Support for non-supervised learning tasks

[lorenzoh]

So right now, much of the data block magic is specialized on supervised learning tasks, e.g. blocklossfn, blockmodel and the visualizations. It would be nice to better support other kinds of tasks as well. The underlying APIs are already pretty general, e.g. encode has no restraints on number of blocks encodings. It's more that right now, BlockMethod assumes a tuple (inputblock, targetblock) and uses that to determine what block the model output is and what blocks are in a batch for visualization.

For the FastAI Q&A @darsnack prepared a variational autoencoder tutorial where the data is really just one image that itself is the target and we ended up having to overwrite some methods to get it to work.

So, the thing that is mostly different for different learning tasks, is how each block of data is used. Below I try to summarize the different kinds of blocks that need to be known to use the block method APIs.

• sampleblock i.e. what are the blocks of one sample
• outputblock i.e. what block is output by the model
• xblock i.e. what block is input to the model
• yblock i.e. what block does the loss function compare outputblock to
• showpredblocks i.e. what blocks are compared when using showpredictions
• showoutputblocks i.e. what blocks are compared when using showoutputs

For a basic supervised learning method, we have

• sampleblock = (inputblock, targetblock) = method.blocks
• outputblock = yblock (can be overwritten)
• xblock, yblock = encode(sampleblock)
• predblock = decode(yblock) (usually same as targetblock)
• showoutputblocks = (xblock, yblock, outputblock)
• showpredblocks = (inputblock, targetblock, predblock) = decode(showoutputblocks)

For the autoencoder example, we have

• sampleblock = inputblock = targetblock = predblock
• outputblock = xblock = yblock = encode(sampleblock)
• showpredblocks = (inputblock, predblock)
• showoutputblocks = (xblock, outputblock)

This is how the blocks are used:

• core interface
  • encode() = encode(sampleblock)
  • encodeinput() = encode(inputblock)
  • encodetarget() = encode(targetblock)
  • decodeypred() = decode(outputblock)
  • decodex() = decode(xblock)
  • decodey() = decode(yblock)
• training interface
  • methodlossfn() = blocklossfn(outputblock, yblock)
  • methodmodel() = blockmodel(xblock, outputblock, blockbackbone(xblock))
• interpretation interface
  • showpredictions() = showblocks(showpredblocks)
  • showoutputs() = showblocksinterpertable(showoutputblocks)
• testing interface
  • mocksample() = mockblock(sampleblock)
  • mockmodel() = mockmodel(xblock, outputblock)

[lorenzoh]

Closed in #188. See https://fluxml.ai/FastAI.jl/dev/notebooks/vae.ipynb.html as an example.