add train code for table

configs/table/table_mv3.yml

+Global:
+  use_gpu: true
+  epoch_num: 40
+  log_smooth_window: 20
+  print_batch_step: 5
+  save_model_dir: ./output/table_mv3/
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 400]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model: 
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/table_structure_dict.txt
+  character_type: en
+  max_text_length: 100
+  max_elem_length: 800
+  max_cell_num: 500
+  infer_mode: False
+  process_total_num: 0
+  process_cut_num: 0
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  clip_norm: 5.0
+  lr:
+    learning_rate: 0.0001
+  regularizer:
+    name: 'L2'
+    factor: 0.00000
+
+Architecture:
+  model_type: table
+  algorithm: TableAttn
+  Backbone:
+    name: MobileNetV3
+    scale: 1.0
+    model_name: large
+  Head:
+    name: TableAttentionHead  # AttentionHead
+    hidden_size: 256 #
+    l2_decay: 0.00001
+#     loc_type: 1
+    loc_type: 2
+
+Loss:
+  name: TableAttentionLoss
+  structure_weight: 100.0
+  loc_weight: 10000.0
+
+PostProcess:
+  name: TableLabelDecode
+
+Metric:
+  name: TableMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: PubTabDataSet
+    data_dir: train_data/table/pubtabnet/train/
+    label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - ResizeTableImage:
+          max_len: 488
+      - TableLabelEncode:
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - PaddingTableImage:
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask']
+  loader:
+    shuffle: True
+    batch_size_per_card: 32
+    drop_last: True
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: PubTabDataSet
+    data_dir: train_data/table/pubtabnet/val/
+    label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - ResizeTableImage:
+          max_len: 488
+      - TableLabelEncode:
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - PaddingTableImage:
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 16
+    num_workers: 4

ppocr/data/__init__.py

@@ -35,6 +35,7 @@ from ppocr.data.imaug import transform, create_operators
 from ppocr.data.simple_dataset import SimpleDataSet
 from ppocr.data.lmdb_dataset import LMDBDataSet
 from ppocr.data.pgnet_dataset import PGDataSet
+from ppocr.data.pubtab_dataset import PubTabDataSet
 
 __all__ = ['build_dataloader', 'transform', 'create_operators']
 
@@ -55,7 +56,7 @@ signal.signal(signal.SIGTERM, term_mp)
 def build_dataloader(config, mode, device, logger, seed=None):
     config = copy.deepcopy(config)
 
-    support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet']
+    support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet']
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(
         'DataSet only support {}'.format(support_dict))

ppocr/data/imaug/label_ops.py

@@ -351,3 +351,182 @@ class SRNLabelEncode(BaseRecLabelEncode):
             assert False, "Unsupport type %s in get_beg_end_flag_idx" \
                           % beg_or_end
         return idx
+
+class TableLabelEncode(object):
+    """ Convert between text-label and text-index """
+    def __init__(self, 
+        max_text_length,
+        max_elem_length,
+        max_cell_num,
+        character_dict_path,
+        span_weight = 1.0, 
+        **kwargs):
+        self.max_text_length = max_text_length
+        self.max_elem_length = max_elem_length
+        self.max_cell_num = max_cell_num
+        list_character, list_elem = self.load_char_elem_dict(character_dict_path)
+        list_character = self.add_special_char(list_character)
+        list_elem = self.add_special_char(list_elem)
+        self.dict_character = {}
+        for i, char in enumerate(list_character):
+            self.dict_character[char] = i
+        self.dict_elem = {}
+        for i, elem in enumerate(list_elem):
+            self.dict_elem[elem] = i
+        self.span_weight = span_weight
+    
+    def load_char_elem_dict(self, character_dict_path):
+        list_character = []
+        list_elem = []
+        with open(character_dict_path, "rb") as fin:
+            lines = fin.readlines()
+            substr = lines[0].decode('utf-8').strip("\n").split("\t")
+            character_num = int(substr[0])
+            elem_num = int(substr[1])
+            for cno in range(1, 1+character_num):
+                character = lines[cno].decode('utf-8').strip("\n")
+                list_character.append(character)
+            for eno in range(1+character_num, 1+character_num+elem_num):
+                elem = lines[eno].decode('utf-8').strip("\n")
+                list_elem.append(elem)
+        return list_character, list_elem
+        
+    def add_special_char(self, list_character):
+        self.beg_str = "sos"
+        self.end_str = "eos"
+        list_character = [self.beg_str] + list_character + [self.end_str]
+        return list_character
+    
+    def get_span_idx_list(self):
+        span_idx_list = []
+        for elem in self.dict_elem:
+            if 'span' in elem:
+                span_idx_list.append(self.dict_elem[elem])
+        return span_idx_list
+        
+    def __call__(self, data):
+        cells = data['cells']
+        structure = data['structure']['tokens']
+        structure = self.encode(structure, 'elem')
+        if structure is None:
+            return None
+        elem_num = len(structure)
+        structure = [0] + structure + [len(self.dict_elem) - 1]
+#         structure = [0] + structure + [0]
+        structure = structure + [0] * (self.max_elem_length + 2 - len(structure))
+        structure = np.array(structure)
+        data['structure'] = structure
+        elem_char_idx1 = self.dict_elem['<td>']
+        elem_char_idx2 = self.dict_elem['<td']
+        span_idx_list = self.get_span_idx_list()
+        td_idx_list = np.logical_or(structure == elem_char_idx1, structure == elem_char_idx2)
+        td_idx_list = np.where(td_idx_list)[0]
+        
+        structure_mask = np.ones((self.max_elem_length + 2, 1), dtype=np.float32)
+        bbox_list = np.zeros((self.max_elem_length + 2, 4), dtype=np.float32)
+        bbox_list_mask = np.zeros((self.max_elem_length + 2, 1), dtype=np.float32)
+        img_height, img_width, img_ch = data['image'].shape
+        if len(span_idx_list) > 0:
+            span_weight = len(td_idx_list) * 1.0 / len(span_idx_list)
+            span_weight = min(max(span_weight, 1.0), self.span_weight)
+        for cno in range(len(cells)):
+            if 'bbox' in cells[cno]:
+                bbox = cells[cno]['bbox'].copy()
+                bbox[0] = bbox[0] * 1.0 / img_width
+                bbox[1] = bbox[1] * 1.0 / img_height
+                bbox[2] = bbox[2] * 1.0 / img_width
+                bbox[3] = bbox[3] * 1.0 / img_height
+                td_idx = td_idx_list[cno]
+                bbox_list[td_idx] = bbox
+                bbox_list_mask[td_idx] = 1.0
+                cand_span_idx = td_idx + 1
+                if cand_span_idx < (self.max_elem_length + 2):
+                    if structure[cand_span_idx] in span_idx_list:
+                        structure_mask[cand_span_idx] = span_weight
+#                         structure_mask[td_idx] = self.span_weight
+#                         structure_mask[cand_span_idx] = self.span_weight
+
+        data['bbox_list'] = bbox_list
+        data['bbox_list_mask'] = bbox_list_mask
+        data['structure_mask'] = structure_mask
+        char_beg_idx = self.get_beg_end_flag_idx('beg', 'char')
+        char_end_idx = self.get_beg_end_flag_idx('end', 'char')
+        elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem')
+        elem_end_idx = self.get_beg_end_flag_idx('end', 'elem')
+        data['sp_tokens'] = np.array([char_beg_idx, char_end_idx, elem_beg_idx, 
+            elem_end_idx, elem_char_idx1, elem_char_idx2, self.max_text_length, 
+            self.max_elem_length, self.max_cell_num, elem_num])
+        return data
+
+        ########
+        # for char decode
+#         cell_list = []
+#         for cell in cells:
+#             char_list = cell['tokens']
+#             cell = self.encode(char_list, 'char')
+#             if cell is None:
+#                 return None
+#             cell = [0] + cell + [len(self.dict_character) - 1]
+#             cell = cell + [0] * (self.max_text_length + 2 - len(cell))
+#             cell_list.append(cell)
+#         cell_list_padding = np.zeros((self.max_cell_num, self.max_text_length + 2))
+#         cell_list = np.array(cell_list)
+#         cell_list_padding[0:cell_list.shape[0]] = cell_list
+#         data['cells'] = cell_list_padding
+#         return data
+
+    def encode(self, text, char_or_elem):
+        """convert text-label into text-index.
+        """
+        if char_or_elem == "char":
+            max_len = self.max_text_length
+            current_dict = self.dict_character
+        else:
+            max_len = self.max_elem_length
+            current_dict = self.dict_elem
+        if len(text) > max_len:
+            return None
+        if len(text) == 0:
+            if char_or_elem == "char":
+                return [self.dict_character['space']]
+            else:
+                return None
+        text_list = []
+        for char in text:
+            if char not in current_dict:
+                return None
+            text_list.append(current_dict[char])
+        if len(text_list) == 0:
+            if char_or_elem == "char":
+                return [self.dict_character['space']]
+            else:
+                return None
+        return text_list
+
+    def get_ignored_tokens(self, char_or_elem):
+        beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem)
+        end_idx = self.get_beg_end_flag_idx("end", char_or_elem)
+        return [beg_idx, end_idx]
+
+    def get_beg_end_flag_idx(self, beg_or_end, char_or_elem):
+        if char_or_elem == "char":
+            if beg_or_end == "beg":
+                idx = np.array(self.dict_character[self.beg_str])
+            elif beg_or_end == "end":
+                idx = np.array(self.dict_character[self.end_str])
+            else:
+                assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \
+                              % beg_or_end
+        elif char_or_elem == "elem":
+            if beg_or_end == "beg":
+                idx = np.array(self.dict_elem[self.beg_str])
+            elif beg_or_end == "end":
+                idx = np.array(self.dict_elem[self.end_str])
+            else:
+                assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \
+                              % beg_or_end  
+        else:
+            assert False, "Unsupport type %s in char_or_elem" \
+                              % char_or_elem 
+        return idx
+    
\ No newline at end of file

