def _compute_tensor(self, y_pred: torch.Tensor,
y: torch.Tensor): # type: ignore
"""
Args:
y_pred: input data to compute, typical segmentation model output.
It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
should be binarized.
y: ground truth to compute the distance. It must be one-hot format and first dim is batch.
The values should be binarized.
Raises:
ValueError: when `y` is not a binarized tensor.
ValueError: when `y_pred` has less than three dimensions.
"""
is_binary_tensor(y_pred, "y_pred")
is_binary_tensor(y, "y")
if y_pred.dim() < 3:
raise ValueError("y_pred should have at least three dimensions.")
# compute (BxC) for each channel for each batch
return compute_average_surface_distance(
y_pred=y_pred,
y=y,
include_background=self.include_background,
symmetric=self.symmetric,
distance_metric=self.distance_metric,
)
def _compute_tensor(self, y_pred: torch.Tensor,
y: torch.Tensor): # type: ignore
"""
Args:
y_pred: input data to compute, typical segmentation model output.
It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
should be binarized.
y: ground truth to compute mean IoU metric. It must be one-hot format and first dim is batch.
The values should be binarized.
Raises:
ValueError: when `y` is not a binarized tensor.
ValueError: when `y_pred` has less than three dimensions.
"""
is_binary_tensor(y_pred, "y_pred")
is_binary_tensor(y, "y")
dims = y_pred.ndimension()
if dims < 3:
raise ValueError(
f"y_pred should have at least 3 dimensions (batch, channel, spatial), got {dims}."
)
# compute IoU (BxC) for each channel for each batch
return compute_meaniou(y_pred=y_pred,
y=y,
include_background=self.include_background,
ignore_empty=self.ignore_empty)
def _compute_tensor(self, y_pred: torch.Tensor,
y: torch.Tensor): # type: ignore
"""
Args:
y_pred: input data to compute. It must be one-hot format and first dim is batch.
The values should be binarized.
y: ground truth to compute the metric. It must be one-hot format and first dim is batch.
The values should be binarized.
Raises:
ValueError: when `y` is not a binarized tensor.
ValueError: when `y_pred` has less than two dimensions.
"""
is_binary_tensor(y_pred, "y_pred")
is_binary_tensor(y, "y")
# check dimension
dims = y_pred.ndimension()
if dims < 2:
raise ValueError("y_pred should have at least two dimensions.")
if dims == 2 or (dims == 3 and y_pred.shape[-1] == 1):
if self.compute_sample:
warnings.warn(
"As for classification task, compute_sample should be False."
)
self.compute_sample = False
return get_confusion_matrix(y_pred=y_pred,
y=y,
include_background=self.include_background)