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+# 1. 鍐呭
+
+<!-- TOC -->
+
+[鏌ョ湅涓枃](./README_CN.md)
+
+- [Retinanet Description](#retinanet-description)
+- [Model Architecture](#model-architecture)
+- [Dataset](#dataset)
+- [Environment Requirements](#environment-requirements)
+- [Script Description](#script-description)
+    - [Script and Sample Code](#ccript-and-sample-code)
+    - [Script Parameters](#script-parameters)
+    - [Training Process](#training-process)
+        - [Usage](#usage1)
+        - [Run](#run1)
+        - [Result](#result1)
+    - [Evaluation Process](#evaluation-process)
+        - [Usage](#usage2)
+        - [Run](#run2)
+        - [Result](#result2)
+    - [Model Export](#model-export)
+        - [Usage](#usage3)
+        - [Run](#run3)
+    - [Inference Process](#inference-process)
+        - [Usage](#usage4)
+        - [Run](#run4)
+        - [Result](#result4)
+    - [Model Description](#model-description)
+        - [Performance](#performance)
+            - [Training Performance](#training-performance)
+            - [Evaluation Performance](#evaluation-performance)
+- [Description of Random State](#description-of-random-situation)
+- [ModelZoo Homepage](#modelzoo-homepage)
+
+<!-- /TOC -->
+
+## [Retinanet Description](#content)
+
+RetinaNet was proposed in "Focal Loss for Dense Object Detection" 2018 paper by Facebook AI Research. The biggest contribution of this paper is to propose Focal Loss to solve the problem of category imbalance, thus creating RetinaNet (One Stage target detection algorithm), a target detection network whose accuracy exceeds the classic Two Stage Faster-RCNN.
+
+[Paper](https://arxiv.org/pdf/1708.02002.pdf)
+Lin T Y , Goyal P , Girshick R , et al. Focal Loss for Dense Object Detection[C]// 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017:2999-3007.
+
+## [Model Architecture](#content)
+
+The overall network architecture of Retinanet is [here](https://arxiv.org/pdf/1708.02002.pdf)
+
+## [Dataset](#content)
+
+Dataset used (refer to paper): [COCO2017](https://cocodataset.org/#download)
+
+- Dataset size: 19.3G, 123287 pcs 80 classes of colored images
+    - [train](http://images.cocodataset.org/zips/train2017.zip): 19.3G, 118287 images
+    - [val](http://images.cocodataset.org/zips/val2017.zip): 814.3M, 5000 images
+- Data format: RGB
+
+>Note锛歍he data will be processed with src/dataset.py.
+
+## [Environment Requirements](#content)
+
+- Hardware (Ascend/GPU)
+    - Prepare hardware environment with Ascend or GPU.
+- Framework
+    - [MindSpore](https://www.mindspore.cn/install)
+- For more information about MindSpore, please check the resources below锛�
+    - [MindSpore 鏁欑▼](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
+
+- Training in ModelArts (If you want to run on ModelArts, you can refer to the following [modelarts documents](https://support.huaweicloud.com/modelarts/))
+
+    ```python
+    # Train with 8 cards on ModelArts
+    # (1) choose a or b
+    #       a. in default_config.yaml set "enable_modelarts=True"
+    #          in default_config.yaml set "distribute=True"
+    #          in default_config.yaml set "coco_root='/cache/data'"
+    #          in default_config.yaml set "epoch_size=500"
+    #          (optional)in default_config.yaml set "checkpoint_path='s3://dir_to_your_pretrained/'"
+    #          in default_config.yaml set other parameters
+    #       b. Set on the web page  "enable_modelarts=True"
+    #          Set on the web page "distribute=True"
+    #          Set on the web page "coco_root=/cache/data"
+    #          Set on the web page "epoch_size=500"
+    #          (optional)Set on the web page "checkpoint_path='s3://dir_to_your_pretrained/'"
+    #          Set on the web page other parameters
+    # (2) Prepare model code
+    # (3) If you choose to fine-tune your model, upload your pretrained model to the S3 bucket
+    # (4) choose a or b (recommended choice a)
+    #       a. First, compress the dataset into a ".zip" file.
+    #          Second, upload your compressed dataset to the S3 bucket (you can also upload the uncompressed dataset, but that may be slow.)
+    #       b. Upload the original dataset to the S3 bucket.
+    #          (Dataset conversion occurs during the training process, which takes more time. It will be re-transformed every time you train.)
+    # (5) Set your code path on the web page to "/path/retinanet"
+    # (6) Set the startup file on the web page as "train.py"
+    # (7) Set "training dataset", "training output file path", "job log path", etc. on the webpage
+    # (8) Create a training job
+    #
+    # Train with 1 card on ModelArts
+    # (1) choose a or b
+    #       a. in default_config.yaml set "enable_modelarts=True"
+    #          in default_config.yaml set "coco_root='/cache/data'"
+    #          in default_config.yaml set "epoch_size=500"
+    #          (optional)in default_config.yaml set "checkpoint_path='s3://dir_to_your_pretrained/'"
+    #          in default_config.yaml set other parameters
+    #       b. Set on the web page "enable_modelarts=True"
+    #          Set on the web page "coco_root='/cache/data'"
+    #          Set on the web page "epoch_size=500"
+    #          (optional)Set on the web page "checkpoint_path='s3://dir_to_your_pretrained/'"
+    #          Set on the web page other parameters
+    # (2) Prepare model code
+    # (3) If you choose to fine-tune your model, upload your pretrained model to the S3 bucket
+    # (4) choose a or b (recommended choice a)
+    #       a. First, compress the dataset into a ".zip" file.
+    #          Second, upload your compressed dataset to the S3 bucket (you can also upload the uncompressed dataset, but that may be slow.)
+    #       b. Upload the original dataset to the S3 bucket.
+    #          (Data set conversion occurs during the training process, which takes more time. It will be re-transformed every time you train.)
+    # (5) Set your code path on the web page to "/path/retinanet"
+    # (6) Set the startup file on the web page as "train.py"
+    # (7) Set "training dataset", "training output file path", "job log path", etc. on the webpage
+    # (8) Create a training job
+    #
+    # Eval on ModelArts
+    # (1) choose a or b
+    #       a. in default_config.yaml set "enable_modelarts=True"
+    #          in default_config.yaml set "checkpoint_path='s3://dir_to_your_trained_model/'"
+    #          in default_config.yaml set "mindrecord_dir='./MindRecord_COCO'"
+    #          in default_config.yaml set "coco_root='/cache/data'"
+    #          in default_config.yaml set other parameters
+    #       b. Set on the web page "enable_modelarts=True"
+    #          Set on the web page "checkpoint_path='s3://dir_to_your_trained_model/'"
+    #          Set on the web page "mindrecord_dir='./MindRecord_COCO'"
+    #          Set on the web page "coco_root='/cache/data'"
+    #          Set on the web page other parameters
+    # (2) Prepare model code
+    # (3) Upload your pretrained model to the S3 bucket
+    # (4) choose a or b (recommended choice a)
+    #       a. First, compress the dataset into a ".zip" file.
+    #          Second, upload your compressed dataset to the S3 bucket (you can also upload the uncompressed dataset, but that may be slow.)
+    #       b. Upload the original dataset to the S3 bucket.
+    #          (Data set conversion occurs during the training process, which takes more time. It will be re-transformed every time you train.)
+    # (5) Set your code path on the web page to "/path/retinanet"
+    # (6) Set the startup file on the web page as "eval.py"
+    # (7) Set "training dataset", "training output file path", "job log path", etc. on the webpage
+    # (8) Create a training job
+    ```
+
+- Export in ModelArts (If you want to run on ModelArts, you can refer to the following [modelarts documents](https://support.huaweicloud.com/modelarts/))
+
+    ```python
+    # (1) choose a or b
+    #       a. in default_config.yaml set "enable_modelarts=True"
+    #          in default_config.yaml set "file_name='retinanet'"
+    #          in default_config.yaml set "file_format='MINDIR'"
+    #          in base_config.yaml set "checkpoint_path='/The path of checkpoint in S3/'"
+    #          in base_config.yaml set other parameters
+    #       b. Set on the web page "enable_modelarts=True"
+    #          Set on the web page "file_name='retinanet'"
+    #          Set on the web page "file_format='MINDIR'"
+    #          Set on the web page "checkpoint_path='/The path of checkpoint in S3/'"
+    #          Set on the web page other parameters
+    # (2) Upload your pretrained model to the S3 bucket
+    # (3) Set your code path on the web page to "/path/retinanet"
+    # (4) Set the startup file on the web page as "export.py"
+    # (5) Set "training dataset", "training output file path", "job log path", etc. on the webpage
+    # (6) Create a training job
+    ```
+
+## [Script Description](#content)
+
+### [Script and Sample Code](#content)
+
+```text
+.
+鈹斺攢Retinanet_resnet152
+  鈹溾攢README.md
+  鈹溾攢README_CN.md
+  鈹溾攢ascend310_infer
+  鈹溾攢scripts
+    鈹溾攢run_single_train.sh                     # training on Ascend single card
+    鈹溾攢run_single_train_gpu.sh                 # training on GPU single card
+    鈹溾攢run_distribute_train.sh                 # training on Ascend multiple card
+    鈹溾攢run_distribute_train_gpu.sh             # training on GPU multiple card
+    鈹溾攢run_eval.sh                             # inference on Ascend
+    鈹溾攢run_eval_gpu.sh                         # inference on GPU
+    鈹斺攢run_infer_310.sh
+  鈹溾攢src
+    鈹溾攢backbone.py
+    鈹溾攢bottleneck.py
+    鈹溾攢dataset.py
+    鈹溾攢retinahead.py
+    鈹溾攢init_params.py
+    鈹溾攢lr_schedule.py
+    鈹溾攢coco_eval
+    鈹溾攢box_utils.py
+    鈹斺攢model_utils
+      鈹溾攢config.py
+      鈹溾攢device_adapter.py
+      鈹溾攢local_adapter.py
+      鈹斺攢moxing_adapter.py
+  鈹溾攢default_config.yaml
+  鈹溾攢export.py
+  鈹溾攢postprocess.py
+  鈹溾攢requirements.txt
+  鈹溾攢train.py
+  鈹斺攢eval.py
+```
+
+### [Script Parameters](#content)
+
+```text
+Main parameteres used in train.py and config.py:
+"img_shape": [640, 640],                                                                        # image size
+"num_retinanet_boxes": 76725,                                                                   # The total number of a priori boxes set
+"match_thershold": 0.5,
+"softnms_sigma": 0.5,
+"nms_thershold": 0.6,
+"min_score": 0.1,
+"max_boxes": 100,                                                                               # Maximum number of detection frames
+"global_step": 0,
+"lr_init": 1e-6,
+"lr_end_rate": 5e-3,                                                                            # The ratio of the final learning rate to the maximum learning rate
+"warmup_epochs1": 2,                                                                            # Number of cycles of the 1st stage warmup
+"warmup_epochs2": 5,                                                                            # Number of cycles of the 2nd stage warmup
+"warmup_epochs3": 23,                                                                           # Number of cycles of the 3d stage warmup
+"warmup_epochs4": 60,                                                                           # Number of cycles of the 4th stage warmup
+"warmup_epochs5": 160,                                                                          # Number of cycles of the 5th stage warmup
+"momentum": 0.9,                                                                                # momentum
+"weight_decay": 1.5e-4,
+"num_default": [9, 9, 9, 9, 9],                                                                 # The number of a priori boxes in a single grid
+"extras_out_channels": [256, 256, 256, 256, 256],                                               # Feature layer output channels
+"feature_size": [75, 38, 19, 10, 5],
+"aspect_ratios": [(0.5,1.0,2.0), (0.5,1.0,2.0), (0.5,1.0,2.0), (0.5,1.0,2.0), (0.5,1.0,2.0)],   # Priori box size change ratio
+"steps": ( 8, 16, 32, 64, 128),                                                                 # Priori box setting step size
+"anchor_size":(32, 64, 128, 256, 512),                                                          # A priori box size
+"prior_scaling": (0.1, 0.2),                                                                    # Used to adjust the ratio of regression and regression in loss
+"gamma": 2.0,                                                                                   # focal loss parameter
+"alpha": 0.75,                                                                                  # focal loss parameter
+"mindrecord_dir": "/opr/root/data/MindRecord_COCO",
+"coco_root": "/opr/root/data/",
+"train_data_type": "train2017",
+"val_data_type": "val2017",
+"instances_set": "annotations_trainval2017/annotations/instances_{}.json",
+"coco_classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+                 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
+                 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
+                 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
+                 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
+                 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
+                 'kite', 'baseball bat', 'baseball glove', 'skateboard',
+                 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
+                 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+                 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
+                 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
+                 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
+                 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
+                 'refrigerator', 'book', 'clock', 'vase', 'scissors',
+                 'teddy bear', 'hair drier', 'toothbrush'),
+"num_classes": 81,
+"voc_root": "",
+"voc_dir": "",
+"image_dir": "",
+"anno_path": "",
+"save_checkpoint": True,
+"save_checkpoint_epochs": 1,
+"keep_checkpoint_max":1,
+"save_checkpoint_path": "./model",                                                              # Path to save checkpoints
+"finish_epoch":0,                                                                               # number of epoch that have been run
+"checkpoint_path":"/opr/root/reretina/retinanet2/LOG0/model/retinanet-400_458.ckpt"             # checkpoint path for evaluation
+```
+
+### [Training process](#content)
+
+#### Usage
+
+You can use python or shell scripts for training. The usage of the shell script is as follows:
+
+- Ascend:
+
+```bash
+# data and the path to store the mindrecord file are set in default_config.yaml
+
+# before training convert dataset to MindRecord format:
+python train.py --only_create_dataset=True --run_platform="Ascend"
+
+# Eight-card parallel training example锛�
+# create RANK_TABLE_FILE
+bash run_distribute_train.sh DEVICE_NUM EPOCH_SIZE LR DATASET RANK_TABLE_FILE PRE_TRAINED(optional) PRE_TRAINED_EPOCH_SIZE(optional)
+
+# Single card training example锛�
+bash run_distribute_train.sh DEVICE_ID EPOCH_SIZE LR DATASET PRE_TRAINED(optional) PRE_TRAINED_EPOCH_SIZE(optional)
+```
+
+> Note: RANK_TABLE_FILE related reference materials see in this [link](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html),
+> for details on how to get device_ip check this [link](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools).
+
+- GPU:
+
+```bash
+# data and the path to store the mindrecord file are set in default_config.yaml
+
+# convert dataset to MindRecord format:
+python train.py --only_create_dataset=True --run_platform="GPU"
+
+# Eight-card parallel training example锛�
+bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
+
+# Single card training example锛�
+bash run_single_train_gpu.sh [DEVICE_ID] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
+```
+
+#### Run
+
+- Ascend:
+
+```bash
+# Ascend 8-card parallel training example (run in the retinanet directory):
+bash scripts/run_distribute_train.sh 8 500 0.1 coco scripts/rank_table_8pcs.json /dataset/retinanet-322_458.ckpt 322
+
+# Ascend single card training example (run in the retinanet directory):
+bash scripts/run_single_train.sh 0 500 0.1 coco /dataset/retinanet-322_458.ckpt 322
+```
+
+- GPU:
+
+```bash
+# Ascend 8-card parallel training example (run in the retinanet directory):
+bash run_distribute_train_gpu.sh 8 500 0.1 coco /dataset/retinanet-322_458.ckpt 322
+
+# Ascend single card training example (run in the retinanet directory):
+bash run_single_train_gpu.sh 0 500 0.1 coco /dataset/retinanet-322_458.ckpt 322
+```
+
+#### Result
+
+Paths are set in `default_config.yaml`. Checkpoints will be save in `./model`. The training log will be recorded to LOG/train.log锛宎n example of the training log is as follows:
+
+```text
+epoch: 117 step: 916, loss is 0.8391866
+lr:[0.025187]
+epoch time: 444315.944 ms, per step time: 485.061 ms
+epoch: 118 step: 916, loss is 1.070719
+lr:[0.025749]
+epoch time: 444288.450 ms, per step time: 485.031 ms
+epoch: 119 step: 916, loss is 1.1607553
+lr:[0.026312]
+epoch time: 444339.538 ms, per step time: 485.087 ms
+epoch: 120 step: 916, loss is 1.174742
+lr:[0.026874]
+epoch time: 444237.851 ms, per step time: 484.976 ms
+```
+
+### [Evaluation process](#content)
+
+#### Usage
+
+You can use python or shell scripts for training. The usage of the shell script is as follows:
+
+- Ascend
+
+```bash
+bash scripts/run_eval.sh [DATASET] [DEVICE_ID]
+```
+
+- GPU
+
+```bash
+bash run_eval_gpu.sh [DATASET] [DEVICE_ID] [CHECKPOINT_PATH]
+```
+
+#### Run
+
+- Ascend
+
+```bash
+bash scripts/run_eval.sh coco 0
+```
+
+- GPU
+
+```bash
+bash run_eval_gpu.sh coco 0 LOG/model/retinanet-500_610.ckpt
+```
+
+> checkpoint can be generated during training.
+
+#### Result
+
+The calculation results will be stored in the example path, which you can view in `eval.log`.
+
+```text
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.357
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.503
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.400
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.381
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.498
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.307
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.447
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.458
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.172
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.487
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.643
+
+========================================
+
+mAP: 0.3571988469737286
+```
+
+### [Model Export](#content)
+
+#### Usage
+
+Before exporting the model, modify the checkpoint_path configuration item in the `default_config.yaml` file. The value is the path of the checkpoint.
+
+```shell
+python export.py --file_name [RUN_PLATFORM] --file_format[EXPORT_FORMAT] --checkpoint_path [CHECKPOINT PATH]
+```
+
+`EXPORT_FORMAT` choose from ["AIR", "MINDIR"]
+
+#### Run
+
+```bash
+python export.py
+```
+
+### [Inference Process](#content)
+
+#### Usage
+
+Before inference, model needs to be exported to the Ascend 910 environment. Pictures with iscrowd set to true should be excluded. The image id after removal is saved in the ascend310_infer directory.
+You also need to modify the coco_root, val_data_type, and instances_set configuration items in the config.py file. The values are respectively taken as the directory of the coco data set, the directory name of the data set used for inference, and the annotation file used to calculate the accuracy after the inference is completed. The instances_set is spliced with val_data_type to ensure that the file is correct and exists.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
+```
+
+#### Run
+
+```shell
+bash run_infer_310.sh  [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
+```
+
+#### Result
+
+The result of the inference is saved in acc.log in the current directory, and the result similar to the following:
+
+```text
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.356
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.499
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.396
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.380
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.506
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.308
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.446
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.457
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.179
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.647
+mAP: 0.35625723922139957
+```
+
+## [Model Description](#content)
+
+### [Performance](#content)
+
+#### Training Performance
+
+| Parameters                 | Ascend (8 pcs)                                                                         | GPU Tesla V100 (1 pcs)                                                                 | GPU Tesla V100 (8 pcs)                                                                 |
+| -------------------------- |----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|
+| Model                      | Retinanet-resnet-152                                                                   | Retinanet-resnet-152                                                                   | Retinanet-resnet-152                                                                   |
+| Environment                | Huawei cloud Modelarts                                                                 | Ubuntu 18.04.6, Tesla V100, CPU 2.7 GHz, 56 cores, RAM 504 GB                          | Ubuntu 18.04.6, Tesla V100 (8 pcs), CPU 2.7 GHz, 56 cores, RAM 504 GB                  |
+| Uploaded Date              | 10/03/2021                                                                             | 27/12/2021                                                                             | 27/12/2021                                                                             |
+| MindSpore version          | 1.0.1                                                                                  | 1.6.0                                                                                  | 1.6.0                                                                                  |
+| Dataset                    | 118287 images                                                                          | 118287 images                                                                          | 118287 images                                                                          |
+| Training Parameters        | batch_size=16                                                                          | batch_size=12, epochs=200, lr=0.1, steps_per_epoch=9772                                | batch_size=12*8, epochs=180, lr=0.1, steps_per_epoch=1221                                |
+| Other training parameters  | default_config.yaml                                                                    | default_config.yaml                                                                    | default_config.yaml                                                                    |
+| Optimizer                  | Momentum                                                                               | Momentum                                                                               | Momentum                                                                               |
+| Loss function              | Focal loss                                                                             | Focal loss                                                                             | Focal loss                                                                             |
+| Final loss                 | 0.69                                                                                   | 0.84                                                                                   | 0.84                                                                                   |
+| Speed                      |                                                                                        | 860 ms/step                                                                            | 1205 ms/step                                                                           |
+| Total training time (8p)   | 41h 32m 20s                                                                            | 440h                                                                                   | 72h                                                                                    |
+| Script                     | [Link](https://gitee.com/mindspore/models/tree/master/research/cv/retinanet_resnet152) | [Link](https://gitee.com/mindspore/models/tree/master/research/cv/retinanet_resnet152) | [Link](https://gitee.com/mindspore/models/tree/master/research/cv/retinanet_resnet152) |
+
+#### Evaluation Performance
+
+| Parameters        | Ascend                      | GPU                                                           |
+|-------------------| --------------------------- |---------------------------------------------------------------|
+| Model             | Retinanet-resnet-152        | Retinanet-resnet-152                                          |
+| Environment       | Huawei cloud Modelarts      | Ubuntu 18.04.6, Tesla V100, CPU 2.7 GHz, 56 cores, RAM 504 GB |
+| Uploaded Date     | 10/03/2021                  | 27/12/2021                                                    |
+| MindSpore version | 1.0.1                       | 1.6.0                                                         |
+| Dataset           | 5k images                   | 5k images                                                     |
+| Batch_size        | 1                           | 1                                                             |
+| Accuracy          | mAP[0.3571]                 |                                                               |
+| Total time        | 12 min and 03 seconds       | 12 min                                                        |
+
+# [Description of Random State](#content)
+
+Random seed is set in function `create_dataset` of `dataset.py` script. It's also set in the `train.py` script.
+
+# [ModelZoo Homepage](#content)
+
+Please check the official [homepage](https://gitee.com/mindspore/models).
diff --git a/research/cv/retinanet_resnet152/README_CN.md b/research/cv/retinanet_resnet152/README_CN.md
index 14fd64a3796aa48aee3ec255ca131f72e09158c4..1dda1c52d4e38e6877f87ae0a08dc3b2c47e4279 100644
--- a/research/cv/retinanet_resnet152/README_CN.md
+++ b/research/cv/retinanet_resnet152/README_CN.md
@@ -2,6 +2,8 @@
 
 <!-- TOC -->
 
+[View English](./README.md)
+
 - [Retinanet 鎻忚堪](#retinanet鎻忚堪)
 - [妯″瀷鏋舵瀯](#妯″瀷鏋舵瀯)
 - [鏁版嵁闆哴(#鏁版嵁闆�)
@@ -48,19 +50,14 @@ Retinanet鐨勬暣浣撶綉缁滄灦鏋勫涓嬫墍绀猴細
 
 ## [鏁版嵁闆哴(#content)
 
-鏁版嵁闆嗗彲鍙傝€冩枃鐚�.
-
-MSCOCO2017
+鏁版嵁闆嗗彲鍙傝€冩枃鐚�. [COCO2017](https://cocodataset.org/#download)
 
 - 鏁版嵁闆嗗ぇ灏�: 19.3G, 123287寮�80绫诲僵鑹插浘鍍�
-
     - 璁粌:19.3G, 118287寮犲浘鐗�
-
     - 娴嬭瘯:1814.3M, 5000寮犲浘鐗�
+- 鏁版嵁鏍煎紡:RGB鍥惧儚
 
-- 鏁版嵁鏍煎紡:RGB鍥惧儚.
-
-    - 娉ㄦ剰锛氭暟鎹皢鍦╯rc/dataset.py 涓澶勭悊
+>娉ㄦ剰锛氭暟鎹皢鍦╯rc/dataset.py 涓澶勭悊
 
 ## [鐜瑕佹眰](#content)
 
@@ -175,7 +172,7 @@ MSCOCO2017
 
 ### [鑴氭湰鍜岀ず渚嬩唬鐮乚(#content)
 
-```shell
+```text
 .
 鈹斺攢Retinanet_resnet152
   鈹溾攢README.md
@@ -201,12 +198,11 @@ MSCOCO2017
   鈹溾攢default_config.yaml                       # 鍙傛暟閰嶇疆
   鈹溾攢train.py                                  # 缃戠粶璁粌鑴氭湰
   鈹斺攢eval.py                                   # 缃戠粶鎺ㄧ悊鑴氭湰
-
 ```
 
 ### [鑴氭湰鍙傛暟](#content)
 
-```python
+```text
 鍦╰rain.py鍜宒efault_config.yaml鑴氭湰涓娇鐢ㄥ埌鐨勪富瑕佸弬鏁版槸:
 "img_shape": [640, 640],                                                                        # 鍥惧儚灏哄
 "num_retinanet_boxes": 76725,                                                                   # 璁剧疆鐨勫厛楠屾鎬绘暟
@@ -275,52 +271,65 @@ MSCOCO2017
 
 - Ascend:
 
-```璁粌
-# 鍏崱骞惰璁粌绀轰緥锛�
+```bash
+# data鍜屽瓨鍌╩indrecord鏂囦欢鐨勮矾寰勫湪default_config.yaml閲岃缃�
 
-鍒涘缓 RANK_TABLE_FILE
+# 璁粌浠ュ墠锛� 璇疯繍琛岋細
+python train.py --only_create_dataset=True --run_platform="Ascend"
+
+# 鍏崱骞惰璁粌绀轰緥锛�
+# 鍒涘缓 RANK_TABLE_FILE
 bash run_distribute_train.sh DEVICE_NUM EPOCH_SIZE LR DATASET RANK_TABLE_FILE PRE_TRAINED(optional) PRE_TRAINED_EPOCH_SIZE(optional)
 
 # 鍗曞崱璁粌绀轰緥锛�
-
 bash run_distribute_train.sh DEVICE_ID EPOCH_SIZE LR DATASET PRE_TRAINED(optional) PRE_TRAINED_EPOCH_SIZE(optional)
-
 ```
 
-> 娉ㄦ剰:
+> 娉ㄦ剰: RANK_TABLE_FILE鐩稿叧鍙傝€冭祫鏂欒[閾炬帴](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html),
+> 鑾峰彇device_ip鏂规硶璇﹁[閾炬帴](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools).
 
-  RANK_TABLE_FILE鐩稿叧鍙傝€冭祫鏂欒[閾炬帴](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html), 鑾峰彇device_ip鏂规硶璇﹁[閾炬帴](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools).
+- GPU:
 
-#### 杩愯
+```bash
+# data鍜屽瓨鍌╩indrecord鏂囦欢鐨勮矾寰勫湪default_config.yaml閲岃缃�
 
-``` 杩愯
-# 璁粌绀轰緥
+# 璁粌浠ュ墠锛� 璇疯繍琛岋細
+python train.py --only_create_dataset=True --run_platform="GPU"
 
-  python:
-    data鍜屽瓨鍌╩indrecord鏂囦欢鐨勮矾寰勫湪config閲岃缃�
+# 鍏崱骞惰璁粌绀轰緥锛�
+bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
 
-      # 鍗曞崱璁粌绀轰緥锛�
+# 鍗曞崱璁粌绀轰緥锛�
+bash run_single_train_gpu.sh [DEVICE_ID] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
+```
 
-      python train.py
-  shell:
-      Ascend:
+#### 杩愯
+
+- Ascend
 
-      # 鍏崱骞惰璁粌绀轰緥(鍦╮etinanet鐩綍涓嬭繍琛�)锛�
+```bash
+# 鍏崱骞惰璁粌绀轰緥(鍦╮etinanet鐩綍涓嬭繍琛�)锛�
+bash scripts/run_distribute_train.sh 8 500 0.1 coco scripts/rank_table_8pcs.json /dataset/retinanet-322_458.ckpt 322
 
-      bash scripts/run_distribute_train.sh 8 500 0.1 coco RANK_TABLE_FILE(鍒涘缓鐨凴ANK_TABLE_FILE鐨勫湴鍧€) PRE_TRAINED(棰勮缁僣heckpoint鍦板潃) PRE_TRAINED_EPOCH_SIZE锛堥璁粌EPOCH澶у皬锛�
-      渚嬪锛歜ash scripts/run_distribute_train.sh 8 500 0.1 coco scripts/rank_table_8pcs.json /dataset/retinanet-322_458.ckpt 322
+# 鍗曞崱璁粌绀轰緥(鍦╮etinanet鐩綍涓嬭繍琛�)锛�
+bash scripts/run_single_train.sh 0 500 0.1 coco /dataset/retinanet-322_458.ckpt 322
+```
 
-      # 鍗曞崱璁粌绀轰緥(鍦╮etinanet鐩綍涓嬭繍琛�)锛�
+- GPU
 
-      bash scripts/run_single_train.sh 0 500 0.1 coco /dataset/retinanet-322_458.ckpt 322
+```bash
+# 鍏崱骞惰璁粌绀轰緥(鍦╮etinanet鐩綍涓嬭繍琛�)锛�
+bash run_distribute_train_gpu.sh 8 500 0.1 coco /dataset/retinanet-322_458.ckpt 322
 
+# 鍗曞崱璁粌绀轰緥(鍦╮etinanet鐩綍涓嬭繍琛�)锛�
+bash run_single_train_gpu.sh 0 500 0.1 coco /dataset/retinanet-322_458.ckpt 322
 ```
 
 #### 缁撴灉
 
-璁粌缁撴灉灏嗗瓨鍌ㄥ湪绀轰緥璺緞涓€俢heckpoint灏嗗瓨鍌ㄥ湪 `./model` 璺緞涓嬶紝璁粌鏃ュ織灏嗚璁板綍鍒� `./log.txt` 涓紝璁粌鏃ュ織閮ㄥ垎绀轰緥濡備笅锛�
+璺緞鍦╜default_config.yaml`閲岃缃€俢heckpoint灏嗗瓨鍌ㄥ湪 `./model` 璺緞涓嬶紝璁粌鏃ュ織灏嗚璁板綍鍒� `./train.log` 涓紝璁粌鏃ュ織閮ㄥ垎绀轰緥濡備笅锛�
 
-``` 璁粌鏃ュ織
+```text
 epoch: 117 step: 916, loss is 0.8391866
 lr:[0.025187]
 epoch time: 444315.944 ms, per step time: 485.061 ms
@@ -337,33 +346,43 @@ epoch time: 444237.851 ms, per step time: 484.976 ms
 
 ### [璇勪及杩囩▼](#content)
 
-#### 鐢� 娉�
+#### 鐢ㄦ硶
 
 鎮ㄥ彲浠ヤ娇鐢╬ython鎴杝hell鑴氭湰杩涜璁粌銆俿hell鑴氭湰鐨勭敤娉曞涓�:
 
-```eval
+- Ascend
+
+```bash
 bash scripts/run_eval.sh [DATASET] [DEVICE_ID]
 ```
 
-#### 杩� 琛�
+- GPU
+
+```bash
+bash run_eval_gpu.sh [DATASET] [DEVICE_ID] [CHECKPOINT_PATH]
+```
+
+#### 杩愯
+
+- Ascend
+
+```bash
+bash scripts/run_eval.sh coco 0
+```
 
-```eval杩愯
-# 楠岃瘉绀轰緥
+- GPU
 
-  python:
-      Ascend: python eval.py
-  checkpoint 鐨勮矾寰勫湪config閲岃缃�
-  shell:
-      Ascend: bash scripts/run_eval.sh coco 0
+```bash
+bash run_eval_gpu.sh coco 0 LOG/model/retinanet-500_610.ckpt
 ```
 
 > checkpoint 鍙互鍦ㄨ缁冭繃绋嬩腑浜х敓.
 
-#### 缁� 鏋�
+#### 缁撴灉
 
 璁＄畻缁撴灉灏嗗瓨鍌ㄥ湪绀轰緥璺緞涓紝鎮ㄥ彲浠ュ湪 `eval.log` 鏌ョ湅.
 
-``` mAP
+```text
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.357
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.503
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.400
@@ -386,7 +405,7 @@ mAP: 0.3571988469737286
 
 #### 鐢ㄩ€�
 
-瀵煎嚭妯″瀷鍓嶈淇敼default_config.yaml鏂囦欢涓殑checkpoint_path閰嶇疆椤癸紝鍊间负checkpoint鐨勮矾寰勩€�
+瀵煎嚭妯″瀷鍓嶈淇敼`default_config.yaml`鏂囦欢涓殑checkpoint_path閰嶇疆椤癸紝鍊间负checkpoint鐨勮矾寰勩€�
 
