def __init__(self, kernel_size, iterations=1, random=True):
if isinstance(kernel_size, int):
kernel_size = kernel_size, kernel_size + 1
elif not mmcv.is_tuple_of(kernel_size, int) or len(kernel_size) != 2:
raise ValueError('kernel_size must be an int or a tuple of 2 int, '
f'but got {kernel_size}')
if isinstance(iterations, int):
iterations = iterations, iterations + 1
elif not mmcv.is_tuple_of(iterations, int) or len(iterations) != 2:
raise ValueError('iterations must be an int or a tuple of 2 int, '
f'but got {iterations}')
self.random = random
if self.random:
min_kernel, max_kernel = kernel_size
self.iterations = iterations
self.kernels = [
cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size, size))
for size in range(min_kernel, max_kernel)
]
else:
erode_ksize, dilate_ksize = kernel_size
self.iterations = iterations
self.kernels = [
cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
(erode_ksize, erode_ksize)),
cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
(dilate_ksize, dilate_ksize))
]
def __init__(self,
keys,
crop_size,
scale=(0.08, 1.0),
ratio=(3. / 4., 4. / 3.),
interpolation='bilinear'):
assert keys, 'Keys should not be empty.'
if isinstance(crop_size, int):
crop_size = (crop_size, crop_size)
elif not mmcv.is_tuple_of(crop_size, int):
raise TypeError('"crop_size" must be an integer '
'or a tuple of integers, but got '
f'{type(crop_size)}')
if not mmcv.is_tuple_of(scale, float):
raise TypeError('"scale" must be a tuple of float, '
f'but got {type(scale)}')
if not mmcv.is_tuple_of(ratio, float):
raise TypeError('"ratio" must be a tuple of float, '
f'but got {type(ratio)}')
self.keys = keys
self.crop_size = crop_size
self.scale = scale
self.ratio = ratio
self.interpolation = interpolation
def __init__(self,
area_range=(0.08, 1.0),
aspect_ratio_range=(3 / 4, 4 / 3),
lazy=False):
self.area_range = area_range
self.aspect_ratio_range = aspect_ratio_range
self.lazy = lazy
if not mmcv.is_tuple_of(self.area_range, float):
raise TypeError(f'Area_range must be a tuple of float, '
f'but got {type(area_range)}')
if not mmcv.is_tuple_of(self.aspect_ratio_range, float):
raise TypeError(f'Aspect_ratio_range must be a tuple of float, '
f'but got {type(aspect_ratio_range)}')
def __init__(self, keys, crop_size, crop_pos=None):
if not mmcv.is_tuple_of(crop_size, int):
raise TypeError(
'Elements of crop_size must be int and crop_size must be'
f' tuple, but got {type(crop_size[0])} in {type(crop_size)}')
if not mmcv.is_tuple_of(crop_pos, int) and (crop_pos is not None):
raise TypeError(
'Elements of crop_pos must be int and crop_pos must be'
f' tuple or None, but got {type(crop_pos[0])} in '
f'{type(crop_pos)}')
self.keys = keys
self.crop_size = crop_size
self.crop_pos = crop_pos
def __init__(self,
channels,
ratio=4,
conv_cfg=None,
act_cfg=(dict(type='ReLU'),
dict(type='HSigmoid', bias=3.0, divisor=6.0)),
init_cfg=None):
super().__init__(init_cfg=init_cfg)
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
assert mmcv.is_tuple_of(act_cfg, dict)
self.channels = channels
self.expansion = 4 # for a1, b1, a2, b2
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = ConvModule(in_channels=channels,
out_channels=int(channels / ratio),
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
act_cfg=act_cfg[0])
self.conv2 = ConvModule(in_channels=int(channels / ratio),
out_channels=channels * self.expansion,
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
act_cfg=act_cfg[1])
def __init__(self,
arch='small',
conv_cfg=None,
norm_cfg=dict(type='BN'),
out_indices=(0, 1, 12),
frozen_stages=-1,
reduction_factor=1,
norm_eval=False,
with_cp=False):
super(MobileNetV3, self).__init__()
assert arch in self.arch_settings
assert isinstance(reduction_factor, int) and reduction_factor > 0
assert mmcv.is_tuple_of(out_indices, int)
for index in out_indices:
if index not in range(0, len(self.arch_settings[arch]) + 2):
raise ValueError(
