add TableMaster

configs/table/table_master.yml

+Global:
+  use_gpu: true
+  epoch_num: 17
+  log_smooth_window: 20
+  print_batch_step: 5
+  save_model_dir: ./output/table_master/
+  save_epoch_step: 17
+  # evaluation is run every 400 iterations after the 0th iteration
+  eval_batch_step: [0, 400]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: ppstructure/docs/table/table.jpg
+  save_res_path: output/table_master
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/table_master_structure_dict.txt
+  infer_mode: False
+  max_text_length: 500
+  process_total_num: 0
+  process_cut_num: 0
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: MultiStepDecay
+    learning_rate: 0.001
+    milestones: [12, 15]
+    gamma: 0.1
+    warmup_epoch: 0.02
+  regularizer:
+    name: 'L2'
+    factor: 0.00000
+
+Architecture:
+  model_type: table
+  algorithm: TableMaster
+  Backbone:
+    name: TableResNetExtra
+    gcb_config:
+      ratio: 0.0625
+      headers: 1
+      att_scale: False
+      fusion_type: channel_add
+      layers: [False, True, True, True]
+    layers: [1,2,5,3]
+  Head:
+    name: TableMasterHead
+    hidden_size: 512
+    headers: 8
+    dropout: 0
+    d_ff: 2024
+    max_text_length: 500
+
+Loss:
+  name: TableMasterLoss
+  ignore_index: 42 # set to len of dict + 3
+
+PostProcess:
+  name: TableMasterLabelDecode
+  box_shape: pad
+
+Metric:
+  name: TableMetric
+  main_indicator: acc
+  compute_bbox_metric: true # cost many time, set False for training
+
+Train:
+  dataset:
+    name: PubTabDataSet
+    data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/train/
+    label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/PubTabNet_2.0.0_train.jsonl]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - TableMasterLabelEncode:
+          learn_empty_box: False
+          merge_no_span_structure: True
+          replace_empty_cell_token: True
+      - ResizeTableImage:
+          max_len: 480
+          resize_bboxes: True
+      - PaddingTableImage:
+          size: [480, 480]
+      - TableBoxEncode:
+          use_xywh: true
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.5, 0.5, 0.5]
+          std: [0.5, 0.5, 0.5]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'structure', 'bboxes', 'bbox_masks','shape']
+  loader:
+    shuffle: True
+    batch_size_per_card: 8
+    drop_last: True
+    num_workers: 1
+
+Eval:
+  dataset:
+    name: PubTabDataSet
+    data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/
+    label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - TableMasterLabelEncode:
+          learn_empty_box: False
+          merge_no_span_structure: True
+          replace_empty_cell_token: True
+      - ResizeTableImage:
+          max_len: 480
+          resize_bboxes: True
+      - PaddingTableImage:
+          size: [ 480, 480 ]
+      - TableBoxEncode:
+          use_xywh: true
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.5, 0.5, 0.5 ]
+          std: [ 0.5, 0.5, 0.5 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks','shape' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 2
+    num_workers: 8

configs/table/table_mv3.yml

@@ -4,21 +4,20 @@ Global:
   log_smooth_window: 20
   print_batch_step: 5
   save_model_dir: ./output/table_mv3/
-  save_epoch_step: 3
+  save_epoch_step: 400
   # evaluation is run every 400 iterations after the 0th iteration
   eval_batch_step: [0, 400]
   cal_metric_during_train: True
   pretrained_model:
-  checkpoints: 
+  checkpoints:
   save_inference_dir:
   use_visualdl: False
-  infer_img: doc/table/table.jpg
+  infer_img: ppstructure/docs/table/table.jpg
+  save_res_path: output/table_mv3
   # 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
+  max_text_length: 500
   infer_mode: False
   process_total_num: 0
   process_cut_num: 0
@@ -44,11 +43,8 @@ Architecture:
   Head:
     name: TableAttentionHead
     hidden_size: 256
-    l2_decay: 0.00001
     loc_type: 2
-    max_text_length: 100
-    max_elem_length: 800
-    max_cell_num: 500
+    max_text_length: 500
 
 Loss:
   name: TableAttentionLoss
@@ -61,6 +57,7 @@ PostProcess:
 Metric:
   name: TableMetric
   main_indicator: acc
+  compute_bbox_metric: False # cost many time, set False for training
 
 Train:
   dataset:
@@ -71,18 +68,23 @@ Train:
       - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
+      - TableLabelEncode:
+          learn_empty_box: False
+          merge_no_span_structure: False
+          replace_empty_cell_token: False
+      - TableBoxEncode:
       - 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:
+          size: [488, 488]
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask']
+          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
   loader:
     shuffle: True
     batch_size_per_card: 32
@@ -92,24 +94,29 @@ Train:
 Eval:
   dataset:
     name: PubTabDataSet
-    data_dir: train_data/table/pubtabnet/val/
-    label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl
+    data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/
+    label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl]
     transforms:
       - DecodeImage: # load image
           img_mode: BGR
           channel_first: False
+      - TableLabelEncode:
+          learn_empty_box: False
+          merge_no_span_structure: False
+          replace_empty_cell_token: False
+      - TableBoxEncode:
       - 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:
+          size: [488, 488]
       - ToCHWImage:
       - KeepKeys:
-          keep_keys: ['image', 'structure', 'bbox_list', 'sp_tokens', 'bbox_list_mask']
+          keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
   loader:
     shuffle: False
     drop_last: False

ppocr/data/imaug/gen_table_mask.py

@@ -32,7 +32,7 @@ class GenTableMask(object):
         self.shrink_h_max = 5
         self.shrink_w_max = 5
         self.mask_type = mask_type
-        
+
     def projection(self, erosion, h, w, spilt_threshold=0):
         # 水平投影
         projection_map = np.ones_like(erosion)
@@ -48,10 +48,12 @@ class GenTableMask(object):
         in_text = False  # 是否遍历到了字符区内
         box_list = []
         for i in range(len(project_val_array)):
-            if in_text == False and project_val_array[i] > spilt_threshold:  # 进入字符区了
+            if in_text == False and project_val_array[
+                    i] > spilt_threshold:  # 进入字符区了
                 in_text = True
                 start_idx = i
-            elif project_val_array[i] <= spilt_threshold and in_text == True:  # 进入空白区了
+            elif project_val_array[
+                    i] <= spilt_threshold and in_text == True:  # 进入空白区了
                 end_idx = i
                 in_text = False
                 if end_idx - start_idx <= 2:
@@ -70,7 +72,8 @@ class GenTableMask(object):
         box_gray_img = cv2.cvtColor(box_img, cv2.COLOR_BGR2GRAY)
         h, w = box_gray_img.shape
         # 灰度图片进行二值化处理
-        ret, thresh1 = cv2.threshold(box_gray_img, 200, 255, cv2.THRESH_BINARY_INV)
+        ret, thresh1 = cv2.threshold(box_gray_img, 200, 255,
+                                     cv2.THRESH_BINARY_INV)
         # 纵向腐蚀
         if h < w:
             kernel = np.ones((2, 1), np.uint8)
@@ -95,10 +98,12 @@ class GenTableMask(object):
         box_list = []
         spilt_threshold = 0
         for i in range(len(project_val_array)):
-            if in_text == False and project_val_array[i] > spilt_threshold:  # 进入字符区了
+            if in_text == False and project_val_array[
+                    i] > spilt_threshold:  # 进入字符区了
                 in_text = True
                 start_idx = i
-            elif project_val_array[i] <= spilt_threshold and in_text == True:  # 进入空白区了
+            elif project_val_array[
+                    i] <= spilt_threshold and in_text == True:  # 进入空白区了
                 end_idx = i
                 in_text = False
                 if end_idx - start_idx <= 2:
@@ -120,7 +125,8 @@ class GenTableMask(object):
                     h_end = h
                 word_img = erosion[h_start:h_end + 1, :]
                 word_h, word_w = word_img.shape
-                w_split_list, w_projection_map = self.projection(word_img.T, word_w, word_h)
+                w_split_list, w_projection_map = self.projection(word_img.T,
+                                                                 word_w, word_h)
                 w_start, w_end = w_split_list[0][0], w_split_list[-1][1]
                 if h_start > 0:
                     h_start -= 1
@@ -170,75 +176,54 @@ class GenTableMask(object):
 
                 for sno in range(len(split_bbox_list)):
                     left, top, right, bottom = split_bbox_list[sno]
-                    left, top, right, bottom = self.shrink_bbox([left, top, right, bottom])
+                    left, top, right, bottom = self.shrink_bbox(
+                        [left, top, right, bottom])
                     if self.mask_type == 1:
                         mask_img[top:bottom, left:right] = 1.0
                         data['mask_img'] = mask_img
                     else:
-                        mask_img[top:bottom, left:right, :] = (255, 255, 255)        
+                        mask_img[top:bottom, left:right, :] = (255, 255, 255)
                         data['image'] = mask_img
         return data
 
+
 class ResizeTableImage(object):
-    def __init__(self, max_len, **kwargs):
+    def __init__(self, max_len, resize_bboxes=False, infer_mode=False,
+                 **kwargs):
         super(ResizeTableImage, self).__init__()
         self.max_len = max_len
+        self.resize_bboxes = resize_bboxes
+        self.infer_mode = infer_mode
 
-    def get_img_bbox(self, cells):
-        bbox_list = []
-        if len(cells) == 0:
-            return bbox_list
-        cell_num = len(cells)
-        for cno in range(cell_num):
-            if "bbox" in cells[cno]:
-                bbox = cells[cno]['bbox']
-                bbox_list.append(bbox)
-        return bbox_list
-
-    def resize_img_table(self, img, bbox_list, max_len):
+    def __call__(self, data):
+        img = data['image']
         height, width = img.shape[0:2]
-        ratio = max_len / (max(height, width) * 1.0)
+        ratio = self.max_len / (max(height, width) * 1.0)
         resize_h = int(height * ratio)
         resize_w = int(width * ratio)
-        img_new = cv2.resize(img, (resize_w, resize_h))
-        bbox_list_new = []
-        for bno in range(len(bbox_list)):
-            left, top, right, bottom = bbox_list[bno].copy()
-            left = int(left * ratio)
-            top = int(top * ratio)
-            right = int(right * ratio)
-            bottom = int(bottom * ratio)
-            bbox_list_new.append([left, top, right, bottom])
-        return img_new, bbox_list_new
-    
-    def __call__(self, data):
-        img = data['image']
-        if 'cells' not in data:
-            cells = []
-        else:
-            cells = data['cells']
-        bbox_list = self.get_img_bbox(cells)
-        img_new, bbox_list_new = self.resize_img_table(img, bbox_list, self.max_len)
-        data['image'] = img_new
-        cell_num = len(cells)
-        bno = 0
-        for cno in range(cell_num):
-            if "bbox" in data['cells'][cno]:
-                data['cells'][cno]['bbox'] = bbox_list_new[bno]
-                bno += 1
+        resize_img = cv2.resize(img, (resize_w, resize_h))
+        if self.resize_bboxes and not self.infer_mode:
+            data['bboxes'] = data['bboxes'] * ratio
+        data['image'] = resize_img
+        data['src_img'] = img
+        data['shape'] = np.array([resize_h, resize_w, ratio, ratio])
         data['max_len'] = self.max_len
         return data
 
