# To support ChainerMN, the target directory should be locked.
with filelock.FileLock(os.path.join(data_root, 'lock')):
if os.path.exists(os.path.join(data_root, 'ADEChallengeData2016')):
return data_root
cache_path = utils.cached_download(url)
utils.extractall(cache_path, data_root, os.path.splitext(url)[1])
return data_root
def _get_sintel():
data_root = download.get_dataset_directory(root)
if os.path.exists(os.path.join(data_root, 'training')):
# skip downloading
return data_root
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, data_root, ext)
return data_root
def get_coco(split, img_split, year, mode):
if not os.path.isdir(coco_dir):
os.makedirs(coco_dir)
# To support ChainerMN, the target directory should be locked.
lockfile_path = os.path.join(coco_dir, 'coco.lock')
with filelock.FileLock(lockfile_path):
annos_root = os.path.join(coco_dir, 'annotations')
img_root = os.path.join(coco_dir, 'images')
created_img_root = os.path.join(img_root,
'{}{}'.format(img_split, year))
img_url = img_urls[year][img_split]
if mode == 'instances':
anno_url = instances_anno_urls[year][split]
anno_path = os.path.join(annos_root,
'instances_{}{}.json'.format(split, year))
elif mode == 'panoptic':
anno_url = panoptic_anno_url
anno_path = os.path.join(annos_root,
'panoptic_{}{}.json'.format(split, year))
if not os.path.exists(created_img_root):
download_file_path = utils.cached_download(img_url)
ext = os.path.splitext(img_url)[1]
utils.extractall(download_file_path, img_root, ext)
if not os.path.exists(anno_path):
download_file_path = utils.cached_download(anno_url)
ext = os.path.splitext(anno_url)[1]
if split in ['train', 'val']:
utils.extractall(download_file_path, coco_dir, ext)
elif split in ['valminusminival', 'minival']:
utils.extractall(download_file_path, annos_root, ext)
if mode == 'panoptic':
pixelmap_path = os.path.join(annos_root,
'panoptic_{}{}'.format(split, year))
if not os.path.exists(pixelmap_path):
utils.extractall(pixelmap_path + '.zip', annos_root, '.zip')
if os.path.isfile(lockfile_path):
os.remove(lockfile_path) # remove lockfile
return coco_dir
def get_cub():
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'CUB_200_2011')
if os.path.exists(base_path):
# skip downloading
return base_path
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def get_cub_prob_map():
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'segmentations')
if os.path.exists(base_path):
# skip downloading
return base_path
prob_map_download_file_path = utils.cached_download(prob_map_url)
prob_map_ext = os.path.splitext(prob_map_url)[1]
utils.extractall(prob_map_download_file_path, data_root, prob_map_ext)
return base_path
def get_voc(data_dir=None):
if data_dir is None:
data_dir = download.get_dataset_directory(root)
base_path = osp.join(data_dir, 'VOCdevkit/VOC2012')
if osp.exists(base_path):
return base_path
download_file_path = utils.cached_download(url)
ext = osp.splitext(url)[1]
utils.extractall(download_file_path, data_dir, ext)
return base_path
def get_sbd(data_dir=None):
if data_dir is None:
data_dir = download.get_dataset_directory(root)
base_path = osp.join(data_dir, 'benchmark_RELEASE/dataset')
if osp.exists(base_path):
return base_path
download_file_path = utils.cached_download(url)
ext = osp.splitext(url)[1]
utils.extractall(download_file_path, data_dir, ext)
return base_path
def _get_online_products():
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'Stanford_Online_Products')
if os.path.exists(base_path):
# skip downloading
return base_path
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def get_cub():
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'CUB_200_2011')
if os.path.exists(base_path):
# skip downloading
return base_path
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def get_cub_mask():
