Merge pull request #5174 from WenmuZhou/fix_vqa

vqa code integrated into ppocr training system

ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.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.
+import numpy as np
+import paddle
+from ppocr.utils.utility import load_vqa_bio_label_maps
+
+
+class VQASerTokenLayoutLMPostProcess(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, class_path, **kwargs):
+        super(VQASerTokenLayoutLMPostProcess, self).__init__()
+        label2id_map, self.id2label_map = load_vqa_bio_label_maps(class_path)
+
+        self.label2id_map_for_draw = dict()
+        for key in label2id_map:
+            if key.startswith("I-"):
+                self.label2id_map_for_draw[key] = label2id_map["B" + key[1:]]
+            else:
+                self.label2id_map_for_draw[key] = label2id_map[key]
+
+        self.id2label_map_for_show = dict()
+        for key in self.label2id_map_for_draw:
+            val = self.label2id_map_for_draw[key]
+            if key == "O":
+                self.id2label_map_for_show[val] = key
+            if key.startswith("B-") or key.startswith("I-"):
+                self.id2label_map_for_show[val] = key[2:]
+            else:
+                self.id2label_map_for_show[val] = key
+
+    def __call__(self, preds, batch=None, *args, **kwargs):
+        if isinstance(preds, paddle.Tensor):
+            preds = preds.numpy()
+
+        if batch is not None:
+            return self._metric(preds, batch[1])
+        else:
+            return self._infer(preds, **kwargs)
+
+    def _metric(self, preds, label):
+        pred_idxs = preds.argmax(axis=2)
+        decode_out_list = [[] for _ in range(pred_idxs.shape[0])]
+        label_decode_out_list = [[] for _ in range(pred_idxs.shape[0])]
+
+        for i in range(pred_idxs.shape[0]):
+            for j in range(pred_idxs.shape[1]):
+                if label[i, j] != -100:
+                    label_decode_out_list[i].append(self.id2label_map[label[i,
+                                                                            j]])
+                    decode_out_list[i].append(self.id2label_map[pred_idxs[i,
+                                                                          j]])
+        return decode_out_list, label_decode_out_list
+
+    def _infer(self, preds, attention_masks, segment_offset_ids, ocr_infos):
+        results = []
+
+        for pred, attention_mask, segment_offset_id, ocr_info in zip(
+                preds, attention_masks, segment_offset_ids, ocr_infos):
+            pred = np.argmax(pred, axis=1)
+            pred = [self.id2label_map[idx] for idx in pred]
+
+            for idx in range(len(segment_offset_id)):
+                if idx == 0:
+                    start_id = 0
+                else:
+                    start_id = segment_offset_id[idx - 1]
+
+                end_id = segment_offset_id[idx]
+
+                curr_pred = pred[start_id:end_id]
+                curr_pred = [self.label2id_map_for_draw[p] for p in curr_pred]
+
+                if len(curr_pred) <= 0:
+                    pred_id = 0
+                else:
+                    counts = np.bincount(curr_pred)
+                    pred_id = np.argmax(counts)
+                ocr_info[idx]["pred_id"] = int(pred_id)
+                ocr_info[idx]["pred"] = self.id2label_map_for_show[int(pred_id)]
+            results.append(ocr_info)
+        return results

ppocr/utils/save_load.py

@@ -44,7 +44,7 @@ def _mkdir_if_not_exist(path, logger):
                 raise OSError('Failed to mkdir {}'.format(path))
 
 
-def load_model(config, model, optimizer=None):
+def load_model(config, model, optimizer=None, model_type='det'):
     """
     load model from checkpoint or pretrained_model
     """
@@ -53,6 +53,33 @@ def load_model(config, model, optimizer=None):
     checkpoints = global_config.get('checkpoints')
     pretrained_model = global_config.get('pretrained_model')
     best_model_dict = {}
+
+    if model_type == 'vqa':
+        checkpoints = config['Architecture']['Backbone']['checkpoints']
+        # load vqa method metric
+        if checkpoints:
+            if os.path.exists(os.path.join(checkpoints, 'metric.states')):
+                with open(os.path.join(checkpoints, 'metric.states'),
+                          'rb') as f:
+                    states_dict = pickle.load(f) if six.PY2 else pickle.load(
+                        f, encoding='latin1')
+                best_model_dict = states_dict.get('best_model_dict', {})
+                if 'epoch' in states_dict:
+                    best_model_dict['start_epoch'] = states_dict['epoch'] + 1
+            logger.info("resume from {}".format(checkpoints))
+
+            if optimizer is not None:
+                if checkpoints[-1] in ['/', '\\']:
+                    checkpoints = checkpoints[:-1]
+                if os.path.exists(checkpoints + '.pdopt'):
+                    optim_dict = paddle.load(checkpoints + '.pdopt')
+                    optimizer.set_state_dict(optim_dict)
+                else:
+                    logger.warning(
+                        "{}.pdopt is not exists, params of optimizer is not loaded".
+                        format(checkpoints))
+        return best_model_dict
+
     if checkpoints:
         if checkpoints.endswith('.pdparams'):
             checkpoints = checkpoints.replace('.pdparams', '')
@@ -130,6 +157,7 @@ def save_model(model,
                optimizer,
                model_path,
                logger,
+               config,
                is_best=False,
                prefix='ppocr',
                **kwargs):
@@ -138,13 +166,20 @@ def save_model(model,
     """
     _mkdir_if_not_exist(model_path, logger)
     model_prefix = os.path.join(model_path, prefix)
-    paddle.save(model.state_dict(), model_prefix + '.pdparams')
     paddle.save(optimizer.state_dict(), model_prefix + '.pdopt')
-
+    if config['Architecture']["model_type"] != 'vqa':
+        paddle.save(model.state_dict(), model_prefix + '.pdparams')
+        metric_prefix = model_prefix
+    else:
+        if config['Global']['distributed']:
+            model._layers.backbone.model.save_pretrained(model_prefix)
+        else:
+            model.backbone.model.save_pretrained(model_prefix)
+        metric_prefix = os.path.join(model_prefix, 'metric')
     # save metric and config
-    with open(model_prefix + '.states', 'wb') as f:
-        pickle.dump(kwargs, f, protocol=2)
     if is_best:
+        with open(metric_prefix + '.states', 'wb') as f:
+            pickle.dump(kwargs, f, protocol=2)
         logger.info('save best model is to {}'.format(model_prefix))
     else:
         logger.info("save model in {}".format(model_prefix))

ppocr/utils/utility.py

@@ -16,6 +16,9 @@ import logging
 import os
 import imghdr
 import cv2
+import random
+import numpy as np
+import paddle
 
 
 def print_dict(d, logger, delimiter=0):
@@ -77,4 +80,28 @@ def check_and_read_gif(img_path):
             frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
         imgvalue = frame[:, :, ::-1]
         return imgvalue, True
-    return None, False
\ No newline at end of file
+    return None, False
+
+
+def load_vqa_bio_label_maps(label_map_path):
+    with open(label_map_path, "r", encoding='utf-8') as fin:
+        lines = fin.readlines()
+    lines = [line.strip() for line in lines]
+    if "O" not in lines:
+        lines.insert(0, "O")
+    labels = []
+    for line in lines:
+        if line == "O":
+            labels.append("O")
+        else:
+            labels.append("B-" + line)
+            labels.append("I-" + line)
+    label2id_map = {label: idx for idx, label in enumerate(labels)}
+    id2label_map = {idx: label for idx, label in enumerate(labels)}
+    return label2id_map, id2label_map
+
+
+def set_seed(seed=1024):
+    random.seed(seed)
+    np.random.seed(seed)
+    paddle.seed(seed)

ppocr/utils/visual.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+import os
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+
+def draw_ser_results(image,
+                     ocr_results,
+                     font_path="doc/fonts/simfang.ttf",
+                     font_size=18):
+    np.random.seed(2021)
+    color = (np.random.permutation(range(255)),
+             np.random.permutation(range(255)),
+             np.random.permutation(range(255)))
+    color_map = {
+        idx: (color[0][idx], color[1][idx], color[2][idx])
+        for idx in range(1, 255)
+    }
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    elif isinstance(image, str) and os.path.isfile(image):
+        image = Image.open(image).convert('RGB')
+    img_new = image.copy()
+    draw = ImageDraw.Draw(img_new)
+
+    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
+    for ocr_info in ocr_results:
+        if ocr_info["pred_id"] not in color_map:
+            continue
+        color = color_map[ocr_info["pred_id"]]
+        text = "{}: {}".format(ocr_info["pred"], ocr_info["text"])
+
+        draw_box_txt(ocr_info["bbox"], text, draw, font, font_size, color)
+
+    img_new = Image.blend(image, img_new, 0.5)
+    return np.array(img_new)
+
+
+def draw_box_txt(bbox, text, draw, font, font_size, color):
+    # draw ocr results outline
+    bbox = ((bbox[0], bbox[1]), (bbox[2], bbox[3]))
+    draw.rectangle(bbox, fill=color)
+
+    # draw ocr results
+    start_y = max(0, bbox[0][1] - font_size)
+    tw = font.getsize(text)[0]
+    draw.rectangle(
+        [(bbox[0][0] + 1, start_y), (bbox[0][0] + tw + 1, start_y + font_size)],
+        fill=(0, 0, 255))
+    draw.text((bbox[0][0] + 1, start_y), text, fill=(255, 255, 255), font=font)
+
+
+def draw_re_results(image,
+                    result,
+                    font_path="doc/fonts/simfang.ttf",
+                    font_size=18):
+    np.random.seed(0)
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    elif isinstance(image, str) and os.path.isfile(image):
+        image = Image.open(image).convert('RGB')
+    img_new = image.copy()
+    draw = ImageDraw.Draw(img_new)
+
+    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
+    color_head = (0, 0, 255)
+    color_tail = (255, 0, 0)
+    color_line = (0, 255, 0)
+
+    for ocr_info_head, ocr_info_tail in result:
+        draw_box_txt(ocr_info_head["bbox"], ocr_info_head["text"], draw, font,
+                     font_size, color_head)
+        draw_box_txt(ocr_info_tail["bbox"], ocr_info_tail["text"], draw, font,
+                     font_size, color_tail)
+
+        center_head = (
+            (ocr_info_head['bbox'][0] + ocr_info_head['bbox'][2]) // 2,
+            (ocr_info_head['bbox'][1] + ocr_info_head['bbox'][3]) // 2)
+        center_tail = (
+            (ocr_info_tail['bbox'][0] + ocr_info_tail['bbox'][2]) // 2,
+            (ocr_info_tail['bbox'][1] + ocr_info_tail['bbox'][3]) // 2)
+
+        draw.line([center_head, center_tail], fill=color_line, width=5)
+
+    img_new = Image.blend(image, img_new, 0.5)
+    return np.array(img_new)

ppstructure/docs/model_list.md

@@ -24,8 +24,8 @@
 
 |模型名称|模型简介|推理模型大小|下载地址|
 | --- | --- | --- | --- |
-|PP-Layout_v1.0_ser_pretrained|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
-|PP-Layout_v1.0_re_pretrained|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
+|PP-Layout_v1.0_ser_pretrained|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+|PP-Layout_v1.0_re_pretrained|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
 
 ## 3. KIE模型
 

ppstructure/vqa/README.md

@@ -20,11 +20,11 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 
 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
 
-| 模型 | 任务 | f1 | 模型下载地址 |
+| 模型 | 任务 | hmean | 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) |
+| LayoutXLM | RE | 0.7483 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+| LayoutXLM | SER | 0.9038 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+| LayoutLM | SER | 0.7731 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
 
 
 
@@ -34,7 +34,7 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 
 ### 2.1 SER
 
-![](./images/result_ser/zh_val_0_ser.jpg) | ![](./images/result_ser/zh_val_42_ser.jpg)
+![](../../doc/vqa/result_ser/zh_val_0_ser.jpg) | ![](../../doc/vqa/result_ser/zh_val_42_ser.jpg)
 ---|---
 
 图中不同颜色的框表示不同的类别，对于XFUN数据集，有`QUESTION`, `ANSWER`, `HEADER` 3种类别
@@ -48,7 +48,7 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 
 ### 2.2 RE
 
-![](./images/result_re/zh_val_21_re.jpg) | ![](./images/result_re/zh_val_40_re.jpg)
+![](../../doc/vqa/result_re/zh_val_21_re.jpg) | ![](../../doc/vqa/result_re/zh_val_40_re.jpg)
 ---|---
 
