add LayoutLM ser

ppstructure/vqa/README.md

@@ -18,12 +18,13 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 
 ## 1 性能
 
-我们在 [XFUN](https://github.com/doc-analysis/XFUND) 评估数据集上对算法进行了评估，性能如下
+我们在 [XFUN](https://github.com/doc-analysis/XFUND) 的中文数据集上对算法进行了评估，性能如下
 
-|任务|    f1 | 模型下载地址|
-|:---:|:---:| :---:|
-|SER|0.9056| [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar)|
-|RE|0.7113| [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar)|
+| 模型 | 任务 | f1 | 模型下载地址 |
+|:---:|:---:|:---:| :---:|
+| LayoutXLM | RE | 0.7113 | [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
+| LayoutXLM | SER | 0.9056 | [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
+| LayoutLM | SER | 0.78 | [链接](https://paddleocr.bj.bcebos.com/pplayout/LayoutLM_ser_pretrained.tar) |
 
 
 
@@ -135,6 +136,7 @@ wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
 ```shell
 python3.7 train_ser.py \
     --model_name_or_path "layoutxlm-base-uncased" \
+    --ser_model_type "LayoutLM" \
     --train_data_dir "XFUND/zh_train/image" \
     --train_label_path "XFUND/zh_train/xfun_normalize_train.json" \
     --eval_data_dir "XFUND/zh_val/image" \
@@ -155,6 +157,7 @@ python3.7 train_ser.py \
 ```shell
 python3.7 train_ser.py \
     --model_name_or_path "model_path" \
+    --ser_model_type "LayoutXLM" \
     --train_data_dir "XFUND/zh_train/image" \
     --train_label_path "XFUND/zh_train/xfun_normalize_train.json" \
     --eval_data_dir "XFUND/zh_val/image" \
@@ -175,6 +178,7 @@ python3.7 train_ser.py \
 export CUDA_VISIBLE_DEVICES=0
 python3 eval_ser.py \
     --model_name_or_path "PP-Layout_v1.0_ser_pretrained/" \
+    --ser_model_type "LayoutXLM" \
     --eval_data_dir "XFUND/zh_val/image" \
     --eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
     --per_gpu_eval_batch_size 8 \
@@ -190,6 +194,7 @@ python3 eval_ser.py \
 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/" \
     --infer_imgs "XFUND/zh_val/image/" \
     --ocr_json_path "XFUND/zh_val/xfun_normalize_val.json"
@@ -203,6 +208,7 @@ python3.7 infer_ser.py \
 export CUDA_VISIBLE_DEVICES=0
 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/" \
     --infer_imgs "images/input/zh_val_0.jpg"

ppstructure/vqa/eval_ser.py

@@ -29,11 +29,21 @@ import paddle
 import numpy as np
 from seqeval.metrics import classification_report, f1_score, precision_score, recall_score
 from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
+from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
+
 from xfun import XFUNDataset
+from losses import SERLoss
 from utils import parse_args, get_bio_label_maps, print_arguments
 
 from ppocr.utils.logging import get_logger
 
+MODELS = {
+    'LayoutXLM':
+    (LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
+    'LayoutLM':
+    (LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
+}
+
 
 def eval(args):
     logger = get_logger()
@@ -42,9 +52,9 @@ def eval(args):
     label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
     pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
 
-    tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
-    model = LayoutXLMForTokenClassification.from_pretrained(
-        args.model_name_or_path)
+    tokenizer_class, base_model_class, model_class = MODELS[args.ser_model_type]
+    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
+    model = model_class.from_pretrained(args.model_name_or_path)
 
     eval_dataset = XFUNDataset(
         tokenizer,
@@ -65,8 +75,11 @@ def eval(args):
         use_shared_memory=True,
         collate_fn=None, )
 
-    results, _ = evaluate(args, model, tokenizer, eval_dataloader, label2id_map,
-                          id2label_map, pad_token_label_id, logger)
+    loss_class = SERLoss(len(label2id_map))
+
+    results, _ = evaluate(args, model, tokenizer, loss_class, eval_dataloader,
+                          label2id_map, id2label_map, pad_token_label_id,
+                          logger)
 
     logger.info(results)
 
