Hello, I am trying to do TTA on the model trained using fp16. By running the following

code

```
dl = dls.valid
a1, target = learn.tta(dl=dl, n=16)
```

I got the following error

```
RuntimeError Traceback (most recent call last)
<ipython-input-50-964f94913b4a> in <module>
1 dl = dls.valid
----> 2 a1, target = learn.tta(dl=dl, n=16)
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in tta(self, ds_idx, dl, n, item_tfms, batch_tfms, beta, use_max)
565 for i in self.progress.mbar if hasattr(self,'progress') else range(n):
566 self.epoch = i #To keep track of progress on mbar since the progress callback will use self.epoch
--> 567 aug_preds.append(self.get_preds(dl=dl, inner=True)[0][None])
568 aug_preds = torch.cat(aug_preds)
569 aug_preds = aug_preds.max(0)[0] if use_max else aug_preds.mean(0)
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in get_preds(self, ds_idx, dl, with_input, with_decoded, with_loss, act, inner, reorder, cbs, **kwargs)
238 pred_i = 1 if with_input else 0
239 if res[pred_i] is not None:
--> 240 res[pred_i] = act(res[pred_i])
241 if with_decoded: res.insert(pred_i+2, getattr(self.loss_func, 'decodes', noop)(res[pred_i]))
242 if reorder and hasattr(dl, 'get_idxs'): res = nested_reorder(res, tensor(idxs).argsort())
/opt/conda/lib/python3.7/site-packages/fastai/losses.py in activation(self, out)
97 return loss*self.eps/c + (1-self.eps) * F.nll_loss(log_preds, target.long(), reduction=self.reduction)
98
---> 99 def activation(self, out): return F.softmax(out, dim=-1)
100 def decodes(self, out): return out.argmax(dim=-1)
101
/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py in softmax(input, dim, _stacklevel, dtype)
1496 dim = _get_softmax_dim('softmax', input.dim(), _stacklevel)
1497 if dtype is None:
-> 1498 ret = input.softmax(dim)
1499 else:
1500 ret = input.softmax(dim, dtype=dtype)
RuntimeError: "softmax_lastdim_kernel_impl" not implemented for 'Half'
```

Tried `learn = learn.to_fp32()`

as per this discussion but I am getting the same error

Thanks in advance for your time – if I’ve missed out anything, over- or under-emphasized a specific point let me know in the comments.