forked from mindspore-Ecosystem/mindspore
!18706 centernet can been used on ModelArts
Merge pull request !18706 from 郑彬/master
This commit is contained in:
Gitee
commit
18eed2fb33
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@ -97,7 +97,7 @@ Dataset used: [COCO2017](https://cocodataset.org/)
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pip install mmcv==0.2.14
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```
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And change the COCO_ROOT and other settings you need in `config.py`. The directory structure is as follows:
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And change the COCO_ROOT and other settings you need in `default_config.yaml`. The directory structure is as follows:
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```path
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.
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@ -115,12 +115,15 @@ Dataset used: [COCO2017](https://cocodataset.org/)
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# [Quick Start](#contents)
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- running on local
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After installing MindSpore via the official website, you can start training and evaluation as follows:
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Note: 1.the first run of training will generate the mindrecord file, which will take a long time.
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2.MINDRECORD_DATASET_PATH is the mindrecord dataset directory.
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3.LOAD_CHECKPOINT_PATH is the pretrained checkpoint file directory, if no just set ""
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4.RUN_MODE support validation and testing, set to be "val"/"test"
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3.For `train.py`, LOAD_CHECKPOINT_PATH is the pretrained checkpoint file directory, if no just set "".
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4.For `eval.py`, LOAD_CHECKPOINT_PATH is the checkpoint to be evaluated.
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5.RUN_MODE support validation and testing, set to be "val"/"test"
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```shell
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# create dataset in mindrecord format
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@ -142,6 +145,61 @@ bash scripts/run_standalone_eval_ascend.sh [DEVICE_ID] [RUN_MODE] [DATA_DIR] [LO
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bash scripts/run_standalone_eval_cpu.sh [RUN_MODE] [DATA_DIR] [LOAD_CHECKPOINT_PATH]
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```
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- running on ModelArts
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If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training as follows
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- Training with single cards on ModelArts
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```python
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# (1) Upload the code folder to S3 bucket.
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# (2) Click to "create training task" on the website UI interface.
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# (3) Set the code directory to "/{path}/centernet" on the website UI interface.
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# (4) Set the startup file to /{path}/centernet/train.py" on the website UI interface.
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# (5) Perform a or b.
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# a. setting parameters in /{path}/centernet/default_config.yaml.
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# 1. Set ”enable_modelarts: True“
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# 2. Set “epoch_size: 350”
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# 3. Set “distribute: 'true'”
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# 4. Set “data_sink_steps: 50”
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# 5. Set “save_checkpoint_path: ./checkpoints”
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# b. adding on the website UI interface.
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# 1. Add ”enable_modelarts=True“
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# 2. Add “epoch_size=350”
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# 3. Add “distribute=true”
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# 4. Add “data_sink_steps=50”
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# 5. Add “save_checkpoint_path=./checkpoints”
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# (6) Upload the mindrecdrd dataset to S3 bucket.
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# (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path.
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# (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
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# (9) Under the item "resource pool selection", select the specification of single cards.
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# (10) Create your job.
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```
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- evaluating with single card on ModelArts
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```python
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# (1) Upload the code folder 'centernet' to S3 bucket.
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# (2) Git clone https://github.com/xingyizhou/CenterNet.git on local, and put the folder 'CenterNet' under the folder 'centernet' on s3 bucket.
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# (3) Click to "create training task" on the website UI interface.
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# (4) Set the code directory to "/{path}/centernet" on the website UI interface.
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# (5) Set the startup file to /{path}/centernet/eval.py" on the website UI interface.
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# (6) Perform a or b.
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# a. setting parameters in /{path}/centernet/default_config.yaml.
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# 1. Set ”enable_modelarts: True“
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# 2. Set “run_mode: 'val'”
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# 3. Set “load_checkpoint_path: ./{path}/*.ckpt”('load_checkpoint_path' indicates the path of the weight file to be evaluated relative to the file `eval.py`, and the weight file must be included in the code directory.)
