add more details

deploy/configs/inference_attr.yaml

@@ -27,7 +27,7 @@ PreProcess:
 PostProcess:
   main_indicator: Attribute
   Attribute:
-    threshold: 0.5
-    glasses_threshold: 0.3
-    hold_threshold: 0.6
+    threshold: 0.5  #default threshold
+    glasses_threshold: 0.3  #threshold only for glasses
+    hold_threshold: 0.6 #threshold only for hold
   
\ No newline at end of file

deploy/python/postprocess.py

@@ -64,9 +64,17 @@ class ThreshOutput(object):
         for idx, probs in enumerate(x):
             score = probs[1]
             if score < self.threshold:
-                result = {"class_ids": [0], "scores":  [1 - score], "label_names": [self.label_0]}
+                result = {
+                    "class_ids": [0],
+                    "scores": [1 - score],
+                    "label_names": [self.label_0]
+                }
             else:
-                result = {"class_ids": [1], "scores": [score], "label_names": [self.label_1]}
+                result = {
+                    "class_ids": [1],
+                    "scores": [score],
+                    "label_names": [self.label_1]
+                }
             if file_names is not None:
                 result["file_name"] = file_names[idx]
             y.append(result)
@@ -264,5 +272,11 @@ class Attribute(object):
             shoe = 'Boots' if res[14] > self.threshold else 'No boots'
             label_res.append(shoe)
 
-            batch_res.append(label_res)
+            threshold_list = [0.5] * len(res)
+            threshold_list[1] = self.glasses_threshold
+            threshold_list[18] = self.hold_threshold
+            pred_res = (np.array(res) > np.array(threshold_list)
+                        ).astype(np.int8).tolist()
+
+            batch_res.append([label_res, pred_res])
         return batch_res

deploy/python/predict_cls.py

@@ -140,9 +140,10 @@ def main(config):
             for number, result_dict in enumerate(batch_results):
                 if "Attribute" in config["PostProcess"]:
                     filename = batch_names[number]
-                    attr_message = result_dict
-                    print("{}:\tclass id(s): {}".format(filename,
-                                                        attr_message))
+                    attr_message = result_dict[0]
+                    pred_res = result_dict[1]
+                    print("{}:\t attributes: {}, \npredict output: {}".format(
+                        filename, attr_message, pred_res))
                 else:
                     filename = batch_names[number]
                     clas_ids = result_dict["class_ids"]

ppcls/metric/metrics.py

@@ -391,6 +391,7 @@ class AccuracyScore(MultiLabelMetric):
 def get_attr_metrics(gt_label, preds_probs, threshold):
     """
     index: evaluated label index
+    adapted from "https://github.com/valencebond/Rethinking_of_PAR/blob/master/metrics/pedestrian_metrics.py"
     """
     pred_label = (preds_probs > threshold).astype(int)