ppocr/data/pubtab_dataset.py

+# copyright (c) 2020 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 numpy as np
+import os
+import random
+from paddle.io import Dataset
+import json
+
+from .imaug import transform, create_operators
+
+class PubTabDataSet(Dataset):
+    def __init__(self, config, mode, logger, seed=None):
+        super(PubTabDataSet, self).__init__()
+        self.logger = logger
+
+        global_config = config['Global']
+        dataset_config = config[mode]['dataset']
+        loader_config = config[mode]['loader']
+
+        label_file_path = dataset_config.pop('label_file_path')
+
+        self.data_dir = dataset_config['data_dir']
+        self.do_shuffle = loader_config['shuffle']
+        self.do_hard_select = False
+        if 'hard_select' in loader_config:
+            self.do_hard_select = loader_config['hard_select']
+            self.hard_prob = loader_config['hard_prob']
+        if self.do_hard_select:
+            self.img_select_prob = self.load_hard_select_prob()
+        self.table_select_type = None
+        if 'table_select_type' in loader_config:
+            self.table_select_type = loader_config['table_select_type']
+            self.table_select_prob = loader_config['table_select_prob']
+
+        self.seed = seed
+        logger.info("Initialize indexs of datasets:%s" % label_file_path)
+        with open(label_file_path, "rb") as f:
+            self.data_lines = f.readlines()
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if mode.lower() == "train":
+            self.shuffle_data_random()
+        self.ops = create_operators(dataset_config['transforms'], global_config)
+
+    def shuffle_data_random(self):
+        if self.do_shuffle:
+            random.seed(self.seed)
+            random.shuffle(self.data_lines)
+        return
+    
+    def load_hard_select_prob(self):
+        label_path = "./pretrained_model/teds_score_exp5_st2_train.txt"
+        img_select_prob = {}
+        with open(label_path, "rb") as fin:
+            lines = fin.readlines()
+            for lno in range(len(lines)):
+                substr = lines[lno].decode('utf-8').strip("\n").split(" ")
+                img_name = substr[0].strip(":")
+                score = float(substr[1])
+                if score <= 0.8:
+                    img_select_prob[img_name] = self.hard_prob[0]
+                elif score <= 0.98:
+                    img_select_prob[img_name] = self.hard_prob[1]
+                else:
+                    img_select_prob[img_name] = self.hard_prob[2]
+        return img_select_prob
+
+    def __getitem__(self, idx):
+        try:
+            data_line = self.data_lines[idx]
+            data_line = data_line.decode('utf-8').strip("\n")
+            info = json.loads(data_line)
+            file_name = info['filename']
+            select_flag = True
+            if self.do_hard_select:
+                prob = self.img_select_prob[file_name]
+                if prob < random.uniform(0, 1):
+                    select_flag = False
+            
+            if self.table_select_type:
+                structure = info['html']['structure']['tokens'].copy()
+                structure_str = ''.join(structure)
+                table_type = "simple"
+                if 'colspan' in structure_str or 'rowspan' in structure_str:
+                    table_type = "complex"
+#                 if self.table_select_type != table_type:
+#                     select_flag = False
+                if table_type == "complex":
+                    if self.table_select_prob < random.uniform(0, 1):
+                        select_flag = False                    
+            
+            if select_flag:
+                cells = info['html']['cells'].copy()
+                structure = info['html']['structure'].copy()
+                img_path = os.path.join(self.data_dir, file_name)
+                data = {'img_path': img_path, 'cells': cells, 'structure':structure}
+                if not os.path.exists(img_path):
+                    raise Exception("{} does not exist!".format(img_path))
+                with open(data['img_path'], 'rb') as f:
+                    img = f.read()
+                    data['image'] = img
+                outs = transform(data, self.ops)
+            else:
+                outs = None
+        except Exception as e:
+            self.logger.error(
+                "When parsing line {}, error happened with msg: {}".format(
+                    data_line, e))
+            outs = None
+        if outs is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        return outs
+
+    def __len__(self):
+        return len(self.data_idx_order_list)

ppocr/losses/__init__.py

@@ -38,11 +38,13 @@ from .basic_loss import DistanceLoss
 # combined loss function
 from .combined_loss import CombinedLoss
 
+# table loss
+from .table_att_loss import TableAttentionLoss
 
 def build_loss(config):
     support_dict = [
         'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
-        'SRNLoss', 'PGLoss', 'CombinedLoss'
+        'SRNLoss', 'PGLoss', 'CombinedLoss', 'TableAttentionLoss'
     ]
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/table_att_loss.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 paddle
+from paddle import nn
+from paddle.nn import functional as F
+from paddle import fluid
+
+class TableAttentionLoss(nn.Layer):
+    def __init__(self, structure_weight, loc_weight, use_giou=False, giou_weight=1.0, **kwargs):
+        super(TableAttentionLoss, self).__init__()
+        self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none')
+        self.structure_weight = structure_weight
+        self.loc_weight = loc_weight
+        self.use_giou = use_giou
+        self.giou_weight = giou_weight
+        
+    def giou_loss(self, preds, bbox, eps=1e-7, reduction='mean'):
+        '''
+        :param preds:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,]
+        :param bbox:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,]
+        :return: loss
+        '''
+        ix1 = fluid.layers.elementwise_max(preds[:, 0], bbox[:, 0])
+        iy1 = fluid.layers.elementwise_max(preds[:, 1], bbox[:, 1])
+        ix2 = fluid.layers.elementwise_min(preds[:, 2], bbox[:, 2])
+        iy2 = fluid.layers.elementwise_min(preds[:, 3], bbox[:, 3])
+
+        iw = fluid.layers.clip(ix2 - ix1 + 1e-3, 0., 1e10)
+        ih = fluid.layers.clip(iy2 - iy1 + 1e-3, 0., 1e10)
+
+        # overlap
+        inters = iw * ih
+
+        # union
+        uni = (preds[:, 2] - preds[:, 0] + 1e-3) * (preds[:, 3] - preds[:, 1] + 1e-3
+            ) + (bbox[:, 2] - bbox[:, 0] + 1e-3) * (
+            bbox[:, 3] - bbox[:, 1] + 1e-3) - inters + eps
+
+        # ious
+        ious = inters / uni
+
+        ex1 = fluid.layers.elementwise_min(preds[:, 0], bbox[:, 0])
+        ey1 = fluid.layers.elementwise_min(preds[:, 1], bbox[:, 1])
+        ex2 = fluid.layers.elementwise_max(preds[:, 2], bbox[:, 2])
+        ey2 = fluid.layers.elementwise_max(preds[:, 3], bbox[:, 3])
+        ew = fluid.layers.clip(ex2 - ex1 + 1e-3, 0., 1e10)
+        eh = fluid.layers.clip(ey2 - ey1 + 1e-3, 0., 1e10)
+
+        # enclose erea
+        enclose = ew * eh + eps
+        giou = ious - (enclose - uni) / enclose
+
+        loss = 1 - giou
+
+        if reduction == 'mean':
+            loss = paddle.mean(loss)
+        elif reduction == 'sum':
+            loss = paddle.sum(loss)
+        else:
+            raise NotImplementedError
+        return loss
+
+    def forward(self, predicts, batch):
+        structure_probs = predicts['structure_probs']
+        structure_targets = batch[1].astype("int64")
+        structure_targets = structure_targets[:, 1:]
+        if len(batch) == 6:
+            structure_mask = batch[5].astype("int64")
+            structure_mask = structure_mask[:, 1:]
+            structure_mask = paddle.reshape(structure_mask, [-1])
+        structure_probs = paddle.reshape(structure_probs, [-1, structure_probs.shape[-1]])
+        structure_targets = paddle.reshape(structure_targets, [-1])
+        structure_loss = self.loss_func(structure_probs, structure_targets)
+        
+        if len(batch) == 6:
+             structure_loss = structure_loss * structure_mask
+            
+#         structure_loss = paddle.sum(structure_loss) * self.structure_weight
+        structure_loss = paddle.mean(structure_loss) * self.structure_weight
+        
+        loc_preds = predicts['loc_preds']
+        loc_targets = batch[2].astype("float32")
+        loc_targets_mask = batch[4].astype("float32")
+        loc_targets = loc_targets[:, 1:, :]
+        loc_targets_mask = loc_targets_mask[:, 1:, :]
+        loc_loss = F.mse_loss(loc_preds * loc_targets_mask, loc_targets) * self.loc_weight
+        if self.use_giou:
+            loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, loc_targets) * self.giou_weight
+            total_loss = structure_loss + loc_loss + loc_loss_giou
+            return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss, "loc_loss_giou":loc_loss_giou}
+        else:
+            total_loss = structure_loss + loc_loss            
+            return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss}
\ No newline at end of file

ppocr/metrics/__init__.py

@@ -26,11 +26,11 @@ from .rec_metric import RecMetric
 from .cls_metric import ClsMetric
 from .e2e_metric import E2EMetric
 from .distillation_metric import DistillationMetric
-
+from .table_metric import TableMetric
 
 def build_metric(config):
     support_dict = [
-        "DetMetric", "RecMetric", "ClsMetric", "E2EMetric", "DistillationMetric"
+        "DetMetric", "RecMetric", "ClsMetric", "E2EMetric", "DistillationMetric", "TableMetric"
     ]
 
     config = copy.deepcopy(config)

ppocr/metrics/table_metric.py

+# copyright (c) 2020 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 numpy as np
+class TableMetric(object):
+    def __init__(self, main_indicator='acc', **kwargs):
+        self.main_indicator = main_indicator
+        self.reset()
+
+    def __call__(self, pred, batch, *args, **kwargs):
+        structure_probs = pred['structure_probs'].numpy()
+        structure_labels = batch[1]
+        correct_num = 0
+        all_num = 0
+        structure_probs = np.argmax(structure_probs, axis=2)
+        structure_labels = structure_labels[:, 1:]
+        batch_size = structure_probs.shape[0]
+        for bno in range(batch_size):
+            all_num += 1
+            if (structure_probs[bno] == structure_labels[bno]).all():
+                correct_num += 1
+        self.correct_num += correct_num
+        self.all_num += all_num
+        return {
+            'acc': correct_num * 1.0 / all_num,
+        }
+
+    def get_metric(self):
+        """
+        return metrics {
+                 'acc': 0,
+            }
+        """
+        acc = 1.0 * self.correct_num / self.all_num
+        self.reset()
+        return {'acc': acc}
+
+    def reset(self):
+        self.correct_num = 0
+        self.all_num = 0

ppocr/modeling/architectures/base_model.py

@@ -69,7 +69,7 @@ class BaseModel(nn.Layer):
 
         self.return_all_feats = config.get("return_all_feats", False)
 
