graftr: an interactive shell to view and edit PyTorch checkpoints.
graftr can be used to remove, rename, and move around the layers and parameters
of your saved model. It's also a handy tool to peek into the structure of pre-trained PyTorch
models that you can find online (e.g. Transformer, DCGAN, etc.).
The screencast above shows an example of taking a pre-trained Densenet
and preparing it for integration into a larger model. We remove the final classification layer
and move the feature extractor into its own
pip install graftr
graftr presents a hierarchical directory structure for
state_dicts and parameters in your
checkpoint. You can list (
ls), move/rename (
mv), and print (
cat) parameters. And, of course,
you can navigate (
cd) through the hierarchy. It also supports standard shell beahvior like
command history, up-arrow, tab-completion, etc.
All changes are kept in-memory until you're ready to write them back to your checkpoint with
cd- change working directory.
pwd- print working directory.
ls- list directory contents.
cat- print the contents of a value or directory.
mv- move/rename value or directory.
rm- remove value or directory.
parameters- print the number of model parameters under a directory.
shape- print tensor shape.
device- get or set the device of a tensor or group of tensors.
save- write back changes to disk.
where- print the location on disk where changes will be saved.
exit- exits the shell.