 ```shell
 python export.py --file_name [RUN_PLATFORM] --file_format[EXPORT_FORMAT] --checkpoint_path [CHECKPOINT PATH]
@@ -396,13 +415,13 @@ python export.py --file_name [RUN_PLATFORM] --file_format[EXPORT_FORMAT] --check
 
 #### 杩愯鏂瑰紡
 
-```杩愯
+```python
 python export.py
 ```
 
 ### [鎺ㄧ悊杩囩▼](#content)
 
-#### 鐢� 閫�
+#### 鐢ㄩ€�
 
 鍦ㄦ帹鐞嗕箣鍓嶉渶瑕佸湪鏄囪吘910鐜涓婂畬鎴愭ā鍨嬬殑瀵煎嚭銆傛帹鐞嗘椂瑕佸皢iscrowd涓簍rue鐨勫浘鐗囨帓闄ゆ帀銆傚湪ascend310_infer鐩綍涓嬩繚瀛樹簡鍘绘帓闄ゅ悗鐨勫浘鐗噄d銆�
 杩橀渶瑕佷慨鏀筪efault_config.yaml鏂囦欢涓殑coco_root銆乿al_data_type銆乮nstances_set閰嶇疆椤癸紝鍊煎垎鍒彇coco鏁版嵁闆嗙殑鐩綍锛屾帹鐞嗘墍鐢ㄦ暟鎹泦鐨勭洰褰曞悕绉帮紝鎺ㄧ悊瀹屾垚鍚庤绠楃簿搴︾敤鐨刟nnotation鏂囦欢锛宨nstances_set鏄敤val_data_type鎷兼帴璧锋潵鐨勶紝瑕佷繚璇佹枃浠舵纭苟涓斿瓨鍦ㄣ€�
@@ -414,7 +433,7 @@ bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
 
 #### 杩愯鍛戒护
 
-```杩愯
+```shell
 bash run_infer_310.sh  [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
 ```
 
@@ -422,7 +441,7 @@ bash run_infer_310.sh  [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
 
 鎺ㄧ悊鐨勭粨鏋滀繚瀛樺湪褰撳墠鐩綍涓嬶紝鍦╝cc.log鏃ュ織鏂囦欢涓彲浠ユ壘鍒扮被浼间互涓嬬殑缁撴灉銆�
 
-```mAP
+```text
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.356
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.499
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.396
@@ -444,34 +463,34 @@ mAP: 0.35625723922139957
 
 #### 璁粌鎬ц兘
 
-| 鍙傛暟                        | Ascend                                |
-| -------------------------- | ------------------------------------- |
-| 妯″瀷鍚嶇О                    | Retinanet                             |
-| 杩愯鐜                    | 鍗庝负浜� Modelarts                      |
-| 涓婁紶鏃堕棿                    | 10/03/2021                           |
-| MindSpore 鐗堟湰             | 1.0.1                                 |
-| 鏁版嵁闆�                      | 123287 寮犲浘鐗�                          |
-| Batch_size                 | 16                                   |
-| 璁粌鍙傛暟                    | default_config.yaml                   |
-| 浼樺寲鍣�                      | Momentum                              |
-| 鎹熷け鍑芥暟                    | Focal loss                            |
-| 鏈€缁堟崯澶�                    | 0.69                               |
-| 绮剧‘搴� (8p)                 | mAP[0.3571]            |
-| 璁粌鎬绘椂闂� (8p)             | 41h32m20s                        |
+| 鍙傛暟                        | Ascend (8pcs)        | GPU Tesla V100 (1 pcs)  | GPU Tesla V100 (8 pcs) |
+| -------------------------- | --------------------- | ---- | ---- |
+| 妯″瀷鍚嶇О                    | Retinanet-resnet-152  | Retinanet-resnet-152 | Retinanet-resnet-152 |
+| 杩愯鐜                    | 鍗庝负浜� Modelarts       | Ubuntu 18.04.6, Tesla V100, CPU 2.7 GHz, 56 cores, RAM 504 GB | Ubuntu 18.04.6, Tesla V100 (8 pcs), CPU 2.7 GHz, 56 cores, RAM 504 GB |
+| 涓婁紶鏃堕棿                    | 10/03/2021            | 27/12/2021 | 27/12/2021 |
+| MindSpore 鐗堟湰             | 1.0.1                  | 1.6.0 | 1.6.0 |
+| 鏁版嵁闆�                      | 123287 寮犲浘鐗�          | 123287 寮犲浘鐗� | 123287 寮犲浘鐗� |
+| 璁粌鍙傛暟                    | 16                    | batch_size=12, epochs=180, lr=0.1, steps_per_epoch=1221 | batch_size=12*8, epochs=180, lr=0.1, steps_per_epoch=1221 |
+| 鍏朵粬璁粌鍙傛暟                 | default_config.yaml   | default_config.yaml | default_config.yaml |
+| 浼樺寲鍣�                      | Momentum              | Momentum | Momentum |
+| 鎹熷け鍑芥暟                    | Focal loss            | Focal loss | Focal loss |
+| 鏈€缁堟崯澶�                    | 0.69                 | 0.84 | 0.84 |
+| 閫熷害 (8p)                  |                      | 860 姣/姝� | 1205 姣/姝� |
+| 璁粌鎬绘椂闂� (8p)             | 41h32m20s             | 440灏忔椂 | 72灏忔椂 |
 | 鑴氭湰                       | [Retianet script](https://gitee.com/mindspore/models/tree/master/research/cv/retinanet_resnet152) |
 
 #### 鎺ㄧ悊鎬ц兘
 
-| 鍙傛暟                 | Ascend                      |
-| ------------------- | :-------------------------- |
-| 妯″瀷鍚嶇О             | Retinanet                    |
-| 杩愯鐜             | 鍗庝负浜� Modelarts             |
-| 涓婁紶鏃堕棿             | 10/03/2021                 |
-| MindSpore 鐗堟湰      | 1.0.1                        |
-| 鏁版嵁闆�              | 5k 寮犲浘鐗�                   |
-| Batch_size          | 1                          |
-| 绮剧‘搴�              | mAP[0.3571]                  |
-| 鎬绘椂闂�              | 12m3s       |
+| 鍙傛暟                 | Ascend                      | GPU |
+| ------------------- | :-------------------------- | --- |
+| 妯″瀷鍚嶇О             | Retinanet-resnet-152        | Retinanet-resnet-152 |
+| 杩愯鐜             | 鍗庝负浜� Modelarts             | Ubuntu 18.04.6, Tesla V100, CPU 2.7 GHz, 56 cores, RAM 504 GB |
+| 涓婁紶鏃堕棿             | 10/03/2021                 | 27/12/2021 |
+| MindSpore 鐗堟湰      | 1.0.1                        | 1.6.0 |
+| 鏁版嵁闆�              | 5k 寮犲浘鐗�                   | 5k 寮犲浘鐗� |
+| Batch_size          | 1                          | 1 |
+| 绮剧‘搴�              | mAP[0.3571]                |  |
+| 鎬绘椂闂�              | 12m3s       | 12m |
 
 # [闅忔満鎯呭喌鐨勬弿杩癩(#鍐呭)
 
diff --git a/research/cv/retinanet_resnet152/default_config.yaml b/research/cv/retinanet_resnet152/default_config.yaml
index ed323a5657fbe4aa4089caac73fd21ca28375073..fb6a952a655db55b807246036ec4e359a1b334cf 100644
--- a/research/cv/retinanet_resnet152/default_config.yaml
+++ b/research/cv/retinanet_resnet152/default_config.yaml
@@ -49,8 +49,8 @@ gamma: 2.0
 alpha: 0.75
 
