vqa code integrated into ppocr training system

ppocr/modeling/architectures/base_model.py

@@ -63,6 +63,10 @@ class BaseModel(nn.Layer):
             in_channels = self.neck.out_channels
 
         # # build head, head is need for det, rec and cls
+        if 'Head' not in config or config['Head'] is None:
+            self.use_head = False
+        else:
+            self.use_head = True
             config["Head"]['in_channels'] = in_channels
             self.head = build_head(config["Head"])
 
@@ -77,6 +81,7 @@ class BaseModel(nn.Layer):
         if self.use_neck:
             x = self.neck(x)
         y["neck_out"] = x
+        if self.use_head:
             x = self.head(x, targets=data)
         if isinstance(x, dict):
             y.update(x)

ppocr/modeling/backbones/__init__.py

@@ -43,6 +43,9 @@ def build_backbone(config, model_type):
         from .table_resnet_vd import ResNet
         from .table_mobilenet_v3 import MobileNetV3
         support_dict = ["ResNet", "MobileNetV3"]
+    elif model_type == 'vqa':
+        from .vqa_layoutlm import LayoutLMForSer, LayoutXLMForSer, LayoutXLMForRe
+        support_dict = ["LayoutLMForSer", "LayoutXLMForSer", 'LayoutXLMForRe']
     else:
         raise NotImplementedError
 

ppocr/modeling/backbones/vqa_layoutlm.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+from paddle import nn
+
+from paddlenlp.transformers import LayoutXLMModel, LayoutXLMForTokenClassification, LayoutXLMForRelationExtraction
+from paddlenlp.transformers import LayoutLMModel, LayoutLMForTokenClassification
+
+__all__ = ["LayoutXLMForSer", 'LayoutLMForSer']
+
+
+class NLPBaseModel(nn.Layer):
+    def __init__(self,
+                 base_model_class,
+                 model_class,
+                 type='ser',
+                 pretrained_model=None,
+                 checkpoints=None,
+                 **kwargs):
+        super(NLPBaseModel, self).__init__()
+        assert pretrained_model is not None or checkpoints is not None, "one of pretrained_model and checkpoints must be not None"
+        if checkpoints is not None:
+            self.model = model_class.from_pretrained(checkpoints)
+        else:
+            base_model = base_model_class.from_pretrained(pretrained_model)
+            if type == 'ser':
+                self.model = model_class(
+                    base_model, num_classes=kwargs['num_classes'], dropout=None)
+            else:
+                self.model = model_class(base_model, dropout=None)
+        self.out_channels = 1
+
+
+class LayoutXLMForSer(NLPBaseModel):
+    def __init__(self,
+                 num_classes,
+                 pretrained_model='layoutxlm-base-uncased',
+                 checkpoints=None,
+                 **kwargs):
+        super(LayoutXLMForSer, self).__init__(
+            LayoutXLMModel,
+            LayoutXLMForTokenClassification,
+            'ser',
+            pretrained_model,
+            checkpoints,
+            num_classes=num_classes)
+
+    def forward(self, x):
+        x = self.model(
+            input_ids=x[0],
+            bbox=x[2],
+            image=x[3],
+            attention_mask=x[4],
+            token_type_ids=x[5],
+            position_ids=None,
+            head_mask=None,
+            labels=None)
+        return x[0]
+
+
+class LayoutLMForSer(NLPBaseModel):
+    def __init__(self,
+                 num_classes,
+                 pretrained_model='layoutxlm-base-uncased',
+                 checkpoints=None,
+                 **kwargs):
+        super(LayoutLMForSer, self).__init__(
+            LayoutLMModel,
+            LayoutLMForTokenClassification,
+            'ser',
+            pretrained_model,
+            checkpoints,
+            num_classes=num_classes)
+
+    def forward(self, x):
+        x = self.model(
+            input_ids=x[0],
+            bbox=x[2],
+            attention_mask=x[4],
+            token_type_ids=x[5],
+            position_ids=None,
+            output_hidden_states=False)
+        return x
+
+
+class LayoutXLMForRe(NLPBaseModel):
+    def __init__(self,
+                 pretrained_model='layoutxlm-base-uncased',
+                 checkpoints=None,
+                 **kwargs):
+        super(LayoutXLMForRe, self).__init__(
+            LayoutXLMModel, LayoutXLMForRelationExtraction, 're',
+            pretrained_model, checkpoints)
+
+    def forward(self, x):
+        x = self.model(
+            input_ids=x[0],
+            bbox=x[1],
+            labels=None,
+            image=x[2],
+            attention_mask=x[3],
+            token_type_ids=x[4],
+            position_ids=None,
+            head_mask=None,
+            entities=x[5],
+            relations=x[6])
+        return x

ppocr/optimizer/__init__.py

@@ -42,7 +42,9 @@ def build_optimizer(config, epochs, step_each_epoch, parameters):
     # step2 build regularization
     if 'regularizer' in config and config['regularizer'] is not None:
         reg_config = config.pop('regularizer')
-        reg_name = reg_config.pop('name') + 'Decay'
+        reg_name = reg_config.pop('name')
+        if not hasattr(regularizer, reg_name):
+            reg_name += 'Decay'
         reg = getattr(regularizer, reg_name)(**reg_config)()
     else:
         reg = None

