def _download(self, root):
if not os.path.isdir("{root}/wikitext-103".format(root=self.root)):
archive_dict = ARCHIVE_DICT["full"]
download_and_extract_archive(
archive_dict["url"],
self.root,
extract_root=self.root,
md5=archive_dict["md5"],
)
if not os.path.isfile(self.file):
file_zip = os.path.join(root, "wikitext-103-v1.zip")
if os.path.isfile(file_zip):
extract_archive(from_path=file_zip, to_path=root)
def _download(self, root, annFile):
if not os.path.isdir(root) or os.path.isdir(annFile):
if '2017' in root:
image_dir_zip = os.path.join(up(root), 'val2017.zip')
elif '2014' in root:
image_dir_zip = os.path.join(up(root), 'val2014.zip')
if '2017' in annFile:
annotations_dir_zip = os.path.join(
up(up(annFile)), 'annotations_trainval2017.zip')
elif '2014' in annFile:
annotations_dir_zip = os.path.join(
up(up(annFile)), 'annotations_trainval2014.zip')
if os.path.isfile(image_dir_zip) and os.path.isfile(
annotations_dir_zip):
extract_archive(from_path=image_dir_zip, to_path=up(root))
extract_archive(from_path=annotations_dir_zip,
to_path=up(root))
def _download(self, root, annFile):
if not os.path.isdir(root) or not os.path.isdir(annFile):
if "2017" in root:
image_dir_zip = os.path.join(up(root), "val2017.zip")
elif "2014" in root:
image_dir_zip = os.path.join(up(root), "val2014.zip")
else:
image_dir_zip = None
if "2017" in annFile:
annotations_dir_zip = os.path.join(
up(up(annFile)), "annotations_trainval2017.zip")
elif "2014" in annFile:
annotations_dir_zip = os.path.join(
up(up(annFile)), "annotations_trainval2014.zip")
else:
annotations_dir_zip = None
if (image_dir_zip is not None and os.path.isfile(image_dir_zip)
and annotations_dir_zip is not None
and os.path.isfile(annotations_dir_zip)):
extract_archive(from_path=image_dir_zip, to_path=up(root))
extract_archive(from_path=annotations_dir_zip,
to_path=up(root))
def _download(self, root):
if not os.path.isfile(self.file):
file_zip = os.path.join(root, "CoNLL2003.zip")
if os.path.isfile(file_zip):
extract_archive(from_path=file_zip, to_path=root)
import copy
import mmcv
import torch
import torch.distributed as dist
from mmcv.runner import load_checkpoint, get_dist_info
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmdet.apis import init_dist
from mmdet.core import coco_eval, wrap_fp16_model
from mmdet.datasets import build_dataloader, get_dataset
from mmdet.models import build_detector
# Extract val2017 zip
from torchbench.utils import extract_archive
image_dir_zip = osp.join('./.data/vision/coco', 'val2017.zip')
extract_archive(from_path=image_dir_zip, to_path='./.data/vision/coco')
def xyxy2xywh(bbox):
_bbox = bbox.tolist()
return [
_bbox[0],
_bbox[1],
_bbox[2] - _bbox[0] + 1,
_bbox[3] - _bbox[1] + 1,
]
def proposal2json(dataset, results):
json_results = []
for idx in range(len(dataset)):