Clean pre_hook/post_hook of JointInference lib code

Signed-off-by: bryson-davis <qinkai1994@qq.com>

examples/helmet_detection_inference/little_model/little_model.py

@@ -82,7 +82,7 @@ def preprocess(image, input_shape):
     new_image.fill(128)
     bh, bw, _ = new_image.shape
     new_image[int((bh - nh) / 2):(nh + int((bh - nh) / 2)),
-    int((bw - nw) / 2):(nw + int((bw - nw) / 2)), :] = image
+              int((bw - nw) / 2):(nw + int((bw - nw) / 2)), :] = image
 
     new_image /= 255.
     new_image = np.expand_dims(new_image, 0)  # Add batch dimension.
@@ -112,7 +112,7 @@ def create_output_fetch(sess):
     return output_fetch
 
 
-def post_hook(model_output):
+def postprocess(model_output):
     all_classes, all_scores, all_bboxes = model_output
     bboxes = []
     for c, s, bbox in zip(all_classes, all_scores, all_bboxes):
@@ -171,7 +171,7 @@ def run():
     # create little model object
     model = neptune.joint_inference.TSLittleModel(
         preprocess=preprocess,
-        postprocess=None,
+        postprocess=postprocess,
         input_shape=input_shape,
         create_input_feed=create_input_feed,
         create_output_fetch=create_output_fetch
@@ -188,9 +188,7 @@ def run():
     # create joint inference object
     inference_instance = neptune.joint_inference.JointInference(
         little_model=model,
-        hard_example_mining_algorithm=hard_example_mining_algorithm,
-        pre_hook=None,
-        post_hook=post_hook,
+        hard_example_mining_algorithm=hard_example_mining_algorithm
     )
 
     # use video streams for testing

lib/neptune/joint_inference/joint_inference.py

@@ -203,7 +203,10 @@ class TSLittleModel(BaseModel):
             input_feed = self.create_input_feed(self.session, new_image,
                                                 img_data_np)
             output_fetch = self.create_output_fetch(self.session)
-            return self.session.run(output_fetch, input_feed)
+            output = self.session.run(output_fetch, input_feed)
+            if self.postprocess:
+                output = self.postprocess(output)
+            return output
 
 
 class LCReporter(threading.Thread):
@@ -294,13 +297,10 @@ class JointInference:
     :param little_model: the little model entity for edge inference
     :param hard_example_mining_algorithm: the algorithm for judging hard
         example
-    :param pre_hook: the pre function of edge inference
-    :param post_hook: the post function of edge inference
     """
 
     def __init__(self, little_model: BaseModel,
-                 hard_example_mining_algorithm=None,
-                 pre_hook=None, post_hook=None):
+                 hard_example_mining_algorithm=None):
         self.little_model = little_model
         self.big_model = BigModelClient()
         # TODO how to deal process use-defined cloud_offload_algorithm,
@@ -329,8 +329,6 @@ class JointInference:
                 hard_example_mining_algorithm = ThresholdFilter()
 
         self.hard_example_mining_algorithm = hard_example_mining_algorithm
-        self.pre_hook = pre_hook
-        self.post_hook = post_hook
 
         self.lc_reporter = LCReporter()
         self.lc_reporter.setDaemon(True)
@@ -339,13 +337,10 @@ class JointInference:
     def inference(self, img_data) -> InferenceResult:
         """Image inference function."""
         img_data_pre = img_data
-        if self.pre_hook:
-            img_data_pre = self.pre_hook(img_data_pre)
         edge_result = self.little_model.inference(img_data_pre)
-        if self.post_hook:
-            edge_result = self.post_hook(edge_result)
         is_hard_example = self.hard_example_mining_algorithm.hard_judge(
-            edge_result)
+            edge_result
+        )
         if not is_hard_example:
             LOG.debug("not hard example, use edge result directly")
             self.lc_reporter.update_for_edge_inference()