delete other rec algorithm

deploy/pphuman/config/infer_cfg_ppvehicle.yml

@@ -19,7 +19,6 @@ VEHICLE_PLATE:
   det_model_dir: output_inference/ch_PP-OCRv3_det_infer/
   det_limit_side_len: 480
   det_limit_type: "max"
-  rec_algorithm: "SVTR_LCNet"
   rec_model_dir: output_inference/ch_PP-OCRv3_rec_infer/
   rec_image_shape: [3, 48, 320]
   rec_batch_num: 6

deploy/pphuman/ppvehicle/vehicle_plate.py

@@ -151,7 +151,6 @@ class TextRecognizer(object):
     def __init__(self, args, cfg, use_gpu=True):
         self.rec_image_shape = cfg['rec_image_shape']
         self.rec_batch_num = cfg['rec_batch_num']
-        self.rec_algorithm = cfg['rec_algorithm']
         word_dict_path = cfg['word_dict_path']
         use_space_char = True
 
@@ -160,30 +159,6 @@ class TextRecognizer(object):
             "character_dict_path": word_dict_path,
             "use_space_char": use_space_char
         }
-        if self.rec_algorithm == "SRN":
-            postprocess_params = {
-                'name': 'SRNLabelDecode',
-                "character_dict_path": word_dict_path,
-                "use_space_char": use_space_char
-            }
-        elif self.rec_algorithm == "RARE":
-            postprocess_params = {
-                'name': 'AttnLabelDecode',
-                "character_dict_path": word_dict_path,
-                "use_space_char": use_space_char
-            }
-        elif self.rec_algorithm == 'NRTR':
-            postprocess_params = {
-                'name': 'NRTRLabelDecode',
-                "character_dict_path": word_dict_path,
-                "use_space_char": use_space_char
-            }
-        elif self.rec_algorithm == "SAR":
-            postprocess_params = {
-                'name': 'SARLabelDecode',
-                "character_dict_path": word_dict_path,
-                "use_space_char": use_space_char
-            }
         self.postprocess_op = build_post_process(postprocess_params)
         self.predictor, self.input_tensor, self.output_tensors, self.config = \
             create_predictor(args, cfg, 'rec')
@@ -191,15 +166,6 @@ class TextRecognizer(object):
 
     def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
-        if self.rec_algorithm == 'NRTR':
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-            # return padding_im
-            image_pil = Image.fromarray(np.uint8(img))
-            img = image_pil.resize([100, 32], Image.ANTIALIAS)
-            img = np.array(img)
-            norm_img = np.expand_dims(img, -1)
-            norm_img = norm_img.transpose((2, 0, 1))
-            return norm_img.astype(np.float32) / 128. - 1.
 
         assert imgC == img.shape[2]
         imgW = int((imgH * max_wh_ratio))
@@ -214,10 +180,6 @@ class TextRecognizer(object):
             resized_w = imgW
         else:
             resized_w = int(math.ceil(imgH * ratio))
-        if self.rec_algorithm == 'RARE':
-            if resized_w > self.rec_image_shape[2]:
-                resized_w = self.rec_image_shape[2]
-            imgW = self.rec_image_shape[2]
         resized_image = cv2.resize(img, (resized_w, imgH))
         resized_image = resized_image.astype('float32')
         resized_image = resized_image.transpose((2, 0, 1)) / 255
@@ -227,124 +189,6 @@ class TextRecognizer(object):
         padding_im[:, :, 0:resized_w] = resized_image
         return padding_im
 
-    def resize_norm_img_svtr(self, img, image_shape):
-
-        imgC, imgH, imgW = image_shape
-        resized_image = cv2.resize(
-            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
-        resized_image = resized_image.astype('float32')
-        resized_image = resized_image.transpose((2, 0, 1)) / 255
-        resized_image -= 0.5
-        resized_image /= 0.5
-        return resized_image
-
-    def resize_norm_img_srn(self, img, image_shape):
-        imgC, imgH, imgW = image_shape
-
-        img_black = np.zeros((imgH, imgW))
-        im_hei = img.shape[0]
-        im_wid = img.shape[1]
-
-        if im_wid <= im_hei * 1:
-            img_new = cv2.resize(img, (imgH * 1, imgH))
-        elif im_wid <= im_hei * 2:
-            img_new = cv2.resize(img, (imgH * 2, imgH))
-        elif im_wid <= im_hei * 3:
-            img_new = cv2.resize(img, (imgH * 3, imgH))
-        else:
-            img_new = cv2.resize(img, (imgW, imgH))
-
-        img_np = np.asarray(img_new)
-        img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
-        img_black[:, 0:img_np.shape[1]] = img_np
-        img_black = img_black[:, :, np.newaxis]
-
-        row, col, c = img_black.shape
-        c = 1
-
-        return np.reshape(img_black, (c, row, col)).astype(np.float32)
-
-    def srn_other_inputs(self, image_shape, num_heads, max_text_length):
-
-        imgC, imgH, imgW = image_shape
-        feature_dim = int((imgH / 8) * (imgW / 8))
-
-        encoder_word_pos = np.array(range(0, feature_dim)).reshape(
-            (feature_dim, 1)).astype('int64')
-        gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
-            (max_text_length, 1)).astype('int64')
-
-        gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
-        gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
-            [-1, 1, max_text_length, max_text_length])
-        gsrm_slf_attn_bias1 = np.tile(
-            gsrm_slf_attn_bias1,
-            [1, num_heads, 1, 1]).astype('float32') * [-1e9]
-
-        gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
-            [-1, 1, max_text_length, max_text_length])
-        gsrm_slf_attn_bias2 = np.tile(
-            gsrm_slf_attn_bias2,
-            [1, num_heads, 1, 1]).astype('float32') * [-1e9]
-
-        encoder_word_pos = encoder_word_pos[np.newaxis, :]
-        gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
-
-        return [
-            encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
-            gsrm_slf_attn_bias2
-        ]
-
-    def process_image_srn(self, img, image_shape, num_heads, max_text_length):
-        norm_img = self.resize_norm_img_srn(img, image_shape)
-        norm_img = norm_img[np.newaxis, :]
-
-        [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
-            self.srn_other_inputs(image_shape, num_heads, max_text_length)
-
-        gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
-        gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
-        encoder_word_pos = encoder_word_pos.astype(np.int64)
-        gsrm_word_pos = gsrm_word_pos.astype(np.int64)
-
-        return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
-                gsrm_slf_attn_bias2)
-
-    def resize_norm_img_sar(self, img, image_shape,
-                            width_downsample_ratio=0.25):
-        imgC, imgH, imgW_min, imgW_max = image_shape
-        h = img.shape[0]
-        w = img.shape[1]
-        valid_ratio = 1.0
-        # make sure new_width is an integral multiple of width_divisor.
-        width_divisor = int(1 / width_downsample_ratio)
-        # resize
-        ratio = w / float(h)
-        resize_w = math.ceil(imgH * ratio)
-        if resize_w % width_divisor != 0:
-            resize_w = round(resize_w / width_divisor) * width_divisor
-        if imgW_min is not None:
-            resize_w = max(imgW_min, resize_w)
-        if imgW_max is not None:
-            valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
-            resize_w = min(imgW_max, resize_w)
-        resized_image = cv2.resize(img, (resize_w, imgH))
-        resized_image = resized_image.astype('float32')
-        # norm 
-        if image_shape[0] == 1:
-            resized_image = resized_image / 255
-            resized_image = resized_image[np.newaxis, :]
-        else:
-            resized_image = resized_image.transpose((2, 0, 1)) / 255
-        resized_image -= 0.5
-        resized_image /= 0.5
-        resize_shape = resized_image.shape
-        padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
-        padding_im[:, :, 0:resize_w] = resized_image
-        pad_shape = padding_im.shape
-
-        return padding_im, resize_shape, pad_shape, valid_ratio
-
     def predict_text(self, img_list):
         img_num = len(img_list)
         # Calculate the aspect ratio of all text bars
@@ -367,103 +211,16 @@ class TextRecognizer(object):
                 wh_ratio = w * 1.0 / h
                 max_wh_ratio = max(max_wh_ratio, wh_ratio)
             for ino in range(beg_img_no, end_img_no):
-
-                if self.rec_algorithm == "SAR":