-    def forward(self, x, data=None):
+    def forward(self, x, data=None, mode='Train'):
         y = dict()
         if self.use_transform:
             x = self.transform(x)
@@ -81,7 +81,10 @@ class BaseModel(nn.Layer):
         if data is None:
             x = self.head(x)
         else:
-            x = self.head(x, data)
+            if mode == 'Eval' or mode == 'Test':
+                x = self.head(x, targets=data, mode=mode)
+            else:
+                x = self.head(x, targets=data)
         y["head_out"] = x
         if self.return_all_feats:
             return y

ppocr/modeling/backbones/__init__.py

@@ -29,6 +29,10 @@ def build_backbone(config, model_type):
     elif model_type == 'e2e':
         from .e2e_resnet_vd_pg import ResNet
         support_dict = ['ResNet']
+    elif model_type == "table":
+        from .table_resnet_vd import ResNet
+        from .table_mobilenet_v3 import MobileNetV3
+        support_dict = ['ResNet', 'MobileNetV3']
     else:
         raise NotImplementedError
 

ppocr/modeling/backbones/table_mobilenet_v3.py

+# copyright (c) 2020 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 paddle
+from paddle import nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+
+__all__ = ['MobileNetV3']
+
+
+def make_divisible(v, divisor=8, min_value=None):
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+class MobileNetV3(nn.Layer):
+    def __init__(self,
+                 in_channels=3,
+                 model_name='large',
+                 scale=0.5,
+                 disable_se=False,
+                 **kwargs):
+        """
+        the MobilenetV3 backbone network for detection module.
+        Args:
+            params(dict): the super parameters for build network
+        """
+        super(MobileNetV3, self).__init__()
+
+        self.disable_se = disable_se
+
+        if model_name == "large":
+            cfg = [
+                # k, exp, c,  se,     nl,  s,
+                [3, 16, 16, False, 'relu', 1],
+                [3, 64, 24, False, 'relu', 2],
+                [3, 72, 24, False, 'relu', 1],
+                [5, 72, 40, True, 'relu', 2],
+                [5, 120, 40, True, 'relu', 1],
+                [5, 120, 40, True, 'relu', 1],
+                [3, 240, 80, False, 'hardswish', 2],
+                [3, 200, 80, False, 'hardswish', 1],
+                [3, 184, 80, False, 'hardswish', 1],
+                [3, 184, 80, False, 'hardswish', 1],
+                [3, 480, 112, True, 'hardswish', 1],
+                [3, 672, 112, True, 'hardswish', 1],
+                [5, 672, 160, True, 'hardswish', 2],
+                [5, 960, 160, True, 'hardswish', 1],
+                [5, 960, 160, True, 'hardswish', 1],
+            ]
+            cls_ch_squeeze = 960
+        elif model_name == "small":
+            cfg = [
+                # k, exp, c,  se,     nl,  s,
+                [3, 16, 16, True, 'relu', 2],
+                [3, 72, 24, False, 'relu', 2],
+                [3, 88, 24, False, 'relu', 1],
+                [5, 96, 40, True, 'hardswish', 2],
+                [5, 240, 40, True, 'hardswish', 1],
+                [5, 240, 40, True, 'hardswish', 1],
+                [5, 120, 48, True, 'hardswish', 1],
+                [5, 144, 48, True, 'hardswish', 1],
+                [5, 288, 96, True, 'hardswish', 2],
+                [5, 576, 96, True, 'hardswish', 1],
+                [5, 576, 96, True, 'hardswish', 1],
+            ]
+            cls_ch_squeeze = 576
+        else:
+            raise NotImplementedError("mode[" + model_name +
+                                      "_model] is not implemented!")
+
+        supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
+        assert scale in supported_scale, \
+            "supported scale are {} but input scale is {}".format(supported_scale, scale)
+        inplanes = 16
+        # conv1
+        self.conv = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=make_divisible(inplanes * scale),
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            groups=1,
+            if_act=True,
+            act='hardswish',
+            name='conv1')
+
+        self.stages = []
+        self.out_channels = []
+        block_list = []
+        i = 0
+        inplanes = make_divisible(inplanes * scale)
+        for (k, exp, c, se, nl, s) in cfg:
+            se = se and not self.disable_se
+            start_idx = 2 if model_name == 'large' else 0
+            if s == 2 and i > start_idx:
+                self.out_channels.append(inplanes)
+                self.stages.append(nn.Sequential(*block_list))
+                block_list = []
+            block_list.append(
+                ResidualUnit(
+                    in_channels=inplanes,
+                    mid_channels=make_divisible(scale * exp),
+                    out_channels=make_divisible(scale * c),
+                    kernel_size=k,
+                    stride=s,
+                    use_se=se,
+                    act=nl,
+                    name="conv" + str(i + 2)))
+            inplanes = make_divisible(scale * c)
+            i += 1
+        block_list.append(
+            ConvBNLayer(
+                in_channels=inplanes,
+                out_channels=make_divisible(scale * cls_ch_squeeze),
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                groups=1,
+                if_act=True,
+                act='hardswish',
+                name='conv_last'))
+        self.stages.append(nn.Sequential(*block_list))
+        self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
+        for i, stage in enumerate(self.stages):
+            self.add_sublayer(sublayer=stage, name="stage{}".format(i))
+
+    def forward(self, x):
+        x = self.conv(x)
+        out_list = []
+        for stage in self.stages:
+            x = stage(x)
+            out_list.append(x)
+        return out_list
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 padding,
+                 groups=1,
+                 if_act=True,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+        self.if_act = if_act
+        self.act = act
+        self.conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + '_weights'),
+            bias_attr=False)
+
+        self.bn = nn.BatchNorm(
+            num_channels=out_channels,
+            act=None,
+            param_attr=ParamAttr(name=name + "_bn_scale"),
+            bias_attr=ParamAttr(name=name + "_bn_offset"),
+            moving_mean_name=name + "_bn_mean",
+            moving_variance_name=name + "_bn_variance")
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.if_act:
+            if self.act == "relu":
+                x = F.relu(x)
+            elif self.act == "hardswish":
+                x = F.hardswish(x)
+            else:
+                print("The activation function({}) is selected incorrectly.".
+                      format(self.act))
+                exit()
+        return x
+
+
+class ResidualUnit(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 mid_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 use_se,
+                 act=None,
+                 name=''):
+        super(ResidualUnit, self).__init__()
+        self.if_shortcut = stride == 1 and in_channels == out_channels
+        self.if_se = use_se
+
+        self.expand_conv = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=mid_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            if_act=True,
+            act=act,
+            name=name + "_expand")
+        self.bottleneck_conv = ConvBNLayer(
+            in_channels=mid_channels,
+            out_channels=mid_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=int((kernel_size - 1) // 2),
+            groups=mid_channels,
+            if_act=True,
+            act=act,
+            name=name + "_depthwise")
+        if self.if_se:
+            self.mid_se = SEModule(mid_channels, name=name + "_se")
+        self.linear_conv = ConvBNLayer(
+            in_channels=mid_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            if_act=False,
+            act=None,
+            name=name + "_linear")
+
+    def forward(self, inputs):
+        x = self.expand_conv(inputs)
+        x = self.bottleneck_conv(x)
+        if self.if_se:
+            x = self.mid_se(x)
+        x = self.linear_conv(x)
+        if self.if_shortcut:
+            x = paddle.add(inputs, x)
+        return x
+
+
+class SEModule(nn.Layer):
+    def __init__(self, in_channels, reduction=4, name=""):
+        super(SEModule, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2D(1)
+        self.conv1 = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=in_channels // reduction,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            weight_attr=ParamAttr(name=name + "_1_weights"),
+            bias_attr=ParamAttr(name=name + "_1_offset"))
+        self.conv2 = nn.Conv2D(
+            in_channels=in_channels // reduction,
+            out_channels=in_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            weight_attr=ParamAttr(name + "_2_weights"),
+            bias_attr=ParamAttr(name=name + "_2_offset"))
+
+    def forward(self, inputs):
+        outputs = self.avg_pool(inputs)
+        outputs = self.conv1(outputs)
+        outputs = F.relu(outputs)
+        outputs = self.conv2(outputs)
+        outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
+        return inputs * outputs
\ No newline at end of file

