Fastai v2 vision

fastai
94

Thank you for sharing and I modified a bit like:

class AUC(Metric):
    "AUC score for each class in single-label multi-class classifications."
    def __init__(self, main_class=0, classes = noop):
        super().__init__()
        self.main_class = main_class
        self.classes = classes

    def reset(self): self.targs, self.preds = [],[]

    def accumulate(self, learn):
        pred = learn.pred
        targ = learn.y
        pred, targ = to_detach(pred), to_detach(targ)
        self.preds.append(pred)
        self.targs.append(targ)

    @property
    def value(self):
        if len(self.preds) == 0: return
        preds = torch.cat(self.preds)
        targs = torch.cat(self.targs)

        idx = (targs==self.main_class)
        targs = torch.zeros(targs.size())
        targs[idx] = 1
        preds = F.softmax(preds, dim=1)[:, self.main_class]

        return skm.roc_auc_score(targs.cpu().numpy(), preds.cpu().numpy())

    @property
    def name(self):  return f'{self.classes[self.main_class]} AUC'

and use it

metrics=[accuracy] + [AUC(c, databunch.vocab) for c in range(databunch.c)]

def get_learner2():
    learn = cnn_learner(databunch, xresnet50, opt_func=opt_func, metrics=metrics)
    return learn.to_fp16()
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