 
@@ -65,10 +65,10 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 python3 -m pip install --upgrade pip
 
 # GPU安装
-python3 -m pip install paddlepaddle-gpu==2.2 -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install "paddlepaddle-gpu>=2.2" -i https://mirror.baidu.com/pypi/simple
 
 # CPU安装
-python3 -m pip install paddlepaddle==2.2 -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simple
 
 ```
 更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
@@ -93,11 +93,10 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 # 注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
 ```
 
-- **（4）安装VQA的`requirements`**
+- **（3）安装VQA的`requirements`**
 
 ```bash
-cd ppstructure/vqa
-python3 -m pip install -r requirements.txt
+python3 -m pip install -r ppstructure/vqa/requirements.txt
 ```
 
 ## 4. 使用
@@ -105,6 +104,10 @@ python3 -m pip install -r requirements.txt
 
 ### 4.1 数据和预训练模型准备
 
+如果希望直接体验预测过程，可以下载我们提供的预训练模型，跳过训练过程，直接预测即可。
+
+* 下载处理好的数据集
+
 处理好的XFUN中文数据集下载地址：[https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。
 
 
@@ -114,98 +117,62 @@ python3 -m pip install -r requirements.txt
 wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
 ```
 
-如果希望转换XFUN中其他语言的数据集，可以参考[XFUN数据转换脚本](helper/trans_xfun_data.py)。
+* 转换数据集
 
-如果希望直接体验预测过程，可以下载我们提供的预训练模型，跳过训练过程，直接预测即可。
+若需进行其他XFUN数据集的训练，可使用下面的命令进行数据集的转换
 
+```bash
+python3 ppstructure/vqa/helper/trans_xfun_data.py --ori_gt_path=path/to/json_path --output_path=path/to/save_path
+```
 
 ### 4.2 SER任务
 
-* 启动训练
+启动训练之前，需要修改下面的四个字段
 
+1. `Train.dataset.data_dir`：指向训练集图片存放目录
+2. `Train.dataset.label_file_list`：指向训练集标注文件
+3. `Eval.dataset.data_dir`：指指向验证集图片存放目录
+4. `Eval.dataset.label_file_list`：指向验证集标注文件
+
+* 启动训练
 ```shell
-python3 train_ser.py \
-    --model_name_or_path "layoutxlm-base-uncased" \
-    --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" \
-    --eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
-    --num_train_epochs 200 \
-    --eval_steps 10 \
-    --output_dir "./output/ser/" \
-    --learning_rate 5e-5 \
-    --warmup_steps 50 \
-    --evaluate_during_training \
-    --seed 2048
+CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/ser/layoutxlm.yml
 ```
 
-最终会打印出`precision`, `recall`, `f1`等指标，模型和训练日志会保存在`./output/ser/`文件夹中。
+最终会打印出`precision`, `recall`, `hmean`等指标。
+在`./output/ser_layoutxlm/`文件夹中会保存训练日志，最优的模型和最新epoch的模型。
 
 * 恢复训练
 
+恢复训练需要将之前训练好的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。
+
 ```shell
-python3 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" \
-    --eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
-    --num_train_epochs 200 \
-    --eval_steps 10 \
-    --output_dir "./output/ser/" \
-    --learning_rate 5e-5 \
-    --warmup_steps 50 \
-    --evaluate_during_training \
-    --num_workers 8 \
-    --seed 2048 \
-    --resume
+CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir
 ```
 
 * 评估
-```shell
-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 \
-    --num_workers 8 \
-    --output_dir "output/ser/"  \
-    --seed 2048
-```
-最终会打印出`precision`, `recall`, `f1`等指标
 
-* 使用评估集合中提供的OCR识别结果进行预测
+评估需要将待评估的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。
 
 ```shell
-export CUDA_VISIBLE_DEVICES=0
-python3 infer_ser.py \
-    --model_name_or_path "PP-Layout_v1.0_ser_pretrained/" \
-    --ser_model_type "LayoutXLM" \
-    --output_dir "output/ser/" \
-    --infer_imgs "XFUND/zh_val/image/" \
-    --ocr_json_path "XFUND/zh_val/xfun_normalize_val.json"
+CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir
 ```
+最终会打印出`precision`, `recall`, `hmean`等指标
 
-最终会在`output_res`目录下保存预测结果可视化图像以及预测结果文本文件，文件名为`infer_results.txt`。
+* 使用`OCR引擎 + SER`串联预测
 
-* 使用`OCR引擎 + SER`串联结果
+使用如下命令即可完成`OCR引擎 + SER`的串联预测
 
 ```shell
-export CUDA_VISIBLE_DEVICES=0
-python3 infer_ser_e2e.py \
-    --model_name_or_path "PP-Layout_v1.0_ser_pretrained/" \
-    --ser_model_type "LayoutXLM" \
-    --max_seq_length 512 \
-    --output_dir "output/ser_e2e/" \
-    --infer_imgs "images/input/zh_val_0.jpg"
+CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml  -o Architecture.Backbone.checkpoints=PP-Layout_v1.0_ser_pretrained/ Global.infer_img=doc/vqa/input/zh_val_42.jpg
 ```
 
+最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。
+
 * 对`OCR引擎 + SER`预测系统进行端到端评估
 
+首先使用 `tools/infer_vqa_token_ser.py` 脚本完成数据集的预测，然后使用下面的命令进行评估。
+
 ```shell
 export CUDA_VISIBLE_DEVICES=0
 python3 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json  --pred_json_path output_res/infer_results.txt
@@ -216,102 +183,48 @@ python3 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_norma
 
 * 启动训练
 
-```shell
-export CUDA_VISIBLE_DEVICES=0
-python3 train_re.py \
-    --model_name_or_path "layoutxlm-base-uncased" \
-    --train_data_dir "XFUND/zh_train/image" \
-    --train_label_path "XFUND/zh_train/xfun_normalize_train.json" \
-    --eval_data_dir "XFUND/zh_val/image" \
-    --eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
-    --label_map_path "labels/labels_ser.txt" \
-    --num_train_epochs 200 \
-    --eval_steps 10 \
-    --output_dir "output/re/"  \
-    --learning_rate 5e-5 \
-    --warmup_steps 50 \
-    --per_gpu_train_batch_size 8 \
-    --per_gpu_eval_batch_size 8 \
-    --num_workers 8 \
-    --evaluate_during_training \
-    --seed 2048
+启动训练之前，需要修改下面的四个字段
 
-```
-
-* 恢复训练
+1. `Train.dataset.data_dir`：指向训练集图片存放目录
+2. `Train.dataset.label_file_list`：指向训练集标注文件
+3. `Eval.dataset.data_dir`：指指向验证集图片存放目录
+4. `Eval.dataset.label_file_list`：指向验证集标注文件
 
 ```shell
-export CUDA_VISIBLE_DEVICES=0
-python3 train_re.py \
-    --model_name_or_path "model_path" \
-    --train_data_dir "XFUND/zh_train/image" \
-    --train_label_path "XFUND/zh_train/xfun_normalize_train.json" \
-    --eval_data_dir "XFUND/zh_val/image" \
-    --eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
-    --label_map_path "labels/labels_ser.txt" \
-    --num_train_epochs 2 \
-    --eval_steps 10 \
-    --output_dir "output/re/"  \
-    --learning_rate 5e-5 \
-    --warmup_steps 50 \
-    --per_gpu_train_batch_size 8 \
-    --per_gpu_eval_batch_size 8 \
-    --num_workers 8 \
-    --evaluate_during_training \
-    --seed 2048 \
-    --resume
-
+CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/re/layoutxlm.yml
 ```
 
-最终会打印出`precision`, `recall`, `f1`等指标，模型和训练日志会保存在`./output/re/`文件夹中。
+最终会打印出`precision`, `recall`, `hmean`等指标。
+在`./output/re_layoutxlm/`文件夹中会保存训练日志，最优的模型和最新epoch的模型。
+
+* 恢复训练
+
+恢复训练需要将之前训练好的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。
 
-* 评估
 ```shell
-export CUDA_VISIBLE_DEVICES=0
-python3 eval_re.py \
-    --model_name_or_path "PP-Layout_v1.0_re_pretrained/" \
-    --max_seq_length 512 \
-    --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/"  \
-    --per_gpu_eval_batch_size 8 \
-    --num_workers 8 \
-    --seed 2048
+CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir
 ```
-最终会打印出`precision`, `recall`, `f1`等指标
 
+* 评估
 
-* 使用评估集合中提供的OCR识别结果进行预测
+评估需要将待评估的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。
 
 ```shell
-export CUDA_VISIBLE_DEVICES=0
-python3 infer_re.py \
-    --model_name_or_path "PP-Layout_v1.0_re_pretrained/" \
-    --max_seq_length 512 \
-    --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/"  \
-    --per_gpu_eval_batch_size 1 \
-    --seed 2048
+CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir
 ```
+最终会打印出`precision`, `recall`, `hmean`等指标
 
-最终会在`output_res`目录下保存预测结果可视化图像以及预测结果文本文件，文件名为`infer_results.txt`。
-
-* 使用`OCR引擎 + SER + RE`串联结果
+* 使用`OCR引擎 + SER + RE`串联预测
 