 # `mindrecord_dir` and `coco_root` are better to use absolute path.
-mindrecord_dir: "/cache/train/MindRecord_COCO"
-coco_root: "/cache/data"
+mindrecord_dir: "/data/Datasets/COCO2017/MindRecord_COCO"
+coco_root: "/data/Datasets/COCO2017"
 train_data_type: "train2017"
 val_data_type: "val2017"
 instances_set: "annotations/instances_{}.json"
@@ -78,7 +78,7 @@ voc_dir: ""
 image_dir: ""
 anno_path: ""
 save_checkpoint: True
-keep_checkpoint_max: 30
+keep_checkpoint_max: 50
 save_checkpoint_path: "./model"
 finish_epoch: 0
 checkpoint_path: "/cache/train/model/retinanet-10_916.ckpt"
@@ -91,7 +91,7 @@ device_num: 1
 lr: 0.1
 mode: "sink"
 dataset: "coco"
-epoch_size: 500
+epoch_size: 180
 batch_size: 16
 pre_trained: ''
 pre_trained_epoch_size: 0
@@ -126,7 +126,7 @@ pre_trained_epoch_size: 'Pretrained epoch size.'
 save_checkpoint_epochs: 'Save checkpoint epochs, default is 1.'
 loss_scale: 'Loss scale, default is 1024.'
 filter_weight: 'Filter weight parameters, default is False.'
-run_platform: 'Run platform, only support Ascend in training and export, only support Ascend and GPU in evaling.'
+run_platform: 'Run platform, only support Ascend and GPU.'
 file_format: 'file format'
 file_name: 'output file name.'
 result_path: 'result file path.'
@@ -139,5 +139,5 @@ train_url: 'Training output url for obs'
 data_path: 'Dataset path for local'
 
 ---
-run_platform: ['Ascend']
+run_platform: ['Ascend', 'GPU']
 file_format: ["AIR", "MINDIR"]
diff --git a/research/cv/retinanet_resnet152/eval.py b/research/cv/retinanet_resnet152/eval.py
index 5910c4e4b14c62f65aa7345cf76034bccd6e0650..ae27c035157eee176a7ec66192870be8f0f677d3 100644
--- a/research/cv/retinanet_resnet152/eval.py
+++ b/research/cv/retinanet_resnet152/eval.py
@@ -38,6 +38,7 @@ def retinanet_eval(dataset_path, ckpt_path):
     net = retinahead(backbone, config)
     net = retinanetInferWithDecoder(net, Tensor(default_boxes), config)
     print("Load Checkpoint!")
+    print("ckpt_path: ", ckpt_path)
     param_dict = load_checkpoint(ckpt_path)
     net.init_parameters_data()
     load_param_into_net(net, param_dict)
@@ -77,6 +78,7 @@ def modelarts_process():
         config.coco_root = os.path.join(config.coco_root, config.modelarts_dataset_unzip_name)
         print(os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
 
+
 @moxing_wrapper(pre_process=modelarts_process)
 def eval_retinanet_resnet152():
     """ eval_retinanet_resnet152 """
@@ -115,5 +117,6 @@ def eval_retinanet_resnet152():
     print("Start Eval!")
     retinanet_eval(mindrecord_file, config.checkpoint_path)
 
+
 if __name__ == '__main__':
     eval_retinanet_resnet152()
diff --git a/research/cv/retinanet_resnet152/export.py b/research/cv/retinanet_resnet152/export.py
index 032d62d074dac0a738cc08b8a3af0814ec734549..8a3f66e8621b6f54f69ec233500e9e1bfc3ed435 100644
--- a/research/cv/retinanet_resnet152/export.py
+++ b/research/cv/retinanet_resnet152/export.py
@@ -28,6 +28,7 @@ from src.backbone import resnet152
 def modelarts_process():
     pass
 
+
 @moxing_wrapper(pre_process=modelarts_process)
 def export_retinanet_resnet152():
     """ export_retinanet_resnet152 """
@@ -44,5 +45,6 @@ def export_retinanet_resnet152():
     input_data = Tensor(np.zeros(shape), mstype.float32)
     export(net, input_data, file_name=config.file_name, file_format=config.file_format)
 
+
 if __name__ == '__main__':
     export_retinanet_resnet152()
diff --git a/research/cv/retinanet_resnet152/postprocess.py b/research/cv/retinanet_resnet152/postprocess.py
index f3994b894bb5ed7970c7c1ed1985dcf03d4f90c0..1e24b3e823a3bf49961e505a7f0a417d55784249 100644
--- a/research/cv/retinanet_resnet152/postprocess.py
+++ b/research/cv/retinanet_resnet152/postprocess.py
@@ -30,10 +30,12 @@ def get_pred(result_path, img_id):
     scores = np.fromfile(scores_file, dtype=np.float32).reshape(76725, config.num_classes)
     return boxes, scores
 
+
 def get_img_size(file_name):
     img = Image.open(file_name)
     return img.size
 
+
 def get_img_id(img_id_file):
     f = open(img_id_file)
     lines = f.readlines()
@@ -44,6 +46,7 @@ def get_img_id(img_id_file):
 
     return ids
 
+
 def cal_acc(result_path, img_path, img_id_file):
     """Calculate acc"""
     ids = get_img_id(img_id_file)
@@ -66,5 +69,6 @@ def cal_acc(result_path, img_path, img_id_file):
     mAP = metrics(pred_data)
     print(f"mAP: {mAP}")
 
