torch.save(net,"resnet18.pth")if__name__=='__main__': main() this 'resnet18.pth' file contains the model structure and weights. 2.load the .pth file and transform it to ONNX format: importtorchdefmain(): model= torch.load('resnet18.pth')#model.eval()inputs = torch.randn(1...
通过调整每个 Res Block 的堆叠数量和通道数可以产生不同的 ResNet,如通过 64-64-128-128-256-256-512-512 通道数配置,共 8 个 Res Block,可得到 ResNet18 的网络模型。每个ResBlock 包含了 2 个主要的卷积层,因此卷积层数量是8 ∙ 2 = 16,加上网络末尾的全连接层,共 18 层。 注意,在卷积层之后...