'the item in out_indices must in '
f'range(0, {len(self.arch_settings[arch])+2}). '
f'But received {index}')
if frozen_stages not in range(-1, len(self.arch_settings[arch]) + 2):
raise ValueError('frozen_stages must be in range(-1, '
f'{len(self.arch_settings[arch])+2}). '
f'But received {frozen_stages}')
self.arch = arch
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.reduction_factor = reduction_factor
self.norm_eval = norm_eval
self.with_cp = with_cp
self.layers = self._make_layer()
def __init__(self,
channels,
ratio=16,
conv_cfg=None,
act_cfg=(dict(type='ReLU'),
dict(type='HSigmoid', bias=3.0, divisor=6.0))):
super(SELayer, self).__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
assert mmcv.is_tuple_of(act_cfg, dict)
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = ConvModule(in_channels=channels,
out_channels=make_divisible(
channels // ratio, 8),
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
act_cfg=act_cfg[0])
self.conv2 = ConvModule(in_channels=make_divisible(
channels // ratio, 8),
out_channels=channels,
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
act_cfg=act_cfg[1])
def __init__(self,
module_keys,
interp_mode='lerp',
interp_cfg=None,
interval=-1,
start_iter=0):
super().__init__()
assert isinstance(module_keys, str) or mmcv.is_tuple_of(
module_keys, str)
self.module_keys = (module_keys, ) if isinstance(module_keys,
str) else module_keys
# sanity check for the format of module keys
for k in self.module_keys:
assert k.endswith(
'_ema'), 'You should give keys that end with "_ema".'
self.interp_mode = interp_mode
self.interp_cfg = dict() if interp_cfg is None else deepcopy(
interp_cfg)
self.interval = interval
self.start_iter = start_iter
assert hasattr(
self, interp_mode
), f'Currently, we do not support {self.interp_mode} for EMA.'
self.interp_func = partial(
getattr(self, interp_mode), **self.interp_cfg)
def __init__(self, input_size, scale=(256, 320)):
if isinstance(input_size, int):
self.input_size = (input_size, input_size)
else:
self.input_size = input_size
assert mmcv.is_tuple_of(self.input_size, int)
self.scale = scale
def __init__(self,
channels,
ratio=32,
conv_cfg=None,
act_cfg=(dict(type='ReLU'), dict(type='Sigmoid'))):
super().__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
assert mmcv.is_tuple_of(act_cfg, dict)
self.pool_h = nn.AdaptiveAvgPool2d((None, 1))
self.pool_w = nn.AdaptiveAvgPool2d((1, None))
mip = max(8, channels // ratio)
self.conv1 = nn.Conv2d(channels,
mip,
kernel_size=1,
stride=1,
padding=0)
self.bn1 = nn.BatchNorm2d(mip)
self.act = h_swish()
self.conv_h = nn.Conv2d(mip,
channels,
kernel_size=1,
stride=1,
padding=0)
self.conv_w = nn.Conv2d(mip,
channels,
kernel_size=1,
stride=1,
padding=0)
def __init__(self,
channels,
ratio=16,
conv_cfg=None,
norm_cfg=None,
act_cfg=(dict(type='ReLU'), dict(type='Sigmoid'))):
super().__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
assert mmcv.is_tuple_of(act_cfg, dict)
self.channels = channels
total_channel = sum(channels)
self.conv1 = ConvModule(in_channels=total_channel,
out_channels=int(total_channel / ratio),
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg[0])
self.conv2 = ConvModule(in_channels=int(total_channel / ratio),
out_channels=total_channel,
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg[1])
def __init__(self,
channels,
squeeze_channels=None,
ratio=16,
divisor=8,
bias='auto',
conv_cfg=None,
act_cfg=(dict(type='ReLU'), dict(type='Sigmoid')),
init_cfg=None):
super(SELayer, self).__init__(init_cfg)
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
assert mmcv.is_tuple_of(act_cfg, dict)
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
if squeeze_channels is None:
squeeze_channels = make_divisible(channels // ratio, divisor)
assert isinstance(squeeze_channels, int) and squeeze_channels > 0, \
'"squeeze_channels" should be a positive integer, but get ' + \
f'{squeeze_channels} instead.'
