Inconsistent with interface name 'initial_model_url' and environment …

…variable 'base_model_url' kubeedge#14 Signed-off-by: Aryan <[email protected]>
AryanNanda17 · Jan 2, 2025 · e05f776 · e05f776
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diff --git a/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py b/core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py
@@ -167,7 +167,7 @@ def _train(self, model, data_index_file, rounds):
             os.makedirs(train_output_dir)
 
         os.environ["MODEL_URL"] = train_output_dir
-        os.environ["BASE_MODEL_URL"] = model
+        os.environ["INITIAL_MODEL_URL"] = model
 
         job = self.build_paradigm_job(ParadigmType.INCREMENTAL_LEARNING.value)
         train_dataset = self.dataset.load_data(data_index_file, "train")

diff --git a/core/testcasecontroller/algorithm/paradigm/multiedge_inference/multiedge_inference.py b/core/testcasecontroller/algorithm/paradigm/multiedge_inference/multiedge_inference.py
@@ -81,7 +81,7 @@ def run(self):
 
     def _inference(self, job, trained_model):
         train_dataset = self.dataset.load_data(self.dataset.train_url, "train")
-        os.environ["BASE_MODEL_URL"] = trained_model
+        os.environ["INITIAL_MODEL_URL"] = trained_model
         inference_dataset = self.dataset.load_data(self.dataset.test_url, "inference")
         inference_output_dir = os.path.join(self.workspace, "output/inference/")
         os.environ["RESULT_SAVED_URL"] = inference_output_dir

diff --git a/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning.py b/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning.py
@@ -110,7 +110,7 @@ def _compress(self, trained_model):
 
     def _train(self, job, initial_model):
         train_output_dir = os.path.join(self.workspace, "output/train/")
-        os.environ["BASE_MODEL_URL"] = initial_model
+        os.environ["INITIAL_MODEL_URL"] = initial_model
 
         train_dataset = self.dataset.load_data(self.dataset.train_url, "train")
         job.train(train_dataset)