+使用如下命令即可完成`OCR引擎 + SER + RE`的串联预测
 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python3 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/" \
-    --ser_model_type "LayoutXLM" \
-    --max_seq_length 512 \
-    --output_dir "output/ser_re_e2e/" \
-    --infer_imgs "images/input/zh_val_21.jpg"
+python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=PP-Layout_v1.0_re_pretrained/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=PP-Layout_v1.0_ser_pretrained/
 ```
 
+最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。
+
+
 ## 参考链接
 
 - LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf

ppstructure/vqa/eval_re.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.
-
-import os
-import sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
-
-import paddle
-
-from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
-
-from xfun import XFUNDataset
-from vqa_utils import parse_args, get_bio_label_maps, print_arguments
-from data_collator import DataCollator
-from metric import re_score
-
-from ppocr.utils.logging import get_logger
-
-
-def cal_metric(re_preds, re_labels, entities):
-    gt_relations = []
-    for b in range(len(re_labels)):
-        rel_sent = []
-        for head, tail in zip(re_labels[b]["head"], re_labels[b]["tail"]):
-            rel = {}
-            rel["head_id"] = head
-            rel["head"] = (entities[b]["start"][rel["head_id"]],
-                           entities[b]["end"][rel["head_id"]])
-            rel["head_type"] = entities[b]["label"][rel["head_id"]]
-
-            rel["tail_id"] = tail
-            rel["tail"] = (entities[b]["start"][rel["tail_id"]],
-                           entities[b]["end"][rel["tail_id"]])
-            rel["tail_type"] = entities[b]["label"][rel["tail_id"]]
-
-            rel["type"] = 1
-            rel_sent.append(rel)
-        gt_relations.append(rel_sent)
-    re_metrics = re_score(re_preds, gt_relations, mode="boundaries")
-    return re_metrics
-
-
-def evaluate(model, eval_dataloader, logger, prefix=""):
-    # Eval!
-    logger.info("***** Running evaluation {} *****".format(prefix))
-    logger.info("  Num examples = {}".format(len(eval_dataloader.dataset)))
-
-    re_preds = []
-    re_labels = []
-    entities = []
-    eval_loss = 0.0
-    model.eval()
-    for idx, batch in enumerate(eval_dataloader):
-        with paddle.no_grad():
-            outputs = model(**batch)
-            loss = outputs['loss'].mean().item()
-            if paddle.distributed.get_rank() == 0:
-                logger.info("[Eval] process: {}/{}, loss: {:.5f}".format(
-                    idx, len(eval_dataloader), loss))
-
-            eval_loss += loss
-        re_preds.extend(outputs['pred_relations'])
-        re_labels.extend(batch['relations'])
-        entities.extend(batch['entities'])
-    re_metrics = cal_metric(re_preds, re_labels, entities)
-    re_metrics = {
-        "precision": re_metrics["ALL"]["p"],
-        "recall": re_metrics["ALL"]["r"],
-        "f1": re_metrics["ALL"]["f1"],
-    }
-    model.train()
-    return re_metrics
-
-
-def eval(args):
-    logger = get_logger()
-    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 = LayoutXLMForRelationExtraction.from_pretrained(
-        args.model_name_or_path)
-
-    eval_dataset = XFUNDataset(
-        tokenizer,
-        data_dir=args.eval_data_dir,
-        label_path=args.eval_label_path,
-        label2id_map=label2id_map,
-        img_size=(224, 224),
-        max_seq_len=args.max_seq_length,
-        pad_token_label_id=pad_token_label_id,
-        contains_re=True,
-        add_special_ids=False,
-        return_attention_mask=True,
-        load_mode='all')
-
-    eval_dataloader = paddle.io.DataLoader(
-        eval_dataset,
-        batch_size=args.per_gpu_eval_batch_size,
-        num_workers=args.num_workers,
-        shuffle=False,
-        collate_fn=DataCollator())
-
-    results = evaluate(model, eval_dataloader, logger)
-    logger.info("eval results: {}".format(results))
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    eval(args)

ppstructure/vqa/eval_ser.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.
-
-import os
-import sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
-
-import random
-import time
-import copy
-import logging
-
-import argparse
-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 vqa_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()
-    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
-
-    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,
-        data_dir=args.eval_data_dir,
-        label_path=args.eval_label_path,
-        label2id_map=label2id_map,
-        img_size=(224, 224),
-        pad_token_label_id=pad_token_label_id,
-        contains_re=False,
-        add_special_ids=False,
-        return_attention_mask=True,
-        load_mode='all')
-
-    eval_dataloader = paddle.io.DataLoader(
-        eval_dataset,
-        batch_size=args.per_gpu_eval_batch_size,
-        num_workers=args.num_workers,
-        use_shared_memory=True,
-        collate_fn=None, )
-
-    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)
-
-
-def evaluate(args,
-             model,
-             tokenizer,
-             loss_class,
-             eval_dataloader,
-             label2id_map,
-             id2label_map,
-             pad_token_label_id,
-             logger,
-             prefix=""):
-
-    eval_loss = 0.0
-    nb_eval_steps = 0
-    preds = None
-    out_label_ids = None
-    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)
-            if args.ser_model_type == 'LayoutXLM':
-                outputs = outputs[0]
-            loss = loss_class(labels, outputs, batch['attention_mask'])
-
-            loss = loss.mean()
-
-            if paddle.distributed.get_rank() == 0:
-                logger.info("[Eval]process: {}/{}, loss: {:.5f}".format(
-                    idx, len(eval_dataloader), loss.numpy()[0]))
-
-            eval_loss += loss.item()
-        nb_eval_steps += 1
-        if preds is None:
-            preds = outputs.numpy()
-            out_label_ids = labels.numpy()
-        else:
-            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)
-
-    # label_map = {i: label.upper() for i, label in enumerate(labels)}
-
-    out_label_list = [[] for _ in range(out_label_ids.shape[0])]
-    preds_list = [[] for _ in range(out_label_ids.shape[0])]
-
-    for i in range(out_label_ids.shape[0]):
-        for j in range(out_label_ids.shape[1]):
-            if out_label_ids[i, j] != pad_token_label_id:
-                out_label_list[i].append(id2label_map[out_label_ids[i][j]])
-                preds_list[i].append(id2label_map[preds[i][j]])
-
-    results = {
-        "loss": eval_loss,
-        "precision": precision_score(out_label_list, preds_list),
-        "recall": recall_score(out_label_list, preds_list),
-        "f1": f1_score(out_label_list, preds_list),
-    }
-
-    with open(
-            os.path.join(args.output_dir, "test_gt.txt"), "w",
-            encoding='utf-8') as fout:
-        for lbl in out_label_list:
-            for l in lbl:
-                fout.write(l + "\t")
-            fout.write("\n")
-    with open(
-            os.path.join(args.output_dir, "test_pred.txt"), "w",
-            encoding='utf-8') as fout:
-        for lbl in preds_list:
-            for l in lbl:
-                fout.write(l + "\t")
-            fout.write("\n")
-
-    report = classification_report(out_label_list, preds_list)
-    logger.info("\n" + report)
-
-    logger.info("***** Eval results %s *****", prefix)
-    for key in sorted(results.keys()):
-        logger.info("  %s = %s", key, str(results[key]))
-    model.train()
-    return results, preds_list
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    eval(args)

ppstructure/vqa/helper/trans_xfun_data.py

@@ -49,4 +49,16 @@ def transfer_xfun_data(json_path=None, output_file=None):
     print("===ok====")
 
 
-transfer_xfun_data("./xfun/zh.val.json", "./xfun_normalize_val.json")
+def parser_args():
+    import argparse
+    parser = argparse.ArgumentParser(description="args for paddleserving")
+    parser.add_argument(
+        "--ori_gt_path", type=str, required=True, help='origin xfun gt path')
+    parser.add_argument(
+        "--output_path", type=str, required=True, help='path to save')
+    args = parser.parse_args()
+    return args
+
+
+args = parser_args()
+transfer_xfun_data(args.ori_gt_path, args.output_path)

ppstructure/vqa/infer.sh

-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"
\ No newline at end of file

ppstructure/vqa/infer_re.py

-import os
-import sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
-
-import random
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import paddle
-
-from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
-
-from xfun import XFUNDataset
-from vqa_utils import parse_args, get_bio_label_maps, draw_re_results
-from data_collator import DataCollator
-
-from ppocr.utils.logging import get_logger
-
-
-def infer(args):
-    os.makedirs(args.output_dir, exist_ok=True)
-    logger = get_logger()
-    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 = LayoutXLMForRelationExtraction.from_pretrained(
-        args.model_name_or_path)
-
-    eval_dataset = XFUNDataset(
-        tokenizer,
-        data_dir=args.eval_data_dir,
-        label_path=args.eval_label_path,
-        label2id_map=label2id_map,
-        img_size=(224, 224),
-        max_seq_len=args.max_seq_length,
-        pad_token_label_id=pad_token_label_id,
-        contains_re=True,
-        add_special_ids=False,
-        return_attention_mask=True,
-        load_mode='all')
-
-    eval_dataloader = paddle.io.DataLoader(
-        eval_dataset,
-        batch_size=args.per_gpu_eval_batch_size,
-        num_workers=8,
-        shuffle=False,
-        collate_fn=DataCollator())
-
-    # 读取gt的oct数据
-    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(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']
-
-        # 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)
-
-        # 进行 relations 到 ocr信息的转换
-        result = []
-        used_tail_id = []
-        for relations in pred_relations:
-            for relation in relations:
-                if relation['tail_id'] in used_tail_id:
-                    continue
-                if relation['head_id'] not in ocr_info or relation[
-                        'tail_id'] not in ocr_info:
-                    continue
-                used_tail_id.append(relation['tail_id'])
-                ocr_info_head = ocr_info[relation['head_id']]
-                ocr_info_tail = ocr_info[relation['tail_id']]
-                result.append((ocr_info_head, ocr_info_tail))
-
-        img = cv2.imread(image_path)
-        img_show = draw_re_results(img, result)
-        cv2.imwrite(save_img_path, img_show)
-
-
-def load_ocr(img_folder, json_path):
-    import json
-    d = []
-    with open(json_path, "r", encoding='utf-8') as fin:
-        lines = fin.readlines()
-        for line in lines:
-            image_name, info_str = line.split("\t")
-            info_dict = json.loads(info_str)
-            info_dict['image_path'] = os.path.join(img_folder, image_name)
-            d.append(info_dict)
-    return d
-
-
-def filter_bg_by_txt(ocr_info, batch, tokenizer):
-    entities = batch['entities'][0]
-    input_ids = batch['input_ids'][0]
-
-    new_info_dict = {}
-    for i in range(len(entities['start'])):
-        entitie_head = entities['start'][i]
-        entitie_tail = entities['end'][i]
-        word_input_ids = input_ids[entitie_head:entitie_tail].numpy().tolist()
-        txt = tokenizer.convert_ids_to_tokens(word_input_ids)
-        txt = tokenizer.convert_tokens_to_string(txt)
-
-        for i, info in enumerate(ocr_info):
-            if info['text'] == txt:
-                new_info_dict[i] = info
-    return new_info_dict
-
-
-def post_process(pred_relations, ocr_info, img):
-    result = []
-    for relations in pred_relations:
-        for relation in relations:
-            ocr_info_head = ocr_info[relation['head_id']]
-            ocr_info_tail = ocr_info[relation['tail_id']]
-            result.append((ocr_info_head, ocr_info_tail))
-    return result
-
-
-def draw_re(result, image_path, output_folder):
-    img = cv2.imread(image_path)
-
-    from matplotlib import pyplot as plt
-    for ocr_info_head, ocr_info_tail in result:
-        cv2.rectangle(
-            img,
-            tuple(ocr_info_head['bbox'][:2]),
-            tuple(ocr_info_head['bbox'][2:]), (255, 0, 0),
-            thickness=2)
-        cv2.rectangle(
-            img,
-            tuple(ocr_info_tail['bbox'][:2]),
-            tuple(ocr_info_tail['bbox'][2:]), (0, 0, 255),
-            thickness=2)
-        center_p1 = [(ocr_info_head['bbox'][0] + ocr_info_head['bbox'][2]) // 2,
-                     (ocr_info_head['bbox'][1] + ocr_info_head['bbox'][3]) // 2]
-        center_p2 = [(ocr_info_tail['bbox'][0] + ocr_info_tail['bbox'][2]) // 2,
-                     (ocr_info_tail['bbox'][1] + ocr_info_tail['bbox'][3]) // 2]
-        cv2.line(
-            img, tuple(center_p1), tuple(center_p2), (0, 255, 0), thickness=2)
-    plt.imshow(img)
-    plt.savefig(
-        os.path.join(output_folder, os.path.basename(image_path)), dpi=600)
-    # plt.show()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    infer(args)

ppstructure/vqa/infer_ser.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.
-
-import os
-import sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-
-import json
-import cv2
-import numpy as np
-from copy import deepcopy
-
-import paddle
-
-# relative reference
-from vqa_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,
-                  encoded_inputs,
-                  max_seq_len=512,
-                  pad_to_max_seq_len=True,
-                  return_attention_mask=True,
-                  return_token_type_ids=True,
-                  return_overflowing_tokens=False,
-                  return_special_tokens_mask=False):
-    # Padding with larger size, reshape is carried out
-    max_seq_len = (
-        len(encoded_inputs["input_ids"]) // max_seq_len + 1) * max_seq_len
-
-    needs_to_be_padded = pad_to_max_seq_len and \
-                         max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len
-
-    if needs_to_be_padded:
-        difference = max_seq_len - len(encoded_inputs["input_ids"])
-        if tokenizer.padding_side == 'right':
-            if return_attention_mask:
-                encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
-                    "input_ids"]) + [0] * difference
-            if return_token_type_ids:
-                encoded_inputs["token_type_ids"] = (
-                    encoded_inputs["token_type_ids"] +
-                    [tokenizer.pad_token_type_id] * difference)
-            if return_special_tokens_mask:
-                encoded_inputs["special_tokens_mask"] = encoded_inputs[
-                    "special_tokens_mask"] + [1] * difference
-            encoded_inputs["input_ids"] = encoded_inputs[
-                "input_ids"] + [tokenizer.pad_token_id] * difference
-            encoded_inputs["bbox"] = encoded_inputs["bbox"] + [[0, 0, 0, 0]
-                                                               ] * difference
-        else:
-            assert False, "padding_side of tokenizer just supports [\"right\"] but got {}".format(
-                tokenizer.padding_side)
-    else:
-        if return_attention_mask:
-            encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
-                "input_ids"])
-
-    return encoded_inputs
-
-
-def split_page(encoded_inputs, max_seq_len=512):
-    """
-    truncate is often used in training process
-    """
-    for key in encoded_inputs:
-        encoded_inputs[key] = paddle.to_tensor(encoded_inputs[key])
-        if encoded_inputs[key].ndim <= 1:  # for input_ids, att_mask and so on
-            encoded_inputs[key] = encoded_inputs[key].reshape([-1, max_seq_len])
-        else:  # for bbox
-            encoded_inputs[key] = encoded_inputs[key].reshape(
-                [-1, max_seq_len, 4])
-    return encoded_inputs
-
-
-def preprocess(
-        tokenizer,
-        ori_img,
-        ocr_info,
-        img_size=(224, 224),
-        pad_token_label_id=-100,
-        max_seq_len=512,
-        add_special_ids=False,
-        return_attention_mask=True, ):
-    ocr_info = deepcopy(ocr_info)
-    height = ori_img.shape[0]
-    width = ori_img.shape[1]
-
-    img = cv2.resize(ori_img,
-                     (224, 224)).transpose([2, 0, 1]).astype(np.float32)
-
-    segment_offset_id = []
-    words_list = []
-    bbox_list = []
-    input_ids_list = []
-    token_type_ids_list = []
-
-    for info in ocr_info:
-        # x1, y1, x2, y2
-        bbox = info["bbox"]
-        bbox[0] = int(bbox[0] * 1000.0 / width)
-        bbox[2] = int(bbox[2] * 1000.0 / width)
-        bbox[1] = int(bbox[1] * 1000.0 / height)
-        bbox[3] = int(bbox[3] * 1000.0 / height)
-
-        text = info["text"]
-        encode_res = tokenizer.encode(
-            text, pad_to_max_seq_len=False, return_attention_mask=True)
-
-        if not add_special_ids:
-            # TODO: use tok.all_special_ids to remove
-            encode_res["input_ids"] = encode_res["input_ids"][1:-1]
-            encode_res["token_type_ids"] = encode_res["token_type_ids"][1:-1]
-            encode_res["attention_mask"] = encode_res["attention_mask"][1:-1]
-
-        input_ids_list.extend(encode_res["input_ids"])
-        token_type_ids_list.extend(encode_res["token_type_ids"])
-        bbox_list.extend([bbox] * len(encode_res["input_ids"]))
-        words_list.append(text)
-        segment_offset_id.append(len(input_ids_list))
-
-    encoded_inputs = {
-        "input_ids": input_ids_list,
-        "token_type_ids": token_type_ids_list,
-        "bbox": bbox_list,
-        "attention_mask": [1] * len(input_ids_list),
-    }
-
-    encoded_inputs = pad_sentences(
-        tokenizer,
-        encoded_inputs,
-        max_seq_len=max_seq_len,
-        return_attention_mask=return_attention_mask)
-
-    encoded_inputs = split_page(encoded_inputs)
-
-    fake_bs = encoded_inputs["input_ids"].shape[0]
-
-    encoded_inputs["image"] = paddle.to_tensor(img).unsqueeze(0).expand(
-        [fake_bs] + list(img.shape))
-
-    encoded_inputs["segment_offset_id"] = segment_offset_id
-
-    return encoded_inputs
-
-
-def postprocess(attention_mask, preds, label_map_path):
-    if isinstance(preds, paddle.Tensor):
-        preds = preds.numpy()
-    preds = np.argmax(preds, axis=2)
-
-    _, label_map = get_bio_label_maps(label_map_path)
-
-    preds_list = [[] for _ in range(preds.shape[0])]
-
-    # keep batch info
-    for i in range(preds.shape[0]):
-        for j in range(preds.shape[1]):
-            if attention_mask[i][j] == 1:
-                preds_list[i].append(label_map[preds[i][j]])
-
-    return preds_list
-
-
-def merge_preds_list_with_ocr_info(label_map_path, ocr_info, segment_offset_id,
-                                   preds_list):
-    # must ensure the preds_list is generated from the same image
-    preds = [p for pred in preds_list for p in pred]
-    label2id_map, _ = get_bio_label_maps(label_map_path)
-    for key in label2id_map:
-        if key.startswith("I-"):
-            label2id_map[key] = label2id_map["B" + key[1:]]
-
-    id2label_map = dict()
-    for key in label2id_map:
-        val = label2id_map[key]
-        if key == "O":
-            id2label_map[val] = key
-        if key.startswith("B-") or key.startswith("I-"):
-            id2label_map[val] = key[2:]
-        else:
-            id2label_map[val] = key
-
-    for idx in range(len(segment_offset_id)):
-        if idx == 0:
-            start_id = 0
-        else:
-            start_id = segment_offset_id[idx - 1]
-
-        end_id = segment_offset_id[idx]
-
-        curr_pred = preds[start_id:end_id]
-        curr_pred = [label2id_map[p] for p in curr_pred]
-
-        if len(curr_pred) <= 0:
-            pred_id = 0
-        else:
-            counts = np.bincount(curr_pred)
-            pred_id = np.argmax(counts)
-        ocr_info[idx]["pred_id"] = int(pred_id)
-        ocr_info[idx]["pred"] = id2label_map[pred_id]
-    return ocr_info
-
-
-@paddle.no_grad()
-def infer(args):
-    os.makedirs(args.output_dir, exist_ok=True)
-
-    # init token and model
-    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
-    ocr_results = dict()
-    with open(args.ocr_json_path, "r", encoding='utf-8') as fin:
-        lines = fin.readlines()
-        for line in lines:
-            img_name, json_info = line.split("\t")
-            ocr_results[os.path.basename(img_name)] = json.loads(json_info)
-
-    # get infer img list
-    infer_imgs = get_image_file_list(args.infer_imgs)
-
-    # loop for infer
-    with open(
-            os.path.join(args.output_dir, "infer_results.txt"),
-            "w",
-            encoding='utf-8') as fout:
-        for idx, img_path in enumerate(infer_imgs):
-            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)
-
-            ocr_info = ocr_results[os.path.basename(img_path)]["ocr_info"]
-            inputs = preprocess(
-                tokenizer=tokenizer,
-                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]
-
-            preds = postprocess(inputs["attention_mask"], preds,
-                                args.label_map_path)
-            ocr_info = merge_preds_list_with_ocr_info(
-                args.label_map_path, ocr_info, inputs["segment_offset_id"],
-                preds)
-
-            fout.write(img_path + "\t" + json.dumps(
-                {
-                    "ocr_info": ocr_info,
-                }, ensure_ascii=False) + "\n")
-
-            img_res = draw_ser_results(img, ocr_info)
-            cv2.imwrite(save_img_path, img_res)
-
-    return
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    infer(args)

ppstructure/vqa/infer_ser_e2e.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.
-
-import os
-import sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-
-import json
-import cv2
-import numpy as np
-from copy import deepcopy
-from PIL import Image
-
-import paddle
-from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
-from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
-
-# relative reference
-from vqa_utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
-
-from vqa_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])
-    x2 = np.max([p[0] for p in poly])
-    y1 = np.min([p[1] for p in poly])
-    y2 = np.max([p[1] for p in poly])
-    return [x1, y1, x2, y2]
-
-
-def parse_ocr_info_for_ser(ocr_result):
-    ocr_info = []
-    for res in ocr_result:
-        ocr_info.append({
-            "text": res[1][0],
-            "bbox": trans_poly_to_bbox(res[0]),
-            "poly": res[0],
-        })
-    return ocr_info
-
-
-class SerPredictor(object):
-    def __init__(self, args):
-        self.args = args
-        self.max_seq_length = args.max_seq_length
-
-        # init ser token and model
-        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
-        from paddleocr import PaddleOCR
-
-        self.ocr_engine = PaddleOCR(
-            rec_model_dir=args.rec_model_dir,
-            det_model_dir=args.det_model_dir,
-            use_angle_cls=False,
-            show_log=False)
-        # init dict
-        label2id_map, self.id2label_map = get_bio_label_maps(
-            args.label_map_path)
-        self.label2id_map_for_draw = dict()
-        for key in label2id_map:
-            if key.startswith("I-"):
-                self.label2id_map_for_draw[key] = label2id_map["B" + key[1:]]
-            else:
-                self.label2id_map_for_draw[key] = label2id_map[key]
-
-    def __call__(self, img):
-        ocr_result = self.ocr_engine.ocr(img, cls=False)
-
-        ocr_info = parse_ocr_info_for_ser(ocr_result)
-
-        inputs = preprocess(
-            tokenizer=self.tokenizer,
-            ori_img=img,
-            ocr_info=ocr_info,
-            max_seq_len=self.max_seq_length)
-
-        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 self.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 = postprocess(inputs["attention_mask"], preds, self.id2label_map)
-        ocr_info = merge_preds_list_with_ocr_info(
-            ocr_info, inputs["segment_offset_id"], preds,
-            self.label2id_map_for_draw)
-        return ocr_info, inputs
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    os.makedirs(args.output_dir, exist_ok=True)
-
-    # get infer img list
-    infer_imgs = get_image_file_list(args.infer_imgs)
-
-    # loop for infer
-    ser_engine = SerPredictor(args)
-    with open(
-            os.path.join(args.output_dir, "infer_results.txt"),
-            "w",
-            encoding='utf-8') as fout:
-        for idx, img_path in enumerate(infer_imgs):
-            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)
-
-            result, _ = ser_engine(img)
-            fout.write(img_path + "\t" + json.dumps(
-                {
-                    "ser_resule": result,
-                }, ensure_ascii=False) + "\n")
-
-            img_res = draw_ser_results(img, result)
-            cv2.imwrite(save_img_path, img_res)

ppstructure/vqa/infer_ser_re_e2e.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.
-
-import os
-import sys
-import json
-import cv2
-import numpy as np
-from copy import deepcopy
-from PIL import Image
-
-import paddle
-from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForRelationExtraction
-
-# relative reference
-from vqa_utils import parse_args, get_image_file_list, draw_re_results
-from infer_ser_e2e import SerPredictor
-
-
-def make_input(ser_input, ser_result, max_seq_len=512):
-    entities_labels = {'HEADER': 0, 'QUESTION': 1, 'ANSWER': 2}
-
-    entities = ser_input['entities'][0]
-    assert len(entities) == len(ser_result)
-
-    # entities
-    start = []
-    end = []
-    label = []
-    entity_idx_dict = {}
-    for i, (res, entity) in enumerate(zip(ser_result, entities)):
-        if res['pred'] == 'O':
-            continue
-        entity_idx_dict[len(start)] = i
-        start.append(entity['start'])
-        end.append(entity['end'])
-        label.append(entities_labels[res['pred']])
-    entities = dict(start=start, end=end, label=label)
-
-    # relations
-    head = []
-    tail = []
-    for i in range(len(entities["label"])):
-        for j in range(len(entities["label"])):
-            if entities["label"][i] == 1 and entities["label"][j] == 2:
-                head.append(i)
-                tail.append(j)
-
-    relations = dict(head=head, tail=tail)
-
-    batch_size = ser_input["input_ids"].shape[0]
-    entities_batch = []
-    relations_batch = []
-    for b in range(batch_size):
-        entities_batch.append(entities)
-        relations_batch.append(relations)
-
-    ser_input['entities'] = entities_batch
-    ser_input['relations'] = relations_batch
