fix format

deploy/python/postprocess.py

@@ -322,16 +322,15 @@ class VehicleAttribute(object):
         return batch_res
 
 
-
 class TableAttribute(object):
-    def __init__(self, 
-                 source_threshold=0.5, 
-                 number_threshold=0.5,
-                 color_threshold=0.5,
-                 clarity_threshold=0.5,
-                 obstruction_threshold=0.5,
-                 angle_threshold=0.5,
-                 ):
+    def __init__(
+            self,
+            source_threshold=0.5,
+            number_threshold=0.5,
+            color_threshold=0.5,
+            clarity_threshold=0.5,
+            obstruction_threshold=0.5,
+            angle_threshold=0.5, ):
         self.source_threshold = source_threshold
         self.number_threshold = number_threshold
         self.color_threshold = color_threshold
@@ -342,19 +341,27 @@ class TableAttribute(object):
     def __call__(self, batch_preds, file_names=None):
         # postprocess output of predictor
         batch_res = []
+
         for res in batch_preds:
             res = res.tolist()
             label_res = []
             source = 'Scanned' if res[0] > self.source_threshold else 'Photo'
             number = 'Little' if res[1] > self.number_threshold else 'Numerous'
-            color = 'Black-and-White' if res[2] > self.color_threshold else 'Multicolor'
+            color = 'Black-and-White' if res[
+                2] > self.color_threshold else 'Multicolor'
             clarity = 'Clear' if res[3] > self.clarity_threshold else 'Blurry'
-            obstruction = 'Without-Obstacles' if res[4] > self.number_threshold else 'With-Obstacles'
-            angle = 'Horizontal' if res[5] > self.number_threshold else 'Tilted'
+            obstruction = 'Without-Obstacles' if res[
+                4] > self.number_threshold else 'With-Obstacles'
+            angle = 'Horizontal' if res[
+                5] > self.number_threshold else 'Tilted'
 
             label_res = [source, number, color, clarity, obstruction, angle]
 
-            threshold_list = [self.source_threshold, self.number_threshold, self.color_threshold, self.clarity_threshold, self.obstruction_threshold, self.angle_threshold]
+            threshold_list = [
+                self.source_threshold, self.number_threshold,
+                self.color_threshold, self.clarity_threshold,
+                self.obstruction_threshold, self.angle_threshold
+            ]
             pred_res = (np.array(res) > np.array(threshold_list)
                         ).astype(np.int8).tolist()
             batch_res.append({"attributes": label_res, "output": pred_res})

ppcls/data/postprocess/attr_rec.py

@@ -71,7 +71,6 @@ class VehicleAttribute(object):
         return batch_res
 
 
-
 class PersonAttribute(object):
     def __init__(self,
                  threshold=0.5,
@@ -173,14 +172,14 @@ class PersonAttribute(object):
 
 
 class TableAttribute(object):
-    def __init__(self,
-                 source_threshold=0.5,
-                 number_threshold=0.5,
-                 color_threshold=0.5,
-                 clarity_threshold=0.5,
-                 obstruction_threshold=0.5,
-                 angle_threshold=0.5,
-                 ):
+    def __init__(
+            self,
+            source_threshold=0.5,
+            number_threshold=0.5,
+            color_threshold=0.5,
+            clarity_threshold=0.5,
+            obstruction_threshold=0.5,
+            angle_threshold=0.5, ):
         self.source_threshold = source_threshold
         self.number_threshold = number_threshold
         self.color_threshold = color_threshold
@@ -195,6 +194,7 @@ class TableAttribute(object):
         if file_names is not None:
             assert x.shape[0] == len(file_names)
         x = F.sigmoid(x).numpy()
+
         # postprocess output of predictor
         batch_res = []
         for idx, res in enumerate(x):
@@ -202,16 +202,26 @@ class TableAttribute(object):
             label_res = []
             source = 'Scanned' if res[0] > self.source_threshold else 'Photo'
             number = 'Little' if res[1] > self.number_threshold else 'Numerous'
-            color = 'Black-and-White' if res[2] > self.color_threshold else 'Multicolor'
+            color = 'Black-and-White' if res[
+                2] > self.color_threshold else 'Multicolor'
             clarity = 'Clear' if res[3] > self.clarity_threshold else 'Blurry'
-            obstruction = 'Without-Obstacles' if res[4] > self.number_threshold else 'With-Obstacles'
-            angle = 'Horizontal' if res[5] > self.number_threshold else 'Tilted'
+            obstruction = 'Without-Obstacles' if res[
+                4] > self.number_threshold else 'With-Obstacles'
+            angle = 'Horizontal' if res[
+                5] > self.number_threshold else 'Tilted'
 
             label_res = [source, number, color, clarity, obstruction, angle]
 
-            threshold_list = [self.source_threshold, self.number_threshold, self.color_threshold, self.clarity_threshold, self.obstruction_threshold, self.angle_threshold]
+            threshold_list = [
+                self.source_threshold, self.number_threshold,
+                self.color_threshold, self.clarity_threshold,
+                self.obstruction_threshold, self.angle_threshold
+            ]
             pred_res = (np.array(res) > np.array(threshold_list)
                         ).astype(np.int8).tolist()
-            batch_res.append({"attributes": label_res, "output": pred_res, "file_name": file_names[idx]})
+            batch_res.append({
+                "attributes": label_res,
+                "output": pred_res,
+                "file_name": file_names[idx]
+            })
         return batch_res
-