@@ -74,6 +87,7 @@ def eval(args):
 def evaluate(args,
              model,
              tokenizer,
+             loss_class,
              eval_dataloader,
              label2id_map,
              id2label_map,
@@ -88,24 +102,29 @@ def evaluate(args,
     model.eval()
     for idx, batch in enumerate(eval_dataloader):
         with paddle.no_grad():
+            if args.ser_model_type == 'LayoutLM':
+                if 'image' in batch:
+                    batch.pop('image')
+            labels = batch.pop('labels')
             outputs = model(**batch)
-            tmp_eval_loss, logits = outputs[:2]
+            if args.ser_model_type == 'LayoutXLM':
+                outputs = outputs[0]
+            loss = loss_class(labels, outputs, batch['attention_mask'])
 
-            tmp_eval_loss = tmp_eval_loss.mean()
+            loss = loss.mean()
 
             if paddle.distributed.get_rank() == 0:
                 logger.info("[Eval]process: {}/{}, loss: {:.5f}".format(
-                    idx, len(eval_dataloader), tmp_eval_loss.numpy()[0]))
+                    idx, len(eval_dataloader), loss.numpy()[0]))
 
-            eval_loss += tmp_eval_loss.item()
+            eval_loss += loss.item()
         nb_eval_steps += 1
         if preds is None:
-            preds = logits.numpy()
-            out_label_ids = batch["labels"].numpy()
+            preds = outputs.numpy()
+            out_label_ids = labels.numpy()
         else:
-            preds = np.append(preds, logits.numpy(), axis=0)
-            out_label_ids = np.append(
-                out_label_ids, batch["labels"].numpy(), axis=0)
+            preds = np.append(preds, outputs.numpy(), axis=0)
+            out_label_ids = np.append(out_label_ids, labels.numpy(), axis=0)
 
     eval_loss = eval_loss / nb_eval_steps
     preds = np.argmax(preds, axis=2)

ppstructure/vqa/infer_re.py

@@ -56,7 +56,11 @@ def infer(args):
     ocr_info_list = load_ocr(args.eval_data_dir, args.eval_label_path)
 
     for idx, batch in enumerate(eval_dataloader):
-        logger.info("[Infer] process: {}/{}".format(idx, len(eval_dataloader)))
+        save_img_path = os.path.join(
+            args.output_dir,
+            os.path.splitext(os.path.basename(img_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']
@@ -85,8 +89,7 @@ def infer(args):
 
         img = cv2.imread(image_path)
         img_show = draw_re_results(img, result)
-        save_path = os.path.join(args.output_dir, os.path.basename(image_path))
-        cv2.imwrite(save_path, img_show)
+        cv2.imwrite(save_img_path, img_show)
 
 
 def load_ocr(img_folder, json_path):

ppstructure/vqa/infer_ser.py

@@ -24,6 +24,14 @@ import paddle
 # relative reference
 from utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
 from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
+from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
+
+MODELS = {
+    'LayoutXLM':
+    (LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
+    'LayoutLM':
+    (LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
+}
 
 
 def pad_sentences(tokenizer,
@@ -217,10 +225,10 @@ def infer(args):
     os.makedirs(args.output_dir, exist_ok=True)
 
     # init token and model
-    tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
-    # model = LayoutXLMModel.from_pretrained(args.model_name_or_path)
-    model = LayoutXLMForTokenClassification.from_pretrained(
-        args.model_name_or_path)
+    tokenizer_class, base_model_class, model_class = MODELS[args.ser_model_type]
+    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
+    model = model_class.from_pretrained(args.model_name_or_path)
+
     model.eval()
 
     # load ocr results json
@@ -240,7 +248,10 @@ def infer(args):
             "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.basename(img_path))
+            print("process: [{}/{}], save_result to {}".format(
+                idx, len(infer_imgs), save_img_path))
 
             img = cv2.imread(img_path)
 
@@ -250,15 +261,21 @@ def infer(args):
                 ori_img=img,
                 ocr_info=ocr_info,
                 max_seq_len=args.max_seq_length)
+            if args.ser_model_type == 'LayoutLM':
+                preds = 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':
+                preds = model(
+                    input_ids=inputs["input_ids"],
+                    bbox=inputs["bbox"],
+                    image=inputs["image"],
+                    token_type_ids=inputs["token_type_ids"],
+                    attention_mask=inputs["attention_mask"])
+                preds = preds[0]
 
