model.load_state_dict(state_dict) return model 1. 2. 3. 4. 5. 6. 7. 8. 可以看到,在_resnet()方法中,又调用了ResNet()方法创建模型,然后加载训练好的模型参数。 ResNet() 首先来看ResNet()方法的构造函数。 构造函数 构造函数的重要参数如下: block:每个layer里面使用的block,可以是BasicBlockBottl...
resnet50.load_state_dict(torch.load('resnet50_weight.pth'))###保存模型 + 参数torch.save(resnet50,'resnet50.pth')#加载模型 + 参数net = torch.load('resnet50.pth')print(net) 3)完整代码 importtorchvision.models as modelsfromtorchsummaryimportsummaryimporttorch#pytorch官网提供好的预训练模型:...
net.load_state_dict(t.load(PARAS_FN)) net_test(net, test_load, 0) return optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum) start_time = datetime.datetime.now() for epoch in range(1, args.epochs + 1): net_train(net, train_load, optimizer, epoch, args....
state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) model.load_state_dict(state_dict) return model 可以看到,在_resnet()方法中,又调用了ResNet()方法创建模型,然后加载训练好的模型参数。 ResNet() 首先来看ResNet()方法的构造函数。 构造函数 构造函数的重要参数如下: block:...
model_ft.load_state_dict(model['state_dict']) Best_ACC=model['Best_ACC'] start_epoch=model['epoch']+1 model_ft.to(DEVICE) 设置模型为fasternet_s,pretrained设置为true,表示加载预训练模型,修改head层,将将输出classes设置为12。 如果resume为True,则加载模型接着resume指向的模型接着训练,使用模型里...
(model_weight_path) net.load_state_dict(torch.load(model_weight_path, map_location=device)) # for param in net.parameters(): # param.requires_grad = False # change fc layer structure in_channel = net.fc.in_features net.fc = nn.Linear(in_channel, 5...
(3, 1) # net = U2NETP(3, 1) checkpoint_path = os.path.join(current_project_path, 'weight/weight18.pth') if torch.cuda.is_available(): net.load_state_dict(paddle.load(checkpoint_path, map_location='cuda:0')) else: net.load_state_dict(paddle.load(checkpoint_path, map_location='...
model_weight_path = "./resnet34-333f7ec4.pth" missing_keys, unexpected_keys = net.load_state_dict(torch.load(model_weight_path), strict=False) inchannel = net.fc.in_features net.fc = nn.Linear(inchannel, 5) net.to(device)注意...
frommainimport*if__name__=='__main__':# 加载效果最好的网络模型checkpoint=torch.load('./checkpoint/cifar10_epoch_20.ckpt')net.load_state_dict(checkpoint['net'])start_epoch=checkpoint['epoch']# 查看前十张图片的预测效果dataiter=iter(testloader)test_images,test_labels=dataiter.next()test_...
open('class_indices.json','w') as json_file:json_file.write(json_str)# 构建网络net=resnet34()# 加载模型参数model_weight_path='./resnet34-pre.pth'net.load_state_dict(torch.load(model_weight_path,map_location=device))# 将每个参数置为False,反向传播时不会进行梯度更新for param in net....