def is_norm(layer, exclude=None):
"""Check if a layer is a normalization layer.
Args:
layer (nn.Module): The layer to be checked.
exclude (type | tuple[type]): Types to be excluded.
Returns:
bool: Whether the layer is a norm layer.
"""
if exclude is not None:
if not isinstance(exclude, tuple):
exclude = (exclude, )
if not is_tuple_of(exclude, type):
raise TypeError(
f'"exclude" must be either None or type or a tuple of types, '
f'but got {type(exclude)}: {exclude}')
if exclude and isinstance(layer, exclude):
return False
all_norm_bases = (_BatchNorm, _InstanceNorm, nn.GroupNorm, nn.LayerNorm)
if isinstance(layer, all_norm_bases):
return True
else:
return False
def __init__(self,
by_epoch=True,
interval=10,
ignore_last=True,
reset_flag=False,
interval_exp_name=1000,
out_dir=None,
out_suffix=('.log.json', '.log', '.py'),
keep_local=True,
file_client_args=None):
super(TextLoggerHook, self).__init__(interval, ignore_last, reset_flag,
by_epoch)
self.by_epoch = by_epoch
self.time_sec_tot = 0
self.interval_exp_name = interval_exp_name
if out_dir is None and file_client_args is not None:
raise ValueError(
'file_client_args should be "None" when `out_dir` is not'
'specified.')
self.out_dir = out_dir
if not (out_dir is None or isinstance(out_dir, str)
or is_tuple_of(out_dir, str)):
raise TypeError('out_dir should be "None" or string or tuple of '
'string, but got {out_dir}')
self.out_suffix = out_suffix
self.keep_local = keep_local
self.file_client_args = file_client_args
if self.out_dir is not None:
self.file_client = FileClient.infer_client(file_client_args,
self.out_dir)
def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)):
assert isinstance(clip_limit, (float, int))
self.clip_limit = clip_limit
assert is_tuple_of(tile_grid_size, int)
assert len(tile_grid_size) == 2
self.tile_grid_size = tile_grid_size
