loss, log_vars = self._parse_losses(losses) File "/home/ggao/z_h_240513_files/z_models/mmseg/models/segmentors/base.py", line 256, in _parse_losses log_vars[loss_name] = loss_value.item()RuntimeError:CUDA error: an illegal memory access was encountered terminate called after throwing...
F-score 和 Dice Loss结果不一致原因及分析 关键词 mDice,mIoU,mFscore Motivation MMSeg中IoUMetric 中有三个评价指标,对三个指标加强理解并进行介绍 config文件中验证阶段可以在tensorboard中显示的评价指标有:mDice,mIoU,mFscore test.py 的参数设置中 --eval 可以设置测试阶段时计算的评价指标:mDice,mIoU,mFsc...
( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), ], # model training and testing settings train_cfg=dict(), test_cfg=dict(mode='whole')) # yapf:disable log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook', by_epoch=False), # dict(type='...
"""Binary Lovasz hinge loss. Args: logits (torch.Tensor): [P], logits at each prediction (between -infty and +infty). labels (torch.Tensor): [P], binary ground truth labels (0 or 1). Returns: torch.Tensor: The calculated loss. ...
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. These parameters are essential for my custom logic, but the current implementation does not support such...
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...
( 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),...
首先我们需要构造一个能够用于可视化的功能函数,我们将其写在tools/visualize_helper.py下,方便在其他函数中调用: visualize_helper.py import os import cv2 import torch import numpy as np # visualize label_colors = np.array([ ...
44 d2l.evaluate_loss(net, test_iter, loss))) 45 print('w的L2范数是:', torch.norm(w).item()) 46 d2l.plt.show() 47 48 49 train(lambd=6) 50 51 52 #简洁实现: 53 54 55 56 def train_concise(wd): 57 net = nn.Sequential(nn.Linear(num_inputs, 1)) ...
一、整体修改的文件 configs |--_base_ | |--datasets | | |--ours_semi.py |--ours | |--fixmatch_ours.py // 配置文件 mmcv_custom |--runner | |--__init__.py | |--custom_runner.py mmseg_custom |--datasets | |--pipelines ...