AI代码解释 classMyModule(torch.nn.Module):def__init__(self):super().__init__()self.param=torch.nn.Parameter(torch.rand(3,4))self.linear=torch.nn.Linear(4,5)defforward(self,x):returnself.linear(x+self.param).clamp(min=0.0,max=1.0) 很简单地继承于torch.nn.Module的Module(熟悉pytorch...
AI代码解释 classCivilNet(nn.Module):def__init__(self):super(CivilNet,self).__init__()gemfieldin=1gemfieldout=1self.conv=nn.Conv2d(gemfieldin,gemfieldout,kernel_size=1,stride=1,padding=0,groups=1,bias=False)self.fc=nn.Linear(3,2,bias=False)self.relu=nn.ReLU(inplace=False)defforward(...
def find_classes(dir): classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))] classes.sort() class_to_idx = {classes[i]: i for i in range(len(classes))} return classes, class_to_idx def make_dataset(dir, class_to_idx): images = [] dir = os.p...
:class:`~ProcessContext` if ``join`` is ``False`` """主要参数介绍如下：fn:处理的主函...
3.1 class torchvision.datasets.ImageFolder 默认读取图像数据方法: __init__( 初始化) classes, class_to_idx = find_classes(root) :得到分类的类别名(classes)和类别名与数字类别的映射关系字典(class_to_idx)
find_unused_parameters=True的设置会带来额外的运行时开销(而且还不小)。 一种更好的办法是构建一个相同的计算图,用0和1这些选择变量来执行选择操作,这样就不用设置find_unused_parameters参数了。例如: from torch.nn import Module from torch import nn import torch class Net(Module): def __init__(self...
class torchvision.datasets.CIFAR10(root='./data', train=True,download=True, transform=transform) 参数说明: root:保存数据集的目录 train:True= 训练集, False = 测试集 download:True = 从互联网上下载数据集,并把数据集放在root目录下. 如果数据集之前下载过,就不用再重复下载。
class ResNet101Graph(oneflow.nn.Graph): def __init__(self, input_shape, input_dtype=oneflow.float32): super().__init__() # 添加 ResNet101 nn.Module self.model = ResNet101Module(input_shape, input_dtype) self.loss_fn = ResNet101_loss_fn # 添加 对应的 Optimizer ...
Fix DCP state_dict cannot correctly find FQN when the leaf module is wrapped by FSDP (#115592) Fix OOM when when returning a AsyncCollectiveTensor by forcing _gather_state_dict() to be synchronous with respect to the mian stream. (#118197) (#119716) Fix Windows runtime torch.distributed....
class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob