[MNT] remove Feature_Extraction in checker

learnware/market/easy2/checker.py

@@ -43,7 +43,7 @@ class EasySemanticChecker(BaseChecker):
                     assert int(k) >= 0 and int(k) < dim, f"Dimension number in [0, {dim})"
                     assert isinstance(v, str), "Description must be string"
 
-            if semantic_spec["Task"]["Values"][0] in ["Classification", "Regression", "Feature Extraction"]:
+            if semantic_spec["Task"]["Values"][0] in ["Classification", "Regression"]:
                 assert semantic_spec["Output"] is not None, "Lack of output semantics"
                 dim = semantic_spec["Output"]["Dimension"]
                 for k, v in semantic_spec["Output"]["Description"].items():
@@ -126,7 +126,7 @@ class EasyStatChecker(BaseChecker):
                 logger.warning(f"learnware {learnware} prediction method is not valid!")
                 return self.INVALID_LEARNWARE
 
-            if semantic_spec["Task"]["Values"][0] in ("Classification", "Regression", "Feature Extraction"):
+            if semantic_spec["Task"]["Values"][0] in ("Classification", "Regression"):
                 # Check output type
                 if isinstance(outputs, torch.Tensor):
                     outputs = outputs.detach().cpu().numpy()