+
 class PaddingTableImage(object):
-    def __init__(self, **kwargs):
+    def __init__(self, size, **kwargs):
         super(PaddingTableImage, self).__init__()
-    
+        self.size = size
+
     def __call__(self, data):
         img = data['image']
-        max_len = data['max_len']
-        padding_img = np.zeros((max_len, max_len, 3), dtype=np.float32)
+        pad_h, pad_w = self.size
+        padding_img = np.zeros((pad_h, pad_w, 3), dtype=np.float32)
         height, width = img.shape[0:2]
         padding_img[0:height, 0:width, :] = img.copy()
         data['image'] = padding_img
+        shape = data['shape'].tolist()
+        shape.extend([pad_h, pad_w])
+        data['shape'] = np.array(shape)
         return data
-            
\ No newline at end of file

ppocr/data/imaug/label_ops.py

@@ -443,7 +443,9 @@ class KieLabelEncode(object):
             elif 'key_cls' in anno.keys():
                 labels.append(anno['key_cls'])
             else:
-                raise ValueError("Cannot found 'key_cls' in ann.keys(), please check your training annotation.")
+                raise ValueError(
+                    "Cannot found 'key_cls' in ann.keys(), please check your training annotation."
+                )
             edges.append(ann.get('edge', 0))
         ann_infos = dict(
             image=data['image'],
@@ -580,171 +582,197 @@ class SRNLabelEncode(BaseRecLabelEncode):
         return idx
 
 
-class TableLabelEncode(object):
+class TableLabelEncode(AttnLabelEncode):
     """ 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,
+                 replace_empty_cell_token=False,
+                 merge_no_span_structure=False,
+                 learn_empty_box=False,
+                 point_num=4,
                  **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 = []
+        self.max_text_len = max_text_length
+        self.lower = False
+        self.learn_empty_box = learn_empty_box
+        self.merge_no_span_structure = merge_no_span_structure
+        self.replace_empty_cell_token = replace_empty_cell_token
+
+        dict_character = []
         with open(character_dict_path, "rb") as fin:
             lines = fin.readlines()
-            substr = lines[0].decode('utf-8').strip("\r\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("\r\n")
-                list_character.append(character)
-            for eno in range(1 + character_num, 1 + character_num + elem_num):
-                elem = lines[eno].decode('utf-8').strip("\r\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
+            for line in lines:
+                line = line.decode('utf-8').strip("\n").strip("\r\n")
+                dict_character.append(line)
+
+        dict_character = self.add_special_char(dict_character)
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+        self.idx2char = {v: k for k, v in self.dict.items()}
 
-    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
+        self.character = dict_character
+        self.point_num = point_num
+        self.pad_idx = self.dict[self.beg_str]
+        self.start_idx = self.dict[self.beg_str]
+        self.end_idx = self.dict[self.end_str]
+
+        self.td_token = ['<td>', '<td', '<eb></eb>', '<td></td>']
+        self.empty_bbox_token_dict = {
+            "[]": '<eb></eb>',
+            "[' ']": '<eb1></eb1>',
+            "['<b>', ' ', '</b>']": '<eb2></eb2>',
+            "['\\u2028', '\\u2028']": '<eb3></eb3>',
+            "['<sup>', ' ', '</sup>']": '<eb4></eb4>',
+            "['<b>', '</b>']": '<eb5></eb5>',
+            "['<i>', ' ', '</i>']": '<eb6></eb6>',
+            "['<b>', '<i>', '</i>', '</b>']": '<eb7></eb7>',
+            "['<b>', '<i>', ' ', '</i>', '</b>']": '<eb8></eb8>',
+            "['<i>', '</i>']": '<eb9></eb9>',
+            "['<b>', ' ', '\\u2028', ' ', '\\u2028', ' ', '</b>']":
+            '<eb10></eb10>',
+        }
+
+    @property
+    def _max_text_len(self):
+        return self.max_text_len + 2
 
     def __call__(self, data):
         cells = data['cells']
-        structure = data['structure']['tokens']
-        structure = self.encode(structure, 'elem')
+        structure = data['structure']
+        if self.merge_no_span_structure:
+            structure = self._merge_no_span_structure(structure)
+        if self.replace_empty_cell_token:
+            structure = self._replace_empty_cell_token(structure, cells)
+        # remove empty token and add " " to span token
+        new_structure = []
+        for token in structure:
+            if token != '':
+                if 'span' in token and token[0] != ' ':
+                    token = ' ' + token
+                new_structure.append(token)
+        # encode structure
+        structure = self.encode(new_structure)
         if structure is None:
             return None
-        elem_num = len(structure)
-        structure = [0] + structure + [len(self.dict_elem) - 1]
-        structure = structure + [0] * (self.max_elem_length + 2 - len(structure)
-                                       )
+
+        structure = [self.start_idx] + structure + [self.end_idx
+                                                    ]  # add sos abd eos
+        structure = structure + [self.pad_idx] * (self._max_text_len -
+                                                  len(structure))  # pad
         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
-
-        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
 
-    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:
+        if len(structure) > self._max_text_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]
+        # encode box
+        bboxes = np.zeros(
+            (self._max_text_len, self.point_num), dtype=np.float32)
+        bbox_masks = np.zeros((self._max_text_len, 1), dtype=np.float32)
+
+        bbox_idx = 0
+        for i, token in enumerate(structure):
+            if self.idx2char[token] in self.td_token:
+                if 'bbox' in cells[bbox_idx]:
+                    bbox = cells[bbox_idx]['bbox'].copy()
+                    bbox = np.array(bbox, dtype=np.float32).reshape(-1)
+                    bboxes[i] = bbox
+                    bbox_masks[i] = 1.0
+                if self.learn_empty_box:
+                    bbox_masks[i] = 1.0
+                bbox_idx += 1
+        data['bboxes'] = bboxes
+        data['bbox_masks'] = bbox_masks
+        return data
 
-    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])
+    def _merge_no_span_structure(self, structure):
+        new_structure = []
+        i = 0
+        while i < len(structure):
+            token = structure[i]
+            if token == '<td>':
+                token = '<td></td>'
+                i += 1
+            new_structure.append(token)
+            i += 1
+        return new_structure
+
+    def _replace_empty_cell_token(self, token_list, cells):
+        bbox_idx = 0
+        add_empty_bbox_token_list = []
+        for token in token_list:
+            if token in ['<td></td>', '<td', '<td>']:
+                if 'bbox' not in cells[bbox_idx].keys():
+                    content = str(cells[bbox_idx]['tokens'])
+                    token = self.empty_bbox_token_dict[content]
+                add_empty_bbox_token_list.append(token)
+                bbox_idx += 1
             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
+                add_empty_bbox_token_list.append(token)
+        return add_empty_bbox_token_list
+
+
+class TableMasterLabelEncode(TableLabelEncode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path,
+                 replace_empty_cell_token=False,
+                 merge_no_span_structure=False,
+                 learn_empty_box=False,
+                 point_num=4,
+                 **kwargs):
+        super(TableMasterLabelEncode, self).__init__(
+            max_text_length, character_dict_path, replace_empty_cell_token,
+            merge_no_span_structure, learn_empty_box, point_num, **kwargs)
+
+    @property
+    def _max_text_len(self):
+        return self.max_text_len
+
+    def add_special_char(self, dict_character):
+        self.beg_str = '<SOS>'
+        self.end_str = '<EOS>'
+        self.unknown_str = '<UKN>'
+        self.pad_str = '<PAD>'
+        dict_character = dict_character
+        dict_character = dict_character + [
+            self.unknown_str, self.beg_str, self.end_str, self.pad_str
+        ]
+        return dict_character
+
+
+class TableBoxEncode(object):
+    def __init__(self, use_xywh=False, **kwargs):
+        self.use_xywh = use_xywh
+
+    def __call__(self, data):
+        img_height, img_width = data['image'].shape[:2]
+        bboxes = data['bboxes']
+        if self.use_xywh and bboxes.shape[1] == 4:
+            bboxes = self.xyxy2xywh(bboxes)
+        bboxes[:, 0::2] /= img_width
+        bboxes[:, 1::2] /= img_height
+        data['bboxes'] = bboxes
+        return data
+
+    def xyxy2xywh(self, bboxes):
+        """
+        Convert coord (x1,y1,x2,y2) to (x,y,w,h).
+        where (x1,y1) is top-left, (x2,y2) is bottom-right.
+        (x,y) is bbox center and (w,h) is width and height.
+        :param bboxes: (x1, y1, x2, y2)
+        :return:
+        """
+        new_bboxes = np.empty_like(bboxes)
+        new_bboxes[:, 0] = (bboxes[:, 0] + bboxes[:, 2]) / 2  # x center
+        new_bboxes[:, 1] = (bboxes[:, 1] + bboxes[:, 3]) / 2  # y center
+        new_bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0]  # width
+        new_bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1]  # height
+        return new_bboxes
 
 
 class SARLabelEncode(BaseRecLabelEncode):
@@ -1030,7 +1058,6 @@ class MultiLabelEncode(BaseRecLabelEncode):
                                          use_space_char, **kwargs)
 
     def __call__(self, data):
-
         data_ctc = copy.deepcopy(data)
         data_sar = copy.deepcopy(data)
         data_out = dict()

ppocr/data/pubtab_dataset.py

@@ -16,6 +16,7 @@ import os
 import random
 from paddle.io import Dataset
 import json
+from copy import deepcopy
 
 from .imaug import transform, create_operators
 
@@ -29,33 +30,63 @@ class PubTabDataSet(Dataset):
         dataset_config = config[mode]['dataset']
         loader_config = config[mode]['loader']
 
-        label_file_path = dataset_config.pop('label_file_path')
+        label_file_list = dataset_config.pop('label_file_list')
+        data_source_num = len(label_file_list)
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+        if isinstance(ratio_list, (float, int)):
+            ratio_list = [float(ratio_list)] * int(data_source_num)
+
+        assert len(
+            ratio_list
+        ) == data_source_num, "The length of ratio_list should be the same as the file_list."
 
