mmseg-语义分割-Runtimerror-C10-CUDA-BUG-loss 最近发现一个使用mmseg由于经验不足,很多新手可能会犯的一个影响模型训练的大错。 报错如下: Traceback (most recent call last): ...(省略号省略细节) loss, log_vars = self._parse_losses(losses) File "/home/ggao/z_h_240513_files/z_models/mmseg/mo...
naive dice loss in which the power of the number in the denominator is the first power instead of the second power.Defaults to False.avg_factor (int, optional): Average factor that is used to average the loss. Defaults to None.ignore...
from mmseg.registry import MODELS from .utils import get_class_weight, weight_reduce_loss def cross_entropy(pred, label, weight=None, class_weight=None, reduction='mean', avg_factor=None, ignore_index=-100, avg_non_ignore=False): """cross_entropy. The wrapper function for ...
How to Add Custom Parameters to loss, predict, and _forward Functions in New Segmentor in mmseg1.2.2 Hello, I am using mmsegmentation to develop a new segmentation model. During the process, I found that I need to pass additional parameters through the loss, predict, and _forward functions....
from mmseg.registry import MODELS from .utils import get_class_weight, weight_reduce_loss def lovasz_grad(gt_sorted): """Computes gradient of the Lovasz extension w.r.t sorted errors.See Alg. 1 in paper.""" p = len(gt_sorted) ...
tversky_loss *= class_weight[i] total_loss += tversky_loss return total_loss / num_classes @weighted_loss def binary_tversky_loss(pred, target, valid_mask, alpha=0.3, beta=0.7, smooth=1): assert pred.shape[0] == target.shape[0] pred = pred.reshape(pred.shape[0],...
returntotal_loss/num_classes @weighted_loss defbinary_tversky_loss(pred, target, valid_mask, alpha=0.3, beta=0.7, smooth=1): assertpred.shape[0]==target.shape[0] pred=pred.reshape(pred.shape[0],-1) target=target.reshape(target.shape[0],-1) ...