-                    norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
-                        img_list[indices[ino]], self.rec_image_shape)
-                    norm_img = norm_img[np.newaxis, :]
-                    valid_ratio = np.expand_dims(valid_ratio, axis=0)
-                    valid_ratios = []
-                    valid_ratios.append(valid_ratio)
-                    norm_img_batch.append(norm_img)
-                elif self.rec_algorithm == "SRN":
-                    norm_img = self.process_image_srn(
-                        img_list[indices[ino]], self.rec_image_shape, 8, 25)
-                    encoder_word_pos_list = []
-                    gsrm_word_pos_list = []
-                    gsrm_slf_attn_bias1_list = []
-                    gsrm_slf_attn_bias2_list = []
-                    encoder_word_pos_list.append(norm_img[1])
-                    gsrm_word_pos_list.append(norm_img[2])
-                    gsrm_slf_attn_bias1_list.append(norm_img[3])
-                    gsrm_slf_attn_bias2_list.append(norm_img[4])
-                    norm_img_batch.append(norm_img[0])
-                elif self.rec_algorithm == "SVTR":
-                    norm_img = self.resize_norm_img_svtr(img_list[indices[ino]],
-                                                         self.rec_image_shape)
-                    norm_img = norm_img[np.newaxis, :]
-                    norm_img_batch.append(norm_img)
-                else:
                 norm_img = self.resize_norm_img(img_list[indices[ino]],
                                                 max_wh_ratio)
                 norm_img = norm_img[np.newaxis, :]
                 norm_img_batch.append(norm_img)
             norm_img_batch = np.concatenate(norm_img_batch)
             norm_img_batch = norm_img_batch.copy()
-
-            if self.rec_algorithm == "SRN":
-                encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
-                gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
-                gsrm_slf_attn_bias1_list = np.concatenate(
-                    gsrm_slf_attn_bias1_list)
-                gsrm_slf_attn_bias2_list = np.concatenate(
-                    gsrm_slf_attn_bias2_list)
-
-                inputs = [
-                    norm_img_batch,
-                    encoder_word_pos_list,
-                    gsrm_word_pos_list,
-                    gsrm_slf_attn_bias1_list,
-                    gsrm_slf_attn_bias2_list,
-                ]
-                if self.use_onnx:
-                    input_dict = {}
-                    input_dict[self.input_tensor.name] = norm_img_batch
-                    outputs = self.predictor.run(self.output_tensors,
-                                                 input_dict)
-                    preds = {"predict": outputs[2]}
-                else:
-                    input_names = self.predictor.get_input_names()
-                    for i in range(len(input_names)):
-                        input_tensor = self.predictor.get_input_handle(
-                            input_names[i])
-                        input_tensor.copy_from_cpu(inputs[i])
-                    self.predictor.run()
-                    outputs = []
-                    for output_tensor in self.output_tensors:
-                        output = output_tensor.copy_to_cpu()
-                        outputs.append(output)
-                    preds = {"predict": outputs[2]}
-            elif self.rec_algorithm == "SAR":
-                valid_ratios = np.concatenate(valid_ratios)
-                inputs = [
-                    norm_img_batch,
-                    valid_ratios,
-                ]
-                if self.use_onnx:
-                    input_dict = {}
-                    input_dict[self.input_tensor.name] = norm_img_batch
-                    outputs = self.predictor.run(self.output_tensors,
-                                                 input_dict)
-                    preds = outputs[0]
-                else:
-                    input_names = self.predictor.get_input_names()
-                    for i in range(len(input_names)):
-                        input_tensor = self.predictor.get_input_handle(
-                            input_names[i])
-                        input_tensor.copy_from_cpu(inputs[i])
-                    self.predictor.run()
-                    outputs = []
-                    for output_tensor in self.output_tensors:
-                        output = output_tensor.copy_to_cpu()
-                        outputs.append(output)
-                    preds = outputs[0]
-            else:
             if self.use_onnx:
                 input_dict = {}
                 input_dict[self.input_tensor.name] = norm_img_batch
-                    outputs = self.predictor.run(self.output_tensors,
-                                                 input_dict)
+                outputs = self.predictor.run(self.output_tensors, input_dict)
                 preds = outputs[0]
             else:
                 self.input_tensor.copy_from_cpu(norm_img_batch)