self.conv1 = ConvModule(in_channels=channels,
out_channels=squeeze_channels,
kernel_size=1,
stride=1,
bias=bias,
conv_cfg=conv_cfg,
act_cfg=act_cfg[0])
self.conv2 = ConvModule(in_channels=squeeze_channels,
out_channels=channels,
kernel_size=1,
stride=1,
bias=bias,
conv_cfg=conv_cfg,
act_cfg=act_cfg[1])
def __init__(self,
channels,
ratio=16,
conv_cfg=None,
norm_cfg=None,
act_cfg=(dict(type='ReLU'), dict(type='Sigmoid'))):
super().__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
assert mmcv.is_tuple_of(act_cfg, dict)
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = ConvModule(in_channels=channels,
out_channels=int(channels / ratio),
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg[0])
self.conv2 = ConvModule(in_channels=int(channels / ratio),
out_channels=channels,
kernel_size=1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg[1])
def __init__(self,
clip_len,
body_segments,
aug_segments,
aug_ratio,
frame_interval=1,
test_interval=6,
start_index=1,
temporal_jitter=False,
mode='train'):
super().__init__(clip_len,
frame_interval=frame_interval,
start_index=start_index,
temporal_jitter=temporal_jitter)
self.body_segments = body_segments
self.aug_segments = aug_segments
self.aug_ratio = _pair(aug_ratio)
if not mmcv.is_tuple_of(self.aug_ratio, (int, float)):
raise TypeError(f'aug_ratio should be int, float'
f'or tuple of int and float, '
f'but got {type(aug_ratio)}')
assert len(self.aug_ratio) == 2
assert mode in ['train', 'val', 'test']
self.mode = mode
self.test_interval = test_interval
def __init__(self,
keys,
crop_sizes,
unknown_source='alpha',
interpolations='bilinear'):
if 'alpha' not in keys:
raise ValueError(f'"alpha" must be in keys, but got {keys}')
self.keys = keys
if not isinstance(crop_sizes, list):
raise TypeError(
f'Crop sizes must be list, but got {type(crop_sizes)}.')
self.crop_sizes = [_pair(crop_size) for crop_size in crop_sizes]
if not mmcv.is_tuple_of(self.crop_sizes[0], int):
raise TypeError('Elements of crop_sizes must be int or tuple of '
f'int, but got {type(self.crop_sizes[0][0])}.')
if unknown_source not in ['alpha', 'trimap']:
raise ValueError('unknown_source must be "alpha" or "trimap", '
f'but got {unknown_source}')
elif unknown_source not in keys:
# it could only be trimap, since alpha is checked before
raise ValueError(
'if unknown_source is "trimap", it must also be set in keys')
self.unknown_source = unknown_source
if isinstance(interpolations, str):
self.interpolations = [interpolations] * len(self.keys)
elif mmcv.is_list_of(interpolations,
str) and len(interpolations) == len(self.keys):
self.interpolations = interpolations
else:
raise TypeError(
'interpolations must be a str or list of str with '
f'the same length as keys, but got {interpolations}')
def __init__(self, crop_size):
if mmcv.is_tuple_of(crop_size, int):
assert len(crop_size) == 2, 'length of crop_size must be 2.'
