align_corners=False,# 解码里调整大小(resize)的 align_corners 参数。loss_decode=dict(# 解码头(decode_head)里的损失函数的配置项。type='CrossEntropyLoss',# 在分割里使用的损失函数的类别。use_sigmoid=False,# 在分割里是否使用 sigmoid 激活。loss_weight=1.0)),# 解码头里损失的权重。auxiliary_head=d...
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), # model training and testing settings train_cfg=dict(), test_cfg=dict(mode='whole')) 这个文件是网络架构配置,type 是用 register 注册过类,根据 type 可以找到对应的类,也可以自己定义模型后使用 Registor 进行注册后,后面跟着参数是这...
use_sigmoid=False, use_mask=False, reduction='mean', class_weight=None, loss_weight=1.0, loss_name='loss_ce', avg_non_ignore=False): super().__init__() assert (use_sigmoid is False) or (use_mask is False) self.use_sigmoid = use_sigmoid self.use_mask = use_mask...
use_sigmoid=True, activate=True, reduction='mean', naive_dice=False, loss_weight=1.0, ignore_index=255, eps=1e-3, loss_name='loss_dice'): """Compute dice loss. Args: use_sigmoid (bool, optional): Whether to the prediction is ...
loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), norm_cfg=dict(requires_grad=True, type='BN'), num_classes=6, num_convs=1, type='FCNHead'), backbone=dict( contract_dilation=True, depth=50, dilations=( 1, 1,
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( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), # model training and testing settings train_cfg=dict(), test_cfg=dict(mode='whole')) # 准备训练各种参数 # yapf:disable # 准备日志 log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook', by_epoch=False),...
Default: dict(type='CrossEntropyLoss', use_sigmoid=True). """ def__init__(self, num_codes=32, use_se_loss=True, add_lateral=False, loss_se_decode=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2), **kwargs): ...
sigmoid() seg_logits = torch.einsum('bqc,bqhw->bchw', mask_cls, mask_pred) return seg_logits def loss(self, x: Tuple[Tensor], batch_data_samples: SampleList, train_cfg: ConfigType) -> dict: """Perform forward propagation and loss calculation of the decoder head ...
use_sigmoid=False, loss_weight=1.0)), auxiliary_head=[ dict( type='FCNHead', in_channels=16, channels=16, num_convs=2, num_classes=12, in_index=1, norm_cfg=norm_cfg, concat_input=False, align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight...