Add deeptext.

2020-12-26 18:49:05 +08:00 · 2020-12-26 18:49:05 +08:00 · e735b85802
parent 4a940e4be6
commit e735b85802
21 changed files with 3651 additions and 0 deletions
--- a/model_zoo/official/cv/deeptext/README.md
+++ b/model_zoo/official/cv/deeptext/README.md
@ -0,0 +1,223 @@
+# DeepText for Ascend
+
+- [DeepText Description](#DeepText-description)
+- [Model Architecture](#model-architecture)
+- [Dataset](#dataset)
+- [Features](#features)
+    - [Mixed Precision](#mixed-precision)
+- [Environment Requirements](#environment-requirements)
+- [Script Description](#script-description)
+    - [Script and Sample Code](#script-and-sample-code)
+    - [Training Process](#training-process)
+    - [Evaluation Process](#evaluation-process)
+        - [Evaluation](#evaluation)
+- [Model Description](#model-description)
+    - [Performance](#performance)  
+        - [Training Performance](#evaluation-performance)
+        - [Inference Performance](#evaluation-performance)
+- [Description of Random Situation](#description-of-random-situation)
+- [ModelZoo Homepage](#modelzoo-homepage)
+
+# [DeepText Description](#contents)
+
+DeepText is a convolutional neural network architecture for text detection in non-specific scenarios. The DeepText system is based on the elegant framwork of Faster R-CNN. This idea was proposed in the paper "DeepText: A new approach for text proposal generation and text detection in natural images.", published in 2017.
+
+[Paper](https://arxiv.org/pdf/1605.07314v1.pdf) Zhuoyao Zhong, Lianwen Jin, Shuangping Huang, South China University of Technology (SCUT), Published in ICASSP 2017.
+
+# [Model architecture](#contents)
+
+The overall network architecture of InceptionV4 is show below:
+
+[Link](https://arxiv.org/pdf/1605.07314v1.pdf)
+
+# [Dataset](#contents)
+
+Here we used 4 datasets for training, and 1 datasets for Evaluation.
+
+- Dataset1: ICDAR 2013: Focused Scene Text
+    - Train: 142MB, 229 images
+    - Test: 110MB, 233 images
+- Dataset2: ICDAR 2013: Born-Digital Images
+    - Train: 27.7MB, 410 images
+- Dataset3: SCUT-FORU: Flickr OCR Universal Database
+    - Train: 388MB, 1715 images
+- Dataset4: CocoText v2(Subset of MSCOCO2014):
+    - Train: 13GB, 63686 images
+
+# [Features](#contents)
+
+# [Environment Requirements](#contents)
+
+- Hardware（Ascend）
+    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+- Framework
+    - [MindSpore](https://www.mindspore.cn/install/en)
+- For more information, please check the resources below：
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
+
+# [Script description](#contents)
+
+## [Script and sample code](#contents)
+
+```shell
+.
+└─deeptext
+  ├─README.md
+  ├─scripts
+    ├─run_standalone_train_ascend.sh    # launch standalone training with ascend platform(1p)
+    ├─run_distribute_train_ascend.sh    # launch distributed training with ascend platform(8p)
+    └─run_eval_ascend.sh                # launch evaluating with ascend platform
+  ├─src
+    ├─DeepText
+      ├─__init__.py                     # package init file
+      ├─anchor_genrator.py              # anchor generator
+      ├─bbox_assign_sample.py           # proposal layer for stage 1
+      ├─bbox_assign_sample_stage2.py    # proposal layer for stage 2
+      ├─deeptext_vgg16.py               # main network defination
+      ├─proposal_generator.py           # proposal generator
+      ├─rcnn.py                         # rcnn
+      ├─roi_align.py                    # roi_align cell wrapper
+      ├─rpn.py                          # region-proposal network
+      └─vgg16.py                        # backbone
+    ├─config.py                       # training configuration
+    ├─dataset.py                      # data proprocessing
+    ├─lr_schedule.py                  # learning rate scheduler
+    ├─network_define.py               # network defination
+    └─utils.py                        # some functions which is commonly used
+  ├─eval.py                           # eval net
+  ├─export.py                         # export checkpoint, surpport .onnx, .air, .mindir convert
+  └─train.py                          # train net
+```
+
+## [Training process](#contents)
+
+### Usage
+
+- Ascend:
+
+```bash
+# distribute training example(8p)
+sh run_distribute_train_ascend.sh [IMGS_PATH] [ANNOS_PATH] [RANK_TABLE_FILE] [PRETRAINED_PATH] [COCO_TEXT_PARSER_PATH]
+# standalone training
+sh run_standalone_train_ascend.sh [IMGS_PATH] [ANNOS_PATH] [PRETRAINED_PATH] [COCO_TEXT_PARSER_PATH]
+# evaluation:
+sh run_eval_ascend.sh [IMGS_PATH] [ANNOS_PATH] [CHECKPOINT_PATH] [COCO_TEXT_PARSER_PATH]
+```
+
+> Notes:
+> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
+>
+> This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
+>
+> The `pretrained_path` should be a checkpoint of vgg16 trained on Imagenet2012. The name of weight in dict should be totally the same, also the batch_norm should be enabled in the trainig of vgg16, otherwise fails in further steps.
+> COCO_TEXT_PARSER_PATH coco_text.py can refer to [Link](https://github.com/andreasveit/coco-text).
+>
+### Launch
+
+```bash
+# training example
+  shell:
+    Ascend:
+      # distribute training example(8p)
+      sh run_distribute_train_ascend.sh [IMGS_PATH] [ANNOS_PATH] [RANK_TABLE_FILE] [PRETRAINED_PATH] [COCO_TEXT_PARSER_PATH]
+      # standalone training
+      sh run_standalone_train_ascend.sh [IMGS_PATH] [ANNOS_PATH] [PRETRAINED_PATH] [COCO_TEXT_PARSER_PATH]
+```
+
+### Result
+
+Training result will be stored in the example path. Checkpoints will be stored at `ckpt_path` by default, and training log  will be redirected to `./log`, also the loss will be redirected to `./loss_0.log` like followings.
+
+```python
+469 epoch: 1 step: 982 ,rpn_loss: 0.03940, rcnn_loss: 0.48169, rpn_cls_loss: 0.02910, rpn_reg_loss: 0.00344, rcnn_cls_loss: 0.41943, rcnn_reg_loss: 0.06223, total_loss: 0.52109
+659 epoch: 2 step: 982 ,rpn_loss: 0.03607, rcnn_loss: 0.32129, rpn_cls_loss: 0.02916, rpn_reg_loss: 0.00230, rcnn_cls_loss: 0.25732, rcnn_reg_loss: 0.06390, total_loss: 0.35736
+847 epoch: 3 step: 982 ,rpn_loss: 0.07074, rcnn_loss: 0.40527, rpn_cls_loss: 0.03494, rpn_reg_loss: 0.01193, rcnn_cls_loss: 0.30591, rcnn_reg_loss: 0.09937, total_loss: 0.47601
+```
+
+## [Eval process](#contents)
+
+### Usage
+
+You can start training using python or shell scripts. The usage of shell scripts as follows:
+
+- Ascend:
+
+```bash
+  sh run_eval_ascend.sh [IMGS_PATH] [ANNOS_PATH] [CHECKPOINT_PATH] [COCO_TEXT_PARSER_PATH]
+```
+
+### Launch
+
+```bash
+# eval example
+  shell:
+      Ascend:
+            sh run_eval_ascend.sh [IMGS_PATH] [ANNOS_PATH] [CHECKPOINT_PATH] [COCO_TEXT_PARSER_PATH]
+```
+
+> checkpoint can be produced in training process.
+
+### Result
+
+Evaluation result will be stored in the example path, you can find result like the followings in `log`.
+
+```python
+========================================
+
+class 1 precision is 88.01%, recall is 82.77%
+```
+
+# [Model description](#contents)
+
+## [Performance](#contents)
+
+### Training Performance
+
+| Parameters                 | Ascend                                                       |
+| -------------------------- | ------------------------------------------------------------ |
+| Model Version              | Deeptext                                                     |
+| Resource                   | Ascend 910, cpu:2.60GHz 192cores, memory:755G                |
+| uploaded Date              | 12/26/2020                                                   |
+| MindSpore Version          | 1.1.0                                                        |
+| Dataset                    | 66040 images                                                 |
+| Batch_size                 | 2                                                            |
+| Training Parameters        | src/config.py                                                |
+| Optimizer                  | Momentum                                                     |
+| Loss Function              | SoftmaxCrossEntropyWithLogits for classification, SmoothL2Loss for bbox regression|
+| Loss                       | ~0.008                                                       |
+| Accuracy (8p)              | precision=0.8854, recall=0.8024                              |
+| Total time (8p)            | 4h                                                           |
+| Scripts                    | [deeptext script](https://gitee.com/mindspore/mindspore/tree/r1.1/mindspore/official/cv/deeptext) |
+
+#### Inference Performance
+
+| Parameters          | Ascend                 |
+| ------------------- | --------------------------- |
+| Model Version       | Deeptext                 |
+| Resource            | Ascend 910, cpu:2.60GHz 192cores, memory:755G         |
+| Uploaded Date       | 12/26/2020                 |
+| MindSpore Version   | 1.1.0              |
+| Dataset             | 229 images                  |
+| Batch_size          | 2                         |
+| Accuracy            | precision=0.8854, recall=0.8024 |
+| Total time          | 1 min                      |
+| Model for inference | 3492M (.ckpt file)   |
+
+#### Training performance results
+
+| **Ascend** | train performance |
+| :--------: | :---------------: |
+|     1p     |     42 img/s      |
+
+| **Ascend** | train performance |
+| :--------: | :---------------: |
+|     8p     |     330 img/s     |
+
+# [Description of Random Situation](#contents)
+
+We set seed to 1 in train.py.
+
+# [ModelZoo Homepage](#contents)
+
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
--- a/model_zoo/official/cv/deeptext/eval.py
+++ b/model_zoo/official/cv/deeptext/eval.py
@ -0,0 +1,138 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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
+#
+# less 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.
+# ============================================================================
+
+"""Evaluation for Deeptext"""
+import argparse
+import os
+import time
+
+import numpy as np
+from src.Deeptext.deeptext_vgg16 import Deeptext_VGG16
+from src.config import config
+from src.dataset import data_to_mindrecord_byte_image, create_deeptext_dataset
+from src.utils import metrics
+
+from mindspore import context
+from mindspore.common import set_seed
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+set_seed(1)
+
+parser = argparse.ArgumentParser(description="Deeptext evaluation")
+parser.add_argument("--checkpoint_path", type=str, default='test', help="Checkpoint file path.")
+parser.add_argument("--imgs_path", type=str, required=True,
+                    help="Test images files paths, multiple paths can be separated by ','.")
+parser.add_argument("--annos_path", type=str, required=True,
+                    help="Annotations files paths of test images, multiple paths can be separated by ','.")
+parser.add_argument("--device_id", type=int, default=7, help="Device id, default is 7.")
+parser.add_argument("--mindrecord_prefix", type=str, default='Deeptext-TEST', help="Prefix of mindrecord.")
+args_opt = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+
+
+def Deeptext_eval_test(dataset_path='', ckpt_path=''):
+    """Deeptext evaluation."""
+    ds = create_deeptext_dataset(dataset_path, batch_size=config.test_batch_size,
+                                 repeat_num=1, is_training=False)
+
+    total = ds.get_dataset_size()
+    net = Deeptext_VGG16(config)
+    param_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, param_dict)
+    net.set_train(False)
+    eval_iter = 0
+
+    print("\n========================================\n")
+    print("Processing, please wait a moment.")
+    max_num = 32
+
+    pred_data = []
+    for data in ds.create_dict_iterator():
+        eval_iter = eval_iter + 1
+
+        img_data = data['image']
+        img_metas = data['image_shape']
+        gt_bboxes = data['box']
+        gt_labels = data['label']
+        gt_num = data['valid_num']
+
+        start = time.time()
+        # run net
+        output = net(img_data, img_metas, gt_bboxes, gt_labels, gt_num)
+        gt_bboxes = gt_bboxes.asnumpy()
+
+        gt_bboxes = gt_bboxes[gt_num.asnumpy().astype(bool), :]
+        print(gt_bboxes)
+        gt_labels = gt_labels.asnumpy()
+        gt_labels = gt_labels[gt_num.asnumpy().astype(bool)]
+        print(gt_labels)
+        end = time.time()
+        print("Iter {} cost time {}".format(eval_iter, end - start))
+
+        # output
+        all_bbox = output[0]
+        all_label = output[1] + 1
+        all_mask = output[2]
+
+        for j in range(config.test_batch_size):
+            all_bbox_squee = np.squeeze(all_bbox.asnumpy()[j, :, :])
+            all_label_squee = np.squeeze(all_label.asnumpy()[j, :, :])
+            all_mask_squee = np.squeeze(all_mask.asnumpy()[j, :, :])
+
+            all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
+            all_labels_tmp_mask = all_label_squee[all_mask_squee]
+
+            if all_bboxes_tmp_mask.shape[0] > max_num:
+                inds = np.argsort(-all_bboxes_tmp_mask[:, -1])
+                inds = inds[:max_num]
+                all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]
+                all_labels_tmp_mask = all_labels_tmp_mask[inds]
+
+            pred_data.append({"boxes": all_bboxes_tmp_mask,
+                              "labels": all_labels_tmp_mask,
+                              "gt_bboxes": gt_bboxes,
+                              "gt_labels": gt_labels})
+
+            percent = round(eval_iter / total * 100, 2)
+
+            print('    %s [%d/%d]' % (str(percent) + '%', eval_iter, total), end='\r')
+
+    precisions, recalls = metrics(pred_data)
+    print("\n========================================\n")
+    for i in range(config.num_classes - 1):
+        j = i + 1
+        print("class {} precision is {:.2f}%, recall is {:.2f}%".format(j, precisions[j] * 100, recalls[j] * 100))
+        if config.use_ambigous_sample:
+            break
+
+
+if __name__ == '__main__':
+    prefix = args_opt.mindrecord_prefix
+    config.test_images = args_opt.imgs_path
+    config.test_txts = args_opt.annos_path
+    mindrecord_dir = config.mindrecord_dir
+    mindrecord_file = os.path.join(mindrecord_dir, prefix)
+    print("CHECKING MINDRECORD FILES ...")
+    if not os.path.exists(mindrecord_file):
+        if not os.path.isdir(mindrecord_dir):
+            os.makedirs(mindrecord_dir)
+        print("Create Mindrecord. It may take some time.")
+        data_to_mindrecord_byte_image(False, prefix, file_num=1)
+        print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+
+    print("CHECKING MINDRECORD FILES DONE!")
+    print("Start Eval!")
+    Deeptext_eval_test(mindrecord_file, args_opt.checkpoint_path)
--- a/model_zoo/official/cv/deeptext/scripts/run_distribute_train_ascend.sh
+++ b/model_zoo/official/cv/deeptext/scripts/run_distribute_train_ascend.sh
@ -0,0 +1,80 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+if [ $# -ne 5 ]
+then 
+    echo "Usage: sh run_distribute_train_ascend.sh [IMGS_PATH] [ANNOS_PATH] [RANK_TABLE_FILE] [PRETRAINED_PATH] [COCO_TEXT_PARSER_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+echo $PATH1
+PATH2=$(get_real_path $2)
+echo $PATH2
+PATH3=$(get_real_path $3)
+echo $PATH3
+PATH4=$(get_real_path $4)
+echo $PATH4
+PATH5=$(get_real_path $5)
+echo $PATH5
+
+if [ ! -f $PATH3 ]
+then 
+    echo "error: RANK_TABLE_FILE=$PATH3 is not a file"
+exit 1
+fi 
+
+if [ ! -f $PATH4 ]
+then 
+    echo "error: PRETRAINED_PATH=$PATH4 is not a file"
+exit 1
+fi
+
+if [ ! -f $PATH5 ]
+then 
+    echo "error: COCO_TEXT_PARSER_PATH=$PATH5 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=8
+export RANK_SIZE=8
+export RANK_TABLE_FILE=$PATH3
+cp $PATH5 ../src/
+
+for((i=0; i<${DEVICE_NUM}; i++))
+do
+    export DEVICE_ID=$i
+    export RANK_ID=$i
+    rm -rf ./train_parallel$i
+    mkdir ./train_parallel$i
+    cp ../*.py ./train_parallel$i
+    cp *.sh ./train_parallel$i
+    cp -r ../src ./train_parallel$i
+    cd ./train_parallel$i || exit
+    echo "start training for rank $RANK_ID, device $DEVICE_ID"
+    env > env.log
+    python train.py --device_id=$i --rank_id=$i --imgs_path=$PATH1 --annos_path=$PATH2 --run_distribute=True --device_num=$DEVICE_NUM --pre_trained=$PATH4 &> log &
+    cd ..
+done
--- a/model_zoo/official/cv/deeptext/scripts/run_eval_ascend.sh
+++ b/model_zoo/official/cv/deeptext/scripts/run_eval_ascend.sh
@ -0,0 +1,70 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+if [ $# != 4 ]
+then 
+    echo "Usage: sh run_eval_ascend.sh [IMGS_PATH] [ANNOS_PATH] [CHECKPOINT_PATH] [COCO_TEXT_PARSER_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+PATH3=$(get_real_path $3)
+PATH4=$(get_real_path $4)
+echo $PATH1
+echo $PATH2
+echo $PATH3
+echo $PATH4
+
+
+if [ ! -f $PATH3 ]
+then 
+    echo "error: CHECKPOINT_PATH=$PATH3 is not a file"
+exit 1
+fi 
+
+if [ ! -f $PATH4 ]
+then 
+    echo "error: COCO_TEXT_PARSER_PATH=$PATH4 is not a file"
+exit 1
+fi 
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export RANK_SIZE=$DEVICE_NUM
+export DEVICE_ID=0
+export RANK_ID=0
+cp $PATH4 ../src/
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ../*.py ./eval
+cp *.sh ./eval
+cp -r ../src ./eval
+cd ./eval || exit
+env > env.log
+echo "start eval for device $DEVICE_ID"
+python eval.py --device_id=$DEVICE_ID --imgs_path=$PATH1 --annos_path=$PATH2 --checkpoint_path=$PATH3 &> log &
+cd ..
