Repository: PaddlePaddle/Paddle2ONNX (language: python)

Issue:
Add output name choose support
1. Add output name choose support.

#### 1. List type
```
paddle2onnx --model_dir ./  --model_filename model.pdmodel  --save_file model.onnx --opset_version 13 --enable_onnx_checker True --output_names "['my_output1','my_output2']"
```
#### 2. Dict type
```
# only change one output:
paddle2onnx --model_dir ./  --model_filename model.pdmodel  --save_file model.onnx --opset_version 13 --enable_onnx_checker True --output_names "{'paddle_output':'my_output1'}"

# change several outputs:
paddle2onnx --model_dir ./  --model_filename model.pdmodel  --save_file model.onnx --opset_version 13 --enable_onnx_checker True --output_names "{'paddle_output1':'my_output1', 'paddle_output2':'my_output2'}"
```


Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 750356abd..e0aa63564 100755
--- a/README.md
+++ b/README.md
@@ -68,6 +68,7 @@ If you need to configure the input shape, use the following command:
 |--enable_paddle_fallback| **[Optional]**  Whether custom op is exported using paddle_fallback mode. Default value is False|
 |--input_shape_dict| **[Optional]**  Configure the input shape, the default is empty|
 |--version |**[Optional]** check the version of paddle2onnx |
+|--output_names| **[Optional]**  Set the output name of the model, the default is empty, support configuration in list form，for example：--output_names "['my_output1','my_output2']"，or in dict form，for example："{'paddle_output1':'my_output1', 'paddle_output2':'my_output2'}"|
 
 - Two types of PaddlePaddle models
    - Combined model, parameters saved in one binary file. --model_filename and --params_filename represents the file name and parameter name under the directory designated by --model_dir. --model_filename and --params_filename are valid only with parameter --model_dir.
diff --git a/README_zh.md b/README_zh.md
index 6de844c88..a5b33154c 100755
--- a/README_zh.md
+++ b/README_zh.md
@@ -63,6 +63,7 @@ Paddle模型的参数保存在一个单独的二进制文件中（combined）:
 |--enable_paddle_fallback| **[可选]**  配置custom op是否使用paddle_fallback模式导出, 默认为False|
 |--input_shape_dict| **[可选]**  配置输入的shape, 默认为空|
 |--version |**[可选]** 查看paddle2onnx版本 |
+|--output_names| **[可选]**  配置模型的输出名, 默认为空，支持配置为list形式，如：--output_names "['my_output1','my_output2']"，或者dict形式，如：--output_names "{'paddle_output1':'my_output1', 'paddle_output2':'my_output2'}"|
 