+
 if __name__ == '__main__':
     cal_acc(config.result_path, config.img_path, config.img_id_file)
diff --git a/research/cv/retinanet_resnet152/requirements.txt b/research/cv/retinanet_resnet152/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..99f22cd1258a69ec92127f440e89b7fe13da59a0
--- /dev/null
+++ b/research/cv/retinanet_resnet152/requirements.txt
@@ -0,0 +1 @@
+pycocotools
diff --git a/research/cv/retinanet_resnet152/scripts/run_distribute_train.sh b/research/cv/retinanet_resnet152/scripts/run_distribute_train.sh
index 7329ad5e5a62af5b7131f76518f4a5453d2d8e62..3629c3b02f4e1e96686f2bdd06a5f43b12514041 100644
--- a/research/cv/retinanet_resnet152/scripts/run_distribute_train.sh
+++ b/research/cv/retinanet_resnet152/scripts/run_distribute_train.sh
@@ -56,9 +56,11 @@ do
     export RANK_ID=$i
     echo "start training for rank $i, device $DEVICE_ID"
     env > env.log
+
     if [ $# == 5 ]
     then
         python train.py  \
+        --run_platform="Ascend" \
         --distribute=True  \
         --lr=$LR \
         --dataset=$DATASET \
@@ -70,6 +72,7 @@ do
     if [ $# == 7 ]
     then
         python train.py  \
+        --run_platform="Ascend" \
         --distribute=True  \
         --lr=$LR \
         --dataset=$DATASET \
diff --git a/research/cv/retinanet_resnet152/scripts/run_distribute_train_gpu.sh b/research/cv/retinanet_resnet152/scripts/run_distribute_train_gpu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c32b540ee20d23203f0d6a1bb8e580699971ebe2
--- /dev/null
+++ b/research/cv/retinanet_resnet152/scripts/run_distribute_train_gpu.sh
@@ -0,0 +1,77 @@
+#!/bin/bash
+# Copyright 2022 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "sh run_distribute_train_gpu.sh DEVICE_NUM EPOCH_SIZE LR DATASET RANK_TABLE_FILE PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
+echo "for example: sh run_distribute_train_gpu.sh 8 500 0.1 coco /data/hccl.json /opt/retinanet-500_458.ckpt(optional) 200(optional)"
+echo "It is better to use absolute path."
+echo "================================================================================================================="
+
+if [ $# != 4 ] && [ $# != 6 ]
+then
+    echo "Usage: sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] \
+[PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)"
+    exit 1
+fi
+
+echo "After running the script, the network runs in the background. The log will be generated in LOG/train_log.txt"
+
+export DEVICE_NUM=$1
+EPOCH_SIZE=$2
+LR=$3
+DATASET=$4
+PRE_TRAINED=$5
+PRE_TRAINED_EPOCH_SIZE=$6
+
+rm -rf LOG
+mkdir ./LOG
+cp ../*.py ./LOG
+cp ../*.yaml ./LOG
+cp -r ../src ./LOG
+cd ./LOG || exit
+
+echo "start training on GPU $DEVICE_NUM devices"
+env > env.log
+
+if [ $# == 4 ]
+then
+  mpirun -n $DEVICE_NUM --output-filename log_output --merge-stderr-to-stdout \
+  python train.py  \
+  --run_platform="GPU" \
+  --batch_size=12 \
+  --distribute=True  \
+  --lr=$LR \
+  --dataset=$DATASET \
+  --device_num=$DEVICE_NUM  \
+  --epoch_size=$EPOCH_SIZE > train_log.txt 2>&1 &
+fi
+
+if [ $# == 6 ]
+then
+  mpirun -n $DEVICE_NUM --output-filename log_output --merge-stderr-to-stdout \
+  python train.py  \
+  --run_platform="GPU" \
+  --batch_size=12 \
+  --distribute=True  \
+  --lr=$LR \
+  --dataset=$DATASET \
+  --device_num=$DEVICE_NUM  \
+  --pre_trained=$PRE_TRAINED \
+  --pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
+  --epoch_size=$EPOCH_SIZE > train_log.txt 2>&1 &
+fi
+cd ..
\ No newline at end of file
diff --git a/research/cv/retinanet_resnet152/scripts/run_eval.sh b/research/cv/retinanet_resnet152/scripts/run_eval.sh
index 615828f919c6b0ed6132336de32e3da61d095dfc..2775ca224d091ba46f3c681ae41f331a81efb437 100644
--- a/research/cv/retinanet_resnet152/scripts/run_eval.sh
+++ b/research/cv/retinanet_resnet152/scripts/run_eval.sh
@@ -44,7 +44,9 @@ cp -r ./src ./eval$2
 cd ./eval$2 || exit
 env > env.log
 echo "start inferring for device $DEVICE_ID"
+
 python eval.py \
+    --run_platform="Ascend" \
     --dataset=$DATASET \
     --device_id=$2 > log.txt 2>&1 &
 cd ..
\ No newline at end of file
diff --git a/research/cv/retinanet_resnet152/scripts/run_eval_gpu.sh b/research/cv/retinanet_resnet152/scripts/run_eval_gpu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..af0a2e1eb4e41c4aab694929076a09be92bb8302
--- /dev/null
+++ b/research/cv/retinanet_resnet152/scripts/run_eval_gpu.sh
@@ -0,0 +1,80 @@
+#!/bin/bash
+# Copyright 2022 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 [ $# != 2 ] && [ $# != 3 ]
+then
+    echo "Usage: sh run_eval_gpu.sh [DATASET] [DEVICE_ID] [CHECKPOINT_PATH](optional)"
+exit 1
+fi
+
+DATASET=$1
+echo $DATASET
+
+export DEVICE_NUM=1
+export DEVICE_ID=$2
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+get_real_path() {
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+if [ $# == 3 ]
+then
+    CHECKPOINT_PATH=$(get_real_path $3)
+    if [ ! -f $CHECKPOINT_PATH ]
+    then
+        echo "error: CHECKPOINT_PATH=$CHECKPOINT_PATH is not a file"
+    exit 1
+    fi
+fi
+
+if [ -d "eval$2" ];
+then
+    rm -rf ./eval$2
+fi
+
+mkdir ./eval$2
+cp ../*.py ./eval$2
+cp ../*.yaml ./eval$2
+cp -r ../src ./eval$2
+cd ./eval$2 || exit
+
+env > env.log
+echo "start inferring for device $DEVICE_ID"
+
+if [ $# == 2 ]
+then
+  python eval.py \
+      --run_platform="GPU" \
+      --dataset=$DATASET \
+      --device_id=$2 > eval_log.txt 2>&1 &
+fi
+
+if [ $# == 3 ]
+then
+  python eval.py \
+      --run_platform="GPU" \
+      --dataset=$DATASET \
+      --checkpoint_path=$CHECKPOINT_PATH \
+      --device_id=$2 > eval_log.txt 2>&1 &
+fi
+
+cd ..
\ No newline at end of file
diff --git a/research/cv/retinanet_resnet152/scripts/run_single_train.sh b/research/cv/retinanet_resnet152/scripts/run_single_train.sh
index 27891cedcd09492b0ffbc5a2cd742d749840c98e..745fe7f0db65140b81c32137cbd6a6ae986eaf52 100644
--- a/research/cv/retinanet_resnet152/scripts/run_single_train.sh
+++ b/research/cv/retinanet_resnet152/scripts/run_single_train.sh
@@ -38,6 +38,8 @@ export DEVICE_ID=$1
 EPOCH_SIZE=$2
 LR=$3
 DATASET=$4
+PRE_TRAINED=$5
+PRE_TRAINED_EPOCH_SIZE=$6
 
 rm -rf LOG$1
 mkdir ./LOG$1
@@ -45,13 +47,33 @@ cp ./*.py ./LOG$1
 cp ./*.yaml ./LOG$1
 cp -r ./src ./LOG$1
 cd ./LOG$1 || exit
+
 echo "start training for device $1"
 env > env.log
-python train.py  \
---distribute=False  \
---lr=$LR \
---dataset=$DATASET \
---device_num=1  \
---device_id=$DEVICE_ID  \
---epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
+
+if [ $# == 4 ]
+then
+      python train.py  \
+      --run_platform="Ascend" \
+      --distribute=False  \
+      --lr=$LR \
+      --dataset=$DATASET \
+      --device_num=1  \
+      --device_id=$DEVICE_ID  \
+      --epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
+fi
+
+if [ $# == 6 ]
+then
+      python train_retinanet.py  \
+      --run_platform="Ascend" \
+      --distribute=False  \
+      --lr=$LR \
+      --dataset=$DATASET \
+      --device_num=1  \
+      --device_id=$DEVICE_ID  \
+      --pre_trained=$PRE_TRAINED \
+      --pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
+      --epoch_size=$EPOCH_SIZE > train_log.txt 2>&1 &
+fi
 cd ../
diff --git a/research/cv/retinanet_resnet152/scripts/run_single_train_gpu.sh b/research/cv/retinanet_resnet152/scripts/run_single_train_gpu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..bb96517591faed80cb880e7787c4363e90c0078e
--- /dev/null
+++ b/research/cv/retinanet_resnet152/scripts/run_single_train_gpu.sh
@@ -0,0 +1,76 @@
+#!/bin/bash
+# Copyright 2022 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
+# 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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "sh run_single_train_gpu.sh DEVICE_ID EPOCH_SIZE LR DATASET PRE_TRAINED PRE_TRAINED_EPOCH_SIZE"
+echo "for example: sh run_single_train_gpu.sh 0 500 0.1 coco /opt/retinanet-500_458.ckpt(optional) 200(optional)"
+echo "It is better to use absolute path."
+echo "================================================================================================================="
+
+if [ $# != 4 ] && [ $# != 6 ]
+then
+    echo "Usage: sh run_single_train_gpu.sh [DEVICE_ID] [EPOCH_SIZE] [LR] [DATASET] \
+[PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)"
+    exit 1
+fi
+
+echo "After running the script, the network runs in the background. The log will be generated in LOGx/train_log.txt"
+
+export DEVICE_ID=$1
+EPOCH_SIZE=$2
+LR=$3
+DATASET=$4
+PRE_TRAINED=$5
+PRE_TRAINED_EPOCH_SIZE=$6
+
+rm -rf LOG$1
+mkdir ./LOG$1
+cp ../*.py ./LOG$1
+cp ../*.yaml ./LOG$1
+cp -r ../src ./LOG$1
+cd ./LOG$1 || exit
+
+echo "start training for device $1"
+env > env.log
+
+if [ $# == 4 ]
+then
+      python train.py  \
+      --run_platform="GPU" \
+      --batch_size=12 \
+      --distribute=False  \
+      --lr=$LR \
+      --dataset=$DATASET \
+      --device_num=1  \
+      --device_id=$DEVICE_ID  \
+      --epoch_size=$EPOCH_SIZE > train_log.txt 2>&1 &
+fi
+
+if [ $# == 6 ]
+then
+      python train_retinanet.py  \
+      --run_platform="GPU" \
+      --batch_size=12 \
+      --distribute=False  \
+      --lr=$LR \
+      --dataset=$DATASET \
+      --device_num=1  \
+      --device_id=$DEVICE_ID  \
+      --pre_trained=$PRE_TRAINED \
+      --pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
+      --epoch_size=$EPOCH_SIZE > train_log.txt 2>&1 &
+fi
+cd ../
diff --git a/research/cv/retinanet_resnet152/src/backbone.py b/research/cv/retinanet_resnet152/src/backbone.py
index 020a48378f8b25e8a57021c71f7a6d3c897b34f2..1c30f362fea1a85f6147a0b976a0bf91d9d78695 100644
--- a/research/cv/retinanet_resnet152/src/backbone.py
+++ b/research/cv/retinanet_resnet152/src/backbone.py
@@ -93,7 +93,7 @@ class ResidualBlock(nn.Cell):
             self.down_sample_layer = nn.Conv2dBnAct(in_channel, out_channel,
                                                     kernel_size=1, stride=stride,
                                                     pad_mode='same', padding=0, has_bn=True, activation='relu')
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.relu = P.ReLU()
 
     def construct(self, x):
@@ -111,6 +111,7 @@ class ResidualBlock(nn.Cell):
 
         return out
 
+
 class resnet(nn.Cell):
     """
     ResNet architecture.
diff --git a/research/cv/retinanet_resnet152/src/bottleneck.py b/research/cv/retinanet_resnet152/src/bottleneck.py
index f4c46c96e8e2eb21a8846037c59c8e96ff573590..8dd365c1dd9c86d894f9c7701ab523c16fc7b0c1 100644
--- a/research/cv/retinanet_resnet152/src/bottleneck.py
+++ b/research/cv/retinanet_resnet152/src/bottleneck.py
@@ -18,6 +18,7 @@
 import mindspore.nn as nn
 from mindspore.ops import operations as P
 