--- a/model_zoo/official/cv/deeptext/scripts/run_standalone_train_ascend.sh
+++ b/model_zoo/official/cv/deeptext/scripts/run_standalone_train_ascend.sh
@ -0,0 +1,70 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+if [ $# -ne 4 ]
+then 
+    echo "Usage: sh run_standalone_train_ascend.sh [IMGS_PATH] [ANNOS_PATH] [PRETRAINED_PATH] [COCO_TEXT_PARSER_PATH]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+echo $PATH1
+PATH2=$(get_real_path $2)
+echo $PATH2
+PATH3=$(get_real_path $3)
+echo $PATH3
+PATH4=$(get_real_path $4)
+echo $PATH4
+
+if [ ! -f $PATH3 ]
+then 
+    echo "error: PRETRAINED_PATH=$PATH3 is not a file"
+exit 1
+fi
+
+if [ ! -f $PATH4 ]
+then 
+    echo "error: COCO_TEXT_PARSER_PATH=$PATH4 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_ID=0
+export RANK_SIZE=1
+cp $PATH4 ../src/
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+echo "start training for device $DEVICE_ID"
+env > env.log
+python train.py --device_id=$DEVICE_ID --imgs_path=$PATH1 --annos_path=$PATH2 --pre_trained=$PATH3 &> log &
+cd ..
--- a/model_zoo/official/cv/deeptext/src/Deeptext/init.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/init.py
@ -0,0 +1,29 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Deeptext Init."""
+
+from .bbox_assign_sample import BboxAssignSample
+from .bbox_assign_sample_stage2 import BboxAssignSampleForRcnn
+from .proposal_generator import Proposal
+from .rcnn import Rcnn
+from .rpn import RPN
+from .roi_align import SingleRoIExtractor
+from .anchor_generator import AnchorGenerator
+
+__all__ = [
+    "BboxAssignSample",
+    "Proposal", "Rcnn",
+    "RPN", "SingleRoIExtractor", "AnchorGenerator"
+]
--- a/model_zoo/official/cv/deeptext/src/Deeptext/anchor_generator.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/anchor_generator.py
@ -0,0 +1,86 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Deeptext anchor generator."""
+
+import numpy as np
+
+
+class AnchorGenerator():
+    """Anchor generator for Deeptext."""
+
+    def __init__(self, base_size, scales, ratios, scale_major=True, ctr=None):
+        """Anchor generator init method."""
+        self.base_size = base_size
+        self.scales = np.array(scales)
+        self.ratios = np.array(ratios)
+        self.scale_major = scale_major
+        self.ctr = ctr
+        self.base_anchors = self.gen_base_anchors()
+
+    def gen_base_anchors(self):
+        """Generate a single anchor."""
+        w = self.base_size
+        h = self.base_size
+        if self.ctr is None:
+            x_ctr = 0.5 * (w - 1)
+            y_ctr = 0.5 * (h - 1)
+        else:
+            x_ctr, y_ctr = self.ctr
+
+        h_ratios = np.sqrt(self.ratios)
+        w_ratios = 1 / h_ratios
+        if self.scale_major:
+            ws = (w * w_ratios[:, None] * self.scales[None, :]).reshape(-1)
+            hs = (h * h_ratios[:, None] * self.scales[None, :]).reshape(-1)
+        else:
+            ws = (w * self.scales[:, None] * w_ratios[None, :]).reshape(-1)
+            hs = (h * self.scales[:, None] * h_ratios[None, :]).reshape(-1)
+
+        base_anchors = np.stack(
+            [
+                x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1),
+                x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1)
+            ],
+            axis=-1).round()
+
+        return base_anchors
+
+    def _meshgrid(self, x, y, row_major=True):
+        """Generate grid."""
+        xx = np.repeat(x.reshape(1, len(x)), len(y), axis=0).reshape(-1)
+        yy = np.repeat(y, len(x))
+        if row_major:
+            return xx, yy
+
+        return yy, xx
+
+    def grid_anchors(self, featmap_size, stride=16):
+        """Generate anchor list."""
+        base_anchors = self.base_anchors
+
+        feat_h, feat_w = featmap_size
+        shift_x = np.arange(0, feat_w) * stride
+        shift_y = np.arange(0, feat_h) * stride
+        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
+        shifts = np.stack([shift_xx, shift_yy, shift_xx, shift_yy], axis=-1)
+        shifts = shifts.astype(base_anchors.dtype)
+        # first feat_w elements correspond to the first row of shifts
+        # add A anchors (1, A, 4) to K shifts (K, 1, 4) to get
+        # shifted anchors (K, A, 4), reshape to (K*A, 4)
+
+        all_anchors = base_anchors[None, :, :] + shifts[:, None, :]
+        all_anchors = all_anchors.reshape(-1, 4)
+
+        return all_anchors
--- a/model_zoo/official/cv/deeptext/src/Deeptext/bbox_assign_sample.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/bbox_assign_sample.py
@ -0,0 +1,165 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Deeptext positive and negative sample screening for RPN."""
+
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+import mindspore.common.dtype as mstype
+
+
+class BboxAssignSample(nn.Cell):
+    """
+    Bbox assigner and sampler defination.
+
+    Args:
+        config (dict): Config.
+        batch_size (int): Batchsize.
+        num_bboxes (int): The anchor nums.
+        add_gt_as_proposals (bool): add gt bboxes as proposals flag.
+
+    Returns:
+        Tensor, output tensor.
+        bbox_targets: bbox location, (batch_size, num_bboxes, 4)
+        bbox_weights: bbox weights, (batch_size, num_bboxes, 1)
+        labels: label for every bboxes, (batch_size, num_bboxes, 1)
+        label_weights: label weight for every bboxes, (batch_size, num_bboxes, 1)
+
+    Examples:
+        BboxAssignSample(config, 2, 1024, True)
+    """
+
+    def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
+        super(BboxAssignSample, self).__init__()
+        cfg = config
+        self.batch_size = batch_size
+
+        self.neg_iou_thr = Tensor(cfg.neg_iou_thr, mstype.float16)
+        self.pos_iou_thr = Tensor(cfg.pos_iou_thr, mstype.float16)
+        self.min_pos_iou = Tensor(cfg.min_pos_iou, mstype.float16)
+        self.zero_thr = Tensor(0.0, mstype.float16)
+
+        self.num_bboxes = num_bboxes
+        self.num_gts = cfg.num_gts
+        self.num_expected_pos = cfg.num_expected_pos
+        self.num_expected_neg = cfg.num_expected_neg
+        self.add_gt_as_proposals = add_gt_as_proposals
+
+        if self.add_gt_as_proposals:
+            self.label_inds = Tensor(np.arange(1, self.num_gts + 1))
+
+        self.concat = P.Concat(axis=0)
+        self.max_gt = P.ArgMaxWithValue(axis=0)
+        self.max_anchor = P.ArgMaxWithValue(axis=1)
+        self.sum_inds = P.ReduceSum()
+        self.iou = P.IOU()
+        self.greaterequal = P.GreaterEqual()
+        self.greater = P.Greater()
+        self.select = P.Select()
+        self.gatherND = P.GatherNd()
+        self.squeeze = P.Squeeze()
+        self.cast = P.Cast()
+        self.logicaland = P.LogicalAnd()
+        self.less = P.Less()
+        self.random_choice_with_mask_pos = P.RandomChoiceWithMask(self.num_expected_pos)
+        self.random_choice_with_mask_neg = P.RandomChoiceWithMask(self.num_expected_neg)
+        self.reshape = P.Reshape()
+        self.equal = P.Equal()
+        self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
+        self.scatterNdUpdate = P.ScatterNdUpdate()
+        self.scatterNd = P.ScatterNd()
+        self.logicalnot = P.LogicalNot()
+        self.tile = P.Tile()
+        self.zeros_like = P.ZerosLike()
+
+        self.assigned_gt_inds = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_gt_zeros = Tensor(np.array(np.zeros(num_bboxes), dtype=np.int32))
+        self.assigned_gt_ones = Tensor(np.array(np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_gt_ignores = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_pos_ones = Tensor(np.array(np.ones(self.num_expected_pos), dtype=np.int32))
+
+        self.check_neg_mask = Tensor(np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool))
+        self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(np.float16))
+        self.check_gt_one = Tensor(np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16))
+        self.check_anchor_two = Tensor(np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16))
+
+    def construct(self, gt_bboxes_i, gt_labels_i, valid_mask, bboxes, gt_valids):
+        gt_bboxes_i = self.select(self.cast(self.tile(self.reshape(self.cast(gt_valids, mstype.int32), \
+                                                                   (self.num_gts, 1)), (1, 4)), mstype.bool_),
+                                  gt_bboxes_i, self.check_gt_one)
+        bboxes = self.select(self.cast(self.tile(self.reshape(self.cast(valid_mask, mstype.int32), \
+                                                              (self.num_bboxes, 1)), (1, 4)), mstype.bool_), bboxes,
+                             self.check_anchor_two)
+
+        overlaps = self.iou(bboxes, gt_bboxes_i)
+
+        max_overlaps_w_gt_index, max_overlaps_w_gt = self.max_gt(overlaps)
+        _, max_overlaps_w_ac = self.max_anchor(overlaps)
+
+        neg_sample_iou_mask = self.logicaland(self.greaterequal(max_overlaps_w_gt, self.zero_thr), \
+                                              self.less(max_overlaps_w_gt, self.neg_iou_thr))
+        assigned_gt_inds2 = self.select(neg_sample_iou_mask, self.assigned_gt_zeros, self.assigned_gt_inds)
+
+        pos_sample_iou_mask = self.greaterequal(max_overlaps_w_gt, self.pos_iou_thr)
+        assigned_gt_inds3 = self.select(pos_sample_iou_mask, \
+                                        max_overlaps_w_gt_index + self.assigned_gt_ones, assigned_gt_inds2)
+        assigned_gt_inds4 = assigned_gt_inds3
+        for j in range(self.num_gts):
+            max_overlaps_w_ac_j = max_overlaps_w_ac[j:j + 1:1]
+            overlaps_w_gt_j = self.squeeze(overlaps[j:j + 1:1, ::])
+
+            pos_mask_j = self.logicaland(self.greaterequal(max_overlaps_w_ac_j, self.min_pos_iou), \
+                                         self.equal(overlaps_w_gt_j, max_overlaps_w_ac_j))
+
+            assigned_gt_inds4 = self.select(pos_mask_j, self.assigned_gt_ones + j, assigned_gt_inds4)
+
+        assigned_gt_inds5 = self.select(valid_mask, assigned_gt_inds4, self.assigned_gt_ignores)
+
+        pos_index, valid_pos_index = self.random_choice_with_mask_pos(self.greater(assigned_gt_inds5, 0))
+
+        pos_check_valid = self.cast(self.greater(assigned_gt_inds5, 0), mstype.float16)
+        pos_check_valid = self.sum_inds(pos_check_valid, -1)
+        valid_pos_index = self.less(self.range_pos_size, pos_check_valid)
+        pos_index = pos_index * self.reshape(self.cast(valid_pos_index, mstype.int32), (self.num_expected_pos, 1))
+
+        pos_assigned_gt_index = self.gatherND(assigned_gt_inds5, pos_index) - self.assigned_pos_ones
+        pos_assigned_gt_index = pos_assigned_gt_index * self.cast(valid_pos_index, mstype.int32)
+        pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, (self.num_expected_pos, 1))
+
+        neg_index, valid_neg_index = self.random_choice_with_mask_neg(self.equal(assigned_gt_inds5, 0))
+
+        num_pos = self.cast(self.logicalnot(valid_pos_index), mstype.float16)
+        num_pos = self.sum_inds(num_pos, -1)
+        unvalid_pos_index = self.less(self.range_pos_size, num_pos)
+        valid_neg_index = self.logicaland(self.concat((self.check_neg_mask, unvalid_pos_index)), valid_neg_index)
+
+        pos_bboxes_ = self.gatherND(bboxes, pos_index)
+        pos_gt_bboxes_ = self.gatherND(gt_bboxes_i, pos_assigned_gt_index)
+        pos_gt_labels = self.gatherND(gt_labels_i, pos_assigned_gt_index)
+
+        pos_bbox_targets_ = self.bounding_box_encode(pos_bboxes_, pos_gt_bboxes_)
+
+        valid_pos_index = self.cast(valid_pos_index, mstype.int32)
+        valid_neg_index = self.cast(valid_neg_index, mstype.int32)
+        bbox_targets_total = self.scatterNd(pos_index, pos_bbox_targets_, (self.num_bboxes, 4))
+        bbox_weights_total = self.scatterNd(pos_index, valid_pos_index, (self.num_bboxes,))
+        labels_total = self.scatterNd(pos_index, pos_gt_labels, (self.num_bboxes,))
+        total_index = self.concat((pos_index, neg_index))
+        total_valid_index = self.concat((valid_pos_index, valid_neg_index))
+        label_weights_total = self.scatterNd(total_index, total_valid_index, (self.num_bboxes,))
+
+        return bbox_targets_total, self.cast(bbox_weights_total, mstype.bool_), \
+               labels_total, self.cast(label_weights_total, mstype.bool_)
--- a/model_zoo/official/cv/deeptext/src/Deeptext/bbox_assign_sample_stage2.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/bbox_assign_sample_stage2.py
@ -0,0 +1,257 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Deeptext tpositive and negative sample screening for Rcnn."""
+
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+
+
+class BboxAssignSampleForRcnn(nn.Cell):
+    """
+    Bbox assigner and sampler defination.
+
+    Args:
+        config (dict): Config.
+        batch_size (int): Batchsize.
+        num_bboxes (int): The anchor nums.
+        add_gt_as_proposals (bool): add gt bboxes as proposals flag.
+
+    Returns:
+        Tensor, output tensor.
