From a0c33908d500fe893d8e79e11399a5ab665f330b Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Thu, 16 Jun 2022 13:24:38 +0000 Subject: [PATCH 01/32] add TableMaster --- configs/table/table_master.yml | 138 + configs/table/table_mv3.yml | 39 +- ppocr/data/imaug/gen_table_mask.py | 95 +- ppocr/data/imaug/label_ops.py | 315 +- ppocr/data/pubtab_dataset.py | 126 +- ppocr/losses/__init__.py | 5 +- ppocr/losses/table_master_loss.py | 65 + ppocr/metrics/table_metric.py | 115 +- ppocr/modeling/backbones/__init__.py | 5 +- .../modeling/backbones/table_master_resnet.py | 369 +++ ppocr/modeling/heads/__init__.py | 3 +- ppocr/modeling/heads/table_att_head.py | 136 +- ppocr/modeling/heads/table_master_head.py | 276 ++ ppocr/optimizer/learning_rate.py | 43 + ppocr/postprocess/__init__.py | 5 +- ppocr/postprocess/rec_postprocess.py | 140 - ppocr/postprocess/table_postprocess.py | 160 + .../dict/table_master_structure_dict.txt | 39 + ppocr/utils/dict/table_structure_dict.txt | 2733 +---------------- ppstructure/table/predict_structure.py | 138 +- ppstructure/utility.py | 3 +- tools/export_model.py | 2 + tools/infer_table.py | 86 +- tools/program.py | 42 +- 24 files changed, 1693 insertions(+), 3385 deletions(-) create mode 100755 configs/table/table_master.yml create mode 100644 ppocr/losses/table_master_loss.py create mode 100644 ppocr/modeling/backbones/table_master_resnet.py create mode 100644 ppocr/modeling/heads/table_master_head.py create mode 100644 ppocr/postprocess/table_postprocess.py create mode 100644 ppocr/utils/dict/table_master_structure_dict.txt diff --git a/configs/table/table_master.yml b/configs/table/table_master.yml new file mode 100755 index 0000000000..9dfc0e2746 --- /dev/null +++ b/configs/table/table_master.yml @@ -0,0 +1,138 @@ +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 diff --git a/configs/table/table_mv3.yml b/configs/table/table_mv3.yml index 1a91ea95af..9159addc31 100755 --- a/configs/table/table_mv3.yml +++ b/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 diff --git a/ppocr/data/imaug/gen_table_mask.py b/ppocr/data/imaug/gen_table_mask.py index 08e35d5d1d..8d139190ab 100644 --- a/ppocr/data/imaug/gen_table_mask.py +++ b/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): - height, width = img.shape[0:2] - ratio = 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 + height, width = img.shape[0:2] + ratio = self.max_len / (max(height, width) * 1.0) + resize_h = int(height * ratio) + resize_w = int(width * ratio) + 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 diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 02a5187dad..a55869a641 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/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 + 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 - def load_char_elem_dict(self, character_dict_path): - list_character = [] - list_elem = [] + 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 + for line in lines: + line = line.decode('utf-8').strip("\n").strip("\r\n") + dict_character.append(line) - 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 + 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 = ['', '', ''] + self.empty_bbox_token_dict = { + "[]": '', + "[' ']": '', + "['', ' ', '']": '', + "['\\u2028', '\\u2028']": '', + "['', ' ', '']": '', + "['', '']": '', + "['', ' ', '']": '', + "['', '', '', '']": '', + "['', '', ' ', '', '']": '', + "['', '']": '', + "['', ' ', '\\u2028', ' ', '\\u2028', ' ', '']": + '', + } + + @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[''] - elem_char_idx2 = self.dict_elem[' 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 + if len(structure) > self._max_text_len: + return None - 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 - ]) + # 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 encode(self, text, char_or_elem): - """convert text-label into text-index. + def _merge_no_span_structure(self, structure): + new_structure = [] + i = 0 + while i < len(structure): + token = structure[i] + if token == '': + token = '' + 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 ['', '']: + 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: + 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 = '' + self.end_str = '' + self.unknown_str = '' + self.pad_str = '' + 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): """ - if char_or_elem == "char": - max_len = self.max_text_length - current_dict = self.dict_character - else: - max_len = self.max_elem_length - current_dict = self.dict_elem - if len(text) > max_len: - return None - if len(text) == 0: - if char_or_elem == "char": - return [self.dict_character['space']] - else: - return None - text_list = [] - for char in text: - if char not in current_dict: - return None - text_list.append(current_dict[char]) - if len(text_list) == 0: - if char_or_elem == "char": - return [self.dict_character['space']] - else: - return None - return text_list - - def get_ignored_tokens(self, char_or_elem): - beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem) - end_idx = self.get_beg_end_flag_idx("end", char_or_elem) - return [beg_idx, end_idx] - - def get_beg_end_flag_idx(self, beg_or_end, char_or_elem): - if char_or_elem == "char": - if beg_or_end == "beg": - idx = np.array(self.dict_character[self.beg_str]) - elif beg_or_end == "end": - idx = np.array(self.dict_character[self.end_str]) - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \ - % beg_or_end - elif char_or_elem == "elem": - if beg_or_end == "beg": - idx = np.array(self.dict_elem[self.beg_str]) - elif beg_or_end == "end": - idx = np.array(self.dict_elem[self.end_str]) - else: - assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \ - % beg_or_end - else: - assert False, "Unsupport type %s in char_or_elem" \ - % char_or_elem - return idx + 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() diff --git a/ppocr/data/pubtab_dataset.py b/ppocr/data/pubtab_dataset.py index 671cda76fb..105f28db42 100644 --- a/ppocr/data/pubtab_dataset.py +++ b/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 + cells = info['html']['cells'].copy() + structure = info['html']['structure']['tokens'].copy() - 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 + 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 + } - 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: + 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) diff --git a/ppocr/losses/__init__.py b/ppocr/losses/__init__.py index de8419b7c1..0f208007b1 100755 --- a/ppocr/losses/__init__.py +++ b/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') diff --git a/ppocr/losses/table_master_loss.py b/ppocr/losses/table_master_loss.py new file mode 100644 index 0000000000..50a773dd9a --- /dev/null +++ b/ppocr/losses/table_master_loss.py @@ -0,0 +1,65 @@ +# 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 diff --git a/ppocr/metrics/table_metric.py b/ppocr/metrics/table_metric.py index ca4d647420..17f3dc92b2 100644 --- a/ppocr/metrics/table_metric.py +++ b/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 diff --git a/ppocr/modeling/backbones/__init__.py b/ppocr/modeling/backbones/__init__.py index 072d6e0f84..2b5fd9142e 100755 --- a/ppocr/modeling/backbones/__init__.py +++ b/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 diff --git a/ppocr/modeling/backbones/table_master_resnet.py b/ppocr/modeling/backbones/table_master_resnet.py new file mode 100644 index 0000000000..82b4f37a74 --- /dev/null +++ b/ppocr/modeling/backbones/table_master_resnet.py @@ -0,0 +1,369 @@ +# 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 diff --git a/ppocr/modeling/heads/__init__.py b/ppocr/modeling/heads/__init__.py index 1670ea38e6..da09e25c0e 100755 --- a/ppocr/modeling/heads/__init__.py +++ b/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 diff --git a/ppocr/modeling/heads/table_att_head.py b/ppocr/modeling/heads/table_att_head.py index e354f40d65..b64713898d 100644 --- a/ppocr/modeling/heads/table_att_head.py +++ b/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 diff --git a/ppocr/modeling/heads/table_master_head.py b/ppocr/modeling/heads/table_master_head.py new file mode 100644 index 0000000000..acd1a9145f --- /dev/null +++ b/ppocr/modeling/heads/table_master_head.py @@ -0,0 +1,276 @@ +# 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) diff --git a/ppocr/optimizer/learning_rate.py b/ppocr/optimizer/learning_rate.py index fe251f36e7..d96ab51896 100644 --- a/ppocr/optimizer/learning_rate.py +++ b/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 diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index f50b5f1c5f..4a08f1531f 100644 --- a/ppocr/postprocess/__init__.py +++ b/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': diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index bf0fd890bf..0d01b34210 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/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 ['', ' 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 """ diff --git a/ppocr/postprocess/table_postprocess.py b/ppocr/postprocess/table_postprocess.py new file mode 100644 index 0000000000..4396ec4f70 --- /dev/null +++ b/ppocr/postprocess/table_postprocess.py @@ -0,0 +1,160 @@ +# 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 = ['', '', ''] + + 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 = '' + self.end_str = '' + self.unknown_str = '' + self.pad_str = '' + 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 diff --git a/ppocr/utils/dict/table_master_structure_dict.txt b/ppocr/utils/dict/table_master_structure_dict.txt new file mode 100644 index 0000000000..95ab2539a7 --- /dev/null +++ b/ppocr/utils/dict/table_master_structure_dict.txt @@ -0,0 +1,39 @@ + + + + + + + + + + + colspan="2" + colspan="3" + + + 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" + + + + + + + + diff --git a/ppocr/utils/dict/table_structure_dict.txt b/ppocr/utils/dict/table_structure_dict.txt index 9c4531e5f3..8edb10b881 100644 --- a/ppocr/utils/dict/table_structure_dict.txt +++ b/ppocr/utils/dict/table_structure_dict.txt @@ -1,281 +1,3 @@ -277 28 1267 1186 - -V -a -r -i -b -l -e - -H -z -d - -t -o -9 -5 -% -C -I - -p - -v -u -* -A -g -( -m -n -) -0 -. -7 -1 -6 -≤ -> -8 -3 -– -2 -G -4 -M -F -T -y -f -s -L -w -c -U -h -D -S -Q -R -x -P -- -E -O -/ -k -, -+ -N -K -q -′ -[ -] -< -≥ - -− - -μ -± -J -j -W -_ -Δ -B -“ -: -Y -α -λ -; - - -? -∼ -= -° -# -̊ -̈ -̂ -’ -Z -X -∗ -— -β -' -† -~ -@ -" -γ -↓ -↑ -& -‡ -χ -” -σ -§ -| -¶ -‐ -× -$ -→ -√ -✓ -‘ -\ -∞ -π -• -® -^ -∆ -≧ - - -́ -♀ -♂ -‒ -⁎ -▲ -· -£ -φ -Ψ -ß -△ -☆ -▪ -η -€ -∧ -̃ -Φ -ρ -̄ -δ -‰ -̧ -Ω -♦ -{ -} -̀ -∑ -∫ -ø -κ -ε -¥ -※ -` -ω -Σ -➔ -‖ -Β -̸ -
 -─ -● -⩾ -Χ -Α -⋅ -◆ -★ -■ -ψ -ǂ -□ -ζ -! -Γ -↔ -θ -⁄ -〈 -〉 -― -υ -τ -⋆ -Ø -© -∥ -С -˂ -➢ -ɛ -⁡ -✗ -← -○ -¢ -⩽ -∖ -˃ -­ -≈ -Π -̌ -≦ -∅ -ᅟ - - -∣ -¤ -♯ -̆ -ξ -÷ -▼ - -ι -ν -║ - - -◦ -​ -◊ -∙ -« -» -ł -ı -Θ -∈ -„ -∘ -✔ -̇ -æ -ʹ -ˆ -♣ -⇓ -∩ -⊕ -⇒ -⇑ -̨ -Ι -Λ -⋯ -А -⋮ @@ -303,2457 +25,4 @@ $ rowspan="8" rowspan="6" rowspan="7" - rowspan="10" -0 2924682 -1 3405345 -2 2363468 -3 2709165 -4 4078680 -5 3250792 -6 1923159 -7 1617890 -8 1450532 -9 1717624 -10 1477550 -11 1489223 -12 915528 -13 819193 -14 593660 -15 518924 -16 682065 -17 494584 -18 400591 -19 396421 -20 340994 -21 280688 -22 250328 -23 226786 -24 199927 -25 182707 -26 164629 -27 141613 -28 127554 -29 116286 -30 107682 -31 96367 -32 88002 -33 79234 -34 72186 -35 65921 -36 60374 -37 55976 -38 52166 -39 47414 -40 44932 -41 41279 -42 38232 -43 35463 -44 33703 -45 30557 -46 29639 -47 27000 -48 25447 -49 23186 -50 22093 -51 20412 -52 19844 -53 18261 -54 17561 -55 16499 -56 15597 -57 14558 -58 14372 -59 13445 -60 13514 -61 12058 -62 11145 -63 10767 -64 10370 -65 9630 -66 9337 -67 8881 -68 8727 -69 8060 -70 7994 -71 7740 -72 7189 -73 6729 -74 6749 -75 6548 -76 6321 -77 5957 -78 5740 -79 5407 -80 5370 -81 5035 -82 4921 -83 4656 -84 4600 -85 4519 -86 4277 -87 4023 -88 3939 -89 3910 -90 3861 -91 3560 -92 3483 -93 3406 -94 3346 -95 3229 -96 3122 -97 3086 -98 3001 -99 2884 -100 2822 -101 2677 -102 2670 -103 2610 -104 2452 -105 2446 -106 2400 -107 2300 -108 2316 -109 2196 -110 2089 -111 2083 -112 2041 -113 1881 -114 1838 -115 1896 -116 1795 -117 1786 -118 1743 -119 1765 -120 1750 -121 1683 -122 1563 -123 1499 -124 1513 -125 1462 -126 1388 -127 1441 -128 1417 -129 1392 -130 1306 -131 1321 -132 1274 -133 1294 -134 1240 -135 1126 -136 1157 -137 1130 -138 1084 -139 1130 -140 1083 -141 1040 -142 980 -143 1031 -144 974 -145 980 -146 932 -147 898 -148 960 -149 907 -150 852 -151 912 -152 859 -153 847 -154 876 -155 792 -156 791 -157 765 -158 788 -159 787 -160 744 -161 673 -162 683 -163 697 -164 666 -165 680 -166 632 -167 677 -168 657 -169 618 -170 587 -171 585 -172 567 -173 549 -174 562 -175 548 -176 542 -177 539 -178 542 -179 549 -180 547 -181 526 -182 525 -183 514 -184 512 -185 505 -186 515 -187 467 -188 475 -189 458 -190 435 -191 443 -192 427 -193 424 -194 404 -195 389 -196 429 -197 404 -198 386 -199 351 -200 388 -201 408 -202 361 -203 346 -204 324 -205 361 -206 363 -207 364 -208 323 -209 336 -210 342 -211 315 -212 325 -213 328 -214 314 -215 327 -216 320 -217 300 -218 295 -219 315 -220 310 -221 295 -222 275 -223 248 -224 274 -225 232 -226 293 -227 259 -228 286 -229 263 -230 242 -231 214 -232 261 -233 231 -234 211 -235 250 -236 233 -237 206 -238 224 -239 210 -240 233 -241 223 -242 216 -243 222 -244 207 -245 212 -246 196 -247 205 -248 201 -249 202 -250 211 -251 201 -252 215 -253 179 -254 163 -255 179 -256 191 -257 188 -258 196 -259 150 -260 154 -261 176 -262 211 -263 166 -264 171 -265 165 -266 149 -267 182 -268 159 -269 161 -270 164 -271 161 -272 141 -273 151 -274 127 -275 129 -276 142 -277 158 -278 148 -279 135 -280 127 -281 134 -282 138 -283 131 -284 126 -285 125 -286 130 -287 126 -288 135 -289 125 -290 135 -291 131 -292 95 -293 135 -294 106 -295 117 -296 136 -297 128 -298 128 -299 118 -300 109 -301 112 -302 117 -303 108 -304 120 -305 100 -306 95 -307 108 -308 112 -309 77 -310 120 -311 104 -312 109 -313 89 -314 98 -315 82 -316 98 -317 93 -318 77 -319 93 -320 77 -321 98 -322 93 -323 86 -324 89 -325 73 -326 70 -327 71 -328 77 -329 87 -330 77 -331 93 -332 100 -333 83 -334 72 -335 74 -336 69 -337 77 -338 68 -339 78 -340 90 -341 98 -342 75 -343 80 -344 63 -345 71 -346 83 -347 66 -348 71 -349 70 -350 62 -351 62 -352 59 -353 63 -354 62 -355 52 -356 64 -357 64 -358 56 -359 49 -360 57 -361 63 -362 60 -363 68 -364 62 -365 55 -366 54 -367 40 -368 75 -369 70 -370 53 -371 58 -372 57 -373 55 -374 69 -375 57 -376 53 -377 43 -378 45 -379 47 -380 56 -381 51 -382 59 -383 51 -384 43 -385 34 -386 57 -387 49 -388 39 -389 46 -390 48 -391 43 -392 40 -393 54 -394 50 -395 41 -396 43 -397 33 -398 27 -399 49 -400 44 -401 44 -402 38 -403 30 -404 32 -405 37 -406 39 -407 42 -408 53 -409 39 -410 34 -411 31 -412 32 -413 52 -414 27 -415 41 -416 34 -417 36 -418 50 -419 35 -420 32 -421 33 -422 45 -423 35 -424 40 -425 29 -426 41 -427 40 -428 39 -429 32 -430 31 -431 34 -432 29 -433 27 -434 26 -435 22 -436 34 -437 28 -438 30 -439 38 -440 35 -441 36 -442 36 -443 27 -444 24 -445 33 -446 31 -447 25 -448 33 -449 27 -450 32 -451 46 -452 31 -453 35 -454 35 -455 34 -456 26 -457 21 -458 25 -459 26 -460 24 -461 27 -462 33 -463 30 -464 35 -465 21 -466 32 -467 19 -468 27 -469 16 -470 28 -471 26 -472 27 -473 26 -474 25 -475 25 -476 27 -477 20 -478 28 -479 22 -480 23 -481 16 -482 25 -483 27 -484 19 -485 23 -486 19 -487 15 -488 15 -489 23 -490 24 -491 19 -492 20 -493 18 -494 17 -495 30 -496 28 -497 20 -498 29 -499 17 -500 19 -501 21 -502 15 -503 24 -504 15 -505 19 -506 25 -507 16 -508 23 -509 26 -510 21 -511 15 -512 12 -513 16 -514 18 -515 24 -516 26 -517 18 -518 8 -519 25 -520 14 -521 8 -522 24 -523 20 -524 18 -525 15 -526 13 -527 17 -528 18 -529 22 -530 21 -531 9 -532 16 -533 17 -534 13 -535 17 -536 15 -537 13 -538 20 -539 13 -540 19 -541 29 -542 10 -543 8 -544 18 -545 13 -546 9 -547 18 -548 10 -549 18 -550 18 -551 9 -552 9 -553 15 -554 13 -555 15 -556 14 -557 14 -558 18 -559 8 -560 13 -561 9 -562 7 -563 12 -564 6 -565 9 -566 9 -567 18 -568 9 -569 10 -570 13 -571 14 -572 13 -573 21 -574 8 -575 16 -576 12 -577 9 -578 16 -579 17 -580 22 -581 6 -582 14 -583 13 -584 15 -585 11 -586 13 -587 5 -588 12 -589 13 -590 15 -591 13 -592 15 -593 12 -594 7 -595 18 -596 12 -597 13 -598 13 -599 13 -600 12 -601 12 -602 10 -603 11 -604 6 -605 6 -606 2 -607 9 -608 8 -609 12 -610 9 -611 12 -612 13 -613 12 -614 14 -615 9 -616 8 -617 9 -618 14 -619 13 -620 12 -621 6 -622 8 -623 8 -624 8 -625 12 -626 8 -627 7 -628 5 -629 8 -630 12 -631 6 -632 10 -633 10 -634 7 -635 8 -636 9 -637 6 -638 9 -639 4 -640 12 -641 4 -642 3 -643 11 -644 10 -645 6 -646 12 -647 12 -648 4 -649 4 -650 9 -651 8 -652 6 -653 5 -654 14 -655 10 -656 11 -657 8 -658 5 -659 5 -660 9 -661 13 -662 4 -663 5 -664 9 -665 11 -666 12 -667 7 -668 13 -669 2 -670 1 -671 7 -672 7 -673 7 -674 10 -675 9 -676 6 -677 5 -678 7 -679 6 -680 3 -681 3 -682 4 -683 9 -684 8 -685 5 -686 3 -687 11 -688 9 -689 2 -690 6 -691 5 -692 9 -693 5 -694 6 -695 5 -696 9 -697 8 -698 3 -699 7 -700 5 -701 9 -702 8 -703 7 -704 2 -705 3 -706 7 -707 6 -708 6 -709 10 -710 2 -711 10 -712 6 -713 7 -714 5 -715 6 -716 4 -717 6 -718 8 -719 4 -720 6 -721 7 -722 5 -723 7 -724 3 -725 10 -726 10 -727 3 -728 7 -729 7 -730 5 -731 2 -732 1 -733 5 -734 1 -735 5 -736 6 -737 2 -738 2 -739 3 -740 7 -741 2 -742 7 -743 4 -744 5 -745 4 -746 5 -747 3 -748 1 -749 4 -750 4 -751 2 -752 4 -753 6 -754 6 -755 6 -756 3 -757 2 -758 5 -759 5 -760 3 -761 4 -762 2 -763 1 -764 8 -765 3 -766 4 -767 3 -768 1 -769 5 -770 3 -771 3 -772 4 -773 4 -774 1 -775 3 -776 2 -777 2 -778 3 -779 3 -780 1 -781 4 -782 3 -783 4 -784 6 -785 3 -786 5 -787 4 -788 2 -789 4 -790 5 -791 4 -792 6 -794 4 -795 1 -796 1 -797 4 -798 2 -799 3 -800 3 -801 1 -802 5 -803 5 -804 3 -805 3 -806 3 -807 4 -808 4 -809 2 -811 5 -812 4 -813 6 -814 3 -815 2 -816 2 -817 3 -818 5 -819 3 -820 1 -821 1 -822 4 -823 3 -824 4 -825 8 -826 3 -827 5 -828 5 -829 3 -830 6 -831 3 -832 4 -833 8 -834 5 -835 3 -836 3 -837 2 -838 4 -839 2 -840 1 -841 3 -842 2 -843 1 -844 3 -846 4 -847 4 -848 3 -849 3 -850 2 -851 3 -853 1 -854 4 -855 4 -856 2 -857 4 -858 1 -859 2 -860 5 -861 1 -862 1 -863 4 -864 2 -865 2 -867 5 -868 1 -869 4 -870 1 -871 1 -872 1 -873 2 -875 5 -876 3 -877 1 -878 3 -879 3 -880 3 -881 2 -882 1 -883 6 -884 2 -885 2 -886 1 -887 1 -888 3 -889 2 -890 