         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.mode = mode.lower()
+        logger.info("Initialize indexs of datasets:%s" % label_file_list)
+        self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
+        # self.check(config['Global']['max_text_length'])
+
+        if mode.lower() == "train" and self.do_shuffle:
             self.shuffle_data_random()
         self.ops = create_operators(dataset_config['transforms'], global_config)
-
-        ratio_list = dataset_config.get("ratio_list", [1.0])
         self.need_reset = True in [x < 1 for x in ratio_list]
 
+    def get_image_info_list(self, file_list, ratio_list):
+        if isinstance(file_list, str):
+            file_list = [file_list]
+        data_lines = []
+        for idx, file in enumerate(file_list):
+            with open(file, "rb") as f:
+                lines = f.readlines()
+                if self.mode == "train" or ratio_list[idx] < 1.0:
+                    random.seed(self.seed)
+                    lines = random.sample(lines,
+                                          round(len(lines) * ratio_list[idx]))
+                data_lines.extend(lines)
+        return data_lines
+
+    def check(self, max_text_length):
+        data_lines = []
+        for line in self.data_lines:
+            data_line = line.decode('utf-8').strip("\n")
+            info = json.loads(data_line)
+            file_name = info['filename']
+            cells = info['html']['cells'].copy()
+            structure = info['html']['structure']['tokens'].copy()
+
+            img_path = os.path.join(self.data_dir, file_name)
+            if not os.path.exists(img_path):
+                self.logger.warning("{} does not exist!".format(img_path))
+                continue
+            if len(structure) == 0 or len(structure) > max_text_length:
+                continue
+            # data = {'img_path': img_path, 'cells': cells, 'structure':structure,'file_name':file_name}
+            data_lines.append(line)
+        self.data_lines = data_lines
+
     def shuffle_data_random(self):
         if self.do_shuffle:
             random.seed(self.seed)
@@ -68,47 +99,34 @@ class PubTabDataSet(Dataset):
             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 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:
+            cells = info['html']['cells'].copy()
+            structure = info['html']['structure']['tokens'].copy()
+
+            img_path = os.path.join(self.data_dir, file_name)
+            if not os.path.exists(img_path):
+                raise Exception("{} does not exist!".format(img_path))
+            data = {
+                'img_path': img_path,
+                'cells': cells,
+                'structure': structure,
+                'file_name': file_name
+            }
+
+            with open(data['img_path'], 'rb') as f:
+                img = f.read()
+                data['image'] = img
+            outs = transform(data, self.ops)
+        except:
+            import traceback
+            err = traceback.format_exc()
             self.logger.error(
-                "When parsing line {}, error happened with msg: {}".format(
-                    data_line, e))
+                "When parsing line {}, error happened with msg: {}".format(err))
             outs = None
         if outs is None:
-            return self.__getitem__(np.random.randint(self.__len__()))
+            rnd_idx = np.random.randint(self.__len__(
+            )) if self.mode == "train" else (idx + 1) % self.__len__()
+            return self.__getitem__(rnd_idx)
         return outs
 
     def __len__(self):
-        return len(self.data_idx_order_list)
+        return len(self.data_lines)

ppocr/losses/__init__.py

@@ -51,7 +51,7 @@ from .combined_loss import CombinedLoss
 
 # table loss
 from .table_att_loss import TableAttentionLoss
-
+from .table_master_loss import TableMasterLoss
 # vqa token loss
 from .vqa_token_layoutlm_loss import VQASerTokenLayoutLMLoss
 
@@ -61,7 +61,8 @@ def build_loss(config):
         'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'FCELoss', 'CTCLoss',
         'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss',
         'NRTRLoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss',
-        'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss'
+        'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss',
+        'TableMasterLoss'
     ]
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/table_master_loss.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
+from paddle import nn
+
+
+class TableMasterLoss(nn.Layer):
+    def __init__(self, ignore_index=-1):
+        super(TableMasterLoss, self).__init__()
+        self.structure_loss = nn.CrossEntropyLoss(
+            ignore_index=ignore_index, reduction='mean')
+        self.box_loss = nn.L1Loss(reduction='sum')
+        self.eps = 1e-12
+
+    def forward(self, predicts, batch):
+        # structure_loss
+        structure_probs = predicts['structure_probs']
+        structure_targets = batch[1]
+        structure_targets = structure_targets[:, 1:]
+        structure_probs = structure_probs.reshape(
+            [-1, structure_probs.shape[-1]])
+        structure_targets = structure_targets.reshape([-1])
+
+        structure_loss = self.structure_loss(structure_probs, structure_targets)
+        structure_loss = structure_loss.mean()
+        losses = dict(structure_loss=structure_loss)
+
+        # box loss
+        bboxes_preds = predicts['loc_preds']
+        bboxes_targets = batch[2][:, 1:, :]
+        bbox_masks = batch[3][:, 1:]
+        # mask empty-bbox or non-bbox structure token's bbox.
+
+        masked_bboxes_preds = bboxes_preds * bbox_masks
+        masked_bboxes_targets = bboxes_targets * bbox_masks
+
+        # horizon loss (x and width)
+        horizon_sum_loss = self.box_loss(masked_bboxes_preds[:, :, 0::2],
+                                         masked_bboxes_targets[:, :, 0::2])
+        horizon_loss = horizon_sum_loss / (bbox_masks.sum() + self.eps)
+        # vertical loss (y and height)
+        vertical_sum_loss = self.box_loss(masked_bboxes_preds[:, :, 1::2],
+                                          masked_bboxes_targets[:, :, 1::2])
+        vertical_loss = vertical_sum_loss / (bbox_masks.sum() + self.eps)
+
+        horizon_loss = horizon_loss.mean()
+        vertical_loss = vertical_loss.mean()
+        all_loss = structure_loss + horizon_loss + vertical_loss
+        losses.update({
+            'loss': all_loss,
+            'horizon_bbox_loss': horizon_loss,
+            'vertical_bbox_loss': vertical_loss
+        })
+        return losses

ppocr/metrics/table_metric.py

@@ -12,29 +12,30 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import numpy as np
+from ppocr.metrics.det_metric import DetMetric
 
 
-class TableMetric(object):
-    def __init__(self, main_indicator='acc', **kwargs):
+class TableStructureMetric(object):
+    def __init__(self, main_indicator='acc', eps=1e-6, **kwargs):
         self.main_indicator = main_indicator
-        self.eps = 1e-5
+        self.eps = eps
         self.reset()
 
-    def __call__(self, pred, batch, *args, **kwargs):
-        structure_probs = pred['structure_probs'].numpy()
-        structure_labels = batch[1]
+    def __call__(self, pred_label, batch=None, *args, **kwargs):
+        preds, labels = pred_label
+        pred_structure_batch_list = preds['structure_batch_list']
+        gt_structure_batch_list = labels['structure_batch_list']
         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():
+        for (pred, pred_conf), target in zip(pred_structure_batch_list,
+                                             gt_structure_batch_list):
+            pred_str = ''.join(pred)
+            target_str = ''.join(target)
+            if pred_str == target_str:
                 correct_num += 1
+            all_num += 1
         self.correct_num += correct_num
         self.all_num += all_num
-        return {'acc': correct_num * 1.0 / (all_num + self.eps), }
 
     def get_metric(self):
         """
@@ -49,3 +50,91 @@ class TableMetric(object):
     def reset(self):
         self.correct_num = 0
         self.all_num = 0
+        self.len_acc_num = 0
+        self.token_nums = 0
+        self.anys_dict = dict()
+        from collections import defaultdict
+        self.error_num_dict = defaultdict(int)
+
+
+class TableMetric(object):
+    def __init__(self,
+                 main_indicator='acc',
+                 compute_bbox_metric=False,
+                 point_num=4,
+                 **kwargs):
+        """
+
+        @param sub_metrics: configs of sub_metric
+        @param main_matric: main_matric for save best_model
+        @param kwargs:
+        """
+        self.structure_metric = TableStructureMetric()
+        self.bbox_metric = DetMetric() if compute_bbox_metric else None
+        self.main_indicator = main_indicator
+        self.point_num = point_num
+        self.reset()
+
+    def __call__(self, pred_label, batch=None, *args, **kwargs):
+        self.structure_metric(pred_label)
+        if self.bbox_metric is not None:
+            self.bbox_metric(*self.prepare_bbox_metric_input(pred_label))
+
+    def prepare_bbox_metric_input(self, pred_label):
+        pred_bbox_batch_list = []
+        gt_ignore_tags_batch_list = []
+        gt_bbox_batch_list = []
+        preds, labels = pred_label
+
+        batch_num = len(preds['bbox_batch_list'])
+        for batch_idx in range(batch_num):
+            # pred
+            pred_bbox_list = [
+                self.format_box(pred_box)
+                for pred_box in preds['bbox_batch_list'][batch_idx]
+            ]
+            pred_bbox_batch_list.append({'points': pred_bbox_list})
+
+            # gt
+            gt_bbox_list = []
+            gt_ignore_tags_list = []
+            for gt_box in labels['bbox_batch_list'][batch_idx]:
+                gt_bbox_list.append(self.format_box(gt_box))
+                gt_ignore_tags_list.append(0)
+            gt_bbox_batch_list.append(gt_bbox_list)
+            gt_ignore_tags_batch_list.append(gt_ignore_tags_list)
+
+        return [
+            pred_bbox_batch_list,
+            [0, 0, gt_bbox_batch_list, gt_ignore_tags_batch_list]
+        ]
+
+    def get_metric(self):
+        structure_metric = self.structure_metric.get_metric()
+        if self.bbox_metric is None:
+            return structure_metric
+        bbox_metric = self.bbox_metric.get_metric()
+        if self.main_indicator == self.bbox_metric.main_indicator:
+            output = bbox_metric
+            for sub_key in structure_metric:
+                output["structure_metric_{}".format(
+                    sub_key)] = structure_metric[sub_key]
+        else:
+            output = structure_metric
+            for sub_key in bbox_metric:
+                output["bbox_metric_{}".format(sub_key)] = bbox_metric[sub_key]
+        return output
+
+    def reset(self):
+        self.structure_metric.reset()
+        if self.bbox_metric is not None:
+            self.bbox_metric.reset()
+
+    def format_box(self, box):
+        if self.point_num == 4:
+            x1, y1, x2, y2 = box
+            box = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
+        elif self.point_num == 8:
+            x1, y1, x2, y2, x3, y3, x4, y4 = box
+            box = [[x1, y1], [x2, y2], [x3, y3], [x4, y4]]
+        return box