-
-    ser_input.pop('segment_offset_id')
-    return ser_input, entity_idx_dict
-
-
-class SerReSystem(object):
-    def __init__(self, args):
-        self.ser_engine = SerPredictor(args)
-        self.tokenizer = LayoutXLMTokenizer.from_pretrained(
-            args.re_model_name_or_path)
-        self.model = LayoutXLMForRelationExtraction.from_pretrained(
-            args.re_model_name_or_path)
-        self.model.eval()
-
-    def __call__(self, img):
-        ser_result, ser_inputs = self.ser_engine(img)
-        re_input, entity_idx_dict = make_input(ser_inputs, ser_result)
-
-        re_result = self.model(**re_input)
-
-        pred_relations = re_result['pred_relations'][0]
-        # 进行 relations 到 ocr信息的转换
-        result = []
-        used_tail_id = []
-        for relation in pred_relations:
-            if relation['tail_id'] in used_tail_id:
-                continue
-            used_tail_id.append(relation['tail_id'])
-            ocr_info_head = ser_result[entity_idx_dict[relation['head_id']]]
-            ocr_info_tail = ser_result[entity_idx_dict[relation['tail_id']]]
-            result.append((ocr_info_head, ocr_info_tail))
-
-        return result
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    os.makedirs(args.output_dir, exist_ok=True)
-
-    # get infer img list
-    infer_imgs = get_image_file_list(args.infer_imgs)
-
-    # loop for infer
-    ser_re_engine = SerReSystem(args)
-    with open(
-            os.path.join(args.output_dir, "infer_results.txt"),
-            "w",
-            encoding='utf-8') as fout:
-        for idx, img_path in enumerate(infer_imgs):
-            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)
-
-            result = ser_re_engine(img)
-            fout.write(img_path + "\t" + json.dumps(
-                {
-                    "result": result,
-                }, ensure_ascii=False) + "\n")
-
-            img_res = draw_re_results(img, result)
-            cv2.imwrite(save_img_path, img_res)

ppstructure/vqa/metric.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.
-
-import os
-import re
-
-import numpy as np
-
-import logging
-
-logger = logging.getLogger(__name__)
-
-PREFIX_CHECKPOINT_DIR = "checkpoint"
-_re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)$")
-
-
-def get_last_checkpoint(folder):
-    content = os.listdir(folder)
-    checkpoints = [
-        path for path in content
-        if _re_checkpoint.search(path) is not None and os.path.isdir(
-            os.path.join(folder, path))
-    ]
-    if len(checkpoints) == 0:
-        return
-    return os.path.join(
-        folder,
-        max(checkpoints,
-            key=lambda x: int(_re_checkpoint.search(x).groups()[0])))
-
-
-def re_score(pred_relations, gt_relations, mode="strict"):
-    """Evaluate RE predictions
-
-    Args:
-        pred_relations (list) :  list of list of predicted relations (several relations in each sentence)
-        gt_relations (list) :    list of list of ground truth relations
-
-            rel = { "head": (start_idx (inclusive), end_idx (exclusive)),
-                    "tail": (start_idx (inclusive), end_idx (exclusive)),
-                    "head_type": ent_type,
-                    "tail_type": ent_type,
-                    "type": rel_type}
-
-        vocab (Vocab) :         dataset vocabulary
-        mode (str) :            in 'strict' or 'boundaries'"""
-
-    assert mode in ["strict", "boundaries"]
-
-    relation_types = [v for v in [0, 1] if not v == 0]
-    scores = {
-        rel: {
-            "tp": 0,
-            "fp": 0,
-            "fn": 0
-        }
-        for rel in relation_types + ["ALL"]
-    }
-
-    # Count GT relations and Predicted relations
-    n_sents = len(gt_relations)
-    n_rels = sum([len([rel for rel in sent]) for sent in gt_relations])
-    n_found = sum([len([rel for rel in sent]) for sent in pred_relations])
-
-    # Count TP, FP and FN per type
-    for pred_sent, gt_sent in zip(pred_relations, gt_relations):
-        for rel_type in relation_types:
-            # strict mode takes argument types into account
-            if mode == "strict":
-                pred_rels = {(rel["head"], rel["head_type"], rel["tail"],
-                              rel["tail_type"])
-                             for rel in pred_sent if rel["type"] == rel_type}
-                gt_rels = {(rel["head"], rel["head_type"], rel["tail"],
-                            rel["tail_type"])
-                           for rel in gt_sent if rel["type"] == rel_type}
-
-            # boundaries mode only takes argument spans into account
-            elif mode == "boundaries":
-                pred_rels = {(rel["head"], rel["tail"])
-                             for rel in pred_sent if rel["type"] == rel_type}
-                gt_rels = {(rel["head"], rel["tail"])
-                           for rel in gt_sent if rel["type"] == rel_type}
-
-            scores[rel_type]["tp"] += len(pred_rels & gt_rels)
-            scores[rel_type]["fp"] += len(pred_rels - gt_rels)
-            scores[rel_type]["fn"] += len(gt_rels - pred_rels)
-
-    # Compute per entity Precision / Recall / F1
-    for rel_type in scores.keys():
-        if scores[rel_type]["tp"]:
-            scores[rel_type]["p"] = scores[rel_type]["tp"] / (
-                scores[rel_type]["fp"] + scores[rel_type]["tp"])
-            scores[rel_type]["r"] = scores[rel_type]["tp"] / (
-                scores[rel_type]["fn"] + scores[rel_type]["tp"])
-        else:
-            scores[rel_type]["p"], scores[rel_type]["r"] = 0, 0
-
-        if not scores[rel_type]["p"] + scores[rel_type]["r"] == 0:
-            scores[rel_type]["f1"] = (
-                2 * scores[rel_type]["p"] * scores[rel_type]["r"] /
-                (scores[rel_type]["p"] + scores[rel_type]["r"]))
-        else:
-            scores[rel_type]["f1"] = 0
-
-    # Compute micro F1 Scores
-    tp = sum([scores[rel_type]["tp"] for rel_type in relation_types])
-    fp = sum([scores[rel_type]["fp"] for rel_type in relation_types])
-    fn = sum([scores[rel_type]["fn"] for rel_type in relation_types])
-
-    if tp:
-        precision = tp / (tp + fp)
-        recall = tp / (tp + fn)
-        f1 = 2 * precision * recall / (precision + recall)
-
-    else:
-        precision, recall, f1 = 0, 0, 0
-
-    scores["ALL"]["p"] = precision
-    scores["ALL"]["r"] = recall
-    scores["ALL"]["f1"] = f1
-    scores["ALL"]["tp"] = tp
-    scores["ALL"]["fp"] = fp
-    scores["ALL"]["fn"] = fn
-
-    # Compute Macro F1 Scores
-    scores["ALL"]["Macro_f1"] = np.mean(
-        [scores[ent_type]["f1"] for ent_type in relation_types])
-    scores["ALL"]["Macro_p"] = np.mean(
-        [scores[ent_type]["p"] for ent_type in relation_types])
-    scores["ALL"]["Macro_r"] = np.mean(
-        [scores[ent_type]["r"] for ent_type in relation_types])
-
-    # logger.info(f"RE Evaluation in *** {mode.upper()} *** mode")
-
-    # logger.info(
-    #     "processed {} sentences with {} relations; found: {} relations; correct: {}.".format(
-    #         n_sents, n_rels, n_found, tp
-    #     )
-    # )
-    # logger.info(
-    #     "\tALL\t TP: {};\tFP: {};\tFN: {}".format(scores["ALL"]["tp"], scores["ALL"]["fp"], scores["ALL"]["fn"])
-    # )
-    # logger.info("\t\t(m avg): precision: {:.2f};\trecall: {:.2f};\tf1: {:.2f} (micro)".format(precision, recall, f1))
-    # logger.info(
-    #     "\t\t(M avg): precision: {:.2f};\trecall: {:.2f};\tf1: {:.2f} (Macro)\n".format(
-    #         scores["ALL"]["Macro_p"], scores["ALL"]["Macro_r"], scores["ALL"]["Macro_f1"]
-    #     )
-    # )
-
-    # for rel_type in relation_types:
-    #     logger.info(
-    #         "\t{}: \tTP: {};\tFP: {};\tFN: {};\tprecision: {:.2f};\trecall: {:.2f};\tf1: {:.2f};\t{}".format(
-    #             rel_type,
-    #             scores[rel_type]["tp"],
-    #             scores[rel_type]["fp"],
-    #             scores[rel_type]["fn"],
-    #             scores[rel_type]["p"],
-    #             scores[rel_type]["r"],
-    #             scores[rel_type]["f1"],
-    #             scores[rel_type]["tp"] + scores[rel_type]["fp"],
-    #         )
-    #     )
-
-    return scores

ppstructure/vqa/train_re.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.
-
-import os
-import sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
-
-import random
-import time
-import numpy as np
-import paddle
-
-from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
-
-from xfun import XFUNDataset
-from vqa_utils import parse_args, get_bio_label_maps, print_arguments, set_seed
-from data_collator import DataCollator
-from eval_re import evaluate
-
-from ppocr.utils.logging import get_logger
-
-
-def train(args):
-    logger = get_logger(log_file=os.path.join(args.output_dir, "train.log"))
-    rank = paddle.distributed.get_rank()
-    distributed = paddle.distributed.get_world_size() > 1
-
-    print_arguments(args, logger)
-
-    # Added here for reproducibility (even between python 2 and 3)
-    set_seed(args.seed)
-
-    label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
-    pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
-
-    # dist mode
-    if distributed:
-        paddle.distributed.init_parallel_env()
-
-    tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
-    if not args.resume:
-        model = LayoutXLMModel.from_pretrained(args.model_name_or_path)
-        model = LayoutXLMForRelationExtraction(model, dropout=None)
-        logger.info('train from scratch')
-    else:
-        logger.info('resume from {}'.format(args.model_name_or_path))
-        model = LayoutXLMForRelationExtraction.from_pretrained(
-            args.model_name_or_path)
-
-    # dist mode
-    if distributed:
-        model = paddle.DataParallel(model)
-
-    train_dataset = XFUNDataset(
-        tokenizer,
-        data_dir=args.train_data_dir,
-        label_path=args.train_label_path,
-        label2id_map=label2id_map,
-        img_size=(224, 224),
-        max_seq_len=args.max_seq_length,
-        pad_token_label_id=pad_token_label_id,
-        contains_re=True,
-        add_special_ids=False,
-        return_attention_mask=True,
-        load_mode='all')
-
-    eval_dataset = XFUNDataset(
-        tokenizer,
-        data_dir=args.eval_data_dir,
-        label_path=args.eval_label_path,
-        label2id_map=label2id_map,
-        img_size=(224, 224),
-        max_seq_len=args.max_seq_length,
-        pad_token_label_id=pad_token_label_id,
-        contains_re=True,
-        add_special_ids=False,
-        return_attention_mask=True,
-        load_mode='all')
-
-    train_sampler = paddle.io.DistributedBatchSampler(
-        train_dataset, batch_size=args.per_gpu_train_batch_size, shuffle=True)
-
-    train_dataloader = paddle.io.DataLoader(
-        train_dataset,
-        batch_sampler=train_sampler,
-        num_workers=args.num_workers,
-        use_shared_memory=True,
-        collate_fn=DataCollator())
-
-    eval_dataloader = paddle.io.DataLoader(
-        eval_dataset,
-        batch_size=args.per_gpu_eval_batch_size,
-        num_workers=args.num_workers,
-        shuffle=False,
-        collate_fn=DataCollator())
-
-    t_total = len(train_dataloader) * args.num_train_epochs
-
-    # build linear decay with warmup lr sch
-    lr_scheduler = paddle.optimizer.lr.PolynomialDecay(
-        learning_rate=args.learning_rate,
-        decay_steps=t_total,
-        end_lr=0.0,
-        power=1.0)
-    if args.warmup_steps > 0:
-        lr_scheduler = paddle.optimizer.lr.LinearWarmup(
-            lr_scheduler,
-            args.warmup_steps,
-            start_lr=0,
-            end_lr=args.learning_rate, )
-    grad_clip = paddle.nn.ClipGradByNorm(clip_norm=10)
-    optimizer = paddle.optimizer.Adam(
-        learning_rate=args.learning_rate,
-        parameters=model.parameters(),
-        epsilon=args.adam_epsilon,
-        grad_clip=grad_clip,
-        weight_decay=args.weight_decay)
-
-    # Train!
-    logger.info("***** Running training *****")
-    logger.info("  Num examples = {}".format(len(train_dataset)))
-    logger.info("  Num Epochs = {}".format(args.num_train_epochs))
-    logger.info("  Instantaneous batch size per GPU = {}".format(
-        args.per_gpu_train_batch_size))
-    logger.info(
-        "  Total train batch size (w. parallel, distributed & accumulation) = {}".
-        format(args.per_gpu_train_batch_size *
-               paddle.distributed.get_world_size()))
-    logger.info("  Total optimization steps = {}".format(t_total))
-
-    global_step = 0
-    model.clear_gradients()
-    train_dataloader_len = len(train_dataloader)
-    best_metirc = {'f1': 0}
-    model.train()
-
-    train_reader_cost = 0.0
-    train_run_cost = 0.0
-    total_samples = 0
-    reader_start = time.time()
-
-    print_step = 1
-
-    for epoch in range(int(args.num_train_epochs)):
-        for step, batch in enumerate(train_dataloader):
-            train_reader_cost += time.time() - reader_start
-            train_start = time.time()
-            outputs = model(**batch)
-            train_run_cost += time.time() - train_start
-            # model outputs are always tuple in ppnlp (see doc)
-            loss = outputs['loss']
-            loss = loss.mean()
-
-            loss.backward()
-            optimizer.step()
-            optimizer.clear_grad()
-            # lr_scheduler.step()  # Update learning rate schedule
-
-            global_step += 1
-            total_samples += batch['image'].shape[0]
-