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# b. adding on the website UI interface.
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# 1. Add ”enable_modelarts=True“
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# 2. Add “run_mode=val”
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# 3. Add “load_checkpoint_path=./{path}/*.ckpt”('load_checkpoint_path' indicates the path of the weight file to be evaluated relative to the file `eval.py`, and the weight file must be included in the code directory.)
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# (7) Upload the dataset(not mindrecord format) to S3 bucket.
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# (8) Check the "data storage location" on the website UI interface and set the "Dataset path" path.
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# (9) Set the "Output file path" and "Job log path" to your path on the website UI interface.
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# (10) Under the item "resource pool selection", select the specification of a single card.
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# (11) Create your job.
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```
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# [Script Description](#contents)
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## [Script and Sample Code](#contents)
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@ -154,12 +212,13 @@ bash scripts/run_standalone_eval_cpu.sh [RUN_MODE] [DATA_DIR] [LOAD_CHECKPOINT_P
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├── eval.py // testing and evaluation outputs
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├── export.py // convert mindspore model to air model
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├── README.md // descriptions about CenterNet
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├── default_config.yaml // parameter configuration
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├── scripts
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│ ├──ascend_distributed_launcher
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│ │ ├──__init__.py
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│ │ ├──hyper_parameter_config.ini // hyper parameter for distributed training
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│ │ ├──get_distribute_train_cmd.py // script for distributed training
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│ │ ├──README.md
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│ │ └──README.md
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│ ├──convert_dataset_to_mindrecord.sh // shell script for converting coco type dataset to mindrecord
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│ ├──run_standalone_train_ascend.sh // shell script for standalone training on ascend
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│ ├──run_distributed_train_ascend.sh // shell script for distributed training on ascend
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@ -167,10 +226,14 @@ bash scripts/run_standalone_eval_cpu.sh [RUN_MODE] [DATA_DIR] [LOAD_CHECKPOINT_P
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│ ├──run_standalone_train_cpu.sh // shell script for standalone training on cpu
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│ ├──run_standalone_eval_cpu.sh // shell script for standalone evaluation on cpu
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└── src
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├──model_utils
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│ ├──config.py // parsing parameter configuration file of "*.yaml"
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│ ├──device_adapter.py // local or ModelArts training
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│ ├──local_adapter.py // get related environment variables on local
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│ └──moxing_adapter.py // get related environment variables abd transfer data on ModelArts
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├──__init__.py
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├──centernet_pose.py // centernet networks, training entry
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├──dataset.py // generate dataloader and data processing entry
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├──config.py // centernet unique configs
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├──dcn_v2.py // deformable convolution operator v2
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├──decode.py // decode the head features
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├──backbone_dla.py // deep layer aggregation backbone
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@ -255,34 +318,34 @@ options:
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### Options and Parameters
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Parameters for training and evaluation can be set in file `config.py` and `finetune_eval_config.py` respectively.
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Parameters for training and evaluation can be set in file `default_config.yaml`.
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#### Options
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```text
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config for training.
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batch_size batch size of input dataset: N, default is 32
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loss_scale_value initial value of loss scale: N, default is 1024
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optimizer optimizer used in the network: Adam, default is Adam
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lr_schedule schedules to get the learning rate
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```python
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train_config:
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batch_size: 32 // batch size of input dataset: N, default is 32
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loss_scale_value: 1024 // initial value of loss scale: N, default is 1024
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optimizer: 'Adam' // optimizer used in the network: Adam, default is Adam
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lr_schedule: 'MultiDecay' // schedules to get the learning rate
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```
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```text
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config for evaluation.