ppocr/modeling/backbones/__init__.py

@@ -20,7 +20,10 @@ def build_backbone(config, model_type):
         from .det_mobilenet_v3 import MobileNetV3
         from .det_resnet_vd import ResNet
         from .det_resnet_vd_sast import ResNet_SAST
-        support_dict = ["MobileNetV3", "ResNet", "ResNet_SAST"]
+        from .table_master_resnet import TableResNetExtra
+        support_dict = [
+            "MobileNetV3", "ResNet", "ResNet_SAST", "TableResNetExtra"
+        ]
     elif model_type == "rec" or model_type == "cls":
         from .rec_mobilenet_v3 import MobileNetV3
         from .rec_resnet_vd import ResNet

ppocr/modeling/backbones/table_master_resnet.py

+# Copyright (c) 2022 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
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class BasicBlock(nn.Layer):
+    expansion = 1
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 stride=1,
+                 downsample=None,
+                 gcb_config=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2D(
+            inplanes,
+            planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias_attr=False)
+        self.bn1 = nn.BatchNorm2D(planes, momentum=0.9)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2D(
+            planes, planes, kernel_size=3, stride=1, padding=1, bias_attr=False)
+        self.bn2 = nn.BatchNorm2D(planes, momentum=0.9)
+        self.downsample = downsample
+        self.stride = stride
+        self.gcb_config = gcb_config
+
+        if self.gcb_config is not None:
+            gcb_ratio = gcb_config['ratio']
+            gcb_headers = gcb_config['headers']
+            att_scale = gcb_config['att_scale']
+            fusion_type = gcb_config['fusion_type']
+            self.context_block = MultiAspectGCAttention(
+                inplanes=planes,
+                ratio=gcb_ratio,
+                headers=gcb_headers,
+                att_scale=att_scale,
+                fusion_type=fusion_type)
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.gcb_config is not None:
+            out = self.context_block(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+def get_gcb_config(gcb_config, layer):
+    if gcb_config is None or not gcb_config['layers'][layer]:
+        return None
+    else:
+        return gcb_config
+
+
+class TableResNetExtra(nn.Layer):
+    def __init__(self, layers, in_channels=3, gcb_config=None):
+        assert len(layers) >= 4
+
+        super(TableResNetExtra, self).__init__()
+        self.inplanes = 128
+        self.conv1 = nn.Conv2D(
+            in_channels,
+            64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias_attr=False)
+        self.bn1 = nn.BatchNorm2D(64)
+        self.relu1 = nn.ReLU()
+
+        self.conv2 = nn.Conv2D(
+            64, 128, kernel_size=3, stride=1, padding=1, bias_attr=False)
+        self.bn2 = nn.BatchNorm2D(128)
+        self.relu2 = nn.ReLU()
+
+        self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2)
+
+        self.layer1 = self._make_layer(
+            BasicBlock,
+            256,
+            layers[0],
+            stride=1,
+            gcb_config=get_gcb_config(gcb_config, 0))
+
+        self.conv3 = nn.Conv2D(
+            256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False)
+        self.bn3 = nn.BatchNorm2D(256)
+        self.relu3 = nn.ReLU()
+
+        self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2)
+
+        self.layer2 = self._make_layer(
+            BasicBlock,
+            256,
+            layers[1],
+            stride=1,
+            gcb_config=get_gcb_config(gcb_config, 1))
+
+        self.conv4 = nn.Conv2D(
+            256, 256, kernel_size=3, stride=1, padding=1, bias_attr=False)
+        self.bn4 = nn.BatchNorm2D(256)
+        self.relu4 = nn.ReLU()
+
+        self.maxpool3 = nn.MaxPool2D(kernel_size=2, stride=2)
+
+        self.layer3 = self._make_layer(
+            BasicBlock,
+            512,
+            layers[2],
+            stride=1,
+            gcb_config=get_gcb_config(gcb_config, 2))
+
+        self.conv5 = nn.Conv2D(
+            512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False)
+        self.bn5 = nn.BatchNorm2D(512)
+        self.relu5 = nn.ReLU()
+
+        self.layer4 = self._make_layer(
+            BasicBlock,
+            512,
+            layers[3],
+            stride=1,
+            gcb_config=get_gcb_config(gcb_config, 3))
+
+        self.conv6 = nn.Conv2D(
+            512, 512, kernel_size=3, stride=1, padding=1, bias_attr=False)
+        self.bn6 = nn.BatchNorm2D(512)
+        self.relu6 = nn.ReLU()
+
+        self.out_channels = [256, 256, 512]
+
+    def _make_layer(self, block, planes, blocks, stride=1, gcb_config=None):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2D(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias_attr=False),
+                nn.BatchNorm2D(planes * block.expansion), )
+
+        layers = []
+        layers.append(
+            block(
+                self.inplanes,
+                planes,
+                stride,
+                downsample,
+                gcb_config=gcb_config))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        f = []
+        x = self.conv1(x)  # 1,64,480,480
+
+        x = self.bn1(x)
+        x = self.relu1(x)
+
+        x = self.conv2(x)  # 1,128,480,480
+        x = self.bn2(x)
+        x = self.relu2(x)
+        # (48, 160)
+
+        x = self.maxpool1(x)  # 1,64,240,240
+        x = self.layer1(x)
+
+        x = self.conv3(x)  # 1,256,240,240
+        x = self.bn3(x)
+        x = self.relu3(x)
+        f.append(x)
+        # (24, 80)
+
+        x = self.maxpool2(x)  # 1,256,120,120
+        x = self.layer2(x)
+
+        x = self.conv4(x)  # 1,256,120,120
+        x = self.bn4(x)
+        x = self.relu4(x)
+        f.append(x)
+        # (12, 40)
+
+        x = self.maxpool3(x)  # 1,256,60,60
+
+        x = self.layer3(x)  # 1,512,60,60
+        x = self.conv5(x)  # 1,512,60,60
+        x = self.bn5(x)
+        x = self.relu5(x)
+
+        x = self.layer4(x)  # 1,512,60,60
+        x = self.conv6(x)  # 1,512,60,60
+        x = self.bn6(x)
+        x = self.relu6(x)
+        f.append(x)
+        # (6, 40)
+        return f
+
+
+class MultiAspectGCAttention(nn.Layer):
+    def __init__(self,
+                 inplanes,
+                 ratio,
+                 headers,
+                 pooling_type='att',
+                 att_scale=False,
+                 fusion_type='channel_add'):
+        super(MultiAspectGCAttention, self).__init__()
+        assert pooling_type in ['avg', 'att']
+
+        assert fusion_type in ['channel_add', 'channel_mul', 'channel_concat']
+        assert inplanes % headers == 0 and inplanes >= 8  # inplanes must be divided by headers evenly
+
+        self.headers = headers
+        self.inplanes = inplanes
+        self.ratio = ratio
+        self.planes = int(inplanes * ratio)
+        self.pooling_type = pooling_type
+        self.fusion_type = fusion_type
+        self.att_scale = False
+
+        self.single_header_inplanes = int(inplanes / headers)
+
+        if pooling_type == 'att':
+            self.conv_mask = nn.Conv2D(
+                self.single_header_inplanes, 1, kernel_size=1)
+            self.softmax = nn.Softmax(axis=2)
+        else:
+            self.avg_pool = nn.AdaptiveAvgPool2D(1)
+
+        if fusion_type == 'channel_add':
+            self.channel_add_conv = nn.Sequential(
+                nn.Conv2D(
+                    self.inplanes, self.planes, kernel_size=1),
+                nn.LayerNorm([self.planes, 1, 1]),
+                nn.ReLU(),
+                nn.Conv2D(
+                    self.planes, self.inplanes, kernel_size=1))
+        elif fusion_type == 'channel_concat':
+            self.channel_concat_conv = nn.Sequential(
+                nn.Conv2D(
+                    self.inplanes, self.planes, kernel_size=1),
+                nn.LayerNorm([self.planes, 1, 1]),
+                nn.ReLU(),
+                nn.Conv2D(
+                    self.planes, self.inplanes, kernel_size=1))
+            # for concat
+            self.cat_conv = nn.Conv2D(
+                2 * self.inplanes, self.inplanes, kernel_size=1)
+        elif fusion_type == 'channel_mul':
+            self.channel_mul_conv = nn.Sequential(
+                nn.Conv2D(
+                    self.inplanes, self.planes, kernel_size=1),
+                nn.LayerNorm([self.planes, 1, 1]),
+                nn.ReLU(),
+                nn.Conv2D(
+                    self.planes, self.inplanes, kernel_size=1))
+
+    def spatial_pool(self, x):
+        batch, channel, height, width = x.shape
+        if self.pooling_type == 'att':
+            # [N*headers, C', H , W] C = headers * C'
+            x = x.reshape([
+                batch * self.headers, self.single_header_inplanes, height, width
+            ])
+            input_x = x
+
+            # [N*headers, C', H * W] C = headers * C'
+            # input_x = input_x.view(batch, channel, height * width)
+            input_x = input_x.reshape([
+                batch * self.headers, self.single_header_inplanes,
+                height * width
+            ])
+
+            # [N*headers, 1, C', H * W]
+            input_x = input_x.unsqueeze(1)
+            # [N*headers, 1, H, W]
+            context_mask = self.conv_mask(x)
+            # [N*headers, 1, H * W]
+            context_mask = context_mask.reshape(
+                [batch * self.headers, 1, height * width])
+
+            # scale variance
+            if self.att_scale and self.headers > 1:
+                context_mask = context_mask / paddle.sqrt(
+                    self.single_header_inplanes)
+
+            # [N*headers, 1, H * W]
+            context_mask = self.softmax(context_mask)
+
+            # [N*headers, 1, H * W, 1]
+            context_mask = context_mask.unsqueeze(-1)
+            # [N*headers, 1, C', 1] = [N*headers, 1, C', H * W] * [N*headers, 1, H * W, 1]
+            context = paddle.matmul(input_x, context_mask)
+
+            # [N, headers * C', 1, 1]
+            context = context.reshape(
+                [batch, self.headers * self.single_header_inplanes, 1, 1])
+        else:
+            # [N, C, 1, 1]
+            context = self.avg_pool(x)
+
+        return context
+
+    def forward(self, x):
+        # [N, C, 1, 1]
+        context = self.spatial_pool(x)
+
+        out = x
+
+        if self.fusion_type == 'channel_mul':
+            # [N, C, 1, 1]
+            channel_mul_term = F.sigmoid(self.channel_mul_conv(context))
+            out = out * channel_mul_term
+        elif self.fusion_type == 'channel_add':
+            # [N, C, 1, 1]
+            channel_add_term = self.channel_add_conv(context)
+            out = out + channel_add_term
+        else:
+            # [N, C, 1, 1]
+            channel_concat_term = self.channel_concat_conv(context)
+
+            # use concat
+            _, C1, _, _ = channel_concat_term.shape
+            N, C2, H, W = out.shape
+
+            out = paddle.concat(
+                [out, channel_concat_term.expand([-1, -1, H, W])], axis=1)
+            out = self.cat_conv(out)
+            out = F.layer_norm(out, [self.inplanes, H, W])
+            out = F.relu(out)
+
+        return out

