pre-commit

ppocr/modeling/backbones/vqa_layoutlm.py

@@ -121,14 +121,14 @@ class LayoutXLMForSer(NLPBaseModel):
 
     def forward(self, x):
         x = self.model(
-                        input_ids=x[0],
-                        bbox=x[1],
-                        attention_mask=x[2],
-                        token_type_ids=x[3],
-                        image=x[4],
-                        position_ids=None,
-                        head_mask=None,
-                        labels=None)
+            input_ids=x[0],
+            bbox=x[1],
+            attention_mask=x[2],
+            token_type_ids=x[3],
+            image=x[4],
+            position_ids=None,
+            head_mask=None,
+            labels=None)
         if not self.training:
             return x
         return x[0]

ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py

@@ -68,7 +68,8 @@ class VQASerTokenLayoutLMPostProcess(object):
     def _infer(self, preds, segment_offset_ids, ocr_infos):
         results = []
 
-        for pred, segment_offset_id, ocr_info in zip(preds, segment_offset_ids, ocr_infos):
+        for pred, segment_offset_id, ocr_info in zip(preds, segment_offset_ids,
+                                                     ocr_infos):
             pred = np.argmax(pred, axis=1)
             pred = [self.id2label_map[idx] for idx in pred]
 

ppstructure/utility.py

@@ -40,7 +40,6 @@ def init_args():
         type=ast.literal_eval,
         default=None,
         help='label map according to ppstructure/layout/README_ch.md')
-
     # params for vqa
     parser.add_argument("--vqa_algorithm", type=str, default='LayoutXLM')
     parser.add_argument("--ser_model_dir", type=str)
@@ -73,7 +72,7 @@ def init_args():
         "--recovery",
         type=bool,
         default=False,
-        help='Whether to enable layout of recovery')        
+        help='Whether to enable layout of recovery')
     return parser
 
 

tools/export_model.py

@@ -97,8 +97,9 @@ def export_single_model(model,
                 shape=[None, 1, 32, 100], dtype="float32"),
         ]
         model = to_static(model, input_spec=other_shape)
+
     elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]:
-        input_spec=[
+        input_spec = [
             paddle.static.InputSpec(
                 shape=[None, 512], dtype="int64"),  # input_ids
             paddle.static.InputSpec(

tools/infer/utility.py

@@ -318,7 +318,7 @@ def create_predictor(args, mode, logger):
         # create predictor
         predictor = inference.create_predictor(config)
         input_names = predictor.get_input_names()
-        if mode in ['ser','re']:
+        if mode in ['ser', 're']:
             input_tensor = []
             for name in input_names:
                 input_tensor.append(predictor.get_input_handle(name))

tools/infer_vqa_token_ser.py

@@ -44,7 +44,7 @@ def to_tensor(data):
     from collections import defaultdict
     data_dict = defaultdict(list)
     to_tensor_idxs = []
-    
+
     for idx, v in enumerate(data):
         if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)):
             if idx not in to_tensor_idxs:
@@ -72,7 +72,10 @@ class SerPredictor(object):
 
         from paddleocr import PaddleOCR
 
-        self.ocr_engine = PaddleOCR(use_angle_cls=False, show_log=False, use_gpu=global_config['use_gpu'])
+        self.ocr_engine = PaddleOCR(
+            use_angle_cls=False,
+            show_log=False,
+            use_gpu=global_config['use_gpu'])
 
         # create data ops
         transforms = []
@@ -82,8 +85,8 @@ class SerPredictor(object):
                 op[op_name]['ocr_engine'] = self.ocr_engine
             elif op_name == 'KeepKeys':
                 op[op_name]['keep_keys'] = [
-                    'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels',
-                    'segment_offset_id', 'ocr_info',
+                    'input_ids', 'bbox', 'attention_mask', 'token_type_ids',
+                    'image', 'labels', 'segment_offset_id', 'ocr_info',
                     'entities'
                 ]
 
@@ -103,11 +106,9 @@ class SerPredictor(object):
         preds = self.model(batch)
         if self.algorithm in ['LayoutLMv2', 'LayoutXLM']:
             preds = preds[0]
-        
+
         post_result = self.post_process_class(
-            preds,
-            segment_offset_ids=batch[6],
-            ocr_infos=batch[7])
+            preds, segment_offset_ids=batch[6], ocr_infos=batch[7])
         return post_result, batch
 
 
@@ -154,4 +155,3 @@ if __name__ == '__main__':
 
             logger.info("process: [{}/{}], save result to {}".format(
                 idx, len(infer_imgs), save_img_path))
-

tools/infer_vqa_token_ser_re.py

@@ -192,6 +192,6 @@ if __name__ == '__main__':
                 }, ensure_ascii=False) + "\n")
             img_res = draw_re_results(img_path, result)
             cv2.imwrite(save_img_path, img_res)
-            
+
             logger.info("process: [{}/{}], save result to {}".format(
-                idx, len(infer_imgs), save_img_path))
+                idx, len(infer_imgs), save_img_path))