deploy/pphuman/ppvehicle/vehicle_plateutils.py

@@ -185,7 +185,6 @@ def create_predictor(args, cfg, mode):
 def get_output_tensors(cfg, mode, predictor):
     output_names = predictor.get_output_names()
     output_tensors = []
-    if mode == "rec" and cfg['rec_algorithm'] in ["CRNN", "SVTR_LCNet"]:
     output_name = 'softmax_0.tmp_0'
     if output_name in output_names:
         return [predictor.get_output_handle(output_name)]
@@ -193,10 +192,6 @@ def get_output_tensors(cfg, mode, predictor):
         for output_name in output_names:
             output_tensor = predictor.get_output_handle(output_name)
             output_tensors.append(output_tensor)
-    else:
-        for output_name in output_names:
-            output_tensor = predictor.get_output_handle(output_name)
-            output_tensors.append(output_tensor)
     return output_tensors
 
 

deploy/pphuman/ppvehicle/vehicleplate_postprocess.py

@@ -23,16 +23,7 @@ import copy
 
 
 def build_post_process(config, global_config=None):
-    support_dict = [
-        'DBPostProcess', 'CTCLabelDecode', 'AttnLabelDecode', 'SRNLabelDecode',
-        'DistillationCTCLabelDecode', 'TableLabelDecode', 'NRTRLabelDecode',
-        'SARLabelDecode', 'SEEDLabelDecode', 'PRENLabelDecode',
-        'DistillationSARLabelDecode'
-    ]
-
-    if config['name'] == 'PSEPostProcess':
-        from .pse_postprocess import PSEPostProcess
-        support_dict.append('PSEPostProcess')
+    support_dict = ['DBPostProcess', 'CTCLabelDecode']
 