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soft_nms nms after decode: True | False, default is True
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keep_res keep original or fix resolution: True | False, default is False
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multi_scales use multi-scales of image: List, default is [1.0]
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pad pad size when keep original resolution, default is 31
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K number of bboxes to be computed by TopK, default is 100
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score_thresh threshold of score when visualize image and annotation info
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eval_config:
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soft_nms: True // nms after decode: True | False, default is True
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keep_res: c // keep original or fix resolution: True | False, default is False
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multi_scales: [1.0] // use multi-scales of image: List, default is [1.0]
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pad: 31 // pad size when keep original resolution, default is 31
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K: 100 // number of bboxes to be computed by TopK, default is 100
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score_thresh: 0.3 // threshold of score when visualize image and annotation info
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```
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```text
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config for export.
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input_res input resolution of the model air, default is [512, 512]
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ckpt_file checkpoint file, default is "./ckkt_file.ckpt"
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export_format the exported format of model air, default is MINDIR
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export_name the exported file name, default is "CentNet_MultiPose"
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input_res: dataset_config.input_res // input resolution of the model air, default is [512, 512]
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ckpt_file: "./ckpt_file.ckpt" // checkpoint file, default is "./ckkt_file.ckpt"
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export_format: "MINDIR" // the exported format of model air, default is MINDIR
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export_name: "CenterNet_MultiPose" // the exported file name, default is "CentNet_MultiPose"
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```
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#### Parameters
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@ -462,8 +525,35 @@ overall performance on coco2017 test-dev dataset
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If you want to infer the network on Ascend 310, you should convert the model to AIR:
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- Export on local
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```python
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python export.py [DEVICE_ID]
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python export.py --device_id [DEVICE_ID] --export_format MINDIR --export_load_ckpt [CKPT_FILE__PATH] --export_name [EXPORT_FILE_NAME]
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```
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- Export on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start as follows)
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```python
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# (1) Upload the code folder to S3 bucket.
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# (2) Click to "create training task" on the website UI interface.
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# (3) Set the code directory to "/{path}/centernet" on the website UI interface.
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# (4) Set the startup file to /{path}/centernet/export.py" on the website UI interface.
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# (5) Perform a or b.
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# a. setting parameters in /{path}/centernet/default_config.yaml.
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# 1. Set ”enable_modelarts: True“
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# 2. Set “export_load_ckpt: ./{path}/*.ckpt”('export_load_ckpt' indicates the path of the weight file to be exported relative to the file `export.py`, and the weight file must be included in the code directory.)
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# 3. Set ”export_name: centernet“
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# 4. Set ”export_format：MINDIR“
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# b. adding on the website UI interface.
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# 1. Add ”enable_modelarts=True“
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# 2. Add “export_load_ckpt=./{path}/*.ckpt”('export_load_ckpt' indicates the path of the weight file to be exported relative to the file `export.py`, and the weight file must be included in the code directory.)
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# 3. Add ”export_name=centernet“
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# 4. Add ”export_format=MINDIR“
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# (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path (This step is useless, but necessary.).
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# (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
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# (9) Under the item "resource pool selection", select the specification of a single card.
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# (10) Create your job.
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# You will see centernet.mindir under {Output file path}.