elif not isinstance(crop_size, int):
raise TypeError('crop_size must be int or a tuple of int, but got '
f'{type(crop_size)}')
self.crop_size = _pair(crop_size)
def __init__(self, keys, crop_size, random_crop=True):
if not mmcv.is_tuple_of(crop_size, int):
raise TypeError(
'Elements of crop_size must be int and crop_size must be'
f' tuple, but got {type(crop_size[0])} in {type(crop_size)}')
self.keys = keys
self.crop_size = crop_size
self.random_crop = random_crop
def __init__(self, input_size, scale=(0.08, 1.0),
ratio=(3. / 4., 4. / 3.)):
if isinstance(input_size, int):
self.input_size = (input_size, input_size)
else:
self.input_size = input_size
assert mmcv.is_tuple_of(self.input_size, int)
self.scale = scale
self.ratio = ratio
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)
def __init__(self, keys, bd_ratio_range=(0.1, 0.4), test_mode=False):
if 'seg' not in keys:
raise ValueError(f'"seg" must be in keys, but got {keys}')
if (not mmcv.is_tuple_of(bd_ratio_range, float)
or len(bd_ratio_range) != 2):
raise TypeError('bd_ratio_range must be a tuple of 2 int, but got '
f'{bd_ratio_range}')
self.keys = keys
self.bd_ratio_range = bd_ratio_range
self.test_mode = test_mode
def __init__(self, bbox_enlarge_range):
assert (is_tuple_of(bbox_enlarge_range, float)
and len(bbox_enlarge_range) == 3) \
or isinstance(bbox_enlarge_range, float), \
f'Invalid arguments bbox_enlarge_range {bbox_enlarge_range}'
if isinstance(bbox_enlarge_range, float):
bbox_enlarge_range = [bbox_enlarge_range] * 3
self.bbox_enlarge_range = np.array(bbox_enlarge_range,
dtype=np.float32)[np.newaxis, :]
def __init__(self,
arch='small',
conv_cfg=None,
norm_cfg=dict(type='BN'),
out_indices=(0, 1, 12),
frozen_stages=-1,
reduction_factor=1,
norm_eval=False,
with_cp=False,
pretrained=None,
init_cfg=None):
super(MobileNetV3, self).__init__(init_cfg)
self.pretrained = pretrained
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be setting at the same time'
if isinstance(pretrained, str):
warnings.warn('DeprecationWarning: pretrained is a deprecated, '
'please use "init_cfg" instead')
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
dict(type='Kaiming', layer='Conv2d'),
dict(
type='Constant',
val=1,
layer=['_BatchNorm', 'GroupNorm'])
]
else:
raise TypeError('pretrained must be a str or None')
assert arch in self.arch_settings
assert isinstance(reduction_factor, int) and reduction_factor > 0
assert mmcv.is_tuple_of(out_indices, int)
for index in out_indices:
if index not in range(0, len(self.arch_settings[arch]) + 2):
raise ValueError(
'the item in out_indices must in '
f'range(0, {len(self.arch_settings[arch])+2}). '
f'But received {index}')
if frozen_stages not in range(-1, len(self.arch_settings[arch]) + 2):
raise ValueError('frozen_stages must be in range(-1, '
f'{len(self.arch_settings[arch])+2}). '
f'But received {frozen_stages}')
self.arch = arch
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.reduction_factor = reduction_factor
self.norm_eval = norm_eval
self.with_cp = with_cp
self.layers = self._make_layer()
def __init__(self, crop_size, groups):
self.crop_size = _pair(crop_size)
self.groups = groups
if not mmcv.is_tuple_of(self.crop_size, int):
raise TypeError(
'Crop size must be int or tuple of int, but got {}'.format(
type(crop_size)))
if not isinstance(groups, int):
raise TypeError(f'Groups must be int, but got {type(groups)}.')
if groups <= 0:
raise ValueError('Groups must be positive.')
def __init__(self, keys, degrees):
if isinstance(degrees, (int, float)):
if degrees < 0.0:
raise ValueError('Degrees must be positive if it is a number.')
else:
degrees = (-degrees, degrees)
elif not mmcv.is_tuple_of(degrees, (int, float)):
raise TypeError(f'Degrees must be float | int or tuple of float | '
'int, but got '
f'{type(degrees)}.')
