根本原因是swa_utils.py中AveragedModel类将函数avg_fn class AveragedModel(Module):def __init__(self, model, device=None, avg_fn=None):super(AveragedModel, self).__init__()self.module = deepcopy(model)if device is not None:self.module = self.module.to(device)self.register_buffer('n_ave...
1. torch.optim.swa_utils.AveragedModel 用于创建 SWA 模型,类方法 update_parameters 用于更新 SWA 模型的权重(均值操作),update_bn 用于更新 BN 层的统计量。 2. torch.optim.swa_utils.SWALR 是一个 Scheduler,用于在 SWA 训练的过程中调整学习率。 下面是作者常用的 SWA 的代码片段,涵盖了整片文章的全部...
I'm gonna use theAveragedModelinswa_utils. But, I got the error, TypeError: tensor(): argument 'device' must be torch.device, not bool I cannot find any clear solutions about this!! Help me!! This is part of my training code, and I got the error at the end of the code ...
from torch.optim.swa_utils import AveragedModel, SWALR # 采用SGD优化器 optimizer = torch.optim.SGD(model.parameters(),lr=1e-4, weight_decay=1e-3, momentum=0.9) # 随机权重平均SWA,实现更好的泛化 swa_model = AveragedModel(model).to(device) # SWA调整学习率 swa_scheduler = SWALR(optimizer...
optim.swa_utils import AveragedModel, SWALR # 采用SGD优化器 optimizer = torch.optim.SGD(model.parameters(),lr=1e-4, weight_decay=1e-3, momentum=0.9) # 随机权重平均SWA,实现更好的泛化 swa_model = AveragedModel(model).to(device) # SWA调整学习率 swa_scheduler = SWALR(optimizer, swa_lr...
from torch.optim.swa_utils import AveragedModel, SWALRfrom torch.optim.lr_scheduler import CosineAnnealingLRloader, optimizer, model, loss_fn = ... # 定义数据加载器,优化器,模型,损失swa_model = AveragedModel(model)scheduler = CosineAnnealingLR(optimizer, T_max=100) # 使用学习率策略(余弦退火)...
The dashed line illustrates the accuracy of individual models averaged by SWA. CIFAR-10 Test accuracy (%) of SGD and SWA on CIFAR-10 for different training budgets. DNN (Budget)SGDSWA 1 BudgetSWA 1.25 BudgetsSWA 1.5 Budgets VGG16 (200) 93.25 ± 0.16 93.59 ± 0.16 93.70 ± 0.22 93.64 ...
fromtqdmimporttqdmfromtorch.optim.swa_utilsimportAveragedModel,SWALR# from python_developer_tools.cv.utils.torch_utils import init_seeds 函数原先在库里,现在专门将函数移出来definit_seeds(seed=0):"""eg:init_seeds(seed=0)"""ifseedisNone:seed=(os.getpid()+int(datetime.now().strftime("%S%f")...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/torch/optim/swa_utils.py at v1.7.1 · pytorch/pytorch
Below we show the convergence plot for SWA and SGD with PreResNet164 on CIFAR-100 and the corresponding learning rates. The dashed line illustrates the accuracy of individual models averaged by SWA. CIFAR-10 Test accuracy (%) of SGD and SWA on CIFAR-10 for different training budgets. ...