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'segmentations')
if os.path.exists(base_path):
# skip downloading
return base_path
download_file_path_mask = utils.cached_download(mask_url)
ext_mask = os.path.splitext(mask_url)[1]
utils.extractall(download_file_path_mask, data_root, ext_mask)
return base_path
def _get_online_products():
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'Stanford_Online_Products')
if os.path.exists(base_path):
# skip downloading
return base_path
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def get_camvid():
data_root = download.get_dataset_directory(root)
download_file_path = utils.cached_download(url)
if len(glob.glob(os.path.join(data_root, '*'))) != 9:
utils.extractall(
download_file_path, data_root, os.path.splitext(url)[1])
data_dir = os.path.join(data_root, 'SegNet-Tutorial-master/CamVid')
if os.path.exists(data_dir):
for fn in glob.glob(os.path.join(data_dir, '*')):
shutil.move(fn, os.path.join(data_root, os.path.basename(fn)))
shutil.rmtree(os.path.dirname(data_dir))
return data_root
def get_cub_prob_map():
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'segmentations')
if os.path.exists(base_path):
# skip downloading
return base_path
prob_map_download_file_path = utils.cached_download(prob_map_url)
prob_map_ext = os.path.splitext(prob_map_url)[1]
utils.extractall(
prob_map_download_file_path, data_root, prob_map_ext)
return base_path
def get_camvid():
data_root = download.get_dataset_directory(root)
download_file_path = utils.cached_download(url)
if len(glob.glob(os.path.join(data_root, '*'))) != 9:
utils.extractall(
download_file_path, data_root, os.path.splitext(url)[1])
data_dir = os.path.join(data_root, 'SegNet-Tutorial-master/CamVid')
if os.path.exists(data_dir):
for fn in glob.glob(os.path.join(data_dir, '*')):
shutil.move(fn, os.path.join(data_root, os.path.basename(fn)))
shutil.rmtree(os.path.dirname(data_dir))
return data_root
def get_coco(split, img_split, year):
url = img_urls[year][img_split]
data_dir = download.get_dataset_directory(root)
img_root = os.path.join(data_dir, 'images')
created_img_root = os.path.join(img_root, '{}{}'.format(img_split, year))
annos_root = os.path.join(data_dir, 'annotations')
anno_path = os.path.join(annos_root,
'instances_{}{}.json'.format(split, year))
if not os.path.exists(created_img_root):
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, img_root, ext)
if not os.path.exists(anno_path):
anno_url = anno_urls[year][split]
download_file_path = utils.cached_download(anno_url)
ext = os.path.splitext(anno_url)[1]
if split in ['train', 'val']:
utils.extractall(download_file_path, data_dir, ext)
elif split in ['valminusminival', 'minival']:
utils.extractall(download_file_path, annos_root, ext)
return data_dir
def get_cub():
data_root = download.get_dataset_directory(root)
# To support ChainerMN, the target directory should be locked.
with filelock.FileLock(os.path.join(data_root, 'lock')):
base_path = os.path.join(data_root, 'CUB_200_2011')
if os.path.exists(base_path):
# skip downloading
return base_path
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def get_camvid():
data_root = download.get_dataset_directory(root)
# To support ChainerMN, the target directory should be locked.
with filelock.FileLock(os.path.join(data_root, 'lock')):
download_file_path = utils.cached_download(url)
if len(glob.glob(os.path.join(data_root, '*'))) != 10:
utils.extractall(download_file_path, data_root,
os.path.splitext(url)[1])
data_dir = os.path.join(data_root, 'SegNet-Tutorial-master/CamVid')
if os.path.exists(data_dir):
for fn in glob.glob(os.path.join(data_dir, '*')):
shutil.move(fn, os.path.join(data_root, os.path.basename(fn)))
shutil.rmtree(os.path.dirname(data_dir))
return data_root
def get_cub_prob_map():
data_root = download.get_dataset_directory(root)
