def update(
self,
y_pred,
target,
encoder_target,
encoder_lengths=None,
) -> torch.Tensor:
"""
Update metric that handles masking of values.
Args:
y_pred (Dict[str, torch.Tensor]): network output
target (Tuple[Union[torch.Tensor, rnn.PackedSequence], torch.Tensor]): tuple of actual values and weights
encoder_target (Union[torch.Tensor, rnn.PackedSequence]): historic actual values
encoder_lengths (torch.Tensor): optional encoder lengths, not necessary if encoder_target
is rnn.PackedSequence. Assumed encoder_target is torch.Tensor
Returns:
torch.Tensor: loss as a single number for backpropagation
"""
# unpack weight
if isinstance(target, (list, tuple)):
weight = target[1]
target = target[0]
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)
# determine lengths for encoder
if encoder_lengths is None:
encoder_target, encoder_lengths = unpack_sequence(target)
else:
assert isinstance(encoder_target, torch.Tensor)
assert not target.requires_grad
# calculate loss with "none" reduction
scaling = self.calculate_scaling(target, lengths, encoder_target,
encoder_lengths)
losses = self.loss(y_pred, target, scaling)
# weight samples
if weight is not None:
losses = losses * weight.unsqueeze(-1)
self._update_losses_and_lengths(losses, lengths)
def update(self, y_pred: Dict[str, torch.Tensor],
target: Union[torch.Tensor, rnn.PackedSequence]):
"""
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
"""
target, lengths = unpack_sequence(target)
assert not target.requires_grad
# calculate loss with "none" reduction
if target.ndim == 3:
weight = target[..., 1]
target = target[..., 0]
else:
weight = None
losses = self.loss(y_pred, target)
# weight samples
if weight is not None:
losses = losses * weight.unsqueeze(-1)
self._update_losses_and_lengths(losses, lengths)
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 _convert(self, y_pred: torch.Tensor,
target: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
# unpack target into target and weights
if isinstance(
target,
(list, tuple)) and not isinstance(target, rnn.PackedSequence):
target, weight = target
if weight is not None:
raise NotImplementedError(
"Weighting is not supported for pure torchmetrics - "
"implement a custom version or use pytorch-forecasting metrics"
)
# convert to point prediction - limits applications of class
y_pred = self.to_prediction(y_pred)
# unpack target if it is PackedSequence
if isinstance(target, rnn.PackedSequence):
target, lengths = unpack_sequence(target)
# create mask for different lengths
length_mask = create_mask(target.size(1), lengths, inverse=True)
target = target.masked_select(length_mask)
y_pred = y_pred.masked_select(length_mask)
y_pred = y_pred.flatten()
target = target.flatten()
return y_pred, target
def update(
self,
y_pred: Dict[str, torch.Tensor],
target: Union[torch.Tensor, rnn.PackedSequence],
encoder_target: Union[torch.Tensor, rnn.PackedSequence],
encoder_lengths: torch.Tensor = None,
) -> torch.Tensor:
"""
Update metric that handles masking of values.
Args:
y_pred (Dict[str, torch.Tensor]): network output
target (Union[torch.Tensor, rnn.PackedSequence]): actual values
encoder_target (Union[torch.Tensor, rnn.PackedSequence]): historic actual values
encoder_lengths (torch.Tensor): optional encoder lengths, not necessary if encoder_target
is rnn.PackedSequence. Assumed encoder_target is torch.Tensor
Returns:
torch.Tensor: loss as a single number for backpropagation
"""
target, lengths = unpack_sequence(target)
if encoder_lengths is None:
encoder_target, encoder_lengths = unpack_sequence(target)
else:
assert isinstance(encoder_target, torch.Tensor)
assert not target.requires_grad
# calculate loss with "none" reduction
if target.ndim == 3:
weight = target[..., 1]
target = target[..., 0]
else:
weight = None
scaling = self.calculate_scaling(target, lengths, encoder_target,
encoder_lengths)
losses = self.loss(y_pred, target, scaling)
# weight samples
if weight is not None:
losses = losses * weight.unsqueeze(-1)
self._update_losses_and_lengths(losses, lengths)
