def track(self, model: BaseModel):
""" Add current model predictions (usually the result of a batch) to the tracking
"""
super().track(model)
outputs = self._convert(model.get_output())
targets = self._convert(model.get_labels())
erp = torch.sqrt(((outputs - targets) / (targets + self._eps)) ** 2)
self._merp = torch.mean(erp).item()
self._mer = (
torch.mean(F.normalize(outputs - targets, p=2, dim=-1))
/ torch.mean((F.normalize(targets, p=2, dim=-1) + self._eps))
).item()
def track(self, model: BaseModel):
""" Add current model predictions (usually the result of a batch) to the tracking
"""
super().track(model)
outputs = self._convert(model.get_output())
targets = self._convert(model.get_labels())
assert outputs.shape[0] == len(targets)
self._confusion_matrix.count_predicted_batch(targets,
np.argmax(outputs, 1))
self._acc = 100 * self._confusion_matrix.get_overall_accuracy()
self._macc = 100 * self._confusion_matrix.get_mean_class_accuracy()
self._miou = 100 * self._confusion_matrix.get_average_intersection_union(
)
