add docstring for codes

Signed-off-by: hsj576 <[email protected]>

examples/robot/lifelong_learning_bench/benchmarkingjob-simple.yaml

@@ -2,7 +2,7 @@ benchmarkingjob:
   # job name of bechmarking; string type;
   name: "benchmarkingjob"
   # the url address of job workspace that will reserve the output of tests; string type;
-  workspace: "/home/vipuser/ianvs/lifelong_learning_bench/robot-workspace-test"
+  workspace: "/ianvs/lifelong_learning_bench/robot-workspace-test"
 
   # the url address of test environment configuration file; string type;
   # the file format supports yaml/yml;

examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-sam.py

@@ -82,6 +82,9 @@ def train(self, train_data, valid_data=None, **kwargs):
         return self.train_model_url
 
     def predict(self, data, **kwargs):
+        """
+        Use the RFNet model to predict at the edge 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}
@@ -103,6 +106,9 @@ def predict(self, data, **kwargs):
         return predictions
 
     def predict_cloud(self, data, **kwargs):
+        """
+        Use the SAM model to predict at the cloud 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}
@@ -124,6 +130,9 @@ def predict_cloud(self, data, **kwargs):
         return predictions
 
     def predict_score(self, data, **kwargs):
+        """
+        Get the prediction scores of RFNet model 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}

examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-simple.py

@@ -82,6 +82,9 @@ def train(self, train_data, valid_data=None, **kwargs):
         return self.train_model_url
 
     def predict(self, data, **kwargs):
+        """
+        Use the RFNet model to predict at the edge 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}
@@ -103,6 +106,9 @@ def predict(self, data, **kwargs):
         return predictions
 
     def predict_cloud(self, data, **kwargs):
+        """
+        Use the SAM model to predict at the cloud 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}
@@ -124,6 +130,9 @@ def predict_cloud(self, data, **kwargs):
         return predictions
 
     def predict_score(self, data, **kwargs):
+        """
+        Get the prediction scores of RFNet model 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}

examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-vit-sam.py

@@ -82,6 +82,9 @@ def train(self, train_data, valid_data=None, **kwargs):
         return self.train_model_url
 
     def predict(self, data, **kwargs):
+        """
+        Use the Segformer model to predict at the edge 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}
@@ -103,6 +106,9 @@ def predict(self, data, **kwargs):
         return predictions
 
     def predict_cloud(self, data, **kwargs):
+        """
+        Use the SAM model to predict at the cloud 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}
@@ -124,6 +130,9 @@ def predict_cloud(self, data, **kwargs):
         return predictions
 
     def predict_score(self, data, **kwargs):
+        """
+        Get the prediction scores of RFNet model 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}

examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel-vit-simple.py

@@ -82,6 +82,9 @@ def train(self, train_data, valid_data=None, **kwargs):
         return self.train_model_url
 
     def predict(self, data, **kwargs):
+        """
+        Use the Segformer model to predict at the edge 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}
@@ -103,6 +106,9 @@ def predict(self, data, **kwargs):
         return predictions
 
     def predict_cloud(self, data, **kwargs):
+        """
+        Use the SAM model to predict at the cloud 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}
@@ -124,6 +130,9 @@ def predict_cloud(self, data, **kwargs):
         return predictions
 
     def predict_score(self, data, **kwargs):
+        """
+        Get the prediction scores of RFNet model 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}

examples/robot/lifelong_learning_bench/testalgorithms/rfnet/basemodel.py

@@ -89,6 +89,9 @@ def train(self, train_data, valid_data=None, **kwargs):
         return self.train_model_url
 
     def predict(self, data, **kwargs):
+        """
+        Use the RFNet model to predict at the edge 
+        """
         if len(data) > 10:
             print("predict start for big data")
             my_kwargs = {'num_workers': self.val_args.workers, 'pin_memory': True}