self.keys = keys
self.degrees = degrees
def __init__(self,
fg_thr=0.2,
border_width=25,
erode_ksize=3,
dilate_ksize=5,
erode_iter_range=(10, 20),
dilate_iter_range=(3, 7),
blur_ksizes=[(21, 21), (31, 31), (41, 41)]):
if not isinstance(fg_thr, float):
raise TypeError(f'fg_thr must be a float, but got {type(fg_thr)}')
if not isinstance(border_width, int):
raise TypeError(
f'border_width must be an int, but got {type(border_width)}')
if not isinstance(erode_ksize, int):
raise TypeError(
f'erode_ksize must be an int, but got {type(erode_ksize)}')
if not isinstance(dilate_ksize, int):
raise TypeError(
f'dilate_ksize must be an int, but got {type(dilate_ksize)}')
if (not mmcv.is_tuple_of(erode_iter_range, int)
or len(erode_iter_range) != 2):
raise TypeError('erode_iter_range must be a tuple of 2 int, '
f'but got {erode_iter_range}')
if (not mmcv.is_tuple_of(dilate_iter_range, int)
or len(dilate_iter_range) != 2):
raise TypeError('dilate_iter_range must be a tuple of 2 int, '
f'but got {dilate_iter_range}')
if not mmcv.is_list_of(blur_ksizes, tuple):
raise TypeError(
f'blur_ksizes must be a list of tuple, but got {blur_ksizes}')
self.fg_thr = fg_thr
self.border_width = border_width
self.erode_ksize = erode_ksize
self.dilate_ksize = dilate_ksize
self.erode_iter_range = erode_iter_range
self.dilate_iter_range = dilate_iter_range
self.blur_ksizes = blur_ksizes
def __init__(self,
channels,
ratio=16,
conv_cfg=None,
act_cfg=(dict(type='ReLU'), dict(type='Sigmoid'))):
super().__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2
assert mmcv.is_tuple_of(act_cfg, dict)
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
# self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
self.conv = nn.Conv1d(1, 1, kernel_size=3, padding=1, bias=False)
self.sigmoid = nn.Sigmoid()
def __init__(self,
in_channels,
vote_per_seed=1,
gt_per_seed=3,
num_points=-1,
conv_channels=(16, 16),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
act_cfg=dict(type='ReLU'),
norm_feats=True,
with_res_feat=True,
vote_xyz_range=None,
vote_loss=None):
super().__init__()
self.in_channels = in_channels
self.vote_per_seed = vote_per_seed
self.gt_per_seed = gt_per_seed
self.num_points = num_points
self.norm_feats = norm_feats
self.with_res_feat = with_res_feat
assert vote_xyz_range is None or is_tuple_of(vote_xyz_range, float)
self.vote_xyz_range = vote_xyz_range
if vote_loss is not None:
self.vote_loss = build_loss(vote_loss)
prev_channels = in_channels
vote_conv_list = list()
for k in range(len(conv_channels)):
vote_conv_list.append(
ConvModule(
prev_channels,
conv_channels[k],
1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
bias=True,
inplace=True))
prev_channels = conv_channels[k]
self.vote_conv = nn.Sequential(*vote_conv_list)
# conv_out predicts coordinate and residual features
if with_res_feat:
out_channel = (3 + in_channels) * self.vote_per_seed
else:
out_channel = 3 * self.vote_per_seed
self.conv_out = nn.Conv1d(prev_channels, out_channel, 1)
def forward(ctx, rois, pts, pts_feature, out_size, max_pts_per_voxel,
mode):
"""
Args:
rois (torch.Tensor): [N, 7], in LiDAR coordinate,
(x, y, z) is the bottom center of rois.
pts (torch.Tensor): [npoints, 3], coordinates of input points.
pts_feature (torch.Tensor): [npoints, C], features of input points.
out_size (int or tuple): The size of output features. n or
[n1, n2, n3].
max_pts_per_voxel (int): The maximum number of points per voxel.
Default: 128.
mode (int): Pooling method of RoIAware, 0 (max pool) or 1 (average
pool).