ppocr/modeling/backbones/table_resnet_vd.py

+# copyright (c) 2020 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 paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+__all__ = ["ResNet"]
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            groups=1,
+            is_vd_mode=False,
+            act=None,
+            name=None, ):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = nn.AvgPool2D(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self._conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = nn.BatchNorm(
+            out_channels,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+
+    def forward(self, inputs):
+        if self.is_vd_mode:
+            inputs = self._pool2d_avg(inputs)
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2b")
+        self.conv2 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels * 4,
+            kernel_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=out_channels * 4,
+                kernel_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.add(x=short, y=conv2)
+        y = F.relu(y)
+        return y
+
+
+class BasicBlock(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BasicBlock, self).__init__()
+        self.stride = stride
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            act=None,
+            name=name + "_branch2b")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.add(x=short, y=conv1)
+        y = F.relu(y)
+        return y
+
+
+class ResNet(nn.Layer):
+    def __init__(self, in_channels=3, layers=50, **kwargs):
+        super(ResNet, self).__init__()
+        
+        self.layers = layers
+        supported_layers = [18, 34, 50, 101, 152, 200]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        elif layers == 200:
+            depth = [3, 12, 48, 3]
+        num_channels = [64, 256, 512,
+                        1024] if layers >= 50 else [64, 64, 128, 256]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv1_1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=32,
+            kernel_size=3,
+            stride=2,
+            act='relu',
+            name="conv1_1")
+        self.conv1_2 = ConvBNLayer(
+            in_channels=32,
+            out_channels=32,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_2")
+        self.conv1_3 = ConvBNLayer(
+            in_channels=32,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_3")
+        self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
+
+        self.stages = []
+        self.out_channels = []
+        if layers >= 50:
+            for block in range(len(depth)):
+                block_list = []
+                shortcut = False
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    bottleneck_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BottleneckBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(bottleneck_block)
+                self.out_channels.append(num_filters[block] * 4)
+                self.stages.append(nn.Sequential(*block_list))
+        else:
+            for block in range(len(depth)):
+                block_list = []
+                shortcut = False
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    basic_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BasicBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(basic_block)
+                self.out_channels.append(num_filters[block])
+                self.stages.append(nn.Sequential(*block_list))
+
+    def forward(self, inputs):
+        y = self.conv1_1(inputs)
+        y = self.conv1_2(y)
+        y = self.conv1_3(y)
+        y = self.pool2d_max(y)
+        out = []
+        for block in self.stages:
+            y = block(y)
+            out.append(y)
+        return out

ppocr/modeling/heads/__init__.py

@@ -31,8 +31,10 @@ def build_head(config):
     from .cls_head import ClsHead
     support_dict = [
         'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
-        'SRNHead', 'PGHead']
+        'SRNHead', 'PGHead', 'TableAttentionHead']
 
+    #table head
+    from .table_att_head import TableAttentionHead
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('head only support {}'.format(

