[FIX] fix checker logic with many specification

learnware/market/easy2/checker.py

@@ -5,6 +5,7 @@ import random
 import string
 
 from ..base import BaseChecker
+from ..utils import parse_specification_type
 from ...config import C
 from ...logger import get_module_logger
 
@@ -61,6 +62,23 @@ class EasySemanticChecker(BaseChecker):
 
 
 class EasyStatisticalChecker(BaseChecker):
+
+    @staticmethod
+    def _generate_random_text_list(num, text_type="en", min_len=10, max_len=1000):
+        text_list = []
+        for i in range(num):
+            length = random.randint(min_len, max_len)
+            if text_type == "en":
+                characters = string.ascii_letters + string.digits + string.punctuation
+                result_str = "".join(random.choice(characters) for i in range(length))
+                text_list.append(result_str)
+            elif text_type == "zh":
+                result_str = "".join(chr(random.randint(0x4E00, 0x9FFF)) for i in range(length))
+                text_list.append(result_str)
+            else:
+                raise ValueError("Type should be en or zh")
+        return text_list
+        
     def __call__(self, learnware):
         semantic_spec = learnware.get_specification().get_semantic_spec()
 
@@ -76,41 +94,30 @@ class EasyStatisticalChecker(BaseChecker):
         try:
             learnware_model = learnware.get_model()
             # Check input shape
-            if semantic_spec["Data"]["Values"][0] == "Table":
-                input_shape = (semantic_spec["Input"]["Dimension"],)
-            else:
-                input_shape = learnware_model.input_shape
-
-            # Check rkme dimension
-            is_text = "RKMETextSpecification" in learnware.get_specification().stat_spec
-            if is_text:
-                stat_spec = learnware.get_specification().get_stat_spec_by_name("RKMETextSpecification")
-            else:
-                stat_spec = learnware.get_specification().get_stat_spec_by_name("RKMETableSpecification")
-            if stat_spec is not None and not is_text:
+            input_shape = learnware_model.input_shape
+
+            ## WHY: why write this?
+            if semantic_spec["Data"]["Values"][0] == "Table" and input_shape != (int(semantic_spec["Input"]["Dimension"]),):
+               logger.warning("input shapes of model and semantic specifications are different")
+               return self.INVALID_LEARNWARE
+                
+            spec_type = parse_specification_type(learnware.get_specification())
+            if spec_type is None:
+                logger.warning(f"No valid specification is found in stat spec {stat_spec}")
+                return self.INVALID_LEARNWARE
+                
+            if spec_type == "RKMETableSpecification":
+                stat_spec = learnware.get_specification().get_stat_spec_by_name(spec_type)
                 if stat_spec.get_z().shape[1:] != input_shape:
                     logger.warning(f"The learnware [{learnware.id}] input dimension mismatch with stat specification.")
                     return self.INVALID_LEARNWARE
-
-            def generate_random_text_list(num, text_type="en", min_len=10, max_len=1000):
-                text_list = []
-                for i in range(num):
-                    length = random.randint(min_len, max_len)
-                    if text_type == "en":
-                        characters = string.ascii_letters + string.digits + string.punctuation
-                        result_str = "".join(random.choice(characters) for i in range(length))
-                        text_list.append(result_str)
-                    elif text_type == "zh":
-                        result_str = "".join(chr(random.randint(0x4E00, 0x9FFF)) for i in range(length))
-                        text_list.append(result_str)
-                    else:
-                        raise ValueError("Type should be en or zh")
-                return text_list
-
-            if is_text:
-                inputs = generate_random_text_list(10)
-            else:
                 inputs = np.random.randn(10, *input_shape)
+            elif spec_type == "RKMETextSpecification":
+                inputs = EasyStatisticalChecker._generate_random_text_list(10)
+            elif spec_type == "RKMEImageSpecification":
+                inputs = np.random.randint(0, 255, size=(10, *input_shape))
+            else:
+                raise ValueError(f"not supported spec type for spec_type = {spec_type}")
             outputs = learnware.predict(inputs)
             # Check output
             if outputs.ndim == 1:
@@ -129,8 +136,7 @@ class EasyStatisticalChecker(BaseChecker):
                     return self.INVALID_LEARNWARE
 
                 # Check output shape
-                output_dim = int(semantic_spec["Output"]["Dimension"])
-                if outputs[0].shape[0] != output_dim:
+                if outputs[0].shape != learnware_model.output_shape or learnware_model.output_shape != int(semantic_spec["Output"]["Dimension"]):
                     logger.warning(f"The learnware [{learnware.id}] output dimention mismatch!")
                     return self.INVALID_LEARNWARE
 

learnware/market/utils.py

@@ -0,0 +1,12 @@
+from ..specification import Specification
+from ..logger import get_module_logger
+
+logger = get_module_logger('market_utils')
+
+def parse_specification_type(stat_spec: Specification):
+    stat_specs = stat_spec.stat_spec
+    spec_list =['RKMETableSpecification', 'RKMETextSpecification', 'RKMEImageSpecification']
+    for spec in spec_list:
+        if spec in stat_specs:
+            return spec
+    return None