-            if rank == 0 and step % print_step == 0:
-                logger.info(
-                    "epoch: [{}/{}], iter: [{}/{}], global_step:{}, train loss: {:.6f}, lr: {:.6f}, avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.5f} images/sec".
-                    format(epoch, args.num_train_epochs, step,
-                           train_dataloader_len, global_step,
-                           np.mean(loss.numpy()),
-                           optimizer.get_lr(), train_reader_cost / print_step, (
-                               train_reader_cost + train_run_cost) / print_step,
-                           total_samples / print_step, total_samples / (
-                               train_reader_cost + train_run_cost)))
-
-                train_reader_cost = 0.0
-                train_run_cost = 0.0
-                total_samples = 0
-
-            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(model, eval_dataloader, logger)
-                if results['f1'] >= best_metirc['f1']:
-                    best_metirc = results
-                    output_dir = os.path.join(args.output_dir, "best_model")
-                    os.makedirs(output_dir, exist_ok=True)
-                    if distributed:
-                        model._layers.save_pretrained(output_dir)
-                    else:
-                        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 {}".format(
-                        output_dir))
-                logger.info("eval results: {}".format(results))
-                logger.info("best_metirc: {}".format(best_metirc))
-            reader_start = time.time()
-
-        if rank == 0:
-            # Save model checkpoint
-            output_dir = os.path.join(args.output_dir, "latest_model")
-            os.makedirs(output_dir, exist_ok=True)
-            if distributed:
-                model._layers.save_pretrained(output_dir)
-            else:
-                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 {}".format(output_dir))
-    logger.info("best_metirc: {}".format(best_metirc))
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    os.makedirs(args.output_dir, exist_ok=True)
-    train(args)

ppstructure/vqa/train_ser.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.
-
-import os
-import sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
-
-import random
-import time
-import copy
-import logging
-
-import argparse
-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 vqa_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)
-    rank = paddle.distributed.get_rank()
-    distributed = paddle.distributed.get_world_size() > 1
-
-    logger = get_logger(log_file=os.path.join(args.output_dir, "train.log"))
-    print_arguments(args, logger)
-
-    label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
-    loss_class = SERLoss(len(label2id_map))
-
-    pad_token_label_id = loss_class.ignore_index
-
-    # dist mode
-    if distributed:
-        paddle.distributed.init_parallel_env()
-
-    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:
-        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 = model_class.from_pretrained(args.model_name_or_path)
-
-    # dist mode
-    if distributed:
-        model = paddle.DataParallel(model)
-
-    train_dataset = XFUNDataset(
-        tokenizer,
-        data_dir=args.train_data_dir,
-        label_path=args.train_label_path,
-        label2id_map=label2id_map,
-        img_size=(224, 224),
-        pad_token_label_id=pad_token_label_id,
-        contains_re=False,
-        add_special_ids=False,
-        return_attention_mask=True,
-        load_mode='all')
-    eval_dataset = XFUNDataset(
-        tokenizer,
-        data_dir=args.eval_data_dir,
-        label_path=args.eval_label_path,
-        label2id_map=label2id_map,
-        img_size=(224, 224),
-        pad_token_label_id=pad_token_label_id,
-        contains_re=False,
-        add_special_ids=False,
-        return_attention_mask=True,
-        load_mode='all')
-
-    train_sampler = paddle.io.DistributedBatchSampler(
-        train_dataset, batch_size=args.per_gpu_train_batch_size, shuffle=True)
-
-    train_dataloader = paddle.io.DataLoader(
-        train_dataset,
-        batch_sampler=train_sampler,
-        num_workers=args.num_workers,
-        use_shared_memory=True,
-        collate_fn=None, )
-
-    eval_dataloader = paddle.io.DataLoader(
-        eval_dataset,
-        batch_size=args.per_gpu_eval_batch_size,
-        num_workers=args.num_workers,
-        use_shared_memory=True,
-        collate_fn=None, )
-
-    t_total = len(train_dataloader) * args.num_train_epochs
-
-    # build linear decay with warmup lr sch
-    lr_scheduler = paddle.optimizer.lr.PolynomialDecay(
-        learning_rate=args.learning_rate,
-        decay_steps=t_total,
-        end_lr=0.0,
-        power=1.0)
-    if args.warmup_steps > 0:
-        lr_scheduler = paddle.optimizer.lr.LinearWarmup(
-            lr_scheduler,
-            args.warmup_steps,
-            start_lr=0,
-            end_lr=args.learning_rate, )
-
-    optimizer = paddle.optimizer.AdamW(
-        learning_rate=lr_scheduler,
-        parameters=model.parameters(),
-        epsilon=args.adam_epsilon,
-        weight_decay=args.weight_decay)
-
-    # Train!
-    logger.info("***** Running training *****")
-    logger.info("  Num examples = %d", len(train_dataset))
-    logger.info("  Num Epochs = %d", args.num_train_epochs)
-    logger.info("  Instantaneous batch size per GPU = %d",
-                args.per_gpu_train_batch_size)
-    logger.info(
-        "  Total train batch size (w. parallel, distributed) = %d",
-        args.per_gpu_train_batch_size * paddle.distributed.get_world_size(), )
-    logger.info("  Total optimization steps = %d", t_total)
-
-    global_step = 0
-    tr_loss = 0.0
-    set_seed(args.seed)
-    best_metrics = None
-
-    train_reader_cost = 0.0
-    train_run_cost = 0.0
-    total_samples = 0
-    reader_start = time.time()
-
-    print_step = 1
-    model.train()
-    for epoch_id in range(args.num_train_epochs):
-        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 = loss.mean()
-            loss.backward()
-            tr_loss += loss.item()
-            optimizer.step()
-            lr_scheduler.step()  # Update learning rate schedule
-            optimizer.clear_grad()
-            global_step += 1
-            total_samples += batch['input_ids'].shape[0]
-
-            if rank == 0 and step % print_step == 0:
-                logger.info(
-                    "epoch: [{}/{}], iter: [{}/{}], global_step:{}, train loss: {:.6f}, lr: {:.6f}, avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.5f} images/sec".
-                    format(epoch_id, args.num_train_epochs, step,
-                           len(train_dataloader), global_step,
-                           loss.numpy()[0],
-                           lr_scheduler.get_lr(), train_reader_cost /
-                           print_step, (train_reader_cost + train_run_cost) /
-                           print_step, total_samples / print_step, total_samples
-                           / (train_reader_cost + train_run_cost)))
-
-                train_reader_cost = 0.0
-                train_run_cost = 0.0
-                total_samples = 0
-
-            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, 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)
-                    output_dir = os.path.join(args.output_dir, "best_model")
-                    os.makedirs(output_dir, exist_ok=True)
-                    if distributed:
-                        model._layers.save_pretrained(output_dir)
-                    else:
-                        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 {}".format(
-                        output_dir))
-
-                logger.info("[epoch {}/{}][iter: {}/{}] results: {}".format(
-                    epoch_id, args.num_train_epochs, step,
-                    len(train_dataloader), results))
-                if best_metrics is not None:
-                    logger.info("best metrics: {}".format(best_metrics))
-            reader_start = time.time()
-        if rank == 0:
-            # Save model checkpoint
-            output_dir = os.path.join(args.output_dir, "latest_model")
-            os.makedirs(output_dir, exist_ok=True)
-            if distributed:
-                model._layers.save_pretrained(output_dir)
-            else:
-                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 {}".format(output_dir))
-    return global_step, tr_loss / global_step
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    train(args)

ppstructure/vqa/vqa_utils.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.
-
-import os
-import argparse
-import cv2
-import random
-import numpy as np
-import imghdr
-from copy import deepcopy
-
-import paddle
-
-from PIL import Image, ImageDraw, ImageFont
-
-
-def set_seed(seed):
-    random.seed(seed)
-    np.random.seed(seed)
-    paddle.seed(seed)
-
-
-def get_bio_label_maps(label_map_path):
-    with open(label_map_path, "r", encoding='utf-8') as fin:
-        lines = fin.readlines()
-    lines = [line.strip() for line in lines]
-    if "O" not in lines:
-        lines.insert(0, "O")
-    labels = []
-    for line in lines:
-        if line == "O":
-            labels.append("O")
-        else:
-            labels.append("B-" + line)
-            labels.append("I-" + line)
-    label2id_map = {label: idx for idx, label in enumerate(labels)}
-    id2label_map = {idx: label for idx, label in enumerate(labels)}
-    return label2id_map, id2label_map
-
-
-def get_image_file_list(img_file):
-    imgs_lists = []
-    if img_file is None or not os.path.exists(img_file):
-        raise Exception("not found any img file in {}".format(img_file))
-
-    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'GIF'}
-    if os.path.isfile(img_file) and imghdr.what(img_file) in img_end:
-        imgs_lists.append(img_file)
-    elif os.path.isdir(img_file):
-        for single_file in os.listdir(img_file):
-            file_path = os.path.join(img_file, single_file)
-            if os.path.isfile(file_path) and imghdr.what(file_path) in img_end:
-                imgs_lists.append(file_path)
-    if len(imgs_lists) == 0:
-        raise Exception("not found any img file in {}".format(img_file))
-    imgs_lists = sorted(imgs_lists)
-    return imgs_lists
-
-
-def draw_ser_results(image,
-                     ocr_results,
-                     font_path="../../doc/fonts/simfang.ttf",
-                     font_size=18):
-    np.random.seed(2021)
-    color = (np.random.permutation(range(255)),
-             np.random.permutation(range(255)),
-             np.random.permutation(range(255)))
-    color_map = {
-        idx: (color[0][idx], color[1][idx], color[2][idx])
-        for idx in range(1, 255)
-    }
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-    img_new = image.copy()
-    draw = ImageDraw.Draw(img_new)
-
-    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
-    for ocr_info in ocr_results:
-        if ocr_info["pred_id"] not in color_map:
-            continue
-        color = color_map[ocr_info["pred_id"]]
-        text = "{}: {}".format(ocr_info["pred"], ocr_info["text"])
-
-        draw_box_txt(ocr_info["bbox"], text, draw, font, font_size, color)
-
-    img_new = Image.blend(image, img_new, 0.5)
-    return np.array(img_new)
-
-
-def draw_box_txt(bbox, text, draw, font, font_size, color):
-    # draw ocr results outline
-    bbox = ((bbox[0], bbox[1]), (bbox[2], bbox[3]))
-    draw.rectangle(bbox, fill=color)
-
-    # draw ocr results
-    start_y = max(0, bbox[0][1] - font_size)
-    tw = font.getsize(text)[0]
-    draw.rectangle(
-        [(bbox[0][0] + 1, start_y), (bbox[0][0] + tw + 1, start_y + font_size)],
-        fill=(0, 0, 255))
-    draw.text((bbox[0][0] + 1, start_y), text, fill=(255, 255, 255), font=font)
-
-
-def draw_re_results(image,
-                    result,
-                    font_path="../../doc/fonts/simfang.ttf",
-                    font_size=18):
-    np.random.seed(0)
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-    img_new = image.copy()
-    draw = ImageDraw.Draw(img_new)
-
-    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
-    color_head = (0, 0, 255)
-    color_tail = (255, 0, 0)
-    color_line = (0, 255, 0)
-
-    for ocr_info_head, ocr_info_tail in result:
-        draw_box_txt(ocr_info_head["bbox"], ocr_info_head["text"], draw, font,
-                     font_size, color_head)
-        draw_box_txt(ocr_info_tail["bbox"], ocr_info_tail["text"], draw, font,
-                     font_size, color_tail)
-
-        center_head = (
-            (ocr_info_head['bbox'][0] + ocr_info_head['bbox'][2]) // 2,
-            (ocr_info_head['bbox'][1] + ocr_info_head['bbox'][3]) // 2)
-        center_tail = (
-            (ocr_info_tail['bbox'][0] + ocr_info_tail['bbox'][2]) // 2,
-            (ocr_info_tail['bbox'][1] + ocr_info_tail['bbox'][3]) // 2)
-
-        draw.line([center_head, center_tail], fill=color_line, width=5)
-
-    img_new = Image.blend(image, img_new, 0.5)
-    return np.array(img_new)
-
-
-# pad sentences
-def pad_sentences(tokenizer,
-                  encoded_inputs,
-                  max_seq_len=512,
-                  pad_to_max_seq_len=True,
-                  return_attention_mask=True,
-                  return_token_type_ids=True,
-                  return_overflowing_tokens=False,
-                  return_special_tokens_mask=False):