ppocr/modeling/heads/table_att_head.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 paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+import numpy as np
+
+class TableAttentionHead(nn.Layer):
+    def __init__(self, in_channels, hidden_size, loc_type, in_max_len=488, **kwargs):
+        super(TableAttentionHead, self).__init__()
+        self.input_size = in_channels[-1]
+        self.hidden_size = hidden_size
+        self.char_num = 280
+        self.elem_num = 30
+
+        self.structure_attention_cell = AttentionGRUCell(
+            self.input_size, hidden_size, self.elem_num, use_gru=False)
+        self.structure_generator = nn.Linear(hidden_size, self.elem_num)
+        self.loc_type = loc_type
+        self.in_max_len = in_max_len
+        
+        if self.loc_type == 1:
+            self.loc_generator = nn.Linear(hidden_size, 4)
+        else:
+            if self.in_max_len == 640:
+                self.loc_fea_trans = nn.Linear(400, 801)
+            elif self.in_max_len == 800:
+                self.loc_fea_trans = nn.Linear(625, 801)
+            else:
+                self.loc_fea_trans = nn.Linear(256, 801)
+            self.loc_generator = nn.Linear(self.input_size + hidden_size, 4)
+            
+    def _char_to_onehot(self, input_char, onehot_dim):
+        input_ont_hot = F.one_hot(input_char, onehot_dim)
+        return input_ont_hot
+
+    def forward(self, inputs, targets=None, mode='Train'):
+        # if and else branch are both needed when you want to assign a variable
+        # if you modify the var in just one branch, then the modification will not work.
+        fea = inputs[-1]
+        if len(fea.shape) == 3:
+            pass
+        else:
+            last_shape = int(np.prod(fea.shape[2:])) # gry added
+            fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape])
+            fea = fea.transpose([0, 2, 1])  # (NTC)(batch, width, channels)
+        batch_size = fea.shape[0]
+        #sp_tokens = targets[2].numpy()
+        #char_beg_idx, char_end_idx = sp_tokens[0, 0:2]
+        #elem_beg_idx, elem_end_idx = sp_tokens[0, 2:4]
+        #elem_char_idx1, elem_char_idx2 = sp_tokens[0, 4:6]
+        #max_text_length, max_elem_length, max_cell_num = sp_tokens[0, 6:9]
+        max_text_length, max_elem_length, max_cell_num = 100, 800, 500
+        
+        hidden = paddle.zeros((batch_size, self.hidden_size))
+        output_hiddens = []
+        if mode == 'Train' and targets is not None:
+            structure = targets[0]
+            for i in range(max_elem_length+1):
+                elem_onehots = self._char_to_onehot(
+                    structure[:, i], onehot_dim=self.elem_num)
+                (outputs, hidden), alpha = self.structure_attention_cell(
+                    hidden, fea, elem_onehots)
+                output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
+            output = paddle.concat(output_hiddens, axis=1)
+            structure_probs = self.structure_generator(output)
+            if self.loc_type == 1:
+                loc_preds = self.loc_generator(output)
+                loc_preds = F.sigmoid(loc_preds)
+            else:
+                loc_fea = fea.transpose([0, 2, 1])
+                loc_fea = self.loc_fea_trans(loc_fea)
+                loc_fea = loc_fea.transpose([0, 2, 1])
+                loc_concat = paddle.concat([output, loc_fea], axis=2)
+                loc_preds = self.loc_generator(loc_concat)
+                loc_preds = F.sigmoid(loc_preds)
+        else:
+            temp_elem = paddle.zeros(shape=[batch_size], dtype="int32")
+            structure_probs = None
+            loc_preds = None
+            elem_onehots = None
+            outputs = None
+            alpha = None
+            max_elem_length = paddle.to_tensor(max_elem_length)
+            i = 0
+            while i < max_elem_length+1:
+                elem_onehots = self._char_to_onehot(
+                    temp_elem, onehot_dim=self.elem_num)
+                (outputs, hidden), alpha = self.structure_attention_cell(
+                    hidden, fea, elem_onehots)
+                output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
+                structure_probs_step = self.structure_generator(outputs)
+                temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
+                i += 1
+                
+            output = paddle.concat(output_hiddens, axis=1)
+            structure_probs = self.structure_generator(output)
+            structure_probs = F.softmax(structure_probs)
+            if self.loc_type == 1:
+                loc_preds = self.loc_generator(output)
+                loc_preds = F.sigmoid(loc_preds)
+            else:
+                loc_fea = fea.transpose([0, 2, 1])
+                loc_fea = self.loc_fea_trans(loc_fea)
+                loc_fea = loc_fea.transpose([0, 2, 1])
+                loc_concat = paddle.concat([output, loc_fea], axis=2)
+                loc_preds = self.loc_generator(loc_concat)
+                loc_preds = F.sigmoid(loc_preds)
+        return {'structure_probs':structure_probs, 'loc_preds':loc_preds}
+
+class AttentionGRUCell(nn.Layer):
+    def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
+        super(AttentionGRUCell, self).__init__()
+        self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
+        self.h2h = nn.Linear(hidden_size, hidden_size)
+        self.score = nn.Linear(hidden_size, 1, bias_attr=False)
+        self.rnn = nn.GRUCell(
+            input_size=input_size + num_embeddings, hidden_size=hidden_size)
+        self.hidden_size = hidden_size
+
+    def forward(self, prev_hidden, batch_H, char_onehots):
+        batch_H_proj = self.i2h(batch_H)
+        prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden), axis=1)
+        res = paddle.add(batch_H_proj, prev_hidden_proj)
+        res = paddle.tanh(res)
+        e = self.score(res)
+        alpha = F.softmax(e, axis=1)
+        alpha = paddle.transpose(alpha, [0, 2, 1])
+        context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
+        concat_context = paddle.concat([context, char_onehots], 1)
+        cur_hidden = self.rnn(concat_context, prev_hidden)
+        return cur_hidden, alpha
+
+
+class AttentionLSTM(nn.Layer):
+    def __init__(self, in_channels, out_channels, hidden_size, **kwargs):
+        super(AttentionLSTM, self).__init__()
+        self.input_size = in_channels
+        self.hidden_size = hidden_size
+        self.num_classes = out_channels
+
+        self.attention_cell = AttentionLSTMCell(
+            in_channels, hidden_size, out_channels, use_gru=False)
+        self.generator = nn.Linear(hidden_size, out_channels)
+
+    def _char_to_onehot(self, input_char, onehot_dim):
+        input_ont_hot = F.one_hot(input_char, onehot_dim)
+        return input_ont_hot
+
+    def forward(self, inputs, targets=None, batch_max_length=25):
+        batch_size = inputs.shape[0]
+        num_steps = batch_max_length
+
+        hidden = (paddle.zeros((batch_size, self.hidden_size)), paddle.zeros(
+            (batch_size, self.hidden_size)))
+        output_hiddens = []
+
+        if targets is not None:
+            for i in range(num_steps):
+                # one-hot vectors for a i-th char
+                char_onehots = self._char_to_onehot(
+                    targets[:, i], onehot_dim=self.num_classes)
+                hidden, alpha = self.attention_cell(hidden, inputs,
+                                                    char_onehots)
+
+                hidden = (hidden[1][0], hidden[1][1])
+                output_hiddens.append(paddle.unsqueeze(hidden[0], axis=1))
+            output = paddle.concat(output_hiddens, axis=1)
+            probs = self.generator(output)
+
+        else:
+            targets = paddle.zeros(shape=[batch_size], dtype="int32")
+            probs = None
+
+            for i in range(num_steps):
+                char_onehots = self._char_to_onehot(
+                    targets, onehot_dim=self.num_classes)
+                hidden, alpha = self.attention_cell(hidden, inputs,
+                                                    char_onehots)
+                probs_step = self.generator(hidden[0])
+                hidden = (hidden[1][0], hidden[1][1])
+                if probs is None:
+                    probs = paddle.unsqueeze(probs_step, axis=1)
+                else:
+                    probs = paddle.concat(
+                        [probs, paddle.unsqueeze(
+                            probs_step, axis=1)], axis=1)
+
+                next_input = probs_step.argmax(axis=1)
+
+                targets = next_input
+
+        return probs
+
+
+class AttentionLSTMCell(nn.Layer):
+    def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
+        super(AttentionLSTMCell, self).__init__()
+        self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
+        self.h2h = nn.Linear(hidden_size, hidden_size)
+        self.score = nn.Linear(hidden_size, 1, bias_attr=False)
+        if not use_gru:
+            self.rnn = nn.LSTMCell(
+                input_size=input_size + num_embeddings, hidden_size=hidden_size)
+        else:
+            self.rnn = nn.GRUCell(
+                input_size=input_size + num_embeddings, hidden_size=hidden_size)
+
+        self.hidden_size = hidden_size
+
+    def forward(self, prev_hidden, batch_H, char_onehots):
+        batch_H_proj = self.i2h(batch_H)
+        prev_hidden_proj = paddle.unsqueeze(self.h2h(prev_hidden[0]), axis=1)
+        res = paddle.add(batch_H_proj, prev_hidden_proj)
+        res = paddle.tanh(res)
+        e = self.score(res)
+
+        alpha = F.softmax(e, axis=1)
+        alpha = paddle.transpose(alpha, [0, 2, 1])
+        context = paddle.squeeze(paddle.mm(alpha, batch_H), axis=1)
+        concat_context = paddle.concat([context, char_onehots], 1)
+        cur_hidden = self.rnn(concat_context, prev_hidden)
+
+        return cur_hidden, alpha