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```
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# [Model Description](#contents)
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@ -0,0 +1,176 @@
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# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
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enable_modelarts: False
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# Url for modelarts
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data_url: ""
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train_url: ""
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checkpoint_url: ""
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# Path for local
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data_path: "/cache/data"
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output_path: "/cache/train"
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load_path: "/cache/checkpoint_path"
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device_target: "Ascend"
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enable_profiling: False
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# ==============================================================================
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# prepare *.mindrecord* data
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coco_data_dir: ""
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mindrecord_dir: "" # also used by train.py
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mindrecord_prefix: "coco_hp.train.mind"
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# train related
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visual_image: "false"
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save_result_dir: ""
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device_id: 0
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device_num: 1
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distribute: 'false'
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need_profiler: "false"
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profiler_path: "./profiler"
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epoch_size: 1
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train_steps: -1
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enable_save_ckpt: "true"
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do_shuffle: "true"
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enable_data_sink: "true"
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data_sink_steps: 1
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save_checkpoint_path: ""
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load_checkpoint_path: ""
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save_checkpoint_steps: 1000
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save_checkpoint_num: 1
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# test related
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data_dir: ""
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run_mode: "test"
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enable_eval: "true"
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# export related
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export_load_ckpt: ''
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export_format: ''
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export_name: ''
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dataset_config:
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num_classes: 1
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num_joints: 17
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max_objs: 32
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input_res: [512, 512]
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output_res: [128, 128]
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rand_crop: False
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shift: 0.1
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scale: 0.4
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aug_rot: 0.0
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rotate: 0
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flip_prop: 0.5
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mean: np.array([0.40789654, 0.44719302, 0.47026115], dtype=np.float32)
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std: np.array([0.28863828, 0.27408164, 0.27809835], dtype=np.float32)
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flip_idx: [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]
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edges: [[0, 1], [0, 2], [1, 3], [2, 4], [4, 6], [3, 5], [5, 6],
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[5, 7], [7, 9], [6, 8], [8, 10], [6, 12], [5, 11], [11, 12],
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[12, 14], [14, 16], [11, 13], [13, 15]]
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eig_val: np.array([0.2141788, 0.01817699, 0.00341571], dtype=np.float32)
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eig_vec: np.array([[-0.58752847, -0.69563484, 0.41340352],
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[-0.5832747, 0.00994535, -0.81221408],
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[-0.56089297, 0.71832671, 0.41158938]], dtype=np.float32)
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categories: [{"supercategory": "person",
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"id": 1,
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"name": "person",
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"keypoints": ["nose", "left_eye", "right_eye", "left_ear", "right_ear",
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"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
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"left_wrist", "right_wrist", "left_hip", "right_hip",
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"left_knee", "right_knee", "left_ankle", "right_ankle"],
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"skeleton": [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13],
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[6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3], [1, 2], [1, 3],
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[2, 4], [3, 5], [4, 6], [5, 7]]}]
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net_config:
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down_ratio: 4
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last_level: 6
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final_kernel: 1
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stage_levels: [1, 1, 1, 2, 2, 1]
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stage_channels: [16, 32, 64, 128, 256, 512]
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head_conv: 256
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dense_hp: True
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hm_hp: True
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reg_hp_offset: True
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reg_offset: True
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hm_weight: 1
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off_weight: 1
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wh_weight: 0.1
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hp_weight: 1
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hm_hp_weight: 1
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mse_loss: False
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reg_loss: 'l1'
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train_config:
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batch_size: 32
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loss_scale_value: 1024
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optimizer: 'Adam'
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lr_schedule: 'MultiDecay'
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Adam:
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weight_decay: 0.0
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decay_filter: "lambda x: x.name.endswith('.bias') or x.name.endswith('.beta') or x.name.endswith('.gamma')"
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PolyDecay:
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learning_rate: 0.00012 # 1.2e-4
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end_learning_rate: 0.0000005 # 5e-7
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power: 5.0
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eps: 0.0000001 # 1e-7
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warmup_steps: 2000
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MultiDecay:
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learning_rate: 0.00012 # 1.2e-4
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eps: 0.0000001 # 1e-7
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warmup_steps: 2000
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multi_epochs: [270, 300]
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factor: 10
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eval_config:
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soft_nms: True
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keep_res: True
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multi_scales: [1.0]
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pad: 31
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K: 100
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score_thresh: 0.3
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export_config:
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input_res: dataset_config.input_res
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ckpt_file: "./ckpt_file.ckpt"
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export_format: "MINDIR"
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export_name: "CenterNet_MultiPose"
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---
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# Help description for each configuration
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enable_modelarts: "Whether training on modelarts, default: False"
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data_url: "Url for modelarts"
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train_url: "Url for modelarts"
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data_path: "The location of the input data."
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output_path: "The location of the output file."
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device_target: "Running platform, choose from Ascend, GPU or CPU, and default is Ascend."
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enable_profiling: 'Whether enable profiling while training, default: False'
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distribute: "Run distribute, default is false."
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need_profiler: "Profiling to parsing runtime info, default is false."
