DL101 Week 1 interp.plot_top_losses plots only one image

[jamescavanagh]

Describe the bug

In the DL101 notebook, the interp.plot_top_losses(5, nrows=5) line will only return one image.
It is critical to the notebook running correctly for students.

To Reproduce
Steps to reproduce the behavior or code you have used

Run the notebook, train the model, then run this cell

interp.plot_top_losses(5, nrows=5)

Expected behavior
A clear and concise description of what you expected to happen.

Screenshots
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Desktop (please complete the following information):

• OS: [e.g. iOS]
  Ubuntu
• Browser [e.g. chrome, safari]
  Firefox
• Version [e.g. 22]

Additional context

This has been mentioned by several students, and is important to their experience.

[jfthuong]

@Dennis-fast-ai

Could you put in the comments of this issue, the fix-up code that you found on fastai forums?

Thanks

Note: as mentioned in our chats, we could add a function in our library that is inspired from fastai with stable code, tests to check it's not breaking, and 2 additional parameters:

• filter_predictions (or something like that): to only keep incorrect predictions
• threshold: to only keep losses under a certain level of confidence

[jamescavanagh]

@jfthuong Here is the code
It is a bit messy, but this is how it came

#@title Getting a visual result for the top losses
def plot_top_losses_fix(interp, k, largest=True, **kwargs):
losses,idx = interp.top_losses(k, largest)
if not isinstance(interp.inputs, tuple): interp.inputs = (interp.inputs,)
if isinstance(interp.inputs[0], Tensor): inps = tuple(o[idx] for o in interp.inputs)
else: inps = interp.dl.create_batch(interp.dl.before_batch([tuple(o[i] for o in interp.inputs) for i in idx]))
b = inps + tuple(o[idx] for o in (interp.targs if is_listy(interp.targs) else (interp.targs,)))
x,y,its = interp.dl._pre_show_batch(b, max_n=k)
b_out = inps + tuple(o[idx] for o in (interp.decoded if is_listy(interp.decoded) else (interp.decoded,)))
x1,y1,outs = interp.dl._pre_show_batch(b_out, max_n=k)
if its is not None:
#plot_top_losses(x, y, its, outs.itemgot(slice(len(inps), None)), L(self.preds).itemgot(idx), losses, **kwargs)
plot_top_losses(x, y, its, outs.itemgot(slice(len(inps), None)), interp.preds[idx], losses, **kwargs)
#TODO: figure out if this is needed
#its None means that a batch knows how to show itself as a whole, so we pass x, x1
#else: show_results(x, x1, its, ctxs=ctxs, max_n=max_n, **kwargs)

plot_top_losses_fix(interp, 10, nrows=2)