def __init__(self,
in_channels,
channels,
*,
num_classes,
dropout_ratio=0.1,
conv_cfg=None,
norm_cfg=None,
act_cfg=dict(type='ReLU'),
in_index=-1,
input_transform=None,
loss_decode=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
ignore_index=255,
sampler=None,
align_corners=False,
init_cfg=dict(
type='Normal', std=0.01, override=dict(name='conv_seg'))):
super(BaseDecodeHead, self).__init__(init_cfg)
self._init_inputs(in_channels, in_index, input_transform)
self.channels = channels
self.num_classes = num_classes
self.dropout_ratio = dropout_ratio
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.in_index = in_index
self.ignore_index = ignore_index
self.align_corners = align_corners
self.loss_decode = nn.ModuleList()
if isinstance(loss_decode, dict):
self.loss_decode.append(build_loss(loss_decode))
elif isinstance(loss_decode, (list, tuple)):
for loss in loss_decode:
self.loss_decode.append(build_loss(loss))
else:
raise TypeError(f'loss_decode must be a dict or sequence of dict,\
but got {type(loss_decode)}')
if sampler is not None:
self.sampler = build_pixel_sampler(sampler, context=self)
else:
self.sampler = None
self.conv_seg = nn.Conv2d(channels, num_classes, kernel_size=1)
if dropout_ratio > 0:
self.dropout = nn.Dropout2d(dropout_ratio)
else:
self.dropout = None
self.fp16_enabled = False
def __init__(self,
in_channels,
channels,
*,
num_classes,
dropout_ratio=0.1,
conv_cfg=None,
norm_cfg=None,
act_cfg=dict(type='ReLU'),
in_index=-1,
input_transform=None,
loss_decode=dict(type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
ignore_index=255,
sampler=None,
align_corners=False):
super(BaseDecodeHead, self).__init__()
self._init_inputs(in_channels, in_index, input_transform)
self.channels = channels
self.num_classes = num_classes
self.dropout_ratio = dropout_ratio
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.in_index = in_index
self.loss_decode = build_loss(loss_decode)
self.ignore_index = ignore_index
self.align_corners = align_corners
if sampler is not None:
self.sampler = build_pixel_sampler(sampler, context=self)
else:
self.sampler = None
if conv_cfg == None:
self.conv_seg = nn.Conv2d(channels, num_classes, kernel_size=1)
if dropout_ratio > 0:
self.dropout = nn.Dropout2d(dropout_ratio)
else:
self.dropout = None
elif conv_cfg['type'] == 'Conv3d':
self.conv_seg = nn.Conv3d(channels, num_classes, kernel_size=1)
if dropout_ratio > 0:
self.dropout = nn.Dropout3d(dropout_ratio)
else:
self.dropout = None
self.fp16_enabled = False