2 -891 3 -892 1 -893 3 -894 1 -895 5 -896 1 -897 3 -899 2 -900 2 -902 1 -903 2 -904 4 -905 4 -906 3 -907 1 -908 1 -909 2 -910 5 -911 2 -912 3 -914 1 -915 1 -916 2 -918 2 -919 2 -920 4 -921 4 -922 1 -923 1 -924 4 -925 5 -926 1 -928 2 -929 1 -930 1 -931 1 -932 1 -933 1 -934 2 -935 1 -936 1 -937 1 -938 2 -939 1 -941 1 -942 4 -944 2 -945 2 -946 2 -947 1 -948 1 -950 1 -951 2 -953 1 -954 2 -955 1 -956 1 -957 2 -958 1 -960 3 -962 4 -963 1 -964 1 -965 3 -966 2 -967 2 -968 1 -969 3 -970 3 -972 1 -974 4 -975 3 -976 3 -977 2 -979 2 -980 1 -981 1 -983 5 -984 1 -985 3 -986 1 -987 2 -988 4 -989 2 -991 2 -992 2 -993 1 -994 1 -996 2 -997 2 -998 1 -999 3 -1000 2 -1001 1 -1002 3 -1003 3 -1004 2 -1005 3 -1006 1 -1007 2 -1009 1 -1011 1 -1013 3 -1014 1 -1016 2 -1017 1 -1018 1 -1019 1 -1020 4 -1021 1 -1022 2 -1025 1 -1026 1 -1027 2 -1028 1 -1030 1 -1031 2 -1032 4 -1034 3 -1035 2 -1036 1 -1038 1 -1039 1 -1040 1 -1041 1 -1042 2 -1043 1 -1044 2 -1045 4 -1048 1 -1050 1 -1051 1 -1052 2 -1054 1 -1055 3 -1056 2 -1057 1 -1059 1 -1061 2 -1063 1 -1064 1 -1065 1 -1066 1 -1067 1 -1068 1 -1069 2 -1074 1 -1075 1 -1077 1 -1078 1 -1079 1 -1082 1 -1085 1 -1088 1 -1090 1 -1091 1 -1092 2 -1094 2 -1097 2 -1098 1 -1099 2 -1101 2 -1102 1 -1104 1 -1105 1 -1107 1 -1109 1 -1111 2 -1112 1 -1114 2 -1115 2 -1116 2 -1117 1 -1118 1 -1119 1 -1120 1 -1122 1 -1123 1 -1127 1 -1128 3 -1132 2 -1138 3 -1142 1 -1145 4 -1150 1 -1153 2 -1154 1 -1158 1 -1159 1 -1163 1 -1165 1 -1169 2 -1174 1 -1176 1 -1177 1 -1178 2 -1179 1 -1180 2 -1181 1 -1182 1 -1183 2 -1185 1 -1187 1 -1191 2 -1193 1 -1195 3 -1196 1 -1201 3 -1203 1 -1206 1 -1210 1 -1213 1 -1214 1 -1215 2 -1218 1 -1220 1 -1221 1 -1225 1 -1226 1 -1233 2 -1241 1 -1243 1 -1249 1 -1250 2 -1251 1 -1254 1 -1255 2 -1260 1 -1268 1 -1270 1 -1273 1 -1274 1 -1277 1 -1284 1 -1287 1 -1291 1 -1292 2 -1294 1 -1295 2 -1297 1 -1298 1 -1301 1 -1307 1 -1308 3 -1311 2 -1313 1 -1316 1 -1321 1 -1324 1 -1325 1 -1330 1 -1333 1 -1334 1 -1338 2 -1340 1 -1341 1 -1342 1 -1343 1 -1345 1 -1355 1 -1357 1 -1360 2 -1375 1 -1376 1 -1380 1 -1383 1 -1387 1 -1389 1 -1393 1 -1394 1 -1396 1 -1398 1 -1410 1 -1414 1 -1419 1 -1425 1 -1434 1 -1435 1 -1438 1 -1439 1 -1447 1 -1455 2 -1460 1 -1461 1 -1463 1 -1466 1 -1470 1 -1473 1 -1478 1 -1480 1 -1483 1 -1484 1 -1485 2 -1492 2 -1499 1 -1509 1 -1512 1 -1513 1 -1523 1 -1524 1 -1525 2 -1529 1 -1539 1 -1544 1 -1568 1 -1584 1 -1591 1 -1598 1 -1600 1 -1604 1 -1614 1 -1617 1 -1621 1 -1622 1 -1626 1 -1638 1 -1648 1 -1658 1 -1661 1 -1679 1 -1682 1 -1693 1 -1700 1 -1705 1 -1707 1 -1722 1 -1728 1 -1758 1 -1762 1 -1763 1 -1775 1 -1776 1 -1801 1 -1810 1 -1812 1 -1827 1 -1834 1 -1846 1 -1847 1 -1848 1 -1851 1 -1862 1 -1866 1 -1877 2 -1884 1 -1888 1 -1903 1 -1912 1 -1925 1 -1938 1 -1955 1 -1998 1 -2054 1 -2058 1 -2065 1 -2069 1 -2076 1 -2089 1 -2104 1 -2111 1 -2133 1 -2138 1 -2156 1 -2204 1 -2212 1 -2237 1 -2246 2 -2298 1 -2304 1 -2360 1 -2400 1 -2481 1 -2544 1 -2586 1 -2622 1 -2666 1 -2682 1 -2725 1 -2920 1 -3997 1 -4019 1 -5211 1 -12 19 -14 1 -16 401 -18 2 -20 421 -22 557 -24 625 -26 50 -28 4481 -30 52 -32 550 -34 5840 -36 4644 -38 87 -40 5794 -41 33 -42 571 -44 11805 -46 4711 -47 7 -48 597 -49 12 -50 678 -51 2 -52 14715 -53 3 -54 7322 -55 3 -56 508 -57 39 -58 3486 -59 11 -60 8974 -61 45 -62 1276 -63 4 -64 15693 -65 15 -66 657 -67 13 -68 6409 -69 10 -70 3188 -71 25 -72 1889 -73 27 -74 10370 -75 9 -76 12432 -77 23 -78 520 -79 15 -80 1534 -81 29 -82 2944 -83 23 -84 12071 -85 36 -86 1502 -87 10 -88 10978 -89 11 -90 889 -91 16 -92 4571 -93 17 -94 7855 -95 21 -96 2271 -97 33 -98 1423 -99 15 -100 11096 -101 21 -102 4082 -103 13 -104 5442 -105 25 -106 2113 -107 26 -108 3779 -109 43 -110 1294 -111 29 -112 7860 -113 29 -114 4965 -115 22 -116 7898 -117 25 -118 1772 -119 28 -120 1149 -121 38 -122 1483 -123 32 -124 10572 -125 25 -126 1147 -127 31 -128 1699 -129 22 -130 5533 -131 22 -132 4669 -133 34 -134 3777 -135 10 -136 5412 -137 21 -138 855 -139 26 -140 2485 -141 46 -142 1970 -143 27 -144 6565 -145 40 -146 933 -147 15 -148 7923 -149 16 -150 735 -151 23 -152 1111 -153 33 -154 3714 -155 27 -156 2445 -157 30 -158 3367 -159 10 -160 4646 -161 27 -162 990 -163 23 -164 5679 -165 25 -166 2186 -167 17 -168 899 -169 32 -170 1034 -171 22 -172 6185 -173 32 -174 2685 -175 17 -176 1354 -177 38 -178 1460 -179 15 -180 3478 -181 20 -182 958 -183 20 -184 6055 -185 23 -186 2180 -187 15 -188 1416 -189 30 -190 1284 -191 22 -192 1341 -193 21 -194 2413 -195 18 -196 4984 -197 13 -198 830 -199 22 -200 1834 -201 19 -202 2238 -203 9 -204 3050 -205 22 -206 616 -207 17 -208 2892 -209 22 -210 711 -211 30 -212 2631 -213 19 -214 3341 -215 21 -216 987 -217 26 -218 823 -219 9 -220 3588 -221 20 -222 692 -223 7 -224 2925 -225 31 -226 1075 -227 16 -228 2909 -229 18 -230 673 -231 20 -232 2215 -233 14 -234 1584 -235 21 -236 1292 -237 29 -238 1647 -239 25 -240 1014 -241 30 -242 1648 -243 19 -244 4465 -245 10 -246 787 -247 11 -248 480 -249 25 -250 842 -251 15 -252 1219 -253 23 -254 1508 -255 8 -256 3525 -257 16 -258 490 -259 12 -260 1678 -261 14 -262 822 -263 16 -264 1729 -265 28 -266 604 -267 11 -268 2572 -269 7 -270 1242 -271 15 -272 725 -273 18 -274 1983 -275 13 -276 1662 -277 19 -278 491 -279 12 -280 1586 -281 14 -282 563 -283 10 -284 2363 -285 10 -286 656 -287 14 -288 725 -289 28 -290 871 -291 9 -292 2606 -293 12 -294 961 -295 9 -296 478 -297 13 -298 1252 -299 10 -300 736 -301 19 -302 466 -303 13 -304 2254 -305 12 -306 486 -307 14 -308 1145 -309 13 -310 955 -311 13 -312 1235 -313 13 -314 931 -315 14 -316 1768 -317 11 -318 330 -319 10 -320 539 -321 23 -322 570 -323 12 -324 1789 -325 13 -326 884 -327 5 -328 1422 -329 14 -330 317 -331 11 -332 509 -333 13 -334 1062 -335 12 -336 577 -337 27 -338 378 -339 10 -340 2313 -341 9 -342 391 -343 13 -344 894 -345 17 -346 664 -347 9 -348 453 -349 6 -350 363 -351 15 -352 1115 -353 13 -354 1054 -355 8 -356 1108 -357 12 -358 354 -359 7 -360 363 -361 16 -362 344 -363 11 -364 1734 -365 12 -366 265 -367 10 -368 969 -369 16 -370 316 -371 12 -372 757 -373 7 -374 563 -375 15 -376 857 -377 9 -378 469 -379 9 -380 385 -381 12 -382 921 -383 15 -384 764 -385 14 -386 246 -387 6 -388 1108 -389 14 -390 230 -391 8 -392 266 -393 11 -394 641 -395 8 -396 719 -397 9 -398 243 -399 4 -400 1108 -401 7 -402 229 -403 7 -404 903 -405 7 -406 257 -407 12 -408 244 -409 3 -410 541 -411 6 -412 744 -413 8 -414 419 -415 8 -416 388 -417 19 -418 470 -419 14 -420 612 -421 6 -422 342 -423 3 -424 1179 -425 3 -426 116 -427 14 -428 207 -429 6 -430 255 -431 4 -432 288 -433 12 -434 343 -435 6 -436 1015 -437 3 -438 538 -439 10 -440 194 -441 6 -442 188 -443 15 -444 524 -445 7 -446 214 -447 7 -448 574 -449 6 -450 214 -451 5 -452 635 -453 9 -454 464 -455 5 -456 205 -457 9 -458 163 -459 2 -460 558 -461 4 -462 171 -463 14 -464 444 -465 11 -466 543 -467 5 -468 388 -469 6 -470 141 -471 4 -472 647 -473 3 -474 210 -475 4 -476 193 -477 7 -478 195 -479 7 -480 443 -481 10 -482 198 -483 3 -484 816 -485 6 -486 128 -487 9 -488 215 -489 9 -490 328 -491 7 -492 158 -493 11 -494 335 -495 8 -496 435 -497 6 -498 174 -499 1 -500 373 -501 5 -502 140 -503 7 -504 330 -505 9 -506 149 -507 5 -508 642 -509 3 -510 179 -511 3 -512 159 -513 8 -514 204 -515 7 -516 306 -517 4 -518 110 -519 5 -520 326 -521 6 -522 305 -523 6 -524 294 -525 7 -526 268 -527 5 -528 149 -529 4 -530 133 -531 2 -532 513 -533 10 -534 116 -535 5 -536 258 -537 4 -538 113 -539 4 -540 138 -541 6 -542 116 -544 485 -545 4 -546 93 -547 9 -548 299 -549 3 -550 256 -551 6 -552 92 -553 3 -554 175 -555 6 -556 253 -557 7 -558 95 -559 2 -560 128 -561 4 -562 206 -563 2 -564 465 -565 3 -566 69 -567 3 -568 157 -569 7 -570 97 -571 8 -572 118 -573 5 -574 130 -575 4 -576 301 -577 6 -578 177 -579 2 -580 397 -581 3 -582 80 -583 1 -584 128 -585 5 -586 52 -587 2 -588 72 -589 1 -590 84 -591 6 -592 323 -593 11 -594 77 -595 5 -596 205 -597 1 -598 244 -599 4 -600 69 -601 3 -602 89 -603 5 -604 254 -605 6 -606 147 -607 3 -608 83 -609 3 -610 77 -611 3 -612 194 -613 1 -614 98 -615 3 -616 243 -617 3 -618 50 -619 8 -620 188 -621 4 -622 67 -623 4 -624 123 -625 2 -626 50 -627 1 -628 239 -629 2 -630 51 -631 4 -632 65 -633 5 -634 188 -636 81 -637 3 -638 46 -639 3 -640 103 -641 1 -642 136 -643 3 -644 188 -645 3 -646 58 -648 122 -649 4 -650 47 -651 2 -652 155 -653 4 -654 71 -655 1 -656 71 -657 3 -658 50 -659 2 -660 177 -661 5 -662 66 -663 2 -664 183 -665 3 -666 50 -667 2 -668 53 -669 2 -670 115 -672 66 -673 2 -674 47 -675 1 -676 197 -677 2 -678 46 -679 3 -680 95 -681 3 -682 46 -683 3 -684 107 -685 1 -686 86 -687 2 -688 158 -689 4 -690 51 -691 1 -692 80 -694 56 -695 4 -696 40 -698 43 -699 3 -700 95 -701 2 -702 51 -703 2 -704 133 -705 1 -706 100 -707 2 -708 121 -709 2 -710 15 -711 3 -712 35 -713 2 -714 20 -715 3 -716 37 -717 2 -718 78 -720 55 -721 1 -722 42 -723 2 -724 218 -725 3 -726 23 -727 2 -728 26 -729 1 -730 64 -731 2 -732 65 -734 24 -735 2 -736 53 -737 1 -738 32 -739 1 -740 60 -742 81 -743 1 -744 77 -745 1 -746 47 -747 1 -748 62 -749 1 -750 19 -751 1 -752 86 -753 3 -754 40 -756 55 -757 2 -758 38 -759 1 -760 101 -761 1 -762 22 -764 67 -765 2 -766 35 -767 1 -768 38 -769 1 -770 22 -771 1 -772 82 -773 1 -774 73 -776 29 -777 1 -778 55 -780 23 -781 1 -782 16 -784 84 -785 3 -786 28 -788 59 -789 1 -790 33 -791 3 -792 24 -794 13 -795 1 -796 110 -797 2 -798 15 -800 22 -801 3 -802 29 -803 1 -804 87 -806 21 -808 29 -810 48 -812 28 -813 1 -814 58 -815 1 -816 48 -817 1 -818 31 -819 1 -820 66 -822 17 -823 2 -824 58 -826 10 -827 2 -828 25 -829 1 -830 29 -831 1 -832 63 -833 1 -834 26 -835 3 -836 52 -837 1 -838 18 -840 27 -841 2 -842 12 -843 1 -844 83 -845 1 -846 7 -847 1 -848 10 -850 26 -852 25 -853 1 -854 15 -856 27 -858 32 -859 1 -860 15 -862 43 -864 32 -865 1 -866 6 -868 39 -870 11 -872 25 -873 1 -874 10 -875 1 -876 20 -877 2 -878 19 -879 1 -880 30 -882 11 -884 53 -886 25 -887 1 -888 28 -890 6 -892 36 -894 10 -896 13 -898 14 -900 31 -902 14 -903 2 -904 43 -906 25 -908 9 -910 11 -911 1 -912 16 -913 1 -914 24 -916 27 -918 6 -920 15 -922 27 -923 1 -924 23 -926 13 -928 42 -929 1 -930 3 -932 27 -934 17 -936 8 -937 1 -938 11 -940 33 -942 4 -943 1 -944 18 -946 15 -948 13 -950 18 -952 12 -954 11 -956 21 -958 10 -960 13 -962 5 -964 32 -966 13 -968 8 -970 8 -971 1 -972 23 -973 2 -974 12 -975 1 -976 22 -978 7 -979 1 -980 14 -982 8 -984 22 -985 1 -986 6 -988 17 -989 1 -990 6 -992 13 -994 19 -996 11 -998 4 -1000 9 -1002 2 -1004 14 -1006 5 -1008 3 -1010 9 -1012 29 -1014 6 -1016 22 -1017 1 -1018 8 -1019 1 -1020 7 -1022 6 -1023 1 -1024 10 -1026 2 -1028 8 -1030 11 -1031 2 -1032 8 -1034 9 -1036 13 -1038 12 -1040 12 -1042 3 -1044 12 -1046 3 -1048 11 -1050 2 -1051 1 -1052 2 -1054 11 -1056 6 -1058 8 -1059 1 -1060 23 -1062 6 -1063 1 -1064 8 -1066 3 -1068 6 -1070 8 -1071 1 -1072 5 -1074 3 -1076 5 -1078 3 -1080 11 -1081 1 -1082 7 -1084 18 -1086 4 -1087 1 -1088 3 -1090 3 -1092 7 -1094 3 -1096 12 -1098 6 -1099 1 -1100 2 -1102 6 -1104 14 -1106 3 -1108 6 -1110 5 -1112 2 -1114 8 -1116 3 -1118 3 -1120 7 -1122 10 -1124 6 -1126 8 -1128 1 -1130 4 -1132 3 -1134 2 -1136 5 -1138 5 -1140 8 -1142 3 -1144 7 -1146 3 -1148 11 -1150 1 -1152 5 -1154 1 -1156 5 -1158 1 -1160 5 -1162 3 -1164 6 -1165 1 -1166 1 -1168 4 -1169 1 -1170 3 -1171 1 -1172 2 -1174 5 -1176 3 -1177 1 -1180 8 -1182 2 -1184 4 -1186 2 -1188 3 -1190 2 -1192 5 -1194 6 -1196 1 -1198 2 -1200 2 -1204 10 -1206 2 -1208 9 -1210 1 -1214 6 -1216 3 -1218 4 -1220 9 -1221 2 -1222 1 -1224 5 -1226 4 -1228 8 -1230 1 -1232 1 -1234 3 -1236 5 -1240 3 -1242 1 -1244 3 -1245 1 -1246 4 -1248 6 -1250 2 -1252 7 -1256 3 -1258 2 -1260 2 -1262 3 -1264 4 -1265 1 -1266 1 -1270 1 -1271 1 -1272 2 -1274 3 -1276 3 -1278 1 -1280 3 -1284 1 -1286 1 -1290 1 -1292 3 -1294 1 -1296 7 -1300 2 -1302 4 -1304 3 -1306 2 -1308 2 -1312 1 -1314 1 -1316 3 -1318 2 -1320 1 -1324 8 -1326 1 -1330 1 -1331 1 -1336 2 -1338 1 -1340 3 -1341 1 -1344 1 -1346 2 -1347 1 -1348 3 -1352 1 -1354 2 -1356 1 -1358 1 -1360 3 -1362 1 -1364 4 -1366 1 -1370 1 -1372 3 -1380 2 -1384 2 -1388 2 -1390 2 -1392 2 -1394 1 -1396 1 -1398 1 -1400 2 -1402 1 -1404 1 -1406 1 -1410 1 -1412 5 -1418 1 -1420 1 -1424 1 -1432 2 -1434 2 -1442 3 -1444 5 -1448 1 -1454 1 -1456 1 -1460 3 -1462 4 -1468 1 -1474 1 -1476 1 -1478 2 -1480 1 -1486 2 -1488 1 -1492 1 -1496 1 -1500 3 -1503 1 -1506 1 -1512 2 -1516 1 -1522 1 -1524 2 -1534 4 -1536 1 -1538 1 -1540 2 -1544 2 -1548 1 -1556 1 -1560 1 -1562 1 -1564 2 -1566 1 -1568 1 -1570 1 -1572 1 -1576 1 -1590 1 -1594 1 -1604 1 -1608 1 -1614 1 -1622 1 -1624 2 -1628 1 -1629 1 -1636 1 -1642 1 -1654 2 -1660 1 -1664 1 -1670 1 -1684 4 -1698 1 -1732 3 -1742 1 -1752 1 -1760 1 -1764 1 -1772 2 -1798 1 -1808 1 -1820 1 -1852 1 -1856 1 -1874 1 -1902 1 -1908 1 -1952 1 -2004 1 -2018 1 -2020 1 -2028 1 -2174 1 -2233 1 -2244 1 -2280 1 -2290 1 -2352 1 -2604 1 -4190 1 + rowspan="10" \ No newline at end of file diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py index 0179c614ae..17ec909582 100755 --- a/ppstructure/table/predict_structure.py +++ b/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) + shape_list = np.expand_dims(data[-1], axis=0) + post_result = self.postprocess_op(preds, [shape_list]) - 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] + 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 = [ '', '', '' ] + structure_str_list + ['
', '', ''] - 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) + 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) - logger.info("result: {}".format(structure_res)) + 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)) - if count > 0: - total_time += elapse - count += 1 - logger.info("Predict time of {}: {}".format(image_file, elapse)) + 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__": diff --git a/ppstructure/utility.py b/ppstructure/utility.py index 1ad902e7e6..05452c23b5 100644 --- a/ppstructure/utility.py +++ b/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 diff --git a/tools/export_model.py b/tools/export_model.py index e971f6cb20..fbb2201e39 100755 --- a/tools/export_model.py +++ b/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=[ diff --git a/tools/infer_table.py b/tools/infer_table.py index 66c2da4421..58e7455cbb 100644 --- a/tools/infer_table.py +++ b/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 = [ + '', '', '' + ] + structure_str_list + ['
', '', ''] + 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__': diff --git a/tools/program.py b/tools/program.py index 7c02dc0149..17079cb86e 100755 --- a/tools/program.py +++ b/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: From 9b1e9ae6dd22504251dbb7190d39d5b7823355de Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Tue, 21 Jun 2022 08:21:21 +0000 Subject: [PATCH 02/32] add table_ops.py --- ppocr/data/imaug/__init__.py | 2 +- ppocr/data/imaug/{gen_table_mask.py => table_ops.py} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename ppocr/data/imaug/{gen_table_mask.py => table_ops.py} (100%) diff --git a/ppocr/data/imaug/__init__.py b/ppocr/data/imaug/__init__.py index 548832fb0d..750a754157 100644 --- a/ppocr/data/imaug/__init__.py +++ b/ppocr/data/imaug/__init__.py @@ -34,7 +34,7 @@ from .label_ops import * from .east_process import * from .sast_process import * from .pg_process import * -from .gen_table_mask import * +from .table_ops import * from .vqa import * diff --git a/ppocr/data/imaug/gen_table_mask.py b/ppocr/data/imaug/table_ops.py similarity index 100% rename from ppocr/data/imaug/gen_table_mask.py rename to ppocr/data/imaug/table_ops.py From 5dfcc98306ff5482f230ca0929ef0fc3c4697639 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Mon, 27 Jun 2022 09:37:07 +0000 Subject: [PATCH 03/32] fix bug --- ppocr/data/imaug/label_ops.py | 12 ++++-- ppocr/losses/table_att_loss.py | 55 +++++++++++++++----------- ppocr/metrics/table_metric.py | 6 ++- ppocr/modeling/heads/table_att_head.py | 15 ++++--- 4 files changed, 54 insertions(+), 34 deletions(-) diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index a55869a641..d63901a03d 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -591,7 +591,7 @@ class TableLabelEncode(AttnLabelEncode): replace_empty_cell_token=False, merge_no_span_structure=False, learn_empty_box=False, - point_num=4, + point_num=2, **kwargs): self.max_text_len = max_text_length self.lower = False @@ -669,13 +669,15 @@ class TableLabelEncode(AttnLabelEncode): # encode box bboxes = np.zeros( - (self._max_text_len, self.point_num), dtype=np.float32) + (self._max_text_len, self.point_num * 2), 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]: + if 'bbox' in cells[bbox_idx] and len(cells[bbox_idx][ + 'tokens']) > 0: bbox = cells[bbox_idx]['bbox'].copy() bbox = np.array(bbox, dtype=np.float32).reshape(-1) bboxes[i] = bbox @@ -723,11 +725,13 @@ class TableMasterLabelEncode(TableLabelEncode): replace_empty_cell_token=False, merge_no_span_structure=False, learn_empty_box=False, - point_num=4, + point_num=2, **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) + self.pad_idx = self.dict[self.pad_str] + self.unknown_idx = self.dict[self.unknown_str] @property def _max_text_len(self): diff --git a/ppocr/losses/table_att_loss.py b/ppocr/losses/table_att_loss.py index d7fd99e695..4bdccad399 100644 --- a/ppocr/losses/table_att_loss.py +++ b/ppocr/losses/table_att_loss.py @@ -21,15 +21,21 @@ from paddle import nn from paddle.nn import functional as F from paddle import fluid + class TableAttentionLoss(nn.Layer): - def __init__(self, structure_weight, loc_weight, use_giou=False, giou_weight=1.0, **kwargs): + def __init__(self, + structure_weight, + loc_weight, + use_giou=False, + giou_weight=1.0, + **kwargs): super(TableAttentionLoss, self).