make the hard_example_mining alg to be a common interface

Signed-off-by: JoeyHwong <joeyhwong@gknow.cn>

examples/incremental_learning/helmet_detection/training/inference.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import os
-import json
 import time
 import warnings
 
@@ -25,6 +24,7 @@
 from sedna.core.incremental_learning import IncrementalLearning
 from interface import Estimator
 
+
 he_saved_url = Context.get_parameters("HE_SAVED_URL", '/tmp')
 rsl_saved_url = Context.get_parameters("RESULT_SAVED_URL", '/tmp')
 class_names = ['person', 'helmet', 'helmet_on', 'helmet_off']
@@ -105,22 +105,11 @@ def deal_infer_rsl(model_output):
 def run():
     camera_address = Context.get_parameters('video_url')
 
-    hard_example_name = Context.get_parameters('HEM_NAME', "IBT")
-    hem_parameters = Context.get_parameters('HEM_PARAMETERS')
-
-    try:
-        hem_parameters = json.loads(hem_parameters)
-        hem_parameters = {
-            p["key"]: p.get("value", "")
-            for p in hem_parameters if "key" in p
-        }
-    except:
-        hem_parameters = {}
-
-    hard_example_mining = {
-        "method": hard_example_name,
-        "param": hem_parameters
-    }
+    # get hard exmaple mining algorithm from config e.g.:
+    # {"method": "IBT", "param": {"threshold_img": 0.9}}
+    hard_example_mining = IncrementalLearning.get_hem_algorithm(
+        threshold_img=0.9
+    )
 
     input_shape_str = Context.get_parameters("input_shape")
     input_shape = tuple(int(v) for v in input_shape_str.split(","))

lib/sedna/common/config.py

@@ -291,3 +291,27 @@ def get_parameters(cls, param, default=None):
         value = cls.parameters.get(
             param) or cls.parameters.get(str(param).upper())
         return value if value else default
+
+    @classmethod
+    def get_crd_algorithm(cls, algorithm, **param) -> dict:
+        """get the algorithm and parameter which define in crd"""
+        hard_example_name = cls.get_parameters(f'{algorithm}_NAME')
+        hem_parameters = cls.get_parameters(f'{algorithm}_PARAMETERS')
+
+        try:
+            hem_parameters = json.loads(hem_parameters)
+            hem_parameters = {
+                p["key"]: p.get("value", "")
+                for p in hem_parameters if "key" in p
+            }
+        except:
+            hem_parameters = {}
+
+        hem_parameters.update(**param)
+
+        hard_example_mining = {
+            "method": hard_example_name,
+            "param": hem_parameters
+        }
+
+        return hard_example_mining

lib/sedna/core/incremental_learning/incremental_learning.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import json
 from copy import deepcopy
 
 from sedna.common.file_ops import FileOps
@@ -50,6 +49,18 @@ def __init__(self, estimator, hard_example_mining: dict = None):
                 ClassType.HEM, hem
             )(**hem_parameters)
 
+    @classmethod
+    def get_hem_algorithm(cls, **param):
+        """
+         get the `algorithm` name and `param` of hard_example_mining from crd
+        :param param: update value in parameters of hard_example_mining
+        :return: dict, e.g.: {"method": "IBT", "param": {"threshold_img": 0.5}}
+        """
+        return cls.parameters.get_crd_algorithm(
+            algorithm="HEM",
+            **param
+        )
+
     def train(self, train_data,
               valid_data=None,
               post_process=None,