+        bbox_targets: bbox location, (batch_size, num_bboxes, 4)
+        bbox_weights: bbox weights, (batch_size, num_bboxes, 1)
+        labels: label for every bboxes, (batch_size, num_bboxes, 1)
+        label_weights: label weight for every bboxes, (batch_size, num_bboxes, 1)
+
+    Examples:
+        BboxAssignSampleForRcnn(config, 2, 1024, True)
+    """
+
+    def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
+        super(BboxAssignSampleForRcnn, self).__init__()
+        cfg = config
+        self.use_ambigous_sample = cfg.use_ambigous_sample
+        self.batch_size = batch_size
+        self.neg_iou_thr = cfg.neg_iou_thr_stage2
+        self.pos_iou_thr = cfg.pos_iou_thr_stage2
+        self.min_pos_iou = cfg.min_pos_iou_stage2
+        self.num_gts = cfg.num_gts
+        self.num_bboxes = num_bboxes
+        self.num_expected_pos = cfg.num_expected_pos_stage2
+        self.num_expected_amb = cfg.num_expected_amb_stage2
+        self.num_expected_neg = cfg.num_expected_neg_stage2
+        self.num_expected_total = cfg.num_expected_total_stage2
+
+        self.add_gt_as_proposals = add_gt_as_proposals
+        self.label_inds = Tensor(np.arange(1, self.num_gts + 1).astype(np.int32))
+        self.add_gt_as_proposals_valid = Tensor(np.array(self.add_gt_as_proposals * np.ones(self.num_gts),
+                                                         dtype=np.int32))
+
+        self.concat = P.Concat(axis=0)
+        self.max_gt = P.ArgMaxWithValue(axis=0)
+        self.max_anchor = P.ArgMaxWithValue(axis=1)
+        self.sum_inds = P.ReduceSum()
+        self.iou = P.IOU()
+        self.greaterequal = P.GreaterEqual()
+        self.greater = P.Greater()
+        self.select = P.Select()
+        self.gatherND = P.GatherNd()
+        self.gatherV2 = P.GatherV2()
+        self.squeeze = P.Squeeze()
+        self.cast = P.Cast()
+        self.logicaland = P.LogicalAnd()
+        self.less = P.Less()
+        self.random_choice_with_mask_pos = P.RandomChoiceWithMask(self.num_expected_pos)
+        self.random_choice_with_mask_amb = P.RandomChoiceWithMask(self.num_expected_amb)
+        self.random_choice_with_mask_neg = P.RandomChoiceWithMask(self.num_expected_neg)
+        self.reshape = P.Reshape()
+        self.equal = P.Equal()
+        self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(0.1, 0.1, 0.2, 0.2))
+        self.concat_axis1 = P.Concat(axis=1)
+        self.logicalnot = P.LogicalNot()
+        self.tile = P.Tile()
+
+        # Check
+        self.check_gt_one = Tensor(np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16))
+        self.check_anchor_two = Tensor(np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16))
+
+        # Init tensor
+        self.assigned_gt_inds = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_gt_zeros = Tensor(np.array(np.zeros(num_bboxes), dtype=np.int32))
+        self.assigned_gt_ones = Tensor(np.array(np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_amb = Tensor(np.array(-3 * np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_gt_ignores = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
+        self.assigned_pos_ones = Tensor(np.array(np.ones(self.num_expected_pos), dtype=np.int32))
+
+        self.gt_ignores = Tensor(np.array(-1 * np.ones(self.num_gts), dtype=np.int32))
+        self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(np.float16))
+        self.range_amb_size = Tensor(np.arange(self.num_expected_amb).astype(np.float16))
+        self.check_neg_mask = Tensor(np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool))
+        if self.use_ambigous_sample:
+            self.check_neg_mask = Tensor(
+                np.array(np.ones(self.num_expected_neg - self.num_expected_pos - self.num_expected_amb), dtype=np.bool))
+        check_neg_mask_ignore_end = np.array(np.ones(self.num_expected_neg), dtype=np.bool)
+        check_neg_mask_ignore_end[-1] = False
+        self.check_neg_mask_ignore_end = Tensor(check_neg_mask_ignore_end)
+        self.bboxs_neg_mask = Tensor(np.zeros((self.num_expected_neg, 4), dtype=np.float16))
+
+        self.bboxs_amb_mask = Tensor(np.zeros((self.num_expected_amb, 4), dtype=np.float16))
+        self.labels_neg_mask = Tensor(np.array(np.zeros(self.num_expected_neg), dtype=np.uint8))
+        self.labels_amb_mask = Tensor(np.array(np.zeros(self.num_expected_amb) + 2, dtype=np.uint8))
+
+        self.reshape_shape_pos = (self.num_expected_pos, 1)
+        self.reshape_shape_amb = (self.num_expected_amb, 1)
+        self.reshape_shape_neg = (self.num_expected_neg, 1)
+
+        self.scalar_zero = Tensor(0.0, dtype=mstype.float16)
+        self.scalar_neg_iou_thr = Tensor(self.neg_iou_thr, dtype=mstype.float16)
+        self.scalar_pos_iou_thr = Tensor(self.pos_iou_thr, dtype=mstype.float16)
+        self.scalar_min_pos_iou = Tensor(self.min_pos_iou, dtype=mstype.float16)
+
+    def construct(self, gt_bboxes_i, gt_labels_i, valid_mask, bboxes, gt_valids):
+        gt_bboxes_i = self.select(self.cast(self.tile(self.reshape(self.cast(gt_valids, mstype.int32), \
+                                                                   (self.num_gts, 1)), (1, 4)), mstype.bool_), \
+                                  gt_bboxes_i, self.check_gt_one)
+        bboxes = self.select(self.cast(self.tile(self.reshape(self.cast(valid_mask, mstype.int32), \
+                                                              (self.num_bboxes, 1)), (1, 4)), mstype.bool_), \
+                             bboxes, self.check_anchor_two)
+
+        overlaps = self.iou(bboxes, gt_bboxes_i)
+
+        max_overlaps_w_gt_index, max_overlaps_w_gt = self.max_gt(overlaps)
+        _, max_overlaps_w_ac = self.max_anchor(overlaps)
+
+        neg_sample_iou_mask = self.logicaland(self.greaterequal(max_overlaps_w_gt,
+                                                                self.scalar_zero),
+                                              self.less(max_overlaps_w_gt,
+                                                        self.scalar_neg_iou_thr))
+
+        assigned_gt_inds = self.assigned_gt_inds
+        if self.use_ambigous_sample:
+            amb_sample_iou_mask = self.logicaland(self.greaterequal(max_overlaps_w_gt,
+                                                                    self.scalar_neg_iou_thr),
+                                                  self.less(max_overlaps_w_gt,
+                                                            self.scalar_pos_iou_thr))
+
+            assigned_gt_inds = self.select(amb_sample_iou_mask, self.assigned_amb, self.assigned_gt_inds)
+        assigned_gt_inds2 = self.select(neg_sample_iou_mask, self.assigned_gt_zeros, assigned_gt_inds)
+
+        pos_sample_iou_mask = self.greaterequal(max_overlaps_w_gt, self.scalar_pos_iou_thr)
+        assigned_gt_inds3 = self.select(pos_sample_iou_mask, \
+                                        max_overlaps_w_gt_index + self.assigned_gt_ones, assigned_gt_inds2)
+
+        for j in range(self.num_gts):
+            max_overlaps_w_ac_j = max_overlaps_w_ac[j:j + 1:1]
+            overlaps_w_ac_j = overlaps[j:j + 1:1, ::]
+            temp1 = self.greaterequal(max_overlaps_w_ac_j, self.scalar_min_pos_iou)
+            temp2 = self.squeeze(self.equal(overlaps_w_ac_j, max_overlaps_w_ac_j))
+            pos_mask_j = self.logicaland(temp1, temp2)
+            assigned_gt_inds3 = self.select(pos_mask_j, (j + 1) * self.assigned_gt_ones, assigned_gt_inds3)
+
+        assigned_gt_inds5 = self.select(valid_mask, assigned_gt_inds3, self.assigned_gt_ignores)
+
+        bboxes = self.concat((gt_bboxes_i, bboxes))
+        label_inds_valid = self.select(gt_valids, self.label_inds, self.gt_ignores)
+        label_inds_valid = label_inds_valid * self.add_gt_as_proposals_valid
+        assigned_gt_inds5 = self.concat((label_inds_valid, assigned_gt_inds5))
+
+        # Get pos index
+        pos_index, valid_pos_index = self.random_choice_with_mask_pos(self.greater(assigned_gt_inds5, 0))
+
+        pos_check_valid = self.cast(self.greater(assigned_gt_inds5, 0), mstype.float16)
+        pos_check_valid = self.sum_inds(pos_check_valid, -1)
+        valid_pos_index = self.less(self.range_pos_size, pos_check_valid)
+        pos_index = pos_index * self.reshape(self.cast(valid_pos_index, mstype.int32), (self.num_expected_pos, 1))
+
+        num_pos = self.sum_inds(self.cast(self.logicalnot(valid_pos_index), mstype.float16), -1)
+        valid_pos_index = self.cast(valid_pos_index, mstype.int32)
+        pos_index = self.reshape(pos_index, self.reshape_shape_pos)
+        valid_pos_index = self.reshape(valid_pos_index, self.reshape_shape_pos)
+        pos_index = pos_index * valid_pos_index
+
+        pos_assigned_gt_index = self.gatherND(assigned_gt_inds5, pos_index) - self.assigned_pos_ones
+        pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, self.reshape_shape_pos)
+        pos_assigned_gt_index = pos_assigned_gt_index * valid_pos_index
+
+        pos_gt_labels = self.gatherND(gt_labels_i, pos_assigned_gt_index)
+
+        # Get ambiguous index
+        num_amb = None
+        amb_index = None
+        valid_amb_index = None
+        if self.use_ambigous_sample:
+            amb_index, valid_amb_index = self.random_choice_with_mask_amb(self.equal(assigned_gt_inds5, -3))
+
+            amb_check_valid = self.cast(self.equal(assigned_gt_inds5, -3), mstype.float16)
+            amb_check_valid = self.sum_inds(amb_check_valid, -1)
+            valid_amb_index = self.less(self.range_amb_size, amb_check_valid)
+            amb_index = amb_index * self.reshape(self.cast(valid_amb_index, mstype.int32), (self.num_expected_amb, 1))
+
+            num_amb = self.sum_inds(self.cast(self.logicalnot(valid_amb_index), mstype.float16), -1)
+            valid_amb_index = self.cast(valid_amb_index, mstype.int32)
+            amb_index = self.reshape(amb_index, self.reshape_shape_amb)
+            valid_amb_index = self.reshape(valid_amb_index, self.reshape_shape_amb)
+            amb_index = amb_index * valid_amb_index
+
+        # Get neg index
+        neg_index, valid_neg_index = self.random_choice_with_mask_neg(self.equal(assigned_gt_inds5, 0))
+
+        unvalid_pos_index = self.less(self.range_pos_size, num_pos)
+        if self.use_ambigous_sample:
+            unvalid_amb_index = self.less(self.range_amb_size, num_amb)
+            valid_neg_index = self.logicaland(self.concat((self.check_neg_mask, unvalid_amb_index, unvalid_pos_index)),
+                                              valid_neg_index)
+        else:
+            valid_neg_index = self.logicaland(self.concat((self.check_neg_mask, unvalid_pos_index)), valid_neg_index)
+        valid_neg_index = self.logicaland(valid_neg_index, self.check_neg_mask_ignore_end)
+        # import pdb
+        # pdb.set_trace()
+        neg_index = self.reshape(neg_index, self.reshape_shape_neg)
+
+        valid_neg_index = self.cast(valid_neg_index, mstype.int32)
+        valid_neg_index = self.reshape(valid_neg_index, self.reshape_shape_neg)
+        neg_index = neg_index * valid_neg_index
+
+        pos_bboxes_ = self.gatherND(bboxes, pos_index)
+
+        amb_bboxes_ = None
+        if self.use_ambigous_sample:
+            amb_bboxes_ = self.gatherND(bboxes, amb_index)
+
+        neg_bboxes_ = self.gatherV2(bboxes, self.squeeze(neg_index), 0)
+        pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, self.reshape_shape_pos)
+        pos_gt_bboxes_ = self.gatherND(gt_bboxes_i, pos_assigned_gt_index)
+        pos_bbox_targets_ = self.bounding_box_encode(pos_bboxes_, pos_gt_bboxes_)
+
+        total_bboxes = self.concat((pos_bboxes_, neg_bboxes_))
+        total_deltas = self.concat((pos_bbox_targets_, self.bboxs_neg_mask))
+        total_labels = self.concat((pos_gt_labels, self.labels_neg_mask))
+
+        if self.use_ambigous_sample:
+            total_bboxes = self.concat((pos_bboxes_, amb_bboxes_, neg_bboxes_))
+            total_deltas = self.concat((pos_bbox_targets_, self.bboxs_amb_mask, self.bboxs_neg_mask))
+            total_labels = self.concat((pos_gt_labels, self.labels_amb_mask, self.labels_neg_mask))
+
+        valid_pos_index = self.reshape(valid_pos_index, self.reshape_shape_pos)
+        valid_neg_index = self.reshape(valid_neg_index, self.reshape_shape_neg)
+        total_mask = self.concat((valid_pos_index, valid_neg_index))
+        if self.use_ambigous_sample:
+            valid_amb_index = self.reshape(valid_amb_index, self.reshape_shape_amb)
+            total_mask = self.concat((valid_pos_index, valid_amb_index, valid_neg_index))
+
+        return total_bboxes, total_deltas, total_labels, total_mask
--- a/model_zoo/official/cv/deeptext/src/Deeptext/deeptext_vgg16.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/deeptext_vgg16.py
@ -0,0 +1,432 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Deeptext based on VGG16."""
+
+import numpy as np
+
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+from mindspore.common.initializer import initializer
+from mindspore.common.tensor import Tensor
+from mindspore.ops import functional as F
+from mindspore.ops import operations as P
+from .anchor_generator import AnchorGenerator
+from .bbox_assign_sample_stage2 import BboxAssignSampleForRcnn
+from .proposal_generator import Proposal
+from .rcnn import Rcnn
+from .rpn import RPN
+from .vgg16 import VGG16FeatureExtraction
+
+
+def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
+    """Conv2D wrapper."""
+    shp_weight_conv = (out_channels, in_channels, kernel_size, kernel_size)
+
+    shp_bias_conv = (out_channels,)
+    weights = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16).to_tensor()
+    bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16).to_tensor()
+
+    layers = []
+    layers += [nn.Conv2d(in_channels, out_channels,
+                         kernel_size=kernel_size, stride=stride, padding=padding,
+                         pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias_conv)]
+    # layers += [nn.BatchNorm2d(out_channels)]
+    return nn.SequentialCell(layers)
+
+
+class Deeptext_VGG16(nn.Cell):
+    """
+    Deeptext_VGG16 Network.
+
+    Note:
+        backbone = vgg16
+
+    Returns:
+        Tuple, tuple of output tensor.
+        rpn_loss: Scalar, Total loss of RPN subnet.
+        rcnn_loss: Scalar, Total loss of RCNN subnet.
+        rpn_cls_loss: Scalar, Classification loss of RPN subnet.
+        rpn_reg_loss: Scalar, Regression loss of RPN subnet.
+        rcnn_cls_loss: Scalar, Classification loss of RCNN subnet.
+        rcnn_reg_loss: Scalar, Regression loss of RCNN subnet.
+
+    Examples:
+        net = Deeptext_VGG16()
+    """
+
+    def __init__(self, config):
+        super(Deeptext_VGG16, self).__init__()
+        self.train_batch_size = config.batch_size
+        self.num_classes = config.num_classes
+        self.anchor_scales = config.anchor_scales
+        self.anchor_ratios = config.anchor_ratios
+        self.anchor_strides = config.anchor_strides
+        self.target_means = tuple(config.rcnn_target_means)
+        self.target_stds = tuple(config.rcnn_target_stds)
+
+        # Anchor generator
+        anchor_base_sizes = None
+        self.anchor_base_sizes = list(
+            self.anchor_strides) if anchor_base_sizes is None else anchor_base_sizes
+
+        self.anchor_generators = []
+        for anchor_base in self.anchor_base_sizes:
+            self.anchor_generators.append(
+                AnchorGenerator(anchor_base, self.anchor_scales, self.anchor_ratios))
+
+        self.num_anchors = len(self.anchor_ratios) * len(self.anchor_scales)
+
+        featmap_sizes = config.feature_shapes
+        assert len(featmap_sizes) == len(self.anchor_generators)
+
+        self.anchor_list = self.get_anchors(featmap_sizes)
+
+        # Rpn and rpn loss
+        self.gt_labels_stage1 = Tensor(np.ones((self.train_batch_size, config.num_gts)).astype(np.uint8))
+        self.rpn_with_loss = RPN(config,
+                                 self.train_batch_size,
+                                 config.rpn_in_channels,
+                                 config.rpn_feat_channels,
+                                 config.num_anchors,
+                                 config.rpn_cls_out_channels)
+
+        # Proposal
+        self.proposal_generator = Proposal(config,
+                                           self.train_batch_size,
+                                           config.activate_num_classes,
+                                           config.use_sigmoid_cls)
+        self.proposal_generator.set_train_local(config, True)
+        self.proposal_generator_test = Proposal(config,
+                                                config.test_batch_size,
+                                                config.activate_num_classes,
+                                                config.use_sigmoid_cls)
+        self.proposal_generator_test.set_train_local(config, False)
+
+        # Assign and sampler stage two
+        self.bbox_assigner_sampler_for_rcnn = BboxAssignSampleForRcnn(config, self.train_batch_size,
+                                                                      config.num_bboxes_stage2, True)
+        self.decode = P.BoundingBoxDecode(max_shape=(576, 960), means=self.target_means, \
+                                          stds=self.target_stds)
+
+        # Rcnn
+        self.rcnn = Rcnn(config, config.rcnn_in_channels * config.roi_layer['out_size'] * config.roi_layer['out_size'],
+                         self.train_batch_size, self.num_classes)
+
+        # Op declare
+        self.squeeze = P.Squeeze()
+        self.cast = P.Cast()
+
+        self.concat = P.Concat(axis=0)
+        self.concat_1 = P.Concat(axis=1)
+        self.concat_2 = P.Concat(axis=2)
+        self.reshape = P.Reshape()
+        self.select = P.Select()
+        self.greater = P.Greater()
+        self.transpose = P.Transpose()
+
+        # Test mode
+        self.test_batch_size = config.test_batch_size
+        self.split = P.Split(axis=0, output_num=self.test_batch_size)
+        self.split_shape = P.Split(axis=0, output_num=4)
+        self.split_scores = P.Split(axis=1, output_num=self.num_classes)
+        self.split_cls = P.Split(axis=0, output_num=self.num_classes - 1)
+        self.tile = P.Tile()
+        self.gather = P.GatherNd()
+
+        self.rpn_max_num = config.rpn_max_num
+
+        self.zeros_for_nms = Tensor(np.zeros((self.rpn_max_num, 3)).astype(np.float16))
+        self.ones_mask = np.ones((self.rpn_max_num, 1)).astype(np.bool)
+        self.zeros_mask = np.zeros((self.rpn_max_num, 1)).astype(np.bool)
+        self.bbox_mask = Tensor(np.concatenate((self.ones_mask, self.zeros_mask,
+                                                self.ones_mask, self.zeros_mask), axis=1))
+        self.nms_pad_mask = Tensor(np.concatenate((self.ones_mask, self.ones_mask,
+                                                   self.ones_mask, self.ones_mask, self.zeros_mask), axis=1))
+
+        self.test_score_thresh = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * config.test_score_thr)
+        self.test_score_zeros = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * 0)
+        self.test_box_zeros = Tensor(np.ones((self.rpn_max_num, 4)).astype(np.float16) * -1)
+        self.test_iou_thr = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * config.test_iou_thr)
+        self.test_max_per_img = config.test_max_per_img
+        self.nms_test = P.NMSWithMask(config.test_iou_thr)
+        self.softmax = P.Softmax(axis=1)
+        self.logicand = P.LogicalAnd()
+        self.oneslike = P.OnesLike()
+        self.test_topk = P.TopK(sorted=True)
+        self.test_num_proposal = self.test_batch_size * self.rpn_max_num
+
+        # Improve speed
+        self.concat_start = (self.num_classes - 2)
+        self.concat_end = (self.num_classes - 1)
+
+        # Init tensor
+        self.use_ambigous_sample = config.use_ambigous_sample
+        roi_align_index = [np.array(np.ones((config.num_expected_pos_stage2 + config.num_expected_neg_stage2, 1)) * i,
+                                    dtype=np.float16) for i in range(self.train_batch_size)]
+        if self.use_ambigous_sample:
+            roi_align_index = [np.array(np.ones((
+                config.num_expected_pos_stage2 + config.num_expected_amb_stage2 + config.num_expected_neg_stage2,
+                1)) * i,
+                                        dtype=np.float16) for i in range(self.train_batch_size)]
+
+        roi_align_index_test = [np.array(np.ones((config.rpn_max_num, 1)) * i, dtype=np.float16) \
+                                for i in range(self.test_batch_size)]
+
+        self.roi_align_index_tensor = Tensor(np.concatenate(roi_align_index))
+        self.roi_align_index_test_tensor = Tensor(np.concatenate(roi_align_index_test))
+
+        self.roi_align4 = P.ROIAlign(pooled_width=7, pooled_height=7, spatial_scale=0.125)
+        self.roi_align5 = P.ROIAlign(pooled_width=7, pooled_height=7, spatial_scale=0.0625)
+
+        self.concat1 = P.Concat(axis=1)
+        self.roi_align_fuse = _conv(in_channels=1024, out_channels=512, kernel_size=1, padding=0, stride=1)
+        self.vgg16_feature_extractor = VGG16FeatureExtraction()
+
+    def construct(self, img_data, img_metas, gt_bboxes, gt_labels, gt_valids):
+        # f1, f2, f3, f4, f5 = self.vgg16_feature_extractor(img_data)
+        _, _, _, f4, f5 = self.vgg16_feature_extractor(img_data)
+        f4 = self.cast(f4, mstype.float16)
+        f5 = self.cast(f5, mstype.float16)
+        x = (f4, f5)
+
+        rpn_loss, cls_score, bbox_pred, rpn_cls_loss, rpn_reg_loss, _ = self.rpn_with_loss(x,
+                                                                                           img_metas,
+                                                                                           self.anchor_list,
+                                                                                           gt_bboxes,
+                                                                                           self.gt_labels_stage1,
+                                                                                           gt_valids)
+
+        if self.training:
+            proposal, proposal_mask = self.proposal_generator(cls_score, bbox_pred, self.anchor_list)
+        else:
+            proposal, proposal_mask = self.proposal_generator_test(cls_score, bbox_pred, self.anchor_list)
+
+        gt_labels = self.cast(gt_labels, mstype.int32)
+        gt_valids = self.cast(gt_valids, mstype.int32)
+        bboxes_tuple = ()
+        deltas_tuple = ()
+        labels_tuple = ()
+        mask_tuple = ()
+        if self.training:
+            for i in range(self.train_batch_size):
+                gt_bboxes_i = self.squeeze(gt_bboxes[i:i + 1:1, ::])
+
+                gt_labels_i = self.squeeze(gt_labels[i:i + 1:1, ::])
+                gt_labels_i = self.cast(gt_labels_i, mstype.uint8)
+
+                gt_valids_i = self.squeeze(gt_valids[i:i + 1:1, ::])
+                gt_valids_i = self.cast(gt_valids_i, mstype.bool_)
+
+                bboxes, deltas, labels, mask = self.bbox_assigner_sampler_for_rcnn(gt_bboxes_i,
+                                                                                   gt_labels_i,
+                                                                                   proposal_mask[i],
+                                                                                   proposal[i][::, 0:4:1],
+                                                                                   gt_valids_i)
+                bboxes_tuple += (bboxes,)
+                deltas_tuple += (deltas,)
+                labels_tuple += (labels,)
+                mask_tuple += (mask,)
+
+            bbox_targets = self.concat(deltas_tuple)
+            rcnn_labels = self.concat(labels_tuple)
+            bbox_targets = F.stop_gradient(bbox_targets)
+            rcnn_labels = F.stop_gradient(rcnn_labels)
+            rcnn_labels = self.cast(rcnn_labels, mstype.int32)
+        else:
+            mask_tuple += proposal_mask
+            bbox_targets = proposal_mask
+            rcnn_labels = proposal_mask
+            for p_i in proposal:
+                bboxes_tuple += (p_i[::, 0:4:1],)
+
+        if self.training:
+            if self.train_batch_size > 1:
+                bboxes_all = self.concat(bboxes_tuple)
+            else:
+                bboxes_all = bboxes_tuple[0]
+            rois = self.concat_1((self.roi_align_index_tensor, bboxes_all))
+        else:
+            if self.test_batch_size > 1:
+                bboxes_all = self.concat(bboxes_tuple)
+            else:
+                bboxes_all = bboxes_tuple[0]
+            rois = self.concat_1((self.roi_align_index_test_tensor, bboxes_all))
+
+        rois = self.cast(rois, mstype.float32)
+        rois = F.stop_gradient(rois)
+
+        roi_feats = self.roi_align5(x[1], rois)
+        roi_align4_out = self.roi_align4(x[0], rois)
+
+        roi_align4_out = self.cast(roi_align4_out, mstype.float32)
+        roi_feats = self.cast(roi_feats, mstype.float32)
+        roi_feats = self.concat1((roi_feats, roi_align4_out))
+
+        roi_feats = self.cast(roi_feats, mstype.float16)
+        roi_feats = self.roi_align_fuse(roi_feats)
+
+        roi_feats = self.cast(roi_feats, mstype.float16)
+
+        rcnn_masks = self.concat(mask_tuple)
+        rcnn_masks = F.stop_gradient(rcnn_masks)
+        rcnn_mask_squeeze = self.squeeze(self.cast(rcnn_masks, mstype.bool_))
+        rcnn_loss, rcnn_cls_loss, rcnn_reg_loss, _ = self.rcnn(roi_feats,
+                                                               bbox_targets,
+                                                               rcnn_labels,
+                                                               rcnn_mask_squeeze)
+
+        output = ()
+        if self.training:
+            output += (rpn_loss, rcnn_loss, rpn_cls_loss, rpn_reg_loss, rcnn_cls_loss, rcnn_reg_loss)
+        else:
+            output = self.get_det_bboxes(rcnn_cls_loss, rcnn_reg_loss, rcnn_masks, bboxes_all, img_metas)
+
+        return output
+
+    def get_det_bboxes(self, cls_logits, reg_logits, mask_logits, rois, img_metas):
+        """Get the actual detection box."""
