# Plot top losses problem?

Plot top losses has always worked great for me, but as of this morning I’ve noticed a problem:

The first picture shows correctly and then the rest of my grid just shows empty graphs. Any idea what is going on here or when it might be fixed?

3 Likes

do you have it in a colab you can share?

It’s like this in all of my notebooks now. Someone else actually tipped me off to the problem. Here’s an example I just built off of fast.ai’s Google Colab NB5:

Other interpreter functions, most_confused and confusion_matrix work great! Top losses is the only one broken.

Thanks,
Alex

Thanks guys, made Jeremy aware of this issue. Hopefully gets resolved soon 2 Likes

Here’s the previous working version if you want to temporarily use it.

``````def plot_top_losses(self, k, largest=True, **kwargs):
losses,idx = self.top_losses(k, largest)
if not isinstance(self.inputs, tuple): self.inputs = (self.inputs,)
if isinstance(self.inputs, Tensor): inps = tuple(o[idx] for o in self.inputs)
else: inps = self.dl.create_batch(self.dl.before_batch([tuple(o[i] for o in self.inputs) for i in idx]))
b = inps + tuple(o[idx] for o in (self.targs if is_listy(self.targs) else (self.targs,)))
x,y,its = self.dl._pre_show_batch(b, max_n=k)
b_out = inps + tuple(o[idx] for o in (self.decoded if is_listy(self.decoded) else (self.decoded,)))
x1,y1,outs = self.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)), self.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)
``````
3 Likes

#can use by passing in your interp obj like so…

``````plot_top_losses(interp, 9, nrows=3)
``````
1 Like

This is fixed in https://github.com/fastai/fastai/pull/3510 so it should work again in the next release. Alternatively you could also install the dev version of fastai for the fix.

2 Likes

Please give an example of how the fix is to be used. I tried to use plot_top_losses I received the following:
TypeError: plot_top_losses() takes from 2 to 3 positional arguments but 6 were given.
my input to interp.plot_top_losses which received the error: interp.plot_top_losses(21, nrows=7, figsize=(18,20))
should be changed to what in plot_top_losses?

Hi! I’ve tried this same as @henry but it didn’t work. Would like to know how to implement this!

I still have the same issue.
I fixed it with this:

I redefined the function:

``````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, 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)
``````

And using the command below I can now plot the top losses:

``````plot_top_losses_fix(interp, 10, nrows=2)
``````

Hope this helps for the people who have still the same problem.

1 Like

thanks.

Also as it is fixed in the current github repo can install dev version in colab from there.

``````# in colab.
``````
1 Like

Thank You All!
Just scratched my head with this problem. Being a newbee everything is really scary. No I might get through page one.

1 Like

Did you try from github?

It hangs when I tried upgrade from github. I keep waiting for a long time

I installed the upgrade and now it doesn’t seem to work at all. Before, it just plotted the first sample, as described above.
Edit: There are more than 5 samples in the test set and yes, it’s the same error for k=1, as well.

self.items is a len()=1 list that contains an array with all the file_paths from which data is read from.
It should try to get self.items[i_], but it doesn’t. Not sure how to solve this. Will revert to an older version for now.

Edit:
I just wanted to tell you that the solution by Valentino et al. works flawlessly for me. I’m wondering, why the updated fastai doesn’t work.

Thank you @valentino it worked for me.

Valentino’s top losses fix no longer works after the recent fast.ai update. It worked up until a few days ago, but now gives this error: