import torch from torch.autograd.functional import hvp from torchvision import models model = models.resnet18() model.eval() # model.train() x = torch.randn((1, 3, 224, 224)) hvp(lambda _x: model(_x).max(), x, x, strict=True)
The above code returns the following message:
RuntimeError: The hessian of the user-provided function is independent of entry 0 in the grad_jacobian. This is not allowed in strict mode as it prevents from using the double backward trick to replace forward mode AD.
If I turn on the training mode, i.e., change to “model.train()” from model.eval(), the function hvp can work.
However, I suspect that this is not good for a model including “Dropout” or “BatchNorm”.
Can anyone help me in resolving this issue?
- Ubuntu 20.04
- Python 3.9.5
- torch 1.10.0
- torchvision 0.11.1