+        scores = self.softmax(cls_logits)
+
+        boxes_all = ()
+        for i in range(self.num_classes):
+            k = i * 4
+            reg_logits_i = self.squeeze(reg_logits[::, k:k + 4:1])
+            out_boxes_i = self.decode(rois, reg_logits_i)
+            boxes_all += (out_boxes_i,)
+
+        # img_metas_all = self.split(img_metas)
+        scores_all = self.split(scores)
+        mask_all = self.split(self.cast(mask_logits, mstype.int32))
+
+        boxes_all_with_batchsize = ()
+        for i in range(self.test_batch_size):
+            # scale = self.split_shape(self.squeeze(img_metas_all[i]))
+            # scale_h = scale[2]
+            # scale_w = scale[3]
+            boxes_tuple = ()
+            for j in range(self.num_classes):
+                boxes_tmp = self.split(boxes_all[j])
+                out_boxes_h = boxes_tmp[i] / 1
+                out_boxes_w = boxes_tmp[i] / 1
+                boxes_tuple += (self.select(self.bbox_mask, out_boxes_w, out_boxes_h),)
+            boxes_all_with_batchsize += (boxes_tuple,)
+
+        output = self.multiclass_nms(boxes_all_with_batchsize, scores_all, mask_all)
+
+        return output
+
+    def multiclass_nms(self, boxes_all, scores_all, mask_all):
+        """Multiscale postprocessing."""
+        all_bboxes = ()
+        all_labels = ()
+        all_masks = ()
+
+        for i in range(self.test_batch_size):
+            bboxes = boxes_all[i]
+            scores = scores_all[i]
+            masks = self.cast(mask_all[i], mstype.bool_)
+
+            res_boxes_tuple = ()
+            res_labels_tuple = ()
+            res_masks_tuple = ()
+
+            for j in range(self.num_classes - 1):
+                k = j + 1
+                _cls_scores = scores[::, k:k + 1:1]
+                _bboxes = self.squeeze(bboxes[k])
+                _mask_o = self.reshape(masks, (self.rpn_max_num, 1))
+
+                cls_mask = self.greater(_cls_scores, self.test_score_thresh)
+                _mask = self.logicand(_mask_o, cls_mask)
+
+                _reg_mask = self.cast(self.tile(self.cast(_mask, mstype.int32), (1, 4)), mstype.bool_)
+
+                _bboxes = self.select(_reg_mask, _bboxes, self.test_box_zeros)
+                _cls_scores = self.select(_mask, _cls_scores, self.test_score_zeros)
+                __cls_scores = self.squeeze(_cls_scores)
+                scores_sorted, topk_inds = self.test_topk(__cls_scores, self.rpn_max_num)
+                topk_inds = self.reshape(topk_inds, (self.rpn_max_num, 1))
+                scores_sorted = self.reshape(scores_sorted, (self.rpn_max_num, 1))
+                _bboxes_sorted = self.gather(_bboxes, topk_inds)
+                _mask_sorted = self.gather(_mask, topk_inds)
+
+                scores_sorted = self.tile(scores_sorted, (1, 4))
+                cls_dets = self.concat_1((_bboxes_sorted, scores_sorted))
+                cls_dets = P.Slice()(cls_dets, (0, 0), (self.rpn_max_num, 5))
+
+                cls_dets, _index, _mask_nms = self.nms_test(cls_dets)
+                _index = self.reshape(_index, (self.rpn_max_num, 1))
+                _mask_nms = self.reshape(_mask_nms, (self.rpn_max_num, 1))
+
+                _mask_n = self.gather(_mask_sorted, _index)
+
+                _mask_n = self.logicand(_mask_n, _mask_nms)
+                cls_labels = self.oneslike(_index) * j
+                res_boxes_tuple += (cls_dets,)
+                res_labels_tuple += (cls_labels,)
+                res_masks_tuple += (_mask_n,)
+
+            res_boxes_start = self.concat(res_boxes_tuple[:self.concat_start])
+            res_labels_start = self.concat(res_labels_tuple[:self.concat_start])
+            res_masks_start = self.concat(res_masks_tuple[:self.concat_start])
+
+            res_boxes_end = self.concat(res_boxes_tuple[self.concat_start:self.concat_end])
+            res_labels_end = self.concat(res_labels_tuple[self.concat_start:self.concat_end])
+            res_masks_end = self.concat(res_masks_tuple[self.concat_start:self.concat_end])
+
+            res_boxes = self.concat((res_boxes_start, res_boxes_end))
+            res_labels = self.concat((res_labels_start, res_labels_end))
+            res_masks = self.concat((res_masks_start, res_masks_end))
+            reshape_size = (self.num_classes - 1) * self.rpn_max_num
+            if self.use_ambigous_sample:
+                res_boxes = res_boxes_tuple[0]
+                res_labels = res_labels_tuple[0]
+                res_masks = res_masks_tuple[0]
+                reshape_size = self.rpn_max_num
+
+            res_boxes = self.reshape(res_boxes, (1, reshape_size, 5))
+            res_labels = self.reshape(res_labels, (1, reshape_size, 1))
+            res_masks = self.reshape(res_masks, (1, reshape_size, 1))
+
+            all_bboxes += (res_boxes,)
+            all_labels += (res_labels,)
+            all_masks += (res_masks,)
+
+        all_bboxes = self.concat(all_bboxes)
+        all_labels = self.concat(all_labels)
+        all_masks = self.concat(all_masks)
+        return all_bboxes, all_labels, all_masks
+
+    def get_anchors(self, featmap_sizes):
+        """Get anchors according to feature map sizes.
+
+        Args:
+            featmap_sizes (list[tuple]): Multi-level feature map sizes.
+            img_metas (list[dict]): Image meta info.
+
+        Returns:
+            tuple: anchors of each image, valid flags of each image
+        """
+        num_levels = len(featmap_sizes)
+
+        # since feature map sizes of all images are the same, we only compute
+        # anchors for one time
+        multi_level_anchors = ()
+        for i in range(num_levels):
+            anchors = self.anchor_generators[i].grid_anchors(
+                featmap_sizes[i], self.anchor_strides[i])
+            multi_level_anchors += (Tensor(anchors.astype(np.float16)),)
+
+        return multi_level_anchors
--- a/model_zoo/official/cv/deeptext/src/Deeptext/proposal_generator.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/proposal_generator.py
@ -0,0 +1,199 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Deeptext proposal generator."""
+
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.ops import operations as P
+from mindspore import Tensor
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Proposal(nn.Cell):
+    """
+    Proposal subnet.
+
+    Args:
+        config (dict): Config.
+        batch_size (int): Batchsize.
+        num_classes (int) - Class number.
+        use_sigmoid_cls (bool) - Select sigmoid or softmax function.
+        target_means (tuple) - Means for encode function. Default: (.0, .0, .0, .0).
+        target_stds (tuple) - Stds for encode function. Default: (1.0, 1.0, 1.0, 1.0).
+
+    Returns:
+        Tuple, tuple of output tensor,(proposal, mask).
+
+    Examples:
+        Proposal(config = config, batch_size = 1, num_classes = 81, use_sigmoid_cls = True, \
+                 target_means=(.0, .0, .0, .0), target_stds=(1.0, 1.0, 1.0, 1.0))
+    """
+
+    def __init__(self,
+                 config,
+                 batch_size,
+                 num_classes,
+                 use_sigmoid_cls,
+                 target_means=(.0, .0, .0, .0),
+                 target_stds=(1.0, 1.0, 1.0, 1.0)
+                 ):
+        super(Proposal, self).__init__()
+        cfg = config
+        self.batch_size = batch_size
+        self.num_classes = num_classes
+        self.target_means = target_means
+        self.target_stds = target_stds
+        self.use_sigmoid_cls = use_sigmoid_cls
+
+        if self.use_sigmoid_cls:
+            self.cls_out_channels = num_classes - 1
+            self.activation = P.Sigmoid()
+            self.reshape_shape = (-1, 1)
+        else:
+            self.cls_out_channels = num_classes
+            self.activation = P.Softmax(axis=1)
+            self.reshape_shape = (-1, 2)
+
+        if self.cls_out_channels <= 0:
+            raise ValueError('num_classes={} is too small'.format(num_classes))
+
+        self.num_pre = cfg.rpn_proposal_nms_pre
+        self.min_box_size = cfg.rpn_proposal_min_bbox_size
+        self.nms_thr = cfg.rpn_proposal_nms_thr
+        self.nms_post = cfg.rpn_proposal_nms_post
+        self.nms_across_levels = cfg.rpn_proposal_nms_across_levels
+        self.max_num = cfg.rpn_proposal_max_num
+        self.num_levels = len(cfg.anchor_strides)
+
+        # Op Define
+        self.squeeze = P.Squeeze()
+        self.reshape = P.Reshape()
+        self.cast = P.Cast()
+
+        self.feature_shapes = cfg.feature_shapes
+
+        self.transpose_shape = (1, 2, 0)
+
+        self.decode = P.BoundingBoxDecode(max_shape=(cfg.img_height, cfg.img_width), \
+                                          means=self.target_means, \
+                                          stds=self.target_stds)
+
+        self.nms = P.NMSWithMask(self.nms_thr)
+        self.concat_axis0 = P.Concat(axis=0)
+        self.concat_axis1 = P.Concat(axis=1)
+        self.split = P.Split(axis=1, output_num=5)
+        self.min = P.Minimum()
+        self.gatherND = P.GatherNd()
+        self.slice = P.Slice()
+        self.select = P.Select()
+        self.greater = P.Greater()
+        self.transpose = P.Transpose()
+        self.tile = P.Tile()
+        self.set_train_local(config, training=True)
+
+        self.multi_10 = Tensor(10.0, mstype.float16)
+
+    def set_train_local(self, config, training=True):
+        """Set training flag."""
+        self.training_local = training
+
+        cfg = config
+        self.topK_stage1 = ()
+        self.topK_shape = ()
+        total_max_topk_input = 0
+        if not self.training_local:
+            self.num_pre = cfg.rpn_nms_pre
+            self.min_box_size = cfg.rpn_min_bbox_min_size
+            self.nms_thr = cfg.rpn_nms_thr
+            self.nms_post = cfg.rpn_nms_post
+            self.nms_across_levels = cfg.rpn_nms_across_levels
+            self.max_num = cfg.rpn_max_num
+
+        for shp in self.feature_shapes:
+            k_num = min(self.num_pre, (shp[0] * shp[1] * 3))
+            total_max_topk_input += k_num
+            self.topK_stage1 += (k_num,)
+            self.topK_shape += ((k_num, 1),)
+
+        self.topKv2 = P.TopK(sorted=True)
+        self.topK_shape_stage2 = (self.max_num, 1)
+        self.min_float_num = -65536.0
+        self.topK_mask = Tensor(self.min_float_num * np.ones(total_max_topk_input, np.float16))
+
+    def construct(self, rpn_cls_score_total, rpn_bbox_pred_total, anchor_list):
+        proposals_tuple = ()
+        masks_tuple = ()
+        for img_id in range(self.batch_size):
+            cls_score_list = ()
+            bbox_pred_list = ()
+            for i in range(self.num_levels):
+                rpn_cls_score_i = self.squeeze(rpn_cls_score_total[i][img_id:img_id + 1:1, ::, ::, ::])
+                rpn_bbox_pred_i = self.squeeze(rpn_bbox_pred_total[i][img_id:img_id + 1:1, ::, ::, ::])
+
+                cls_score_list = cls_score_list + (rpn_cls_score_i,)
+                bbox_pred_list = bbox_pred_list + (rpn_bbox_pred_i,)
+
+            proposals, masks = self.get_bboxes_single(cls_score_list, bbox_pred_list, anchor_list)
+            proposals_tuple += (proposals,)
+            masks_tuple += (masks,)
+        return proposals_tuple, masks_tuple
+
+    def get_bboxes_single(self, cls_scores, bbox_preds, mlvl_anchors):
+        """Get proposal boundingbox."""
+        mlvl_proposals = ()
+        mlvl_mask = ()
+        for idx in range(self.num_levels):
+            rpn_cls_score = self.transpose(cls_scores[idx], self.transpose_shape)
+            rpn_bbox_pred = self.transpose(bbox_preds[idx], self.transpose_shape)
+            anchors = mlvl_anchors[idx]
+
+            rpn_cls_score = self.reshape(rpn_cls_score, self.reshape_shape)
+            rpn_cls_score = self.activation(rpn_cls_score)
+            rpn_cls_score_process = self.cast(self.squeeze(rpn_cls_score[::, 0::]), mstype.float16)
+
+            rpn_bbox_pred_process = self.cast(self.reshape(rpn_bbox_pred, (-1, 4)), mstype.float16)
+
+            scores_sorted, topk_inds = self.topKv2(rpn_cls_score_process, self.topK_stage1[idx])
+
+            topk_inds = self.reshape(topk_inds, self.topK_shape[idx])
+
+            bboxes_sorted = self.gatherND(rpn_bbox_pred_process, topk_inds)
+            anchors_sorted = self.cast(self.gatherND(anchors, topk_inds), mstype.float16)
+
+            proposals_decode = self.decode(anchors_sorted, bboxes_sorted)
+
+            proposals_decode = self.concat_axis1((proposals_decode, self.reshape(scores_sorted, self.topK_shape[idx])))
+            proposals, _, mask_valid = self.nms(proposals_decode)
+
+            mlvl_proposals = mlvl_proposals + (proposals,)
+            mlvl_mask = mlvl_mask + (mask_valid,)
+
+        proposals = self.concat_axis0(mlvl_proposals)
+        masks = self.concat_axis0(mlvl_mask)
+
+        _, _, _, _, scores = self.split(proposals)
+        scores = self.squeeze(scores)
+        topk_mask = self.cast(self.topK_mask, mstype.float16)
+        scores_using = self.select(masks, scores, topk_mask)
+
+        _, topk_inds = self.topKv2(scores_using, self.max_num)
+
+        topk_inds = self.reshape(topk_inds, self.topK_shape_stage2)
+        proposals = self.gatherND(proposals, topk_inds)
+        masks = self.gatherND(masks, topk_inds)
+        return proposals, masks
--- a/model_zoo/official/cv/deeptext/src/Deeptext/rcnn.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/rcnn.py
@ -0,0 +1,181 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Deeptext Rcnn network."""
