resnet.fc = nn.Linear(2048, 21) from torchvision import models from torch import nn # 加载预训练好的模型,保存到 ~/.torch/models/ 下面resnet34= models.resnet34(pretrained=True, num_classes=1000) # 默认是ImageNet上的1000分类,这里修改最后的全连接层为10分类问题 resnet34.fc = nn.Linear(5...
model_urls={'resnet18':'https://download.pytorch.org/models/resnet18-5c106cde.pth','resnet34':'https://download.pytorch.org/models/resnet34-333f7ec4.pth','resnet50':'https://download.pytorch.org/models/resnet50-19c8e357.pth','resnet101':'https://download.pytorch.org/models/res...
importtorch.nnasnnimportmathimporttorch.utils.model_zooasmodel_zoo__all__=['ResNet','resnet18','resnet34','resnet50','resnet101','resnet152']model_urls={'resnet18':'https://download.pytorch.org/models/resnet18-5c106cde.pth','resnet34':'https://download.pytorch.org/models...
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet...
def resnet101(num_classes=1000, avgpool_size=7, use_dropout=False, pretrained=True): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(resnet.Bottleneck, [3, 4, 23, 3], num_classes=num_classes, avg...
首先是导入必要的库,其中model_zoo是和导入预训练模型相关的包,另外all变量定义了可以从外部import的函数名或类名。这也是前面为什么可以用torchvision.models.resnet50()来调用的原因。model_urls这个字典是预训练模型的下载地址。 代码语言:javascript 复制
import torchvision.models as models resnet = models.resnet18(pretrained=True) 三、图像预处理 在将图像输入到神经网络之前,通常需要进行一些预处理操作,如缩放、裁剪、归一化等。torchvision提供了丰富的图像转换功能,可以轻松地构建预处理流程。 以下是一个简单的预处理示例,将图像缩放到256x256像素,然后裁剪到...
from model.backbone import ResNet18 model1 = ResNet18(1)model2 = torchvision.models.resnet18(progress=False)fc = model2.fc model2.fc = torch.nn.Linear(512, 1)# print(model)model_dict1 = model1.state_dict()model_dict2 = torch.load('resnet18.pth')model_list1 = list(model_dict1...
model = models.densenet121(pretrained=False) if pretrained is not None: settings = pretrained_settings['densenet121'][pretrained] model = load_pretrained(model, num_classes, settings) model = modify_densenets(model) return model def densenet169(num_classes=1000, pretrained='imagenet'): r""...
torchvision.models: 提供深度学习中各种经典的网络结构、预训练好的模型,如:Alex-Net、VGG、ResNet、Inception等。 torchvision.datasets:提供常用的数据集,设计上继承 torch.utils.data.Dataset,主要包括:MNIST、CIFAR10/100、ImageNet、COCO等。 torchvision.transforms:提供常用的数据预处理操作,主要包括对Tensor及PIL ...