ppocr/modeling/heads/__init__.py

@@ -41,12 +41,13 @@ def build_head(config):
     from .kie_sdmgr_head import SDMGRHead
 
     from .table_att_head import TableAttentionHead
+    from .table_master_head import TableMasterHead
 
     support_dict = [
         'DBHead', 'PSEHead', 'FCEHead', 'EASTHead', 'SASTHead', 'CTCHead',
         'ClsHead', 'AttentionHead', 'SRNHead', 'PGHead', 'Transformer',
         'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead', 'PRENHead',
-        'MultiHead'
+        'MultiHead', 'TableMasterHead'
     ]
 
     #table head

ppocr/modeling/heads/table_att_head.py

@@ -21,6 +21,8 @@ import paddle.nn as nn
 import paddle.nn.functional as F
 import numpy as np
 
+from .rec_att_head import AttentionGRUCell
+
 
 class TableAttentionHead(nn.Layer):
     def __init__(self,
@@ -28,17 +30,13 @@ class TableAttentionHead(nn.Layer):
                  hidden_size,
                  loc_type,
                  in_max_len=488,
-                 max_text_length=100,
-                 max_elem_length=800,
-                 max_cell_num=500,
+                 max_text_length=800,
                  **kwargs):
         super(TableAttentionHead, self).__init__()
         self.input_size = in_channels[-1]
         self.hidden_size = hidden_size
         self.elem_num = 30
         self.max_text_length = max_text_length
-        self.max_elem_length = max_elem_length
-        self.max_cell_num = max_cell_num
 
         self.structure_attention_cell = AttentionGRUCell(
             self.input_size, hidden_size, self.elem_num, use_gru=False)
@@ -50,11 +48,11 @@ class TableAttentionHead(nn.Layer):
             self.loc_generator = nn.Linear(hidden_size, 4)
         else:
             if self.in_max_len == 640:
-                self.loc_fea_trans = nn.Linear(400, self.max_elem_length + 1)
+                self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1)
             elif self.in_max_len == 800:
-                self.loc_fea_trans = nn.Linear(625, self.max_elem_length + 1)
+                self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1)
             else:
-                self.loc_fea_trans = nn.Linear(256, self.max_elem_length + 1)
+                self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1)
             self.loc_generator = nn.Linear(self.input_size + hidden_size, 4)
 
     def _char_to_onehot(self, input_char, onehot_dim):
@@ -77,7 +75,7 @@ class TableAttentionHead(nn.Layer):
         output_hiddens = []
         if self.training and targets is not None:
             structure = targets[0]
-            for i in range(self.max_elem_length + 1):
+            for i in range(self.max_text_length + 1):
                 elem_onehots = self._char_to_onehot(
                     structure[:, i], onehot_dim=self.elem_num)
                 (outputs, hidden), alpha = self.structure_attention_cell(
@@ -102,9 +100,9 @@ class TableAttentionHead(nn.Layer):
             elem_onehots = None
             outputs = None
             alpha = None
-            max_elem_length = paddle.to_tensor(self.max_elem_length)
+            max_text_length = paddle.to_tensor(self.max_text_length)
             i = 0
-            while i < max_elem_length + 1:
+            while i < max_text_length + 1:
                 elem_onehots = self._char_to_onehot(
                     temp_elem, onehot_dim=self.elem_num)
                 (outputs, hidden), alpha = self.structure_attention_cell(
@@ -128,119 +126,3 @@ class TableAttentionHead(nn.Layer):
                 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/heads/table_master_head.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 copy
+import math
+import paddle
+from paddle import nn
+from paddle.nn import functional as F
+
+
+class TableMasterHead(nn.Layer):
+    """
+    Split to two transformer header at the last layer.
+    Cls_layer is used to structure token classification.
+    Bbox_layer is used to regress bbox coord.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels=30,
+                 headers=8,
+                 d_ff=2048,
+                 dropout=0,
+                 max_text_length=500,
+                 point_num=4,
+                 **kwargs):
+        super(TableMasterHead, self).__init__()
+        hidden_size = in_channels[-1]
+        self.layers = clones(
+            DecoderLayer(headers, hidden_size, dropout, d_ff), 2)
+        self.cls_layer = clones(
+            DecoderLayer(headers, hidden_size, dropout, d_ff), 1)
+        self.bbox_layer = clones(
+            DecoderLayer(headers, hidden_size, dropout, d_ff), 1)
+        self.cls_fc = nn.Linear(hidden_size, out_channels)
+        self.bbox_fc = nn.Sequential(
+            # nn.Linear(hidden_size, hidden_size),
+            nn.Linear(hidden_size, point_num),
+            nn.Sigmoid())
+        self.norm = nn.LayerNorm(hidden_size)
+        self.embedding = Embeddings(d_model=hidden_size, vocab=out_channels)
+        self.positional_encoding = PositionalEncoding(d_model=hidden_size)
+
+        self.SOS = out_channels - 3
+        self.PAD = out_channels - 1
+        self.out_channels = out_channels
+        self.point_num = point_num
+        self.max_text_length = max_text_length
+
+    def make_mask(self, tgt):
+        """
+        Make mask for self attention.
+        :param src: [b, c, h, l_src]
+        :param tgt: [b, l_tgt]
+        :return:
+        """
+        trg_pad_mask = (tgt != self.PAD).unsqueeze(1).unsqueeze(3)
+
+        tgt_len = paddle.shape(tgt)[1]
+        trg_sub_mask = paddle.tril(
+            paddle.ones(
+                ([tgt_len, tgt_len]), dtype=paddle.float32))
+
+        tgt_mask = paddle.logical_and(
+            trg_pad_mask.astype(paddle.float32), trg_sub_mask)
+        return tgt_mask.astype(paddle.float32)
+
+    def decode(self, input, feature, src_mask, tgt_mask):
+        # main process of transformer decoder.
+        x = self.embedding(input)  # x: 1*x*512, feature: 1*3600,512
+        x = self.positional_encoding(x)
+
+        # origin transformer layers
+        for i, layer in enumerate(self.layers):
+            x = layer(x, feature, src_mask, tgt_mask)
+
+        # cls head
+        for layer in self.cls_layer:
+            cls_x = layer(x, feature, src_mask, tgt_mask)
+        cls_x = self.norm(cls_x)
+
+        # bbox head
+        for layer in self.bbox_layer:
+            bbox_x = layer(x, feature, src_mask, tgt_mask)
+        bbox_x = self.norm(bbox_x)
+        return self.cls_fc(cls_x), self.bbox_fc(bbox_x)
+
+    def greedy_forward(self, SOS, feature):
+        input = SOS
+        output = paddle.zeros(
+            [input.shape[0], self.max_text_length + 1, self.out_channels])
+        bbox_output = paddle.zeros(
+            [input.shape[0], self.max_text_length + 1, self.point_num])
+        max_text_length = paddle.to_tensor(self.max_text_length)
+        for i in range(max_text_length + 1):
+            target_mask = self.make_mask(input)
+            out_step, bbox_output_step = self.decode(input, feature, None,
+                                                     target_mask)
+            prob = F.softmax(out_step, axis=-1)
+            next_word = prob.argmax(axis=2, dtype="int64")
+            input = paddle.concat(
+                [input, next_word[:, -1].unsqueeze(-1)], axis=1)
+            if i == self.max_text_length:
+                output = out_step
+                bbox_output = bbox_output_step
+        return output, bbox_output
+
+    def forward_train(self, out_enc, targets):
+        # x is token of label
+        # feat is feature after backbone before pe.
+        # out_enc is feature after pe.
+        padded_targets = targets[0]
+        src_mask = None
+        tgt_mask = self.make_mask(padded_targets[:, :-1])
+        output, bbox_output = self.decode(padded_targets[:, :-1], out_enc,
+                                          src_mask, tgt_mask)
+        return {'structure_probs': output, 'loc_preds': bbox_output}
+
+    def forward_test(self, out_enc):
+        batch_size = out_enc.shape[0]
+        SOS = paddle.zeros([batch_size, 1], dtype='int64') + self.SOS
+        output, bbox_output = self.greedy_forward(SOS, out_enc)
+        # output = F.softmax(output)
+        return {'structure_probs': output, 'loc_preds': bbox_output}
+
+    def forward(self, feat, targets=None):
+        feat = feat[-1]
+        b, c, h, w = feat.shape
+        feat = feat.reshape([b, c, h * w])  # flatten 2D feature map
+        feat = feat.transpose((0, 2, 1))
+        out_enc = self.positional_encoding(feat)
+        if self.training:
+            return self.forward_train(out_enc, targets)
+
+        return self.forward_test(out_enc)
+
+
+class DecoderLayer(nn.Layer):
+    """
+    Decoder is made of self attention, srouce attention and feed forward.
+    """
+
+    def __init__(self, headers, d_model, dropout, d_ff):
+        super(DecoderLayer, self).__init__()
+        self.self_attn = MultiHeadAttention(headers, d_model, dropout)
+        self.src_attn = MultiHeadAttention(headers, d_model, dropout)
+        self.feed_forward = FeedForward(d_model, d_ff, dropout)
+        self.sublayer = clones(SubLayerConnection(d_model, dropout), 3)
+
+    def forward(self, x, feature, src_mask, tgt_mask):
+        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
+        x = self.sublayer[1](
+            x, lambda x: self.src_attn(x, feature, feature, src_mask))
+        return self.sublayer[2](x, self.feed_forward)
+
+
+class MultiHeadAttention(nn.Layer):
+    def __init__(self, headers, d_model, dropout):
+        super(MultiHeadAttention, self).__init__()
+
+        assert d_model % headers == 0
+        self.d_k = int(d_model / headers)
+        self.headers = headers
+        self.linears = clones(nn.Linear(d_model, d_model), 4)
+        self.attn = None
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, query, key, value, mask=None):
+        B = query.shape[0]
+
+        # 1) Do all the linear projections in batch from d_model => h x d_k
+        query, key, value = \
+            [l(x).reshape([B, 0, self.headers, self.d_k]).transpose([0, 2, 1, 3])
+             for l, x in zip(self.linears, (query, key, value))]
+        # 2) Apply attention on all the projected vectors in batch
+        x, self.attn = self_attention(
+            query, key, value, mask=mask, dropout=self.dropout)
+        x = x.transpose([0, 2, 1, 3]).reshape([B, 0, self.headers * self.d_k])
+        return self.linears[-1](x)
+
+
+class FeedForward(nn.Layer):
+    def __init__(self, d_model, d_ff, dropout):
+        super(FeedForward, self).__init__()
+        self.w_1 = nn.Linear(d_model, d_ff)
+        self.w_2 = nn.Linear(d_ff, d_model)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        return self.w_2(self.dropout(F.relu(self.w_1(x))))
+
+
+class SubLayerConnection(nn.Layer):
+    """
+    A residual connection followed by a layer norm.
+    Note for code simplicity the norm is first as opposed to last.
+    """
+
+    def __init__(self, size, dropout):
+        super(SubLayerConnection, self).__init__()
+        self.norm = nn.LayerNorm(size)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, sublayer):
+        return x + self.dropout(sublayer(self.norm(x)))
+
+
+def masked_fill(x, mask, value):
+    mask = mask.astype(x.dtype)
+    return x * paddle.logical_not(mask).astype(x.dtype) + mask * value
+
+
+def self_attention(query, key, value, mask=None, dropout=None):
+    """
+    Compute 'Scale Dot Product Attention'
+    """
+    d_k = value.shape[-1]
+
+    score = paddle.matmul(query, key.transpose([0, 1, 3, 2]) / math.sqrt(d_k))
+    if mask is not None:
+        # score = score.masked_fill(mask == 0, -1e9) # b, h, L, L
+        score = masked_fill(score, mask == 0, -6.55e4)  # for fp16
+
+    p_attn = F.softmax(score, axis=-1)
+
+    if dropout is not None:
+        p_attn = dropout(p_attn)
+    return paddle.matmul(p_attn, value), p_attn
+
+
+def clones(module, N):
+    """ Produce N identical layers """
+    return nn.LayerList([copy.deepcopy(module) for _ in range(N)])
+
+
+class Embeddings(nn.Layer):
+    def __init__(self, d_model, vocab):
+        super(Embeddings, self).__init__()
+        self.lut = nn.Embedding(vocab, d_model)
+        self.d_model = d_model
+
+    def forward(self, *input):
+        x = input[0]
+        return self.lut(x) * math.sqrt(self.d_model)
+
+
+class PositionalEncoding(nn.Layer):
+    """ Implement the PE function. """
+
+    def __init__(self, d_model, dropout=0., max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        self.dropout = nn.Dropout(p=dropout)
+
+        # Compute the positional encodings once in log space.
+        pe = paddle.zeros([max_len, d_model])
+        position = paddle.arange(0, max_len).unsqueeze(1).astype('float32')
+        div_term = paddle.exp(
+            paddle.arange(0, d_model, 2) * -math.log(10000.0) / d_model)
+        pe[:, 0::2] = paddle.sin(position * div_term)
+        pe[:, 1::2] = paddle.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, feat, **kwargs):
+        feat = feat + self.pe[:, :paddle.shape(feat)[1]]  # pe 1*5000*512
+        return self.dropout(feat)

