def __call__(self, force_pt=False, *input, **kwargs):
pt_call = len(input) > 0 or force_pt
if pt_call:
with t.no_grad():
return self.forward(*input, **kwargs)
else:
return NeuralModule.__call__(self, **kwargs)
def __call__(self, force_pt=False, *input, **kwargs):
pt_call = len(input) > 0 or force_pt
if pt_call:
# [inds] = kwargs.values()
inds = kwargs["indices"]
np_inds = inds.detach().cpu().numpy().tolist()
result = []
for lst in np_inds:
sublst = []
for tid in lst:
if tid != 1:
sublst.append(tid)
else:
break
result.append(self._detokenizer(sublst))
return result
else:
return NeuralModule.__call__(self, **kwargs)
def __call__(self, force_pt=False, *input, **kwargs):
pt_call = len(input) > 0 or force_pt
if pt_call:
# [inds] = kwargs.values()
# np_inds = inds.detach().cpu().numpy().reshape(-1)
# result = [self._ids2classes[i] for i in np_inds]
# #result = list(map(lambda x: self._ids2classes[x], np_inds))
# return result
inds = kwargs["indices"]
np_inds = inds.detach().transpose_(1, 0).cpu().numpy().tolist()
result = []
for lst in np_inds:
sublst = []
for tid in lst:
if tid != 1:
sublst.append(tid)
else:
break
result.append(list(map(lambda x: self._ids2classes[x],
sublst)))
return [result]
else:
return NeuralModule.__call__(self, **kwargs)
def __call__(self, *input, force_pt=False, **kwargs):
pt_call = len(input) > 0 or force_pt
if pt_call:
return nn.Module.__call__(self, *input, **kwargs)
else:
return NeuralModule.__call__(self, **kwargs)
def __call__(self, force_pt=False, *input, **kwargs):
if force_pt:
return self._loss_function(**kwargs)
else:
return NeuralModule.__call__(self, **kwargs)
