CRZbulabula commented on code in PR #16907:
URL: https://github.com/apache/iotdb/pull/16907#discussion_r2622626382
##########
iotdb-core/ainode/iotdb/ainode/core/inference/pipeline/basic_pipeline.py:
##########
@@ -29,59 +29,61 @@ def __init__(self, model_info, **model_kwargs):
self.device = model_kwargs.get("device", "cpu")
self.model = load_model(model_info, device_map=self.device,
**model_kwargs)
- def _preprocess(self, inputs):
+ @abstractmethod
+ def preprocess(self, inputs):
"""
Preprocess the input before inference, including shape validation and
value transformation.
"""
- return inputs
+ raise NotImplementedError("preprocess not implemented")
- def _postprocess(self, output: torch.Tensor):
+ @abstractmethod
+ def postprocess(self, output: torch.Tensor):
"""
Post-process the outputs after the entire inference task.
"""
- return output
+ raise NotImplementedError("postprocess not implemented")
class ForecastPipeline(BasicPipeline):
def __init__(self, model_info, **model_kwargs):
super().__init__(model_info, model_kwargs=model_kwargs)
- def _preprocess(self, inputs):
+ def preprocess(self, inputs):
return inputs
@abstractmethod
def forecast(self, inputs, **infer_kwargs):
pass
- def _postprocess(self, output: torch.Tensor):
+ def postprocess(self, output: torch.Tensor):
Review Comment:
And the shape of `inputs` should be 3-d.
##########
iotdb-core/ainode/iotdb/ainode/core/inference/pipeline/basic_pipeline.py:
##########
@@ -29,59 +29,61 @@ def __init__(self, model_info, **model_kwargs):
self.device = model_kwargs.get("device", "cpu")
self.model = load_model(model_info, device_map=self.device,
**model_kwargs)
- def _preprocess(self, inputs):
+ @abstractmethod
+ def preprocess(self, inputs):
"""
Preprocess the input before inference, including shape validation and
value transformation.
"""
- return inputs
+ raise NotImplementedError("preprocess not implemented")
- def _postprocess(self, output: torch.Tensor):
+ @abstractmethod
+ def postprocess(self, output: torch.Tensor):
"""
Post-process the outputs after the entire inference task.
"""
- return output
+ raise NotImplementedError("postprocess not implemented")
class ForecastPipeline(BasicPipeline):
def __init__(self, model_info, **model_kwargs):
super().__init__(model_info, model_kwargs=model_kwargs)
- def _preprocess(self, inputs):
+ def preprocess(self, inputs):
return inputs
@abstractmethod
def forecast(self, inputs, **infer_kwargs):
pass
- def _postprocess(self, output: torch.Tensor):
+ def postprocess(self, output: torch.Tensor):
Review Comment:
U should add some annotations to notify the shape of `inputs` and `output`
for forecasting task.
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