     config = copy.deepcopy(config)
     module_name = config.pop('name')
@@ -298,651 +289,3 @@ class CTCLabelDecode(BaseRecLabelDecode):
     def add_special_char(self, dict_character):
         dict_character = ['blank'] + dict_character
         return dict_character
-
-
-class DistillationCTCLabelDecode(CTCLabelDecode):
-    """
-    Convert 
-    Convert between text-label and text-index
-    """
-
-    def __init__(self,
-                 character_dict_path=None,
-                 use_space_char=False,
-                 model_name=["student"],
-                 key=None,
-                 multi_head=False,
-                 **kwargs):
-        super(DistillationCTCLabelDecode, self).__init__(character_dict_path,
-                                                         use_space_char)
-        if not isinstance(model_name, list):
-            model_name = [model_name]
-        self.model_name = model_name
-
-        self.key = key
-        self.multi_head = multi_head
-
-    def __call__(self, preds, label=None, *args, **kwargs):
-        output = dict()
-        for name in self.model_name:
-            pred = preds[name]
-            if self.key is not None:
-                pred = pred[self.key]
-            if self.multi_head and isinstance(pred, dict):
-                pred = pred['ctc']
-            output[name] = super().__call__(pred, label=label, *args, **kwargs)
-        return output
-
-
-class NRTRLabelDecode(BaseRecLabelDecode):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
-        super(NRTRLabelDecode, self).__init__(character_dict_path,
-                                              use_space_char)
-
-    def __call__(self, preds, label=None, *args, **kwargs):
-
-        if len(preds) == 2:
-            preds_id = preds[0]
-            preds_prob = preds[1]
-            if isinstance(preds_id, paddle.Tensor):
-                preds_id = preds_id.numpy()
-            if isinstance(preds_prob, paddle.Tensor):
-                preds_prob = preds_prob.numpy()
-            if preds_id[0][0] == 2:
-                preds_idx = preds_id[:, 1:]
-                preds_prob = preds_prob[:, 1:]
-            else:
-                preds_idx = preds_id
-            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
-            if label is None:
-                return text
-            label = self.decode(label[:, 1:])
-        else:
-            if isinstance(preds, paddle.Tensor):
-                preds = preds.numpy()
-            preds_idx = preds.argmax(axis=2)
-            preds_prob = preds.max(axis=2)
-            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
-            if label is None:
-                return text
-            label = self.decode(label[:, 1:])
-        return text, label
-
-    def add_special_char(self, dict_character):
-        dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
-        return dict_character
-
-    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
-        """ convert text-index into text-label. """
-        result_list = []
-        batch_size = len(text_index)
-        for batch_idx in range(batch_size):
-            char_list = []
-            conf_list = []
-            for idx in range(len(text_index[batch_idx])):
-                if text_index[batch_idx][idx] == 3:  # end
-                    break
-                try:
-                    char_list.append(self.character[int(text_index[batch_idx][
-                        idx])])
-                except:
-                    continue
-                if text_prob is not None:
-                    conf_list.append(text_prob[batch_idx][idx])
-                else:
-                    conf_list.append(1)
-            text = ''.join(char_list)
-            result_list.append((text.lower(), np.mean(conf_list).tolist()))
-        return result_list
-
-
-class AttnLabelDecode(BaseRecLabelDecode):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, character_dict_path=None, use_space_char=False,
-                 **kwargs):
-        super(AttnLabelDecode, self).__init__(character_dict_path,
-                                              use_space_char)
-
-    def add_special_char(self, dict_character):
-        self.beg_str = "sos"
-        self.end_str = "eos"
-        dict_character = dict_character
-        dict_character = [self.beg_str] + dict_character + [self.end_str]
-        return dict_character
-
-    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
-        """ convert text-index into text-label. """
-        result_list = []
-        ignored_tokens = self.get_ignored_tokens()
-        [beg_idx, end_idx] = self.get_ignored_tokens()
-        batch_size = len(text_index)
-        for batch_idx in range(batch_size):
-            char_list = []
-            conf_list = []
-            for idx in range(len(text_index[batch_idx])):
-                if text_index[batch_idx][idx] in ignored_tokens:
-                    continue
-                if int(text_index[batch_idx][idx]) == int(end_idx):
-                    break
-                if is_remove_duplicate:
-                    # only for predict
-                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
-                            batch_idx][idx]:
-                        continue
-                char_list.append(self.character[int(text_index[batch_idx][
-                    idx])])
-                if text_prob is not None:
-                    conf_list.append(text_prob[batch_idx][idx])
-                else:
-                    conf_list.append(1)
-            text = ''.join(char_list)
-            result_list.append((text, np.mean(conf_list).tolist()))
-        return result_list
-
-    def __call__(self, preds, label=None, *args, **kwargs):
-        """
-        text = self.decode(text)
-        if label is None:
-            return text
-        else:
-            label = self.decode(label, is_remove_duplicate=False)
-            return text, label
-        """
-        if isinstance(preds, paddle.Tensor):
-            preds = preds.numpy()
-
-        preds_idx = preds.argmax(axis=2)
-        preds_prob = preds.max(axis=2)
-        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
-        if label is None:
-            return text