+
+import numpy as np
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore.common.tensor import Tensor
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
+
+
+class DenseNoTranpose(nn.Cell):
+    """Dense method"""
+
+    def __init__(self, input_channels, output_channels, weight_init):
+        super(DenseNoTranpose, self).__init__()
+
+        self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float16),
+                                name="weight")
+        self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16).to_tensor(), name="bias")
+
+        self.matmul = P.MatMul(transpose_b=False)
+        self.bias_add = P.BiasAdd()
+
+    def construct(self, x):
+        output = self.bias_add(self.matmul(x, self.weight), self.bias)
+        return output
+
+
+class Rcnn(nn.Cell):
+    """
+    Rcnn subnet.
+
+    Args:
+        config (dict) - Config.
+        representation_size (int) - Channels of shared dense.
+        batch_size (int) - Batchsize.
+        num_classes (int) - Class number.
+        target_means (list) - Means for encode function. Default: (.0, .0, .0, .0]).
+        target_stds (list) - Stds for encode function. Default: (0.1, 0.1, 0.2, 0.2).
+
+    Returns:
+        Tuple, tuple of output tensor.
+
+    Examples:
+        Rcnn(config=config, representation_size = 1024, batch_size=2, num_classes = 81, \
+             target_means=(0., 0., 0., 0.), target_stds=(0.1, 0.1, 0.2, 0.2))
+    """
+
+    def __init__(self,
+                 config,
+                 representation_size,
+                 batch_size,
+                 num_classes,
+                 target_means=(0., 0., 0., 0.),
+                 target_stds=(0.1, 0.1, 0.2, 0.2)
+                 ):
+        super(Rcnn, self).__init__()
+        cfg = config
+        self.rcnn_loss_cls_weight = Tensor(np.array(cfg.rcnn_loss_cls_weight).astype(np.float16))
+        self.rcnn_loss_reg_weight = Tensor(np.array(cfg.rcnn_loss_reg_weight).astype(np.float16))
+        self.rcnn_fc_out_channels = cfg.rcnn_fc_out_channels
+        self.target_means = target_means
+        self.target_stds = target_stds
+        self.num_classes = num_classes
+        self.in_channels = cfg.rcnn_in_channels
+        self.train_batch_size = batch_size
+        self.test_batch_size = cfg.test_batch_size
+        self.use_ambigous_sample = cfg.use_ambigous_sample
+
+        shape_0 = (self.rcnn_fc_out_channels, representation_size)
+        weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16).to_tensor()
+        shape_1 = (self.rcnn_fc_out_channels, self.rcnn_fc_out_channels)
+        weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16).to_tensor()
+        self.shared_fc_0 = DenseNoTranpose(representation_size, self.rcnn_fc_out_channels, weights_0)
+        self.shared_fc_1 = DenseNoTranpose(self.rcnn_fc_out_channels, self.rcnn_fc_out_channels, weights_1)
+
+        cls_weight = initializer('Normal', shape=[num_classes, self.rcnn_fc_out_channels][::-1],
+                                 dtype=mstype.float16).to_tensor()
+        reg_weight = initializer('Normal', shape=[num_classes * 4, self.rcnn_fc_out_channels][::-1],
+                                 dtype=mstype.float16).to_tensor()
+        self.cls_scores = DenseNoTranpose(self.rcnn_fc_out_channels, num_classes, cls_weight)
+        self.reg_scores = DenseNoTranpose(self.rcnn_fc_out_channels, num_classes * 4, reg_weight)
+
+        self.flatten = P.Flatten()
+        self.relu = P.ReLU()
+        self.logicaland = P.LogicalAnd()
+        self.loss_cls = P.SoftmaxCrossEntropyWithLogits()
+        self.loss_bbox = P.SmoothL1Loss(beta=1.0)
+        self.reshape = P.Reshape()
+        self.onehot = P.OneHot()
+        self.greater = P.Greater()
+        self.equal = P.Equal()
+        self.cast = P.Cast()
+        self.sum_loss = P.ReduceSum()
+        self.tile = P.Tile()
+        self.expandims = P.ExpandDims()
+
+        self.gather = P.GatherNd()
+        self.argmax = P.ArgMaxWithValue(axis=1)
+
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+        self.value = Tensor(1.0, mstype.float16)
+
+        self.num_bboxes = (cfg.num_expected_pos_stage2 + cfg.num_expected_neg_stage2) * batch_size
+        if self.use_ambigous_sample:
+            self.num_bboxes = (
+                cfg.num_expected_pos_stage2 + cfg.num_expected_amb_stage2 + cfg.num_expected_neg_stage2) * batch_size
+
+        rmv_first = np.ones((self.num_bboxes, self.num_classes))
+        rmv_first[:, 0] = np.zeros((self.num_bboxes,))
+        self.rmv_first_tensor = Tensor(rmv_first.astype(np.float16))
+
+        self.num_bboxes_test = cfg.rpn_max_num * cfg.test_batch_size
+
+        range_max = np.arange(self.num_bboxes_test).astype(np.int32)
+        self.range_max = Tensor(range_max)
+
+    def construct(self, featuremap, bbox_targets, labels, mask):
+        x = self.flatten(featuremap)
+
+        x = self.relu(self.shared_fc_0(x))
+
+        x = self.relu(self.shared_fc_1(x))
+
+        x_cls = self.cls_scores(x)
+        x_reg = self.reg_scores(x)
+
+        if self.training:
+            bbox_weights = self.cast(self.logicaland(self.greater(labels, 0), mask), mstype.int32) * labels
+            if self.use_ambigous_sample:
+                bbox_weights = self.cast(self.logicaland(self.equal(labels, 1), mask), mstype.int32) * labels
+            labels = self.cast(self.onehot(labels, self.num_classes, self.on_value, self.off_value), mstype.float16)
+            bbox_targets = self.tile(self.expandims(bbox_targets, 1), (1, self.num_classes, 1))
+
+            loss, loss_cls, loss_reg, loss_print = self.loss(x_cls, x_reg, bbox_targets, bbox_weights, labels, mask)
+            out = (loss, loss_cls, loss_reg, loss_print)
+        else:
+            out = (x_cls, (x_cls / self.value), x_reg, x_cls)
+
+        return out
+
+    def loss(self, cls_score, bbox_pred, bbox_targets, bbox_weights, labels, weights):
+        """Loss method."""
+        loss_print = ()
+        loss_cls, _ = self.loss_cls(cls_score, labels)
+
+        weights = self.cast(weights, mstype.float16)
+        loss_cls = loss_cls * weights
+        loss_cls = self.sum_loss(loss_cls, (0,)) / self.sum_loss(weights, (0,))
+
+        bbox_weights = self.cast(self.onehot(bbox_weights, self.num_classes, self.on_value, self.off_value),
+                                 mstype.float16)
+        if not self.use_ambigous_sample:
+            bbox_weights = bbox_weights * self.rmv_first_tensor
+        pos_bbox_pred = self.reshape(bbox_pred, (self.num_bboxes, -1, 4))
+        loss_reg = self.loss_bbox(pos_bbox_pred, bbox_targets)
+        loss_reg = self.sum_loss(loss_reg, (2,))
+        loss_reg = loss_reg * bbox_weights
+        loss_reg = loss_reg / self.sum_loss(weights, (0,))
+        loss_reg = self.sum_loss(loss_reg, (0, 1))
+
+        loss = self.rcnn_loss_cls_weight * loss_cls + self.rcnn_loss_reg_weight * loss_reg
+        loss_print += (loss_cls, loss_reg)
+
+        return loss, loss_cls, loss_reg, loss_print
--- a/model_zoo/official/cv/deeptext/src/Deeptext/roi_align.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/roi_align.py
@ -0,0 +1,181 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Deeptext ROIAlign module."""
+
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.ops import operations as P
+from mindspore.ops import composite as C
+from mindspore.nn import layer as L
+from mindspore.common.tensor import Tensor
+
+
+class ROIAlign(nn.Cell):
+    """
+    Extract RoI features from mulitple feature map.
+
+    Args:
+        out_size_h (int) - RoI height.
+        out_size_w (int) - RoI width.
+        spatial_scale (int) - RoI spatial scale.
+        sample_num (int) - RoI sample number.
+    """
+
+    def __init__(self,
+                 out_size_h,
+                 out_size_w,
+                 spatial_scale,
+                 sample_num=0):
+        super(ROIAlign, self).__init__()
+
+        self.out_size = (out_size_h, out_size_w)
+        self.spatial_scale = float(spatial_scale)
+        self.sample_num = int(sample_num)
+        self.align_op = P.ROIAlign(self.out_size[0], self.out_size[1],
+                                   self.spatial_scale, self.sample_num)
+
+    def construct(self, features, rois):
+        return self.align_op(features, rois)
+
+    def __repr__(self):
+        format_str = self.__class__.__name__
+        format_str += '(out_size={}, spatial_scale={}, sample_num={}'.format(
+            self.out_size, self.spatial_scale, self.sample_num)
+        return format_str
+
+
+class SingleRoIExtractor(nn.Cell):
+    """
+    Extract RoI features from a single level feature map.
+
+    If there are mulitple input feature levels, each RoI is mapped to a level
+    according to its scale.
+
+    Args:
+        config (dict): Config
+        roi_layer (dict): Specify RoI layer type and arguments.
+        out_channels (int): Output channels of RoI layers.
+        featmap_strides (int): Strides of input feature maps.
+        batch_size (int)： Batchsize.
+        finest_scale (int): Scale threshold of mapping to level 0.
+    """
+
+    def __init__(self,
+                 config,
+                 roi_layer,
+                 out_channels,
+                 featmap_strides,
+                 batch_size=1,
+                 finest_scale=56):
+        super(SingleRoIExtractor, self).__init__()
+        cfg = config
+        self.train_batch_size = batch_size
+        self.out_channels = out_channels
+        self.featmap_strides = featmap_strides
+        self.num_levels = len(self.featmap_strides)
+        self.out_size = roi_layer['out_size']
+        self.sample_num = roi_layer['sample_num']
+        self.roi_layers = self.build_roi_layers(self.featmap_strides)
+        self.roi_layers = L.CellList(self.roi_layers)
+
+        self.sqrt = P.Sqrt()
+        self.log = P.Log()
+        self.finest_scale_ = finest_scale
+        self.clamp = C.clip_by_value
+
+        self.cast = P.Cast()
+        self.equal = P.Equal()
+        self.select = P.Select()
+
+        _mode_16 = False
+        self.dtype = np.float16 if _mode_16 else np.float32
+        self.ms_dtype = mstype.float16 if _mode_16 else mstype.float32
+        self.set_train_local(cfg, training=True)
+
+    def set_train_local(self, config, training=True):
+        """Set training flag."""
+        self.training_local = training
+
+        cfg = config
+        # Init tensor
+        self.batch_size = cfg.roi_sample_num if self.training_local else cfg.rpn_max_num
+        self.batch_size = self.train_batch_size * self.batch_size \
+            if self.training_local else cfg.test_batch_size * self.batch_size
+        self.ones = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype))
+        finest_scale = np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * self.finest_scale_
+        self.finest_scale = Tensor(finest_scale)
+        self.epslion = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * self.dtype(1e-6))
+        self.zeros = Tensor(np.array(np.zeros((self.batch_size, 1)), dtype=np.int32))
+        self.max_levels = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=np.int32) * (self.num_levels - 1))
+        self.twos = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * 2)
+        self.res_ = Tensor(np.array(np.zeros((self.batch_size, self.out_channels,
+                                              self.out_size, self.out_size)), dtype=self.dtype))
+
+    def num_inputs(self):
+        return len(self.featmap_strides)
+
+    def init_weights(self):
+        pass
+
+    def log2(self, value):
+        return self.log(value) / self.log(self.twos)
+
+    def build_roi_layers(self, featmap_strides):
+        roi_layers = []
+        for s in featmap_strides:
+            layer_cls = ROIAlign(self.out_size, self.out_size,
+                                 spatial_scale=1 / s,
+                                 sample_num=self.sample_num)
+            roi_layers.append(layer_cls)
+        return roi_layers
+
+    def _c_map_roi_levels(self, rois):
+        """Map rois to corresponding feature levels by scales.
+
+        - scale < finest_scale * 2: level 0
+        - finest_scale * 2 <= scale < finest_scale * 4: level 1
+        - finest_scale * 4 <= scale < finest_scale * 8: level 2
+        - scale >= finest_scale * 8: level 3
+
+        Args:
+            rois (Tensor): Input RoIs, shape (k, 5).
+            num_levels (int): Total level number.
+
+        Returns:
+            Tensor: Level index (0-based) of each RoI, shape (k, )
+        """
+        scale = self.sqrt(rois[::, 3:4:1] - rois[::, 1:2:1] + self.ones) * \
+                self.sqrt(rois[::, 4:5:1] - rois[::, 2:3:1] + self.ones)
+
+        target_lvls = self.log2(scale / self.finest_scale + self.epslion)
+        target_lvls = P.Floor()(target_lvls)
+        target_lvls = self.cast(target_lvls, mstype.int32)
+        target_lvls = self.clamp(target_lvls, self.zeros, self.max_levels)
+
+        return target_lvls
+
+    def construct(self, rois, feat1, feat2):
+        feats = (feat1, feat2)
+        res = self.res_
+        target_lvls = self._c_map_roi_levels(rois)
+        for i in range(self.num_levels):
+            mask = self.equal(target_lvls, P.ScalarToArray()(i))
+            mask = P.Reshape()(mask, (-1, 1, 1, 1))
+            roi_feats_t = self.roi_layers[i](feats[i], rois)
+            mask = self.cast(P.Tile()(self.cast(mask, mstype.int32), (1, 256, 7, 7)), mstype.bool_)
+            res = self.select(mask, roi_feats_t, res)
+
+        return res
--- a/model_zoo/official/cv/deeptext/src/Deeptext/rpn.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/rpn.py
@ -0,0 +1,332 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""RPN for deeptext"""
+import numpy as np
+import mindspore.nn as nn
+import mindspore.common.dtype as mstype
+from mindspore.ops import operations as P
+from mindspore import Tensor
+from mindspore.ops import functional as F
+from mindspore.common.initializer import initializer
+from .bbox_assign_sample import BboxAssignSample
+
+
+def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
+    """Conv2D wrapper."""
+    shp_weight_conv = (out_channels, in_channels, kernel_size, kernel_size)
+
+    shp_bias_conv = (out_channels,)
+    weights = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16).to_tensor()
+    bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16).to_tensor()
+
+    layers = []
+    layers += [nn.Conv2d(in_channels, out_channels,
+                         kernel_size=kernel_size, stride=stride, padding=padding,
+                         pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias_conv)]
+    return nn.SequentialCell(layers)
+
+
+class RpnRegClsBlock(nn.Cell):
+    """
+    Rpn reg cls block for rpn layer
+
+    Args:
+        in_channels (int) - Input channels of shared convolution.
+        feat_channels (int) - Output channels of shared convolution.
+        num_anchors (int) - The anchor number.
+        cls_out_channels (int) - Output channels of classification convolution.
+        weight_conv (Tensor) - weight init for rpn conv.
+        bias_conv (Tensor) - bias init for rpn conv.
+        weight_cls (Tensor) - weight init for rpn cls conv.
+        bias_cls (Tensor) - bias init for rpn cls conv.
+        weight_reg (Tensor) - weight init for rpn reg conv.
+        bias_reg (Tensor) - bias init for rpn reg conv.
+
+    Returns:
+        Tensor, output tensor.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 feat_channels,
+                 num_anchors,
+                 cls_out_channels,
+                 weight_conv,
+                 bias_conv,
+                 weight_cls,
+                 bias_cls,
+                 weight_reg,
+                 bias_reg):
+        super(RpnRegClsBlock, self).__init__()
+        self.rpn_conv = nn.Conv2d(in_channels, feat_channels, kernel_size=3, stride=1, pad_mode='same',
+                                  has_bias=True, weight_init=weight_conv, bias_init=bias_conv)
+        self.relu = nn.ReLU()
+
+        self.rpn_cls = nn.Conv2d(feat_channels, num_anchors * cls_out_channels, kernel_size=1, pad_mode='valid',
+                                 has_bias=True, weight_init=weight_cls, bias_init=bias_cls)
+        self.rpn_reg = nn.Conv2d(feat_channels, num_anchors * 4, kernel_size=1, pad_mode='valid',
+                                 has_bias=True, weight_init=weight_reg, bias_init=bias_reg)
+
+        self.rpn_conv1x1 = _conv(in_channels=in_channels, out_channels=128, kernel_size=1, stride=1, padding=0)
+        self.rpn_conv3x3 = _conv(in_channels=in_channels, out_channels=384, kernel_size=3, stride=1, padding=1)
+        self.rpn_conv5x5 = _conv(in_channels=in_channels, out_channels=128, kernel_size=5, stride=1, padding=2)
+
+        self.rpn_output = P.Concat(axis=1)
+
+    def construct(self, x):
+        x1 = self.rpn_conv1x1(x)
+        x2 = self.rpn_conv3x3(x)
+        x3 = self.rpn_conv5x5(x)
+
+        x = self.relu(self.rpn_output((x1, x2, x3)))
+
+        x1 = self.rpn_cls(x)
+        x2 = self.rpn_reg(x)
+
+        return x1, x2
+
+
+class RPN(nn.Cell):
+    """
+    ROI proposal network..