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profiler_path: "The path to save profiling data"
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epoch_size: "Epoch size, default is 1."
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train_steps: "Training Steps, default is -1, i.e. run all steps according to epoch number."
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device_id: "Device id, default is 0."
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device_num: "Use device nums, default is 1."
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enable_save_ckpt: "Enable save checkpoint, default is true."
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do_shuffle: "Enable shuffle for dataset, default is true."
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enable_data_sink: "Enable data sink, default is true."
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data_sink_steps: "Sink steps for each epoch, default is 1."
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save_checkpoint_path: "Save checkpoint path"
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load_checkpoint_path: "Load checkpoint file path"
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save_checkpoint_steps: "Save checkpoint steps, default is 1000."
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save_checkpoint_num: "Save checkpoint numbers, default is 1."
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mindrecord_dir: "Mindrecord dataset files directory"
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mindrecord_prefix: "Prefix of MindRecord dataset filename."
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visual_image: "Visulize the ground truth and predicted image"
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save_result_dir: "The path to save the predict results"
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data_dir: "Dataset directory, the absolute image path is joined by the data_dir, and the relative path in anno_path"
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run_mode: "test or validation, default is test."
|
||||
enable_eval: "Whether evaluate accuracy after prediction"
|
||||
---
|
||||
|
||||
device_target: ['Ascend', 'CPU']
|
||||
distribute: ["true", "false"]
|
||||
need_profiler: ["true", "false"]
|
||||
enable_save_ckpt: ["true", "false"]
|
||||
do_shuffle: ["true", "false"]
|
||||
enable_data_sink: ["true", "false"]
|
||||
export_format: ["MINDIR"]
|
||||
|
|
@ -20,7 +20,6 @@ import os
|
|||
import time
|
||||
import copy
|
||||
import json
|
||||
import argparse
|
||||
import cv2
|
||||
from pycocotools.coco import COCO
|
||||
from pycocotools.cocoeval import COCOeval
|
||||
|
|
@ -31,53 +30,62 @@ import mindspore.log as logger
|
|||
from src import COCOHP, CenterNetMultiPoseEval
|
||||
from src import convert_eval_format, post_process, merge_outputs
|
||||
from src import visual_image
|
||||
from src.config import dataset_config, net_config, eval_config
|
||||
from src.model_utils.config import config, dataset_config, net_config, eval_config
|
||||
from src.model_utils.moxing_adapter import moxing_wrapper
|
||||
from src.model_utils.device_adapter import get_device_id
|
||||
|
||||
_current_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
parser = argparse.ArgumentParser(description='CenterNet evaluation')
|
||||
parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'CPU'],
|
||||
help='device where the code will be implemented. (Default: Ascend)')
|
||||
parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
|
||||
parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path")
|
||||
parser.add_argument("--data_dir", type=str, default="", help="Dataset directory, "
|
||||
"the absolute image path is joined by the data_dir "
|
||||
"and the relative path in anno_path")
|
||||
parser.add_argument("--run_mode", type=str, default="test", help="test or validation, default is test.")
|
||||
parser.add_argument("--visual_image", type=str, default="false", help="Visulize the ground truth and predicted image")
|
||||
parser.add_argument("--enable_eval", type=str, default="true", help="Whether evaluate accuracy after prediction")
|
||||
parser.add_argument("--save_result_dir", type=str, default="", help="The path to save the predict results")
|
||||
|
||||
args_opt = parser.parse_args()
|
||||
def modelarts_pre_process():
|
||||
'''modelarts pre process function.'''
|
||||
try:
|
||||
from nms import soft_nms_39
|
||||
print('soft_nms_39_attributes: {}'.format(soft_nms_39.__dir__()))
|
||||
except ImportError:
|
||||
print('NMS not installed! trying installing...\n')
|
||||
cur_path = os.path.dirname(os.path.abspath(__file__))
|
||||
os.system('cd {}/CenterNet/src/lib/external/ && make && python setup.py install && cd - '.format(cur_path))
|
||||
try:
|
||||
from nms import soft_nms_39
|
||||
print('soft_nms_39_attributes: {}'.format(soft_nms_39.__dir__()))
|
||||
except ImportError:
|
||||
print('Installing failed! check if the folder "./CenterNet" exists.')
