CRZbulabula commented on code in PR #16923:
URL: https://github.com/apache/iotdb/pull/16923#discussion_r2626750609
##########
iotdb-core/ainode/iotdb/ainode/core/model/sundial/pipeline_sundial.py:
##########
@@ -26,26 +26,36 @@ class SundialPipeline(ForecastPipeline):
def __init__(self, model_info, **model_kwargs):
super().__init__(model_info, model_kwargs=model_kwargs)
- def _preprocess(self, inputs):
- if len(inputs.shape) != 2:
+ def preprocess(self, inputs):
+ """
+ The inputs shape should be 3D, but Sundial only supports 2D tensor:
[batch_size, sequence_length],
+ we need to squeeze the target_count dimension.
+ """
+ inputs = super().preprocess(inputs)
+ if inputs.shape[1] != 1:
raise InferenceModelInternalException(
- f"[Inference] Input shape must be: [batch_size, seq_len], but
receives {inputs.shape}"
+ f"[Inference] Sundial model only supports single target, but
receives {inputs.shape[1]} series."
Review Comment:
```suggestion
f"[Inference] Model sundial only supports univarate
forecast, but receives {inputs.shape[1]} target variables."
```
##########
iotdb-core/ainode/iotdb/ainode/core/model/timer_xl/pipeline_timer.py:
##########
@@ -26,22 +26,31 @@ class TimerPipeline(ForecastPipeline):
def __init__(self, model_info, **model_kwargs):
super().__init__(model_info, model_kwargs=model_kwargs)
- def _preprocess(self, inputs):
- if len(inputs.shape) != 2:
+ def preprocess(self, inputs):
+ """
+ The inputs shape should be 3D, but Timer-XL only supports 2D tensor:
[batch_size, sequence_length],
+ we need to squeeze the target_count dimension.
+ """
+ inputs = super().preprocess(inputs)
+ if inputs.shape[1] != 1:
raise InferenceModelInternalException(
- f"[Inference] Input shape must be: [batch_size, seq_len], but
receives {inputs.shape}"
+ f"[Inference] Timer-XL model only supports single target, but
receives {inputs.shape[1]} series."
Review Comment:
Same as above.
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