 - PaddlePaddle模型的两种存储形式：
    - 参数被保存在一个单独的二进制文件中（combined），需要在指定--model_dir的前提下，指定--model_filename, --params_filename, 分别表示--model_dir目录下的网络文件名称和参数文件名称。
diff --git a/model_zoo/ocr/README.md b/model_zoo/ocr/README.md
new file mode 100644
index 000000000..404332cb6
--- /dev/null
+++ b/model_zoo/ocr/README.md
@@ -0,0 +1,93 @@
+# OCR模型库
+
+本文档中模型库均来源于PaddleOCR [release/2.4分支](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.4)，在下表中提供了部分已经转换好的模型，如有更多模型或自行模型训练导出需求，可参考 [PP-OCR系列模型列表](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.4/doc/doc_ch/models_list.md).
+
+| 模型简介                              | 模型名称                | 推荐场景        | 检测模型                                                     | 方向分类器                                                   | 识别模型                                                     |
+| ------------------------------------- | ----------------------- | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| 中英文超轻量PP-OCRv2模型（13.0M）     | ch_PP-OCRv2_xx          | 移动端&服务器端 | [Paddle模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [ONNX模型]() | [Paddle模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [ONNX模型]() | [Paddle模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [ONNX模型]() |
+
+
+
+# ONNX模型推理示例
+
+各模型的推理前后处理参考本目录下的infer.py，以中英文超轻量PP-OCRv2模型为例，如下命令即可得到推理结果
+
+```bash
+# 安装onnxruntime
+pip3 install onnxruntime
+
+# 下载det模型
+wget https://bj.bcebos.com/paddle2onnx/model_zoo/ocr_det.onnx
+
+# 下载rec模型
+wget https://bj.bcebos.com/paddle2onnx/model_zoo/ocr_rec.onnx
+
+# 下载cls模型
+wget https://bj.bcebos.com/paddle2onnx/model_zoo/ocr_cls.onnx
+
+python3 predict_system.py --use_onnx=True \
+--det_model_dir=./ocr_det.onnx  \
+--rec_model_dir=./ocr_rec.onnx  \
+--cls_model_dir=./ocr_cls.onnx  \
+--image_dir=./images/lite_demo.png
+```
+
+你也可以使用Paddle框架进行推理验证
+
+```bash
+wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+cd ./inference && tar xf ch_PP-OCRv2_det_infer.tar && cd ..
+
+wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+cd ./inference && tar xf ch_PP-OCRv2_rec_infer.tar && cd ..
+
+wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+cd ./inference && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && cd ..
+
+python3 predict_system.py \
+--cls_model_dir=./inference/ch_ppocr_mobile_v2.0_cls_infer \
+--rec_model_dir=./inference/ch_PP-OCRv2_rec_infer \
+--det_model_dir=./inference/ch_PP-OCRv2_det_infer \
+--image_dir=./images/lite_demo.png
+```
+
+最后ONNXRuntime和Paddle的终端端输出结果为：
+```
+The, 0.984
+visualized, 0.882
+etect18片, 0.720
+image saved in./vis.jpg, 0.947
+纯臻营养护发素0.993604, 0.996
+产品信息/参数, 0.922
+0.992728, 0.914
+（45元／每公斤，100公斤起订）, 0.926
+0.97417, 0.977
+每瓶22元，1000瓶起订）0.993976, 0.962
+【品牌】：代加工方式/0EMODM, 0.945
+0.985133, 0.980
+【品名】：纯臻营养护发素, 0.921
+0.995007, 0.883
+【产品编号】：YM-X-30110.96899, 0.955
+【净含量】：220ml, 0.943
+Q.996577, 0.932
+【适用人群】：适合所有肤质, 0.913
+0.995842, 0.969
+【主要成分】：鲸蜡硬脂醇、燕麦B-葡聚, 0.883
+0.961928, 0.964
+10, 0.812
+糖、椰油酰胺丙基甜菜碱、泛醒, 0.866
+0.925898, 0.943
+（成品包材）, 0.974
+心, 0.691
+0.972573, 0.961
+【主要功能】：可紧致头发磷层，从而达到, 0.936
+0.994448, 0.952
+13, 0.998
+即时持久改善头发光泽的效果，给干燥的头, 0.994
+0.990198, 0.975
+14, 0.977
+发足够的滋养, 0.991
+0.997668, 0.918
+花费了0.457335秒, 0.901
+Finish!
+```
diff --git a/model_zoo/ocr/images/lite_demo.png b/model_zoo/ocr/images/lite_demo.png
new file mode 100644
index 000000000..c9daf1b2e
Binary files /dev/null and b/model_zoo/ocr/images/lite_demo.png differ
diff --git a/model_zoo/ocr/predict_system.py b/model_zoo/ocr/predict_system.py
new file mode 100755
index 000000000..d0d1be2af
--- /dev/null
+++ b/model_zoo/ocr/predict_system.py
@@ -0,0 +1,203 @@
+# 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.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import copy
+import numpy as np
+import utils.predict_rec as predict_rec
+import utils.predict_det as predict_det
+import utils.predict_cls as predict_cls
+
+import argparse
+
+
+def str2bool(v):
+    return v.lower() in ("true", "t", "1")
+
+
+def init_args():
+    parser = argparse.ArgumentParser()
+    # params for text detector
+    parser.add_argument(
+        "--image_dir",
+        type=str,
+        default='/Users/huangshenghui/PP/Paddle2ONNX/model_zoo/ocr/images/lite_demo.png'
+    )
+    parser.add_argument("--det_algorithm", type=str, default='DB')
+    parser.add_argument(
+        "--det_model_dir",
+        type=str,
+        default='/Users/huangshenghui/PP/Paddle2ONNX/model_zoo/ocr/inference/ch_PP-OCRv2_det_infer'
+    )
+    parser.add_argument("--det_limit_side_len", type=float, default=960)
+    parser.add_argument("--det_limit_type", type=str, default='max')
+
+    # DB parmas
+    parser.add_argument("--det_db_thresh", type=float, default=0.3)
+    parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
+    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
+    parser.add_argument("--max_batch_size", type=int, default=10)
+    parser.add_argument("--use_dilation", type=str2bool, default=False)
+    parser.add_argument("--det_db_score_mode", type=str, default="fast")
+
+    parser.add_argument("--rec_algorithm", type=str, default='CRNN')
+    parser.add_argument(
+        "--rec_model_dir",
+        type=str,
+        default='/Users/huangshenghui/PP/Paddle2ONNX/model_zoo/ocr/inference/ch_PP-OCRv2_rec_infer'
+    )
+    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
+    parser.add_argument("--rec_batch_num", type=int, default=6)
+    parser.add_argument("--max_text_length", type=int, default=25)
+    parser.add_argument(
+        "--rec_char_dict_path",
+        type=str,
+        default="./utils/doc/ppocr_keys_v1.txt")
+    parser.add_argument("--use_space_char", type=str2bool, default=True)
+    parser.add_argument(
+        "--vis_font_path", type=str, default="./utils/doc/fonts/simfang.ttf")
+    parser.add_argument("--drop_score", type=float, default=0.5)
+
+    # params for text classifier
+    parser.add_argument("--use_angle_cls", type=str2bool, default=True)
+    parser.add_argument(
+        "--cls_model_dir",
+        type=str,
+        default='/Users/huangshenghui/PP/Paddle2ONNX/model_zoo/ocr/inference/ch_ppocr_mobile_v2.0_cls_infer'
+    )
+    parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
+    parser.add_argument("--label_list", type=list, default=['0', '180'])
+    parser.add_argument("--cls_batch_num", type=int, default=6)
+    parser.add_argument("--cls_thresh", type=float, default=0.9)
+
+    parser.add_argument("--use_onnx", type=str2bool, default=False)
+    return parser.parse_args()
+
+
+class TextSystem(object):
+    def __init__(self, args):
+        self.text_detector = predict_det.TextDetector(args)
+        self.text_recognizer = predict_rec.TextRecognizer(args)
+        self.use_angle_cls = args.use_angle_cls
+        self.drop_score = args.drop_score
+        if self.use_angle_cls:
+            self.text_classifier = predict_cls.TextClassifier(args)
+
+        self.args = args
+        self.crop_image_res_index = 0
+
+    def __call__(self, img, cls=True):
+        ori_im = img.copy()
+        dt_boxes = self.text_detector(img)
+
+        if dt_boxes is None:
+            return None, None
+        img_crop_list = []
+
+        dt_boxes = sorted_boxes(dt_boxes)
+
+        for bno in range(len(dt_boxes)):
+            tmp_box = copy.deepcopy(dt_boxes[bno])
+            img_crop = get_rotate_crop_image(ori_im, tmp_box)
+            img_crop_list.append(img_crop)
+        if self.use_angle_cls and cls:
+            img_crop_list, angle_list = self.text_classifier(img_crop_list)
+
+        rec_res = self.text_recognizer(img_crop_list)
+
+        filter_boxes, filter_rec_res = [], []
+        for box, rec_result in zip(dt_boxes, rec_res):
+            text, score = rec_result
+            if score >= self.drop_score:
+                filter_boxes.append(box)
+                filter_rec_res.append(rec_result)
+        return filter_boxes, filter_rec_res
+
+
+def get_rotate_crop_image(img, points):
+    '''
+    img_height, img_width = img.shape[0:2]
+    left = int(np.min(points[:, 0]))
+    right = int(np.max(points[:, 0]))
+    top = int(np.min(points[:, 1]))
+    bottom = int(np.max(points[:, 1]))
+    img_crop = img[top:bottom, left:right, :].copy()
+    points[:, 0] = points[:, 0] - left
+    points[:, 1] = points[:, 1] - top
+    '''
+    assert len(points) == 4, "shape of points must be 4*2"
+    img_crop_width = int(
+        max(
+            np.linalg.norm(points[0] - points[1]),
+            np.linalg.norm(points[2] - points[3])))
+    img_crop_height = int(
+        max(
+            np.linalg.norm(points[0] - points[3]),
+            np.linalg.norm(points[1] - points[2])))
+    pts_std = np.float32([[0, 0], [img_crop_width, 0],
+                          [img_crop_width, img_crop_height],
+                          [0, img_crop_height]])
+    M = cv2.getPerspectiveTransform(points, pts_std)
+    dst_img = cv2.warpPerspective(
+        img,
+        M, (img_crop_width, img_crop_height),
+        borderMode=cv2.BORDER_REPLICATE,
+        flags=cv2.INTER_CUBIC)
+    dst_img_height, dst_img_width = dst_img.shape[0:2]
+    if dst_img_height * 1.0 / dst_img_width >= 1.5:
+        dst_img = np.rot90(dst_img)
+    return dst_img
+
+
+def sorted_boxes(dt_boxes):
+    """
+    Sort text boxes in order from top to bottom, left to right
+    args:
+        dt_boxes(array):detected text boxes with shape [4, 2]
+    return:
+        sorted boxes(array) with shape [4, 2]
+    """
+    num_boxes = dt_boxes.shape[0]
+    sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
+    _boxes = list(sorted_boxes)
+
+    for i in range(num_boxes - 1):
+        if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
+                (_boxes[i + 1][0][0] < _boxes[i][0][0]):
+            tmp = _boxes[i]
+            _boxes[i] = _boxes[i + 1]
+            _boxes[i + 1] = tmp
+    return _boxes
+
+
+def main(args):
+    text_sys = TextSystem(args)
+    img = cv2.imread(args.image_dir)
+    dt_boxes, rec_res = text_sys(img)
+    for text, score in rec_res:
+        print("{}, {:.3f}".format(text, score))
+    print("Finish!")
+
+
+if __name__ == "__main__":
+    args = init_args()
+    main(args)
diff --git a/model_zoo/ocr/utils/__init__.py b/model_zoo/ocr/utils/__init__.py
new file mode 100644
index 000000000..abf198b97
--- /dev/null
+++ b/model_zoo/ocr/utils/__init__.py
@@ -0,0 +1,13 @@
+# 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.
diff --git a/model_zoo/ocr/utils/doc/ppocr_keys_v1.txt b/model_zoo/ocr/utils/doc/ppocr_keys_v1.txt
new file mode 100644
index 000000000..84b885d83
--- /dev/null
+++ b/model_zoo/ocr/utils/doc/ppocr_keys_v1.txt
@@ -0,0 +1,6623 @@
+'
+疗
+绚
+诚
+娇
+溜
+题
+贿
+者
+廖
+更
+纳
+加
+奉
+公
+一
+就
+汴
+计
+与
+路
+房
+原
+妇
+2
+0
+8
+-
+7
+其
+>
+:
+]
+,
+，
+骑
+刈
+全
+消
+昏
+傈
+安
+久
+钟
+嗅
+不
+影
+处
+驽
+蜿
+资
+关
+椤
+地
+瘸
+专
+问
+忖
+票
+嫉
+炎
+韵
+要
+月
+田
+节
+陂
+鄙
+捌
+备
+拳
+伺
+眼
+网
+盎
+大
+傍
+心
+东
+愉
+汇
+蹿
+科
+每
+业
+里
+航
+晏
+字
+平
+录
+先
+1
+3
+彤
+鲶
+产
+稍
+督
+腴
+有
+象
+岳
+注
+绍
+在
+泺
+文
+定
+核
+名
+水
+过
+理
+让
+偷
+率
+等
+这
+发
+”
+为
+含
+肥
+酉
+相
+鄱
+七
+编
+猥
+锛
+日
+镀
+蒂
+掰
+倒
+辆
+栾
+栗
+综
+涩
+州
+雌
+滑
+馀
+了
+机
+块
+司
+宰
+甙
+兴
+矽
+抚
+保
+用
+沧
+秩
+如
+收
+息
+滥
+页
+疑
+埠
+!
+！
+姥
+异
+橹
+钇
+向
+下
+跄
+的
+椴
+沫
+国
+绥
+獠
+报
+开
+民
+蜇
+何
+分
+凇
+长
+讥
+藏
+掏
+施
+羽
+中
+讲
+派
+嘟
+人
+提
+浼
+间
+世
+而
+古
+多
+倪
+唇
+饯
+控
+庚
+首
+赛
+蜓
+味
+断
+制
+觉
+技
+替
+艰
+溢
+潮
+夕
+钺
+外
+摘
+枋
+动
+双
+单
+啮
+户
+枇
+确
+锦
+曜
+杜
+或
+能
+效
+霜
+盒
+然
+侗
+电
+晁
+放
+步
+鹃
+新
+杖
+蜂
+吒
+濂
+瞬
+评
+总
+隍
+对
+独
+合
+也
+是
+府
+青
+天
+诲
+墙
+组
+滴
+级
+邀
+帘
+示
+已
+时
+骸
+仄
+泅
+和
+遨
+店
+雇
+疫
+持
+巍
+踮
+境
+只
+亨
+目
+鉴
+崤
+闲
+体
+泄
+杂
+作
+般
+轰
+化
+解
+迂
+诿
+蛭
+璀
+腾
+告
+版
+服
+省
+师
+小
+规
+程
+线
+海
+办
+引
+二
+桧
+牌
+砺
+洄
+裴
+修
+图
+痫
+胡
+许
+犊
+事
+郛
+基
+柴
+呼
+食
+研
+奶
+律
+蛋
+因
+葆
+察
+戏
+褒
+戒
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\ No newline at end of file
diff --git a/model_zoo/ocr/utils/predict_cls.py b/model_zoo/ocr/utils/predict_cls.py
new file mode 100755
index 000000000..5e6da2638
--- /dev/null
+++ b/model_zoo/ocr/utils/predict_cls.py
@@ -0,0 +1,127 @@
+# 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 cv2
+import copy
+import numpy as np
+import math
+import paddle
+
+
+class ClsPostProcess(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, label_list, **kwargs):
+        super(ClsPostProcess, self).__init__()
+        self.label_list = label_list
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        if isinstance(preds, paddle.Tensor):
+            preds = preds.numpy()
+        pred_idxs = preds.argmax(axis=1)
+        decode_out = [(self.label_list[idx], preds[i, idx])
+                      for i, idx in enumerate(pred_idxs)]
+        if label is None:
+            return decode_out
+        label = [(self.label_list[idx], 1.0) for idx in label]
+        return decode_out, label
+
+
+class TextClassifier(object):
+    def __init__(self, args):
+        self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
+        self.cls_batch_num = args.cls_batch_num
+        self.cls_thresh = args.cls_thresh
+        self.postprocess_op = ClsPostProcess(args.label_list)
+        self.use_onnx = args.use_onnx
+        self.args = args
+
+        model_dir = args.cls_model_dir
+        if args.use_onnx:
+            import onnxruntime as ort
+            model_file_path = model_dir
+            sess = ort.InferenceSession(model_file_path)
+            self.predictor = sess
+        else:
+            model = paddle.jit.load(model_dir + "/inference")
+            model.eval()
+            self.predictor = model
+
+    def resize_norm_img(self, img):
+        imgC, imgH, imgW = self.cls_image_shape
+        h = img.shape[0]
+        w = img.shape[1]
+        ratio = w / float(h)
+        if math.ceil(imgH * ratio) > imgW:
+            resized_w = imgW
+        else:
+            resized_w = int(math.ceil(imgH * ratio))
+        resized_image = cv2.resize(img, (resized_w, imgH))
+        resized_image = resized_image.astype('float32')
+        if self.cls_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
+        padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+        padding_im[:, :, 0:resized_w] = resized_image
+        return padding_im
+
+    def __call__(self, img_list):
+        img_list = copy.deepcopy(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 cls process
+        indices = np.argsort(np.array(width_list))
+
+        cls_res = [['', 0.0]] * img_num
+        batch_num = self.cls_batch_num
+
+        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 = []
+            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):
+                norm_img = self.resize_norm_img(img_list[indices[ino]])
+                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.use_onnx:
+                input_dict = {}
+                input_dict[self.predictor.get_inputs()[0].name] = norm_img_batch
+                outputs = self.predictor.run(None, input_dict)
+                prob_out = outputs[0]
+            else:
+                outputs = self.predictor(norm_img_batch).numpy()
+                prob_out = outputs
+
+            cls_result = self.postprocess_op(prob_out)
+            for rno in range(len(cls_result)):
+                label, score = cls_result[rno]
+                cls_res[indices[beg_img_no + rno]] = [label, score]
+                if '180' in label and score > self.cls_thresh:
+                    img_list[indices[beg_img_no + rno]] = cv2.rotate(
+                        img_list[indices[beg_img_no + rno]], 1)
+        return img_list, cls_res
diff --git a/model_zoo/ocr/utils/predict_det.py b/model_zoo/ocr/utils/predict_det.py
new file mode 100755
index 000000000..874c211ca
--- /dev/null
+++ b/model_zoo/ocr/utils/predict_det.py
@@ -0,0 +1,339 @@
+# 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
+
+import paddle
+from shapely.geometry import Polygon
+import pyclipper
+
+import cv2
+import numpy as np
+
+
+class DBPreProcess(object):
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self, data):
+        data = self.DetResizeForTest(self.args.det_limit_side_len,
+                                     self.args.det_limit_type, data)
+        data = self.NormalizeImage(data)
+        data = self.ToCHWImage(data)
+        data = self.KeepKeys(['image', 'shape'], data)
+        return data
+
+    def NormalizeImage(self, data):
+        scale = 1.0 / 255
+        mean = [0.485, 0.456, 0.406]
+        std = [0.229, 0.224, 0.225]
+        order = 'hwc'
+        shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
+        mean = np.array(mean).reshape(shape).astype('float32')
+        std = np.array(std).reshape(shape).astype('float32')
+        img = data['image']
+        data['image'] = (img.astype('float32') * scale - mean) / std
+        return data
+
+    def ToCHWImage(self, data):
+        img = data['image']
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+        data['image'] = img.transpose((2, 0, 1))
+        return data
+
+    def KeepKeys(self, keep_keys, data):
+        data_list = []
+        for key in keep_keys:
+            data_list.append(data[key])
+        return data_list
+
+    def DetResizeForTest(self, limit_side_len, limit_type, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+
+        limit_side_len = limit_side_len
+        h, w, c = img.shape
+
+        # limit the max side
+        if limit_type == 'max':
+            if max(h, w) > limit_side_len:
+                if h > w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        else:
+            raise Exception('not support limit type, image ')
+        resize_h = int(h * ratio)
+        resize_w = int(w * ratio)
+
+        resize_h = max(int(round(resize_h / 32) * 32), 32)
+        resize_w = max(int(round(resize_w / 32) * 32), 32)
+
+        try:
+            if int(resize_w) <= 0 or int(resize_h) <= 0:
+                return None, (None, None)
+            img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        except:
+            print(img.shape, resize_w, resize_h)
+            sys.exit(0)
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        data['image'] = img
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        return data
+
+
+class DBPostProcess(object):
+    """
+    The post process for Differentiable Binarization (DB).
+    """
+
+    def __init__(self,
+                 thresh=0.3,
+                 box_thresh=0.7,
+                 max_candidates=1000,
+                 unclip_ratio=2.0,
+                 use_dilation=False,
+                 score_mode="fast",
+                 **kwargs):
+        self.thresh = thresh
+        self.box_thresh = box_thresh
+        self.max_candidates = max_candidates
+        self.unclip_ratio = unclip_ratio
+        self.min_size = 3
+        self.score_mode = score_mode
+        assert score_mode in [
+            "slow", "fast"
+        ], "Score mode must be in [slow, fast] but got: {}".format(score_mode)
+
+        self.dilation_kernel = None if not use_dilation else np.array(
+            [[1, 1], [1, 1]])
+
+    def boxes_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
+
+        outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
+                                cv2.CHAIN_APPROX_SIMPLE)
+        if len(outs) == 3:
+            img, contours, _ = outs[0], outs[1], outs[2]
+        elif len(outs) == 2:
+            contours, _ = outs[0], outs[1]
+
+        num_contours = min(len(contours), self.max_candidates)
