Unneeded elements in state dict

[ternaus]

State_dict keys after training resnet18:

dict_keys(['model.conv1.weight', 'model.bn1.weight', 'model.bn1.bias', 'model.bn1.running_mean', 'model.bn1.running_var', 'model.bn1.num_batches_tracked', 'model.layer1.0.conv1.weight', 'model.layer1.0.bn1.weight', 'model.layer1.0.bn1.bias', 'model.layer1.0.bn1.running_mean', 'model.layer1.0.bn1.running_var', 'model.layer1.0.bn1.num_batches_tracked', 'model.layer1.0.conv2.weight', 'model.layer1.0.bn2.weight', 'model.layer1.0.bn2.bias', 'model.layer1.0.bn2.running_mean', 'model.layer1.0.bn2.running_var', 'model.layer1.0.bn2.num_batches_tracked', 'model.layer1.1.conv1.weight', 'model.layer1.1.bn1.weight', 'model.layer1.1.bn1.bias', 'model.layer1.1.bn1.running_mean', 'model.layer1.1.bn1.running_var', 'model.layer1.1.bn1.num_batches_tracked', 'model.layer1.1.conv2.weight', 'model.layer1.1.bn2.weight', 'model.layer1.1.bn2.bias', 'model.layer1.1.bn2.running_mean', 'model.layer1.1.bn2.running_var', 'model.layer1.1.bn2.num_batches_tracked', 'model.layer2.0.conv1.weight', 'model.layer2.0.bn1.weight', 'model.layer2.0.bn1.bias', 'model.layer2.0.bn1.running_mean', 'model.layer2.0.bn1.running_var', 'model.layer2.0.bn1.num_batches_tracked', 'model.layer2.0.conv2.weight', 'model.layer2.0.bn2.weight', 'model.layer2.0.bn2.bias', 'model.layer2.0.bn2.running_mean', 'model.layer2.0.bn2.running_var', 'model.layer2.0.bn2.num_batches_tracked', 'model.layer2.0.downsample.0.weight', 'model.layer2.0.downsample.1.weight', 'model.layer2.0.downsample.1.bias', 'model.layer2.0.downsample.1.running_mean', 'model.layer2.0.downsample.1.running_var', 'model.layer2.0.downsample.1.num_batches_tracked', 'model.layer2.1.conv1.weight', 'model.layer2.1.bn1.weight', 'model.layer2.1.bn1.bias', 'model.layer2.1.bn1.running_mean', 'model.layer2.1.bn1.running_var', 'model.layer2.1.bn1.num_batches_tracked', 'model.layer2.1.conv2.weight', 'model.layer2.1.bn2.weight', 'model.layer2.1.bn2.bias', 'model.layer2.1.bn2.running_mean', 'model.layer2.1.bn2.running_var', 'model.layer2.1.bn2.num_batches_tracked', 'model.layer3.0.conv1.weight', 'model.layer3.0.bn1.weight', 'model.layer3.0.bn1.bias', 'model.layer3.0.bn1.running_mean', 'model.layer3.0.bn1.running_var', 'model.layer3.0.bn1.num_batches_tracked', 'model.layer3.0.conv2.weight', 'model.layer3.0.bn2.weight', 'model.layer3.0.bn2.bias', 'model.layer3.0.bn2.running_mean', 'model.layer3.0.bn2.running_var', 'model.layer3.0.bn2.num_batches_tracked', 'model.layer3.0.downsample.0.weight', 'model.layer3.0.downsample.1.weight', 'model.layer3.0.downsample.1.bias', 'model.layer3.0.downsample.1.running_mean', 'model.layer3.0.downsample.1.running_var', 'model.layer3.0.downsample.1.num_batches_tracked', 'model.layer3.1.conv1.weight', 'model.layer3.1.bn1.weight', 'model.layer3.1.bn1.bias', 'model.layer3.1.bn1.running_mean', 'model.layer3.1.bn1.running_var', 'model.layer3.1.bn1.num_batches_tracked', 'model.layer3.1.conv2.weight', 'model.layer3.1.bn2.weight', 'model.layer3.1.bn2.bias', 'model.layer3.1.bn2.running_mean', 'model.layer3.1.bn2.running_var', 'model.layer3.1.bn2.num_batches_tracked', 'model.layer4.0.conv1.weight', 'model.layer4.0.bn1.weight', 'model.layer4.0.bn1.bias', 'model.layer4.0.bn1.running_mean', 'model.layer4.0.bn1.running_var', 'model.layer4.0.bn1.num_batches_tracked', 'model.layer4.0.conv2.weight', 'model.layer4.0.bn2.weight', 'model.layer4.0.bn2.bias', 'model.layer4.0.bn2.running_mean', 'model.layer4.0.bn2.running_var', 'model.layer4.0.bn2.num_batches_tracked', 'model.layer4.0.downsample.0.weight', 'model.layer4.0.downsample.1.weight', 'model.layer4.0.downsample.1.bias', 'model.layer4.0.downsample.1.running_mean', 'model.layer4.0.downsample.1.running_var', 'model.layer4.0.downsample.1.num_batches_tracked', 'model.layer4.1.conv1.weight', 'model.layer4.1.bn1.weight', 'model.layer4.1.bn1.bias', 'model.layer4.1.bn1.running_mean', 'model.layer4.1.bn1.running_var', 'model.layer4.1.bn1.num_batches_tracked', 'model.layer4.1.conv2.weight', 'model.layer4.1.bn2.weight', 'model.layer4.1.bn2.bias', 'model.layer4.1.bn2.running_mean', 'model.layer4.1.bn2.running_var', 'model.layer4.1.bn2.num_batches_tracked', 'model.fc.weight', 'model.fc.bias', 

'train_accuracy.correct', 'train_accuracy.total', 'val_accuracy.correct', 'val_accuracy.total'])

I have elements with keys
'train_accuracy.correct', 'train_accuracy.total', 'val_accuracy.correct', 'val_accuracy.total in the state dict. Which are metrics that are logged during training.

If it is a feature - it is a strange feature. Now I need to manually delete them so that I will be able to use the checkpoint with the resnet18 model.

[Vozf]

Metric.add_state(..., persistent=False) to fix this, but I agree that True as default is strange