ppocr/modeling/necks/__init__.py

@@ -21,7 +21,8 @@ def build_neck(config):
     from .sast_fpn import SASTFPN
     from .rnn import SequenceEncoder
     from .pg_fpn import PGFPN
-    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN']
+    from .table_fpn import TableFPN
+    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('neck only support {}'.format(

ppocr/modeling/necks/table_fpn.py

+# copyright (c) 2019 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 paddle
+from paddle import nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+
+
+class TableFPN(nn.Layer):
+    def __init__(self, in_channels, out_channels, **kwargs):
+        super(TableFPN, self).__init__()
+        self.out_channels = 512
+        weight_attr = paddle.nn.initializer.KaimingUniform()
+        self.in2_conv = nn.Conv2D(
+            in_channels=in_channels[0],
+            out_channels=self.out_channels,
+            kernel_size=1,
+            weight_attr=ParamAttr(
+                name='conv2d_51.w_0', initializer=weight_attr),
+            bias_attr=False)
+        self.in3_conv = nn.Conv2D(
+            in_channels=in_channels[1],
+            out_channels=self.out_channels,
+            kernel_size=1,
+            stride = 1,
+            weight_attr=ParamAttr(
+                name='conv2d_50.w_0', initializer=weight_attr),
+            bias_attr=False)
+        self.in4_conv = nn.Conv2D(
+            in_channels=in_channels[2],
+            out_channels=self.out_channels,
+            kernel_size=1,
+            weight_attr=ParamAttr(
+                name='conv2d_49.w_0', initializer=weight_attr),
+            bias_attr=False)
+        self.in5_conv = nn.Conv2D(
+            in_channels=in_channels[3],
+            out_channels=self.out_channels,
+            kernel_size=1,
+            weight_attr=ParamAttr(
+                name='conv2d_48.w_0', initializer=weight_attr),
+            bias_attr=False)
+        self.p5_conv = nn.Conv2D(
+            in_channels=self.out_channels,
+            out_channels=self.out_channels // 4,
+            kernel_size=3,
+            padding=1,
+            weight_attr=ParamAttr(
+                name='conv2d_52.w_0', initializer=weight_attr),
+            bias_attr=False)
+        self.p4_conv = nn.Conv2D(
+            in_channels=self.out_channels,
+            out_channels=self.out_channels // 4,
+            kernel_size=3,
+            padding=1,
+            weight_attr=ParamAttr(
+                name='conv2d_53.w_0', initializer=weight_attr),
+            bias_attr=False)
+        self.p3_conv = nn.Conv2D(
+            in_channels=self.out_channels,
+            out_channels=self.out_channels // 4,
+            kernel_size=3,
+            padding=1,
+            weight_attr=ParamAttr(
+                name='conv2d_54.w_0', initializer=weight_attr),
+            bias_attr=False)
+        self.p2_conv = nn.Conv2D(
+            in_channels=self.out_channels,
+            out_channels=self.out_channels // 4,
+            kernel_size=3,
+            padding=1,
+            weight_attr=ParamAttr(
+                name='conv2d_55.w_0', initializer=weight_attr),
+            bias_attr=False)
+        self.fuse_conv = nn.Conv2D(
+            in_channels=self.out_channels * 4,
+            out_channels=512,
+            kernel_size=3,
+            padding=1,
+            weight_attr=ParamAttr(
+                name='conv2d_fuse.w_0', initializer=weight_attr), bias_attr=False)
+
+    def forward(self, x):
+        c2, c3, c4, c5 = x
+
+        in5 = self.in5_conv(c5)
+        in4 = self.in4_conv(c4)
+        in3 = self.in3_conv(c3)
+        in2 = self.in2_conv(c2)
+
+        out4 = in4 + F.upsample(
+            in5, size=in4.shape[2:4], mode="nearest", align_mode=1)  # 1/16
+        out3 = in3 + F.upsample(
+            out4, size=in3.shape[2:4], mode="nearest", align_mode=1)  # 1/8
+        out2 = in2 + F.upsample(
+            out3, size=in2.shape[2:4], mode="nearest", align_mode=1)  # 1/4
+
+        p4 = F.upsample(out4, size=in5.shape[2:4], mode="nearest", align_mode=1)
+        p3 = F.upsample(out3, size=in5.shape[2:4], mode="nearest", align_mode=1)
+        p2 = F.upsample(out2, size=in5.shape[2:4], mode="nearest", align_mode=1)
+        fuse = paddle.concat([in5, p4, p3, p2], axis=1)
+        fuse_conv = self.fuse_conv(fuse) * 0.005
+        return [c5 + fuse_conv]

ppocr/postprocess/rec_postprocess.py

@@ -325,8 +325,14 @@ class TableLabelDecode(object):
     """  """
 
     def __init__(self,
+                 max_text_length,
+                 max_elem_length,
+                 max_cell_num,
                  character_dict_path,
                  **kwargs):
+        self.max_text_length = max_text_length
+        self.max_elem_length = max_elem_length
+        self.max_cell_num = max_cell_num
         list_character, list_elem = self.load_char_elem_dict(character_dict_path)
         list_character = self.add_special_char(list_character)
         list_elem = self.add_special_char(list_elem)
@@ -363,6 +369,18 @@ class TableLabelDecode(object):
         list_character = [self.beg_str] + list_character + [self.end_str]
         return list_character
 
