[MNT] modify load_learnware in LearnwareClient

learnware/client/learnware_client.py

@@ -64,10 +64,9 @@ class LearnwareClient:
             self.host = C.backend_host
         else:
             self.host = host
-            pass
 
         self.chunk_size = 1024 * 1024
-        pass
+        self.tempdir_list = []
 
     def login(self, email, token):
         url = f"{self.host}/auth/login_by_token"
@@ -305,40 +304,36 @@ class LearnwareClient:
         return semantic_conf[key.value]["Values"]
 
     def load_learnware(self, learnware_file: str, load_model: bool = True):
-        with tempfile.TemporaryDirectory(prefix="learnware_") as tempdir:
-            with zipfile.ZipFile(learnware_file, "r") as z_file:
-                z_file.extractall(tempdir)
-                pass
+        self.tempdir_list.append(tempfile.TemporaryDirectory(prefix="learnware_"))
+        tempdir = self.tempdir_list[-1].name
+        
+        with zipfile.ZipFile(learnware_file, "r") as z_file:
+            z_file.extractall(tempdir)
 
-            yaml_file = C.learnware_folder_config["yaml_file"]
+        yaml_file = C.learnware_folder_config["yaml_file"]
 
-            with open(os.path.join(tempdir, yaml_file), "r") as fin:
-                learnware_info = yaml.safe_load(fin)
-                pass
+        with open(os.path.join(tempdir, yaml_file), "r") as fin:
+            learnware_info = yaml.safe_load(fin)
 
-            learnware_id = learnware_info.get("id")
-            if learnware_id is None:
-                learnware_id = "test_id"
-                pass
+        learnware_id = learnware_info.get("id")
+        if learnware_id is None:
+            learnware_id = "test_id"
 
-            semantic_specification = learnware_info.get("semantic_specification")
-            if semantic_specification is None:
-                semantic_specification = {}
-                pass
-            else:
-                semantic_file = semantic_specification.get("file_name")
+        semantic_specification = learnware_info.get("semantic_specification")
+        if semantic_specification is None:
+            semantic_specification = {}
+        else:
+            semantic_file = semantic_specification.get("file_name")
 
-                with open(os.path.join(tempdir, semantic_file), "r") as fin:
-                    semantic_specification = json.load(fin)
-                    pass
-                pass
+            with open(os.path.join(tempdir, semantic_file), "r") as fin:
+                semantic_specification = json.load(fin)
 
-            learnware_obj = learnware.get_learnware_from_dirpath(learnware_id, semantic_specification, tempdir)
+        learnware_obj = learnware.get_learnware_from_dirpath(learnware_id, semantic_specification, tempdir)
 
-            if load_model:
-                learnware_obj.instantiate_model()
+        if load_model:
+            learnware_obj.instantiate_model()
 
-            return learnware_obj
+        return learnware_obj
 
     def system(self, command):
         retcd = os.system(command)
@@ -424,3 +419,7 @@ class LearnwareClient:
 
         logger.info("test ok")
         pass
+    
+    def __del__(self):
+        for tempdir in self.tempdir_list:
+            tempdir.cleanup()

tests/test_client/test_download.py

@@ -9,47 +9,6 @@ from learnware.client.container import ModelEnvContainer, LearnwaresContainer
 from learnware.learnware.reuse import AveragingReuser
 
 
-def test_container(zip_paths):
-    semantic_specification = dict()
-    semantic_specification["Data"] = {"Type": "Class", "Values": ["Text"]}
-    semantic_specification["Task"] = {"Type": "Class", "Values": ["Ranking"]}
-    semantic_specification["Library"] = {"Type": "Class", "Values": ["Scikit-learn"]}
-    semantic_specification["Scenario"] = {"Type": "Tag", "Values": "Financial"}
-    semantic_specification["Name"] = {"Type": "String", "Values": "test"}
-    semantic_specification["Description"] = {"Type": "String", "Values": "test"}
-
-    learnware_list = []
-    for id, zip_path in enumerate(zip_paths):
-        dir_path = zip_path[:-4]
-        with zipfile.ZipFile(zip_path, "r") as z_file:
-            z_file.extractall(dir_path)
-
-        learnware = get_learnware_from_dirpath(f"test_id{id}", semantic_specification, dir_path)
-        learnware_list.append(learnware)
-
-    with LearnwaresContainer(learnware_list, zip_paths) as env_container:
-        learnware_list = env_container.get_learnware_list_with_container()
-        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
-        input_array = np.random.random(size=(20, 13))
-        print(reuser.predict(input_array))
-
-        for idx, learnware in enumerate(learnware_list):
-            print(f"learnware_{idx}", learnware.predict(input_array))
-
-
-def test_load(zip_paths):
-    learnware_list = [client.load_learnware(file, load_model=False) for file in zip_paths]
-
-    with LearnwaresContainer(learnware_list, zip_paths) as env_container:
-        learnware_list = env_container.get_learnware_list_with_container()
-        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
-        input_array = np.random.random(size=(20, 13))
-        print(reuser.predict(input_array))
-
-        for idx, learnware in enumerate(learnware_list):
-            print(f"learnware_{idx}", learnware.predict(input_array))
-
-
 if __name__ == "__main__":
     email = "liujd@lamda.nju.edu.cn"
     token = "f7e647146a314c6e8b4e2e1079c4bca4"
@@ -64,5 +23,13 @@ if __name__ == "__main__":
         zip_paths[i] = os.path.join(root, zip_paths[i])
         client.download_learnware(learnware_ids[i], zip_paths[i])
 
-    test_container(zip_paths)
-    # test_load(zip_paths)
+    learnware_list = [client.load_learnware(file, load_model=False) for file in zip_paths]
+
+    with LearnwaresContainer(learnware_list, zip_paths) as env_container:
+        learnware_list = env_container.get_learnware_list_with_container()
+        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
+        input_array = np.random.random(size=(20, 13))
+        print(reuser.predict(input_array))
+
+        for learnware in learnware_list:
+            print(learnware.id, learnware.predict(input_array))