+
+    Args:
+        config (dict) - Config.
+        batch_size (int) - Batchsize.
+        in_channels (int) - Input channels of shared convolution.
+        feat_channels (int) - Output channels of shared convolution.
+        num_anchors (int) - The anchor number.
+        cls_out_channels (int) - Output channels of classification convolution.
+
+    Returns:
+        Tuple, tuple of output tensor.
+
+    Examples:
+        RPN(config=config, batch_size=2, in_channels=256, feat_channels=1024,
+            num_anchors=3, cls_out_channels=512)
+    """
+
+    def __init__(self,
+                 config,
+                 batch_size,
+                 in_channels,
+                 feat_channels,
+                 num_anchors,
+                 cls_out_channels):
+        super(RPN, self).__init__()
+        cfg_rpn = config
+        self.num_bboxes = cfg_rpn.num_bboxes
+        self.slice_index = ()
+        self.feature_anchor_shape = ()
+        self.slice_index += (0,)
+        index = 0
+        for shape in cfg_rpn.feature_shapes:
+            self.slice_index += (self.slice_index[index] + shape[0] * shape[1] * num_anchors,)
+            self.feature_anchor_shape += (shape[0] * shape[1] * num_anchors * batch_size,)
+            index += 1
+
+        self.num_anchors = num_anchors
+        self.batch_size = batch_size
+        self.test_batch_size = cfg_rpn.test_batch_size
+        self.num_layers = 1
+        self.real_ratio = Tensor(np.ones((1, 1)).astype(np.float16))
+
+        self.rpn_convs_list = nn.layer.CellList(self._make_rpn_layer(self.num_layers, in_channels, feat_channels,
+                                                                     num_anchors, cls_out_channels))
+
+        self.transpose = P.Transpose()
+        self.reshape = P.Reshape()
+        self.concat = P.Concat(axis=0)
+        self.fill = P.Fill()
+        self.placeh1 = Tensor(np.ones((1,)).astype(np.float16))
+
+        self.trans_shape = (0, 2, 3, 1)
+
+        self.reshape_shape_reg = (-1, 4)
+        self.reshape_shape_cls = (-1,)
+        self.rpn_loss_reg_weight = Tensor(np.array(cfg_rpn.rpn_loss_reg_weight).astype(np.float16))
+        self.rpn_loss_cls_weight = Tensor(np.array(cfg_rpn.rpn_loss_cls_weight).astype(np.float16))
+        self.num_expected_total = Tensor(np.array(cfg_rpn.num_expected_neg * self.batch_size).astype(np.float16))
+        self.num_bboxes = cfg_rpn.num_bboxes
+        self.get_targets = BboxAssignSample(cfg_rpn, self.batch_size, self.num_bboxes, False)
+        self.CheckValid = P.CheckValid()
+        self.sum_loss = P.ReduceSum()
+        self.loss_cls = P.SigmoidCrossEntropyWithLogits()
+        self.loss_bbox = P.SmoothL1Loss(beta=1.0 / 9.0)
+        self.squeeze = P.Squeeze()
+        self.cast = P.Cast()
+        self.tile = P.Tile()
+        self.zeros_like = P.ZerosLike()
+        self.loss = Tensor(np.zeros((1,)).astype(np.float16))
+        self.clsloss = Tensor(np.zeros((1,)).astype(np.float16))
+        self.regloss = Tensor(np.zeros((1,)).astype(np.float16))
+
+    def _make_rpn_layer(self, num_layers, in_channels, feat_channels, num_anchors, cls_out_channels):
+        """
+        make rpn layer for rpn proposal network
+
+        Args:
+        num_layers (int) - layer num.
+        in_channels (int) - Input channels of shared convolution.
+        feat_channels (int) - Output channels of shared convolution.
+        num_anchors (int) - The anchor number.
+        cls_out_channels (int) - Output channels of classification convolution.
+
+        Returns:
+        List, list of RpnRegClsBlock cells.
+        """
+        rpn_layer = []
+
+        shp_weight_conv = (feat_channels, in_channels, 3, 3)
+        shp_bias_conv = (feat_channels,)
+        weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16).to_tensor()
+        bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16).to_tensor()
+
+        shp_weight_cls = (num_anchors * cls_out_channels, feat_channels, 1, 1)
+        shp_bias_cls = (num_anchors * cls_out_channels,)
+        weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16).to_tensor()
+        bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16).to_tensor()
+
+        shp_weight_reg = (num_anchors * 4, feat_channels, 1, 1)
+        shp_bias_reg = (num_anchors * 4,)
+        weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16).to_tensor()
+        bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16).to_tensor()
+
+        rpn_layer.append(RpnRegClsBlock(in_channels, feat_channels, num_anchors, cls_out_channels, \
+                                        weight_conv, bias_conv, weight_cls, \
+                                        bias_cls, weight_reg, bias_reg))
+
+        rpn_layer[0].rpn_conv.weight = rpn_layer[0].rpn_conv.weight
+        rpn_layer[0].rpn_cls.weight = rpn_layer[0].rpn_cls.weight
+        rpn_layer[0].rpn_reg.weight = rpn_layer[0].rpn_reg.weight
+
+        rpn_layer[0].rpn_conv.bias = rpn_layer[0].rpn_conv.bias
+        rpn_layer[0].rpn_cls.bias = rpn_layer[0].rpn_cls.bias
+        rpn_layer[0].rpn_reg.bias = rpn_layer[0].rpn_reg.bias
+
+        return rpn_layer
+
+    def construct(self, inputs, img_metas, anchor_list, gt_bboxes, gt_labels, gt_valids):
+        loss_print = ()
+        rpn_cls_score = ()
+        rpn_bbox_pred = ()
+        rpn_cls_score_total = ()
+        rpn_bbox_pred_total = ()
+
+        x1, x2 = self.rpn_convs_list[0](inputs[1])
+
+        rpn_cls_score_total = rpn_cls_score_total + (x1,)
+        rpn_bbox_pred_total = rpn_bbox_pred_total + (x2,)
+
+        x1 = self.transpose(x1, self.trans_shape)
+        x1 = self.reshape(x1, self.reshape_shape_cls)
+
+        x2 = self.transpose(x2, self.trans_shape)
+        x2 = self.reshape(x2, self.reshape_shape_reg)
+
+        rpn_cls_score = rpn_cls_score + (x1,)
+        rpn_bbox_pred = rpn_bbox_pred + (x2,)
+
+        loss = self.loss
+        clsloss = self.clsloss
+        regloss = self.regloss
+        bbox_targets = ()
+        bbox_weights = ()
+        labels = ()
+        label_weights = ()
+
+        output = ()
+        if self.training:
+            for i in range(self.batch_size):
+                multi_level_flags = ()
+                anchor_list_tuple = ()
+
+                res = self.cast(self.CheckValid(anchor_list[0], self.squeeze(img_metas[i:i + 1:1, ::])),
+                                mstype.int32)
+                multi_level_flags = multi_level_flags + (res,)
+                anchor_list_tuple = anchor_list_tuple + (anchor_list[0],)
+
+                valid_flag_list = self.concat(multi_level_flags)
+                anchor_using_list = self.concat(anchor_list_tuple)
+
+                gt_bboxes_i = self.squeeze(gt_bboxes[i:i + 1:1, ::])
+                gt_labels_i = self.squeeze(gt_labels[i:i + 1:1, ::])
+                gt_valids_i = self.squeeze(gt_valids[i:i + 1:1, ::])
+
+                bbox_target, bbox_weight, label, label_weight = self.get_targets(gt_bboxes_i,
+                                                                                 gt_labels_i,
+                                                                                 self.cast(valid_flag_list,
+                                                                                           mstype.bool_),
+                                                                                 anchor_using_list, gt_valids_i)
+
+                bbox_weight = self.cast(bbox_weight, mstype.float16)
+                label = self.cast(label, mstype.float16)
+                label_weight = self.cast(label_weight, mstype.float16)
+
+                begin = self.slice_index[0]
+                end = self.slice_index[0 + 1]
+                stride = 1
+                bbox_targets += (bbox_target[begin:end:stride, ::],)
+                bbox_weights += (bbox_weight[begin:end:stride],)
+                labels += (label[begin:end:stride],)
+                label_weights += (label_weight[begin:end:stride],)
+
+            bbox_target_using = ()
+            bbox_weight_using = ()
+            label_using = ()
+            label_weight_using = ()
+
+            for j in range(self.batch_size):
+                bbox_target_using += (bbox_targets[0 + (self.num_layers * j)],)
+                bbox_weight_using += (bbox_weights[0 + (self.num_layers * j)],)
+                label_using += (labels[0 + (self.num_layers * j)],)
+                label_weight_using += (label_weights[0 + (self.num_layers * j)],)
+
+            bbox_target_with_batchsize = self.concat(bbox_target_using)
+            bbox_weight_with_batchsize = self.concat(bbox_weight_using)
+            label_with_batchsize = self.concat(label_using)
+            label_weight_with_batchsize = self.concat(label_weight_using)
+
+            # stop
+            bbox_target_ = F.stop_gradient(bbox_target_with_batchsize)
+            bbox_weight_ = F.stop_gradient(bbox_weight_with_batchsize)
+            label_ = F.stop_gradient(label_with_batchsize)
+            label_weight_ = F.stop_gradient(label_weight_with_batchsize)
+
+            cls_score_i = rpn_cls_score[0]
+            reg_score_i = rpn_bbox_pred[0]
+
+            loss_cls = self.loss_cls(cls_score_i, label_)
+            loss_cls_item = loss_cls * label_weight_
+            loss_cls_item = self.sum_loss(loss_cls_item, (0,)) / self.num_expected_total
+
+            loss_reg = self.loss_bbox(reg_score_i, bbox_target_)
+            bbox_weight_ = self.tile(self.reshape(bbox_weight_, (self.feature_anchor_shape[0], 1)), (1, 4))
+            loss_reg = loss_reg * bbox_weight_
+            loss_reg_item = self.sum_loss(loss_reg, (1,))
+            loss_reg_item = self.sum_loss(loss_reg_item, (0,)) / self.num_expected_total
+
+            loss_total = self.rpn_loss_cls_weight * loss_cls_item + self.rpn_loss_reg_weight * loss_reg_item
+
+            loss += loss_total
+            loss_print += (loss_total, loss_cls_item, loss_reg_item)
+            clsloss += loss_cls_item
+            regloss += loss_reg_item
+
+            output = (loss, rpn_cls_score_total, rpn_bbox_pred_total, clsloss, regloss, loss_print)
+        else:
+            output = (self.placeh1, rpn_cls_score_total, rpn_bbox_pred_total, self.placeh1, self.placeh1, self.placeh1)
+
+        return output
--- a/model_zoo/official/cv/deeptext/src/Deeptext/vgg16.py
+++ b/model_zoo/official/cv/deeptext/src/Deeptext/vgg16.py
@ -0,0 +1,104 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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
+#
+# less 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 mindspore.common.dtype as mstype
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+
+# """VGG16 for deeptext"""
+
+
+def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
+    """Conv2D wrapper."""
+    # shape = (out_channels, in_channels, kernel_size, kernel_size)
+    weights = 'ones'
+    layers = []
+    layers += [nn.Conv2d(in_channels, out_channels,
+                         kernel_size=kernel_size, stride=stride, padding=padding,
+                         pad_mode=pad_mode, weight_init=weights, has_bias=False)]
+    layers += [nn.BatchNorm2d(out_channels)]
+    return nn.SequentialCell(layers)
+
+
+class VGG16FeatureExtraction(nn.Cell):
+    """VGG16FeatureExtraction for deeptext"""
+
+    def __init__(self):
+        super(VGG16FeatureExtraction, self).__init__()
+        self.relu = nn.ReLU()
+        self.max_pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2)
+
+        self.conv1_1 = _conv(in_channels=3, out_channels=64, kernel_size=3, padding=1)
+        self.conv1_2 = _conv(in_channels=64, out_channels=64, kernel_size=3, padding=1)
+
+        self.conv2_1 = _conv(in_channels=64, out_channels=128, kernel_size=3, padding=1)
+        self.conv2_2 = _conv(in_channels=128, out_channels=128, kernel_size=3, padding=1)
+
+        self.conv3_1 = _conv(in_channels=128, out_channels=256, kernel_size=3, padding=1)
+        self.conv3_2 = _conv(in_channels=256, out_channels=256, kernel_size=3, padding=1)
+        self.conv3_3 = _conv(in_channels=256, out_channels=256, kernel_size=3, padding=1)
+
+        self.conv4_1 = _conv(in_channels=256, out_channels=512, kernel_size=3, padding=1)
+        self.conv4_2 = _conv(in_channels=512, out_channels=512, kernel_size=3, padding=1)
+        self.conv4_3 = _conv(in_channels=512, out_channels=512, kernel_size=3, padding=1)
+
+        self.conv5_1 = _conv(in_channels=512, out_channels=512, kernel_size=3, padding=1)
+        self.conv5_2 = _conv(in_channels=512, out_channels=512, kernel_size=3, padding=1)
+        self.conv5_3 = _conv(in_channels=512, out_channels=512, kernel_size=3, padding=1)
+        self.cast = P.Cast()
+
+    def construct(self, x):
+        x = self.cast(x, mstype.float32)
+        x = self.conv1_1(x)
+        x = self.relu(x)
+        x = self.conv1_2(x)
+        x = self.relu(x)
+        x = self.max_pool(x)
+        f1 = x
+
+        x = self.conv2_1(x)
+        x = self.relu(x)
+        x = self.conv2_2(x)
+        x = self.relu(x)
+        x = self.max_pool(x)
+        f2 = x
+
+        x = self.conv3_1(x)
+        x = self.relu(x)
+        x = self.conv3_2(x)
+        x = self.relu(x)
+        x = self.conv3_3(x)
+        x = self.relu(x)
+        x = self.max_pool(x)
+        f3 = x
+
+        x = self.conv4_1(x)
+        x = self.relu(x)
+        x = self.conv4_2(x)
+        x = self.relu(x)
+        x = self.conv4_3(x)
+        x = self.relu(x)
+        f4 = x
+
+        x = self.max_pool(x)
+        x = self.conv5_1(x)
+        x = self.relu(x)
+        x = self.conv5_2(x)
+        x = self.relu(x)
+        x = self.conv5_3(x)
+        x = self.relu(x)
+        f5 = x
+
+        return f1, f2, f3, f4, f5
--- a/model_zoo/official/cv/deeptext/src/config.py
+++ b/model_zoo/official/cv/deeptext/src/config.py
@ -0,0 +1,130 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# " :===========================================================================
+"""
+network config setting, will be used in train.py and eval.py
+"""
+from easydict import EasyDict as ed
+
+config = ed({
+    "img_width": 960,
+    "img_height": 576,
+    "keep_ratio": False,
+    "flip_ratio": 0.0,
+    "photo_ratio": 0.0,
+    "expand_ratio": 0.3,
+
+    # anchor
+    "feature_shapes": [(36, 60)],
+    "anchor_scales": [2, 4, 6, 8, 12],
+    "anchor_ratios": [0.2, 0.5, 0.8, 1.0, 1.2, 1.5],
+    "anchor_strides": [16],
+    "num_anchors": 5 * 6,
+
+    # rpn
+    "rpn_in_channels": 512,
+    "rpn_feat_channels": 640,
+    "rpn_loss_cls_weight": 1.0,
+    "rpn_loss_reg_weight": 3.0,
+    "rpn_cls_out_channels": 1,
+    "rpn_target_means": [0., 0., 0., 0.],
+    "rpn_target_stds": [1.0, 1.0, 1.0, 1.0],
+
+    # bbox_assign_sampler
+    "neg_iou_thr": 0.3,
+    "pos_iou_thr": 0.5,
+    "min_pos_iou": 0.3,
+    "num_bboxes": 5 * 6 * 36 * 60,
+    "num_gts": 128,
+    "num_expected_neg": 256,
+    "num_expected_pos": 128,
+
+    # proposal
+    "activate_num_classes": 2,
+    "use_sigmoid_cls": True,
+
+    # roi_align
+    "roi_layer": dict(type='RoIAlign', out_size=7, sample_num=2),
+
+    # bbox_assign_sampler_stage2
+    "neg_iou_thr_stage2": 0.2,
+    "pos_iou_thr_stage2": 0.5,
+    "min_pos_iou_stage2": 0.5,
+    "num_bboxes_stage2": 2000,
+    "use_ambigous_sample": True,
+    "num_expected_pos_stage2": 128,
+    "num_expected_amb_stage2": 128,
+    "num_expected_neg_stage2": 640,
+    "num_expected_total_stage2": 640,
+
+    # rcnn
+    "rcnn_in_channels": 512,
+    "rcnn_fc_out_channels": 4096,
+    "rcnn_loss_cls_weight": 1,
+    "rcnn_loss_reg_weight": 1,
+    "rcnn_target_means": [0., 0., 0., 0.],
+    "rcnn_target_stds": [0.1, 0.1, 0.2, 0.2],
+
+    # train proposal
+    "rpn_proposal_nms_across_levels": False,
+    "rpn_proposal_nms_pre": 2000,
+    "rpn_proposal_nms_post": 2000,
+    "rpn_proposal_max_num": 2000,
+    "rpn_proposal_nms_thr": 0.7,
+    "rpn_proposal_min_bbox_size": 0,
+
+    # test proposal
+    "rpn_nms_across_levels": False,
+    "rpn_nms_pre": 300,
+    "rpn_nms_post": 300,
+    "rpn_max_num": 300,
+    "rpn_nms_thr": 0.7,
+    "rpn_min_bbox_min_size": 0,
+    "test_score_thr": 0.95,
+    "test_iou_thr": 0.5,
+    "test_max_per_img": 100,
+    "test_batch_size": 2,
+
+    "rpn_head_loss_type": "CrossEntropyLoss",
+    "rpn_head_use_sigmoid": True,
+    "rpn_head_weight": 1.0,
+
+    # LR
+    "base_lr": 0.02,
+    "base_step": 982 * 8,
+    "total_epoch": 70,
+    "warmup_step": 50,
+    "warmup_mode": "linear",
+    "warmup_ratio": 1 / 3.0,
+    "sgd_step": [8, 11],
+    "sgd_momentum": 0.9,
+
+    # train
+    "batch_size": 2,
+    "loss_scale": 1,
+    "momentum": 0.91,
+    "weight_decay": 1e-4,
+    "epoch_size": 70,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 10,
+    "keep_checkpoint_max": 5,
+    "save_checkpoint_path": "./",
+
+    "mindrecord_dir": "/home/deeptext_sustech/data/mindrecord/full_ori",
+    "use_coco": True,
+    "coco_root": "/d0/dataset/coco2017",
+    "cocotext_json": "/home/deeptext_sustech/data/cocotext.v2.json",
+    "coco_train_data_type": "train2017",
+    "num_classes": 3
+})
--- a/model_zoo/official/cv/deeptext/src/dataset.py
+++ b/model_zoo/official/cv/deeptext/src/dataset.py
@ -0,0 +1,504 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""Deeptext dataset"""
+from __future__ import division
+
+import os
+import numpy as np
+from numpy import random
+
+import mmcv
+import mindspore.dataset as de
+import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms.c_transforms as CC
+import mindspore.common.dtype as mstype
+from mindspore.mindrecord import FileWriter
+from src.config import config
+
+
+def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
+    """Calculate the ious between each bbox of bboxes1 and bboxes2.