__init__() self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none') self.structure_weight = structure_weight self.loc_weight = loc_weight self.use_giou = use_giou self.giou_weight = giou_weight - + def giou_loss(self, preds, bbox, eps=1e-7, reduction='mean'): ''' :param preds:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,] @@ -48,9 +54,10 @@ class TableAttentionLoss(nn.Layer): inters = iw * ih # union - uni = (preds[:, 2] - preds[:, 0] + 1e-3) * (preds[:, 3] - preds[:, 1] + 1e-3 - ) + (bbox[:, 2] - bbox[:, 0] + 1e-3) * ( - bbox[:, 3] - bbox[:, 1] + 1e-3) - inters + eps + uni = (preds[:, 2] - preds[:, 0] + 1e-3) * ( + preds[:, 3] - preds[:, 1] + 1e-3) + (bbox[:, 2] - bbox[:, 0] + 1e-3 + ) * (bbox[:, 3] - bbox[:, 1] + + 1e-3) - inters + eps # ious ious = inters / uni @@ -80,30 +87,34 @@ class TableAttentionLoss(nn.Layer): structure_probs = predicts['structure_probs'] structure_targets = batch[1].astype("int64") structure_targets = structure_targets[:, 1:] - if len(batch) == 6: - structure_mask = batch[5].astype("int64") - structure_mask = structure_mask[:, 1:] - structure_mask = paddle.reshape(structure_mask, [-1]) - structure_probs = paddle.reshape(structure_probs, [-1, structure_probs.shape[-1]]) + structure_probs = paddle.reshape(structure_probs, + [-1, structure_probs.shape[-1]]) structure_targets = paddle.reshape(structure_targets, [-1]) structure_loss = self.loss_func(structure_probs, structure_targets) - - if len(batch) == 6: - structure_loss = structure_loss * structure_mask - -# structure_loss = paddle.sum(structure_loss) * self.structure_weight + structure_loss = paddle.mean(structure_loss) * self.structure_weight - + loc_preds = predicts['loc_preds'] loc_targets = batch[2].astype("float32") - loc_targets_mask = batch[4].astype("float32") + loc_targets_mask = batch[3].astype("float32") loc_targets = loc_targets[:, 1:, :] loc_targets_mask = loc_targets_mask[:, 1:, :] - loc_loss = F.mse_loss(loc_preds * loc_targets_mask, loc_targets) * self.loc_weight + loc_loss = F.mse_loss(loc_preds * loc_targets_mask, + loc_targets) * self.loc_weight if self.use_giou: - loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, loc_targets) * self.giou_weight + loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, + loc_targets) * self.giou_weight total_loss = structure_loss + loc_loss + loc_loss_giou - return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss, "loc_loss_giou":loc_loss_giou} + return { + 'loss': total_loss, + "structure_loss": structure_loss, + "loc_loss": loc_loss, + "loc_loss_giou": loc_loss_giou + } else: - total_loss = structure_loss + loc_loss - return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss} \ No newline at end of file + total_loss = structure_loss + loc_loss + return { + 'loss': total_loss, + "structure_loss": structure_loss, + "loc_loss": loc_loss + } diff --git a/ppocr/metrics/table_metric.py b/ppocr/metrics/table_metric.py index 17f3dc92b2..26f577a03a 100644 --- a/ppocr/metrics/table_metric.py +++ b/ppocr/metrics/table_metric.py @@ -31,6 +31,8 @@ class TableStructureMetric(object): gt_structure_batch_list): pred_str = ''.join(pred) target_str = ''.join(target) + # pred_str = pred_str.replace('','').replace('','').replace('','').replace('','') + # target_str = target_str.replace('','').replace('','').replace('','').replace('','') if pred_str == target_str: correct_num += 1 all_num += 1 @@ -131,10 +133,10 @@ class TableMetric(object): self.bbox_metric.reset() def format_box(self, box): - if self.point_num == 4: + if self.point_num == 2: x1, y1, x2, y2 = box box = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] - elif self.point_num == 8: + elif self.point_num == 4: x1, y1, x2, y2, x3, y3, x4, y4 = box box = [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] return box diff --git a/ppocr/modeling/heads/table_att_head.py b/ppocr/modeling/heads/table_att_head.py index b64713898d..4f39d6253d 100644 --- a/ppocr/modeling/heads/table_att_head.py +++ b/ppocr/modeling/heads/table_att_head.py @@ -31,16 +31,18 @@ class TableAttentionHead(nn.Layer): loc_type, in_max_len=488, max_text_length=800, + out_channels=30, + point_num=2, **kwargs): super(TableAttentionHead, self).__init__() self.input_size = in_channels[-1] self.hidden_size = hidden_size - self.elem_num = 30 + self.out_channels = out_channels self.max_text_length = max_text_length self.structure_attention_cell = AttentionGRUCell( - self.input_size, hidden_size, self.elem_num, use_gru=False) - self.structure_generator = nn.Linear(hidden_size, self.elem_num) + self.input_size, hidden_size, self.out_channels, use_gru=False) + self.structure_generator = nn.Linear(hidden_size, self.out_channels) self.loc_type = loc_type self.in_max_len = in_max_len @@ -53,7 +55,8 @@ class TableAttentionHead(nn.Layer): self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1) else: self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1) - self.loc_generator = nn.Linear(self.input_size + hidden_size, 4) + self.loc_generator = nn.Linear(self.input_size + hidden_size, + point_num * 2) def _char_to_onehot(self, input_char, onehot_dim): input_ont_hot = F.one_hot(input_char, onehot_dim) @@ -77,7 +80,7 @@ class TableAttentionHead(nn.Layer): structure = targets[0] for i in range(self.max_text_length + 1): elem_onehots = self._char_to_onehot( - structure[:, i], onehot_dim=self.elem_num) + structure[:, i], onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) @@ -104,7 +107,7 @@ class TableAttentionHead(nn.Layer): i = 0 while i < max_text_length + 1: elem_onehots = self._char_to_onehot( - temp_elem, onehot_dim=self.elem_num) + temp_elem, onehot_dim=self.out_channels) (outputs, hidden), alpha = self.structure_attention_cell( hidden, fea, elem_onehots) output_hiddens.append(paddle.unsqueeze(outputs, axis=1)) From 7c92b441a88925a82a4d4880bb21710ba0d5c945 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Mon, 27 Jun 2022 09:39:35 +0000 Subject: [PATCH 04/32] update mv3 config --- configs/table/table_mv3.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/configs/table/table_mv3.yml b/configs/table/table_mv3.yml index 9159addc31..66c1c83e12 100755 --- a/configs/table/table_mv3.yml +++ b/configs/table/table_mv3.yml @@ -17,7 +17,7 @@ Global: # for data or label process character_dict_path: ppocr/utils/dict/table_structure_dict.txt character_type: en - max_text_length: 500 + max_text_length: 800 infer_mode: False process_total_num: 0 process_cut_num: 0 @@ -44,7 +44,7 @@ Architecture: name: TableAttentionHead hidden_size: 256 loc_type: 2 - max_text_length: 500 + max_text_length: 800 Loss: name: TableAttentionLoss @@ -57,13 +57,13 @@ PostProcess: Metric: name: TableMetric main_indicator: acc - compute_bbox_metric: False # cost many time, set False for training + compute_bbox_metric: false # cost many time, set False for training Train: dataset: name: PubTabDataSet data_dir: train_data/table/pubtabnet/train/ - label_file_path: train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl] transforms: - DecodeImage: # load image img_mode: BGR From 7a9c8af11d38bb246a9be048219d8574abb3b0de Mon Sep 17 00:00:00 2001 From: Liliya WU Date: Thu, 30 Jun 2022 13:39:21 +0800 Subject: [PATCH 05/32] update scripts to run in cloud-based avh --- deploy/avh/README.md | 54 +++++++++++++++---------- deploy/avh/configure_avh.sh | 79 +++++++++++++++++++++++++++++++++++++ deploy/avh/run_demo.sh | 53 ++++++++++++++++++++----- 3 files changed, 155 insertions(+), 31 deletions(-) create mode 100755 deploy/avh/configure_avh.sh diff --git a/deploy/avh/README.md b/deploy/avh/README.md index 922fb4da92..a64bfd3ad6 100644 --- a/deploy/avh/README.md +++ b/deploy/avh/README.md @@ -15,20 +15,22 @@ -Running PaddleOCR text recognition model via TVM on bare metal Arm(R) Cortex(R)-M55 CPU and CMSIS-NN -=============================================================== +Running PaddleOCR text recognition model on bare metal Arm(R) Cortex(R)-M55 CPU using Arm Virtual Hardware +====================================================================== -This folder contains an example of how to use TVM to run a PaddleOCR model -on bare metal Cortex(R)-M55 CPU and CMSIS-NN. +This folder contains an example of how to run a PaddleOCR model on bare metal [Cortex(R)-M55 CPU](https://www.arm.com/products/silicon-ip-cpu/cortex-m/cortex-m55) using [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware). -Prerequisites + +Running environment and prerequisites ------------- -If the demo is run in the ci_cpu Docker container provided with TVM, then the following -software will already be installed. +Case 1: If the demo is run in Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by [AWS](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g?sr=0-1&ref_=beagle&applicationId=AWSMPContessa)/[AWS China](https://awsmarketplace.amazonaws.cn/marketplace/pp/prodview-2y7nefntbmybu), the following software will be installed through [configure_avh.sh](./configure_avh.sh) script. It will install automatically when you run the application through [run_demo.sh](./run_demo.sh) script. +You can refer to this [guide](https://arm-software.github.io/AVH/main/examples/html/MicroSpeech.html#amilaunch) to launch an Arm Virtual Hardware AMI instance. -If the demo is not run in the ci_cpu Docker container, then you will need the following: +Case 2: If the demo is run in the [ci_cpu Docker container](https://github.com/apache/tvm/blob/main/docker/Dockerfile.ci_cpu) provided with [TVM](https://github.com/apache/tvm), then the following software will already be installed. + +Case 3: If the demo is not run in the ci_cpu Docker container, then you will need the following: - Software required to build and run the demo (These can all be installed by running - https://github.com/apache/tvm/blob/main/docker/install/ubuntu_install_ethosu_driver_stack.sh .) + tvm/docker/install/ubuntu_install_ethosu_driver_stack.sh.) - [Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps) - [cmake 3.19.5](https://github.com/Kitware/CMake/releases/) - [GCC toolchain from Arm(R)](https://developer.arm.com/-/media/Files/downloads/gnu-rm/10-2020q4/gcc-arm-none-eabi-10-2020-q4-major-x86_64-linux.tar.bz2) @@ -40,20 +42,23 @@ If the demo is not run in the ci_cpu Docker container, then you will need the fo pip install -r ./requirements.txt ``` -You will also need TVM which can either be: - - Built from source (see [Install from Source](https://tvm.apache.org/docs/install/from_source.html)) - - When building from source, the following need to be set in config.cmake: - - set(USE_CMSISNN ON) - - set(USE_MICRO ON) - - set(USE_LLVM ON) - - Installed from TLCPack nightly(see [TLCPack](https://tlcpack.ai/)) - +In case2 and case3: + You will need to update your PATH environment variable to include the path to cmake 3.19.5 and the FVP. For example if you've installed these in ```/opt/arm``` , then you would do the following: ```bash export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/bin:$PATH ``` +You will also need TVM which can either be: + - Installed from TLCPack(see [TLCPack](https://tlcpack.ai/)) + - Built from source (see [Install from Source](https://tvm.apache.org/docs/install/from_source.html)) + - When building from source, the following need to be set in config.cmake: + - set(USE_CMSISNN ON) + - set(USE_MICRO ON) + - set(USE_LLVM ON) + + Running the demo application ---------------------------- Type the following command to run the bare metal text recognition application ([src/demo_bare_metal.c](./src/demo_bare_metal.c)): @@ -62,6 +67,12 @@ Type the following command to run the bare metal text recognition application ([ ./run_demo.sh ``` +If you are not able to use Arm Virtual Hardware Amazon Machine Image(AMI) instance hosted by AWS/AWS China, specify argument --enable_FVP to 1 to make the application run on local Fixed Virtual Platforms (FVPs) executables. + +```bash +./run_demo.sh --enable_FVP 1 +``` + If the Ethos(TM)-U platform and/or CMSIS have not been installed in /opt/arm/ethosu then the locations for these can be specified as arguments to run_demo.sh, for example: @@ -70,13 +81,14 @@ the locations for these can be specified as arguments to run_demo.sh, for exampl --ethosu_platform_path /home/tvm-user/ethosu/core_platform ``` -This will: +With [run_demo.sh](./run_demo.sh) to run the demo application, it will: +- Set up running environment by installing the required prerequisites automatically if running in Arm Virtual Hardware Amazon AMI instance(not specify --enable_FVP to 1) - Download a PaddleOCR text recognition model - Use tvmc to compile the text recognition model for Cortex(R)-M55 CPU and CMSIS-NN - Create a C header file inputs.c containing the image data as a C array - Create a C header file outputs.c containing a C array where the output of inference will be stored - Build the demo application -- Run the demo application on a Fixed Virtual Platform (FVP) based on Arm(R) Corstone(TM)-300 software +- Run the demo application on a Arm Virtual Hardware based on Arm(R) Corstone(TM)-300 software - The application will report the text on the image and the corresponding score. Using your own image @@ -92,9 +104,9 @@ python3 ./convert_image.py path/to/image Model description ----------------- -In this demo, the model we use is an English recognition model based on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md). PP-OCRv3 is the third version of the PP-OCR series model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). This series of models has the following features: +The example is built on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md) English recognition model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). PP-OCRv3 is the third version of the PP-OCR series model. This series of models has the following features: - PP-OCRv3: ultra-lightweight OCR system: detection (3.6M) + direction classifier (1.4M) + recognition (12M) = 17.0M - Support more than 80 kinds of multi-language recognition models, including English, Chinese, French, German, Arabic, Korean, Japanese and so on. For details - Support vertical text recognition, and long text recognition -The text recognition model in PP-OCRv3 supports more than 80 languages. In the process of model development, since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current model. \ No newline at end of file +The text recognition model in PP-OCRv3 supports more than 80 languages. In the process of model development, since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current model. diff --git a/deploy/avh/configure_avh.sh b/deploy/avh/configure_avh.sh new file mode 100755 index 0000000000..8042fd81d2 --- /dev/null +++ b/deploy/avh/configure_avh.sh @@ -0,0 +1,79 @@ +#!/bin/bash +# Copyright (c) 2022 Arm Limited and Contributors. All rights reserved. +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +set -e +set -u +set -o pipefail + +# Show usage +function show_usage() { + cat <&2 + show_usage >&2 + exit 1 + fi + ;; -*|--*) echo "Error: Unknown flag: $1" >&2 @@ -100,17 +123,27 @@ while (( $# )); do esac done +# Choose running environment: cloud(default) or local environment +Platform="VHT_Corstone_SSE-300_Ethos-U55" +if [ $FVP_enable == "1" ]; then + Platform="FVP_Corstone_SSE-300_Ethos-U55" + echo -e "\e[36mRun application on local Fixed Virtual Platforms (FVPs)\e[0m" +else + if [ ! -d "/opt/arm/" ]; then + sudo ./configure_avh.sh + fi +fi # Directories script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" - # Make build directory -rm -rf build make cleanall mkdir -p build cd build +# Get PaddlePaddle inference model +echo -e "\e[36mDownload PaddlePaddle inference model\e[0m" wget https://paddleocr.bj.bcebos.com/tvm/ocr_en.tar tar -xf ocr_en.tar @@ -144,9 +177,9 @@ cd ${script_dir} echo ${script_dir} make -# Run demo executable on the FVP -FVP_Corstone_SSE-300_Ethos-U55 -C cpu0.CFGDTCMSZ=15 \ +# Run demo executable on the AVH +$Platform -C cpu0.CFGDTCMSZ=15 \ -C cpu0.CFGITCMSZ=15 -C mps3_board.uart0.out_file=\"-\" -C mps3_board.uart0.shutdown_tag=\"EXITTHESIM\" \ -C mps3_board.visualisation.disable-visualisation=1 -C mps3_board.telnetterminal0.start_telnet=0 \ -C mps3_board.telnetterminal1.start_telnet=0 -C mps3_board.telnetterminal2.start_telnet=0 -C mps3_board.telnetterminal5.start_telnet=0 \ -./build/demo \ No newline at end of file +./build/demo --stat From c26e7aee61b74f7e20e6fe62b5942d753b1440f7 Mon Sep 17 00:00:00 2001 From: wangjingyeye <1025993141@qq.com> Date: Thu, 30 Jun 2022 08:57:25 +0000 Subject: [PATCH 06/32] add db++ --- tools/program.