diff --git a/tools/infer/predict_det_eval.py b/tools/infer/predict_det_eval.py
deleted file mode 100755
index d1f832036bad342cfc54261976e97a40f8c86969..0000000000000000000000000000000000000000
--- a/tools/infer/predict_det_eval.py
+++ /dev/null
@@ -1,363 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import os
-import sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
-
-os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
-
-import cv2
-import numpy as np
-import time
-import sys
-
-import tools.infer.utility as utility
-from ppocr.utils.logging import get_logger
-from ppocr.utils.utility import get_image_file_list, check_and_read_gif
-from ppocr.data import create_operators, transform
-from ppocr.postprocess import build_post_process
-import json
-logger = get_logger()
-
-
-class TextDetector(object):
-    def __init__(self, args):
-        self.args = args
-        self.det_algorithm = args.det_algorithm
-        self.use_onnx = args.use_onnx
-        pre_process_list = [{
-            'DetResizeForTest': {
-                'limit_side_len': args.det_limit_side_len,
-                'limit_type': args.det_limit_type,
-            }
-        }, {
-            'NormalizeImage': {
-                'std': [0.229, 0.224, 0.225],
-                'mean': [0.485, 0.456, 0.406],
-                'scale': '1./255.',
-                'order': 'hwc'
-            }
-        }, {
-            'ToCHWImage': None
-        }, {
-            'KeepKeys': {
-                'keep_keys': ['image', 'shape']
-            }
-        }]
-        postprocess_params = {}
-        if self.det_algorithm == "DB":
-            postprocess_params['name'] = 'DBPostProcess'
-            postprocess_params["thresh"] = args.det_db_thresh
-            postprocess_params["box_thresh"] = args.det_db_box_thresh
-            postprocess_params["max_candidates"] = 1000
-            postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
-            postprocess_params["use_dilation"] = args.use_dilation
-            postprocess_params["score_mode"] = args.det_db_score_mode
-        elif self.det_algorithm == "EAST":
-            postprocess_params['name'] = 'EASTPostProcess'
-            postprocess_params["score_thresh"] = args.det_east_score_thresh
-            postprocess_params["cover_thresh"] = args.det_east_cover_thresh
-            postprocess_params["nms_thresh"] = args.det_east_nms_thresh
-        elif self.det_algorithm == "SAST":
-            pre_process_list[0] = {
-                'DetResizeForTest': {
-                    'resize_long': args.det_limit_side_len
-                }
-            }
-            postprocess_params['name'] = 'SASTPostProcess'
-            postprocess_params["score_thresh"] = args.det_sast_score_thresh
-            postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
-            self.det_sast_polygon = args.det_sast_polygon
-            if self.det_sast_polygon:
-                postprocess_params["sample_pts_num"] = 6
-                postprocess_params["expand_scale"] = 1.2
-                postprocess_params["shrink_ratio_of_width"] = 0.2
-            else:
-                postprocess_params["sample_pts_num"] = 2
-                postprocess_params["expand_scale"] = 1.0
-                postprocess_params["shrink_ratio_of_width"] = 0.3
-        elif self.det_algorithm == "PSE":
-            postprocess_params['name'] = 'PSEPostProcess'
-            postprocess_params["thresh"] = args.det_pse_thresh
-            postprocess_params["box_thresh"] = args.det_pse_box_thresh
-            postprocess_params["min_area"] = args.det_pse_min_area
-            postprocess_params["box_type"] = args.det_pse_box_type
-            postprocess_params["scale"] = args.det_pse_scale
-            self.det_pse_box_type = args.det_pse_box_type
-        elif self.det_algorithm == "FCE":
-            pre_process_list[0] = {
-                'DetResizeForTest': {
-                    'rescale_img': [1080, 736]
-                }
-            }
-            postprocess_params['name'] = 'FCEPostProcess'
-            postprocess_params["scales"] = args.scales
-            postprocess_params["alpha"] = args.alpha
-            postprocess_params["beta"] = args.beta
-            postprocess_params["fourier_degree"] = args.fourier_degree
-            postprocess_params["box_type"] = args.det_fce_box_type
-        else:
-            logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
-            sys.exit(0)
-
-        self.preprocess_op = create_operators(pre_process_list)
-        self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
-            args, 'det', logger)
-
-        if self.use_onnx:
-            img_h, img_w = self.input_tensor.shape[2:]