-            outputs = model(
-                input_ids=inputs["input_ids"],
-                bbox=inputs["bbox"],
-                image=inputs["image"],
-                token_type_ids=inputs["token_type_ids"],
-                attention_mask=inputs["attention_mask"])
-
-            preds = outputs[0]
             preds = postprocess(inputs["attention_mask"], preds,
                                 args.label_map_path)
             ocr_info = merge_preds_list_with_ocr_info(
@@ -271,9 +288,7 @@ def infer(args):
                 }, ensure_ascii=False) + "\n")
 
             img_res = draw_ser_results(img, ocr_info)
-            cv2.imwrite(
-                os.path.join(args.output_dir, os.path.basename(img_path)),
-                img_res)
+            cv2.imwrite(save_img_path, img_res)
 
     return
 

ppstructure/vqa/infer_ser_e2e.py

@@ -22,12 +22,20 @@ from PIL import Image
 
 import paddle
 from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
+from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
 
 # relative reference
 from utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
 
 from utils import pad_sentences, split_page, preprocess, postprocess, merge_preds_list_with_ocr_info
 
+MODELS = {
+    'LayoutXLM':
+    (LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
+    'LayoutLM':
+    (LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
+}
+
 
 def trans_poly_to_bbox(poly):
     x1 = np.min([p[0] for p in poly])
@@ -50,14 +58,15 @@ def parse_ocr_info_for_ser(ocr_result):
 
 class SerPredictor(object):
     def __init__(self, args):
-
+        self.args = args
         self.max_seq_length = args.max_seq_length
 
         # init ser token and model
-        self.tokenizer = LayoutXLMTokenizer.from_pretrained(
-            args.model_name_or_path)
-        self.model = LayoutXLMForTokenClassification.from_pretrained(
+        tokenizer_class, base_model_class, model_class = MODELS[
+            args.ser_model_type]
+        self.tokenizer = tokenizer_class.from_pretrained(
             args.model_name_or_path)
+        self.model = model_class.from_pretrained(args.model_name_or_path)
         self.model.eval()
 
         # init ocr_engine
@@ -89,14 +98,21 @@ class SerPredictor(object):
             ocr_info=ocr_info,
             max_seq_len=self.max_seq_length)
 
-        outputs = self.model(
-            input_ids=inputs["input_ids"],
-            bbox=inputs["bbox"],
-            image=inputs["image"],
-            token_type_ids=inputs["token_type_ids"],
-            attention_mask=inputs["attention_mask"])
+        if 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':
+            preds = self.model(
+                input_ids=inputs["input_ids"],
+                bbox=inputs["bbox"],
+                image=inputs["image"],
+                token_type_ids=inputs["token_type_ids"],
+                attention_mask=inputs["attention_mask"])
+            preds = preds[0]
 
-        preds = outputs[0]
         preds = postprocess(inputs["attention_mask"], preds, self.id2label_map)
         ocr_info = merge_preds_list_with_ocr_info(
             ocr_info, inputs["segment_offset_id"], preds,
@@ -118,7 +134,11 @@ 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] + "_ser.jpg")
+            print("process: [{}/{}], save_result to {}".format(
+                idx, len(infer_imgs), save_img_path))
 
             img = cv2.imread(img_path)
 
@@ -129,7 +149,4 @@ if __name__ == "__main__":
                 }, ensure_ascii=False) + "\n")
 
             img_res = draw_ser_results(img, result)
-            cv2.imwrite(
-                os.path.join(args.output_dir,
-                             os.path.splitext(os.path.basename(img_path))[0] +
-                             "_ser.jpg"), img_res)
+            cv2.imwrite(save_img_path, img_res)

ppstructure/vqa/losses.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle import nn
+
+
+class SERLoss(nn.Layer):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.loss_class = nn.CrossEntropyLoss()
+        self.num_classes = num_classes
+        self.ignore_index = self.loss_class.ignore_index
+
+    def forward(self, labels, outputs, attention_mask):
+        if attention_mask is not None:
+            active_loss = attention_mask.reshape([-1, ]) == 1
+            active_outputs = outputs.reshape(
+                [-1, self.num_classes])[active_loss]
+            active_labels = labels.reshape([-1, ])[active_loss]
+            loss = self.loss_class(active_outputs, active_labels)
+        else:
+            loss = self.loss_class(
+                outputs.reshape([-1, self.num_classes]), labels.reshape([-1, ]))
+        return loss

ppstructure/vqa/train_ser.py

@@ -29,11 +29,21 @@ import paddle
 import numpy as np
 from seqeval.metrics import classification_report, f1_score, precision_score, recall_score
 from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
+from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
+
 from xfun import XFUNDataset
 from utils import parse_args, get_bio_label_maps, print_arguments, set_seed
 from eval_ser import evaluate
+from losses import SERLoss
 from ppocr.utils.logging import get_logger
 