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tools/program.py b/tools/program.py index 620c61f093..e8e72fdd2d 100755 --- a/tools/program.py +++ b/tools/program.py @@ -574,7 +574,8 @@ 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', 'DB++' + 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE', + 'SVTR', 'ViTSTR', 'ABINet', 'DB++' ] if use_xpu: From 7c4d0d1e9154ebaf44e1ed6284411728db92bce7 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Fri, 1 Jul 2022 18:05:39 +0800 Subject: [PATCH 07/32] fix kl js loss --- .../det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml | 2 +- ppocr/losses/basic_loss.py | 19 +++++++++++++------ 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml b/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml index e1831f2639..e85127a2ee 100644 --- a/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml +++ b/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_dml.yml @@ -60,7 +60,7 @@ Loss: - ["Student", "Student2"] maps_name: "thrink_maps" weight: 1.0 - act: "softmax" + # act: None model_name_pairs: ["Student", "Student2"] key: maps - DistillationDBLoss: diff --git a/ppocr/losses/basic_loss.py b/ppocr/losses/basic_loss.py index 2df96ea264..74490791c2 100644 --- a/ppocr/losses/basic_loss.py +++ b/ppocr/losses/basic_loss.py @@ -57,17 +57,24 @@ class CELoss(nn.Layer): class KLJSLoss(object): def __init__(self, mode='kl'): assert mode in ['kl', 'js', 'KL', 'JS' - ], "mode can only be one of ['kl', 'js', 'KL', 'JS']" + ], "mode can only be one of ['kl', 'KL', 'js', 'JS']" self.mode = mode def __call__(self, p1, p2, reduction="mean"): - loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)) - - if self.mode.lower() == "js": + if self.mode.lower() == 'kl': + loss = paddle.multiply(p2, paddle.log((p2 + 1e-5) / (p1 + 1e-5) + 1e-5)) loss += paddle.multiply( - p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)) + p1, paddle.log((p1 + 1e-5) / (p2 + 1e-5) + 1e-5)) loss *= 0.5 + elif self.mode.lower() == "js": + loss = paddle.multiply(p2, paddle.log((2*p2 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5)) + loss += paddle.multiply( + p1, paddle.log((2*p1 + 1e-5) / (p1 + p2 + 1e-5) + 1e-5)) + loss *= 0.5 + else: + raise ValueError("The mode.lower() if KLJSLoss should be one of ['kl', 'js']") + if reduction == "mean": loss = paddle.mean(loss, axis=[1, 2]) elif reduction == "none" or reduction is None: @@ -95,7 +102,7 @@ class DMLLoss(nn.Layer): self.act = None self.use_log = use_log - self.jskl_loss = KLJSLoss(mode="js") + self.jskl_loss = KLJSLoss(mode="kl") def _kldiv(self, x, target): eps = 1.0e-10 From 1861d5dda3b19d0b7e636e5df0c227c23887cea1 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Fri, 1 Jul 2022 18:22:46 +0800 Subject: [PATCH 08/32] fix inference doc --- doc/doc_ch/inference_ppocr.md | 20 +++++++++++++++++++- doc/doc_en/inference_ppocr_en.md | 25 ++++++++++++++++++++++--- 2 files changed, 41 insertions(+), 4 deletions(-) diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md index 472c0003f9..a9f6e6300a 100644 --- a/doc/doc_ch/inference_ppocr.md +++ b/doc/doc_ch/inference_ppocr.md @@ -78,9 +78,27 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.9956803321838379) ``` + + +### 2.2 英文模型的推理 + +英文识别模型推理，可以执行如下命令， 注意修改字典路径： + +``` +# 下载英文数字识别模型： +wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar +tar xf en_PP-OCRv3_det_infer.tar +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt" +``` + +执行命令后，上图的预测结果为： + +``` +Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521) +``` -### 2.2 多语言模型的推理 +### 2.3 多语言模型的推理 如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别： ``` diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md index 935f92f514..97c176527f 100755 --- a/doc/doc_en/inference_ppocr_en.md +++ b/doc/doc_en/inference_ppocr_en.md @@ -8,7 +8,8 @@ This article introduces the use of the Python inference engine for the PP-OCR mo - [Text Detection Model Inference](#text-detection-model-inference) - [Text Recognition Model Inference](#text-recognition-model-inference) - [1. Lightweight Chinese Recognition Model Inference](#1-lightweight-chinese-recognition-model-inference) - - [2. Multilingual Model Inference](#2-multilingual-model-inference) + - [2. English Recognition Model Inference](#2-english-recognition-model-inference) + - [3. Multilingual Model Inference](#3-multilingual-model-inference) - [Angle Classification Model Inference](#angle-classification-model-inference) - [Text Detection Angle Classification and Recognition Inference Concatenation](#text-detection-angle-classification-and-recognition-inference-concatenation) @@ -76,10 +77,28 @@ After executing the command, the prediction results (recognized text and score) ```bash Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.988671) ``` + +### 2. English Recognition Model Inference - +For English recognition model inference, you can execute the following commands,you need to specify the dictionary path used by `--rec_char_dict_path`: -### 2. Multilingual Model Inference +``` +# download en model： +wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar +tar xf en_PP-OCRv3_det_infer.tar +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt" +``` + +After executing the command, the prediction result of the above figure is: + +``` +Predicts of ./doc/imgs_words/en/word_1.png: ('JOINT', 0.998160719871521) +``` + + + + +### 3. Multilingual Model Inference If you need to predict [other language models](./models_list_en.md#Multilingual), when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results, You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition: From 7233ed6c01a356ca253e8bedc030276024640ea2 Mon Sep 17 00:00:00 2001 From: tink2123 Date: Fri, 1 Jul 2022 18:25:29 +0800 Subject: [PATCH 09/32] fix inference doc --- doc/doc_ch/inference_ppocr.md | 9 ++++++--- doc/doc_en/inference_ppocr_en.md | 3 +++ 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md index a9f6e6300a..622ac995d3 100644 --- a/doc/doc_ch/inference_ppocr.md +++ b/doc/doc_ch/inference_ppocr.md @@ -7,7 +7,8 @@ - [1. 文本检测模型推理](#1-文本检测模型推理) - [2. 文本识别模型推理](#2-文本识别模型推理) - [2.1 超轻量中文识别模型推理](#21-超轻量中文识别模型推理) - - [2.2 多语言模型的推理](#22-多语言模型的推理) + - [2.2 英文识别模型推理](#22-英文识别模型推理) + - [2.3 多语言模型的推理](#23-多语言模型的推理) - [3. 方向分类模型推理](#3-方向分类模型推理) - [4. 文本检测、方向分类和文字识别串联推理](#4-文本检测方向分类和文字识别串联推理) @@ -78,9 +79,9 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.9956803321838379) ``` - + -### 2.2 英文模型的推理 +### 2.2 英文识别模型推理 英文识别模型推理，可以执行如下命令， 注意修改字典路径： @@ -91,6 +92,8 @@ tar xf en_PP-OCRv3_det_infer.tar python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt" ``` +![](../imgs_words/en/word_1.png) + 执行命令后，上图的预测结果为： ``` diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md index 97c176527f..0f57b0ba6b 100755 --- a/doc/doc_en/inference_ppocr_en.md +++ b/doc/doc_en/inference_ppocr_en.md @@ -89,6 +89,9 @@ tar xf en_PP-OCRv3_det_infer.tar python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./en_PP-OCRv3_det_infer/" --rec_char_dict_path="ppocr/utils/en_dict.txt" ``` +![](../imgs_words/en/word_1.png) + + After executing the command, the prediction result of the above figure is: ``` From 24712d933c3b3669356a812d177c62a271bc17a4 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Fri, 1 Jul 2022 18:28:02 +0800 Subject: [PATCH 10/32] fix --- configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml index f3ad966488..8b160f6353 100644 --- a/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml +++ b/configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml @@ -65,7 +65,7 @@ Loss: - ["Student", "Teacher"] maps_name: "thrink_maps" weight: 1.0 - act: "softmax" + # act: None model_name_pairs: ["Student", "Teacher"] key: maps - DistillationDBLoss: From b541cc12be890212adcd20d5cd99b5767c7fb0f0 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Mon, 4 Jul 2022 04:26:54 +0000 Subject: [PATCH 11/32] fix shape unmatch error --- ppocr/postprocess/pse_postprocess/pse_postprocess.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ppocr/postprocess/pse_postprocess/pse_postprocess.py b/ppocr/postprocess/pse_postprocess/pse_postprocess.py index 34f1b8c9b5..962f3efe92 100755 --- a/ppocr/postprocess/pse_postprocess/pse_postprocess.py +++ b/ppocr/postprocess/pse_postprocess/pse_postprocess.py @@ -58,6 +58,8 @@ class PSEPostProcess(object): kernels = (pred > self.thresh).astype('float32') text_mask = kernels[:, 0, :, :] + text_mask = paddle.unsqueeze(text_mask, axis=1) + kernels[:, 0:, :, :] = kernels[:, 0:, :, :] * text_mask score = score.numpy() From 8d9cfade49c43dd3c4cdf7fe49fd7da6260944c7 Mon Sep 17 00:00:00 2001 From: wangjingyeye <1025993141@qq.com> Date: Mon, 4 Jul 2022 08:40:32 +0000 Subject: [PATCH 12/32] add polygons --- ppocr/metrics/eval_det_iou.py | 12 +---- ppocr/postprocess/db_postprocess.py | 69 ++++++++++++++++++++++++++--- tools/infer_det.py | 6 +-- 3 files changed, 68 insertions(+), 19 deletions(-) diff --git a/ppocr/metrics/eval_det_iou.py b/ppocr/metrics/eval_det_iou.py index bc05e7df7d..c144886b3f 100644 --- a/ppocr/metrics/eval_det_iou.py +++ b/ppocr/metrics/eval_det_iou.py @@ -83,14 +83,10 @@ class DetectionIoUEvaluator(object): evaluationLog = "" - # print(len(gt)) for n in range(len(gt)): points = gt[n]['points'] - # transcription = gt[n]['text'] dontCare = gt[n]['ignore'] - # points = Polygon(points) - # points = points.buffer(0) - if not Polygon(points).is_valid or not Polygon(points).is_simple: + if not Polygon(points).is_valid: continue gtPol = points @@ -105,9 +101,7 @@ class DetectionIoUEvaluator(object): for n in range(len(pred)): points = pred[n]['points'] - # points = Polygon(points) - # points = points.buffer(0) - if not Polygon(points).is_valid or not Polygon(points).is_simple: + if not Polygon(points).is_valid: continue detPol = points @@ -191,8 +185,6 @@ class DetectionIoUEvaluator(object): methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \ methodRecall * methodPrecision / ( methodRecall + methodPrecision) - # print(methodRecall, methodPrecision, methodHmean) - # sys.exit(-1) methodMetrics = { 'precision': methodPrecision, 'recall': methodRecall, diff --git a/ppocr/postprocess/db_postprocess.py b/ppocr/postprocess/db_postprocess.py index 27b428ef2e..c61534d6db 100755 --- a/ppocr/postprocess/db_postprocess.py +++ b/ppocr/postprocess/db_postprocess.py @@ -38,6 +38,7 @@ class DBPostProcess(object): unclip_ratio=2.0, use_dilation=False, score_mode="fast", + use_polygon=False, **kwargs): self.thresh = thresh self.box_thresh = box_thresh @@ -45,6 +46,7 @@ class DBPostProcess(object): self.unclip_ratio = unclip_ratio self.min_size = 3 self.score_mode = score_mode + self.use_polygon = use_polygon assert score_mode in [ "slow", "fast" ], "Score mode must be in [slow, fast] but got: {}".format(score_mode) @@ -52,6 +54,56 @@ class DBPostProcess(object): self.dilation_kernel = None if not use_dilation else np.array( [[1, 1], [1, 1]]) + def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height): + ''' + _bitmap: single map with shape (1, H, W), + whose values are binarized as {0, 1} + ''' + + bitmap = _bitmap + height, width = bitmap.shape + + boxes = [] + scores = [] + + contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), + cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) + + for contour in contours[:self.max_candidates]: + epsilon = 0.002 * cv2.arcLength(contour, True) + approx = cv2.approxPolyDP(contour, epsilon, True) + points = approx.reshape((-1, 2)) + # print(points) + if points.shape[0] < 4: + continue + + score = self.box_score_fast(pred, points.reshape(-1, 2)) + if self.box_thresh > score: + continue + + if points.shape[0] > 2: + box = self.unclip(points, self.unclip_ratio) + if len(box) > 1: + continue + else: + continue + box = box.reshape(-1, 2) + # print(box) + + _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2))) + if sside < self.min_size + 2: + continue + + box = np.array(box) + box[:, 0] = np.clip( + np.round(box[:, 0] / width * dest_width), 0, dest_width) + box[:, 1] = np.clip( + np.round(box[:, 1] / height * dest_height), 0, dest_height) + boxes.append(box.tolist()) + scores.append(score) + # print(boxes) + return boxes, scores + def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height): ''' _bitmap: single map with shape (1, H, W), @@ -85,7 +137,7 @@ class DBPostProcess(object): if self.box_thresh > score: continue - box = self.unclip(points).reshape(-1, 1, 2) + box = self.unclip(points, self.unclip_ratio).reshape(-1, 1, 2) box, sside = self.get_mini_boxes(box) if sside < self.min_size + 2: continue @@ -99,8 +151,7 @@ class DBPostProcess(object): scores.append(score) return np.array(boxes, dtype=np.int16), scores - def unclip(self, box): - unclip_ratio = self.unclip_ratio + def unclip(self, box, unclip_ratio): poly = Polygon(box) distance = poly.area * unclip_ratio / poly.length offset = pyclipper.PyclipperOffset() @@ -185,8 +236,12 @@ class DBPostProcess(object): self.dilation_kernel) else: mask = segmentation[batch_index] - boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, - src_w, src_h) + if self.use_polygon: + boxes, scores = self.polygons_from_bitmap(pred[batch_index], + mask, src_w, src_h) + else: + boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, + src_w, src_h) boxes_batch.append({'points': boxes}) return boxes_batch @@ -202,6 +257,7 @@ class DistillationDBPostProcess(object): unclip_ratio=1.5, use_dilation=False, score_mode="fast", + use_polygon=False, **kwargs): self.model_name = model_name self.key = key @@ -211,7 +267,8 @@ class DistillationDBPostProcess(object): max_candidates=max_candidates, unclip_ratio=unclip_ratio, use_dilation=use_dilation, - score_mode=score_mode) + score_mode=score_mode, + use_polygon=use_polygon) def __call__(self, predicts, shape_list): results = {} diff --git a/tools/infer_det.py b/tools/infer_det.py index 1acecedf3e..df34652389 100755 --- a/tools/infer_det.py +++ b/tools/infer_det.py @@ -44,7 +44,7 @@ def draw_det_res(dt_boxes, config, img, img_name, save_path): import cv2 src_im = img for box in dt_boxes: - box = box.astype(np.int32).reshape((-1, 1, 2)) + box = np.array(box).astype(np.int32).reshape((-1, 1, 2)) cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2) if not os.path.exists(save_path): os.makedirs(save_path) @@ -106,7 +106,7 @@ def main(): dt_boxes_list = [] for box in boxes: tmp_json = {"transcription": ""} - tmp_json['points'] = box.tolist() + tmp_json['points'] = list(box) dt_boxes_list.append(tmp_json) det_box_json[k] = dt_boxes_list save_det_path = os.path.dirname(config['Global'][ @@ -118,7 +118,7 @@ def main(): # write result for box in boxes: tmp_json = {"transcription": ""} - tmp_json['points'] = box.tolist() + tmp_json['points'] = list(box) dt_boxes_json.append(tmp_json) save_det_path = os.path.dirname(config['Global'][ 'save_res_path']) + "/det_results/" From 72f93cf977ffed232e85c1736ba4355c7afb3fd7 Mon Sep 17 00:00:00 2001 From: wangjingyeye <1025993141@qq.com> Date: Mon, 4 Jul 2022 08:49:34 +0000 Subject: [PATCH 13/32] add polygons --- ppocr/postprocess/db_postprocess.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ppocr/postprocess/db_postprocess.py b/ppocr/postprocess/db_postprocess.py index c61534d6db..e1041b00f9 100755 --- a/ppocr/postprocess/db_postprocess.py +++ b/ppocr/postprocess/db_postprocess.py @@ -236,7 +236,7 @@ class DBPostProcess(object): self.dilation_kernel) else: mask = segmentation[batch_index] - if self.use_polygon: + if self.use_polygon is True: boxes, scores = self.polygons_from_bitmap(pred[batch_index], mask, src_w, src_h) else: From d1b31bf85cd6e23c8a09758ea6ca03938209ed69 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Mon, 4 Jul 2022 10:22:25 +0000 Subject: [PATCH 14/32] add ref --- configs/table/table_master.yml | 64 +++++----- doc/doc_ch/algorithm_det_fcenet.md | 22 ++-- doc/doc_ch/algorithm_overview.md | 16 ++- doc/doc_ch/algorithm_table_master.md | 114 ++++++++++++++++++ doc/doc_en/algorithm_overview_en.md | 13 ++ doc/doc_en/algorithm_table_master_en.md | 112 +++++++++++++++++ ppocr/data/imaug/label_ops.py | 9 ++ ppocr/losses/table_master_loss.py | 5 + .../modeling/backbones/table_master_resnet.py | 4 + ppocr/modeling/heads/table_master_head.py | 5 + ppocr/postprocess/__init__.py | 2 +- ppstructure/docs/models_list.md | 2 +- 12 files changed, 319 insertions(+), 49 deletions(-) create mode 100644 doc/doc_ch/algorithm_table_master.md create mode 100644 doc/doc_en/algorithm_table_master_en.md diff --git a/configs/table/table_master.yml b/configs/table/table_master.yml index 9dfc0e2746..cfd98346c4 100755 --- a/configs/table/table_master.yml +++ b/configs/table/table_master.yml @@ -2,21 +2,19 @@ Global: use_gpu: true epoch_num: 17 log_smooth_window: 20 - print_batch_step: 5 + print_batch_step: 100 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 + eval_batch_step: [0, 6259] + cal_metric_during_train: true + pretrained_model: null + checkpoints: + save_inference_dir: output/table_master/infer + use_visualdl: false infer_img: ppstructure/docs/table/table.jpg - save_res_path: output/table_master - # for data or label process + save_res_path: ./output/table_master character_dict_path: ppocr/utils/dict/table_master_structure_dict.txt - infer_mode: False + infer_mode: false max_text_length: 500 process_total_num: 0 process_cut_num: 0 @@ -33,8 +31,8 @@ Optimizer: gamma: 0.1 warmup_epoch: 0.02 regularizer: - name: 'L2' - factor: 0.00000 + name: L2 + factor: 0.0 Architecture: model_type: table @@ -67,15 +65,15 @@ PostProcess: Metric: name: TableMetric main_indicator: acc - compute_bbox_metric: true # cost many time, set False for training + compute_bbox_metric: False 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] + data_dir: train_data/table/pubtabnet/train/ + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl] transforms: - - DecodeImage: # load image + - DecodeImage: img_mode: BGR channel_first: False - TableMasterLabelEncode: @@ -88,20 +86,20 @@ Train: - PaddingTableImage: size: [480, 480] - TableBoxEncode: - use_xywh: true + use_xywh: True - NormalizeImage: scale: 1./255. mean: [0.5, 0.5, 0.5] std: [0.5, 0.5, 0.5] - order: 'hwc' - - ToCHWImage: + order: hwc + - ToCHWImage: null - KeepKeys: - keep_keys: ['image', 'structure', 'bboxes', 'bbox_masks','shape'] + keep_keys: [image, structure, bboxes, bbox_masks, shape] loader: shuffle: True - batch_size_per_card: 8 + batch_size_per_card: 10 drop_last: True - num_workers: 1 + num_workers: 8 Eval: dataset: @@ -109,7 +107,7 @@ Eval: data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/ label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl] transforms: - - DecodeImage: # load image + - DecodeImage: img_mode: BGR channel_first: False - TableMasterLabelEncode: @@ -120,19 +118,19 @@ Eval: max_len: 480 resize_bboxes: True - PaddingTableImage: - size: [ 480, 480 ] + size: [480, 480] - TableBoxEncode: - use_xywh: true + use_xywh: True - NormalizeImage: scale: 1./255. - mean: [ 0.5, 0.5, 0.5 ] - std: [ 0.5, 0.5, 0.5 ] - order: 'hwc' - - ToCHWImage: + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + order: hwc + - ToCHWImage: null - KeepKeys: - keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks','shape' ] + keep_keys: [image, structure, bboxes, bbox_masks, shape] loader: shuffle: False drop_last: False - batch_size_per_card: 2 - num_workers: 8 + batch_size_per_card: 10 + num_workers: 8 \ No newline at end of file diff --git a/doc/doc_ch/algorithm_det_fcenet.md b/doc/doc_ch/algorithm_det_fcenet.md index bd2e734204..a70caa29fb 100644 --- a/doc/doc_ch/algorithm_det_fcenet.md +++ b/doc/doc_ch/algorithm_det_fcenet.md @@ -1,17 +1,15 @@ # FCENet -- [1. 算法简介](#1) -- [2. 环境配置](#2) -- [3. 模型训练、评估、预测](#3) - - [3.1 训练](#3-1) - - [3.2 评估](#3-2) - - [3.3 预测](#3-3) -- [4. 推理部署](#4) - - [4.1 Python推理](#4-1) - - [4.2 C++推理](#4-2) - - [4.3 Serving服务化部署](#4-3) - - [4.4 更多推理部署](#4-4) -- [5. FAQ](#5) +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理](#42-c推理) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) ## 1. 算法简介 diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index ef96f6ec12..84af5fdcce 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -1,9 +1,10 @@ # OCR算法 - [1. 两阶段算法](#1-两阶段算法) - - [1.1 文本检测算法](#11-文本检测算法) - - [1.2 文本识别算法](#12-文本识别算法) + - [1.1 文本检测算法](#11-文本检测算法) + - [1.2 文本识别算法](#12-文本识别算法) - [2. 端到端算法](#2-端到端算法) +- [3. 表格识别算法](#3-表格识别算法) 本文给出了PaddleOCR已支持的OCR算法列表，以及每个算法在**英文公开数据集**上的模型和指标，主要用于算法简介和算法性能对比，更多包括中文在内的其他数据集上的模型请参考[PP-OCR v2.0 系列模型下载](./models_list.md)。 @@ -96,3 +97,14 @@ 已支持的端到端OCR算法列表（戳链接获取使用教程）： - [x] [PGNet](./algorithm_e2e_pgnet.md) + +## 3. 表格识别算法 + +已支持的表格识别算法列表（戳链接获取使用教程）： +- [x] [TableMaster](./algorithm_table_master.md) + +在PubTabNet表格识别公开数据集上，算法效果如下： + +|模型|骨干网络|配置文件|acc|下载链接| +|---|---|---|---|---| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型]|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_ch/algorithm_table_master.md b/doc/doc_ch/algorithm_table_master.md new file mode 100644 index 0000000000..6bfd0f36fc --- /dev/null +++ b/doc/doc_ch/algorithm_table_master.md @@ -0,0 +1,114 @@ +# 表格识别算法-TableMASTER + +- [1. 算法简介](#1-算法简介) +- [2. 环境配置](#2-环境配置) +- [3. 模型训练、评估、预测](#3-模型训练评估预测) +- [4. 推理部署](#4-推理部署) + - [4.1 Python推理](#41-python推理) + - [4.2 C++推理部署](#42-c推理部署) + - [4.3 Serving服务化部署](#43-serving服务化部署) + - [4.4 更多推理部署](#44-更多推理部署) +- [5. FAQ](#5-faq) +- [引用](#引用) + + +## 1. 算法简介 + +论文信息： +> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf) +> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong +> 2021 + +在PubTabNet表格识别公开数据集上，算法复现效果如下： + +|模型|骨干网络|配置文件|acc|下载链接| +| --- | --- | --- | --- | --- | +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| + + + +## 2. 环境配置 +请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。 + + + +## 3. 模型训练、评估、预测 + +上述TableMaster模型使用PubTabNet表格识别公开数据集训练得到，数据集下载可参考 [table_datasets](./dataset/table_datasets.md)。 + +数据下载完成后，请参考[文本识别教程](./recognition.md)进行训练。PaddleOCR对代码进行了模块化，训练不同的模型只需要**更换配置文件**即可。 + + +## 4. 推理部署 + + +### 4.1 Python推理 +首先将训练得到best模型，转换成inference model。以基于TableResNetExtra骨干网络，在PubTabNet数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/contribution/table_master.tar))，可以使用如下命令进行转换： + +```shell +# 注意将pretrained_model的路径设置为本地路径。 +python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master +``` + +**注意：** +- 如果您是在自己的数据集上训练的模型，并且调整了字典文件，请注意修改配置文件中的`character_dict_path`是否为所正确的字典文件。 + +转换成功后，在目录下有三个文件： +``` +/inference/table_master/ + ├── inference.pdiparams # 识别inference模型的参数文件 + ├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略 + └── inference.pdmodel # 识别inference模型的program文件 +``` + + +执行如下命令进行模型推理： + +```shell +cd ppstructure/ +python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg +# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='docs/table'。 +``` + +执行命令后，上面图像的预测结果（结构信息和表格中每个单元格的坐标）会打印到屏幕上，同时会保存单元格坐标的可视化结果。示例如下： +结果如下： +```shell +[2022/06/16 13:06:54] ppocr INFO: result: ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '
', '', ''], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344], +... +[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]] +[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg +[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246 +``` + +**注意**： + +- TableMaster在推理时比较慢，建议使用GPU进行使用。 + + +### 4.2 C++推理部署 + +由于C++预处理后处理还未支持TableMaster，所以暂未支持 + + +### 4.3 Serving服务化部署 + +暂不支持 + + +### 4.4 更多推理部署 + +暂不支持 + + +## 5. FAQ + +## 引用 + +```bibtex +@article{ye2021pingan, + title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML}, + author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong}, + journal={arXiv preprint arXiv:2105.01848}, + year={2021} +} +``` diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index bc96cdf235..cd277c74a3 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -4,6 +4,7 @@ * [1.1 Text Detection Algorithms](#11) * [1.2 Text Recognition Algorithms](#12) - [2. End-to-end Algorithms](#2) +- [3. Table Recognition Algorithms](#3) This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md). @@ -95,3 +96,15 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r Supported end-to-end algorithms (Click the link to get the tutorial): - [x] [PGNet](./algorithm_e2e_pgnet_en.md) + + +## 3. Table Recognition Algorithms + +Supported table recognition algorithms (Click the link to get the tutorial): +- [x] [TableMaster](./algorithm_table_master_en.md) + +On the PubTabNet dataset, the algorithm result is as follows: + +|Model|Backbone|Config|Acc|Download link| +|---|---|---|---|---| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型]|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_en/algorithm_table_master_en.md b/doc/doc_en/algorithm_table_master_en.md new file mode 100644 index 0000000000..a557a60901 --- /dev/null +++ b/doc/doc_en/algorithm_table_master_en.md @@ -0,0 +1,112 @@ +# Torm Recognition Algorithm-TableMASTER + +- [1. Introduction](#1-introduction) +- [2. Environment](#2-environment) +- [3. Model Training / Evaluation / Prediction](#3-model-training--evaluation--prediction) +- [4. Inference and Deployment](#4-inference-and-deployment) + - [4.1 Python Inference](#41-python-inference) + - [4.2 C++ Inference](#42-c-inference) + - [4.3 Serving](#43-serving) + - [4.4 More](#44-more) +- [5. FAQ](#5-faq) +- [Citation](#citation) + + +## 1. Introduction + +Paper: +> [TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML](https://arxiv.org/pdf/2105.01848.pdf) +> Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong +> 2021 + + +On the PubTabNet table recognition public data set, the algorithm reproduction acc is as follows: + +|Model|Backbone|Cnnfig|Acc|Download link| +| --- | --- | --- | --- | --- | +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[train model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| + + + +## 2. Environment +Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. + + + +## 3. Model Training / Evaluation / Prediction + +The above TableMaster model is trained using the PubTabNet table recognition public dataset. For the download of the dataset, please refer to [table_datasets](./dataset/table_datasets_en.md). + +After the data download is complete, please refer to [Text Recognition Training Tutorial](./recognition_en.md) for training. PaddleOCR has modularized the code structure, so that you only need to **replace the configuration file** to train different models. + + + +## 4. Inference and Deployment + + +### 4.1 Python Inference + +First, convert the model saved in the TableMaster table recognition training process into an inference model. Taking the model based on the TableResNetExtra backbone network and trained on the PubTabNet dataset as example ([model download link](https://paddleocr.bj.bcebos.com/contribution/table_master.tar)), you can use the following command to convert: + + +```shell +python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretrained_model=output/table_master/best_accuracy Global.save_inference_dir=./inference/table_master +``` + +**Note: ** +- If you trained the model on your own dataset and adjusted the dictionary file, please pay attention to whether the `character_dict_path` in the modified configuration file is the correct dictionary file + + +Execute the following command for model inference: + +```shell +cd ppstructure/ +# When predicting all images in a folder, you can modify image_dir to a folder, such as --image_dir='docs/table'. +python3.7 table/predict_structure.py --table_model_dir=../output/table_master/table_structure_tablemaster_infer/ --table_algorithm=TableMaster --table_char_dict_path=../ppocr/utils/dict/table_master_structure_dict.txt --table_max_len=480 --image_dir=docs/table/table.jpg + +``` + +After executing the command, the prediction results of the above image (structural information and the coordinates of each cell in the table) are printed to the screen, and the visualization of the cell coordinates is also saved. An example is as follows: + +result： +```shell +[2022/06/16 13:06:54] ppocr INFO: result: ['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '
', '', ''], [[72.17591094970703, 10.759100914001465, 60.29658508300781, 16.6805362701416], [161.85562133789062, 10.884308815002441, 14.9495210647583, 16.727018356323242], [277.79876708984375, 29.54340362548828, 31.490320205688477, 18.143272399902344], +... +[336.11724853515625, 280.3601989746094, 39.456939697265625, 18.121286392211914]] +[2022/06/16 13:06:54] ppocr INFO: save vis result to ./output/table.jpg +[2022/06/16 13:06:54] ppocr INFO: Predict time of docs/table/table.jpg: 17.36806297302246 +``` + +**Note**: + +- TableMaster is relatively slow during inference, and it is recommended to use GPU for use. + + +### 4.2 C++ Inference + +Since the post-processing is not written in CPP, the TableMaster does not support CPP inference. + + + +### 4.3 Serving + +Not supported + + +### 4.4 More + +Not supported + + +## 5. FAQ + +## Citation + +```bibtex +@article{ye2021pingan, + title={PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML}, + author={Ye, Jiaquan and Qi, Xianbiao and He, Yelin and Chen, Yihao and Gu, Dengyi and Gao, Peng and Xiao, Rong}, + journal={arXiv preprint arXiv:2105.01848}, + year={2021} +} +``` diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 007927011a..96f3c98674 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -670,6 +670,10 @@ class TableLabelEncode(AttnLabelEncode): return data def _merge_no_span_structure(self, structure): + """ + This fun code is refer from: + https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py + """ new_structure = [] i = 0 while i < len(structure): @@ -682,6 +686,11 @@ class TableLabelEncode(AttnLabelEncode): return new_structure def _replace_empty_cell_token(self, token_list, cells): + """ + This fun code is refer from: + https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py + """ + bbox_idx = 0 add_empty_bbox_token_list = [] for token in token_list: diff --git a/ppocr/losses/table_master_loss.py b/ppocr/losses/table_master_loss.py index 50a773dd9a..216c4e521a 100644 --- a/ppocr/losses/table_master_loss.py +++ b/ppocr/losses/table_master_loss.py @@ -11,6 +11,11 @@ # 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. +""" +This fun code is refer from: +https://github.com/JiaquanYe/TableMASTER-mmocr/tree/master/mmocr/models/textrecog/losses +""" + import paddle from paddle import nn diff --git a/ppocr/modeling/backbones/table_master_resnet.py b/ppocr/modeling/backbones/table_master_resnet.py index 82b4f37a74..f1c506ca45 100644 --- a/ppocr/modeling/backbones/table_master_resnet.py +++ b/ppocr/modeling/backbones/table_master_resnet.py @@ -11,6 +11,10 @@ # 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. +""" +This fun code is refer from: +https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/backbones/table_resnet_extra.py +""" import paddle import paddle.nn as nn diff --git a/ppocr/modeling/heads/table_master_head.py b/ppocr/modeling/heads/table_master_head.py index 4da6e9b59f..887630a87e 100644 --- a/ppocr/modeling/heads/table_master_head.py +++ b/ppocr/modeling/heads/table_master_head.py @@ -11,6 +11,11 @@ # 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. +""" +This fun code is refer from: +https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/decoders/master_decoder.py +""" + import copy import math import paddle diff --git a/ppocr/postprocess/__init__.py b/ppocr/postprocess/__init__.py index 26a23f1ea4..1d414eb2e8 100644 --- a/ppocr/postprocess/__init__.py +++ b/ppocr/postprocess/__init__.py @@ -26,7 +26,7 @@ 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, ViTSTRLabelDecode, ABINetLabelDecode from .cls_postprocess import ClsPostProcess from .pg_postprocess import PGPostProcess diff --git a/ppstructure/docs/models_list.md b/ppstructure/docs/models_list.md index dabce3a514..42d44009da 100644 --- a/ppstructure/docs/models_list.md +++ b/ppstructure/docs/models_list.md @@ -35,7 +35,7 @@ |模型名称|模型简介|推理模型大小|下载地址| | --- | --- | --- | --- | -|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) | +|en_ppocr_mobile_v2.0_table_structure|PubTabNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) | ## 3. VQA模型 From bedd89f916fb24ca349bfe0abdf593dee3aec22e Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Mon, 4 Jul 2022 20:04:57 +0800 Subject: [PATCH 15/32] fix bug --- deploy/lite/crnn_process.cc | 3 ++- deploy/lite/ocr_db_crnn.cc | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/deploy/lite/crnn_process.cc b/deploy/lite/crnn_process.cc index 6d5fc1504e..7cd95cddb5 100644 --- a/deploy/lite/crnn_process.cc +++ b/deploy/lite/crnn_process.cc @@ -34,12 +34,13 @@ cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio, int rec_image_height) { resize_w = imgW; else resize_w = int(ceilf(imgH * ratio)); - + cv::Mat resize_img; cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, cv::INTER_LINEAR); cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, int(imgW - resize_img.cols), cv::BORDER_CONSTANT, {127, 127, 127}); + return resize_img; } std::vector ReadDict(std::string path) { diff --git a/deploy/lite/ocr_db_crnn.cc b/deploy/lite/ocr_db_crnn.cc index cb2bf7791a..fde0d07d6c 100644 --- a/deploy/lite/ocr_db_crnn.cc +++ b/deploy/lite/ocr_db_crnn.cc @@ -474,7 +474,7 @@ void system(char **argv){ std::vector rec_times; RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score, - charactor_dict, cls_predictor, use_direction_classify, &rec_times); + charactor_dict, cls_predictor, use_direction_classify, &rec_times, rec_image_height); //// visualization auto img_vis = Visualization(srcimg, boxes); From 4ec14d0e8f5539f42b3c7b527e6a5792752a23d1 Mon Sep 17 00:00:00 2001 From: Liliya Wu <94680624+Liliyaw@users.noreply.github.com> Date: Mon, 4 Jul 2022 21:33:38 +0800 Subject: [PATCH 16/32] Update run_demo.sh --- deploy/avh/run_demo.sh | 1 - 1 file changed, 1 deletion(-) diff --git a/deploy/avh/run_demo.sh b/deploy/avh/run_demo.sh index 01f950c13d..63de91afc8 100755 --- a/deploy/avh/run_demo.sh +++ b/deploy/avh/run_demo.sh @@ -1,5 +1,4 @@ #!/bin/bash -# Copyright (c) 2022 Arm Limited and Contributors. All rights reserved. # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information From adda5ddce933b9973d1c9b5296d15d437d266290 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Tue, 5 Jul 2022 02:51:07 +0000 Subject: [PATCH 17/32] fix bug --- doc/doc_ch/algorithm_overview.md | 2 +- doc/doc_ch/algorithm_table_master.md | 2 +- doc/doc_en/algorithm_overview_en.md | 12 ++++---- doc/doc_en/algorithm_table_master_en.md | 4 +-- ppocr/data/imaug/label_ops.py | 2 +- ppocr/losses/table_master_loss.py | 2 +- ppocr/metrics/table_metric.py | 4 --- .../modeling/backbones/table_master_resnet.py | 28 ++++++++----------- ppocr/modeling/heads/table_master_head.py | 4 +-- ppocr/utils/visual.py | 13 +++++++++ ppstructure/table/predict_structure.py | 14 +--------- tools/infer_table.py | 14 +--------- 12 files changed, 41 insertions(+), 60 deletions(-) diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index 84af5fdcce..1efd564ce1 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -107,4 +107,4 @@ |模型|骨干网络|配置文件|acc|下载链接| |---|---|---|---|---| -|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型]|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_ch/algorithm_table_master.md b/doc/doc_ch/algorithm_table_master.md index 6bfd0f36fc..36455ed9f9 100644 --- a/doc/doc_ch/algorithm_table_master.md +++ b/doc/doc_ch/algorithm_table_master.md @@ -55,7 +55,7 @@ python3 tools/export_model.py -c configs/table/table_master.yml -o Global.pretra 转换成功后，在目录下有三个文件： ``` -/inference/table_master/ +./inference/table_master/ ├── inference.pdiparams # 识别inference模型的参数文件 ├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略 └── inference.pdmodel # 识别inference模型的program文件 diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index cd277c74a3..8b9b3f7b0b 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -1,10 +1,10 @@ # OCR Algorithms -- [1. Two-stage Algorithms](#1) - * [1.1 Text Detection Algorithms](#11) - * [1.2 Text Recognition Algorithms](#12) -- [2. End-to-end Algorithms](#2) -- [3. Table Recognition Algorithms](#3) +- [1. Two-stage Algorithms](#1-two-stage-algorithms) + - [1.1 Text Detection Algorithms](#11-text-detection-algorithms) + - [1.2 Text Recognition Algorithms](#12-text-recognition-algorithms) +- [2. End-to-end Algorithms](#2-end-to-end-algorithms) +- [3. Table Recognition Algorithms](#3-table-recognition-algorithms) This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md). @@ -107,4 +107,4 @@ On the PubTabNet dataset, the algorithm result is as follows: |Model|Backbone|Config|Acc|Download link| |---|---|---|---|---| -|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[训练模型]|[训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar) / [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/doc/doc_en/algorithm_table_master_en.md b/doc/doc_en/algorithm_table_master_en.md index a557a60901..e9249a2a05 100644 --- a/doc/doc_en/algorithm_table_master_en.md +++ b/doc/doc_en/algorithm_table_master_en.md @@ -1,4 +1,4 @@ -# Torm Recognition Algorithm-TableMASTER +# Table Recognition Algorithm-TableMASTER - [1. Introduction](#1-introduction) - [2. Environment](#2-environment) @@ -24,7 +24,7 @@ On the PubTabNet table recognition public data set, the algorithm reproduction a |Model|Backbone|Cnnfig|Acc|Download link| | --- | --- | --- | --- | --- | -|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[train model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| +|TableMaster|TableResNetExtra|[configs/table/table_master.yml](../../configs/table/table_master.yml)|77.47%|[trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar)/[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_infer.tar)| diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 96f3c98674..7cc4cef46a 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -671,7 +671,7 @@ class TableLabelEncode(AttnLabelEncode): def _merge_no_span_structure(self, structure): """ - This fun code is refer from: + This code is refer from: https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/table_recognition/data_preprocess.py """ new_structure = [] diff --git a/ppocr/losses/table_master_loss.py b/ppocr/losses/table_master_loss.py index 216c4e521a..dca982dbd4 100644 --- a/ppocr/losses/table_master_loss.py +++ b/ppocr/losses/table_master_loss.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """ -This fun code is refer from: +This code is refer from: https://github.com/JiaquanYe/TableMASTER-mmocr/tree/master/mmocr/models/textrecog/losses """ diff --git a/ppocr/metrics/table_metric.py b/ppocr/metrics/table_metric.py index fb0075f7cb..fd2631e442 100644 --- a/ppocr/metrics/table_metric.py +++ b/ppocr/metrics/table_metric.py @@ -31,8 +31,6 @@ class TableStructureMetric(object): gt_structure_batch_list): pred_str = ''.join(pred) target_str = ''.join(target) - # pred_str = pred_str.replace('','').replace('','').replace('','').replace('','') - # target_str = target_str.replace('','').replace('','').replace('','').replace('','') if pred_str == target_str: correct_num += 1 all_num += 1 @@ -55,8 +53,6 @@ class TableStructureMetric(object): 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): diff --git a/ppocr/modeling/backbones/table_master_resnet.py b/ppocr/modeling/backbones/table_master_resnet.py index f1c506ca45..dacf5ed26e 100644 --- a/ppocr/modeling/backbones/table_master_resnet.py +++ b/ppocr/modeling/backbones/table_master_resnet.