+    def get_sp_tokens(self):
+        char_beg_idx = self.get_beg_end_flag_idx('beg', 'char')
+        char_end_idx = self.get_beg_end_flag_idx('end', 'char')
+        elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem')
+        elem_end_idx = self.get_beg_end_flag_idx('end', 'elem')
+        elem_char_idx1 = self.dict_elem['<td>']
+        elem_char_idx2 = self.dict_elem['<td']
+        sp_tokens = np.array([char_beg_idx, char_end_idx, elem_beg_idx, 
+            elem_end_idx, elem_char_idx1, elem_char_idx2, self.max_text_length, 
+            self.max_elem_length, self.max_cell_num])
+        return sp_tokens
+    
     def __call__(self, preds):
         structure_probs = preds['structure_probs']
         loc_preds = preds['loc_preds']

tools/eval.py

@@ -48,6 +48,7 @@ def main():
             getattr(post_process_class, 'character'))
     model = build_model(config['Architecture'])
     use_srn = config['Architecture']['algorithm'] == "SRN"
+    model_type = config['Architecture']['model_type']
 
     best_model_dict = init_model(config, model)
     if len(best_model_dict):
@@ -60,7 +61,7 @@ def main():
 
     # start eval
     metric = program.eval(model, valid_dataloader, post_process_class,
-                          eval_class, use_srn)
+                          eval_class, model_type, use_srn)
     logger.info('metric eval ***************')
     for k, v in metric.items():
         logger.info('{}:{}'.format(k, v))

tools/infer_table.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import os
+import sys
+import json
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import paddle
+from paddle.jit import to_static
+
+from ppocr.data import create_operators, transform
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import init_model
+from ppocr.utils.utility import get_image_file_list
+import tools.program as program
+import cv2
+
+def main(config, device, logger, vdl_writer):
+    global_config = config['Global']
+
+    # build post process
+    post_process_class = build_post_process(config['PostProcess'],
+                                            global_config)
+
+    # build model
+    if hasattr(post_process_class, 'character'):
+        config['Architecture']["Head"]['out_channels'] = len(
+            getattr(post_process_class, 'character'))
+
+    model = build_model(config['Architecture'])
+
+    init_model(config, model, logger)
+
+    # create data ops
+    transforms = []
+    use_padding = False
+    for op in config['Eval']['dataset']['transforms']:
+        op_name = list(op)[0]
+        if 'Label' in op_name:
+            continue
+        if op_name == 'KeepKeys':
+            op[op_name]['keep_keys'] = ['image']
+        if op_name == "ResizeTableImage":
+            use_padding = True
+            padding_max_len = op['ResizeTableImage']['max_len']
+        transforms.append(op)
+
+    global_config['infer_mode'] = True
+    ops = create_operators(transforms, global_config)
+
+    model.eval()
+    for file in get_image_file_list(config['Global']['infer_img']):
+        logger.info("infer_img: {}".format(file))
+        with open(file, 'rb') as f:
+            img = f.read()
+            data = {'image': img}
+        batch = transform(data, ops)
+        sp_tokens = post_process_class.get_sp_tokens()
+        targets = [[], [], paddle.to_tensor([sp_tokens])]
+        images = np.expand_dims(batch[0], axis=0)
+        images = paddle.to_tensor(images)
+        preds = model(images, data=targets, mode='Test')
+        post_result = post_process_class(preds)
+        res_html_code = post_result['res_html_code']
+        res_loc = post_result['res_loc']
+        img = cv2.imread(file)
+        imgh, imgw = img.shape[0:2]
+        res_loc_final = []
+        for rno in range(len(res_loc[0])):
+            x0, y0, x1, y1 = res_loc[0][rno]
+            left = max(int(imgw * x0), 0)
+            top = max(int(imgh * y0), 0)
+            right = min(int(imgw * x1), imgw - 1)
+            bottom = min(int(imgh * y1), imgh - 1)
+            cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
+            res_loc_final.append([left, top, right, bottom])
+        res_loc_str = json.dumps(res_loc_final)
+        logger.info("result: {}, {}".format(res_html_code, res_loc_final))
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess()
+    main(config, device, logger, vdl_writer)
+

tools/program.py

@@ -186,7 +186,8 @@ def train(config,
     model.train()
 
     use_srn = config['Architecture']['algorithm'] == "SRN"
-
+    model_type = config['Architecture']['model_type']
+    
     if 'start_epoch' in best_model_dict:
         start_epoch = best_model_dict['start_epoch']
     else:
@@ -211,6 +212,9 @@ def train(config,
                 others = batch[-4:]
                 preds = model(images, others)
                 model_average = True
+            elif model_type == "table":
+                others = batch[1:]
+                preds = model(images, others)
             else:
                 preds = model(images)
             loss = loss_class(preds, batch)
@@ -232,8 +236,11 @@ def train(config,
 
             if cal_metric_during_train:  # only rec and cls need
                 batch = [item.numpy() for item in batch]
-                post_result = post_process_class(preds, batch[1])
-                eval_class(post_result, batch)
+                if model_type == 'table':
+                    eval_class(preds, batch)
+                else:
+                    post_result = post_process_class(preds, batch[1])
+                    eval_class(post_result, batch)
                 metric = eval_class.get_metric()
                 train_stats.update(metric)
 
@@ -337,7 +344,7 @@ def train(config,
 
 
 def eval(model, valid_dataloader, post_process_class, eval_class,
-         use_srn=False):
+         model_type, use_srn=False):
     model.eval()
     with paddle.no_grad():
         total_frame = 0.0
@@ -359,10 +366,13 @@ def eval(model, valid_dataloader, post_process_class, eval_class,
 
             batch = [item.numpy() for item in batch]
             # Obtain usable results from post-processing methods
-            post_result = post_process_class(preds, batch[1])
             total_time += time.time() - start
             # Evaluate the results of the current batch
-            eval_class(post_result, batch)
+            if model_type == 'table':
+                eval_class(preds, batch)
+            else:
+                post_result = post_process_class(preds, batch[1])
+                eval_class(post_result, batch)
             pbar.update(1)
             total_frame += len(images)
         # Get final metric，eg. acc or hmean
@@ -386,7 +396,7 @@ def preprocess(is_train=False):
     alg = config['Architecture']['algorithm']
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
-        'CLS', 'PGNet', 'Distillation'
+        'CLS', 'PGNet', 'Distillation', 'TableAttn'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'