本文整理汇总了Python中detectron.core.config.cfg.DOWNLOAD_CACHE属性的典型用法代码示例。如果您正苦于以下问题：Python cfg.DOWNLOAD_CACHE属性的具体用法？Python cfg.DOWNLOAD_CACHE怎么用？Python cfg.DOWNLOAD_CACHE使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类detectron.core.config.cfg的用法示例。
在下文中一共展示了cfg.DOWNLOAD_CACHE属性的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: check_args
# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import DOWNLOAD_CACHE [as 别名]
def check_args(args):
assert (
(args.rpn_pkl is not None and args.rpn_cfg is not None) or
(args.rpn_pkl is None and args.rpn_cfg is None)
)
if args.rpn_pkl is not None:
args.rpn_pkl = cache_url(args.rpn_pkl, cfg.DOWNLOAD_CACHE)
assert os.path.exists(args.rpn_pkl)
assert os.path.exists(args.rpn_cfg)
if args.models_to_run is not None:
assert len(args.models_to_run) % 2 == 0
for i, model_file in enumerate(args.models_to_run):
if len(model_file) > 0:
if i % 2 == 0:
model_file = cache_url(model_file, cfg.DOWNLOAD_CACHE)
args.models_to_run[i] = model_file
assert os.path.exists(model_file), \
'\'{}\' does not exist'.format(model_file) 示例2: main
# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import DOWNLOAD_CACHE [as 别名]
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
) 示例3: main
# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import DOWNLOAD_CACHE [as 别名]
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
)