-            if img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
-                pre_process_list[0] = {
-                    'DetResizeForTest': {
-                        'image_shape': [img_h, img_w]
-                    }
-                }
-        self.preprocess_op = create_operators(pre_process_list)
-
-        if args.benchmark:
-            import auto_log
-            pid = os.getpid()
-            gpu_id = utility.get_infer_gpuid()
-            self.autolog = auto_log.AutoLogger(
-                model_name="det",
-                model_precision=args.precision,
-                batch_size=1,
-                data_shape="dynamic",
-                save_path=None,
-                inference_config=self.config,
-                pids=pid,
-                process_name=None,
-                gpu_ids=gpu_id if args.use_gpu else None,
-                time_keys=[
-                    'preprocess_time', 'inference_time', 'postprocess_time'
-                ],
-                warmup=2,
-                logger=logger)
-
-    def order_points_clockwise(self, pts):
-        rect = np.zeros((4, 2), dtype="float32")
-        s = pts.sum(axis=1)
-        rect[0] = pts[np.argmin(s)]
-        rect[2] = pts[np.argmax(s)]
-        tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
-        diff = np.diff(np.array(tmp), axis=1)
-        rect[1] = tmp[np.argmin(diff)]
-        rect[3] = tmp[np.argmax(diff)]
-        return rect
-
-    def clip_det_res(self, points, img_height, img_width):
-        for pno in range(points.shape[0]):
-            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
-            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
-        return points
-
-    def filter_tag_det_res(self, dt_boxes, image_shape):
-        img_height, img_width = image_shape[0:2]
-        dt_boxes_new = []
-        for box in dt_boxes:
-            box = self.order_points_clockwise(box)
-            box = self.clip_det_res(box, img_height, img_width)
-            rect_width = int(np.linalg.norm(box[0] - box[1]))
-            rect_height = int(np.linalg.norm(box[0] - box[3]))
-            if rect_width <= 3 or rect_height <= 3:
-                continue
-            dt_boxes_new.append(box)
-        dt_boxes = np.array(dt_boxes_new)
-        return dt_boxes
-
-    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
-        img_height, img_width = image_shape[0:2]
-        dt_boxes_new = []
-        for box in dt_boxes:
-            box = self.clip_det_res(box, img_height, img_width)
-            dt_boxes_new.append(box)
-        dt_boxes = np.array(dt_boxes_new)
-        return dt_boxes
-
-    def __call__(self, img):
-        ori_im = img.copy()
-        data = {'image': img}
-
-        st = time.time()
-
-        if self.args.benchmark:
-            self.autolog.times.start()
-
-        data = transform(data, self.preprocess_op)
-        img, shape_list = data
-        if img is None:
-            return None, 0
-        img = np.expand_dims(img, axis=0)
-        shape_list = np.expand_dims(shape_list, axis=0)
-        img = img.copy()
-
-        if self.args.benchmark:
-            self.autolog.times.stamp()
-        if self.use_onnx:
-            input_dict = {}
-            input_dict[self.input_tensor.name] = img
-            outputs = self.predictor.run(self.output_tensors, input_dict)
-        else:
-            self.input_tensor.copy_from_cpu(img)
-            self.predictor.run()
-            outputs = []
-            for output_tensor in self.output_tensors:
-                output = output_tensor.copy_to_cpu()
-                outputs.append(output)
-            if self.args.benchmark:
-                self.autolog.times.stamp()
-
-        preds = {}
-        if self.det_algorithm == "EAST":
-            preds['f_geo'] = outputs[0]
-            preds['f_score'] = outputs[1]
-        elif self.det_algorithm == 'SAST':
-            preds['f_border'] = outputs[0]
-            preds['f_score'] = outputs[1]
-            preds['f_tco'] = outputs[2]
-            preds['f_tvo'] = outputs[3]
-        elif self.det_algorithm in ['DB', 'PSE']:
-            preds['maps'] = outputs[0]
-        elif self.det_algorithm == 'FCE':
-            for i, output in enumerate(outputs):
-                preds['level_{}'.format(i)] = output
-        else:
-            raise NotImplementedError
-
-        #self.predictor.try_shrink_memory()
-        post_result = self.postprocess_op(preds, shape_list)
-        dt_boxes = post_result[0]['points']
-        if (self.det_algorithm == "SAST" and self.det_sast_polygon) or (
-                self.det_algorithm in ["PSE", "FCE"] and
-                self.postprocess_op.box_type == 'poly'):
-            dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
-        else:
-            dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
-
-        if self.args.benchmark:
-            self.autolog.times.end(stamp=True)
-        et = time.time()
-        return dt_boxes, et - st
-
-
-if __name__ == "__main__":
-    from ppocr.metrics.eval_det_iou import DetectionIoUEvaluator
-    evaluator = DetectionIoUEvaluator()
-    args = utility.parse_args()
-
-    # image_file_list = get_image_file_list(args.image_dir)
-    def _check_image_file(path):
-        img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
-        return any([path.lower().endswith(e) for e in img_end])
-
-    def get_image_file_list_from_txt(img_file):
-        imgs_lists = []
-        label_lists = []
-        if img_file is None or not os.path.exists(img_file):
-            raise Exception("not found any img file in {}".format(img_file))
-
-        img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
-        root_dir = img_file.split('/')[0]
-        with open(img_file, 'r') as f:
-            lines = f.readlines()
-            for line in lines:
-                line = line.replace('\n', '').split('\t')
-                file_path, label = line[0], line[1]
-                file_path = os.path.join(root_dir, file_path)
-                if os.path.isfile(file_path) and _check_image_file(file_path):
-                    imgs_lists.append(file_path)
-                    label_lists.append(label)
-
-        if len(imgs_lists) == 0:
-            raise Exception("not found any img file in {}".format(img_file))
-        return imgs_lists, label_lists
-
-    image_file_list, label_list = get_image_file_list_from_txt(args.image_dir)
-
-    text_detector = TextDetector(args)
-    count = 0
-    total_time = 0
-    draw_img_save = "./inference_results"
-
-    if args.warmup:
-        img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
-        for i in range(2):
-            res = text_detector(img)
-
-    if not os.path.exists(draw_img_save):
-        os.makedirs(draw_img_save)
-    save_results = []
-    results = []
-    for idx in range(len(image_file_list)):
-        image_file = image_file_list[idx]
-        label = json.loads(label_list[idx])
-        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
-        st = time.time()
-        dt_boxes, _ = text_detector(img)
-        elapse = time.time() - st
-        if count > 0:
-            total_time += elapse
-        count += 1
-        save_pred = os.path.basename(image_file) + "\t" + str(
-            json.dumps([x.tolist() for x in dt_boxes])) + "\n"
-        save_results.append(save_pred)
-
-        # for eval
-        gt_info_list = []
-        det_info_list = []
-        for dt_box in dt_boxes:
-            det_info = {
-                'points': np.array(
-                    dt_box, dtype=np.float32),
-                'text': ''
-            }
-            det_info_list.append(det_info)
-        for lab in label:
-            gt_info = {
-                'points': np.array(
-                    lab['points'], dtype=np.float32),
-                'text': '',
-                'ignore': False
-            }
-            gt_info_list.append(gt_info)
-        result = evaluator.evaluate_image(gt_info_list, det_info_list)
-        results.append(result)
-
-    metrics = evaluator.combine_results(results)
-    print('predict det eval on ', args.image_dir)
-    print('metrics: ', metrics)
-
-# logger.info(save_pred)
-# logger.info("The predict time of {}: {}".format(image_file, elapse))
-# src_im = utility.draw_text_det_res(dt_boxes, image_file)
-# img_name_pure = os.path.split(image_file)[-1]
-# img_path = os.path.join(draw_img_save,
-#                         "det_res_{}".format(img_name_pure))
-# cv2.imwrite(img_path, src_im)
-# logger.info("The visualized image saved in {}".format(img_path))
-
-# with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f:
-#     f.writelines(save_results)
-#     f.close()
-# if args.benchmark:
-#     text_detector.autolog.report()
diff --git a/tools/infer/predict_rec_eval.py b/tools/infer/predict_rec_eval.py
deleted file mode 100755
index 3150d11ddfb454a90b3a59ef3b9f31b31b3ef3ea..0000000000000000000000000000000000000000
--- a/tools/infer/predict_rec_eval.py
+++ /dev/null
@@ -1,534 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import os
-import sys
-from PIL import Image
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
-
-os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
-
-import cv2
-import numpy as np
-import math
-import time
-import traceback
-import paddle
-
-import tools.infer.utility as utility
-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
-
-logger = get_logger()
-
-
-class TextRecognizer(object):
-    def __init__(self, args):