-        label = self.decode(label, is_remove_duplicate=False)
-        return text, label
-
-    def get_ignored_tokens(self):
-        beg_idx = self.get_beg_end_flag_idx("beg")
-        end_idx = self.get_beg_end_flag_idx("end")
-        return [beg_idx, end_idx]
-
-    def get_beg_end_flag_idx(self, beg_or_end):
-        if beg_or_end == "beg":
-            idx = np.array(self.dict[self.beg_str])
-        elif beg_or_end == "end":
-            idx = np.array(self.dict[self.end_str])
-        else:
-            assert False, "unsupport type %s in get_beg_end_flag_idx" \
-                          % beg_or_end
-        return idx
-
-
-class SEEDLabelDecode(BaseRecLabelDecode):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, character_dict_path=None, use_space_char=False,
-                 **kwargs):
-        super(SEEDLabelDecode, self).__init__(character_dict_path,
-                                              use_space_char)
-
-    def add_special_char(self, dict_character):
-        self.padding_str = "padding"
-        self.end_str = "eos"
-        self.unknown = "unknown"
-        dict_character = dict_character + [
-            self.end_str, self.padding_str, self.unknown
-        ]
-        return dict_character
-
-    def get_ignored_tokens(self):
-        end_idx = self.get_beg_end_flag_idx("eos")
-        return [end_idx]
-
-    def get_beg_end_flag_idx(self, beg_or_end):
-        if beg_or_end == "sos":
-            idx = np.array(self.dict[self.beg_str])
-        elif beg_or_end == "eos":
-            idx = np.array(self.dict[self.end_str])
-        else:
-            assert False, "unsupport type %s in get_beg_end_flag_idx" % beg_or_end
-        return idx
-
-    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
-        """ convert text-index into text-label. """
-        result_list = []
-        [end_idx] = self.get_ignored_tokens()
-        batch_size = len(text_index)
-        for batch_idx in range(batch_size):
-            char_list = []
-            conf_list = []
-            for idx in range(len(text_index[batch_idx])):
-                if int(text_index[batch_idx][idx]) == int(end_idx):
-                    break
-                if is_remove_duplicate:
-                    # only for predict
-                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
-                            batch_idx][idx]:
-                        continue
-                char_list.append(self.character[int(text_index[batch_idx][
-                    idx])])
-                if text_prob is not None:
-                    conf_list.append(text_prob[batch_idx][idx])
-                else:
-                    conf_list.append(1)
-            text = ''.join(char_list)
-            result_list.append((text, np.mean(conf_list).tolist()))
-        return result_list
-
-    def __call__(self, preds, label=None, *args, **kwargs):
-        """
-        text = self.decode(text)
-        if label is None:
-            return text
-        else:
-            label = self.decode(label, is_remove_duplicate=False)
-            return text, label
-        """
-        preds_idx = preds["rec_pred"]
-        if isinstance(preds_idx, paddle.Tensor):
-            preds_idx = preds_idx.numpy()
-        if "rec_pred_scores" in preds:
-            preds_idx = preds["rec_pred"]
-            preds_prob = preds["rec_pred_scores"]
-        else:
-            preds_idx = preds["rec_pred"].argmax(axis=2)
-            preds_prob = preds["rec_pred"].max(axis=2)
-        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
-        if label is None:
-            return text
-        label = self.decode(label, is_remove_duplicate=False)
-        return text, label
-
-
-class SRNLabelDecode(BaseRecLabelDecode):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, character_dict_path=None, use_space_char=False,
-                 **kwargs):
-        super(SRNLabelDecode, self).__init__(character_dict_path,
-                                             use_space_char)
-        self.max_text_length = kwargs.get('max_text_length', 25)
-
-    def __call__(self, preds, label=None, *args, **kwargs):
-        pred = preds['predict']
-        char_num = len(self.character_str) + 2
-        if isinstance(pred, paddle.Tensor):
-            pred = pred.numpy()
-        pred = np.reshape(pred, [-1, char_num])
-
-        preds_idx = np.argmax(pred, axis=1)
-        preds_prob = np.max(pred, axis=1)
-
-        preds_idx = np.reshape(preds_idx, [-1, self.max_text_length])
-
-        preds_prob = np.reshape(preds_prob, [-1, self.max_text_length])
-
-        text = self.decode(preds_idx, preds_prob)
-
-        if label is None:
-            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
-            return text
-        label = self.decode(label)
-        return text, label
-
-    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
-        """ convert text-index into text-label. """
-        result_list = []
-        ignored_tokens = self.get_ignored_tokens()
-        batch_size = len(text_index)
-
-        for batch_idx in range(batch_size):
-            char_list = []
-            conf_list = []
-            for idx in range(len(text_index[batch_idx])):
-                if text_index[batch_idx][idx] in ignored_tokens:
-                    continue
-                if is_remove_duplicate:
-                    # only for predict
-                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
-                            batch_idx][idx]:
-                        continue
-                char_list.append(self.character[int(text_index[batch_idx][
-                    idx])])
-                if text_prob is not None:
-                    conf_list.append(text_prob[batch_idx][idx])
-                else:
-                    conf_list.append(1)
-
-            text = ''.join(char_list)
-            result_list.append((text, np.mean(conf_list).tolist()))
-        return result_list
-
-    def add_special_char(self, dict_character):
-        dict_character = dict_character + [self.beg_str, self.end_str]
-        return dict_character
-
-    def get_ignored_tokens(self):
-        beg_idx = self.get_beg_end_flag_idx("beg")
-        end_idx = self.get_beg_end_flag_idx("end")
