[MNT] add details in stat checker

learnware/market/easy/checker.py

@@ -131,7 +131,13 @@ class EasyStatChecker(BaseChecker):
                 message += "\r\n" + traceback.format_exc()
                 return self.INVALID_LEARNWARE, message
 
-            if semantic_spec["Task"]["Values"][0] in ("Classification", "Regression"):
+            # Check length of input and output
+            if len(inputs) != len(outputs):
+                message = f"The learnware {learnware.id} output length must be equal to input length!"
+                logger.warning(message)
+                return self.INVALID_LEARNWARE, message
+
+            if semantic_spec["Task"]["Values"][0] in ["Classification", "Regression", "Feature Extraction"]:
                 # Check output type
                 if isinstance(outputs, torch.Tensor):
                     outputs = outputs.detach().cpu().numpy()
@@ -142,11 +148,19 @@ class EasyStatChecker(BaseChecker):
 
                 if outputs.ndim == 1:
                     outputs = outputs.reshape(-1, 1)
+
                 # Check output shape
-                if outputs[0].shape != learnware_model.output_shape or learnware_model.output_shape != (
-                    int(semantic_spec["Output"]["Dimension"]),
-                ):
-                    message = f"The learnware [{learnware.id}] output dimension mismatch!, where pred_shape={outputs[0].shape}, model_shape={learnware_model.output_shape}, semantic_shape={(int(semantic_spec['Output']['Dimension']), )}"
+                if outputs[0].shape != learnware_model.output_shape:
+                    message = f"The learnware [{learnware.id}] output dimension mismatch!, where pred_shape={outputs[0].shape}, model_shape={learnware_model.output_shape}"
+                    logger.warning(message)
+                    return self.INVALID_LEARNWARE, message
+
+                # Check output dimension
+                if semantic_spec["Task"]["Values"][0] in [
+                    "Classification",
+                    "Regression",
+                ] and learnware_model.output_shape != int(semantic_spec["Output"]["Dimension"]):
+                    message = f"The learnware [{learnware.id}] output dimension mismatch!, where pred_shape={outputs[0].shape}, semantic_shape={(int(semantic_spec['Output']['Dimension']), )}"
                     logger.warning(message)
                     return self.INVALID_LEARNWARE, message