-        self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
-        self.rec_batch_num = args.rec_batch_num
-        self.rec_algorithm = args.rec_algorithm
-        postprocess_params = {
-            'name': 'CTCLabelDecode',
-            "character_dict_path": args.rec_char_dict_path,
-            "use_space_char": args.use_space_char
-        }
-        if self.rec_algorithm == "SRN":
-            postprocess_params = {
-                'name': 'SRNLabelDecode',
-                "character_dict_path": args.rec_char_dict_path,
-                "use_space_char": args.use_space_char
-            }
-        elif self.rec_algorithm == "RARE":
-            postprocess_params = {
-                'name': 'AttnLabelDecode',
-                "character_dict_path": args.rec_char_dict_path,
-                "use_space_char": args.use_space_char
-            }
-        elif self.rec_algorithm == 'NRTR':
-            postprocess_params = {
-                'name': 'NRTRLabelDecode',
-                "character_dict_path": args.rec_char_dict_path,
-                "use_space_char": args.use_space_char
-            }
-        elif self.rec_algorithm == "SAR":
-            postprocess_params = {
-                'name': 'SARLabelDecode',
-                "character_dict_path": args.rec_char_dict_path,
-                "use_space_char": args.use_space_char
-            }
-        elif self.rec_algorithm == 'ViTSTR':
-            postprocess_params = {
-                'name': 'ViTSTRLabelDecode',
-                "character_dict_path": args.rec_char_dict_path,
-                "use_space_char": args.use_space_char
-            }
-        elif self.rec_algorithm == 'ABINet':
-            postprocess_params = {
-                'name': 'ABINetLabelDecode',
-                "character_dict_path": args.rec_char_dict_path,
-                "use_space_char": args.use_space_char
-            }
-        self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.input_tensor, self.output_tensors, self.config = \
-            utility.create_predictor(args, 'rec', logger)
-        self.benchmark = args.benchmark
-        self.use_onnx = args.use_onnx
-        if args.benchmark:
-            import auto_log
-            pid = os.getpid()
-            gpu_id = utility.get_infer_gpuid()
-            self.autolog = auto_log.AutoLogger(
-                model_name="rec",
-                model_precision=args.precision,
-                batch_size=args.rec_batch_num,
-                data_shape="dynamic",
-                save_path=None,  #args.save_log_path,
-                inference_config=self.config,
-                pids=pid,
-                process_name=None,
-                gpu_ids=gpu_id if args.use_gpu else None,
-                time_keys=[
-                    'preprocess_time', 'inference_time', 'postprocess_time'
-                ],
-                warmup=0,
-                logger=logger)
-
-    def resize_norm_img(self, img, max_wh_ratio):
-        imgC, imgH, imgW = self.rec_image_shape
-        if self.rec_algorithm == 'NRTR' or self.rec_algorithm == 'ViTSTR':
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-            # return padding_im
-            image_pil = Image.fromarray(np.uint8(img))
-            if self.rec_algorithm == 'ViTSTR':
-                img = image_pil.resize([imgW, imgH], Image.BICUBIC)
-            else:
-                img = image_pil.resize([imgW, imgH], Image.ANTIALIAS)
-            img = np.array(img)
-            norm_img = np.expand_dims(img, -1)
-            norm_img = norm_img.transpose((2, 0, 1))
-            if self.rec_algorithm == 'ViTSTR':
-                norm_img = norm_img.astype(np.float32) / 255.
-            else:
-                norm_img = norm_img.astype(np.float32) / 128. - 1.
-            return norm_img
-
-        assert imgC == img.shape[2]
-        imgW = int((imgH * max_wh_ratio))
-        if self.use_onnx:
-            w = self.input_tensor.shape[3:][0]
-            if w is not None and w > 0:
-                imgW = w
-
-        h, w = img.shape[:2]
-        ratio = w / float(h)
-        if math.ceil(imgH * ratio) > imgW:
-            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
-        resized_image -= 0.5
-        resized_image /= 0.5
-        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
-        padding_im[:, :, 0:resized_w] = resized_image
-        return padding_im
-
-    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 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_abinet(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 / 255.
-
-        mean = np.array([0.485, 0.456, 0.406])
-        std = np.array([0.229, 0.224, 0.225])
-        resized_image = (
-            resized_image - mean[None, None, ...]) / std[None, None, ...]
