def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: """ Actual metric computation Args: pred: predicted labels target: ground truth labels Return: A Tensor with the mse loss. """ return mse(pred, target, self.reduction)
def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: """ Actual metric computation Args: pred: predicted labels target: ground truth labels Return: A Tensor with the rmsle loss. """ return mse(torch.log(pred + 1), torch.log(target + 1), self.reduction, return_state=True)
def __step(self, batch, batch_idx, prefix: str): x = batch.x edge_index = batch.edge_index mini_batch = batch.batch edge_attr = batch.edge_attr y_hat = self.forward(x, edge_index, edge_attr, mini_batch).squeeze(-1) loss = pl_regression.mse(y_hat, batch.y) rmse = torch.sqrt(loss) mae = pl_regression.mae(y_hat, batch.y) predictions = y_hat.detach().cpu().numpy() r2 = r2_score(predictions, batch.y.cpu()) return { f'{prefix}_loss': loss, f'{prefix}_size': len(y_hat), f'{prefix}_rmse': rmse, f'{prefix}_mae': mae, f'{prefix}_r2': r2, }