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """ -This fun code is refer from: +This code is refer from: https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/backbones/table_resnet_extra.py """ @@ -193,47 +193,43 @@ class TableResNetExtra(nn.Layer): def forward(self, x): f = [] - x = self.conv1(x) # 1,64,480,480 + x = self.conv1(x) x = self.bn1(x) x = self.relu1(x) - x = self.conv2(x) # 1,128,480,480 + x = self.conv2(x) x = self.bn2(x) x = self.relu2(x) - # (48, 160) - x = self.maxpool1(x) # 1,64,240,240 + x = self.maxpool1(x) x = self.layer1(x) - x = self.conv3(x) # 1,256,240,240 + x = self.conv3(x) x = self.bn3(x) x = self.relu3(x) f.append(x) - # (24, 80) - x = self.maxpool2(x) # 1,256,120,120 + x = self.maxpool2(x) x = self.layer2(x) - x = self.conv4(x) # 1,256,120,120 + x = self.conv4(x) x = self.bn4(x) x = self.relu4(x) f.append(x) - # (12, 40) - x = self.maxpool3(x) # 1,256,60,60 + x = self.maxpool3(x) - x = self.layer3(x) # 1,512,60,60 - x = self.conv5(x) # 1,512,60,60 + x = self.layer3(x) + x = self.conv5(x) 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.layer4(x) + x = self.conv6(x) x = self.bn6(x) x = self.relu6(x) f.append(x) - # (6, 40) return f diff --git a/ppocr/modeling/heads/table_master_head.py b/ppocr/modeling/heads/table_master_head.py index 887630a87e..fddbcc63fc 100644 --- a/ppocr/modeling/heads/table_master_head.py +++ b/ppocr/modeling/heads/table_master_head.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """ -This fun code is refer from: +This code is refer from: https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/mmocr/models/textrecog/decoders/master_decoder.py """ @@ -135,7 +135,7 @@ class TableMasterHead(nn.Layer): 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) + output = F.softmax(output) return {'structure_probs': output, 'loc_preds': bbox_output} def forward(self, feat, targets=None): diff --git a/ppocr/utils/visual.py b/ppocr/utils/visual.py index 235eb572a3..b20222bdbd 100644 --- a/ppocr/utils/visual.py +++ b/ppocr/utils/visual.py @@ -110,3 +110,16 @@ def draw_re_results(image, img_new = Image.blend(image, img_new, 0.5) return np.array(img_new) + + +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 \ No newline at end of file diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py index 17ec909582..00385c7665 100755 --- a/ppstructure/table/predict_structure.py +++ b/ppstructure/table/predict_structure.py @@ -30,6 +30,7 @@ from ppocr.data import create_operators, transform from ppocr.postprocess import build_post_process from ppocr.utils.logging import get_logger from ppocr.utils.utility import get_image_file_list, check_and_read_gif +from ppocr.utils.visual import draw_rectangle from ppstructure.utility import parse_args logger = get_logger() @@ -120,19 +121,6 @@ class TableStructurer(object): 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): image_file_list = get_image_file_list(args.image_dir) table_structurer = TableStructurer(args) diff --git a/tools/infer_table.py b/tools/infer_table.py index 58e7455cbb..6c02dd8640 100644 --- a/tools/infer_table.py +++ b/tools/infer_table.py @@ -36,6 +36,7 @@ from ppocr.modeling.architectures import build_model from ppocr.postprocess import build_post_process from ppocr.utils.save_load import load_model from ppocr.utils.utility import get_image_file_list +from ppocr.utils.visual import draw_rectangle import tools.program as program import cv2 @@ -111,19 +112,6 @@ def main(config, device, logger, vdl_writer): 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__': config, device, logger, vdl_writer = program.preprocess() main(config, device, logger, vdl_writer) From da6636dceebb8af3e0a39341e7beb1a8faf9c7cc Mon Sep 17 00:00:00 2001 From: wangjingyeye <1025993141@qq.com> Date: Tue, 5 Jul 2022 03:01:10 +0000 Subject: [PATCH 18/32] add polygons --- ppocr/postprocess/db_postprocess.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/ppocr/postprocess/db_postprocess.py b/ppocr/postprocess/db_postprocess.py index e1041b00f9..5e2553c3a0 100755 --- a/ppocr/postprocess/db_postprocess.py +++ b/ppocr/postprocess/db_postprocess.py @@ -73,7 +73,6 @@ class DBPostProcess(object): epsilon = 0.002 * cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, epsilon, True) points = approx.reshape((-1, 2)) - # print(points) if points.shape[0] < 4: continue @@ -88,7 +87,6 @@ class DBPostProcess(object): else: continue box = box.reshape(-1, 2) - # print(box) _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2))) if sside < self.min_size + 2: @@ -101,7 +99,6 @@ class DBPostProcess(object): np.round(box[:, 1] / height * dest_height), 0, dest_height) boxes.append(box.tolist()) scores.append(score) - # print(boxes) return boxes, scores def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height): From 52d5b54569d1a10e9272c278eb98e2775ce58680 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Tue, 5 Jul 2022 03:29:43 +0000 Subject: [PATCH 19/32] add import cv2 --- ppocr/utils/visual.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ppocr/utils/visual.py b/ppocr/utils/visual.py index b20222bdbd..e0fbf06abb 100644 --- a/ppocr/utils/visual.py +++ b/ppocr/utils/visual.py @@ -11,6 +11,7 @@ # 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 cv2 import os import numpy as np from PIL import Image, ImageDraw, ImageFont From f39ceb193591f6b205ed4e389a48940b359424bf Mon Sep 17 00:00:00 2001 From: mmglove Date: Tue, 5 Jul 2022 15:30:14 +0800 Subject: [PATCH 20/32] fix benchmark --- test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt | 2 +- test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt | 2 +- test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt b/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt index ab3aa59b60..2c8aa95344 100644 --- a/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt +++ b/test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt @@ -54,6 +54,6 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] ===========================train_benchmark_params========================== batch_size:8|16 fp_items:fp32|fp16 -epoch:2 +epoch:15 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096 diff --git a/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt b/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt index 33e4dbf233..df88c0e543 100644 --- a/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt +++ b/test_tipc/configs/det_r18_vd_db_v2_0/train_infer_python.txt @@ -54,5 +54,5 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] ===========================train_benchmark_params========================== batch_size:8|16 fp_items:fp32|fp16 -epoch:2 +epoch:15 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile diff --git a/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt b/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt index 62da89fe1c..53511e6ae2 100644 --- a/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt +++ b/test_tipc/configs/det_r50_vd_pse_v2_0/train_infer_python.txt @@ -54,5 +54,5 @@ random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}] ===========================train_benchmark_params========================== batch_size:8 fp_items:fp32|fp16 -epoch:2 +epoch:10 --profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile From 96f3c6a7c51ffefbdc7fd569e731a34e35286d69 Mon Sep 17 00:00:00 2001 From: wangjingyeye <1025993141@qq.com> Date: Tue, 5 Jul 2022 08:21:40 +0000 Subject: [PATCH 21/32] add polygons --- tools/program.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/program.py b/tools/program.py index e8e72fdd2d..c6422ac96c 100755 --- a/tools/program.py +++ b/tools/program.py @@ -575,7 +575,7 @@ def preprocess(is_train=False): 'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE', 'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE', - 'SVTR', 'ViTSTR', 'ABINet', 'DB++' + 'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster' ] if use_xpu: From 27181149a9eaa5ffc2da085fd1ad7eaff2eb68f8 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Tue, 5 Jul 2022 09:31:53 +0000 Subject: [PATCH 22/32] add tablemaster to tipc --- doc/doc_ch/algorithm.md | 3 +- doc/doc_ch/algorithm_overview.md | 12 +- doc/doc_en/algorithm_en.md | 3 +- doc/doc_en/algorithm_overview_en.md | 10 +- ppstructure/table/predict_structure.py | 6 +- .../configs/en_table_structure/table_mv3.yml | 41 +++--- .../configs/table_master/table_master.yml | 136 ++++++++++++++++++ .../table_master/train_infer_python.txt | 53 +++++++ test_tipc/prepare.sh | 4 + test_tipc/readme.md | 1 + 10 files changed, 237 insertions(+), 32 deletions(-) create mode 100644 test_tipc/configs/table_master/table_master.yml create mode 100644 test_tipc/configs/table_master/train_infer_python.txt diff --git a/doc/doc_ch/algorithm.md b/doc/doc_ch/algorithm.md index 3056f35d52..d50a5aa4e8 100644 --- a/doc/doc_ch/algorithm.md +++ b/doc/doc_ch/algorithm.md @@ -5,9 +5,10 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，已支持的 - [文本检测算法](./algorithm_overview.md#11-%E6%96%87%E6%9C%AC%E6%A3%80%E6%B5%8B%E7%AE%97%E6%B3%95) - [文本识别算法](./algorithm_overview.md#12-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95) - [端到端算法](./algorithm_overview.md#2-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95) +- [表格识别]](./algorithm_overview.md#3-%E8%A1%A8%E6%A0%BC%E8%AF%86%E5%88%AB%E7%AE%97%E6%B3%95) **欢迎广大开发者合作共建，贡献更多算法，合入有奖🎁！具体可查看[社区常规赛](https://github.com/PaddlePaddle/PaddleOCR/issues/4982)。** 新增算法可参考如下教程： -- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md) \ No newline at end of file +- [使用PaddleOCR架构添加新算法](./add_new_algorithm.md) diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index 1efd564ce1..5c7adc715f 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -1,10 +1,10 @@ # OCR算法 -- [1. 两阶段算法](#1-两阶段算法) - - [1.1 文本检测算法](#11-文本检测算法) - - [1.2 文本识别算法](#12-文本识别算法) -- [2. 端到端算法](#2-端到端算法) -- [3. 表格识别算法](#3-表格识别算法) +- [1. 两阶段算法](#1) + - [1.1 文本检测算法](#11) + - [1.2 文本识别算法](#12) +- [2. 端到端算法](#2) +- [3. 表格识别算法](#3) 本文给出了PaddleOCR已支持的OCR算法列表，以及每个算法在**英文公开数据集**上的模型和指标，主要用于算法简介和算法性能对比，更多包括中文在内的其他数据集上的模型请参考[PP-OCR v2.0 系列模型下载](./models_list.md)。 @@ -98,6 +98,8 @@ 已支持的端到端OCR算法列表（戳链接获取使用教程）： - [x] [PGNet](./algorithm_e2e_pgnet.md) + + ## 3. 表格识别算法 已支持的表格识别算法列表（戳链接获取使用教程）： diff --git a/doc/doc_en/algorithm_en.md b/doc/doc_en/algorithm_en.md index fa7887eb26..c880336b4a 100644 --- a/doc/doc_en/algorithm_en.md +++ b/doc/doc_en/algorithm_en.md @@ -6,5 +6,6 @@ PaddleOCR will add cutting-edge OCR algorithms and models continuously. Check ou - [text detection algorithms](./algorithm_overview_en.md#11) - [text recognition algorithms](./algorithm_overview_en.md#12) - [end-to-end algorithms](./algorithm_overview_en.md#2) +- [table recognition algorithms](./algorithm_overview_en.md#3) -Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline. \ No newline at end of file +Developers are welcome to contribute more algorithms! Please refer to [add new algorithm](./add_new_algorithm_en.md) guideline. diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index 8b9b3f7b0b..f3c96b620c 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -1,10 +1,10 @@ # OCR Algorithms -- [1. Two-stage Algorithms](#1-two-stage-algorithms) - - [1.1 Text Detection Algorithms](#11-text-detection-algorithms) - - [1.2 Text Recognition Algorithms](#12-text-recognition-algorithms) -- [2. End-to-end Algorithms](#2-end-to-end-algorithms) -- [3. Table Recognition Algorithms](#3-table-recognition-algorithms) +- [1. Two-stage Algorithms](#1) + - [1.1 Text Detection Algorithms](#11) + - [1.2 Text Recognition Algorithms](#12) +- [2. End-to-end Algorithms](#2) +- [3. Table Recognition Algorithms](#3) This tutorial lists the OCR algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md). diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py index 00385c7665..7a7d3169d5 100755 --- a/ppstructure/table/predict_structure.py +++ b/ppstructure/table/predict_structure.py @@ -118,7 +118,7 @@ class TableStructurer(object): '', '', '' ] + structure_str_list + ['
', '', ''] elapse = time.time() - starttime - return structure_str_list, bbox_list, elapse + return (structure_str_list, bbox_list), elapse def main(args): @@ -138,8 +138,8 @@ def main(args): if img is None: logger.info("error in loading image:{}".format(image_file)) continue - structure_str_list, bbox_list, elapse = table_structurer(img) - + structure_res, elapse = table_structurer(img) + structure_str_list, bbox_list = structure_res bbox_list_str = json.dumps(bbox_list.tolist()) logger.info("result: {}, {}".format(structure_str_list, bbox_list_str)) diff --git a/test_tipc/configs/en_table_structure/table_mv3.yml b/test_tipc/configs/en_table_structure/table_mv3.yml index adf326bd02..6df5a1a20b 100755 --- a/test_tipc/configs/en_table_structure/table_mv3.yml +++ b/test_tipc/configs/en_table_structure/table_mv3.yml @@ -1,24 +1,23 @@ Global: use_gpu: true - epoch_num: 10 + epoch_num: 400 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: 800 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: 800 Loss: name: TableAttentionLoss @@ -61,28 +57,34 @@ PostProcess: Metric: name: TableMetric main_indicator: acc + compute_bbox_metric: false # cost many time, set False for training Train: dataset: name: PubTabDataSet data_dir: ./train_data/pubtabnet/train - label_file_path: ./train_data/pubtabnet/train.jsonl + label_file_list: [./train_data/pubtabnet/train.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: True batch_size_per_card: 32 @@ -93,23 +95,28 @@ Eval: dataset: name: PubTabDataSet data_dir: ./train_data/pubtabnet/test/ - label_file_path: ./train_data/pubtabnet/test.jsonl + label_file_list: [./train_data/pubtabnet/test.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 diff --git a/test_tipc/configs/table_master/table_master.yml b/test_tipc/configs/table_master/table_master.yml new file mode 100644 index 0000000000..c519b5b8f4 --- /dev/null +++ b/test_tipc/configs/table_master/table_master.yml @@ -0,0 +1,136 @@ +Global: + use_gpu: true + epoch_num: 17 + log_smooth_window: 20 + print_batch_step: 100 + save_model_dir: ./output/table_master/ + save_epoch_step: 17 + eval_batch_step: [0, 6259] + cal_metric_during_train: true + pretrained_model: null + checkpoints: + save_inference_dir: output/table_master/infer + use_visualdl: false + infer_img: ppstructure/docs/table/table.jpg + save_res_path: ./output/table_master + 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: 0.0 + +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: False + +Train: + dataset: + name: PubTabDataSet + data_dir: ./train_data/pubtabnet/train + label_file_list: [./train_data/pubtabnet/train.jsonl] + transforms: + - DecodeImage: + 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: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: True + batch_size_per_card: 10 + drop_last: True + num_workers: 8 + +Eval: + dataset: + name: PubTabDataSet + data_dir: ./train_data/pubtabnet/test/ + label_file_list: [./train_data/pubtabnet/test.jsonl] + transforms: + - DecodeImage: + 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: null + - KeepKeys: + keep_keys: [image, structure, bboxes, bbox_masks, shape] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 10 + num_workers: 8 \ No newline at end of file diff --git a/test_tipc/configs/table_master/train_infer_python.txt b/test_tipc/configs/table_master/train_infer_python.txt new file mode 100644 index 0000000000..56b8e63602 --- /dev/null +++ b/test_tipc/configs/table_master/train_infer_python.txt @@ -0,0 +1,53 @@ +===========================train_params=========================== +model_name:table_master +python:python3.7 +gpu_list:0|0,1 +Global.use_gpu:True|True +Global.auto_cast:fp32 +Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=17 +Global.save_model_dir:./output/ +Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4 +Global.pretrained_model:./pretrain_models/table_structure_tablemaster_train/best_accuracy +train_model_name:latest +train_infer_img_dir:./ppstructure/docs/table/table.jpg +null:null +## +trainer:norm_train +norm_train:tools/train.py -c test_tipc/configs/table_master/table_master.yml -o Global.print_batch_step=10 +pact_train:null +fpgm_train:null +distill_train:null +null:null +null:null +## +===========================eval_params=========================== +eval:null +null:null +## +===========================infer_params=========================== +Global.save_inference_dir:./output/ +Global.checkpoints: +norm_export:tools/export_model.py -c test_tipc/configs/table_master/table_master.yml -o +quant_export: +fpgm_export: +distill_export:null +export1:null +export2:null +## +infer_model:null +infer_export:null +infer_quant:False +inference:ppstructure/table/predict_structure.py --table_char_dict_path=./ppocr/utils/dict/table_master_structure_dict.txt --image_dir=./ppstructure/docs/table/table.jpg --output ./output/table --table_algorithm=TableMaster --table_max_len=480 +--use_gpu:True|False +--enable_mkldnn:False +--cpu_threads:6 +--rec_batch_num:1 +--use_tensorrt:False +--precision:fp32 +--table_model_dir: +--image_dir:./ppstructure/docs/table/table.jpg +null:null +--benchmark:False +null:null +===========================infer_benchmark_params========================== +random_infer_input:[{float32,[3,480,480]}] diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index 32df8e7865..593cd872b7 100644 --- a/test_tipc/prepare.sh +++ b/test_tipc/prepare.sh @@ -62,6 +62,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then if [[ ${model_name} =~ "det_r50_db++" ]];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams --no-check-certificate fi + if [ ${model_name} == "table_master" ];then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/ppstructure/models/tablemaster/table_structure_tablemaster_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf table_structure_tablemaster_train.tar && cd ../ + fi cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../ rm -rf ./train_data/icdar2015 rm -rf ./train_data/ic15_data diff --git a/test_tipc/readme.md b/test_tipc/readme.md index effb2f168b..1c637d76f9 100644 --- a/test_tipc/readme.md +++ b/test_tipc/readme.md @@ -54,6 +54,7 @@ | NRTR |rec_mtb_nrtr | 识别 | 支持 | 多机多卡
