本文整理匯總了Python中mmcv.parallel.DataContainer方法的典型用法代碼示例。如果您正苦於以下問題：Python parallel.DataContainer方法的具體用法？Python parallel.DataContainer怎麽用？Python parallel.DataContainer使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mmcv.parallel的用法示例。
在下文中一共展示了parallel.DataContainer方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: __call__
# 需要導入模塊: from mmcv import parallel [as 別名]
# 或者: from mmcv.parallel import DataContainer [as 別名]
def __call__(self, results):
"""Call function to convert data in results to
:obj:`mmcv.DataContainer`.
Args:
results (dict): Result dict contains the data to convert.
Returns:
dict: The result dict contains the data converted to
:obj:`mmcv.DataContainer`.
"""
for field in self.fields:
field = field.copy()
key = field.pop('key')
results[key] = DC(results[key], **field)
return results 示例2: __call__
# 需要導入模塊: from mmcv import parallel [as 別名]
# 或者: from mmcv.parallel import DataContainer [as 別名]
def __call__(self, results):
for field in self.fields:
field = field.copy()
key = field.pop('key')
results[key] = DC(results[key], **field)
return results 示例3: prepare_test_img
# 需要導入模塊: from mmcv import parallel [as 別名]
# 或者: from mmcv.parallel import DataContainer [as 別名]
def prepare_test_img(self, idx):
"""Prepare an image for testing (multi-scale and flipping)"""
img_info = self.img_infos[idx]
img = mmcv.imread(osp.join(self.img_prefix, img_info['filename']))
if self.proposals is not None:
proposal = self.proposals[idx][:self.num_max_proposals]
if not (proposal.shape[1] == 4 or proposal.shape[1] == 5):
raise AssertionError(
'proposals should have shapes (n, 4) or (n, 5), '
'but found {}'.format(proposal.shape))
else:
proposal = None
def prepare_single(img, scale, flip, proposal=None):
_img, img_shape, pad_shape, scale_factor = self.img_transform(
img, scale, flip, keep_ratio=self.resize_keep_ratio)
_img = to_tensor(_img)
_img_meta = dict(
ori_shape=(img_info['height'], img_info['width'], 3),
img_shape=img_shape,
pad_shape=pad_shape,
scale_factor=scale_factor,
flip=flip)
if proposal is not None:
if proposal.shape[1] == 5:
score = proposal[:, 4, None]
proposal = proposal[:, :4]
else:
score = None
_proposal = self.bbox_transform(proposal, img_shape,
scale_factor, flip)
_proposal = np.hstack(
[_proposal, score]) if score is not None else _proposal
_proposal = to_tensor(_proposal)
else:
_proposal = None
return _img, _img_meta, _proposal
imgs = []
img_metas = []
proposals = []
for scale in self.img_scales:
_img, _img_meta, _proposal = prepare_single(
img, scale, False, proposal)
imgs.append(_img)
img_metas.append(DC(_img_meta, cpu_only=True))
proposals.append(_proposal)
if self.flip_ratio > 0:
_img, _img_meta, _proposal = prepare_single(
img, scale, True, proposal)
imgs.append(_img)
img_metas.append(DC(_img_meta, cpu_only=True))
proposals.append(_proposal)
data = dict(img=imgs, img_meta=img_metas)
if self.proposals is not None:
data['proposals'] = proposals
return data