-    # Padding with larger size, reshape is carried out
-    max_seq_len = (
-        len(encoded_inputs["input_ids"]) // max_seq_len + 1) * max_seq_len
-
-    needs_to_be_padded = pad_to_max_seq_len and \
-        max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len
-
-    if needs_to_be_padded:
-        difference = max_seq_len - len(encoded_inputs["input_ids"])
-        if tokenizer.padding_side == 'right':
-            if return_attention_mask:
-                encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
-                    "input_ids"]) + [0] * difference
-            if return_token_type_ids:
-                encoded_inputs["token_type_ids"] = (
-                    encoded_inputs["token_type_ids"] +
-                    [tokenizer.pad_token_type_id] * difference)
-            if return_special_tokens_mask:
-                encoded_inputs["special_tokens_mask"] = encoded_inputs[
-                    "special_tokens_mask"] + [1] * difference
-            encoded_inputs["input_ids"] = encoded_inputs[
-                "input_ids"] + [tokenizer.pad_token_id] * difference
-            encoded_inputs["bbox"] = encoded_inputs["bbox"] + [[0, 0, 0, 0]
-                                                               ] * difference
-    else:
-        if return_attention_mask:
-            encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
-                "input_ids"])
-
-    return encoded_inputs
-
-
-def split_page(encoded_inputs, max_seq_len=512):
-    """
-    truncate is often used in training process
-    """
-    for key in encoded_inputs:
-        if key == 'entities':
-            encoded_inputs[key] = [encoded_inputs[key]]
-            continue
-        encoded_inputs[key] = paddle.to_tensor(encoded_inputs[key])
-        if encoded_inputs[key].ndim <= 1:  # for input_ids, att_mask and so on
-            encoded_inputs[key] = encoded_inputs[key].reshape([-1, max_seq_len])
-        else:  # for bbox
-            encoded_inputs[key] = encoded_inputs[key].reshape(
-                [-1, max_seq_len, 4])
-    return encoded_inputs
-
-
-def preprocess(
-        tokenizer,
-        ori_img,
-        ocr_info,
-        img_size=(224, 224),
-        pad_token_label_id=-100,
-        max_seq_len=512,
-        add_special_ids=False,
-        return_attention_mask=True, ):
-    ocr_info = deepcopy(ocr_info)
-    height = ori_img.shape[0]
-    width = ori_img.shape[1]
-
-    img = cv2.resize(ori_img, img_size).transpose([2, 0, 1]).astype(np.float32)
-
-    segment_offset_id = []
-    words_list = []
-    bbox_list = []
-    input_ids_list = []
-    token_type_ids_list = []
-    entities = []
-
-    for info in ocr_info:
-        # x1, y1, x2, y2
-        bbox = info["bbox"]
-        bbox[0] = int(bbox[0] * 1000.0 / width)
-        bbox[2] = int(bbox[2] * 1000.0 / width)
-        bbox[1] = int(bbox[1] * 1000.0 / height)
-        bbox[3] = int(bbox[3] * 1000.0 / height)
-
-        text = info["text"]
-        encode_res = tokenizer.encode(
-            text, pad_to_max_seq_len=False, return_attention_mask=True)
-
-        if not add_special_ids:
-            # TODO: use tok.all_special_ids to remove
-            encode_res["input_ids"] = encode_res["input_ids"][1:-1]
-            encode_res["token_type_ids"] = encode_res["token_type_ids"][1:-1]
-            encode_res["attention_mask"] = encode_res["attention_mask"][1:-1]
-
-        # for re
-        entities.append({
-            "start": len(input_ids_list),
-            "end": len(input_ids_list) + len(encode_res["input_ids"]),
-            "label": "O",
-        })
-
-        input_ids_list.extend(encode_res["input_ids"])
-        token_type_ids_list.extend(encode_res["token_type_ids"])
-        bbox_list.extend([bbox] * len(encode_res["input_ids"]))
-        words_list.append(text)
-        segment_offset_id.append(len(input_ids_list))
-
-    encoded_inputs = {
-        "input_ids": input_ids_list,
-        "token_type_ids": token_type_ids_list,
-        "bbox": bbox_list,
-        "attention_mask": [1] * len(input_ids_list),
-        "entities": entities
-    }
-
-    encoded_inputs = pad_sentences(
-        tokenizer,
-        encoded_inputs,
-        max_seq_len=max_seq_len,
-        return_attention_mask=return_attention_mask)
-
-    encoded_inputs = split_page(encoded_inputs)
-
-    fake_bs = encoded_inputs["input_ids"].shape[0]
-
-    encoded_inputs["image"] = paddle.to_tensor(img).unsqueeze(0).expand(
-        [fake_bs] + list(img.shape))
-
-    encoded_inputs["segment_offset_id"] = segment_offset_id
-
-    return encoded_inputs
-
-
-def postprocess(attention_mask, preds, id2label_map):
-    if isinstance(preds, paddle.Tensor):
-        preds = preds.numpy()
-    preds = np.argmax(preds, axis=2)
-
-    preds_list = [[] for _ in range(preds.shape[0])]
-
-    # keep batch info
-    for i in range(preds.shape[0]):
-        for j in range(preds.shape[1]):
-            if attention_mask[i][j] == 1:
-                preds_list[i].append(id2label_map[preds[i][j]])
-
-    return preds_list
-
-
-def merge_preds_list_with_ocr_info(ocr_info, segment_offset_id, preds_list,
-                                   label2id_map_for_draw):
-    # must ensure the preds_list is generated from the same image
-    preds = [p for pred in preds_list for p in pred]
-
-    id2label_map = dict()
-    for key in label2id_map_for_draw:
-        val = label2id_map_for_draw[key]
-        if key == "O":
-            id2label_map[val] = key
-        if key.startswith("B-") or key.startswith("I-"):
-            id2label_map[val] = key[2:]
-        else:
-            id2label_map[val] = key
-
-    for idx in range(len(segment_offset_id)):
-        if idx == 0:
-            start_id = 0
-        else:
-            start_id = segment_offset_id[idx - 1]
-
-        end_id = segment_offset_id[idx]
-
-        curr_pred = preds[start_id:end_id]
-        curr_pred = [label2id_map_for_draw[p] for p in curr_pred]
-
-        if len(curr_pred) <= 0:
-            pred_id = 0
-        else:
-            counts = np.bincount(curr_pred)
-            pred_id = np.argmax(counts)
-        ocr_info[idx]["pred_id"] = int(pred_id)
-        ocr_info[idx]["pred"] = id2label_map[int(pred_id)]
-    return ocr_info
-
-
-def print_arguments(args, logger=None):
-    print_func = logger.info if logger is not None else print
-    """print arguments"""
-    print_func('-----------  Configuration Arguments -----------')
-    for arg, value in sorted(vars(args).items()):
-        print_func('%s: %s' % (arg, value))
-    print_func('------------------------------------------------')
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    # 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,
-                        type=str, required=False,)
-    parser.add_argument("--train_label_path", default=None,
-                        type=str, required=False,)
-    parser.add_argument("--eval_data_dir", default=None,
-                        type=str, required=False,)
-    parser.add_argument("--eval_label_path", default=None,
-                        type=str, required=False,)
-    parser.add_argument("--output_dir", default=None, type=str, required=True,)
-    parser.add_argument("--max_seq_length", default=512, type=int,)
-    parser.add_argument("--evaluate_during_training", action="store_true",)
-    parser.add_argument("--num_workers", default=8, type=int,)
-    parser.add_argument("--per_gpu_train_batch_size", default=8,
-                        type=int, help="Batch size per GPU/CPU for training.",)
-    parser.add_argument("--per_gpu_eval_batch_size", default=8,
-                        type=int, help="Batch size per GPU/CPU for eval.",)
-    parser.add_argument("--learning_rate", default=5e-5,
-                        type=float, help="The initial learning rate for Adam.",)
-    parser.add_argument("--weight_decay", default=0.0,
-                        type=float, help="Weight decay if we apply some.",)
-    parser.add_argument("--adam_epsilon", default=1e-8,
-                        type=float, help="Epsilon for Adam optimizer.",)
-    parser.add_argument("--max_grad_norm", default=1.0,
-                        type=float, help="Max gradient norm.",)
-    parser.add_argument("--num_train_epochs", default=3, type=int,
-                        help="Total number of training epochs to perform.",)
-    parser.add_argument("--warmup_steps", default=0, type=int,
-                        help="Linear warmup over warmup_steps.",)
-    parser.add_argument("--eval_steps", type=int, default=10,
-                        help="eval every X updates steps.",)
-    parser.add_argument("--seed", type=int, default=2048,
-                        help="random seed for initialization",)
-
-    parser.add_argument("--rec_model_dir", default=None, type=str, )
-    parser.add_argument("--det_model_dir", default=None, type=str, )
-    parser.add_argument(
-        "--label_map_path", default="./labels/labels_ser.txt", type=str, required=False, )
-    parser.add_argument("--infer_imgs", default=None, type=str, required=False)
-    parser.add_argument("--resume", action='store_true')
-    parser.add_argument("--ocr_json_path", default=None,
-                        type=str, required=False, help="ocr prediction results")
-    # yapf: enable
-    args = parser.parse_args()
-    return args

ppstructure/vqa/xfun.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.
-
-import json
-import os
-import cv2
-import numpy as np
-import paddle
-import copy
-from paddle.io import Dataset
-
-__all__ = ["XFUNDataset"]
-
-
-class XFUNDataset(Dataset):
-    """
-    Example:
-        print("=====begin to build dataset=====")
-        from paddlenlp.transformers import LayoutXLMTokenizer
-        tokenizer = LayoutXLMTokenizer.from_pretrained("/paddle/models/transformers/layoutxlm-base-paddle/")
-        tok_res = tokenizer.tokenize("Maribyrnong")
-        # res = tokenizer.convert_ids_to_tokens(val_data["input_ids"][0])
-        dataset = XfunDatasetForSer(
-            tokenizer,
-            data_dir="./zh.val/",
-            label_path="zh.val/xfun_normalize_val.json",
-            img_size=(224,224))
-        print(len(dataset))
-
-        data = dataset[0]
-        print(data.keys())
-        print("input_ids: ", data["input_ids"])
-        print("labels: ", data["labels"])
-        print("token_type_ids: ", data["token_type_ids"])
-        print("words_list: ", data["words_list"])
-        print("image shape: ", data["image"].shape)
-    """
-
-    def __init__(self,
-                 tokenizer,
-                 data_dir,
-                 label_path,
-                 contains_re=False,
-                 label2id_map=None,
-                 img_size=(224, 224),
-                 pad_token_label_id=None,
-                 add_special_ids=False,
-                 return_attention_mask=True,
-                 load_mode='all',
-                 max_seq_len=512):
-        super().__init__()
-        self.tokenizer = tokenizer
-        self.data_dir = data_dir
-        self.label_path = label_path
-        self.contains_re = contains_re
-        self.label2id_map = label2id_map
-        self.img_size = img_size
-        self.pad_token_label_id = pad_token_label_id
-        self.add_special_ids = add_special_ids
-        self.return_attention_mask = return_attention_mask
-        self.load_mode = load_mode
-        self.max_seq_len = max_seq_len
-
-        if self.pad_token_label_id is None:
-            self.pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
-
-        self.all_lines = self.read_all_lines()
-
-        self.entities_labels = {'HEADER': 0, 'QUESTION': 1, 'ANSWER': 2}
-        self.return_keys = {
-            'bbox': {
-                'type': 'np',
-                'dtype': 'int64'
-            },
-            'input_ids': {
-                'type': 'np',
-                'dtype': 'int64'
-            },
-            'labels': {
-                'type': 'np',
-                'dtype': 'int64'
-            },
-            'attention_mask': {
-                'type': 'np',
-                'dtype': 'int64'
-            },
-            'image': {
-                'type': 'np',
-                'dtype': 'float32'
-            },
-            'token_type_ids': {
-                'type': 'np',
-                'dtype': 'int64'
-            },
-            'entities': {
-                'type': 'dict'
-            },
-            'relations': {
-                'type': 'dict'
-            }
-        }
-
-        if load_mode == "all":
-            self.encoded_inputs_all = self._parse_label_file_all()
-
-    def pad_sentences(self,
-                      encoded_inputs,
-                      max_seq_len=512,
-                      pad_to_max_seq_len=True,
-                      return_attention_mask=True,
-                      return_token_type_ids=True,
-                      truncation_strategy="longest_first",
-                      return_overflowing_tokens=False,
-                      return_special_tokens_mask=False):
-        # Padding
-        needs_to_be_padded = pad_to_max_seq_len and \
-            max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len
-
-        if needs_to_be_padded:
-            difference = max_seq_len - len(encoded_inputs["input_ids"])
-            if self.tokenizer.padding_side == 'right':
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
-                        "input_ids"]) + [0] * difference
-                if return_token_type_ids:
-                    encoded_inputs["token_type_ids"] = (
-                        encoded_inputs["token_type_ids"] +
-                        [self.tokenizer.pad_token_type_id] * difference)
-                if return_special_tokens_mask:
-                    encoded_inputs["special_tokens_mask"] = encoded_inputs[
-                        "special_tokens_mask"] + [1] * difference
-                encoded_inputs["input_ids"] = encoded_inputs[
-                    "input_ids"] + [self.tokenizer.pad_token_id] * difference
-                encoded_inputs["labels"] = encoded_inputs[
-                    "labels"] + [self.pad_token_label_id] * difference
-                encoded_inputs["bbox"] = encoded_inputs[
-                    "bbox"] + [[0, 0, 0, 0]] * difference
-            elif self.tokenizer.padding_side == 'left':
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = [0] * difference + [
-                        1
-                    ] * len(encoded_inputs["input_ids"])
-                if return_token_type_ids:
-                    encoded_inputs["token_type_ids"] = (
-                        [self.tokenizer.pad_token_type_id] * difference +
-                        encoded_inputs["token_type_ids"])
-                if return_special_tokens_mask:
-                    encoded_inputs["special_tokens_mask"] = [
-                        1
-                    ] * difference + encoded_inputs["special_tokens_mask"]
-                encoded_inputs["input_ids"] = [
-                    self.tokenizer.pad_token_id
-                ] * difference + encoded_inputs["input_ids"]
-                encoded_inputs["labels"] = [
-                    self.pad_token_label_id
-                ] * difference + encoded_inputs["labels"]
-                encoded_inputs["bbox"] = [
-                    [0, 0, 0, 0]
-                ] * difference + encoded_inputs["bbox"]
-        else:
-            if return_attention_mask:
-                encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
-                    "input_ids"])
-
-        return encoded_inputs
-
-    def truncate_inputs(self, encoded_inputs, max_seq_len=512):
-        for key in encoded_inputs:
-            if key == "sample_id":
-                continue
-            length = min(len(encoded_inputs[key]), max_seq_len)
-            encoded_inputs[key] = encoded_inputs[key][:length]
-        return encoded_inputs
-
-    def read_all_lines(self, ):
-        with open(self.label_path, "r", encoding='utf-8') as fin:
-            lines = fin.readlines()
-        return lines
-
-    def _parse_label_file_all(self):
-        """
-        parse all samples
-        """
-        encoded_inputs_all = []
-        for line in self.all_lines:
-            encoded_inputs_all.extend(self._parse_label_file(line))
-        return encoded_inputs_all
-
-    def _parse_label_file(self, line):
-        """
-        parse single sample
-        """
-
-        image_name, info_str = line.split("\t")
-        image_path = os.path.join(self.data_dir, image_name)
-
-        def add_imgge_path(x):
-            x['image_path'] = image_path
-            return x
-
-        encoded_inputs = self._read_encoded_inputs_sample(info_str)
-        if self.contains_re:
-            encoded_inputs = self._chunk_re(encoded_inputs)
-        else:
-            encoded_inputs = self._chunk_ser(encoded_inputs)
-        encoded_inputs = list(map(add_imgge_path, encoded_inputs))
-        return encoded_inputs
-
-    def _read_encoded_inputs_sample(self, info_str):
-        """
-        parse label info
-        """
-        # read text info
-        info_dict = json.loads(info_str)
-        height = info_dict["height"]
-        width = info_dict["width"]
-
-        words_list = []
-        bbox_list = []
-        input_ids_list = []
-        token_type_ids_list = []
-        gt_label_list = []
-
-        if self.contains_re:
-            # for re
-            entities = []
-            relations = []
-            id2label = {}
-            entity_id_to_index_map = {}
-            empty_entity = set()
-        for info in info_dict["ocr_info"]:
-            if self.contains_re:
-                # for re
-                if len(info["text"]) == 0:
-                    empty_entity.add(info["id"])
-                    continue
-                id2label[info["id"]] = info["label"]
-                relations.extend([tuple(sorted(l)) for l in info["linking"]])
-
-            # x1, y1, x2, y2
-            bbox = info["bbox"]
-            label = info["label"]
-            bbox[0] = int(bbox[0] * 1000.0 / width)
-            bbox[2] = int(bbox[2] * 1000.0 / width)
-            bbox[1] = int(bbox[1] * 1000.0 / height)
-            bbox[3] = int(bbox[3] * 1000.0 / height)
-
-            text = info["text"]
-            encode_res = self.tokenizer.encode(
-                text, pad_to_max_seq_len=False, return_attention_mask=True)
-
-            gt_label = []
-            if not self.add_special_ids:
-                # TODO: use tok.all_special_ids to remove
-                encode_res["input_ids"] = encode_res["input_ids"][1:-1]
-                encode_res["token_type_ids"] = encode_res["token_type_ids"][1:
-                                                                            -1]
-                encode_res["attention_mask"] = encode_res["attention_mask"][1:
-                                                                            -1]
-            if label.lower() == "other":
-                gt_label.extend([0] * len(encode_res["input_ids"]))
-            else:
-                gt_label.append(self.label2id_map[("b-" + label).upper()])
-                gt_label.extend([self.label2id_map[("i-" + label).upper()]] *
-                                (len(encode_res["input_ids"]) - 1))
-            if self.contains_re:
-                if gt_label[0] != self.label2id_map["O"]:
-                    entity_id_to_index_map[info["id"]] = len(entities)
-                    entities.append({
-                        "start": len(input_ids_list),
-                        "end":
-                        len(input_ids_list) + len(encode_res["input_ids"]),
-                        "label": label.upper(),
-                    })
-            input_ids_list.extend(encode_res["input_ids"])
-            token_type_ids_list.extend(encode_res["token_type_ids"])
-            bbox_list.extend([bbox] * len(encode_res["input_ids"]))
-            gt_label_list.extend(gt_label)
-            words_list.append(text)
-
-        encoded_inputs = {
-            "input_ids": input_ids_list,
-            "labels": gt_label_list,
-            "token_type_ids": token_type_ids_list,
-            "bbox": bbox_list,
-            "attention_mask": [1] * len(input_ids_list),
-            # "words_list": words_list,
-        }
-        encoded_inputs = self.pad_sentences(
-            encoded_inputs,
-            max_seq_len=self.max_seq_len,
-            return_attention_mask=self.return_attention_mask)
-        encoded_inputs = self.truncate_inputs(encoded_inputs)
-
-        if self.contains_re:
-            relations = self._relations(entities, relations, id2label,
-                                        empty_entity, entity_id_to_index_map)
-            encoded_inputs['relations'] = relations
-            encoded_inputs['entities'] = entities
-        return encoded_inputs
-
-    def _chunk_ser(self, encoded_inputs):
-        encoded_inputs_all = []
-        seq_len = len(encoded_inputs['input_ids'])
-        chunk_size = 512
-        for chunk_id, index in enumerate(range(0, seq_len, chunk_size)):
-            chunk_beg = index
-            chunk_end = min(index + chunk_size, seq_len)
-            encoded_inputs_example = {}
-            for key in encoded_inputs:
-                encoded_inputs_example[key] = encoded_inputs[key][chunk_beg:
-                                                                  chunk_end]
-
-            encoded_inputs_all.append(encoded_inputs_example)
-        return encoded_inputs_all
-
-    def _chunk_re(self, encoded_inputs):
-        # prepare data
-        entities = encoded_inputs.pop('entities')
-        relations = encoded_inputs.pop('relations')
-        encoded_inputs_all = []
-        chunk_size = 512
-        for chunk_id, index in enumerate(
-                range(0, len(encoded_inputs["input_ids"]), chunk_size)):
-            item = {}
-            for k in encoded_inputs:
-                item[k] = encoded_inputs[k][index:index + chunk_size]
-
-            # select entity in current chunk
-            entities_in_this_span = []
-            global_to_local_map = {}  #
-            for entity_id, entity in enumerate(entities):
-                if (index <= entity["start"] < index + chunk_size and
-                        index <= entity["end"] < index + chunk_size):
-                    entity["start"] = entity["start"] - index
-                    entity["end"] = entity["end"] - index
-                    global_to_local_map[entity_id] = len(entities_in_this_span)
-                    entities_in_this_span.append(entity)
-
-            # select relations in current chunk
-            relations_in_this_span = []
-            for relation in relations:
-                if (index <= relation["start_index"] < index + chunk_size and
-                        index <= relation["end_index"] < index + chunk_size):
-                    relations_in_this_span.append({
-                        "head": global_to_local_map[relation["head"]],
-                        "tail": global_to_local_map[relation["tail"]],
-                        "start_index": relation["start_index"] - index,
-                        "end_index": relation["end_index"] - index,
-                    })
-            item.update({
-                "entities": reformat(entities_in_this_span),
-                "relations": reformat(relations_in_this_span),
-            })
-            item['entities']['label'] = [
-                self.entities_labels[x] for x in item['entities']['label']
-            ]
-            encoded_inputs_all.append(item)
-        return encoded_inputs_all
-
-    def _relations(self, entities, relations, id2label, empty_entity,
-                   entity_id_to_index_map):
-        """
-        build relations
-        """
-        relations = list(set(relations))
-        relations = [
-            rel for rel in relations
-            if rel[0] not in empty_entity and rel[1] not in empty_entity
-        ]
-        kv_relations = []
-        for rel in relations:
-            pair = [id2label[rel[0]], id2label[rel[1]]]
-            if pair == ["question", "answer"]:
-                kv_relations.append({
-                    "head": entity_id_to_index_map[rel[0]],
-                    "tail": entity_id_to_index_map[rel[1]]
-                })
-            elif pair == ["answer", "question"]:
-                kv_relations.append({
-                    "head": entity_id_to_index_map[rel[1]],
-                    "tail": entity_id_to_index_map[rel[0]]
-                })
-            else:
-                continue
-        relations = sorted(
-            [{
-                "head": rel["head"],
-                "tail": rel["tail"],
-                "start_index": get_relation_span(rel, entities)[0],
-                "end_index": get_relation_span(rel, entities)[1],
-            } for rel in kv_relations],
-            key=lambda x: x["head"], )
-        return relations
-
-    def load_img(self, image_path):
-        # read img
-        img = cv2.imread(image_path)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        resize_h, resize_w = self.img_size
-        im_shape = img.shape[0:2]
-        im_scale_y = resize_h / im_shape[0]
-        im_scale_x = resize_w / im_shape[1]
-        img_new = cv2.resize(
-            img, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=2)
-        mean = np.array([0.485, 0.456, 0.406])[np.newaxis, np.newaxis, :]
-        std = np.array([0.229, 0.224, 0.225])[np.newaxis, np.newaxis, :]
-        img_new = img_new / 255.0
-        img_new -= mean
-        img_new /= std
-        img = img_new.transpose((2, 0, 1))
-        return img
-
-    def __getitem__(self, idx):
-        if self.load_mode == "all":
-            data = copy.deepcopy(self.encoded_inputs_all[idx])
-        else:
-            data = self._parse_label_file(self.all_lines[idx])[0]
-
-        image_path = data.pop('image_path')
-        data["image"] = self.load_img(image_path)
-
-        return_data = {}
-        for k, v in data.items():
-            if k in self.return_keys:
-                if self.return_keys[k]['type'] == 'np':
-                    v = np.array(v, dtype=self.return_keys[k]['dtype'])
-                return_data[k] = v
-        return return_data
-
-    def __len__(self, ):
-        if self.load_mode == "all":
-            return len(self.encoded_inputs_all)
-        else:
-            return len(self.all_lines)
-
-
-def get_relation_span(rel, entities):
-    bound = []
-    for entity_index in [rel["head"], rel["tail"]]:
-        bound.append(entities[entity_index]["start"])
-        bound.append(entities[entity_index]["end"])
-    return min(bound), max(bound)
-
-
-def reformat(data):
-    new_data = {}
-    for item in data:
-        for k, v in item.items():
-            if k not in new_data:
-                new_data[k] = []
-            new_data[k].append(v)
-    return new_data

requirements.txt

@@ -12,4 +12,4 @@ cython
 lxml
 premailer
 openpyxl
-paddlenlp>=2.2.1
+fasttext==0.9.1