ppocr/optimizer/learning_rate.py

@@ -308,3 +308,46 @@ class Const(object):
                 end_lr=self.learning_rate,
                 last_epoch=self.last_epoch)
         return learning_rate
+
+
+class MultiStepDecay(object):
+    """
+    Piecewise learning rate decay
+    Args:
+        step_each_epoch(int): steps each epoch
+        learning_rate (float): The initial learning rate. It is a python float number.
+        step_size (int): the interval to update.
+        gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` .
+            It should be less than 1.0. Default: 0.1.
+        last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
+    """
+
+    def __init__(self,
+                 learning_rate,
+                 milestones,
+                 step_each_epoch,
+                 gamma,
+                 warmup_epoch=0,
+                 last_epoch=-1,
+                 **kwargs):
+        super(MultiStepDecay, self).__init__()
+        self.milestones = [step_each_epoch * e for e in milestones]
+        self.learning_rate = learning_rate
+        self.gamma = gamma
+        self.last_epoch = last_epoch
+        self.warmup_epoch = round(warmup_epoch * step_each_epoch)
+
+    def __call__(self):
+        learning_rate = lr.MultiStepDecay(
+            learning_rate=self.learning_rate,
+            milestones=self.milestones,
+            gamma=self.gamma,
+            last_epoch=self.last_epoch)
+        if self.warmup_epoch > 0:
+            learning_rate = lr.LinearWarmup(
+                learning_rate=learning_rate,
+                warmup_steps=self.warmup_epoch,
+                start_lr=0.0,
+                end_lr=self.learning_rate,
+                last_epoch=self.last_epoch)
+        return learning_rate
\ No newline at end of file

ppocr/postprocess/__init__.py

@@ -26,8 +26,9 @@ from .east_postprocess import EASTPostProcess
 from .sast_postprocess import SASTPostProcess
 from .fce_postprocess import FCEPostProcess
 from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, \
-    DistillationCTCLabelDecode, TableLabelDecode, NRTRLabelDecode, SARLabelDecode, \
+    DistillationCTCLabelDecode, NRTRLabelDecode, SARLabelDecode, \
     SEEDLabelDecode, PRENLabelDecode
+from .table_postprocess import TableMasterLabelDecode, TableLabelDecode
 from .cls_postprocess import ClsPostProcess
 from .pg_postprocess import PGPostProcess
 from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess
@@ -42,7 +43,7 @@ def build_post_process(config, global_config=None):
         'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode',
         'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess',
         'VQAReTokenLayoutLMPostProcess', 'PRENLabelDecode',
-        'DistillationSARLabelDecode'
+        'DistillationSARLabelDecode', 'TableMasterLabelDecode'
     ]
 
     if config['name'] == 'PSEPostProcess':

ppocr/postprocess/rec_postprocess.py

@@ -444,146 +444,6 @@ class SRNLabelDecode(BaseRecLabelDecode):
         return idx
 
 
-class TableLabelDecode(object):
-    """  """
-
-    def __init__(self, character_dict_path, **kwargs):
-        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 = {}
-        self.dict_idx_character = {}
-        for i, char in enumerate(list_character):
-            self.dict_idx_character[i] = char
-            self.dict_character[char] = i
-        self.dict_elem = {}
-        self.dict_idx_elem = {}
-        for i, elem in enumerate(list_elem):
-            self.dict_idx_elem[i] = elem
-            self.dict_elem[elem] = i
-
-    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").strip("\r\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").strip("\r\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").strip("\r\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 __call__(self, preds):
-        structure_probs = preds['structure_probs']
-        loc_preds = preds['loc_preds']
-        if isinstance(structure_probs, paddle.Tensor):
-            structure_probs = structure_probs.numpy()
-        if isinstance(loc_preds, paddle.Tensor):
-            loc_preds = loc_preds.numpy()
-        structure_idx = structure_probs.argmax(axis=2)
-        structure_probs = structure_probs.max(axis=2)
-        structure_str, structure_pos, result_score_list, result_elem_idx_list = self.decode(
-            structure_idx, structure_probs, 'elem')
-        res_html_code_list = []
-        res_loc_list = []
-        batch_num = len(structure_str)
-        for bno in range(batch_num):
-            res_loc = []
-            for sno in range(len(structure_str[bno])):
-                text = structure_str[bno][sno]
-                if text in ['<td>', '<td']:
-                    pos = structure_pos[bno][sno]
-                    res_loc.append(loc_preds[bno, pos])
-            res_html_code = ''.join(structure_str[bno])
-            res_loc = np.array(res_loc)
-            res_html_code_list.append(res_html_code)
-            res_loc_list.append(res_loc)
-        return {
-            'res_html_code': res_html_code_list,
-            'res_loc': res_loc_list,
-            'res_score_list': result_score_list,
-            'res_elem_idx_list': result_elem_idx_list,
-            'structure_str_list': structure_str
-        }
-
-    def decode(self, text_index, structure_probs, char_or_elem):
-        """convert text-label into text-index.
-        """
-        if char_or_elem == "char":
-            current_dict = self.dict_idx_character
-        else:
-            current_dict = self.dict_idx_elem
-            ignored_tokens = self.get_ignored_tokens('elem')
-            beg_idx, end_idx = ignored_tokens
-
-        result_list = []
-        result_pos_list = []
-        result_score_list = []
-        result_elem_idx_list = []
-        batch_size = len(text_index)
-        for batch_idx in range(batch_size):
-            char_list = []
-            elem_pos_list = []
-            elem_idx_list = []
-            score_list = []
-            for idx in range(len(text_index[batch_idx])):
-                tmp_elem_idx = int(text_index[batch_idx][idx])
-                if idx > 0 and tmp_elem_idx == end_idx:
-                    break
-                if tmp_elem_idx in ignored_tokens:
-                    continue
-
-                char_list.append(current_dict[tmp_elem_idx])
-                elem_pos_list.append(idx)
-                score_list.append(structure_probs[batch_idx, idx])
-                elem_idx_list.append(tmp_elem_idx)
-            result_list.append(char_list)
-            result_pos_list.append(elem_pos_list)
-            result_score_list.append(score_list)
-            result_elem_idx_list.append(elem_idx_list)
-        return result_list, result_pos_list, result_score_list, result_elem_idx_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 = self.dict_character[self.beg_str]
-            elif beg_or_end == "end":
-                idx = 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 = self.dict_elem[self.beg_str]
-            elif beg_or_end == "end":
-                idx = 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
-
-
 class SARLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """
 

