以下是使用PyTorch实现的VGG网络提取图像特征的代码示例: importtorchimporttorchvision.modelsasmodelsimporttorchvision.transformsastransformsfromPILimportImage# 加载预训练的VGG模型vgg=models.vgg16(pretrained=True)vgg_features=vgg.features# 图像预处理transform=transforms.Compose([transforms.Resize((224,224)),transfo...
pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return_vgg('vgg16','D',False, pretrained, progress, **kwargs) defvgg16_bn(pretrained=False, progress=True, **kwargs): r"""VGG 16...
torchvision.models.vgg19_bn(pretrained=False, progress=True, **kwargs) pretrained: 如果设置为 True,则返回在 ImageNet 预训练过的模型。
importtorchimporttorch.optimasoptimfromtorchvisionimporttransforms, models vgg = models.vgg19(pretrained=True).features forparaminvgg.parameters():param.requires_grad_(False) device = torch.device("cuda"iftorch.cuda.is_available()else"cpu") vgg.to(device) ...
def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG: r...
model = models.mobilenet_v3_small(pretrained=True) modules = list(model.children()) modules = modules[0][:4] self.model = nn.Sequential(*modules) def forward(self, x): return self.model(x) class MobileNet2(nn.Module): # out 48 channel ...
net2=models.vgg16(pretrained=True)# pytorch提供的预训练模型dim_in=net2.classifier[-1].in_features# 最后一个分类层的输入维度net2.classifier[-1]=nn.Linear(dim_in,10)# 网络最后一层改为分类到10类train(net2,train_loader_cifar,test_loader_cifar,lr=1e-5,epochs=10) ...
vgg = models.vgg19(pretrained=True).features.eval() print (vgg) Feature Map可利用下面的class class Vgg16(nn.Module): def __init__(self, pretrained=True): super(Vgg16, self).__init__() self.net = models.vgg16(pretrained).features.eval() ...
def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs): if pretrained: kwargs['init_weights'] = False model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs) if pretrained: state_dict = load_state_dict_from_url(model_urls[arch], ...
VGG(# features = models.vgg19(pretrained=True).features# 此项为 获取的 pretrained vgg19 参数;如下注释显示 self.relu 使用的参数块(features):Sequential(# self.to_relu_1_1 -> 1/1(0):Conv2d(3,64,kernel_size=(3,3),stride=(1,1),padding=(1,1))(1):ReLU(inplace=True)# self.to_re...