+MODELS = {
+    'LayoutXLM':
+    (LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
+    'LayoutLM':
+    (LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
+}
+
 
 def train(args):
     os.makedirs(args.output_dir, exist_ok=True)
@@ -44,22 +54,24 @@ def train(args):
     print_arguments(args, logger)
 
     label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
-    pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
+    loss_class = SERLoss(len(label2id_map))
+
+    pad_token_label_id = loss_class.ignore_index
 
     # dist mode
     if distributed:
         paddle.distributed.init_parallel_env()
 
-    tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
+    tokenizer_class, base_model_class, model_class = MODELS[args.ser_model_type]
+    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
     if not args.resume:
-        model = LayoutXLMModel.from_pretrained(args.model_name_or_path)
-        model = LayoutXLMForTokenClassification(
-            model, num_classes=len(label2id_map), dropout=None)
+        base_model = base_model_class.from_pretrained(args.model_name_or_path)
+        model = model_class(
+            base_model, num_classes=len(label2id_map), dropout=None)
         logger.info('train from scratch')
     else:
         logger.info('resume from {}'.format(args.model_name_or_path))
-        model = LayoutXLMForTokenClassification.from_pretrained(
-            args.model_name_or_path)
+        model = model_class.from_pretrained(args.model_name_or_path)
 
     # dist mode
     if distributed:
@@ -153,12 +165,19 @@ def train(args):
         for step, batch in enumerate(train_dataloader):
             train_reader_cost += time.time() - reader_start
 
+            if args.ser_model_type == 'LayoutLM':
+                if 'image' in batch:
+                    batch.pop('image')
+            labels = batch.pop('labels')
+
             train_start = time.time()
             outputs = model(**batch)
             train_run_cost += time.time() - train_start
+            if args.ser_model_type == 'LayoutXLM':
+                outputs = outputs[0]
+            loss = loss_class(labels, outputs, batch['attention_mask'])
 
             # model outputs are always tuple in ppnlp (see doc)
-            loss = outputs[0]
             loss = loss.mean()
             loss.backward()
             tr_loss += loss.item()
@@ -166,7 +185,7 @@ def train(args):
             lr_scheduler.step()  # Update learning rate schedule
             optimizer.clear_grad()
             global_step += 1
-            total_samples += batch['image'].shape[0]
+            total_samples += batch['input_ids'].shape[0]
 
             if rank == 0 and step % print_step == 0:
                 logger.info(
@@ -186,9 +205,9 @@ def train(args):
             if rank == 0 and args.eval_steps > 0 and global_step % args.eval_steps == 0 and args.evaluate_during_training:
                 # Log metrics
                 # Only evaluate when single GPU otherwise metrics may not average well
-                results, _ = evaluate(args, model, tokenizer, eval_dataloader,
-                                      label2id_map, id2label_map,
-                                      pad_token_label_id, logger)
+                results, _ = evaluate(args, model, tokenizer, loss_class,
+                                      eval_dataloader, label2id_map,
+                                      id2label_map, pad_token_label_id, logger)
 
                 if best_metrics is None or results["f1"] >= best_metrics["f1"]:
                     best_metrics = copy.deepcopy(results)
@@ -201,7 +220,8 @@ def train(args):
                     tokenizer.save_pretrained(output_dir)
                     paddle.save(args,
                                 os.path.join(output_dir, "training_args.bin"))
-                    logger.info("Saving model checkpoint to %s", output_dir)
+                    logger.info("Saving model checkpoint to {}".format(
+                        output_dir))
 
                 logger.info("[epoch {}/{}][iter: {}/{}] results: {}".format(
                     epoch_id, args.num_train_epochs, step,
@@ -219,7 +239,7 @@ def train(args):
                 model.save_pretrained(output_dir)
             tokenizer.save_pretrained(output_dir)
             paddle.save(args, os.path.join(output_dir, "training_args.bin"))
-            logger.info("Saving model checkpoint to %s", output_dir)
+            logger.info("Saving model checkpoint to {}".format(output_dir))
     return global_step, tr_loss / global_step
 
 

ppstructure/vqa/utils.py

@@ -350,6 +350,8 @@ def parse_args():
     # yapf: disable
     parser.add_argument("--model_name_or_path",
                         default=None, type=str, required=True,)
+    parser.add_argument("--ser_model_type",
+                        default='LayoutXLM', type=str)
     parser.add_argument("--re_model_name_or_path",
                         default=None, type=str, required=False,)
     parser.add_argument("--train_data_dir", default=None,