Extending dtoolAI

dtoolAI provides everything needed to train image classification networks “out of the box”. Different types of Deep Learning network will require both new models and possibly classes for training data.

New forms of training data

dtoolAI provides two classes for managing training data - TensorDataSet and ImageDataSet. Our examples use these to train models and capture provenance.

The class should:

• Inherit from dtoolai.data.WrappedDataSet. This ensures that it provides both the methods required by Pytorch (to feed into the model) and dtoolAI (to capture metadata).
• Implement __len__ which should return how many items are in the dataset.
• Implement __getitem__, which should return either torch.Tensor objects or numpy arrays that Pytorch is capable of converting to tensors.

Instances of this class can then be passed to dtoolai.training.train_model_with_metadata_capture.