+
+        boxes = []
+        scores = []
+        for index in range(num_contours):
+            contour = contours[index]
+            points, sside = self.get_mini_boxes(contour)
+            if sside < self.min_size:
+                continue
+            points = np.array(points)
+            score = self.box_score_fast(pred, points.reshape(-1, 2))
+            if self.box_thresh > score:
+                continue
+
+            box = self.unclip(points).reshape(-1, 1, 2)
+            box, sside = self.get_mini_boxes(box)
+            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.astype(np.int16))
+            scores.append(score)
+        return np.array(boxes, dtype=np.int16), scores
+
+    def unclip(self, box):
+        unclip_ratio = self.unclip_ratio
+        poly = Polygon(box)
+        distance = poly.area * unclip_ratio / poly.length
+        offset = pyclipper.PyclipperOffset()
+        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+        expanded = np.array(offset.Execute(distance))
+        return expanded
+
+    def get_mini_boxes(self, contour):
+        bounding_box = cv2.minAreaRect(contour)
+        points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
+
+        index_1, index_2, index_3, index_4 = 0, 1, 2, 3
+        if points[1][1] > points[0][1]:
+            index_1 = 0
+            index_4 = 1
+        else:
+            index_1 = 1
+            index_4 = 0
+        if points[3][1] > points[2][1]:
+            index_2 = 2
+            index_3 = 3
+        else:
+            index_2 = 3
+            index_3 = 2
+
+        box = [
+            points[index_1], points[index_2], points[index_3], points[index_4]
+        ]
+        return box, min(bounding_box[1])
+
+    def box_score_fast(self, bitmap, _box):
+        '''
+        box_score_fast: use bbox mean score as the mean score
+        '''
+        h, w = bitmap.shape[:2]
+        box = _box.copy()
+        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+        box[:, 0] = box[:, 0] - xmin
+        box[:, 1] = box[:, 1] - ymin
+        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
+    def __call__(self, outs_dict, shape_list):
+        pred = outs_dict['maps']
+        if isinstance(pred, paddle.Tensor):
+            pred = pred.numpy()
+        pred = pred[:, 0, :, :]
+        segmentation = pred > self.thresh
+
+        boxes_batch = []
+        for batch_index in range(pred.shape[0]):
+            src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
+            if self.dilation_kernel is not None:
+                mask = cv2.dilate(
+                    np.array(segmentation[batch_index]).astype(np.uint8),
+                    self.dilation_kernel)
+            else:
+                mask = segmentation[batch_index]
+            boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
+                                                   src_w, src_h)
+
+            boxes_batch.append({'points': boxes})
+        return boxes_batch
+
+
+class TextDetector(object):
+    def __init__(self, args):
+        self.args = args
+        self.use_onnx = args.use_onnx
+
+        self.preprocess_op = DBPreProcess(args)
+        self.postprocess_op = DBPostProcess(
+            thresh=args.det_db_thresh,
+            box_thresh=args.det_db_box_thresh,
+            max_candidates=1000,
+            unclip_ratio=args.det_db_unclip_ratio,
+            use_dilation=args.use_dilation,
+            score_mode=args.det_db_score_mode)
+
+        model_dir = args.det_model_dir
+        if args.use_onnx:
+            import onnxruntime as ort
+            model_file_path = model_dir
+            sess = ort.InferenceSession(model_file_path)
+            self.predictor = sess
+        else:
+            model = paddle.jit.load(model_dir + "/inference")
+            model.eval()
+            self.predictor = model
+
+    def order_points_clockwise(self, pts):
+        """
+        reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
+        # sort the points based on their x-coordinates
+        """
+        xSorted = pts[np.argsort(pts[:, 0]), :]
+
+        # grab the left-most and right-most points from the sorted
+        # x-roodinate points
+        leftMost = xSorted[:2, :]
+        rightMost = xSorted[2:, :]
+
+        # now, sort the left-most coordinates according to their
+        # y-coordinates so we can grab the top-left and bottom-left
+        # points, respectively
+        leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+        (tl, bl) = leftMost
+
+        rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
+        (tr, br) = rightMost
+
+        rect = np.array([tl, tr, br, bl], dtype="float32")
+        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 __call__(self, img):
+        ori_im = img.copy()
+        data = {'image': img}
+
+        data = self.preprocess_op(data)
+        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.use_onnx:
+            input_dict = {}
+            input_dict[self.predictor.get_inputs()[0].name] = img
+            outputs = self.predictor.run(None, input_dict)
+        else:
+            output = self.predictor(img).numpy()
+            outputs = []
+            outputs.append(output)
+
+        preds = {}
+        preds['maps'] = outputs[0]
+        post_result = self.postprocess_op(preds, shape_list)
+        dt_boxes = post_result[0]['points']
+        dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
+        return dt_boxes
diff --git a/model_zoo/ocr/utils/predict_rec.py b/model_zoo/ocr/utils/predict_rec.py
new file mode 100755
index 000000000..aee385fb5
--- /dev/null
+++ b/model_zoo/ocr/utils/predict_rec.py
@@ -0,0 +1,196 @@
+# 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 cv2
+import math
+
+import numpy as np
+import paddle
+
+
+class BaseRecLabelDecode(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False):
+        self.beg_str = "sos"
+        self.end_str = "eos"
+
+        self.character_str = []
+        if character_dict_path is None:
+            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+            dict_character = list(self.character_str)
+        else:
+            with open(character_dict_path, "rb") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    line = line.decode('utf-8').strip("\n").strip("\r\n")
+                    self.character_str.append(line)
+            if use_space_char:
+                self.character_str.append(" ")
+            dict_character = list(self.character_str)
+
+        dict_character = self.add_special_char(dict_character)
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+        self.character = dict_character
+
+    def add_special_char(self, 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 = []
+        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)))
+        return result_list
+
+    def get_ignored_tokens(self):
+        return [0]  # for ctc blank
+
+
+class CTCLabelDecode(BaseRecLabelDecode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character_dict_path=None, use_space_char=False,
+                 **kwargs):
+        super(CTCLabelDecode, self).__init__(character_dict_path,
+                                             use_space_char)
+
+    def __call__(self, preds, label=None, *args, **kwargs):
+        if isinstance(preds, tuple):
+            preds = preds[-1]
+        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=True)
+        if label is None:
+            return text
+        label = self.decode(label)
+        return text, label
+
+    def add_special_char(self, dict_character):
+        dict_character = ['blank'] + dict_character
+        return dict_character
+
+
+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.postprocess_op = CTCLabelDecode(args.rec_char_dict_path,
+                                             args.use_space_char)
+        self.use_onnx = args.use_onnx
+        self.args = args
+
+        model_dir = args.rec_model_dir
+        if args.use_onnx:
+            import onnxruntime as ort
+            model_file_path = model_dir
+            sess = ort.InferenceSession(model_file_path)
+            self.predictor = sess
+        else:
+            model = paddle.jit.load(model_dir + "/inference")
+            model.eval()
+            self.predictor = model
+
+    def resize_norm_img(self, img, max_wh_ratio):
+        imgC, imgH, imgW = self.rec_image_shape
+        assert imgC == img.shape[2]
+        imgW = int((32 * max_wh_ratio))
+        if self.use_onnx:
+            w = self.predictor.get_inputs()[0].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))
+        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 __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
+
+        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 = []
+            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):
+                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.use_onnx:
+                input_dict = {}
+                input_dict[self.predictor.get_inputs()[0].name] = norm_img_batch
+                outputs = self.predictor.run(None, input_dict)
+                preds = outputs[0]
+            else:
+                output = self.predictor(norm_img_batch).numpy()
+                outputs = []
+                outputs.append(output)
+                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]
+        return rec_res
diff --git a/paddle2onnx/command.py b/paddle2onnx/command.py
index 88967349a..e82d8182e 100755
--- a/paddle2onnx/command.py
+++ b/paddle2onnx/command.py
@@ -22,6 +22,14 @@
 from paddle2onnx.utils import logging
 
