openfold/docs/source/convert_of_v1_weights.md

38 lines
1.9 KiB
Markdown

## Weights Renaming
As part of the [OpenFold v2 update](https://github.com/aqlaboratory/openfold/releases/tag/v2.0.0) with the integration of multimer prediction, certain model layers of the AlphaFold model were renamed. For example.
`module.model.template_angle_embedder.*` is now referred to as
`module.model.template_embedder.template_single_embedder.*`
If you have some checkpoints that were trained using OpenFold v1 or older, and now want to resume training on OpenFold v2, you may need to convert your checkpoints.
## FAQ
### Do I need to convert my checkpoints / model weights?
If you want to run inference or resume training from a checkpoint that was trained with OpenFold V1, you will need to convert your checkpoint.
If you want load model weights only, without starting from a specific time step, then you should not need to convert your checkpoints. The training of the model will begin from `step=0` in this case. To do so, you'll need both the `--resume_from_ckpt` and `--resume_model_weights_only` flags. This example allows you train starting from the pre-trained openfold weights:
```bash
$ python3 $OPENFOLD_DIR/train_openfold.py test_data_epoch/mmcifs test_data_epoch/alignments test_data_epoch/template_mmcifs $OUTPUT_DIR 2021-09-30 \
...
--resume_from_ckpt openfold/resources/openfold_params/finetuning_2.pt \
--resume_model_weights_only
```
### How do I convert my checkpoints?
Use [`scripts/convert_v1_to_v2_weights.py`](https://github.com/aqlaboratory/openfold/blob/main/scripts/convert_v1_to_v2_weights.py) e.g.
`python scripts/convert_v1_to_v2_weights.py checkpoints/6-209.ckpt checkpoints/6-209.ckpt.converted`
Then, to resume training, set the following flags:
```bash
$ python3 $OPENFOLD_DIR/train_openfold.py test_data_epoch/mmcifs test_data_epoch/alignments test_data_epoch/template_mmcifs $OUTPUT_DIR 2021-09-30 \
...
--resume_from_ckpt checkpoints/6-209.ckpt.converted
```