如何通过使用 pytorch 读取我学习的权重的 .ckpt 文件来使用 resnet

如何解决如何通过使用 pytorch 读取我学习的权重的 .ckpt 文件来使用 resnet

在 pytorch 中，如何编写加载 .ckpt 文件的代码而不是

model = torchvision.models.resnet50(pretrained=True)

下面是我的尝试

model = torchvision.models.resnet50(pretrained=False)

PATH = "/content/drive/MyDrive/Colab Notebooks/mlearning2/multi_logs/resnet_2/version_0/checkpoints/epoch=1-step=2543.ckpt"

model.load_state_dict(torch.load(PATH,map_location=torch.device('cpu')))

但它无法工作并出现以下错误。

RuntimeError: Error(s) in loading state_dict for ResNet:
    Missing key(s) in state_dict: "conv1.weight","bn1.weight","bn1.bias","bn1.running_mean","bn1.running_var","layer1.0.conv1.weight","layer1.0.bn1.weight","layer1.0.bn1.bias","layer1.0.bn1.running_mean","layer1.0.bn1.running_var","layer1.0.conv2.weight","layer1.0.bn2.weight","layer1.0.bn2.bias","layer1.0.bn2.running_mean","layer1.0.bn2.running_var","layer1.0.conv3.weight","layer1.0.bn3.weight","layer1.0.bn3.bias","layer1.0.bn3.running_mean","layer1.0.bn3.running_var","layer1.0.downsample.0.weight","layer1.0.downsample.1.weight","layer1.0.downsample.1.bias","layer1.0.downsample.1.running_mean","layer1.0.downsample.1.running_var","layer1.1.conv1.weight","layer1.1.bn1.weight","layer1.1.bn1.bias","layer1.1.bn1.running_mean","layer1.1.bn1.running_var","layer1.1.conv2.weight","layer1.1.bn2.weight","layer1.1.bn2.bias","layer1.1.bn2.running_mean","layer1.1.bn2.running_var","layer1.1.conv3.weight","layer1.1.bn3.weight","layer1.1.bn3.bias","layer1.1.bn3.running_mean","layer1.1.bn3.running_var","layer1.2.conv1.weight","layer1.2.bn1.weight","layer1.2.bn1.bias","layer1.2.bn1.running_mean","layer1.2.bn1.running_var","layer1.2.conv2.weight","layer1.2.bn2.weight","layer1.2.bn2.bias","layer1.2.bn2.running_mean","layer1.2.bn2.running_var","layer1.2.conv3.weight","layer1.2.bn3.weight","layer1.2.bn3.bias","layer1.2.bn3.running_mean","layer1.2.bn3.running_var","layer2.0.conv1.weight","layer2.0.bn1.weight","layer2.0.bn1.bias","layer2.0.bn1...
    Unexpected key(s) in state_dict: "epoch","global_step","pytorch-lightning_version","state_dict","callbacks","optimizer_states","lr_schedulers","hparams_name","hyper_parameters". 

我该怎么做？

@Shai 我试着跑了

model.load_state_dict(torch.load(PATH,map_location=torch.device('cpu'))['state_dict'])

但是出现以下错误。

解决方法

您保存的检查点不仅包含模型训练权重的快照，还包含有关训练状态（例如优化器的状态等）的其他一些有用信息。

尝试仅选择已保存检查点的相关部分：

model.load_state_dict(torch.load(PATH,map_location=torch.device('cpu'))['state_dict'])

---

更新
根据您所做的修改和收到的新错误，保存的模型似乎是 model.backbone = torchvision.models.resnet50()。

您需要以与训练期间相同的方式实例化您的 model。