Metrics

src/skelcast/metrics/__init__.py

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+from skelcast.core.registry import Registry
+
+METRICS = Registry()
+

src/skelcast/metrics/mae.py

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+import torch
+
+from typing import Tuple
+
+from skelcast.metrics import METRICS
+from skelcast.metrics.metric import Metric
+
+
+@METRICS.register_module()
+class MeanPerJointPositionError(Metric): # Inherits from our abstract Metric class
+ def __init__(self, keep_time_dim: bool = True):
+ self.keep_time_dim = keep_time_dim
+ self.reset() # Initialize/reset the state
+
+ def reset(self):
+ # Reset the state of the metric
+ self.y = None
+ self.y_pred = None
+
+ def update(self, output: Tuple[torch.Tensor, torch.Tensor]):
+ y_pred, y = output # Unpack the output tuple, assuming output is already in the desired format
+
+ # Initialize or update the stored tensors
+ if self.y is None:
+ self.y = y
+ self.y_pred = y_pred
+ else:
+ self.y = torch.cat([self.y, y], dim=0)
+ self.y_pred = torch.cat([self.y_pred, y_pred], dim=0)
+
+ def result(self):
+ # Compute the Mean Per Joint Position Error
+ if self.y is None:
+ raise ValueError('MeanPerJointPositionError must have at least one example before it can be computed.')
+
+ error = (self.y - self.y_pred).norm(dim=-1) # Calculate the L2 norm over the last dimension (joints)
+ mean_error = error.mean(dim=[0, 2]) # Take the mean over the batch and time dimensions
+
+ if not self.keep_time_dim:
+ mean_error = mean_error.mean() # Further reduce mean over all joints if time dimension is not kept
+
+ return mean_error

src/skelcast/metrics/metric.py

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+from abc import ABC, abstractmethod
+
+class Metric(ABC):
+ @abstractmethod
+ def update(self, predictions, targets):
+ """
+ Update the metric's state using the predictions and the targets.
+
+ Args:
+ - predictions: The predicted values.
+ - targets: The ground truth values.
+ """
+ pass
+
+ @abstractmethod
+ def result(self):
+ """
+ Calculates and returns the final metric result based on the state.
+
+ Returns:
+ - The calculated metric.
+ """
+ pass
+
+ @abstractmethod
+ def reset(self):
+ """
+ Resets the metric state.
+ """
+ pass

src/skelcast/metrics/mpjpe.py

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+import torch
+
+from typing import Tuple
+
+from skelcast.metrics import METRICS
+from skelcast.metrics.metric import Metric
+
+
+@METRICS.register_module()
+class MeanPerJointPositionError(Metric):
+ def __init__(self, keep_time_dim: bool = True):
+ self.keep_time_dim = keep_time_dim
+ self.reset()
+
+ def reset(self):
+ # Reset the state of the metric
+ self.y = torch.tensor([])
+ self.y_pred = torch.tensor([])
+
+ def update(self, output: Tuple[torch.Tensor, torch.Tensor]):
+ y_pred, y = output
+
+ # Concatenate new predictions and targets to the stored tensors
+ if self.y.numel() == 0:
+ self.y = y
+ self.y_pred = y_pred
+ else:
+ self.y = torch.cat([self.y, y], dim=0)
+ self.y_pred = torch.cat([self.y_pred, y_pred], dim=0)
+
+ def result(self):
+ # Compute the Mean Per Joint Position Error
+ if self.y.numel() == 0:
+ raise ValueError('MeanPerJointPositionError must have at least one example before it can be computed.')
+
+ error = (self.y - self.y_pred).norm(dim=-1)
+ mean_error = error.mean(dim=[0, 2])
+
+ if not self.keep_time_dim:
+ mean_error = mean_error.mean()
+
+ return mean_error