混合精度 | - | - | | SAR |rec_r31_sar | 识别 | 支持 | 多机多卡
混合精度 | - | - | | PGNet |rec_r34_vd_none_none_ctc_v2.0 | 端到端| 支持 | 多机多卡
混合精度 | - | - | +| TableMaster |table_structure_tablemaster_train | 表格识别| 支持 | 多机多卡
混合精度 | - | - | From 029223c6074b54ef4bed3cbebc5517deae306d90 Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Tue, 5 Jul 2022 09:48:26 +0000 Subject: [PATCH 23/32] reduce epoch_num --- test_tipc/configs/en_table_structure/table_mv3.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test_tipc/configs/en_table_structure/table_mv3.yml b/test_tipc/configs/en_table_structure/table_mv3.yml index 6df5a1a20b..281038b968 100755 --- a/test_tipc/configs/en_table_structure/table_mv3.yml +++ b/test_tipc/configs/en_table_structure/table_mv3.yml @@ -1,10 +1,10 @@ Global: use_gpu: true - epoch_num: 400 + epoch_num: 10 log_smooth_window: 20 print_batch_step: 5 save_model_dir: ./output/table_mv3/ - save_epoch_step: 400 + save_epoch_step: 3 # evaluation is run every 400 iterations after the 0th iteration eval_batch_step: [0, 400] cal_metric_during_train: True From 5a0108b8acb6733030a752b0291ea4b1b78c65a2 Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Wed, 6 Jul 2022 11:17:41 +0800 Subject: [PATCH 24/32] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index b168ecc5e5..f57672e505 100644 --- a/README.md +++ b/README.md @@ -72,6 +72,7 @@ PaddleOCR support a variety of cutting-edge algorithms related to OCR, and devel + ## PP-OCR Series Model List（Update on September 8th） | Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model | From 7a99588dd8b678233eff0391aac13ebd0f7000f6 Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Wed, 6 Jul 2022 13:58:46 +0800 Subject: [PATCH 25/32] add more dataset yamls and fix re exceptions (#6791) * add more dataset yamls and fix re exceptions --- configs/vqa/re/layoutlmv2_funsd.yml | 125 +++++++++++++++ ...{layoutlmv2.yml => layoutlmv2_xund_zh.yml} | 6 +- configs/vqa/re/layoutxlm_funsd.yml | 129 +++++++++++++++ .../{layoutxlm.yml => layoutxlm_xfund_zh.yml} | 0 configs/vqa/ser/layoutlm_funsd.yml | 124 ++++++++++++++ configs/vqa/ser/layoutlm_sroie.yml | 124 ++++++++++++++ .../{layoutlm.yml => layoutlm_xfund_zh.yml} | 6 +- configs/vqa/ser/layoutlmv2_funsd.yml | 123 ++++++++++++++ configs/vqa/ser/layoutlmv2_sroie.yml | 123 ++++++++++++++ ...layoutlmv2.yml => layoutlmv2_xfund_zh.yml} | 4 +- configs/vqa/ser/layoutxlm_funsd.yml | 123 ++++++++++++++ configs/vqa/ser/layoutxlm_sroie.yml | 123 ++++++++++++++ configs/vqa/ser/layoutxlm_wildreceipt.yml | 123 ++++++++++++++ .../{layoutxlm.yml => layoutxlm_xfund_zh.yml} | 4 +- ppocr/data/imaug/label_ops.py | 79 +++++++-- ppocr/metrics/vqa_token_re_metric.py | 33 ++-- ppocr/modeling/backbones/vqa_layoutlm.py | 4 +- ppstructure/vqa/tools/trans_funsd_label.py | 151 ++++++++++++++++++ 18 files changed, 1366 insertions(+), 38 deletions(-) create mode 100644 configs/vqa/re/layoutlmv2_funsd.yml rename configs/vqa/re/{layoutlmv2.yml => layoutlmv2_xund_zh.yml} (95%) create mode 100644 configs/vqa/re/layoutxlm_funsd.yml rename configs/vqa/re/{layoutxlm.yml => layoutxlm_xfund_zh.yml} (100%) create mode 100644 configs/vqa/ser/layoutlm_funsd.yml create mode 100644 configs/vqa/ser/layoutlm_sroie.yml rename configs/vqa/ser/{layoutlm.yml => layoutlm_xfund_zh.yml} (95%) create mode 100644 configs/vqa/ser/layoutlmv2_funsd.yml create mode 100644 configs/vqa/ser/layoutlmv2_sroie.yml rename configs/vqa/ser/{layoutlmv2.yml => layoutlmv2_xfund_zh.yml} (96%) create mode 100644 configs/vqa/ser/layoutxlm_funsd.yml create mode 100644 configs/vqa/ser/layoutxlm_sroie.yml create mode 100644 configs/vqa/ser/layoutxlm_wildreceipt.yml rename configs/vqa/ser/{layoutxlm.yml => layoutxlm_xfund_zh.yml} (96%) create mode 100644 ppstructure/vqa/tools/trans_funsd_label.py diff --git a/configs/vqa/re/layoutlmv2_funsd.yml b/configs/vqa/re/layoutlmv2_funsd.yml new file mode 100644 index 0000000000..1c3d8f7854 --- /dev/null +++ b/configs/vqa/re/layoutlmv2_funsd.yml @@ -0,0 +1,125 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/re_layoutlmv2_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/re_layoutlmv2_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLMv2" + Transform: + Backbone: + name: LayoutLMv2ForRe + pretrained: True + checkpoints: + +Loss: + name: LossFromOutput + key: loss + reduction: mean + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + clip_norm: 10 + lr: + learning_rate: 0.00005 + warmup_epoch: 10 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQAReTokenLayoutLMPostProcess + +Metric: + name: VQAReTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: &class_path train_data/FUNSD/class_list.txt + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + collate_fn: ListCollator + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + collate_fn: ListCollator diff --git a/configs/vqa/re/layoutlmv2.yml b/configs/vqa/re/layoutlmv2_xund_zh.yml similarity index 95% rename from configs/vqa/re/layoutlmv2.yml rename to configs/vqa/re/layoutlmv2_xund_zh.yml index 737dbf6b60..986b9b5cef 100644 --- a/configs/vqa/re/layoutlmv2.yml +++ b/configs/vqa/re/layoutlmv2_xund_zh.yml @@ -3,16 +3,16 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/re_layoutlmv2/ + save_model_dir: ./output/re_layoutlmv2_xfund_zh save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration - eval_batch_step: [ 0, 19 ] + eval_batch_step: [ 0, 57 ] cal_metric_during_train: False save_inference_dir: use_visualdl: False seed: 2048 infer_img: ppstructure/docs/vqa/input/zh_val_21.jpg - save_res_path: ./output/re/ + save_res_path: ./output/re_layoutlmv2_xfund_zh/res/ Architecture: model_type: vqa diff --git a/configs/vqa/re/layoutxlm_funsd.yml b/configs/vqa/re/layoutxlm_funsd.yml new file mode 100644 index 0000000000..af28be10d6 --- /dev/null +++ b/configs/vqa/re/layoutxlm_funsd.yml @@ -0,0 +1,129 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/re_layoutxlm_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/re_layoutxlm_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForRe + pretrained: True + checkpoints: + +Loss: + name: LossFromOutput + key: loss + reduction: mean + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + clip_norm: 10 + lr: + learning_rate: 0.00005 + warmup_epoch: 10 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQAReTokenLayoutLMPostProcess + +Metric: + name: VQAReTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train_v4.json + # - ./train_data/FUNSD/train.json + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: &class_path ./train_data/FUNSD/class_list.txt + use_textline_bbox_info: &use_textline_bbox_info True + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 16 + collate_fn: ListCollator + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test_v4.json + # - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: True + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: *use_textline_bbox_info + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQAReTokenRelation: + - VQAReTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'entities', 'relations'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 8 + collate_fn: ListCollator diff --git a/configs/vqa/re/layoutxlm.yml b/configs/vqa/re/layoutxlm_xfund_zh.yml similarity index 100% rename from configs/vqa/re/layoutxlm.yml rename to configs/vqa/re/layoutxlm_xfund_zh.yml diff --git a/configs/vqa/ser/layoutlm_funsd.yml b/configs/vqa/ser/layoutlm_funsd.yml new file mode 100644 index 0000000000..0ef3502bc8 --- /dev/null +++ b/configs/vqa/ser/layoutlm_funsd.yml @@ -0,0 +1,124 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlm_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/ser_layoutlm_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLM" + Transform: + Backbone: + name: LayoutLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 7 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/FUNSD/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: &use_textline_bbox_info True + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: *use_textline_bbox_info + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlm_sroie.yml b/configs/vqa/ser/layoutlm_sroie.yml new file mode 100644 index 0000000000..6abb1151e5 --- /dev/null +++ b/configs/vqa/ser/layoutlm_sroie.yml @@ -0,0 +1,124 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlm_sroie + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/SROIE/test/X00016469670.jpg + save_res_path: ./output/ser_layoutlm_sroie/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLM" + Transform: + Backbone: + name: LayoutLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 9 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/SROIE/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/train + label_file_list: + - ./train_data/SROIE/train.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: &use_textline_bbox_info True + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/test + label_file_list: + - ./train_data/SROIE/test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + use_textline_bbox_info: *use_textline_bbox_info + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlm.yml b/configs/vqa/ser/layoutlm_xfund_zh.yml similarity index 95% rename from configs/vqa/ser/layoutlm.yml rename to configs/vqa/ser/layoutlm_xfund_zh.yml index 53e114defd..99763c1963 100644 --- a/configs/vqa/ser/layoutlm.yml +++ b/configs/vqa/ser/layoutlm_xfund_zh.yml @@ -3,16 +3,16 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/ser_layoutlm/ + save_model_dir: ./output/ser_layoutlm_xfund_zh save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration - eval_batch_step: [ 0, 19 ] + eval_batch_step: [ 0, 57 ] cal_metric_during_train: False save_inference_dir: use_visualdl: False seed: 2022 infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg - save_res_path: ./output/ser/ + save_res_path: ./output/ser_layoutlm_xfund_zh/res/ Architecture: model_type: vqa diff --git a/configs/vqa/ser/layoutlmv2_funsd.yml b/configs/vqa/ser/layoutlmv2_funsd.yml new file mode 100644 index 0000000000..438edc1aa7 --- /dev/null +++ b/configs/vqa/ser/layoutlmv2_funsd.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlmv2_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 100 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: ./output/ser_layoutlmv2_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLMv2" + Transform: + Backbone: + name: LayoutLMv2ForSer + pretrained: True + checkpoints: + num_classes: &num_classes 7 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path train_data/FUNSD/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlmv2_sroie.yml b/configs/vqa/ser/layoutlmv2_sroie.yml new file mode 100644 index 0000000000..549beb8ec5 --- /dev/null +++ b/configs/vqa/ser/layoutlmv2_sroie.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutlmv2_sroie + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/SROIE/test/X00016469670.jpg + save_res_path: ./output/ser_layoutlmv2_sroie/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutLMv2" + Transform: + Backbone: + name: LayoutLMv2ForSer + pretrained: True + checkpoints: + num_classes: &num_classes 9 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/SROIE/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/train + label_file_list: + - ./train_data/SROIE/train.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/test + label_file_list: + - ./train_data/SROIE/test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutlmv2.yml b/configs/vqa/ser/layoutlmv2_xfund_zh.yml similarity index 96% rename from configs/vqa/ser/layoutlmv2.yml rename to configs/vqa/ser/layoutlmv2_xfund_zh.yml index e48c746956..ebdc5f3169 100644 --- a/configs/vqa/ser/layoutlmv2.yml +++ b/configs/vqa/ser/layoutlmv2_xfund_zh.yml @@ -3,7 +3,7 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/ser_layoutlmv2/ + save_model_dir: ./output/ser_layoutlmv2_xfund_zh/ save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration eval_batch_step: [ 0, 19 ] @@ -12,7 +12,7 @@ Global: use_visualdl: False seed: 2022 infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg - save_res_path: ./output/ser/ + save_res_path: ./output/ser_layoutlmv2_xfund_zh/res/ Architecture: model_type: vqa diff --git a/configs/vqa/ser/layoutxlm_funsd.yml b/configs/vqa/ser/layoutxlm_funsd.yml new file mode 100644 index 0000000000..be1e9d4f1e --- /dev/null +++ b/configs/vqa/ser/layoutxlm_funsd.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutxlm_funsd + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 57 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/FUNSD/testing_data/images/83624198.png + save_res_path: output/ser_layoutxlm_funsd/res/ + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 7 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/FUNSD/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/FUNSD/training_data/images/ + label_file_list: + - ./train_data/FUNSD/train.json + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/FUNSD/testing_data/images/ + label_file_list: + - ./train_data/FUNSD/test.json + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutxlm_sroie.yml b/configs/vqa/ser/layoutxlm_sroie.yml new file mode 100644 index 0000000000..dd63d888d0 --- /dev/null +++ b/configs/vqa/ser/layoutxlm_sroie.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 200 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutxlm_sroie + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data/SROIE/test/X00016469670.jpg + save_res_path: res_img_aug_with_gt + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 9 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/SROIE/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/SROIE/train + label_file_list: + - ./train_data/SROIE/train.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/SROIE/test + label_file_list: + - ./train_data/SROIE/test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutxlm_wildreceipt.yml b/configs/vqa/ser/layoutxlm_wildreceipt.yml new file mode 100644 index 0000000000..92c0394296 --- /dev/null +++ b/configs/vqa/ser/layoutxlm_wildreceipt.yml @@ -0,0 +1,123 @@ +Global: + use_gpu: True + epoch_num: &epoch_num 100 + log_smooth_window: 10 + print_batch_step: 10 + save_model_dir: ./output/ser_layoutxlm_wildreceipt + save_epoch_step: 2000 + # evaluation is run every 10 iterations after the 0th iteration + eval_batch_step: [ 0, 200 ] + cal_metric_during_train: False + save_inference_dir: + use_visualdl: False + seed: 2022 + infer_img: train_data//wildreceipt/image_files/Image_12/10/845be0dd6f5b04866a2042abd28d558032ef2576.jpeg + save_res_path: ./output/ser_layoutxlm_wildreceipt/res + +Architecture: + model_type: vqa + algorithm: &algorithm "LayoutXLM" + Transform: + Backbone: + name: LayoutXLMForSer + pretrained: True + checkpoints: + num_classes: &num_classes 51 + +Loss: + name: VQASerTokenLayoutLMLoss + num_classes: *num_classes + +Optimizer: + name: AdamW + beta1: 0.9 + beta2: 0.999 + lr: + name: Linear + learning_rate: 0.00005 + epochs: *epoch_num + warmup_epoch: 2 + regularizer: + name: L2 + factor: 0.00000 + +PostProcess: + name: VQASerTokenLayoutLMPostProcess + class_path: &class_path ./train_data/wildreceipt/class_list.txt + +Metric: + name: VQASerTokenMetric + main_indicator: hmean + +Train: + dataset: + name: SimpleDataSet + data_dir: ./train_data/wildreceipt/ + label_file_list: + - ./train_data/wildreceipt/wildreceipt_train.txt + ratio_list: [ 1.0 ] + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: &max_seq_len 512 + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: True + drop_last: False + batch_size_per_card: 8 + num_workers: 4 + +Eval: + dataset: + name: SimpleDataSet + data_dir: train_data/wildreceipt + label_file_list: + - ./train_data/wildreceipt/wildreceipt_test.txt + transforms: + - DecodeImage: # load image + img_mode: RGB + channel_first: False + - VQATokenLabelEncode: # Class handling label + contains_re: False + algorithm: *algorithm + class_path: *class_path + - VQATokenPad: + max_seq_len: *max_seq_len + return_attention_mask: True + - VQASerTokenChunk: + max_seq_len: *max_seq_len + - Resize: + size: [224,224] + - NormalizeImage: + scale: 1 + mean: [ 123.675, 116.28, 103.53 ] + std: [ 58.395, 57.12, 57.375 ] + order: 'hwc' + - ToCHWImage: + - KeepKeys: + # dataloader will return list in this order + keep_keys: [ 'input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'labels'] + loader: + shuffle: False + drop_last: False + batch_size_per_card: 8 + num_workers: 4 diff --git a/configs/vqa/ser/layoutxlm.yml b/configs/vqa/ser/layoutxlm_xfund_zh.yml similarity index 96% rename from configs/vqa/ser/layoutxlm.yml rename to configs/vqa/ser/layoutxlm_xfund_zh.yml index fa9df192af..68df7d9f03 100644 --- a/configs/vqa/ser/layoutxlm.yml +++ b/configs/vqa/ser/layoutxlm_xfund_zh.yml @@ -3,7 +3,7 @@ Global: epoch_num: &epoch_num 200 log_smooth_window: 10 print_batch_step: 10 - save_model_dir: ./output/ser_layoutxlm/ + save_model_dir: ./output/ser_layoutxlm_xfund_zh save_epoch_step: 2000 # evaluation is run every 10 iterations after the 0th iteration eval_batch_step: [ 0, 19 ] @@ -12,7 +12,7 @@ Global: use_visualdl: False seed: 2022 infer_img: ppstructure/docs/vqa/input/zh_val_42.jpg - save_res_path: ./output/ser + save_res_path: ./output/ser_layoutxlm_xfund_zh/res Architecture: model_type: vqa diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py index 7cc4cef46a..a4087d5328 100644 --- a/ppocr/data/imaug/label_ops.py +++ b/ppocr/data/imaug/label_ops.py @@ -869,6 +869,7 @@ class VQATokenLabelEncode(object): contains_re=False, add_special_ids=False, algorithm='LayoutXLM', + use_textline_bbox_info=True, infer_mode=False, ocr_engine=None, **kwargs): @@ -897,11 +898,51 @@ class VQATokenLabelEncode(object): self.add_special_ids = add_special_ids self.infer_mode = infer_mode self.ocr_engine = ocr_engine + self.use_textline_bbox_info = use_textline_bbox_info + + def split_bbox(self, bbox, text, tokenizer): + words = text.split() + token_bboxes = [] + curr_word_idx = 0 + x1, y1, x2, y2 = bbox + unit_w = (x2 - x1) / len(text) + for idx, word in enumerate(words): + curr_w = len(word) * unit_w + word_bbox = [x1, y1, x1 + curr_w, y2] + token_bboxes.extend([word_bbox] * len(tokenizer.tokenize(word))) + x1 += (len(word) + 1) * unit_w + return token_bboxes + + def filter_empty_contents(self, ocr_info): + """ + find out the empty texts and remove the links + """ + new_ocr_info = [] + empty_index = [] + for idx, info in enumerate(ocr_info): + if len(info["transcription"]) > 0: + new_ocr_info.append(copy.deepcopy(info)) + else: + empty_index.append(info["id"]) + + for idx, info in enumerate(new_ocr_info): + new_link = [] + for link in info["linking"]: + if link[0] in empty_index or link[1] in empty_index: + continue + new_link.append(link) + new_ocr_info[idx]["linking"] = new_link + return new_ocr_info def __call__(self, data): # load bbox and label info ocr_info = self._load_ocr_info(data) + # for re + train_re = self.contains_re and not self.infer_mode + if train_re: + ocr_info = self.filter_empty_contents(ocr_info) + height, width, _ = data['image'].shape words_list = [] @@ -913,8 +954,6 @@ class VQATokenLabelEncode(object): entities = [] - # for re - train_re = self.contains_re and not self.infer_mode if train_re: relations = [] id2label = {} @@ -924,18 +963,19 @@ class VQATokenLabelEncode(object): data['ocr_info'] = copy.deepcopy(ocr_info) for info in ocr_info: + text = info["transcription"] + if len(text) <= 0: + continue if train_re: # for re - if len(info["transcription"]) == 0: + if len(text) == 0: empty_entity.add(info["id"]) continue id2label[info["id"]] = info["label"] relations.extend([tuple(sorted(l)) for l in info["linking"]]) # smooth_box info["bbox"] = self.trans_poly_to_bbox(info["points"]) - bbox = self._smooth_box(info["bbox"], height, width) - text = info["transcription"] encode_res = self.tokenizer.encode( text, pad_to_max_seq_len=False, return_attention_mask=True) @@ -946,6 +986,19 @@ class VQATokenLabelEncode(object): -1] encode_res["attention_mask"] = encode_res["attention_mask"][1: -1] + + if self.use_textline_bbox_info: + bbox = [info["bbox"]] * len(encode_res["input_ids"]) + else: + bbox = self.split_bbox(info["bbox"], info["transcription"], + self.tokenizer) + if len(bbox) <= 0: + continue + bbox = self._smooth_box(bbox, height, width) + if self.add_special_ids: + bbox.insert(0, [0, 0, 0, 0]) + bbox.append([0, 0, 0, 0]) + # parse label if not self.infer_mode: label = info['label'] @@ -970,7 +1023,7 @@ class VQATokenLabelEncode(object): }) input_ids_list.extend(encode_res["input_ids"]) token_type_ids_list.extend(encode_res["token_type_ids"]) - bbox_list.extend([bbox] * len(encode_res["input_ids"])) + bbox_list.extend(bbox) words_list.append(text) segment_offset_id.append(len(input_ids_list)) if not self.infer_mode: @@ -1019,12 +1072,14 @@ class VQATokenLabelEncode(object): info_dict = json.loads(info) return info_dict - def _smooth_box(self, bbox, height, width): - bbox[0] = int(bbox[0] * 1000.0 / width) - bbox[2] = int(bbox[2] * 1000.0 / width) - bbox[1] = int(bbox[1] * 1000.0 / height) - bbox[3] = int(bbox[3] * 1000.0 / height) - return bbox + def _smooth_box(self, bboxes, height, width): + bboxes = np.array(bboxes) + bboxes[:, 0] = bboxes[:, 0] * 1000 / width + bboxes[:, 2] = bboxes[:, 2] * 1000 / width + bboxes[:, 1] = bboxes[:, 1] * 1000 / height + bboxes[:, 3] = bboxes[:, 3] * 1000 / height + bboxes = bboxes.astype("int64").tolist() + return bboxes def _parse_label(self, label, encode_res): gt_label = [] diff --git a/ppocr/metrics/vqa_token_re_metric.py b/ppocr/metrics/vqa_token_re_metric.py index 8a13bc0812..f84387d8be 100644 --- a/ppocr/metrics/vqa_token_re_metric.py +++ b/ppocr/metrics/vqa_token_re_metric.py @@ -37,23 +37,26 @@ class VQAReTokenMetric(object): gt_relations = [] for b in range(len(self.relations_list)): rel_sent = [] - for head, tail in zip(self.relations_list[b]["head"], - self.relations_list[b]["tail"]): - rel = {} - rel["head_id"] = head - rel["head"] = (self.entities_list[b]["start"][rel["head_id"]], - self.entities_list[b]["end"][rel["head_id"]]) - rel["head_type"] = self.entities_list[b]["label"][rel[ - "head_id"]] + if "head" in self.relations_list[b]: + for head, tail in zip(self.relations_list[b]["head"], + self.relations_list[b]["tail"]): + rel = {} + rel["head_id"] = head + rel["head"] = ( + self.entities_list[b]["start"][rel["head_id"]], + self.entities_list[b]["end"][rel["head_id"]]) + rel["head_type"] = self.entities_list[b]["label"][rel[ + "head_id"]] - rel["tail_id"] = tail - rel["tail"] = (self.entities_list[b]["start"][rel["tail_id"]], - self.entities_list[b]["end"][rel["tail_id"]]) - rel["tail_type"] = self.entities_list[b]["label"][rel[ - "tail_id"]] + rel["tail_id"] = tail + rel["tail"] = ( + self.entities_list[b]["start"][rel["tail_id"]], + self.entities_list[b]["end"][rel["tail_id"]]) + rel["tail_type"] = self.entities_list[b]["label"][rel[ + "tail_id"]] - rel["type"] = 1 - rel_sent.append(rel) + rel["type"] = 1 + rel_sent.append(rel) gt_relations.append(rel_sent) re_metrics = self.re_score( self.pred_relations_list, gt_relations, mode="boundaries") diff --git a/ppocr/modeling/backbones/vqa_layoutlm.py b/ppocr/modeling/backbones/vqa_layoutlm.py index 2fd1b1b2a7..34dd9d10ea 100644 --- a/ppocr/modeling/backbones/vqa_layoutlm.py +++ b/ppocr/modeling/backbones/vqa_layoutlm.py @@ -43,9 +43,11 @@ class NLPBaseModel(nn.Layer): super(NLPBaseModel, self).__init__() if checkpoints is not None: self.model = model_class.from_pretrained(checkpoints) + elif isinstance(pretrained, (str, )) and os.path.exists(pretrained): + self.model = model_class.from_pretrained(pretrained) else: pretrained_model_name = pretrained_model_dict[base_model_class] - if pretrained: + if pretrained is True: base_model = base_model_class.from_pretrained( pretrained_model_name) else: diff --git a/ppstructure/vqa/tools/trans_funsd_label.py b/ppstructure/vqa/tools/trans_funsd_label.py new file mode 100644 index 0000000000..ef7d1db010 --- /dev/null +++ b/ppstructure/vqa/tools/trans_funsd_label.py @@ -0,0 +1,151 @@ +# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import sys +import cv2 +import numpy as np +from copy import deepcopy + + +def trans_poly_to_bbox(poly): + x1 = np.min([p[0] for p in poly]) + x2 = np.max([p[0] for p in poly]) + y1 = np.min([p[1] for p in poly]) + y2 = np.max([p[1] for p in poly]) + return [x1, y1, x2, y2] + + +def get_outer_poly(bbox_list): + x1 = min([bbox[0] for bbox in bbox_list]) + y1 = min([bbox[1] for bbox in bbox_list]) + x2 = max([bbox[2] for bbox in bbox_list]) + y2 = max([bbox[3] for bbox in bbox_list]) + return [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] + + +def load_funsd_label(image_dir, anno_dir): + imgs = os.listdir(image_dir) + annos = os.listdir(anno_dir) + + imgs = [img.replace(".png", "") for img in imgs] + annos = [anno.replace(".json", "") for anno in annos] + + fn_info_map = dict() + for anno_fn in annos: + res = [] + with open(os.path.join(anno_dir, anno_fn + ".json"), "r") as fin: + infos = json.load(fin) + infos = infos["form"] + old_id2new_id_map = dict() + global_new_id = 0 + for info in infos: + if info["text"] is None: + continue + words = info["words"] + if len(words) <= 0: + continue + word_idx = 1 + curr_bboxes = [words[0]["box"]] + curr_texts = [words[0]["text"]] + while word_idx < len(words): + # switch to a new link + if words[word_idx]["box"][0] + 10 <= words[word_idx - 1][ + "box"][2]: + if len("".join(curr_texts[0])) > 0: + res.append({ + "transcription": " ".join(curr_texts), + "label": info["label"], + "points": get_outer_poly(curr_bboxes), + "linking": info["linking"], + "id": global_new_id, + }) + if info["id"] not in old_id2new_id_map: + old_id2new_id_map[info["id"]] = [] + old_id2new_id_map[info["id"]].append(global_new_id) + global_new_id += 1 + curr_bboxes = [words[word_idx]["box"]] + curr_texts = [words[word_idx]["text"]] + else: + curr_bboxes.append(words[word_idx]["box"]) + curr_texts.append(words[word_idx]["text"]) + word_idx += 1 + if len("".join(curr_texts[0])) > 0: + res.append({ + "transcription": " ".join(curr_texts), + "label": info["label"], + "points": get_outer_poly(curr_bboxes), + "linking": info["linking"], + "id": global_new_id, + }) + if info["id"] not in old_id2new_id_map: + old_id2new_id_map[info["id"]] = [] + old_id2new_id_map[info["id"]].append(global_new_id) + global_new_id += 1 + res = sorted( + res, key=lambda r: (r["points"][0][1], r["points"][0][0])) + for i in range(len(res) - 1): + for j in range(i, 0, -1): + if abs(res[j + 1]["points"][0][1] - res[j]["points"][0][1]) < 20 and \ + (res[j + 1]["points"][0][0] < res[j]["points"][0][0]): + tmp = deepcopy(res[j]) + res[j] = deepcopy(res[j + 1]) + res[j + 1] = deepcopy(tmp) + else: + break + # re-generate unique ids + for idx, r in enumerate(res): + new_links = [] + for link in r["linking"]: + # illegal links will be removed + if link[0] not in old_id2new_id_map or link[ + 1] not in old_id2new_id_map: + continue + for src in old_id2new_id_map[link[0]]: + for dst in old_id2new_id_map[link[1]]: + new_links.append([src, dst]) + res[idx]["linking"] = deepcopy(new_links) + + fn_info_map[anno_fn] = res + + return fn_info_map + + +def main(): + test_image_dir = "train_data/FUNSD/testing_data/images/" + test_anno_dir = "train_data/FUNSD/testing_data/annotations/" + test_output_dir = "train_data/FUNSD/test.json" + + fn_info_map = load_funsd_label(test_image_dir, test_anno_dir) + with open(test_output_dir, "w") as fout: + for fn in fn_info_map: + fout.write(fn + ".png" + "\t" + json.dumps( + fn_info_map[fn], ensure_ascii=False) + "\n") + + train_image_dir = "train_data/FUNSD/training_data/images/" + train_anno_dir = "train_data/FUNSD/training_data/annotations/" + train_output_dir = "train_data/FUNSD/train.json" + + fn_info_map = load_funsd_label(train_image_dir, train_anno_dir) + with open(train_output_dir, "w") as fout: + for fn in fn_info_map: + fout.write(fn + ".png" + "\t" + json.dumps( + fn_info_map[fn], ensure_ascii=False) + "\n") + print("====ok====") + return + + +if __name__ == "__main__": + main() From 9a0aa1373ee6cc3acb18a0b3e4ecd9e3b5dc6ed5 Mon Sep 17 00:00:00 2001 From: wangjingyeye <1025993141@qq.com> Date: Wed, 6 Jul 2022 11:36:34 +0000 Subject: [PATCH 26/32] add tipc --- .../train_infer_python.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename test_tipc/configs/{det_r50_db++ => det_r50_db_plusplus}/train_infer_python.txt (98%) diff --git a/test_tipc/configs/det_r50_db++/train_infer_python.txt b/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt similarity index 98% rename from test_tipc/configs/det_r50_db++/train_infer_python.txt rename to test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt index bcf393a52b..04a3e84585 100644 --- a/test_tipc/configs/det_r50_db++/train_infer_python.txt +++ b/test_tipc/configs/det_r50_db_plusplus/train_infer_python.txt @@ -1,5 +1,5 @@ ===========================train_params=========================== -model_name:det_r50_db++ +model_name:det_r50_db_plusplus python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True From d92fa1c6a555b925fa8ec05fc88066adb18f33e4 Mon Sep 17 00:00:00 2001 From: wangjingyeye <1025993141@qq.com> Date: Wed, 6 Jul 2022 16:30:41 +0000 Subject: [PATCH 27/32] add tipc --- test_tipc/prepare.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index 32df8e7865..6387795e8a 100644 --- a/test_tipc/prepare.sh +++ b/test_tipc/prepare.sh @@ -59,7 +59,7 @@ if [ ${MODE} = "lite_train_lite_infer" ];then wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar --no-check-certificate cd ./inference/ && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar && cd ../ fi - if [[ ${model_name} =~ "det_r50_db++" ]];then + if [[ ${model_name} =~ "det_r50_db_plusplus" ]];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/ResNet50_dcn_asf_synthtext_pretrained.pdparams --no-check-certificate fi cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../ From d6b81c6c77b1c00faaf2bb0b2d2927c1304d7c4c Mon Sep 17 00:00:00 2001 From: Evezerest <50011306+Evezerest@users.noreply.github.com> Date: Thu, 7 Jul 2022 10:12:48 +0800 Subject: [PATCH 28/32] Update README_ch.md --- deploy/avh/README_ch.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/deploy/avh/README_ch.md b/deploy/avh/README_ch.md index 7e6272ae12..35cc9f2b7c 100644 --- a/deploy/avh/README_ch.md +++ b/deploy/avh/README_ch.md @@ -14,6 +14,7 @@ +[English](README.md) | 简体中文 通过TVM在 Arm(R) Cortex(R)-M55 CPU 上运行 PaddleOCR文 本能识别模型 =============================================================== @@ -85,9 +86,9 @@ export PATH=/opt/arm/FVP_Corstone_SSE-300/models/Linux64_GCC-6.4:/opt/arm/cmake/ 模型描述 ----------------- -在这个demo中，我们使用的模型是基于[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md)的英文识别模型。 PP-OCRv3是[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)发布的PP-OCR系列模型的第三个版本。 该系列模型具有以下特点： +在这个demo中，我们使用的模型是基于[PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md)的英文识别模型。由于Arm(R) Cortex(R)-M55 CPU不支持rnn算子，我们在PP-OCRv3原始文本识别模型的基础上进行适配，最终模型大小为2.7M。 + +PP-OCRv3是[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)发布的PP-OCR系列模型的第三个版本，该系列模型具有以下特点： - 超轻量级OCR系统：检测（3.6M）+方向分类器（1.4M）+识别（12M）=17.0M。 - 支持80多种多语言识别模型，包括英文、中文、法文、德文、阿拉伯文、韩文、日文等。 - 支持竖排文本识别，长文本识别。 - -PP-OCRv3 中的文本识别模型支持 80 多种语言。 在模型开发过程中，由于Arm(R) Cortex(R)-M55 CPU不支持rnn算子，我们在PP-OCRv3文本识别模型的基础上删除了不支持的算子，得到当前模型。 \ No newline at end of file From ca7c4f5d1935243721b33b81a0922416e5dfa4bc Mon Sep 17 00:00:00 2001 From: Evezerest <50011306+Evezerest@users.noreply.github.com> Date: Thu, 7 Jul 2022 10:15:41 +0800 Subject: [PATCH 29/32] Update README.md --- deploy/avh/README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/deploy/avh/README.md b/deploy/avh/README.md index a64bfd3ad6..0087103146 100644 --- a/deploy/avh/README.md +++ b/deploy/avh/README.md @@ -15,6 +15,8 @@ +English | [简体中文](README_ch.md) + Running PaddleOCR text recognition model on bare metal Arm(R) Cortex(R)-M55 CPU using Arm Virtual Hardware ====================================================================== @@ -104,9 +106,11 @@ python3 ./convert_image.py path/to/image Model description ----------------- -The example is built on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md) English recognition model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). PP-OCRv3 is the third version of the PP-OCR series model. This series of models has the following features: +The example is built on [PP-OCRv3](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/PP-OCRv3_introduction.md) English recognition model released by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). Since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current 2.7M English recognition model. + +PP-OCRv3 is the third version of the PP-OCR series model. This series of models has the following features: - PP-OCRv3: ultra-lightweight OCR system: detection (3.6M) + direction classifier (1.4M) + recognition (12M) = 17.0M - Support more than 80 kinds of multi-language recognition models, including English, Chinese, French, German, Arabic, Korean, Japanese and so on. For details - Support vertical text recognition, and long text recognition -The text recognition model in PP-OCRv3 supports more than 80 languages. In the process of model development, since Arm(R) Cortex(R)-M55 CPU does not support rnn operator, we delete the unsupported operator based on the PP-OCRv3 text recognition model to obtain the current model. + From f6160082105ab364204847402236ae89fb0abe39 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 7 Jul 2022 11:38:33 +0800 Subject: [PATCH 30/32] fix benchmark train for east --- test_tipc/prepare.sh | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index 73016ca44a..9c62dc5e2c 100644 --- a/test_tipc/prepare.sh +++ b/test_tipc/prepare.sh @@ -28,6 +28,10 @@ if [ ${MODE} = "benchmark_train" ];then wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate cd ./train_data/ && tar xf icdar2015.tar && cd ../ fi + if [[ ${model_name} =~ "det_r50_vd_east_v2_0"; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf det_r50_vd_east_v2.0_train.tar && cd ../ + fi if [[ ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate From 495c4b6f0e9df0e5e8935bfe94d3a8fc57536c13 Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Thu, 7 Jul 2022 13:59:02 +0800 Subject: [PATCH 31/32] fix pretrain --- test_tipc/prepare.sh | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index 9c62dc5e2c..8e1758abb8 100644 --- a/test_tipc/prepare.sh +++ b/test_tipc/prepare.sh @@ -22,17 +22,19 @@ trainer_list=$(func_parser_value "${lines[14]}") if [ ${MODE} = "benchmark_train" ];then pip install -r requirements.txt - if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then + if [[ ${model_name} =~ "det_mv3_db_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" || ${model_name} =~ "det_r18_db_v2_0" ]];then rm -rf ./train_data/icdar2015 wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate cd ./train_data/ && tar xf icdar2015.tar && cd ../ fi - if [[ ${model_name} =~ "det_r50_vd_east_v2_0"; then + if [[ ${model_name} =~ "det_r50_vd_east_v2_0" ]]; then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar --no-check-certificate cd ./pretrain_models/ && tar xf det_r50_vd_east_v2.0_train.tar && cd ../ + wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate + cd ./train_data/ && tar xf icdar2015.tar && cd ../ fi - if [[ ${model_name} =~ "det_r50_vd_east_v2_0" || ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then + if [[ ${model_name} =~ "det_r50_vd_pse_v2_0" ]];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate cd ./train_data/ && tar xf icdar2015.tar && cd ../ From e727b09709ac366e3af58109928885489b975b0d Mon Sep 17 00:00:00 2001 From: zhoujun Date: Thu, 7 Jul 2022 15:40:23 +0800 Subject: [PATCH 32/32] fix config (#6823) --- configs/table/table_master.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/configs/table/table_master.yml b/configs/table/table_master.yml index cfd98346c4..1e6efe32db 100755 --- a/configs/table/table_master.yml +++ b/configs/table/table_master.yml @@ -104,8 +104,8 @@ Train: Eval: dataset: name: PubTabDataSet - data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/ - label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl] + data_dir: train_data/table/pubtabnet/train/ + label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl] transforms: - DecodeImage: img_mode: BGR