[ENH] Update check procudre

learnware/__init__.py

@@ -23,9 +23,5 @@ def init(make_dir: bool = False, tf_loglevel: str = "2", **kwargs):
         logger.info(f"make learnware dir successfully!")
 
     ## ignore tensorflow warning
-    os.environ["TF_CPP_MIN_LOG_LEVEL"] = tf_loglevel
-    logger.info(f"The tensorflow log level is setted to {tf_loglevel}")
-
-
-## call init method by default
-init()
+    # os.environ["TF_CPP_MIN_LOG_LEVEL"] = tf_loglevel
+    # logger.info(f"The tensorflow log level is setted to {tf_loglevel}")

learnware/learnware/reuse.py

@@ -1,5 +1,6 @@
 import torch
 import numpy as np
+
 # import tensorflow as tf
 from typing import Tuple, Any, List, Union, Dict
 from cvxopt import matrix, solvers
@@ -58,7 +59,7 @@ class JobSelectorReuser(BaseReuser):
                     pred_y = pred_y.detach().cpu().numpy()
                 # elif isinstance(pred_y, tf.Tensor):
                 #     pred_y = pred_y.numpy()
-                
+
                 if not isinstance(pred_y, np.ndarray):
                     raise TypeError(f"Model output must be np.ndarray or torch.Tensor")
 
@@ -300,7 +301,7 @@ class AveragingReuser(BaseReuser):
 
             if not isinstance(pred_y, np.ndarray):
                 raise TypeError(f"Model output must be np.ndarray or torch.Tensor")
-            
+
             if self.mode == "mean":
                 if mean_pred_y is None:
                     mean_pred_y = pred_y

learnware/market/easy.py

@@ -83,15 +83,28 @@ class EasyMarket(BaseMarket):
             - The NOPREDICTION_LEARNWARE denotes the leanrware pass the check and can make prediction
         """
         try:
+            # check model instantiation
             learnware.instantiate_model()
+
         except Exception as e:
             logger.warning(f"The learnware [{learnware.id}] is instantiated failed! Due to {e}")
             return cls.NONUSABLE_LEARNWARE
 
         try:
             learnware_model = learnware.get_model()
+
+            # check input shape
             inputs = np.random.randn(10, *learnware_model.input_shape)
             outputs = learnware.predict(inputs)
+
+            # check output type
+            if isinstance(outputs, torch.Tensor):
+                outputs = outputs.detach().cpu().numpy()
+            if not isinstance(outputs, np.ndarray):
+                logger.warning(f"The learnware [{learnware.id}] output must be np.ndarray or torch.Tensor")
+                return cls.NONUSABLE_LEARNWARE
+
+            # check output shape
             if outputs.shape[1:] != learnware_model.output_shape:
                 logger.warning(f"The learnware [{learnware.id}] input and output dimention is error")
                 return cls.NONUSABLE_LEARNWARE