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Author: JanFidor

darts/models/forecasting/transformer_model.py

@@ -289,15 +289,16 @@ def _permute_transformer_inputs(self, data):
 
     def forward(self, x_in: Tuple):
         """
-        When teacher forcing, x_in = (past_target + past_covariates, static_covariates, future_targets)
-        When inference, x_in = (past_target + past_covariates, static_covariates)
-
+        During training (teacher forcing) x_in = tuple(past_target + past_covariates, static_covariates, future_targets)
+        During inference x_in = tuple(past_target + past_covariates, static_covariates)
         """
         data = x_in[0]
-
         pad_size = (0, self.input_size - self.target_size)
+
+        # start token consists only of target series, past covariates are substituted with 0 padding
         start_token = self._permute_transformer_inputs(data[:, -1:, : self.target_size])
         start_token_padded = F.pad(start_token, pad_size)
+
         if len(x_in) == 3:
             src, _, tgt = x_in
             src = self._permute_transformer_inputs(src)
@@ -340,7 +341,7 @@ def _prediction_step(self, src: torch.Tensor, tgt: torch.Tensor):
         # Here we change the data format
         # from (1, batch_size, output_chunk_length * output_size)
         # to (batch_size, output_chunk_length, output_size, nr_params)
-        predictions = out.permute(1, 0, 2)
+        predictions = self._permute_transformer_inputs(out)
         predictions = predictions.view(batch_size, -1, self.target_size, self.nr_params)
 
         return predictions
@@ -357,13 +358,11 @@ def _produce_train_output(self, input_batch: Tuple):
         Feeds PastCovariatesTorchModel with input and output chunks of a PastCovariatesSequentialDataset for
         training.
 
-        Parameters:        if len(inp
-        # print([x.shape if x is not None else x for x in train_batch], "TRAIN")ut_batch) != 4:
-            a = 1
-        ----------
-        a = 1
+        Parameters:
         input_batch
-            ``(past_target, past_covariates, static_covariates)``
+            ``(past_target, past_covariates, static_covariates, future_target)`` during training
+
+            ``(past_target, past_covariates, static_covariates)`` during validation (not teacher forced)
         """
 
         past_target, past_covariates, static_covariates = input_batch[:3]
@@ -375,7 +374,7 @@ def _produce_train_output(self, input_batch: Tuple):
             static_covariates,
         ]
 
-        # add future targets when teacher forcing
+        # add future targets when training (teacher forcing)
         if len(input_batch) == 4:
             inpt.append(input_batch[-1])
         return self(inpt)