ppocr/optimizer/optimizer.py

@@ -158,3 +158,38 @@ class Adadelta(object):
             name=self.name,
             parameters=parameters)
         return opt
+
+
+class AdamW(object):
+    def __init__(self,
+                 learning_rate=0.001,
+                 beta1=0.9,
+                 beta2=0.999,
+                 epsilon=1e-08,
+                 weight_decay=0.01,
+                 grad_clip=None,
+                 name=None,
+                 lazy_mode=False,
+                 **kwargs):
+        self.learning_rate = learning_rate
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.epsilon = epsilon
+        self.learning_rate = learning_rate
+        self.weight_decay = 0.01 if weight_decay is None else weight_decay
+        self.grad_clip = grad_clip
+        self.name = name
+        self.lazy_mode = lazy_mode
+
+    def __call__(self, parameters):
+        opt = optim.AdamW(
+            learning_rate=self.learning_rate,
+            beta1=self.beta1,
+            beta2=self.beta2,
+            epsilon=self.epsilon,
+            weight_decay=self.weight_decay,
+            grad_clip=self.grad_clip,
+            name=self.name,
+            lazy_mode=self.lazy_mode,
+            parameters=parameters)
+        return opt

ppocr/optimizer/regularizer.py

@@ -50,3 +50,18 @@ class L2Decay(object):
     def __call__(self):
         reg = paddle.regularizer.L2Decay(self.regularization_coeff)
         return reg
+
+
+class ConstDecay(object):
+    """
+    Const L2 Weight Decay Regularization, which encourages the weights to be sparse.
+    Args:
+        factor(float): regularization coeff. Default:0.0.
+    """
+
+    def __init__(self, factor=0.0):
+        super(ConstDecay, self).__init__()
+        self.regularization_coeff = factor
+
+    def __call__(self):
+        return self.regularization_coeff

ppocr/postprocess/__init__.py

@@ -28,6 +28,8 @@ from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, Di
     TableLabelDecode, NRTRLabelDecode, SARLabelDecode, SEEDLabelDecode
 from .cls_postprocess import ClsPostProcess
 from .pg_postprocess import PGPostProcess
+from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess
+from .vqa_token_re_layoutlm_postprocess import VQAReTokenLayoutLMPostProcess
 
 
 def build_post_process(config, global_config=None):
@@ -36,7 +38,8 @@ def build_post_process(config, global_config=None):
         'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
         'DistillationCTCLabelDecode', 'TableLabelDecode',
         'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode',
-        'SEEDLabelDecode'
+        'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess',
+        'VQAReTokenLayoutLMPostProcess'
     ]
 
     if config['name'] == 'PSEPostProcess':

ppocr/postprocess/vqa_token_re_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 paddle
+
+
+class VQAReTokenLayoutLMPostProcess(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, **kwargs):
+        super(VQAReTokenLayoutLMPostProcess, self).__init__()
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        if label is not None:
+            return self._metric(preds, label)
+        else:
+            return self._infer(preds, *args, **kwargs)
+
+    def _metric(self, preds, label):
+        return preds['pred_relations'], label[6], label[5]
+
+    def _infer(self, preds, *args, **kwargs):
+        ser_results = kwargs['ser_results']
+        entity_idx_dict_batch = kwargs['entity_idx_dict_batch']
+        pred_relations = preds['pred_relations']
+
+        # 进行 relations 到 ocr信息的转换
+        results = []
+        for pred_relation, ser_result, entity_idx_dict in zip(
+                pred_relations, ser_results, entity_idx_dict_batch):
+            result = []
+            used_tail_id = []
+            for relation in pred_relation:
+                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))
+            results.append(result)
+        return results

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', '')
@@ -127,6 +154,7 @@ def save_model(model,
                optimizer,
                model_path,
                logger,
+               config,
                is_best=False,
                prefix='ppocr',
                **kwargs):
@@ -135,13 +163,20 @@ 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

@@ -78,3 +78,21 @@ def check_and_read_gif(img_path):
         imgvalue = frame[:, :, ::-1]
         return imgvalue, True
     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

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) |
 
 
 
@@ -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
 pip3 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。
@@ -79,7 +79,7 @@ python3 -m pip install paddlepaddle==2.2 -i https://mirror.baidu.com/pypi/simple
 - **（1）pip快速安装PaddleOCR whl包（仅预测）**
 
 ```bash
-pip install paddleocr
+python3 -m pip install paddleocr
 ```
 
 - **（2）下载VQA源码（预测+训练）**
@@ -93,18 +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。
 ```
 
-- **（3）安装PaddleNLP**
+- **（3）安装VQA的`requirements`**
 
 ```bash
-pip3 install "paddlenlp>=2.2.1"
-```
-
-
-- **（4）安装VQA的`requirements`**
-
-```bash
-cd ppstructure/vqa
-pip install -r requirements.txt
+python3 -m pip install -r ppstructure/vqa/requirements.txt
 ```
 
 ## 4. 使用
@@ -112,6 +104,10 @@ 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)。
 
 
@@ -121,98 +117,62 @@ 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
-python3.7 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
-python3.7 train_ser.py \
-    --model_name_or_path "model_path" \
-    --ser_model_type "LayoutXLM" \
-    --train_data_dir "XFUND/zh_train/image" \
-    --train_label_path "XFUND/zh_train/xfun_normalize_train.json" \
-    --eval_data_dir "XFUND/zh_val/image" \
-    --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
-python3.7 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
-python3.7 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=ppstructure/vqa/images/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
 python3.7 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json  --pred_json_path output_res/infer_results.txt
@@ -223,102 +183,48 @@ python3.7 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_nor
 
 * 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
-python3.7 infer_ser_re_e2e.py \
-    --model_name_or_path "PP-Layout_v1.0_ser_pretrained/" \
-    --re_model_name_or_path "PP-Layout_v1.0_re_pretrained/" \
-    --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=ppstructure/vqa/images/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)