Pretraining-and-finetuning is a widely used approach in other fields such as Computer Vision (CV) or Natural Language Processing (NLP) to vastly reduce the training cost, while it's not trivial in potential models. Compositions and configurations of data samples or even computational parameters in upstream software (such as VASP) may be different between the pretrained and target datasets, leading to energy shifts or other diversities of training data.
Recently the emerging of methods such as DPA-1 has brought us to a new stage where we can
perform similar pretraining-finetuning approaches.
DPA-1 can hopefully learn the common knowledge in the pretrained dataset (especially the force information)
and thus reduce the computational cost in downstream training tasks.
If you have a pretrained model pretrained.pb
(here we support models using se_atten descriptor and ener fitting net)
on a large dataset (for example, OC2M in
DPA-1 paper), a finetuning strategy can be performed by simply running:
$ dp train input.json --finetune pretrained.pbThe command above will change the energy bias in the last layer of the fitting net in pretrained.pb,
according to the training dataset in input.json.
:::{warning} Note that the elements in the training dataset must be contained in the pretrained dataset. :::
The finetune procedure will inherit the model structures in pretrained.pb,
and thus it will ignore the model parameters in input.json,
such as {ref}descriptor <model/descriptor>, {ref}fitting_net <model/fitting_net>,
{ref}type_embedding <model/type_embedding> and {ref}type_map <model/type_map>.
However, you can still set the trainable parameters in each part of input.json to control the training procedure.
To obtain a more simplified script, for example, you can change the {ref}model <model> part in input.json to perform finetuning:
"model": {
"type_map": ["O", "H"],
"type_embedding": {"trainable": true},
"descriptor" : {},
"fitting_net" : {}
}