ppocr/postprocess/table_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 .rec_postprocess import AttnLabelDecode
+
+
+class TableLabelDecode(AttnLabelDecode):
+    """  """
+
+    def __init__(self, character_dict_path, **kwargs):
+        super(TableLabelDecode, self).__init__(character_dict_path)
+        self.td_token = ['<td>', '<td', '<eb></eb>', '<td></td>']
+
+    def __call__(self, preds, batch=None):
+        structure_probs = preds['structure_probs']
+        bbox_preds = preds['loc_preds']
+        if isinstance(structure_probs, paddle.Tensor):
+            structure_probs = structure_probs.numpy()
+        if isinstance(bbox_preds, paddle.Tensor):
+            bbox_preds = bbox_preds.numpy()
+        shape_list = batch[-1]
+        result = self.decode(structure_probs, bbox_preds, shape_list)
+        if len(batch) == 1:  # only contains shape
+            return result
+
+        label_decode_result = self.decode_label(batch)
+        return result, label_decode_result
+
+    def decode(self, structure_probs, bbox_preds, shape_list):
+        """convert text-label into text-index.
+        """
+        ignored_tokens = self.get_ignored_tokens()
+        end_idx = self.dict[self.end_str]
+
+        structure_idx = structure_probs.argmax(axis=2)
+        structure_probs = structure_probs.max(axis=2)
+
+        structure_batch_list = []
+        bbox_batch_list = []
+        batch_size = len(structure_idx)
+        for batch_idx in range(batch_size):
+            structure_list = []
+            bbox_list = []
+            score_list = []
+            for idx in range(len(structure_idx[batch_idx])):
+                char_idx = int(structure_idx[batch_idx][idx])
+                if idx > 0 and char_idx == end_idx:
+                    break
+                if char_idx in ignored_tokens:
+                    continue
+                text = self.character[char_idx]
+                if text in self.td_token:
+                    bbox = bbox_preds[batch_idx, idx]
+                    bbox = self._bbox_decode(bbox, shape_list[batch_idx])
+                    bbox_list.append(bbox)
+                structure_list.append(text)
+                score_list.append(structure_probs[batch_idx, idx])
+            structure_batch_list.append([structure_list, np.mean(score_list)])
+            bbox_batch_list.append(np.array(bbox_list))
+        result = {
+            'bbox_batch_list': bbox_batch_list,
+            'structure_batch_list': structure_batch_list,
+        }
+        return result
+
+    def decode_label(self, batch):
+        """convert text-label into text-index.
+        """
+        structure_idx = batch[1]
+        gt_bbox_list = batch[2]
+        shape_list = batch[-1]
+        ignored_tokens = self.get_ignored_tokens()
+        end_idx = self.dict[self.end_str]
+
+        structure_batch_list = []
+        bbox_batch_list = []
+        batch_size = len(structure_idx)
+        for batch_idx in range(batch_size):
+            structure_list = []
+            bbox_list = []
+            for idx in range(len(structure_idx[batch_idx])):
+                char_idx = int(structure_idx[batch_idx][idx])
+                if idx > 0 and char_idx == end_idx:
+                    break
+                if char_idx in ignored_tokens:
+                    continue
+                structure_list.append(self.character[char_idx])
+
+                bbox = gt_bbox_list[batch_idx][idx]
+                if bbox.sum() != 0:
+                    bbox = self._bbox_decode(bbox, shape_list[batch_idx])
+                    bbox_list.append(bbox)
+            structure_batch_list.append(structure_list)
+            bbox_batch_list.append(bbox_list)
+        result = {
+            'bbox_batch_list': bbox_batch_list,
+            'structure_batch_list': structure_batch_list,
+        }
+        return result
+
+    def _bbox_decode(self, bbox, shape):
+        h, w, ratio_h, ratio_w, pad_h, pad_w = shape
+        src_h = h / ratio_h
+        src_w = w / ratio_w
+        bbox[0::2] *= src_w
+        bbox[1::2] *= src_h
+        return bbox
+
+
+class TableMasterLabelDecode(TableLabelDecode):
+    """  """
+
+    def __init__(self, character_dict_path, box_shape='ori', **kwargs):
+        super(TableMasterLabelDecode, self).__init__(character_dict_path)
+        self.box_shape = box_shape
+        assert box_shape in [
+            'ori', 'pad'
+        ], 'The shape used for box normalization must be ori or pad'
+
+    def add_special_char(self, dict_character):
+        self.beg_str = '<SOS>'
+        self.end_str = '<EOS>'
+        self.unknown_str = '<UKN>'
+        self.pad_str = '<PAD>'
+        dict_character = dict_character
+        dict_character = dict_character + [
+            self.unknown_str, self.beg_str, self.end_str, self.pad_str
+        ]
+        return dict_character
+
+    def get_ignored_tokens(self):
+        pad_idx = self.dict[self.pad_str]
+        start_idx = self.dict[self.beg_str]
+        end_idx = self.dict[self.end_str]
+        unknown_idx = self.dict[self.unknown_str]
+        return [start_idx, end_idx, pad_idx, unknown_idx]
+
+    def _bbox_decode(self, bbox, shape):
+        h, w, ratio_h, ratio_w, pad_h, pad_w = shape
+        if self.box_shape == 'pad':
+            h, w = pad_h, pad_w
+        bbox[0::2] *= w
+        bbox[1::2] *= h
+        bbox[0::2] /= ratio_w
+        bbox[1::2] /= ratio_h
+        return bbox

ppocr/utils/dict/table_master_structure_dict.txt

+<thead>
+<tr>
+<td></td>
+</tr>
+</thead>
+<tbody>
+<eb></eb>
+</tbody>
+<td
+ colspan="5"
+>
+</td>
+ colspan="2"
+ colspan="3"
+<eb2></eb2>
+<eb1></eb1>
+ rowspan="2"
+ colspan="4"
+ colspan="6"
+ rowspan="3"
+ colspan="9"
+ colspan="10"
+ colspan="7"
+ rowspan="4"
+ rowspan="5"
+ rowspan="9"
+ colspan="8"
+ rowspan="8"
+ rowspan="6"
+ rowspan="7"
+ rowspan="10"
+<eb3></eb3>
+<eb4></eb4>
+<eb5></eb5>
+<eb6></eb6>
+<eb7></eb7>
+<eb8></eb8>
+<eb9></eb9>
+<eb10></eb10>

ppstructure/table/predict_structure.py

@@ -23,6 +23,7 @@ os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
 import cv2
 import numpy as np
 import time
+import json
 
 import tools.infer.utility as utility
 from ppocr.data import create_operators, transform
@@ -34,32 +35,50 @@ from ppstructure.utility import parse_args
 logger = get_logger()
 
 
+def build_pre_process_list(args):
+    resize_op = {'ResizeTableImage': {'max_len': args.table_max_len, }}
+    pad_op = {
+        'PaddingTableImage': {
+            'size': [args.table_max_len, args.table_max_len]
+        }
+    }
+    normalize_op = {
+        'NormalizeImage': {
+            'std': [0.229, 0.224, 0.225] if
+            args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5],
+            'mean': [0.485, 0.456, 0.406] if
+            args.table_algorithm not in ['TableMaster'] else [0.5, 0.5, 0.5],
+            'scale': '1./255.',
+            'order': 'hwc'
+        }
+    }
+    to_chw_op = {'ToCHWImage': None}
+    keep_keys_op = {'KeepKeys': {'keep_keys': ['image', 'shape']}}
+    if args.table_algorithm not in ['TableMaster']:
+        pre_process_list = [
+            resize_op, normalize_op, pad_op, to_chw_op, keep_keys_op
+        ]
+    else:
+        pre_process_list = [
+            resize_op, pad_op, normalize_op, to_chw_op, keep_keys_op
+        ]
+    return pre_process_list
+
+
 class TableStructurer(object):
     def __init__(self, args):
-        pre_process_list = [{
-            'ResizeTableImage': {
-                'max_len': args.table_max_len
+        pre_process_list = build_pre_process_list(args)
+        if args.table_algorithm not in ['TableMaster']:
+            postprocess_params = {
+                'name': 'TableLabelDecode',
+                "character_dict_path": args.table_char_dict_path,
             }
-        }, {
-            'NormalizeImage': {
-                'std': [0.229, 0.224, 0.225],
-                'mean': [0.485, 0.456, 0.406],
-                'scale': '1./255.',
-                'order': 'hwc'
+        else:
+            postprocess_params = {
+                'name': 'TableMasterLabelDecode',
+                "character_dict_path": args.table_char_dict_path,
+                'box_shape': 'pad'
             }
-        }, {
-            'PaddingTableImage': None
-        }, {
-            'ToCHWImage': None
-        }, {
-            'KeepKeys': {
-                'keep_keys': ['image']
-            }
-        }]
-        postprocess_params = {
-            'name': 'TableLabelDecode',
-            "character_dict_path": args.table_char_dict_path,
-        }
 
         self.preprocess_op = create_operators(pre_process_list)
         self.postprocess_op = build_post_process(postprocess_params)
@@ -88,27 +107,30 @@ class TableStructurer(object):
         preds['structure_probs'] = outputs[1]
         preds['loc_preds'] = outputs[0]
 