# To support ChainerMN, the target directory should be locked.
with filelock.FileLock(os.path.join(data_root, 'lock')):
base_path = os.path.join(data_root, 'segmentations')
if os.path.exists(base_path):
# skip downloading
return base_path
prob_map_download_file_path = utils.cached_download(prob_map_url)
prob_map_ext = os.path.splitext(prob_map_url)[1]
utils.extractall(
prob_map_download_file_path, data_root, prob_map_ext)
return base_path
def get_coco_pretrained_model():
data_dir = download.get_dataset_directory(root)
pretraind_model_dir = os.path.join(data_dir, 'model')
makedirs(pretraind_model_dir)
pretrained_model_filename = os.path.join(pretraind_model_dir,
'coco_posenet.npz')
pretrained_model_url = 'http://posefs1.perception.cs.cmu.edu/OpenPose/'\
'models/pose/coco/pose_iter_440000.caffemodel'
download_file_path = utils.cached_download(pretrained_model_url)
if os.path.exists(pretrained_model_filename):
return pretrained_model_filename
model = OpenPoseNet(len(JointType) + 1, len(coco_joint_pairs) * 2)
caffe_model = caffe.CaffeFunction(download_file_path)
layer_names = [
"conv1_1", "conv1_2", "conv2_1", "conv2_2", "conv3_1", "conv3_2",
"conv3_3", "conv3_4", "conv4_1", "conv4_2", "conv4_3_CPM",
"conv4_4_CPM", "conv5_1_CPM_L1", "conv5_2_CPM_L1", "conv5_3_CPM_L1",
"conv5_4_CPM_L1", "conv5_1_CPM_L2", "conv5_2_CPM_L2", "conv5_3_CPM_L2",
"conv5_4_CPM_L2", "conv5_5_CPM_L2", "Mconv1_stage2_L1",
"Mconv2_stage2_L1", "Mconv3_stage2_L1", "Mconv4_stage2_L1",
"Mconv5_stage2_L1", "Mconv6_stage2_L1", "Mconv7_stage2_L1",
"Mconv1_stage2_L2", "Mconv2_stage2_L2", "Mconv3_stage2_L2",
"Mconv4_stage2_L2", "Mconv5_stage2_L2", "Mconv6_stage2_L2",
"Mconv7_stage2_L2", "Mconv1_stage3_L1", "Mconv2_stage3_L1",
"Mconv3_stage3_L1", "Mconv4_stage3_L1", "Mconv5_stage3_L1",
"Mconv6_stage3_L1", "Mconv7_stage3_L1", "Mconv1_stage3_L2",
"Mconv2_stage3_L2", "Mconv3_stage3_L2", "Mconv4_stage3_L2",
"Mconv5_stage3_L2", "Mconv6_stage3_L2", "Mconv7_stage3_L2",
"Mconv1_stage4_L1", "Mconv2_stage4_L1", "Mconv3_stage4_L1",
"Mconv4_stage4_L1", "Mconv5_stage4_L1", "Mconv6_stage4_L1",
"Mconv7_stage4_L1", "Mconv1_stage4_L2", "Mconv2_stage4_L2",
"Mconv3_stage4_L2", "Mconv4_stage4_L2", "Mconv5_stage4_L2",
"Mconv6_stage4_L2", "Mconv7_stage4_L2", "Mconv1_stage5_L1",
"Mconv2_stage5_L1", "Mconv3_stage5_L1", "Mconv4_stage5_L1",
"Mconv5_stage5_L1", "Mconv6_stage5_L1", "Mconv7_stage5_L1",
"Mconv1_stage5_L2", "Mconv2_stage5_L2", "Mconv3_stage5_L2",
"Mconv4_stage5_L2", "Mconv5_stage5_L2", "Mconv6_stage5_L2",
"Mconv7_stage5_L2", "Mconv1_stage6_L1", "Mconv2_stage6_L1",
"Mconv3_stage6_L1", "Mconv4_stage6_L1", "Mconv5_stage6_L1",
"Mconv6_stage6_L1", "Mconv7_stage6_L1", "Mconv1_stage6_L2",
"Mconv2_stage6_L2", "Mconv3_stage6_L2", "Mconv4_stage6_L2",
"Mconv5_stage6_L2", "Mconv6_stage6_L2", "Mconv7_stage6_L2"
]
# copy layer params
for layer_name in layer_names:
model[layer_name].copyparams(caffe_model[layer_name])
chainer.serializers.save_npz(pretrained_model_filename, model)
return pretrained_model_filename
def get_pascal_voc(year):
if year not in urls:
raise ValueError
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'VOCdevkit/VOC{}'.format(year))
if os.path.exists(base_path):
# skip downloading
return base_path
download_file_path = utils.cached_download(urls[year])
ext = os.path.splitext(urls[year])[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def _get_online_products():
