fix

ppstructure/infer.sh

@@ -1,4 +0,0 @@
-python3.7 vqa/predict_vqa_token_ser.py --vqa_algorithm=LayoutXLM --ser_model_dir=../models/ser_LayoutXLM_xfun_zh/infer --ser_dict_path=../train_data/XFUND/class_list_xfun.txt --image_dir=docs/vqa/input/zh_val_42.jpg
-
-
-python3.7 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=models/re_LayoutXLM_xfun_zh/ Global.infer_img=ppstructure/docs/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=models/ser_LayoutXLM_xfun_zh/

ppstructure/vqa/predict_vqa_token_ser.py

@@ -16,7 +16,7 @@ import sys
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
 
 os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
 
@@ -50,18 +50,18 @@ class SerPredictor(object):
                 'ocr_engine': self.ocr_engine
             }
         }, {
-            'VQATokenPad':{
-                'max_seq_len':512,
+            'VQATokenPad': {
+                'max_seq_len': 512,
                 'return_attention_mask': True
             }
         }, {
-            'VQASerTokenChunk':{
-                'max_seq_len':512,
+            'VQASerTokenChunk': {
+                'max_seq_len': 512,
                 'return_attention_mask': True
             }
         }, {
-            'Resize':{
-                'size' : [224, 224]
+            'Resize': {
+                'size': [224, 224]
             }
         }, {
             'NormalizeImage': {
@@ -75,8 +75,8 @@ class SerPredictor(object):
         }, {
             'KeepKeys': {
                 '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'
                 ]
             }
@@ -86,7 +86,8 @@ class SerPredictor(object):
             "class_path": args.ser_dict_path,
         }
 
-        self.preprocess_op = create_operators(pre_process_list, {'infer_mode':True})
+        self.preprocess_op = create_operators(pre_process_list,
+                                              {'infer_mode': True})
         self.postprocess_op = build_post_process(postprocess_params)
         self.predictor, self.input_tensor, self.output_tensors, self.config = \
             utility.create_predictor(args, 'ser', logger)
@@ -113,11 +114,9 @@ class SerPredictor(object):
             output = output_tensor.copy_to_cpu()
             outputs.append(output)
         preds = outputs[0]
-        
+
         post_result = self.postprocess_op(
-            preds,
-            segment_offset_ids=[data[6]],
-            ocr_infos=[data[7]])
+            preds, segment_offset_ids=[data[6]], ocr_infos=[data[7]])
         elapse = time.time() - starttime
         return post_result, elapse
 
@@ -136,17 +135,25 @@ def main(args):
             img, flag = check_and_read_gif(image_file)
             if not flag:
                 img = cv2.imread(image_file)
-                img = img[:,:,::-1]
+                img = img[:, :, ::-1]
             if img is None:
                 logger.info("error in loading image:{}".format(image_file))
                 continue
             ser_res, elapse = ser_predictor(img)
             ser_res = ser_res[0]
 
-            res_str = '{}\t{}\n'.format(image_file,json.dumps({"ocr_info": ser_res,}, ensure_ascii=False))
+            res_str = '{}\t{}\n'.format(
+                image_file,
+                json.dumps(
+                    {
+                        "ocr_info": ser_res,
+                    }, ensure_ascii=False))
             f_w.write(res_str)
 
-            img_res = draw_ser_results(image_file, ser_res,  font_path="../doc/fonts/simfang.ttf",)
+            img_res = draw_ser_results(
+                image_file,
+                ser_res,
+                font_path="../doc/fonts/simfang.ttf", )
 
             img_save_path = os.path.join(args.output,
                                          os.path.basename(image_file))
@@ -157,5 +164,6 @@ def main(args):
             count += 1
             logger.info("Predict time of {}: {}".format(image_file, elapse))
 
+
 if __name__ == "__main__":
     main(parse_args())