-        resized_image = resized_image.transpose((2, 0, 1))
-        resized_image = resized_image.astype('float32')
-
-        return resized_image
-
-    def __call__(self, img_list):
-        img_num = len(img_list)
-        # Calculate the aspect ratio of all text bars
-        width_list = []
-        for img in img_list:
-            width_list.append(img.shape[1] / float(img.shape[0]))
-        # Sorting can speed up the recognition process
-        indices = np.argsort(np.array(width_list))
-        rec_res = [['', 0.0]] * img_num
-        batch_num = self.rec_batch_num
-        st = time.time()
-        if self.benchmark:
-            self.autolog.times.start()
-        for beg_img_no in range(0, img_num, batch_num):
-            end_img_no = min(img_num, beg_img_no + batch_num)
-            norm_img_batch = []
-            imgC, imgH, imgW = self.rec_image_shape
-            max_wh_ratio = imgW / imgH
-            # max_wh_ratio = 0
-            for ino in range(beg_img_no, end_img_no):
-                h, w = img_list[indices[ino]].shape[0:2]
-                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)
-                elif self.rec_algorithm == "ABINet":
-                    norm_img = self.resize_norm_img_abinet(
-                        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.benchmark:
-                self.autolog.times.stamp()
-
-            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)
-                    if self.benchmark:
-                        self.autolog.times.stamp()
-                    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)
-                    if self.benchmark:
-                        self.autolog.times.stamp()
-                    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)
-                    preds = outputs[0]
-                else:
-                    self.input_tensor.copy_from_cpu(norm_img_batch)
-                    self.predictor.run()
-                    outputs = []
-                    for output_tensor in self.output_tensors:
-                        output = output_tensor.copy_to_cpu()
-                        outputs.append(output)
-                    if self.benchmark:
-                        self.autolog.times.stamp()
-                    if len(outputs) != 1:
-                        preds = outputs
-                    else:
-                        preds = outputs[0]
-            rec_result = self.postprocess_op(preds)
-            for rno in range(len(rec_result)):
-                rec_res[indices[beg_img_no + rno]] = rec_result[rno]
-            if self.benchmark:
-                self.autolog.times.end(stamp=True)
-        return rec_res, time.time() - st
-
-
-def main(args):
-    # image_file_list = get_image_file_list(args.image_dir)
-
-    def _check_image_file(path):
-        img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
-        return any([path.lower().endswith(e) for e in img_end])
-
-    def get_image_file_list_from_txt(img_file):
-        imgs_lists = []
-        label_lists = []
-        if img_file is None or not os.path.exists(img_file):
-            raise Exception("not found any img file in {}".format(img_file))
-
-        img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
-        root_dir = img_file.split('/')[0]
-        with open(img_file, 'r') as f:
-            lines = f.readlines()
-            for line in lines:
-                line = line.replace('\n', '').split('\t')
-                file_path, label = line[0], line[1]
-                file_path = os.path.join(root_dir, file_path)
-                if os.path.isfile(file_path) and _check_image_file(file_path):
-                    imgs_lists.append(file_path)
-                    label_lists.append(label)
-
-        if len(imgs_lists) == 0:
-            raise Exception("not found any img file in {}".format(img_file))
-        return imgs_lists, label_lists
-
-    image_file_list, label_list = get_image_file_list_from_txt(args.image_dir)
-
-    text_recognizer = TextRecognizer(args)
-    valid_image_file_list = []
-    img_list = []
-
-    logger.info(
-        "In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', "
-        "if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320"
-    )
-    # warmup 2 times
-    if args.warmup:
-        img = np.random.uniform(0, 255, [48, 320, 3]).astype(np.uint8)
-        for i in range(2):
-            res = text_recognizer([img] * int(args.rec_batch_num))
-
-    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
-        valid_image_file_list.append(image_file)
-        img_list.append(img)
-
-    try:
-        rec_res, _ = text_recognizer(img_list)
-    except Exception as E:
-        logger.info(traceback.format_exc())
-        logger.info(E)
-        exit()
-    correct_num = 0
-    for ino in range(len(img_list)):
-        pred = rec_res[ino][0]
-        gt = label_list[ino]
-        if pred == gt:
-            correct_num += 1
-    acc = correct_num * 1.0 / len(img_list)
-    print('predict rec eval on ', args.image_dir)
-    print('acc: ', acc)
-
-    # for debug bad case
-    bad_case_lines = []
-    for ino in range(len(img_list)):
-        pred = rec_res[ino][0]
-        gt = label_list[ino]
-        if pred != gt and len(gt) <= 25:
-            bad_case = valid_image_file_list[
-                ino] + '\t' + 'pred:' + pred + '\t' + 'gt:' + gt + '\n'
-            bad_case_lines.append(bad_case)
-
-    with open('bad_case_hwdb2.txt', 'a+') as f:
-        f.writelines(bad_case_lines)
-    # end debug case
-
-    if args.benchmark:
-        text_recognizer.autolog.report()
-
-
-if __name__ == "__main__":
-    main(utility.parse_args())