fix: 🐛 DeepAR runner bug

baselines/DeepAR/arch/deepar_arch.py

@@ -103,5 +103,9 @@ def forward(self, history_data: torch.Tensor, future_data: torch.Tensor, train:
         mus = torch.concat(mus, dim=1)
         sigmas = torch.concat(sigmas, dim=1)
         reals = input_feat_full[:, -preds.shape[1]:, :, :]
-
+        if not train:
+            preds = preds[:, -len_out:, :, :]
+            reals = reals[:, -len_out:, :, :]
+            mus = mus[:, -len_out:, :, :]
+            sigmas = sigmas[:, -len_out:, :, :]
         return {"prediction": preds, "target": reals, "mus": mus, "sigmas": sigmas}

baselines/DeepAR/runner/deepar_runner.py

@@ -36,55 +36,6 @@ def postprocessing(self, input_data: Dict) -> Dict:
         # TODO: add more postprocessing steps as needed.
         return input_data
 
-    @torch.no_grad()
-    @master_only
-    def test(self, train_epoch: Optional[int] = None, save_metrics: bool = False, save_results: bool = False) -> Dict:
-        """Test process.
-
-        Args:
-            train_epoch (Optional[int]): Current epoch if in training process.
-            save_metrics (bool): Save the test metrics. Defaults to False.
-            save_results (bool): Save the test results. Defaults to False.
-        """
-
-        prediction, target, inputs = [], [], []
-
-        for data in tqdm(self.test_data_loader):
-            forward_return = self.forward(data, epoch=None, iter_num=None, train=False)
-            loss = self.metric_forward(self.loss, forward_return)
-            self.update_epoch_meter('test/loss', loss.item())
-
-            if not self.if_evaluate_on_gpu:
-                forward_return['prediction'] = forward_return['prediction'].detach().cpu()
-                forward_return['target'] = forward_return['target'].detach().cpu()
-                forward_return['inputs'] = forward_return['inputs'].detach().cpu()
-
-            prediction.append(forward_return['prediction'])
-            target.append(forward_return['target'])
-            inputs.append(forward_return['inputs'])
-
-        prediction = torch.cat(prediction, dim=0)
-        target = torch.cat(target, dim=0)
-        inputs = torch.cat(inputs, dim=0)
-
-        returns_all = {'prediction': prediction[:, -self.output_seq_len:, :, :],
-                        'target': target[:, -self.output_seq_len:, :, :],
-                        'inputs': inputs}
-        metrics_results = self.compute_evaluation_metrics(returns_all)
-
-        # save
-        if save_results:
-            # save returns_all to self.ckpt_save_dir/test_results.npz
-            test_results = {k: v.cpu().numpy() for k, v in returns_all.items()}
-            np.savez(os.path.join(self.ckpt_save_dir, 'test_results.npz'), **test_results)
-
-        if save_metrics:
-            # save metrics_results to self.ckpt_save_dir/test_metrics.json
-            with open(os.path.join(self.ckpt_save_dir, 'test_metrics.json'), 'w') as f:
-                json.dump(metrics_results, f, indent=4)
-
-        return returns_all
-
     def forward(self, data: tuple, epoch:int = None, iter_num: int = None, train:bool = True, **kwargs) -> tuple:
         """feed forward process for train, val, and test. Note that the outputs are NOT re-scaled.