+
 class FPN(nn.Cell):
     """FPN"""
     def __init__(self, config, backbone, is_training=True):
diff --git a/research/cv/retinanet_resnet152/src/box_utils.py b/research/cv/retinanet_resnet152/src/box_utils.py
index 0d7780e772cf3a9ac68b5f39e4e03fcb8e0f08c3..30258dd7cb6b7f871471e3b8639b246649591de0 100644
--- a/research/cv/retinanet_resnet152/src/box_utils.py
+++ b/research/cv/retinanet_resnet152/src/box_utils.py
@@ -74,7 +74,7 @@ def retinanet_bboxes_encode(boxes):
     Labels anchors with ground truth inputs.
 
     Args:
-        boxex: ground truth with shape [N, 5], for each row, it stores [y, x, h, w, cls].
+        boxes: ground truth with shape [N, 5], for each row, it stores [y, x, h, w, cls].
 
     Returns:
         gt_loc: location ground truth with shape [num_anchors, 4].
diff --git a/research/cv/retinanet_resnet152/src/dataset.py b/research/cv/retinanet_resnet152/src/dataset.py
index eac1a6451166923f033ac172933abc150add34fe..f1dad5f446e0c7273ae567cb09e2080c2b3c9006 100644
--- a/research/cv/retinanet_resnet152/src/dataset.py
+++ b/research/cv/retinanet_resnet152/src/dataset.py
@@ -215,10 +215,13 @@ def create_voc_label(is_training):
             if not is_training:
                 o_width = abs(x_max - x_min)
                 o_height = abs(y_max - y_min)
-                ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': \
-                    img_id, 'bbox': [x_min, y_min, o_width, o_height], \
-                       'category_id': cls_map[cls_name], 'id': bnd_id, \
-                       'ignore': 0, \
+                ann = {'area': o_width * o_height,
+                       'iscrowd': 0,
+                       'image_id': img_id,
+                       'bbox': [x_min, y_min, o_width, o_height],
+                       'category_id': cls_map[cls_name],
+                       'id': bnd_id,
+                       'ignore': 0,
                        'segmentation': []}
                 json_dict['annotations'].append(ann)
                 bnd_id = bnd_id + 1
@@ -390,8 +393,8 @@ def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="reti
 
 
 def create_retinanet_dataset(mindrecord_file, batch_size, repeat_num, device_num=1, rank=0,
-                             is_training=True, num_parallel_workers=64):
-    """Creatr retinanet dataset with MindDataset."""
+                             is_training=True, num_parallel_workers=8):
+    """Create retinanet dataset with MindDataset."""
     ds = de.MindDataset(mindrecord_file, columns_list=["img_id", "image", "annotation"], num_shards=device_num,
                         shard_id=rank, num_parallel_workers=num_parallel_workers, shuffle=is_training)
     decode = C.Decode()
@@ -427,6 +430,8 @@ def create_mindrecord(dataset="coco", prefix="retinanet.mindrecord", is_training
 
     mindrecord_dir = config.mindrecord_dir
     mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
+    if dataset == "voc":
+        config.coco_root = config.voc_root
     if not os.path.exists(mindrecord_file):
         if not os.path.isdir(mindrecord_dir):
             os.makedirs(mindrecord_dir)
@@ -438,12 +443,12 @@ def create_mindrecord(dataset="coco", prefix="retinanet.mindrecord", is_training
             else:
                 print("coco_root not exits.")
         elif dataset == "voc":
-            if os.path.isdir(config.voc_dir):
+            if os.path.isdir(config.voc_dir) and os.path.isdir(config.voc_root):
                 print("Create Mindrecord.")
                 voc_data_to_mindrecord(mindrecord_dir, is_training, prefix)
                 print("Create Mindrecord Done, at {}".format(mindrecord_dir))
             else:
-                print("voc_dir not exits.")
+                print("voc_root or voc_dir not exits.")
         else:
             if os.path.isdir(config.image_dir) and os.path.exists(config.anno_path):
                 print("Create Mindrecord.")
@@ -451,4 +456,7 @@ def create_mindrecord(dataset="coco", prefix="retinanet.mindrecord", is_training
                 print("Create Mindrecord Done, at {}".format(mindrecord_dir))
             else:
                 print("image_dir or anno_path not exits.")
+    else:
+        print("Mindrecord file exists.")
+
     return mindrecord_file
diff --git a/research/cv/retinanet_resnet152/src/model_utils/config.py b/research/cv/retinanet_resnet152/src/model_utils/config.py
index 7f1ff6e2b8df2da6ddd42d411da05be2066f2c84..ec9300cfa38a03c150f10ebb36ab6ab7469ee27f 100644
--- a/research/cv/retinanet_resnet152/src/model_utils/config.py
+++ b/research/cv/retinanet_resnet152/src/model_utils/config.py
@@ -119,9 +119,9 @@ def get_config():
                         help="Config file path")
     path_args, _ = parser.parse_known_args()
     default, helper, choices = parse_yaml(path_args.config_path)
-    pprint(default)
     args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
     final_config = merge(args, default)
+    pprint(final_config)
     return Config(final_config)
 
 config = get_config()
diff --git a/research/cv/retinanet_resnet152/train.py b/research/cv/retinanet_resnet152/train.py
index 5402c6a4f0228b0190a8c5f7fc72b2fac7fcf22b..d8b580090ec0ff9bd60bbbe6fd2b2cecc2a32409 100644
--- a/research/cv/retinanet_resnet152/train.py
+++ b/research/cv/retinanet_resnet152/train.py
@@ -19,7 +19,7 @@ import os
 import mindspore
 import mindspore.nn as nn
 from mindspore import context, Tensor
-from mindspore.communication.management import init, get_rank
+from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor, Callback
 from mindspore.train import Model
 from mindspore.context import ParallelMode
@@ -65,11 +65,12 @@ def modelarts_process():
         config.coco_root = os.path.join(config.coco_root, config.modelarts_dataset_unzip_name)
         print(os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
 
+
 @moxing_wrapper(pre_process=modelarts_process)
 def train_retinanet_resnet152():
     """ train_retinanet_resnet152 """
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.run_platform)
     if config.run_platform == "Ascend":
-        context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
         if config.distribute:
             if os.getenv("DEVICE_ID", "not_set").isdigit():
                 context.set_context(device_id=int(os.getenv("DEVICE_ID")))
@@ -83,6 +84,15 @@ def train_retinanet_resnet152():
             device_num = 1
             context.set_context(device_id=get_device_id())
 
+    elif config.run_platform == "GPU":
+        rank = config.device_id
+        device_num = config.device_num
+        if config.distribute:
+            init()
+            rank = get_rank()
+            device_num = get_group_size()
+            context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
+                                              device_num=device_num)
     else:
         raise ValueError("Unsupported platform.")
 
@@ -102,7 +112,7 @@ def train_retinanet_resnet152():
         backbone = resnet152(config.num_classes)
         retinanet = retinahead(backbone, config)
         net = retinanetWithLossCell(retinanet, config)
-        net.to_float(mindspore.float16)
+        net.to_float(mindspore.float32)
         init_net_param(net)
 
         if config.pre_trained:
@@ -137,5 +147,6 @@ def train_retinanet_resnet152():
             cb += [ckpt_cb]
             model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=True)
 
+
 if __name__ == '__main__':
     train_retinanet_resnet152()