add LayoutLM ser

ppstructure/vqa/README.md

@@ -195,7 +195,7 @@ export CUDA_VISIBLE_DEVICES=0
 python3.7 infer_ser.py \
     --model_name_or_path "./PP-Layout_v1.0_ser_pretrained/" \
     --ser_model_type "LayoutXLM" \
-    --output_dir "output_res/" \
+    --output_dir "output/ser/" \
     --infer_imgs "XFUND/zh_val/image/" \
     --ocr_json_path "XFUND/zh_val/xfun_normalize_val.json"
 ```
@@ -210,7 +210,7 @@ python3.7 infer_ser_e2e.py \
     --model_name_or_path "./output/PP-Layout_v1.0_ser_pretrained/" \
     --ser_model_type "LayoutXLM" \
     --max_seq_length 512 \
-    --output_dir "output_res_e2e/" \
+    --output_dir "output/ser_e2e/" \
     --infer_imgs "images/input/zh_val_0.jpg"
 ```
 
@@ -284,7 +284,7 @@ python3 eval_re.py \
     --eval_data_dir "XFUND/zh_val/image" \
     --eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
     --label_map_path 'labels/labels_ser.txt' \
-    --output_dir "output/re_test/"  \
+    --output_dir "output/re/"  \
     --per_gpu_eval_batch_size 8 \
     --num_workers 8 \
     --seed 2048
@@ -302,7 +302,7 @@ python3 infer_re.py \
     --eval_data_dir "XFUND/zh_val/image" \
     --eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
     --label_map_path 'labels/labels_ser.txt' \
-    --output_dir "output_res"  \
+    --output_dir "output/re/"  \
     --per_gpu_eval_batch_size 1 \
     --seed 2048
 ```
@@ -317,7 +317,7 @@ python3.7 infer_ser_re_e2e.py \
     --model_name_or_path "./PP-Layout_v1.0_ser_pretrained/" \
     --re_model_name_or_path "./PP-Layout_v1.0_re_pretrained/" \
     --max_seq_length 512 \
-    --output_dir "output_ser_re_e2e_train/" \
+    --output_dir "output/ser_re_e2e/" \
     --infer_imgs "images/input/zh_val_21.jpg"
 ```
 

ppstructure/vqa/infer.sh

@@ -0,0 +1,61 @@
+export CUDA_VISIBLE_DEVICES=6
+# python3.7 infer_ser_e2e.py \
+#     --model_name_or_path "output/ser_distributed/best_model" \
+#     --max_seq_length 512 \
+#     --output_dir "output_res_e2e/" \
+#     --infer_imgs "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/zh.val/zh_val_0.jpg"
+
+
+# python3.7 infer_ser_re_e2e.py \
+#     --model_name_or_path "output/ser_distributed/best_model" \
+#     --re_model_name_or_path "output/re_test/best_model" \
+#     --max_seq_length 512 \
+#     --output_dir "output_ser_re_e2e_train/" \
+#     --infer_imgs "images/input/zh_val_21.jpg"
+
+# python3.7 infer_ser.py \
+#     --model_name_or_path "output/ser_LayoutLM/best_model" \
+#     --ser_model_type "LayoutLM" \
+#     --output_dir "ser_LayoutLM/" \
+#     --infer_imgs "images/input/zh_val_21.jpg" \
+#     --ocr_json_path "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/xfun_normalize_val.json"
+
+python3.7 infer_ser.py \
+    --model_name_or_path "output/ser_new/best_model" \
+    --ser_model_type "LayoutXLM" \
+    --output_dir "ser_new/" \
+    --infer_imgs "images/input/zh_val_21.jpg" \
+    --ocr_json_path "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/xfun_normalize_val.json"
+
+# python3.7 infer_ser_e2e.py \
+#     --model_name_or_path "output/ser_new/best_model" \
+#     --ser_model_type "LayoutXLM" \
+#     --max_seq_length 512 \
+#     --output_dir "output/ser_new/" \
+#     --infer_imgs "images/input/zh_val_0.jpg"
+
+
+# python3.7 infer_ser_e2e.py \
+#     --model_name_or_path "output/ser_LayoutLM/best_model" \
+#     --ser_model_type "LayoutLM" \
+#     --max_seq_length 512 \
+#     --output_dir "output/ser_LayoutLM/" \
+#     --infer_imgs "images/input/zh_val_0.jpg"
+
+# python3 infer_re.py \
+#     --model_name_or_path "/ssd1/zhoujun20/VQA/PaddleOCR/ppstructure/vqa/output/re_test/best_model/" \
+#     --max_seq_length 512 \
+#     --eval_data_dir "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/zh.val" \
+#     --eval_label_path "/ssd1/zhoujun20/VQA/data/XFUN_v1.0_data/xfun_normalize_val.json" \
+#     --label_map_path 'labels/labels_ser.txt' \
+#     --output_dir "output_res"  \
+#     --per_gpu_eval_batch_size 1 \
+#     --seed 2048
+
+# python3.7 infer_ser_re_e2e.py \
+#     --model_name_or_path "output/ser_LayoutLM/best_model" \
+#     --ser_model_type "LayoutLM" \
+#     --re_model_name_or_path "output/re_new/best_model" \
+#     --max_seq_length 512 \
+#     --output_dir "output_ser_re_e2e/" \
+#     --infer_imgs "images/input/zh_val_21.jpg"