+
+    Args:
+        bboxes1(ndarray): shape (n, 4)
+        bboxes2(ndarray): shape (k, 4)
+        mode(str): iou (intersection over union) or iof (intersection
+            over foreground)
+
+    Returns:
+        ious(ndarray): shape (n, k)
+    """
+
+    assert mode in ['iou', 'iof']
+
+    bboxes1 = bboxes1.astype(np.float32)
+    bboxes2 = bboxes2.astype(np.float32)
+    rows = bboxes1.shape[0]
+    cols = bboxes2.shape[0]
+    ious = np.zeros((rows, cols), dtype=np.float32)
+    if rows * cols == 0:
+        return ious
+    exchange = False
+    if bboxes1.shape[0] > bboxes2.shape[0]:
+        bboxes1, bboxes2 = bboxes2, bboxes1
+        ious = np.zeros((cols, rows), dtype=np.float32)
+        exchange = True
+    area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (bboxes1[:, 3] - bboxes1[:, 1] + 1)
+    area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (bboxes2[:, 3] - bboxes2[:, 1] + 1)
+    for i in range(bboxes1.shape[0]):
+        x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])
+        y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])
+        x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])
+        y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])
+        overlap = np.maximum(x_end - x_start + 1, 0) * np.maximum(
+            y_end - y_start + 1, 0)
+        if mode == 'iou':
+            union = area1[i] + area2 - overlap
+        else:
+            union = area1[i] if not exchange else area2
+        ious[i, :] = overlap / union
+    if exchange:
+        ious = ious.T
+    return ious
+
+
+class PhotoMetricDistortion:
+    """Photo Metric Distortion"""
+
+    def __init__(self,
+                 brightness_delta=32,
+                 contrast_range=(0.5, 1.5),
+                 saturation_range=(0.5, 1.5),
+                 hue_delta=18):
+        self.brightness_delta = brightness_delta
+        self.contrast_lower, self.contrast_upper = contrast_range
+        self.saturation_lower, self.saturation_upper = saturation_range
+        self.hue_delta = hue_delta
+
+    def __call__(self, img, boxes, labels):
+        # random brightness
+        img = img.astype('float32')
+
+        if random.randint(2):
+            delta = random.uniform(-self.brightness_delta,
+                                   self.brightness_delta)
+            img += delta
+
+        # mode == 0 --> do random contrast first
+        # mode == 1 --> do random contrast last
+        mode = random.randint(2)
+        if mode == 1:
+            if random.randint(2):
+                alpha = random.uniform(self.contrast_lower,
+                                       self.contrast_upper)
+                img *= alpha
+
+        # convert color from BGR to HSV
+        img = mmcv.bgr2hsv(img)
+
+        # random saturation
+        if random.randint(2):
+            img[..., 1] *= random.uniform(self.saturation_lower,
+                                          self.saturation_upper)
+
+        # random hue
+        if random.randint(2):
+            img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
+            img[..., 0][img[..., 0] > 360] -= 360
+            img[..., 0][img[..., 0] < 0] += 360
+
+        # convert color from HSV to BGR
+        img = mmcv.hsv2bgr(img)
+
+        # random contrast
+        if mode == 0:
+            if random.randint(2):
+                alpha = random.uniform(self.contrast_lower,
+                                       self.contrast_upper)
+                img *= alpha
+
+        # randomly swap channels
+        if random.randint(2):
+            img = img[..., random.permutation(3)]
+
+        return img, boxes, labels
+
+
+class Expand:
+    """expand image"""
+
+    def __init__(self, mean=(0, 0, 0), to_rgb=True, ratio_range=(1, 4)):
+        if to_rgb:
+            self.mean = mean[::-1]
+        else:
+            self.mean = mean
+        self.min_ratio, self.max_ratio = ratio_range
+
+    def __call__(self, img, boxes, labels):
+        if random.randint(2):
+            return img, boxes, labels
+
+        h, w, c = img.shape
+        ratio = random.uniform(self.min_ratio, self.max_ratio)
+        expand_img = np.full((int(h * ratio), int(w * ratio), c),
+                             self.mean).astype(img.dtype)
+        left = int(random.uniform(0, w * ratio - w))
+        top = int(random.uniform(0, h * ratio - h))
+        expand_img[top:top + h, left:left + w] = img
+        img = expand_img
+        boxes += np.tile((left, top), 2)
+        return img, boxes, labels
+
+
+def resize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
+    """resize operation for image"""
+    img_data = img
+    img_data, w_scale, h_scale = mmcv.imresize(
+        img_data, (config.img_width, config.img_height), return_scale=True)
+    scale_factor = np.array(
+        [w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
+    img_shape = (config.img_height, config.img_width, 1.0)
+    img_shape = np.asarray(img_shape, dtype=np.float32)
+
+    gt_bboxes = gt_bboxes * scale_factor
+
+    gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1)
+    gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1)
+
+    return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
+
+
+def resize_column_test(img, img_shape, gt_bboxes, gt_label, gt_num):
+    """resize operation for image of eval"""
+    img_data = img
+    img_data, w_scale, h_scale = mmcv.imresize(
+        img_data, (config.img_width, config.img_height), return_scale=True)
+    scale_factor = np.array(
+        [w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
+    img_shape = (config.img_height, config.img_width)
+    img_shape = np.append(img_shape, (h_scale, w_scale))
+    img_shape = np.asarray(img_shape, dtype=np.float32)
+
+    gt_bboxes = gt_bboxes * scale_factor
+
+    gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1)
+    gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1)
+
+    return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
+
+
+def impad_to_multiple_column(img, img_shape, gt_bboxes, gt_label, gt_num):
+    """impad operation for image"""
+    img_data = mmcv.impad(img, (config.img_height, config.img_width))
+    img_data = img_data.astype(np.float32)
+    return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
+
+
+def imnormalize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
+    """imnormalize operation for image"""
+    img_data = mmcv.imnormalize(img, [123.675, 116.28, 103.53], [58.395, 57.12, 57.375], True)
+    img_data = img_data.astype(np.float32)
+    return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
+
+
+def flip_column(img, img_shape, gt_bboxes, gt_label, gt_num):
+    """flip operation for image"""
+    img_data = img
+    img_data = mmcv.imflip(img_data)
+    flipped = gt_bboxes.copy()
+    _, w, _ = img_data.shape
+
+    flipped[..., 0::4] = w - gt_bboxes[..., 2::4] - 1
+    flipped[..., 2::4] = w - gt_bboxes[..., 0::4] - 1
+
+    return (img_data, img_shape, flipped, gt_label, gt_num)
+
+
+def flipped_generation(img, img_shape, gt_bboxes, gt_label, gt_num):
+    """flipped generation"""
+    img_data = img
+    flipped = gt_bboxes.copy()
+    _, w, _ = img_data.shape
+
+    flipped[..., 0::4] = w - gt_bboxes[..., 2::4] - 1
+    flipped[..., 2::4] = w - gt_bboxes[..., 0::4] - 1
+
+    return (img_data, img_shape, flipped, gt_label, gt_num)
+
+
+def image_bgr_rgb(img, img_shape, gt_bboxes, gt_label, gt_num):
+    img_data = img[:, :, ::-1]
+    return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
+
+
+def transpose_column(img, img_shape, gt_bboxes, gt_label, gt_num):
+    """transpose operation for image"""
+    img_data = img.transpose(2, 0, 1).copy()
+    img_data = img_data.astype(np.float16)
+    img_shape = img_shape.astype(np.float16)
+    gt_bboxes = gt_bboxes.astype(np.float16)
+    gt_label = gt_label.astype(np.int32)
+    gt_num = gt_num.astype(np.bool)
+
+    return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
+
+
+def photo_crop_column(img, img_shape, gt_bboxes, gt_label, gt_num):
+    """photo crop operation for image"""
+    random_photo = PhotoMetricDistortion()
+    img_data, gt_bboxes, gt_label = random_photo(img, gt_bboxes, gt_label)
+
+    return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
+
+
+def expand_column(img, img_shape, gt_bboxes, gt_label, gt_num):
+    """expand operation for image"""
+    expand = Expand()
+    img, gt_bboxes, gt_label = expand(img, gt_bboxes, gt_label)
+
+    return (img, img_shape, gt_bboxes, gt_label, gt_num)
+
+
+def preprocess_fn(image, box, is_training):
+    """Preprocess function for dataset."""
+
+    def _infer_data(image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert):
+        image_shape = image_shape[:2]
+        input_data = image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert
+        input_data = resize_column_test(*input_data)
+        input_data = image_bgr_rgb(*input_data)
+        output_data = input_data
+        return output_data
+
+    def _data_aug(image, box, is_training):
+        """Data augmentation function."""
+        image_bgr = image.copy()
+        image_bgr[:, :, 0] = image[:, :, 2]
+        image_bgr[:, :, 1] = image[:, :, 1]
+        image_bgr[:, :, 2] = image[:, :, 0]
+        image_shape = image_bgr.shape[:2]
+        gt_box = box[:, :4]
+        gt_label = box[:, 4]
+        gt_iscrowd = box[:, 5]
+
+        pad_max_number = 128
+        if box.shape[0] < 128:
+            gt_box_new = np.pad(gt_box, ((0, pad_max_number - box.shape[0]), (0, 0)), mode="constant",
+                                constant_values=0)
+            gt_label_new = np.pad(gt_label, ((0, pad_max_number - box.shape[0])), mode="constant", constant_values=-1)
+            gt_iscrowd_new = np.pad(gt_iscrowd, ((0, pad_max_number - box.shape[0])), mode="constant",
+                                    constant_values=1)
+        else:
+            gt_box_new = gt_box[0:pad_max_number]
+            gt_label_new = gt_label[0:pad_max_number]
+            gt_iscrowd_new = gt_iscrowd[0:pad_max_number]
+
+        gt_iscrowd_new_revert = (~(gt_iscrowd_new.astype(np.bool))).astype(np.int32)
+
+        if not is_training:
+            return _infer_data(image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert)
+
+        input_data = image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert
+
+        expand = (np.random.rand() < config.expand_ratio)
+        if expand:
+            input_data = expand_column(*input_data)
+
+        input_data = photo_crop_column(*input_data)
+        input_data = resize_column(*input_data)
+        input_data = image_bgr_rgb(*input_data)
+
+        output_data = input_data
+        return output_data
+
+    return _data_aug(image, box, is_training)
+
+
+def create_label(is_training):
+    """Create image label."""
+    image_files = []
+    image_anno_dict = {}
+
+    if is_training:
+        img_dirs = config.train_images.split(',')
+        txt_dirs = config.train_txts.split(',')
+    else:
+        img_dirs = config.test_images.split(',')
+        txt_dirs = config.test_txts.split(',')
+
+    for img_dir, txt_dir in zip(img_dirs, txt_dirs):
+        img_basenames = []
+        for file in os.listdir(img_dir):
+            # Filter git file.
+            if 'gif' not in file:
+                img_basenames.append(os.path.basename(file))
+
+        img_names = []
+        for item in img_basenames:
+            temp1, _ = os.path.splitext(item)
+            img_names.append((temp1, item))
+
+        for img, img_basename in img_names:
+            image_path = img_dir + '/' + img_basename
+            annos = []
+            # Parse annotation of dataset in paper.
+            if len(img) == 6 and '_' not in img_basename:
+                gt = open(txt_dir + '/' + img + '.txt').read().splitlines()
+                if img.isdigit() and int(img) > 1200:
+                    continue
+                for img_each_label in gt:
+                    spt = img_each_label.replace(',', '').split(' ')
+                    if ' ' not in img_each_label:
+                        spt = img_each_label.split(',')
+                    annos.append(
+                        [spt[0], spt[1], str(int(spt[0]) + int(spt[2])), str(int(spt[1]) + int(spt[3]))] + [1] + [
+                            int(0)])
+            else:
+                anno_file = txt_dir + '/gt_img_' + img.split('_')[-1] + '.txt'
+                if not os.path.exists(anno_file):
+                    anno_file = txt_dir + '/gt_' + img.split('_')[-1] + '.txt'
+                if not os.path.exists(anno_file):
+                    anno_file = txt_dir + '/img_' + img.split('_')[-1] + '.txt'
+                gt = open(anno_file).read().splitlines()
+                for img_each_label in gt:
+                    spt = img_each_label.replace(',', '').split(' ')
+                    if ' ' not in img_each_label:
+                        spt = img_each_label.split(',')
+                    annos.append([spt[0], spt[1], spt[2], spt[3]] + [1] + [int(0)])
+
+            image_files.append(image_path)
+            if annos:
+                image_anno_dict[image_path] = np.array(annos)
+            else:
+                image_anno_dict[image_path] = np.array([0, 0, 0, 0, 0, 1])
+
+    if is_training and config.use_coco:
+        coco_root = config.coco_root
+        data_type = config.coco_train_data_type
+        from src.coco_text import COCO_Text
+        anno_json = config.cocotext_json
+        ct = COCO_Text(anno_json)
+        image_ids = ct.getImgIds(imgIds=ct.train,
+                                 catIds=[('legibility', 'legible')])
+        for img_id in image_ids:
+            image_info = ct.loadImgs(img_id)[0]
+            file_name = image_info['file_name'][15:]
+            anno_ids = ct.getAnnIds(imgIds=img_id)
+            anno = ct.loadAnns(anno_ids)
+            image_path = os.path.join(coco_root, data_type, file_name)
+            annos = []
+            for label in anno:
+                # if label["utf8_string"] != '':
+                bbox = label["bbox"]
+                x1, x2 = bbox[0], bbox[0] + bbox[2]
+                y1, y2 = bbox[1], bbox[1] + bbox[3]
+                annos.append([x1, y1, x2, y2] + [1] + [int(0)])
+
+            image_files.append(image_path)
+            if annos:
+                image_anno_dict[image_path] = np.array(annos)
+            else:
+                image_anno_dict[image_path] = np.array([0, 0, 0, 0, 0, 1])
+
+    return image_files, image_anno_dict
+
+
+def anno_parser(annos_str):
+    """Parse annotation from string to list."""
+    annos = []
+    for anno_str in annos_str:
+        anno = list(map(int, anno_str.strip().split(',')))
+        annos.append(anno)
+    return annos
+
+
+def data_to_mindrecord_byte_image(is_training=True, prefix="deeptext.mindrecord", file_num=8):
+    """Create MindRecord file."""
+    mindrecord_dir = config.mindrecord_dir
+    mindrecord_path = os.path.join(mindrecord_dir, prefix)
+    writer = FileWriter(mindrecord_path, file_num)
+    image_files, image_anno_dict = create_label(is_training)
+
+    deeptext_json = {
+        "image": {"type": "bytes"},
+        "annotation": {"type": "int32", "shape": [-1, 6]},
+    }
+    writer.add_schema(deeptext_json, "deeptext_json")
+
+    for image_name in image_files:
+        with open(image_name, 'rb') as f:
+            img = f.read()
+        annos = np.array(image_anno_dict[image_name], dtype=np.int32)
+        row = {"image": img, "annotation": annos}
+        writer.write_raw_data([row])
+    writer.commit()
+
+
+def create_deeptext_dataset(mindrecord_file, batch_size=2, repeat_num=12, device_num=1, rank_id=0,
+                            is_training=True, num_parallel_workers=4):
+    """Creatr deeptext dataset with MindDataset."""