Returns:
pooled_features (torch.Tensor): [N, out_x, out_y, out_z, C], output
pooled features.
"""
if isinstance(out_size, int):
out_x = out_y = out_z = out_size
else:
assert len(out_size) == 3
assert mmcv.is_tuple_of(out_size, int)
out_x, out_y, out_z = out_size
num_rois = rois.shape[0]
num_channels = pts_feature.shape[-1]
num_pts = pts.shape[0]
pooled_features = pts_feature.new_zeros(
(num_rois, out_x, out_y, out_z, num_channels))
argmax = pts_feature.new_zeros(
(num_rois, out_x, out_y, out_z, num_channels), dtype=torch.int)
pts_idx_of_voxels = pts_feature.new_zeros(
(num_rois, out_x, out_y, out_z, max_pts_per_voxel),
dtype=torch.int)
ext_module.roiaware_pool3d_forward(rois,
pts,
pts_feature,
argmax,
pts_idx_of_voxels,
pooled_features,
pool_method=mode)
ctx.roiaware_pool3d_for_backward = (pts_idx_of_voxels, argmax, mode,
num_pts, num_channels)
return pooled_features
def __init__(self,
module_keys,
interp_mode='lerp',
interp_cfg=None,
interval=-1,
start_iter=0,
momentum_policy='fixed',
momentum_cfg=None):
super().__init__()
# check args
assert interp_mode in self._registered_interp_funcs, (
'Supported '
f'interpolation functions are {self._registered_interp_funcs}, '
f'but got {interp_mode}')
assert momentum_policy in self._registered_momentum_updaters, (
'Supported momentum policy are'
f'{self._registered_momentum_updaters},'
f' but got {momentum_policy}')
assert isinstance(module_keys, str) or mmcv.is_tuple_of(
module_keys, str)
self.module_keys = (module_keys, ) if isinstance(module_keys,
str) else module_keys
# sanity check for the format of module keys
for k in self.module_keys:
assert k.endswith(
'_ema'), 'You should give keys that end with "_ema".'
self.interp_mode = interp_mode
self.interp_cfg = dict() if interp_cfg is None else deepcopy(
interp_cfg)
self.interval = interval
self.start_iter = start_iter
assert hasattr(
self, interp_mode
), f'Currently, we do not support {self.interp_mode} for EMA.'
self.interp_func = getattr(self, interp_mode)
self.momentum_cfg = dict() if momentum_cfg is None else deepcopy(
momentum_cfg)
self.momentum_policy = momentum_policy
if momentum_policy != 'fixed':
assert hasattr(
self, momentum_policy
), f'Currently, we do not support {self.momentum_policy} for EMA.'
self.momentum_updater = getattr(self, momentum_policy)
def __init__(self,
keys,
scale=None,
keep_ratio=False,
size_factor=None,
max_size=None,
interpolation='bilinear',
backend=None,
output_keys=None):
assert keys, 'Keys should not be empty.'
if output_keys:
assert len(output_keys) == len(keys)
else:
output_keys = keys
if size_factor:
assert scale is None, ('When size_factor is used, scale should ',
f'be None. But received {scale}.')
assert keep_ratio is False, ('When size_factor is used, '
'keep_ratio should be False.')
if max_size:
assert size_factor is not None, (
'When max_size is used, '
f'size_factor should also be set. But received {size_factor}.')
if isinstance(scale, float):
if scale <= 0:
raise ValueError(f'Invalid scale {scale}, must be positive.')
elif mmcv.is_tuple_of(scale, int):
max_long_edge = max(scale)
max_short_edge = min(scale)
if max_short_edge == -1:
# assign np.inf to long edge for rescaling short edge later.
scale = (np.inf, max_long_edge)
elif scale is not None:
raise TypeError(
f'Scale must be None, float or tuple of int, but got '
f'{type(scale)}.')
self.keys = keys
self.output_keys = output_keys
self.scale = scale
self.size_factor = size_factor
self.max_size = max_size
self.keep_ratio = keep_ratio
self.interpolation = interpolation
self.backend = backend