ppstructure/vqa/infer_re.py

@@ -56,19 +56,19 @@ def infer(args):
     ocr_info_list = load_ocr(args.eval_data_dir, args.eval_label_path)
 
     for idx, batch in enumerate(eval_dataloader):
+        ocr_info = ocr_info_list[idx]
+        image_path = ocr_info['image_path']
+        ocr_info = ocr_info['ocr_info']
+
         save_img_path = os.path.join(
             args.output_dir,
-            os.path.splitext(os.path.basename(img_path))[0] + "_re.jpg")
+            os.path.splitext(os.path.basename(image_path))[0] + "_re.jpg")
         logger.info("[Infer] process: {}/{}, save_result to {}".format(
             idx, len(eval_dataloader), save_img_path))
         with paddle.no_grad():
             outputs = model(**batch)
         pred_relations = outputs['pred_relations']
 
-        ocr_info = ocr_info_list[idx]
-        image_path = ocr_info['image_path']
-        ocr_info = ocr_info['ocr_info']
-
         # 根据entity里的信息，做token解码后去过滤不要的ocr_info
         ocr_info = filter_bg_by_txt(ocr_info, batch, tokenizer)
 

ppstructure/vqa/infer_ser_e2e.py

@@ -98,13 +98,13 @@ class SerPredictor(object):
             ocr_info=ocr_info,
             max_seq_len=self.max_seq_length)
 
-        if args.ser_model_type == 'LayoutLM':
+        if self.args.ser_model_type == 'LayoutLM':
             preds = self.model(
                 input_ids=inputs["input_ids"],
                 bbox=inputs["bbox"],
                 token_type_ids=inputs["token_type_ids"],
                 attention_mask=inputs["attention_mask"])
-        elif args.ser_model_type == 'LayoutXLM':
+        elif self.args.ser_model_type == 'LayoutXLM':
             preds = self.model(
                 input_ids=inputs["input_ids"],
                 bbox=inputs["bbox"],

ppstructure/vqa/infer_ser_re_e2e.py

@@ -117,7 +117,11 @@ if __name__ == "__main__":
             "w",
             encoding='utf-8') as fout:
         for idx, img_path in enumerate(infer_imgs):
-            print("process: [{}/{}], {}".format(idx, len(infer_imgs), img_path))
+            save_img_path = os.path.join(
+                args.output_dir,
+                os.path.splitext(os.path.basename(img_path))[0] + "_re.jpg")
+            print("process: [{}/{}], save_result to {}".format(
+                idx, len(infer_imgs), save_img_path))
 
             img = cv2.imread(img_path)
 
@@ -128,7 +132,4 @@ if __name__ == "__main__":
                 }, ensure_ascii=False) + "\n")
 
             img_res = draw_re_results(img, result)
-            cv2.imwrite(
+            cv2.imwrite(save_img_path, img_res)
-                os.path.join(args.output_dir,
-                             os.path.splitext(os.path.basename(img_path))[0] +
-                             "_re.jpg"), img_res)