same code but after pre-commit

dan/decoder.py

@@ -382,7 +382,7 @@ class GlobalHTADecoder(Module):
         cache=None,
         num_pred=None,
         keep_all_weights=False,
-        temperature = 1,
+        temperature=1,
         token_line=None,
         token_pg=None,
     ):
@@ -454,9 +454,9 @@ class GlobalHTADecoder(Module):
             weights = torch.sum(weights, dim=1, keepdim=True).reshape(
                 -1, 1, features_size[2], features_size[3]
             )
-            
+
         temperature = self.temperature
-        
+
         return output, preds, hidden_predict, cache, weights, temperature
 
     def generate_enc_mask(self, batch_reduced_size, total_size, device):

dan/predict/prediction.py

@@ -3,7 +3,6 @@
 import os
 import pickle
 from pathlib import Path
-from typing import DefaultDict
 
 import cv2
 import numpy as np
@@ -159,7 +158,14 @@ class DAN:
             ).permute(2, 0, 1)
 
             for i in range(0, self.max_chars):
-                output, pred, hidden_predict, cache, weights, temperature = self.decoder(
+                (
+                    output,
+                    pred,
+                    hidden_predict,
+                    cache,
+                    weights,
+                    temperature,
+                ) = self.decoder(
                     features,
                     enhanced_features,
                     predicted_tokens,
@@ -170,9 +176,9 @@ class DAN:
                     hidden_predict=hidden_predict,
                     cache=cache,
                     num_pred=1,
-                    temperature = 1,
+                    temperature=1,
                 )
-                pred = pred / temperature 
+                pred = pred / temperature
                 whole_output.append(output)
                 attention_maps.append(weights)
                 confidence_scores.append(
@@ -199,7 +205,6 @@ class DAN:
             confidence_scores = (
                 torch.cat(confidence_scores, dim=1).cpu().detach().numpy()
             )
-            token_confidence_scores = confidence_scores
             attention_maps = torch.cat(attention_maps, dim=1).cpu().detach().numpy()
 
             # Remove bot and eot tokens
@@ -293,7 +298,7 @@ def run(
 
     # Load image and pre-process it
     im = read_image(image, scale=scale)
-    h, w, c = read_image(image, scale= 1).shape
+    h, w, c = read_image(image, scale=1).shape
     ratio = 1800 / w
     im = read_image(image, ratio)
     logger.info("Image loaded.")
@@ -334,22 +339,31 @@ def run(
     if confidence_score:
         result["confidences"] = {}
         char_confidences = prediction["confidences"][0]
-        text = result['text']
-        #retrieve the index of the token ner
-        index = [pos for pos, char in enumerate(text) if char in ["ⓝ", "ⓟ",  "ⓓ" , "ⓡ"]]
-        
+        text = result["text"]
+        # retrieve the index of the token ner
+        index = [pos for pos, char in enumerate(text) if char in ["ⓝ", "ⓟ", "ⓓ", "ⓡ"]]
+
         # calculates scores by token
         score_by_token = []
         for rang, position in enumerate(index[:-1]):
-            score_by_token.append({'text':f'{text[position: index[rang+1]-1]}', 'confidence_ner' : f'{np.around(np.mean(char_confidences[position : index[rang+1]-1]), 2)}'})
-            
-        score_by_token.append({'text':f'{text[index[-2]: index[-1]]}', 'confidence_ner' : f'{np.around(np.mean(char_confidences[index[-2] : index[-1]]), 2)}'})
+            score_by_token.append(
+                {
+                    "text": f"{text[position: index[rang+1]-1]}",
+                    "confidence_ner": f"{np.around(np.mean(char_confidences[position : index[rang+1]-1]), 2)}",
+                }
+            )
 
-        result["confidences"]["total"] = np.around(np.mean(char_confidences), 2)
-        result['confidences']['by ner token']=[]
-        for entity in score_by_token:  
-            result["confidences"]['by ner token'].append(entity)
+        score_by_token.append(
+            {
+                "text": f"{text[index[-2]: index[-1]]}",
+                "confidence_ner": f"{np.around(np.mean(char_confidences[index[-2] : index[-1]]), 2)}",
+            }
+        )
 
+        result["confidences"]["total"] = np.around(np.mean(char_confidences), 2)
+        result["confidences"]["by ner token"] = []
+        for entity in score_by_token:
+            result["confidences"]["by ner token"].append(entity)
 
         for level in confidence_score_levels:
             result["confidences"][level] = []

dan/utils.py

@@ -202,7 +202,6 @@ def read_image(filename, scale=1.0):
     return image
 
 
-
 def round_floats(float_list, decimals=2):
     """
     Round list of floats with fixed decimals