本文整理汇总了Python中mmcv.is_list_of方法的典型用法代码示例。如果您正苦于以下问题:Python mmcv.is_list_of方法的具体用法?Python mmcv.is_list_of怎么用?Python mmcv.is_list_of使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mmcv
的用法示例。
在下文中一共展示了mmcv.is_list_of方法的9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: __init__
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import is_list_of [as 别名]
def __init__(self,
img_scale=None,
multiscale_mode='range',
ratio_range=None,
keep_ratio=True):
if img_scale is None:
self.img_scale = None
else:
if isinstance(img_scale, list):
self.img_scale = img_scale
else:
self.img_scale = [img_scale]
assert mmcv.is_list_of(self.img_scale, tuple)
if ratio_range is not None:
# mode 1: given a scale and a range of image ratio
assert len(self.img_scale) == 1
else:
# mode 2: given multiple scales or a range of scales
assert multiscale_mode in ['value', 'range']
self.multiscale_mode = multiscale_mode
self.ratio_range = ratio_range
self.keep_ratio = keep_ratio
示例2: random_select
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import is_list_of [as 别名]
def random_select(img_scales):
"""Randomly select an img_scale from given candidates.
Args:
img_scales (list[tuple]): Images scales for selection.
Returns:
(tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
where ``img_scale`` is the selected image scale and
``scale_idx`` is the selected index in the given candidates.
"""
assert mmcv.is_list_of(img_scales, tuple)
scale_idx = np.random.randint(len(img_scales))
img_scale = img_scales[scale_idx]
return img_scale, scale_idx
示例3: random_sample
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import is_list_of [as 别名]
def random_sample(img_scales):
"""Randomly sample an img_scale when ``multiscale_mode=='range'``.
Args:
img_scales (list[tuple]): Images scale range for sampling.
There must be two tuples in img_scales, which specify the lower
and uper bound of image scales.
Returns:
(tuple, None): Returns a tuple ``(img_scale, None)``, where
``img_scale`` is sampled scale and None is just a placeholder
to be consistent with :func:`random_select`.
"""
assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
img_scale_long = [max(s) for s in img_scales]
img_scale_short = [min(s) for s in img_scales]
long_edge = np.random.randint(
min(img_scale_long),
max(img_scale_long) + 1)
short_edge = np.random.randint(
min(img_scale_short),
max(img_scale_short) + 1)
img_scale = (long_edge, short_edge)
return img_scale, None
示例4: __init__
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import is_list_of [as 别名]
def __init__(self,
transforms,
img_scale=None,
scale_factor=None,
flip=False,
flip_direction='horizontal'):
self.transforms = Compose(transforms)
assert (img_scale is None) ^ (scale_factor is None), (
'Must have but only one variable can be setted')
if img_scale is not None:
self.img_scale = img_scale if isinstance(img_scale,
list) else [img_scale]
self.scale_key = 'scale'
assert mmcv.is_list_of(self.img_scale, tuple)
else:
self.img_scale = scale_factor if isinstance(
scale_factor, list) else [scale_factor]
self.scale_key = 'scale_factor'
self.flip = flip
self.flip_direction = flip_direction if isinstance(
flip_direction, list) else [flip_direction]
assert mmcv.is_list_of(self.flip_direction, str)
if not self.flip and self.flip_direction != ['horizontal']:
warnings.warn(
'flip_direction has no effect when flip is set to False')
if (self.flip
and not any([t['type'] == 'RandomFlip' for t in transforms])):
warnings.warn(
'flip has no effect when RandomFlip is not in transforms')
示例5: test_is_seq_of
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import is_list_of [as 别名]
def test_is_seq_of():
assert mmcv.is_seq_of([1.0, 2.0, 3.0], float)
assert mmcv.is_seq_of([(1, ), (2, ), (3, )], tuple)
assert mmcv.is_seq_of((1.0, 2.0, 3.0), float)
assert mmcv.is_list_of([1.0, 2.0, 3.0], float)
assert not mmcv.is_seq_of((1.0, 2.0, 3.0), float, seq_type=list)
assert not mmcv.is_tuple_of([1.0, 2.0, 3.0], float)
assert not mmcv.is_seq_of([1.0, 2, 3], int)
assert not mmcv.is_seq_of((1.0, 2, 3), int)
示例6: random_select
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import is_list_of [as 别名]
def random_select(img_scales):
assert mmcv.is_list_of(img_scales, tuple)
scale_idx = np.random.randint(len(img_scales))
img_scale = img_scales[scale_idx]
return img_scale, scale_idx
示例7: random_sample
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import is_list_of [as 别名]
def random_sample(img_scales):
assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
img_scale_long = [max(s) for s in img_scales]
img_scale_short = [min(s) for s in img_scales]
long_edge = np.random.randint(
min(img_scale_long),
max(img_scale_long) + 1)
short_edge = np.random.randint(
min(img_scale_short),
max(img_scale_short) + 1)
img_scale = (long_edge, short_edge)
return img_scale, None
示例8: __init__
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import is_list_of [as 别名]
def __init__(self, transforms, img_scale, flip=False):
self.transforms = Compose(transforms)
self.img_scale = img_scale if isinstance(img_scale,
list) else [img_scale]
assert mmcv.is_list_of(self.img_scale, tuple)
self.flip = flip
示例9: run_step_alter
# 需要导入模块: import mmcv [as 别名]
# 或者: from mmcv import is_list_of [as 别名]
def run_step_alter(self, data_loaders, workflow, max_epochs, arch_update_epoch, **kwargs):
"""Start running. Arch and weight optimization alternates by step.
Args:
data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
and validation.
workflow (list[tuple]): A list of (phase, epochs) to specify the
running order and epochs. E.g, [('train', 2), ('val', 1)] means
running 2 epochs for training and 1 epoch for validation,
iteratively.
max_epochs (int): Total training epochs.
"""
assert isinstance(data_loaders, list)
assert mmcv.is_list_of(workflow, tuple)
self._max_epochs = max_epochs
work_dir = self.work_dir if self.work_dir is not None else 'NONE'
self.logger.info('Start running, host: %s, work_dir: %s',
get_host_info(), work_dir)
self.logger.info('workflow: %s, max: %d epochs', workflow, max_epochs)
self.call_hook('before_run', 'train')
while self.epoch < max_epochs:
self.search_stage = 0 if self.epoch<self.cfg.arch_update_epoch else 1
self.train_step_alter(data_loaders)
time.sleep(1) # wait for some hooks like loggers to finish
self.call_hook('after_run', 'train')