 
+def str2list(v):
+    if len(v) == 0:
+        return None
+    v = v.replace(" ", "")
+    v = eval(v)
+    return v
+
+
 def arg_parser():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -81,6 +89,14 @@ def arg_parser():
         action="store_true",
         default=False,
         help="get version of paddle2onnx")
+    parser.add_argument(
+        "--output_names",
+        "-on",
+        type=str2list,
+        default=None,
+        help="define output names, e.g --output_names=\"[\"output1\"]\" or \
+       --output_names=\"[\"output1\", \"output2\", \"output3\"]\" or \
+       --output_names=\"{\"Paddleoutput\":\"Onnxoutput\"}\"")
     return parser
 
 
@@ -91,7 +107,8 @@ def program2onnx(model_dir,
                  opset_version=9,
                  enable_onnx_checker=False,
                  operator_export_type="ONNX",
-                 input_shape_dict=None):
+                 input_shape_dict=None,
+                 output_names=None):
     try:
         import paddle
     except:
@@ -147,7 +164,6 @@ def program2onnx(model_dir,
             if program.blocks[0].ops[i].type in OP_WITHOUT_KERNEL_SET:
                 continue
             program.blocks[0].ops[i].desc.infer_shape(program.blocks[0].desc)
-
     p2o.program2onnx(
         program,
         fluid.global_scope(),
@@ -156,7 +172,8 @@ def program2onnx(model_dir,
         target_vars=fetch_vars,
         opset_version=opset_version,
         enable_onnx_checker=enable_onnx_checker,
-        operator_export_type=operator_export_type)
+        operator_export_type=operator_export_type,
+        output_names=output_names)
 
