model = Resnet18(num_classes=10, improved=args.my_improved) else: model = resnet18(pretrained=False, num_classes=10) 这里通过配置pretrained决定是否加载预训练权重。另外,pytorch内置resnet18最后一个全连接层是1000个输出,而分类cifar10我们需要设定全连接层为10个输出,所以我们加载权重的时候不加载全连接...
self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512, num_classes) #这个函数主要是用来,重复同一个残差块 def _make_layer(self, block , out_channels, strides): #在调用的时候要给出block是BasicBlock layers = [] for stride in strides: layers.append(block(self.in_channe...
importtorchimporttorch.nnasnnimporttorchvision.modelsasmodels# 加载预训练的ResNet18模型resnet=models.resnet18(pretrained=True)# 替换最后一层全连接层num_classes=10resnet.fc=nn.Linear(resnet.fc.in_features,num_classes)# 定义损失函数和优化器criterion=nn.CrossEntropyLoss()optimizer=torch.optim.SGD(re...
self.bn8_2= torch.nn.BatchNorm2d(512) self.relu8_2=torch.nn.ReLU()#第18层self.fc = torch.nn.Linear(512, num_classes)defforward(self, x):#batch_size, 3, 224, 224x = self.conv0_1(x)#bs, 64, 112, 112x =self.bn0_1(x) x=self.relu0_1(x) x1= self.dmp(x)#bs, 64,...
def __init__(self, layer_dims, num_classes= 10): #[2,2,2,2] super(ResNet, self).__init__() # 根网络,预处理 self.stem= Sequential([ layers.Conv2D(64, kernel_size= (3,3), strides= (1,1)), layers.BatchNormalization(), ...
调整模型和数据集的配置,使其与 Cifar-10 一起工作。 # Configure model exp_config.task.model.num_classes = 10 exp_config.task.model.input_size = list(ds_info.features["image"].shape) exp_config.task.model.backbone.resnet.model_id = 18 ...
num_classes = 10 # 根据具体问题设置类别数 model = CustomResNet18(num_classes) 定义损失函数和优化器: 代码语言:txt 复制 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) 加载数据集并进行数据预处理: 代码语言:txt 复制 # TODO: 加载...
classResNet(nn.Module):def__init__(self,ResBlock,num_classes=10):super(ResNet,self).__init__()self.inchannel=64self.conv1=nn.Sequential(nn.Conv2d(3,64,kernel_size=3,stride=1,padding=1,bias=False),nn.BatchNorm2d(64),nn.ReLU())self.layer1=self.make_layer(ResBlock,64,2,stride=...
Module): def __init__(self, BasicBlock, num_classes=10) -> None: super(ResNet18, self).__init__() self.in_channels = 64 # 第一层作为单独的 因为没有残差快 self.conv1 = nn.Sequential( nn.Conv2d(3,64,kernel_size=7,stride=2,padding=3,bias=False), nn.BatchNorm2d(64), nn....
将num_classes参数去掉,同时修改ResNet结构去掉FC等操作 添加is_training参数,取代num_classes用于区分训练和推理场景的标识符 Steps to reproduce the issue 修改yolov3.py源码,将num_classes设置为非空 按照文档要求配置default_config.yaml脚本 执行python train.py启动训练进程 Describe the current behavior 执行之后...