-        return [beg_idx, end_idx]
-
-    def get_beg_end_flag_idx(self, beg_or_end):
-        if beg_or_end == "beg":
-            idx = np.array(self.dict[self.beg_str])
-        elif beg_or_end == "end":
-            idx = np.array(self.dict[self.end_str])
-        else:
-            assert False, "unsupport type %s in get_beg_end_flag_idx" \
-                          % beg_or_end
-        return idx
-
-
-class TableLabelDecode(object):
-    """  """
-
-    def __init__(self, character_dict_path, **kwargs):
-        list_character, list_elem = self.load_char_elem_dict(
-            character_dict_path)
-        list_character = self.add_special_char(list_character)
-        list_elem = self.add_special_char(list_elem)
-        self.dict_character = {}
-        self.dict_idx_character = {}
-        for i, char in enumerate(list_character):
-            self.dict_idx_character[i] = char
-            self.dict_character[char] = i
-        self.dict_elem = {}
-        self.dict_idx_elem = {}
-        for i, elem in enumerate(list_elem):
-            self.dict_idx_elem[i] = elem
-            self.dict_elem[elem] = i
-
-    def load_char_elem_dict(self, character_dict_path):
-        list_character = []
-        list_elem = []
-        with open(character_dict_path, "rb") as fin:
-            lines = fin.readlines()
-            substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split(
-                "\t")
-            character_num = int(substr[0])
-            elem_num = int(substr[1])
-            for cno in range(1, 1 + character_num):
-                character = lines[cno].decode('utf-8').strip("\n").strip("\r\n")
-                list_character.append(character)
-            for eno in range(1 + character_num, 1 + character_num + elem_num):
-                elem = lines[eno].decode('utf-8').strip("\n").strip("\r\n")
-                list_elem.append(elem)
-        return list_character, list_elem
-
-    def add_special_char(self, list_character):
-        self.beg_str = "sos"
-        self.end_str = "eos"
-        list_character = [self.beg_str] + list_character + [self.end_str]
-        return list_character
-
-    def __call__(self, preds):
-        structure_probs = preds['structure_probs']
-        loc_preds = preds['loc_preds']
-        if isinstance(structure_probs, paddle.Tensor):
-            structure_probs = structure_probs.numpy()
-        if isinstance(loc_preds, paddle.Tensor):
-            loc_preds = loc_preds.numpy()
-        structure_idx = structure_probs.argmax(axis=2)
-        structure_probs = structure_probs.max(axis=2)
-        structure_str, structure_pos, result_score_list, result_elem_idx_list = self.decode(
-            structure_idx, structure_probs, 'elem')
-        res_html_code_list = []
-        res_loc_list = []
-        batch_num = len(structure_str)
-        for bno in range(batch_num):
-            res_loc = []
-            for sno in range(len(structure_str[bno])):
-                text = structure_str[bno][sno]
-                if text in ['<td>', '<td']:
-                    pos = structure_pos[bno][sno]
-                    res_loc.append(loc_preds[bno, pos])
-            res_html_code = ''.join(structure_str[bno])
-            res_loc = np.array(res_loc)
-            res_html_code_list.append(res_html_code)
-            res_loc_list.append(res_loc)
-        return {
-            'res_html_code': res_html_code_list,
-            'res_loc': res_loc_list,
-            'res_score_list': result_score_list,
-            'res_elem_idx_list': result_elem_idx_list,
-            'structure_str_list': structure_str
-        }
-
-    def decode(self, text_index, structure_probs, char_or_elem):
-        """convert text-label into text-index.
-        """
-        if char_or_elem == "char":
-            current_dict = self.dict_idx_character
-        else:
-            current_dict = self.dict_idx_elem
-            ignored_tokens = self.get_ignored_tokens('elem')
-            beg_idx, end_idx = ignored_tokens
-
-        result_list = []
-        result_pos_list = []
-        result_score_list = []
-        result_elem_idx_list = []
-        batch_size = len(text_index)
-        for batch_idx in range(batch_size):
-            char_list = []
-            elem_pos_list = []
-            elem_idx_list = []
-            score_list = []
-            for idx in range(len(text_index[batch_idx])):
-                tmp_elem_idx = int(text_index[batch_idx][idx])
-                if idx > 0 and tmp_elem_idx == end_idx:
-                    break
-                if tmp_elem_idx in ignored_tokens:
-                    continue
-
-                char_list.append(current_dict[tmp_elem_idx])
-                elem_pos_list.append(idx)
-                score_list.append(structure_probs[batch_idx, idx])
-                elem_idx_list.append(tmp_elem_idx)
-            result_list.append(char_list)
-            result_pos_list.append(elem_pos_list)
-            result_score_list.append(score_list)
-            result_elem_idx_list.append(elem_idx_list)
-        return result_list, result_pos_list, result_score_list, result_elem_idx_list
-
-    def get_ignored_tokens(self, char_or_elem):
-        beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem)
-        end_idx = self.get_beg_end_flag_idx("end", char_or_elem)
-        return [beg_idx, end_idx]
-
-    def get_beg_end_flag_idx(self, beg_or_end, char_or_elem):
-        if char_or_elem == "char":
-            if beg_or_end == "beg":
-                idx = self.dict_character[self.beg_str]
-            elif beg_or_end == "end":
-                idx = self.dict_character[self.end_str]
-            else:
-                assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \
-                              % beg_or_end
-        elif char_or_elem == "elem":
-            if beg_or_end == "beg":
-                idx = self.dict_elem[self.beg_str]
-            elif beg_or_end == "end":
-                idx = self.dict_elem[self.end_str]
-            else:
-                assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \
-                              % beg_or_end
-        else:
-            assert False, "Unsupport type %s in char_or_elem" \
-                          % char_or_elem
