FAQs

Does it support data format X / augmentation Y / layer Z?

The library tries to support everything, but it could not really include everything.

The interface tries to be flexible enough so you can put any XYZ on it. You can either implement them under the interface or simply wrap some existing Python code. See Extend Tensorpack for more details.

If you think:

  1. The framework has limitation in its interface so your XYZ cannot be supported, OR

  2. Your XYZ is super common / very well-defined / very useful, so it would be nice to include it.

Then it is a good time to open an issue.

How to dump/inspect a model

When you enable ModelSaver as a callback, trained models will be stored in TensorFlow checkpoint format, which typically includes a .data-xxxxx file and a .index file. Both are necessary.

To inspect a checkpoint, the easiest tool is tf.train.NewCheckpointReader. Please note that it expects a model path without the extension.

You can dump a cleaner version of the model (without unnecessary variables), using scripts/dump-model-params.py, as a simple var-name: value dict saved in npy format. The script expects a metagraph file which is also saved by ModelSaver.

How to load a model / do transfer learning

All model loading (in either training or testing) is through the session_init option in TrainConfig or PredictConfig. It accepts a SessionInit instance, where the common options are SaverRestore which restores TF checkpoint, or DictRestore which restores a dict. (get_model_loader is a small helper to decide which one to use from a file name.)

Doing transfer learning is straightforward. Variable restoring is completely based on name match between the current graph and the SessionInit initializer. Therefore, if you want to load some model, just use the same variable name. If you want to re-train some layer, just rename it. Unmatched variables on both sides will be printed as a warning.

How to freeze some variables in training

  1. You can simply use tf.stop_gradient in your model code in some situations (e.g. to freeze first several layers).

  2. varreplace.freeze_variables can wrap some variables with tf.stop_gradient.

  3. ScaleGradient can be used to set the gradients of some variables to 0.

Note that the above methods only prevent variables being updated by SGD. Some variables may be updated by other means, e.g., BatchNorm statistics are updated through the UPDATE_OPS collection and the RunUpdateOps callback.