 
 def main():
@@ -184,6 +201,13 @@ def main():
     operator_export_type = "ONNX"
     if args.enable_paddle_fallback:
         operator_export_type = "PaddleFallback"
+
+    if args.output_names is not None:
+        if not isinstance(args.output_names, (list, dict)):
+            raise TypeError(
+                "The output_names should be 'list' or 'dict', but received type is %s."
+                % type(args.output_names))
+
     program2onnx(
         args.model_dir,
         args.save_file,
@@ -192,7 +216,8 @@ def main():
         opset_version=args.opset_version,
         enable_onnx_checker=args.enable_onnx_checker,
         operator_export_type=operator_export_type,
-        input_shape_dict=input_shape_dict)
+        input_shape_dict=input_shape_dict,
+        output_names=args.output_names)
 
 
 if __name__ == "__main__":
diff --git a/paddle2onnx/convert.py b/paddle2onnx/convert.py
index 65dea91f5..257646226 100755
--- a/paddle2onnx/convert.py
+++ b/paddle2onnx/convert.py
@@ -30,13 +30,13 @@ def export_onnx(paddle_graph,
                 enable_onnx_checker=False,
                 operator_export_type="ONNX",
                 verbose=False,
-                auto_update_opset=True):
+                auto_update_opset=True,
+                output_names=None):
     onnx_graph = ONNXGraph.build(paddle_graph, opset_version,
                                  operator_export_type, verbose,
                                  auto_update_opset)
     onnx_graph = PassManager.run_pass(onnx_graph, ['inplace_node_pass'])
-
-    onnx_proto = onnx_graph.export_proto(enable_onnx_checker)
+    onnx_proto = onnx_graph.export_proto(enable_onnx_checker, output_names)
 
     if save_file is None:
         return onnx_proto
@@ -83,9 +83,22 @@ def program2onnx(program,
 
         paddle_graph = PaddleGraph.build_from_program(program, feed_var_names,
                                                       target_vars, scope)
-        return export_onnx(paddle_graph, save_file, opset_version,
-                           enable_onnx_checker, operator_export_type,
-                           auto_update_opset)
+        output_names = None
+        if 'output_names' in configs:
+            output_names = configs['output_names']
+            if output_names is not None and not isinstance(output_names,
+                                                           (list, dict)):
+                raise TypeError(
+                    "The output_names should be 'list' or dict, but received type is %s."
+                    % type(output_names))
+        return export_onnx(
+            paddle_graph,
+            save_file,
+            opset_version,
+            enable_onnx_checker,
+            operator_export_type,
+            auto_update_opset,
+            output_names=output_names)
     else:
         raise TypeError(
             "the input 'program' should be 'Program', but received type is %s."
@@ -179,6 +192,14 @@ def dygraph2onnx(layer, save_file, input_spec=None, opset_version=9, **configs):
                 "The auto_update_opset should be 'bool', but received type is %s."
                 % type(configs['auto_update_opset']))
 