# To support ChainerMN, the target directory should be locked.
with filelock.FileLock(
os.path.join(
download.get_dataset_directory('pfnet/chainercv/.lock'),
'online_products.lock')):
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'Stanford_Online_Products')
if os.path.exists(base_path):
# skip downloading
return base_path
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def _get_imagenet(urls):
data_root = download.get_dataset_directory(root)
# this is error prone
if os.path.exists(os.path.join(data_root, 'train')):
return data_root
for key, url in urls.items():
download_file_path = utils.cached_download(url)
d = os.path.join(data_root, key)
if not os.path.exists(d):
os.makedirs(d)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, d, ext)
# train dataset contains tar inside of tar
train_dir = os.path.join(data_root, 'train')
for tar_fn in os.listdir(train_dir):
if tar_fn[-3:] == 'tar':
with tarfile.TarFile(os.path.join(train_dir, tar_fn), 'r') as t:
t.extractall(train_dir)
# parse developers kit
developers_kit_dir = os.path.join(data_root, 'developers_kit')
synsets = _parse_meta_mat(developers_kit_dir)
wnid_to_ilsvrc_id = {val['WNID']: key for key, val in synsets.items()}
# prepare train_pairs.txt
train_pairs_fn = os.path.join(data_root, 'train_pairs.txt')
with open(train_pairs_fn, 'w') as f:
for fn in os.listdir(train_dir):
synset = fn[:9]
if synset in wnid_to_ilsvrc_id and fn[-4:] == 'JPEG':
int_key = wnid_to_ilsvrc_id[synset] # starting from 0
f.write('{} {}\n'.format(fn, int_key))
# prepare val_pairs.txt
val_pairs_fn = os.path.join(data_root, 'val_pairs.txt')
val_gt_fn = os.path.join(developers_kit_dir, 'ILSVRC2012_devkit_t12/data',
'ILSVRC2012_validation_ground_truth.txt')
with open(val_pairs_fn, 'w') as f:
for i, l in enumerate(open(val_gt_fn)):
key = int(l) # starting from 0
index = i + 1
fn = 'ILSVRC2012_val_{0:08}.JPEG'.format(index)
f.write('{} {}\n'.format(fn, key))
return data_root
def get_sbd():
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'benchmark_RELEASE/dataset')
train_voc2012_file = os.path.join(base_path, 'train_voc2012.txt')
if os.path.exists(train_voc2012_file):
# skip downloading
return base_path
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, data_root, ext)
six.moves.urllib.request.urlretrieve(train_voc2012_url, train_voc2012_file)
_generate_voc2012_txt(base_path)
return base_path
def get_voc(year, split):
if year not in urls:
raise ValueError
key = year
if split == 'test' and year == '2007':
key = '2007_test'
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'VOCdevkit/VOC{}'.format(year))
split_file = os.path.join(base_path, 'ImageSets/Main/{}.txt'.format(split))
if os.path.exists(split_file):
# skip downloading
return base_path
download_file_path = utils.cached_download(urls[key])
ext = os.path.splitext(urls[key])[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def get_voc(year, split):
if year not in urls:
raise ValueError
key = year
if split == 'test' and year == '2007':
key = '2007_test'
data_root = download.get_dataset_directory(root)
base_path = os.path.join(data_root, 'VOCdevkit/VOC{}'.format(year))
split_file = os.path.join(base_path, 'ImageSets/Main/{}.txt'.format(split))
if os.path.exists(split_file):
# skip downloading
return base_path
download_file_path = utils.cached_download(urls[key])
ext = os.path.splitext(urls[key])[1]
utils.extractall(download_file_path, data_root, ext)
return base_path
def get_sbd():
data_root = download.get_dataset_directory(root)
# To support ChainerMN, the target directory should be locked.
with filelock.FileLock(os.path.join(data_root, 'lock')):
base_path = os.path.join(data_root, 'benchmark_RELEASE/dataset')
train_voc2012_file = os.path.join(base_path, 'train_voc2012.txt')
if os.path.exists(train_voc2012_file):
# skip downloading
return base_path
download_file_path = utils.cached_download(url)
ext = os.path.splitext(url)[1]
utils.extractall(download_file_path, data_root, ext)
six.moves.urllib.request.urlretrieve(train_voc2012_url,
train_voc2012_file)
_generate_voc2012_txt(base_path)
return base_path
def get_ade20k(root, url):
data_root = download.get_dataset_directory(root)
cache_path = utils.cached_download(url)
utils.extractall(cache_path, data_root, os.path.splitext(url)[1])
return data_root