+    ds = de.MindDataset(mindrecord_file, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank_id,
+                        num_parallel_workers=1, shuffle=is_training)
+    decode = C.Decode()
+    ds = ds.map(operations=decode, input_columns=["image"], num_parallel_workers=1)
+    compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
+
+    hwc_to_chw = C.HWC2CHW()
+    normalize_op = C.Normalize((123.675, 116.28, 103.53), (58.395, 57.12, 57.375))
+    horizontally_op = C.RandomHorizontalFlip(1)
+    type_cast0 = CC.TypeCast(mstype.float32)
+    type_cast1 = CC.TypeCast(mstype.float16)
+    type_cast2 = CC.TypeCast(mstype.int32)
+    type_cast3 = CC.TypeCast(mstype.bool_)
+
+    if is_training:
+        ds = ds.map(operations=compose_map_func, input_columns=["image", "annotation"],
+                    output_columns=["image", "image_shape", "box", "label", "valid_num"],
+                    column_order=["image", "image_shape", "box", "label", "valid_num"],
+                    num_parallel_workers=num_parallel_workers)
+
+        flip = (np.random.rand() < config.flip_ratio)
+        if flip:
+            ds = ds.map(operations=[normalize_op, type_cast0, horizontally_op], input_columns=["image"],
+                        num_parallel_workers=12)
+            ds = ds.map(operations=flipped_generation,
+                        input_columns=["image", "image_shape", "box", "label", "valid_num"],
+                        num_parallel_workers=num_parallel_workers)
+        else:
+            ds = ds.map(operations=[normalize_op, type_cast0], input_columns=["image"],
+                        num_parallel_workers=12)
+        ds = ds.map(operations=[hwc_to_chw, type_cast1], input_columns=["image"],
+                    num_parallel_workers=12)
+
+    else:
+        ds = ds.map(operations=compose_map_func,
+                    input_columns=["image", "annotation"],
+                    output_columns=["image", "image_shape", "box", "label", "valid_num"],
+                    column_order=["image", "image_shape", "box", "label", "valid_num"],
+                    num_parallel_workers=num_parallel_workers)
+
+        ds = ds.map(operations=[normalize_op, hwc_to_chw, type_cast1], input_columns=["image"],
+                    num_parallel_workers=24)
+
+    # transpose_column from python to c
+    ds = ds.map(operations=[type_cast1], input_columns=["image_shape"])
+    ds = ds.map(operations=[type_cast1], input_columns=["box"])
+    ds = ds.map(operations=[type_cast2], input_columns=["label"])
+    ds = ds.map(operations=[type_cast3], input_columns=["valid_num"])
+    ds = ds.batch(batch_size, drop_remainder=True)
+    ds = ds.repeat(repeat_num)
+
+    return ds
--- a/model_zoo/official/cv/deeptext/src/lr_schedule.py
+++ b/model_zoo/official/cv/deeptext/src/lr_schedule.py
@ -0,0 +1,44 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""lr generator for deeptext"""
+import math
+
+
+def linear_warmup_learning_rate(current_step, warmup_steps, base_lr, init_lr):
+    lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
+    learning_rate = float(init_lr) + lr_inc * current_step
+    return learning_rate
+
+
+def a_cosine_learning_rate(current_step, base_lr, warmup_steps, decay_steps):
+    base = float(current_step - warmup_steps) / float(decay_steps)
+    learning_rate = (1 + math.cos(base * math.pi)) / 2 * base_lr
+    return learning_rate
+
+
+def dynamic_lr(config, rank_size=1):
+    """dynamic learning rate generator"""
+    base_lr = config.base_lr
+
+    base_step = (config.base_step // rank_size) + rank_size
+    total_steps = int(base_step * config.total_epoch)
+    warmup_steps = int(config.warmup_step)
+    lr = []
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr.append(linear_warmup_learning_rate(i, warmup_steps, base_lr, base_lr * config.warmup_ratio))
+        else:
+            lr.append(a_cosine_learning_rate(i, base_lr, warmup_steps, total_steps))
+    return lr
--- a/model_zoo/official/cv/deeptext/src/network_define.py
+++ b/model_zoo/official/cv/deeptext/src/network_define.py
@ -0,0 +1,188 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Deeptext training network wrapper."""
+
+import time
+import numpy as np
+import mindspore.nn as nn
+from mindspore.common.tensor import Tensor
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+from mindspore import ParameterTuple
+from mindspore.train.callback import Callback
+from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
+
+time_stamp_init = False
+time_stamp_first = 0
+
+
+class LossCallBack(Callback):
+    """
+    Monitor the loss in training.
+
+    If the loss is NAN or INF terminating training.
+
+    Note:
+        If per_print_times is 0 do not print loss.
+
+    Args:
+        per_print_times (int): Print loss every times. Default: 1.
+    """
+
+    def __init__(self, per_print_times=1, rank_id=0):
+        super(LossCallBack, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0.")
+        self._per_print_times = per_print_times
+        self.count = 0
+        self.rpn_loss_sum = 0
+        self.rcnn_loss_sum = 0
+        self.rpn_cls_loss_sum = 0
+        self.rpn_reg_loss_sum = 0
+        self.rcnn_cls_loss_sum = 0
+        self.rcnn_reg_loss_sum = 0
+        self.rank_id = rank_id
+
+        global time_stamp_init, time_stamp_first
+        if not time_stamp_init:
+            time_stamp_first = time.time()
+            time_stamp_init = True
+
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        rpn_loss = cb_params.net_outputs[0].asnumpy()
+        rcnn_loss = cb_params.net_outputs[1].asnumpy()
+        rpn_cls_loss = cb_params.net_outputs[2].asnumpy()
+
+        rpn_reg_loss = cb_params.net_outputs[3].asnumpy()
+        rcnn_cls_loss = cb_params.net_outputs[4].asnumpy()
+        rcnn_reg_loss = cb_params.net_outputs[5].asnumpy()
+
+        self.count += 1
+        self.rpn_loss_sum += float(rpn_loss)
+        self.rcnn_loss_sum += float(rcnn_loss)
+        self.rpn_cls_loss_sum += float(rpn_cls_loss)
+        self.rpn_reg_loss_sum += float(rpn_reg_loss)
+        self.rcnn_cls_loss_sum += float(rcnn_cls_loss)
+        self.rcnn_reg_loss_sum += float(rcnn_reg_loss)
+
+        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
+
+        if self.count >= 1:
+            global time_stamp_first
+            time_stamp_current = time.time()
+
+            rpn_loss = self.rpn_loss_sum / self.count
+            rcnn_loss = self.rcnn_loss_sum / self.count
+            rpn_cls_loss = self.rpn_cls_loss_sum / self.count
+
+            rpn_reg_loss = self.rpn_reg_loss_sum / self.count
+            rcnn_cls_loss = self.rcnn_cls_loss_sum / self.count
+            rcnn_reg_loss = self.rcnn_reg_loss_sum / self.count
+
+            total_loss = rpn_loss + rcnn_loss
+
+            loss_file = open("./loss_{}.log".format(self.rank_id), "a+")
+            loss_file.write("%lu epoch: %s step: %s ,rpn_loss: %.5f, rcnn_loss: %.5f, rpn_cls_loss: %.5f, "
+                            "rpn_reg_loss: %.5f, rcnn_cls_loss: %.5f, rcnn_reg_loss: %.5f, total_loss: %.5f" %
+                            (time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cur_step_in_epoch,
+                             rpn_loss, rcnn_loss, rpn_cls_loss, rpn_reg_loss,
+                             rcnn_cls_loss, rcnn_reg_loss, total_loss))
+            loss_file.write("\n")
+            loss_file.close()
+
+            self.count = 0
+            self.rpn_loss_sum = 0
+            self.rcnn_loss_sum = 0
+            self.rpn_cls_loss_sum = 0
+            self.rpn_reg_loss_sum = 0
+            self.rcnn_cls_loss_sum = 0
+            self.rcnn_reg_loss_sum = 0
+
+
+class LossNet(nn.Cell):
+    """Deeptext loss method"""
+
+    def construct(self, x1, x2, x3, x4, x5, x6):
+        return x1 + x2
+
+
+class WithLossCell(nn.Cell):
+    """
+    Wrap the network with loss function to compute loss.
+
+    Args:
+        backbone (Cell): The target network to wrap.
+        loss_fn (Cell): The loss function used to compute loss.
+    """
+
+    def __init__(self, backbone, loss_fn):
+        super(WithLossCell, self).__init__(auto_prefix=False)
+        self._backbone = backbone
+        self._loss_fn = loss_fn
+
+    def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num):
+        loss1, loss2, loss3, loss4, loss5, loss6 = self._backbone(x, img_shape, gt_bboxe, gt_label, gt_num)
+        return self._loss_fn(loss1, loss2, loss3, loss4, loss5, loss6)
+
+    @property
+    def backbone_network(self):
+        """
+        Get the backbone network.
+
+        Returns:
+            Cell, return backbone network.
+        """
+        return self._backbone
+
+
+class TrainOneStepCell(nn.Cell):
+    """
+    Network training package class.
+
+    Append an optimizer to the training network after that the construct function
+    can be called to create the backward graph.
+
+    Args:
+        network (Cell): The training network.
+        network_backbone (Cell): The forward network.
+        optimizer (Cell): Optimizer for updating the weights.
+        sens (Number): The adjust parameter. Default value is 1.0.
+        reduce_flag (bool): The reduce flag. Default value is False.
+        mean (bool): Allreduce method. Default value is False.
+        degree (int): Device number. Default value is None.
+    """
+
+    def __init__(self, network, network_backbone, optimizer, sens=1.0, reduce_flag=False, mean=True, degree=None):
+        super(TrainOneStepCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.network.set_grad()
+        self.backbone = network_backbone
+        self.weights = ParameterTuple(network.trainable_params())
+        self.optimizer = optimizer
+        self.grad = C.GradOperation(get_by_list=True,
+                                    sens_param=True)
+        self.sens = Tensor((np.ones((1,)) * sens).astype(np.float16))
+        self.reduce_flag = reduce_flag
+        if reduce_flag:
+            self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
+
+    def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num):
+        weights = self.weights
+        loss1, loss2, loss3, loss4, loss5, loss6 = self.backbone(x, img_shape, gt_bboxe, gt_label, gt_num)
+        grads = self.grad(self.network, weights)(x, img_shape, gt_bboxe, gt_label, gt_num, self.sens)
+        if self.reduce_flag:
+            grads = self.grad_reducer(grads)
+        return F.depend(loss1, self.optimizer(grads)), loss2, loss3, loss4, loss5, loss6
--- a/model_zoo/official/cv/deeptext/src/utils.py
+++ b/model_zoo/official/cv/deeptext/src/utils.py
@ -0,0 +1,99 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""metrics utils"""
+
+import numpy as np
+from src.config import config
+
+
+def calc_iou(bbox_pred, bbox_ground):
+    """Calculate iou of predicted bbox and ground truth."""
+    x1 = float(bbox_pred[0])
+    y1 = float(bbox_pred[1])
+    width1 = float(bbox_pred[2] - bbox_pred[0])
+    height1 = float(bbox_pred[3] - bbox_pred[1])
+
+    x2 = float(bbox_ground[0])
+    y2 = float(bbox_ground[1])
+    width2 = float(bbox_ground[2] - bbox_ground[0])
+    height2 = float(bbox_ground[3] - bbox_ground[1])
+
+    endx = max(x1 + width1, x2 + width2)
+    startx = min(x1, x2)
+    width = width1 + width2 - (endx - startx)
+
+    endy = max(y1 + height1, y2 + height2)
+    starty = min(y1, y2)
+    height = height1 + height2 - (endy - starty)
+
+    if width <= 0 or height <= 0:
+        iou = 0
+    else:
+        area = width * height
+        area1 = width1 * height1
+        area2 = width2 * height2
+        iou = area * 1. / (area1 + area2 - area)
+
+    return iou
+
+
+def metrics(pred_data):
+    """Calculate precision and recall of predicted bboxes."""
+    num_classes = config.num_classes
+    count_corrects = [1e-6 for _ in range(num_classes)]
+    count_grounds = [1e-6 for _ in range(num_classes)]
+    count_preds = [1e-6 for _ in range(num_classes)]
+    ious = []
+    for i, sample in enumerate(pred_data):
+        gt_bboxes = sample['gt_bboxes']
+        gt_labels = sample['gt_labels']
+
+        print('gt_bboxes', gt_bboxes)
+        print('gt_labels', gt_labels)
+
+        boxes = sample['boxes']
+        classes = sample['labels']
+        print('boxes', boxes)
+        print('labels', classes)
+
+        # metric
+        count_correct = [1e-6 for _ in range(num_classes)]
+        count_ground = [1e-6 for _ in range(num_classes)]
+        count_pred = [1e-6 for _ in range(num_classes)]
+
+        for gt_label in gt_labels:
+            count_ground[gt_label] += 1
+
+        for box_index, box in enumerate(boxes):
+            bbox_pred = [box[0], box[1], box[2], box[3]]
+            count_pred[classes[box_index]] += 1
+
+            for gt_index, gt_label in enumerate(gt_labels):
+                class_ground = gt_label
+
+                if classes[box_index] == class_ground:
+                    iou = calc_iou(bbox_pred, gt_bboxes[gt_index])
+                    ious.append(iou)
+                    if iou >= 0.5:
+                        count_correct[class_ground] += 1
+                        break
+
+        count_corrects = [count_corrects[i] + count_correct[i] for i in range(num_classes)]
+        count_preds = [count_preds[i] + count_pred[i] for i in range(num_classes)]
+        count_grounds = [count_grounds[i] + count_ground[i] for i in range(num_classes)]
+
+    precision = np.array([count_corrects[ix] / count_preds[ix] for ix in range(num_classes)])
+    recall = np.array([count_corrects[ix] / count_grounds[ix] for ix in range(num_classes)])
+    return precision, recall * config.test_batch_size
--- a/model_zoo/official/cv/deeptext/train.py
+++ b/model_zoo/official/cv/deeptext/train.py
@ -0,0 +1,139 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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
+#
+# less 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.
+# ============================================================================
+
+"""train Deeptext and get checkpoint files."""
+
+import argparse
+import ast
+import os
+import time
+
+import numpy as np
+from src.Deeptext.deeptext_vgg16 import Deeptext_VGG16
+from src.config import config
+from src.dataset import data_to_mindrecord_byte_image, create_deeptext_dataset
+from src.lr_schedule import dynamic_lr
+from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
+
+import mindspore.common.dtype as mstype
+from mindspore import context, Tensor
+from mindspore.common import set_seed
+from mindspore.communication.management import init
+from mindspore.context import ParallelMode
+from mindspore.nn import Momentum
+from mindspore.train import Model
+from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+np.set_printoptions(threshold=np.inf)
+
+set_seed(1)
+
+parser = argparse.ArgumentParser(description="Deeptext training")
+parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default: False.")
+parser.add_argument("--dataset", type=str, default="coco", help="Dataset name, default: coco.")
+parser.add_argument("--pre_trained", type=str, default="", help="Pretrained file path.")
+parser.add_argument("--device_id", type=int, default=5, help="Device id, default: 5.")
+parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default: 1.")
+parser.add_argument("--rank_id", type=int, default=0, help="Rank id, default: 0.")
+parser.add_argument("--imgs_path", type=str, required=True,
+                    help="Train images files paths, multiple paths can be separated by ','.")
+parser.add_argument("--annos_path", type=str, required=True,
+                    help="Annotations files paths of train images, multiple paths can be separated by ','.")
+parser.add_argument("--mindrecord_prefix", type=str, default='Deeptext-TRAIN', help="Prefix of mindrecord.")
+args_opt = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
+
+if __name__ == '__main__':
+    if args_opt.run_distribute:
+        rank = args_opt.rank_id
+        device_num = args_opt.device_num
+        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          gradients_mean=True)
+        init()
+    else:
+        rank = 0
+        device_num = 1
+
+    print("Start create dataset!")
+
+    # It will generate mindrecord file in args_opt.mindrecord_dir,
+    # and the file name is DeepText.mindrecord0, 1, ... file_num.
+    prefix = args_opt.mindrecord_prefix
+    config.train_images = args_opt.imgs_path
+    config.train_txts = args_opt.annos_path
+    mindrecord_dir = config.mindrecord_dir
+    mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
+    print("CHECKING MINDRECORD FILES ...")
+
+    if rank == 0 and not os.path.exists(mindrecord_file):
+        if not os.path.isdir(mindrecord_dir):
+            os.makedirs(mindrecord_dir)
+        if os.path.isdir(config.coco_root):
+            if not os.path.exists(config.coco_root):
+                print("Please make sure config:coco_root is valid.")
+                raise ValueError(config.coco_root)
+            print("Create Mindrecord. It may take some time.")
+            data_to_mindrecord_byte_image(True, prefix)
+            print("Create Mindrecord Done, at {}".format(mindrecord_dir))
+        else:
+            print("coco_root not exits.")
+
+    while not os.path.exists(mindrecord_file + ".db"):
+        time.sleep(5)
+
+    print("CHECKING MINDRECORD FILES DONE!")
+
+    loss_scale = float(config.loss_scale)
+
+    # When create MindDataset, using the fitst mindrecord file, such as FasterRcnn.mindrecord0.
+    dataset = create_deeptext_dataset(mindrecord_file, repeat_num=1,
+                                      batch_size=config.batch_size, device_num=device_num, rank_id=rank)
+
+    dataset_size = dataset.get_dataset_size()
+    print("Create dataset done! dataset_size = ", dataset_size)
+    net = Deeptext_VGG16(config=config)
+    net = net.set_train()
+
+    load_path = args_opt.pre_trained
+    if load_path != "":
+        param_dict = load_checkpoint(load_path)
+        load_param_into_net(net, param_dict)
+
+    loss = LossNet()
+    lr = Tensor(dynamic_lr(config, rank_size=device_num), mstype.float32)
+
+    opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,
+                   weight_decay=config.weight_decay, loss_scale=config.loss_scale)
+    net_with_loss = WithLossCell(net, loss)
+    if args_opt.run_distribute:
+        net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale, reduce_flag=True,
+                               mean=True, degree=device_num)
+    else:
+        net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale)
+
+    time_cb = TimeMonitor(data_size=dataset_size)
+    loss_cb = LossCallBack(rank_id=rank)
+    cb = [time_cb, loss_cb]
+    if config.save_checkpoint:
+        ckptconfig = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * dataset_size,
+                                      keep_checkpoint_max=config.keep_checkpoint_max)
+        save_checkpoint_path = os.path.join(config.save_checkpoint_path, "ckpt_" + str(rank) + "/")
+        ckpoint_cb = ModelCheckpoint(prefix='deeptext', directory=save_checkpoint_path, config=ckptconfig)
+        cb += [ckpoint_cb]
+
+    model = Model(net)
+    model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=True)