+    output_names = None
+    if 'output_names' in configs:
+        output_names = configs['output_names']
+        if not isinstance(output_names, (list, dict)):
+            raise TypeError(
+                "The output_names should be 'list' or dict, but received type is %s."
+                % type(output_names))
+
     return export_onnx(paddle_graph, save_file, opset_version,
                        enable_onnx_checker, operator_export_type, verbose,
-                       auto_update_opset)
+                       auto_update_opset, output_names)
diff --git a/paddle2onnx/graph/onnx_graph.py b/paddle2onnx/graph/onnx_graph.py
index 342a873c1..a6bedd25b 100755
--- a/paddle2onnx/graph/onnx_graph.py
+++ b/paddle2onnx/graph/onnx_graph.py
@@ -223,7 +223,74 @@ def add_output_node(self, name, shape, dtype):
         vi = self.make_value_info(name, shape, dtype)
         self.output_nodes.append(vi)
 
-    def export_proto(self, enable_onnx_checker=False):
+    def find_index(self, node_inout, name):
+        for i in range(len(node_inout)):
+            if node_inout[i] == name:
+                return i
+        return -1
+
+    def change_output_names(self, onnx_proto, output_names):
+        logging.info("The output of the ONNX model is set to: {}".format(
+            output_names))
+        if isinstance(output_names, list):
+            assert len(output_names) == len(
+                onnx_proto.graph.output
+            ), "The provided output names are inconsistent with the output number of the onnx model when output_names is list"
+            origin_output_names = []
+            for i in range(len(onnx_proto.graph.output)):
+                origin_output_names.append(onnx_proto.graph.output[i].name)
+                onnx_proto.graph.output[i].name = output_names[i]
+
+            for i in range(len(onnx_proto.graph.node)):
+                node = onnx_proto.graph.node[i]
+                # Prevent changed names from being changed again
+                output_visited_node = []
+                input_visited_node = []
+                for j in range(len(origin_output_names)):
+                    if origin_output_names[j] in node.output:
+                        index = self.find_index(node.output,
+                                                origin_output_names[j])
+                        if index in output_visited_node:
+                            continue
+                        output_visited_node.append(index)
+                        onnx_proto.graph.node[i].output[index] = output_names[j]
+                    if origin_output_names[j] in node.input:
+                        index = self.find_index(node.input,
+                                                origin_output_names[j])
+                        if index in input_visited_node:
+                            continue
+                        input_visited_node.append(index)
+                        onnx_proto.graph.node[i].input[index] = output_names[j]
+        if isinstance(output_names, dict):
+            for i in range(len(onnx_proto.graph.output)):
+                for key, value in output_names.items():
+                    if onnx_proto.graph.output[i].name == key:
+                        onnx_proto.graph.output[i].name = value
+                        break
+
+            for i in range(len(onnx_proto.graph.node)):
+                node = onnx_proto.graph.node[i]
+                # Prevent changed names from being changed again
+                output_visited_node = []
+                input_visited_node = []
+                for key, value in output_names.items():
+                    if key in node.output:
+                        index = self.find_index(node.output, key)
+                        if index in output_visited_node:
+                            continue
+                        output_visited_node.append(index)
+                        onnx_proto.graph.node[i].output[index] = value
+                    if key in node.input:
+                        index = self.find_index(node.input, key)
+                        if index in input_visited_node:
+                            continue
+                        input_visited_node.append(index)
+                        onnx_proto.graph.node[i].input[index] = value
+
+        return onnx_proto
+
+    def export_proto(self, enable_onnx_checker=False, output_names=None):
+
         op_nodes = [node.onnx_node for node in self.node_map.values()]
         weight_nodes = [node for node in self.parameters.values()]
 
@@ -239,6 +306,8 @@ def export_proto(self, enable_onnx_checker=False):
             opset_imports.append(helper.make_opsetid(custom_domain, 1))
         onnx_proto = helper.make_model(
             onnx_graph, producer_name=PRODUCER, opset_imports=opset_imports)
+        if output_names is not None:
+            onnx_proto = self.change_output_names(onnx_proto, output_names)
 
         if enable_onnx_checker:
             check_model(onnx_proto)
diff --git a/paddle2onnx/op_mapper/tensor.py b/paddle2onnx/op_mapper/tensor.py
index 02f449139..18fcf691b 100755
--- a/paddle2onnx/op_mapper/tensor.py
+++ b/paddle2onnx/op_mapper/tensor.py
@@ -2019,3 +2019,143 @@ def opset_8(cls, graph, node, **kw):
                     'Expand',
                     inputs=[t_reshaped, out_shape],
                     outputs=node.output('Out')[i]))
+
+
+@op_mapper('flip')
+class Flip():
+    support_opset_version_range = (7, 15)
+
+    @classmethod
+    def opset_7(cls, graph, node, **kw):
+        inputs = node.input('X')
+        x_dtype = node.input_dtype('X', 0)
+        if x_dtype == paddle.bool or x_dtype == paddle.float64:
+            inputs = [
+                graph.make_node(
+                    "Cast", inputs=inputs, to=dtypes.ONNX.FLOAT)
+            ]
+        axes = node.attr("axis")
+        if not isinstance(axes, list):
+            axes = [axes]
+        input_shape = node.input_shape('X', 0)
+
+        for i, axis in enumerate(axes):
+            if axis < 0:
+                axes[i] += len(input_shape)
+            assert input_shape[
+                axis] > 0, "The dimension in axis of input must be fixed for flip operator, but now the input shape({}) in axis({}) is unknow.".format(
+                    input_shape, axis)
+
+        temp_input = inputs[0]
+        for i, axis in enumerate(axes):
+            if input_shape[axis] == 1:
+                if i != len(axes) - 1:
+                    continue
+                else:
+                    if x_dtype == paddle.bool or x_dtype == paddle.float64:
+                        graph.make_node(
+                            "Cast",
+                            inputs=[temp_input],
+                            outputs=node.output("Out"),
+                            to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+                    else:
+                        graph.make_node(
+                            "Identity",
+                            inputs=[temp_input],
+                            outputs=node.output("Out"))
+            else:
+                splits = graph.make_node(
+                    "Split",
+                    inputs=[temp_input],
+                    outputs=input_shape[axis],
+                    axis=axis,
+                    split=[1] * input_shape[axis])
+                reversed_splits = splits[::-1]
+                if i != len(axes) - 1:
+                    temp_input = graph.make_node(
+                        "Concat", inputs=reversed_splits, axis=axis)
+                else:
+                    if x_dtype == paddle.bool or x_dtype == paddle.float64:
+                        out = graph.make_node(
+                            "Concat", inputs=reversed_splits, axis=axis)
+                        graph.make_node(
+                            "Cast",
+                            inputs=[out],
+                            outputs=node.output("Out"),
+                            to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+                    else:
+                        graph.make_node(
+                            "Concat",
+                            inputs=reversed_splits,
+                            outputs=node.output("Out"),
+                            axis=axis)
+
+    @classmethod
+    def opset_13(cls, graph, node, **kw):
+        inputs = node.input('X')
+        x_dtype = node.input_dtype('X', 0)
+        if x_dtype == paddle.bool or x_dtype == paddle.float64:
+            inputs = [
+                graph.make_node(
+                    "Cast", inputs=inputs, to=dtypes.ONNX.FLOAT)
+            ]
+        axes = node.attr("axis")
+        if not isinstance(axes, list):
+            axes = [axes]
+        input_shape = node.input_shape('X', 0)
+
+        for i, axis in enumerate(axes):
+            if axis < 0:
+                axes[i] += len(input_shape)
+            assert input_shape[
+                axis] > 0, "The dimension in axis of input must be fixed for flip operator, but now the input shape({}) in axis({}) is unknow.".format(
+                    input_shape, axis)
+
+        temp_input = inputs[0]
+        for i, axis in enumerate(axes):
+            if input_shape[axis] == 1:
+                if i != len(axes) - 1:
+                    continue
+                else:
+                    if x_dtype == paddle.bool or x_dtype == paddle.float64:
+                        graph.make_node(
+                            "Cast",
+                            inputs=[temp_input],
+                            outputs=node.output("Out"),
+                            to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+                    else:
+                        graph.make_node(
+                            "Identity",
+                            inputs=[temp_input],
+                            outputs=node.output("Out"))
+            else:
+                split = graph.make_node(
+                    'Constant',
+                    attrs={
+                        'dtype': dtypes.ONNX.INT64,
+                        'value': [1] * input_shape[axis]
+                    })
+                splits = graph.make_node(
+                    "Split",
+                    inputs=[temp_input, split],
+                    outputs=input_shape[axis],
+                    axis=axis)
+                reversed_splits = splits[::-1]
+                if i != len(axes) - 1:
+                    temp_input = graph.make_node(
+                        "Concat", inputs=reversed_splits, axis=axis)
+                else:
+                    if x_dtype == paddle.bool or x_dtype == paddle.float64:
+                        out = graph.make_node(
+                            "Concat", inputs=reversed_splits, axis=axis)
+                        graph.make_node(
+                            "Cast",
+                            inputs=[out],
+                            outputs=node.output("Out"),
+                            to=dtypes.DTYPE_PADDLE_ONNX_MAP[x_dtype])
+                    else:
+                        graph.make_node(
+                            "Concat",
+                            inputs=reversed_splits,
+                            outputs=node.output("Out"),
+                            axis=axis)


Test patch:
diff --git a/tests/test_auto_scan_flip.py b/tests/test_auto_scan_flip.py
new file mode 100644
index 000000000..235ca53fc
--- /dev/null
+++ b/tests/test_auto_scan_flip.py
@@ -0,0 +1,85 @@
+# 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.
+
+from auto_scan_test import OPConvertAutoScanTest, BaseNet
+from hypothesis import reproduce_failure
+import hypothesis.strategies as st
+import numpy as np
+import unittest
+import paddle
+
+
+class Net(BaseNet):
+    """
+    simple Net
+    """
+
+    def forward(self, x):
+        """
+        forward
+        """
+        x = paddle.flip(x, axis=self.config["axis"])
+        return x
+
+
+class TestFlattenConvert(OPConvertAutoScanTest):
+    """
+    api: paddle.flip
+    OPset version: 7, 11, 15
+    """
+
+    def sample_convert_config(self, draw):
+        input_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=20), min_size=2, max_size=5))
+
+        dtype = draw(
+            st.sampled_from(["bool", "int32", "int64", "float32", "float64"]))
+
+        axis = draw(
+            st.lists(
+                st.integers(
+                    min_value=0, max_value=len(input_shape) - 1),
+                min_size=1,
+                max_size=len(input_shape)))
+        axis = list(set(axis))
+
+        for i in range(len(axis)):
+            if draw(st.booleans()):
+                axis[i] -= len(input_shape)
+
+        input_spec_shape = [-1] * len(input_shape)
+        for i in range(len(axis)):
+            input_spec_shape[axis[i]] = input_shape[axis[i]]
+
+        config = {
+            "op_names": ["flip"],
+            "test_data_shapes": [input_shape],
+            "test_data_types": [[dtype]],
+            "opset_version": [7, 8, 9, 10, 11, 12, 13, 14, 15],
+            "input_spec_shape": [input_spec_shape],
+            "axis": axis
+        }
+
+        models = Net(config)
+
+        return (config, models)
+
+    def test(self):
+        self.run_and_statis(max_examples=300)
+
+
+if __name__ == "__main__":
+    unittest.main()