-        return idx
-
-
-class SARLabelDecode(BaseRecLabelDecode):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, character_dict_path=None, use_space_char=False,
-                 **kwargs):
-        super(SARLabelDecode, self).__init__(character_dict_path,
-                                             use_space_char)
-
-        self.rm_symbol = kwargs.get('rm_symbol', False)
-
-    def add_special_char(self, dict_character):
-        beg_end_str = "<BOS/EOS>"
-        unknown_str = "<UKN>"
-        padding_str = "<PAD>"
-        dict_character = dict_character + [unknown_str]
-        self.unknown_idx = len(dict_character) - 1
-        dict_character = dict_character + [beg_end_str]
-        self.start_idx = len(dict_character) - 1
-        self.end_idx = len(dict_character) - 1
-        dict_character = dict_character + [padding_str]
-        self.padding_idx = len(dict_character) - 1
-        return dict_character
-
-    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
-        """ convert text-index into text-label. """
-        result_list = []
-        ignored_tokens = self.get_ignored_tokens()
-
-        batch_size = len(text_index)
-        for batch_idx in range(batch_size):
-            char_list = []
-            conf_list = []
-            for idx in range(len(text_index[batch_idx])):
-                if text_index[batch_idx][idx] in ignored_tokens:
-                    continue
-                if int(text_index[batch_idx][idx]) == int(self.end_idx):
-                    if text_prob is None and idx == 0:
-                        continue
-                    else:
-                        break
-                if is_remove_duplicate:
-                    # only for predict
-                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
-                            batch_idx][idx]:
-                        continue
-                char_list.append(self.character[int(text_index[batch_idx][
-                    idx])])
-                if text_prob is not None:
-                    conf_list.append(text_prob[batch_idx][idx])
-                else:
-                    conf_list.append(1)
-            text = ''.join(char_list)
-            if self.rm_symbol:
-                comp = re.compile('[^A-Z^a-z^0-9^\u4e00-\u9fa5]')
-                text = text.lower()
-                text = comp.sub('', text)
-            result_list.append((text, np.mean(conf_list).tolist()))
-        return result_list
-
-    def __call__(self, preds, label=None, *args, **kwargs):
-        if isinstance(preds, paddle.Tensor):
-            preds = preds.numpy()
-        preds_idx = preds.argmax(axis=2)
-        preds_prob = preds.max(axis=2)
-
-        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
-
-        if label is None:
-            return text
-        label = self.decode(label, is_remove_duplicate=False)
-        return text, label
-
-    def get_ignored_tokens(self):
-        return [self.padding_idx]
-
-
-class DistillationSARLabelDecode(SARLabelDecode):
-    """
-    Convert 
-    Convert between text-label and text-index
-    """
-
-    def __init__(self,
-                 character_dict_path=None,
-                 use_space_char=False,
-                 model_name=["student"],
-                 key=None,
-                 multi_head=False,
-                 **kwargs):
-        super(DistillationSARLabelDecode, self).__init__(character_dict_path,
-                                                         use_space_char)
-        if not isinstance(model_name, list):
-            model_name = [model_name]
-        self.model_name = model_name
-
-        self.key = key
-        self.multi_head = multi_head
-
-    def __call__(self, preds, label=None, *args, **kwargs):
-        output = dict()
-        for name in self.model_name:
-            pred = preds[name]
-            if self.key is not None:
-                pred = pred[self.key]
-            if self.multi_head and isinstance(pred, dict):
-                pred = pred['sar']
-            output[name] = super().__call__(pred, label=label, *args, **kwargs)
-        return output
-
-
-class PRENLabelDecode(BaseRecLabelDecode):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, character_dict_path=None, use_space_char=False,
-                 **kwargs):
-        super(PRENLabelDecode, self).__init__(character_dict_path,
-                                              use_space_char)
-
-    def add_special_char(self, dict_character):
-        padding_str = '<PAD>'  # 0 
-        end_str = '<EOS>'  # 1
-        unknown_str = '<UNK>'  # 2
-
-        dict_character = [padding_str, end_str, unknown_str] + dict_character
-        self.padding_idx = 0
-        self.end_idx = 1
-        self.unknown_idx = 2
-
-        return dict_character
-
-    def decode(self, text_index, text_prob=None):
-        """ convert text-index into text-label. """
-        result_list = []
-        batch_size = len(text_index)
-
-        for batch_idx in range(batch_size):
-            char_list = []
-            conf_list = []
-            for idx in range(len(text_index[batch_idx])):
-                if text_index[batch_idx][idx] == self.end_idx:
-                    break
-                if text_index[batch_idx][idx] in \
-                    [self.padding_idx, self.unknown_idx]:
-                    continue
-                char_list.append(self.character[int(text_index[batch_idx][
-                    idx])])
-                if text_prob is not None:
-                    conf_list.append(text_prob[batch_idx][idx])
-                else:
-                    conf_list.append(1)
-
-            text = ''.join(char_list)
-            if len(text) > 0:
-                result_list.append((text, np.mean(conf_list).tolist()))
-            else:
-                # here confidence of empty recog result is 1
-                result_list.append(('', 1))
-        return result_list
-
-    def __call__(self, preds, label=None, *args, **kwargs):
-        preds = preds.numpy()
-        preds_idx = preds.argmax(axis=2)
-        preds_prob = preds.max(axis=2)
-        text = self.decode(preds_idx, preds_prob)
-        if label is None:
-            return text
-        label = self.decode(label)
-        return text, label