-        post_result = self.postprocess_op(preds)
-
-        structure_str_list = post_result['structure_str_list']
-        res_loc = post_result['res_loc']
-        imgh, imgw = ori_im.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)
-            res_loc_final.append([left, top, right, bottom])
-
-        structure_str_list = structure_str_list[0][:-1]
+        shape_list = np.expand_dims(data[-1], axis=0)
+        post_result = self.postprocess_op(preds, [shape_list])
+
+        structure_str_list = post_result['structure_batch_list'][0]
+        bbox_list = post_result['bbox_batch_list'][0]
+        structure_str_list = structure_str_list[0]
         structure_str_list = [
             '<html>', '<body>', '<table>'
         ] + structure_str_list + ['</table>', '</body>', '</html>']
-
         elapse = time.time() - starttime
-        return (structure_str_list, res_loc_final), elapse
+        return structure_str_list, bbox_list, elapse
+
+
+def draw_rectangle(img_path, boxes, use_xywh=False):
+    img = cv2.imread(img_path)
+    img_show = img.copy()
+    for box in boxes.astype(int):
+        if use_xywh:
+            x, y, w, h = box
+            x1, y1, x2, y2 = x - w // 2, y - h // 2, x + w // 2, y + h // 2
+        else:
+            x1, y1, x2, y2 = box
+        cv2.rectangle(img_show, (x1, y1), (x2, y2), (255, 0, 0), 2)
+    return img_show
 
 
 def main(args):
@@ -116,21 +138,35 @@ def main(args):
     table_structurer = TableStructurer(args)
     count = 0
     total_time = 0
-    for image_file in image_file_list:
-        img, flag = check_and_read_gif(image_file)
-        if not flag:
-            img = cv2.imread(image_file)
-        if img is None:
-            logger.info("error in loading image:{}".format(image_file))
-            continue
-        structure_res, elapse = table_structurer(img)
-
-        logger.info("result: {}".format(structure_res))
-
-        if count > 0:
-            total_time += elapse
-        count += 1
-        logger.info("Predict time of {}: {}".format(image_file, elapse))
+    use_xywh = args.table_algorithm in ['TableMaster']
+    os.makedirs(args.output, exist_ok=True)
+    with open(
+            os.path.join(args.output, 'infer.txt'), mode='w',
+            encoding='utf-8') as f_w:
+        for image_file in image_file_list:
+            img, flag = check_and_read_gif(image_file)
+            if not flag:
+                img = cv2.imread(image_file)
+            if img is None:
+                logger.info("error in loading image:{}".format(image_file))
+                continue
+            structure_str_list, bbox_list, elapse = table_structurer(img)
+
+            bbox_list_str = json.dumps(bbox_list.tolist())
+            logger.info("result: {}, {}".format(structure_str_list,
+                                                bbox_list_str))
+            f_w.write("result: {}, {}\n".format(structure_str_list,
+                                                bbox_list_str))
+
+            img = draw_rectangle(image_file, bbox_list, use_xywh)
+            img_save_path = os.path.join(args.output,
+                                         os.path.basename(image_file))
+            cv2.imwrite(img_save_path, img)
+            logger.info("save vis result to {}".format(img_save_path))
+            if count > 0:
+                total_time += elapse
+            count += 1
+            logger.info("Predict time of {}: {}".format(image_file, elapse))
 
 
 if __name__ == "__main__":

ppstructure/utility.py

@@ -25,6 +25,7 @@ def init_args():
     parser.add_argument("--output", type=str, default='./output')
     # params for table structure
     parser.add_argument("--table_max_len", type=int, default=488)
+    parser.add_argument("--table_algorithm", type=str, default='TableAttn')
     parser.add_argument("--table_model_dir", type=str)
     parser.add_argument(
         "--table_char_dict_path",
@@ -65,7 +66,7 @@ def init_args():
         "--recovery",
         type=bool,
         default=False,
-        help='Whether to enable layout of recovery')        
+        help='Whether to enable layout of recovery')
     return parser
 
 

tools/export_model.py

@@ -88,6 +88,8 @@ def export_single_model(model, arch_config, save_path, logger, quanter=None):
                 infer_shape = [1, 32, 100]
         elif arch_config["model_type"] == "table":
             infer_shape = [3, 488, 488]
+            if arch_config["algorithm"] == "TableMaster":
+                infer_shape = [3, 480, 480]
         model = to_static(
             model,
             input_spec=[

tools/infer_table.py

@@ -40,6 +40,7 @@ import tools.program as program
 import cv2
 
 
+@paddle.no_grad()
 def main(config, device, logger, vdl_writer):
     global_config = config['Global']
 
@@ -53,53 +54,74 @@ def main(config, device, logger, vdl_writer):
             getattr(post_process_class, 'character'))
 
     model = build_model(config['Architecture'])
+    algorithm = config['Architecture']['algorithm']
+    use_xywh = algorithm in ['TableMaster']
 
     load_model(config, model)
 
     # create data ops
     transforms = []
-    use_padding = False
     for op in config['Eval']['dataset']['transforms']:
         op_name = list(op)[0]
-        if 'Label' in op_name:
+        if 'Encode' 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']
+            op[op_name]['keep_keys'] = ['image', 'shape']
         transforms.append(op)
 
     global_config['infer_mode'] = True
     ops = create_operators(transforms, global_config)
 
+    save_res_path = config['Global']['save_res_path']
+    os.makedirs(save_res_path, exist_ok=True)
+
     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)
-        images = np.expand_dims(batch[0], axis=0)
-        images = paddle.to_tensor(images)
-        preds = model(images)
-        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!")
+    with open(
+            os.path.join(save_res_path, 'infer.txt'), mode='w',
+            encoding='utf-8') as f_w:
+        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)
+            images = np.expand_dims(batch[0], axis=0)
+            shape_list = np.expand_dims(batch[1], axis=0)
+
+            images = paddle.to_tensor(images)
+            preds = model(images)
+            post_result = post_process_class(preds, [shape_list])
+
+            structure_str_list = post_result['structure_batch_list'][0]
+            bbox_list = post_result['bbox_batch_list'][0]
+            structure_str_list = structure_str_list[0]
+            structure_str_list = [
+                '<html>', '<body>', '<table>'
+            ] + structure_str_list + ['</table>', '</body>', '</html>']
+            bbox_list_str = json.dumps(bbox_list.tolist())
+
+            logger.info("result: {}, {}".format(structure_str_list,
+                                                bbox_list_str))
+            f_w.write("result: {}, {}\n".format(structure_str_list,
+                                                bbox_list_str))
+
+            img = draw_rectangle(file, bbox_list, use_xywh)
+            cv2.imwrite(
+                os.path.join(save_res_path, os.path.basename(file)), img)
+        logger.info("success!")
+
+
+def draw_rectangle(img_path, boxes, use_xywh=False):
+    img = cv2.imread(img_path)
+    img_show = img.copy()
+    for box in boxes.astype(int):
+        if use_xywh:
+            x, y, w, h = box
+            x1, y1, x2, y2 = x - w // 2, y - h // 2, x + w // 2, y + h // 2
+        else:
+            x1, y1, x2, y2 = box
+        cv2.rectangle(img_show, (x1, y1), (x2, y2), (255, 0, 0), 2)
+    return img_show
 
 
 if __name__ == '__main__':

tools/program.py

@@ -274,8 +274,11 @@ def train(config,
 
             if cal_metric_during_train and epoch % calc_epoch_interval == 0:  # only rec and cls need
                 batch = [item.numpy() for item in batch]
-                if model_type in ['table', 'kie']:
+                if model_type in ['kie']:
                     eval_class(preds, batch)
+                elif model_type in ['table']:
+                    post_result = post_process_class(preds, batch)
+                    eval_class(post_result, batch)
                 else:
                     if config['Loss']['name'] in ['MultiLoss', 'MultiLoss_v2'
                                                   ]:  # for multi head loss
@@ -302,7 +305,8 @@ def train(config,
             train_stats.update(stats)
 
             if log_writer is not None and dist.get_rank() == 0:
-                log_writer.log_metrics(metrics=train_stats.get(), prefix="TRAIN", step=global_step)
+                log_writer.log_metrics(
+                    metrics=train_stats.get(), prefix="TRAIN", step=global_step)
 
             if dist.get_rank() == 0 and (
                 (global_step > 0 and global_step % print_batch_step == 0) or
@@ -349,7 +353,8 @@ def train(config,
 
                 # logger metric
                 if log_writer is not None:
-                    log_writer.log_metrics(metrics=cur_metric, prefix="EVAL", step=global_step)
+                    log_writer.log_metrics(
+                        metrics=cur_metric, prefix="EVAL", step=global_step)
 
                 if cur_metric[main_indicator] >= best_model_dict[
                         main_indicator]:
@@ -372,11 +377,18 @@ def train(config,
                 logger.info(best_str)
                 # logger best metric
                 if log_writer is not None:
-                    log_writer.log_metrics(metrics={
-                        "best_{}".format(main_indicator): best_model_dict[main_indicator]
-                        }, prefix="EVAL", step=global_step)
-                    
-                    log_writer.log_model(is_best=True, prefix="best_accuracy", metadata=best_model_dict)
+                    log_writer.log_metrics(
+                        metrics={
+                            "best_{}".format(main_indicator):
+                            best_model_dict[main_indicator]
+                        },
+                        prefix="EVAL",
+                        step=global_step)
+
+                    log_writer.log_model(
+                        is_best=True,
+                        prefix="best_accuracy",
+                        metadata=best_model_dict)
 
             reader_start = time.time()
         if dist.get_rank() == 0:
@@ -408,7 +420,8 @@ def train(config,
                 epoch=epoch,
                 global_step=global_step)
             if log_writer is not None:
-                log_writer.log_model(is_best=False, prefix='iter_epoch_{}'.format(epoch))
+                log_writer.log_model(
+                    is_best=False, prefix='iter_epoch_{}'.format(epoch))
 
     best_str = 'best metric, {}'.format(', '.join(
         ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
@@ -446,7 +459,6 @@ def eval(model,
                 preds = model(batch)
             else:
                 preds = model(images)
-
             batch_numpy = []
             for item in batch:
                 if isinstance(item, paddle.Tensor):
@@ -456,9 +468,9 @@ def eval(model,
             # Obtain usable results from post-processing methods
             total_time += time.time() - start
             # Evaluate the results of the current batch
-            if model_type in ['table', 'kie']:
+            if model_type in ['kie']:
                 eval_class(preds, batch_numpy)
-            elif model_type in ['vqa']:
+            elif model_type in ['table', 'vqa']:
                 post_result = post_process_class(preds, batch_numpy)
                 eval_class(post_result, batch_numpy)
             else:
@@ -559,7 +571,8 @@ def preprocess(is_train=False):
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
         'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
-        'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR'
+        'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'PREN', 'FCE', 'SVTR',
+        'TableMaster'
     ]
 
     device = 'cpu'
@@ -578,7 +591,8 @@ def preprocess(is_train=False):
         vdl_writer_path = '{}/vdl/'.format(save_model_dir)
         log_writer = VDLLogger(save_model_dir)
         loggers.append(log_writer)
-    if ('use_wandb' in config['Global'] and config['Global']['use_wandb']) or 'wandb' in config:
+    if ('use_wandb' in config['Global'] and
+            config['Global']['use_wandb']) or 'wandb' in config:
         save_dir = config['Global']['save_model_dir']
         wandb_writer_path = "{}/wandb".format(save_dir)
         if "wandb" in config: