def update(self, y_pred: torch.Tensor,
y_actual: torch.Tensor) -> torch.Tensor:
"""
Calculate composite metric
Args:
y_pred: network output
y_actual: actual values
Returns:
torch.Tensor: metric value on which backpropagation can be applied
"""
# extract target and weight
if isinstance(
y_actual,
(tuple, list)) and not isinstance(y_actual, rnn.PackedSequence):
target, weight = y_actual
else:
target = y_actual
weight = None
# handle rnn sequence as target
if isinstance(target, rnn.PackedSequence):
target, lengths = rnn.pad_packed_sequence(target, batch_first=True)
# batch sizes reside on the CPU by default -> we need to bring them to GPU
lengths = lengths.to(target.device)
# calculate mask for time steps
length_mask = create_mask(target.size(1), lengths, inverse=True)
# modify weight
if weight is None:
weight = length_mask
else:
weight = weight * length_mask
if weight is None:
y_mean = target.mean(0)
y_pred_mean = y_pred.mean(0)
else:
# calculate weighted sums
y_mean = (target *
unsqueeze_like(weight, y_pred)).sum(0) / weight.sum(0)
y_pred_sum = (y_pred * unsqueeze_like(weight, y_pred)).sum(0)
y_pred_mean = y_pred_sum / unsqueeze_like(weight.sum(0),
y_pred_sum)
# update metric. unsqueeze first batch dimension (as batches are collapsed)
self.metric.update(y_pred_mean.unsqueeze(0), y_mean.unsqueeze(0))
def update(self, y_pred, target):
"""
Update method of metric that handles masking of values.
Do not override this method but :py:meth:`~loss` instead
Args:
y_pred (Dict[str, torch.Tensor]): network output
target (Union[torch.Tensor, rnn.PackedSequence]): actual values
Returns:
torch.Tensor: loss as a single number for backpropagation
"""
# unpack weight
if isinstance(
target,
(list, tuple)) and not isinstance(target, rnn.PackedSequence):
target, weight = target
else:
weight = None
# unpack target
if isinstance(target, rnn.PackedSequence):
target, lengths = unpack_sequence(target)
else:
lengths = torch.full((target.size(0), ),
fill_value=target.size(1),
dtype=torch.long,
device=target.device)
losses = self.loss(y_pred, target)
# weight samples
if weight is not None:
losses = losses * unsqueeze_like(weight, losses)
self._update_losses_and_lengths(losses, lengths)
def _calculate_mean(
y_pred: torch.Tensor,
y_actual: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
# extract target and weight
if isinstance(
y_actual,
(tuple, list)) and not isinstance(y_actual, rnn.PackedSequence):
target, weight = y_actual
else:
target = y_actual
weight = None
# handle rnn sequence as target
if isinstance(target, rnn.PackedSequence):
target, lengths = rnn.pad_packed_sequence(target, batch_first=True)
# batch sizes reside on the CPU by default -> we need to bring them to GPU
lengths = lengths.to(target.device)
# calculate mask for time steps
length_mask = create_mask(target.size(1), lengths, inverse=True)
# modify weight
if weight is None:
weight = length_mask
else:
weight = weight * length_mask
if weight is None:
y_mean = target.mean(0)
y_pred_mean = y_pred.mean(0)
else:
# calculate weighted sums
y_mean = (target *
unsqueeze_like(weight, y_pred)).sum(0) / weight.sum(0)
y_pred_sum = (y_pred * unsqueeze_like(weight, y_pred)).sum(0)
y_pred_mean = y_pred_sum / unsqueeze_like(weight.sum(0),
y_pred_sum)
return y_pred_mean.unsqueeze(0), y_mean.unsqueeze(0)