|
||||
else:
|
||||
print('Install nms successfully')
|
||||
config.data_dir = config.data_path
|
||||
config.load_checkpoint_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), config.load_checkpoint_path)
|
||||
|
||||
|
||||
@moxing_wrapper(pre_process=modelarts_pre_process)
|
||||
def predict():
|
||||
'''
|
||||
Predict function
|
||||
'''
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
|
||||
if args_opt.device_target == "Ascend":
|
||||
context.set_context(device_id=args_opt.device_id)
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
|
||||
if config.device_target == "Ascend":
|
||||
context.set_context(device_id=get_device_id())
|
||||
enable_nms_fp16 = True
|
||||
else:
|
||||
enable_nms_fp16 = False
|
||||
|
||||
logger.info("Begin creating {} dataset".format(args_opt.run_mode))
|
||||
coco = COCOHP(dataset_config, run_mode=args_opt.run_mode, net_opt=net_config,
|
||||
enable_visual_image=(args_opt.visual_image == "true"), save_path=args_opt.save_result_dir,)
|
||||
coco.init(args_opt.data_dir, keep_res=eval_config.keep_res)
|
||||
logger.info("Begin creating {} dataset".format(config.run_mode))
|
||||
coco = COCOHP(dataset_config, run_mode=config.run_mode, net_opt=net_config,
|
||||
enable_visual_image=(config.visual_image == "true"), save_path=config.save_result_dir,)
|
||||
coco.init(config.data_dir, keep_res=eval_config.keep_res)
|
||||
dataset = coco.create_eval_dataset()
|
||||
|
||||
net_for_eval = CenterNetMultiPoseEval(net_config, eval_config.K, enable_nms_fp16)
|
||||
net_for_eval.set_train(False)
|
||||
|
||||
param_dict = load_checkpoint(args_opt.load_checkpoint_path)
|
||||
param_dict = load_checkpoint(config.load_checkpoint_path)
|
||||
load_param_into_net(net_for_eval, param_dict)
|
||||
|
||||
# save results
|
||||
save_path = os.path.join(args_opt.save_result_dir, args_opt.run_mode)
|
||||
save_path = os.path.join(config.save_result_dir, config.run_mode)
|
||||
if not os.path.exists(save_path):
|
||||
os.makedirs(save_path)
|
||||
if args_opt.visual_image == "true":
|
||||
if config.visual_image == "true":
|
||||
save_pred_image_path = os.path.join(save_path, "pred_image")
|
||||
if not os.path.exists(save_pred_image_path):
|
||||
os.makedirs(save_pred_image_path)
|
||||
|
|
@ -119,25 +127,25 @@ def predict():
|
|||
pred_annos["images"].append(image_info)
|
||||
for image_anno in pred_json["annotations"]:
|
||||
pred_annos["annotations"].append(image_anno)
|
||||
if args_opt.visual_image == "true":
|
||||
if config.visual_image == "true":
|
||||
img_file = os.path.join(coco.image_path, gt_image_info[0]['file_name'])
|
||||
gt_image = cv2.imread(img_file)
|
||||
if args_opt.run_mode != "test":
|
||||
if config.run_mode != "test":
|
||||
annos = coco.coco.loadAnns(coco.anns[image_id])
|
||||
visual_image(copy.deepcopy(gt_image), annos, save_gt_image_path)
|
||||
anno = copy.deepcopy(pred_json["annotations"])
|
||||
visual_image(gt_image, anno, save_pred_image_path, score_threshold=eval_config.score_thresh)
|
||||
|
||||
# save results
|
||||
save_path = os.path.join(args_opt.save_result_dir, args_opt.run_mode)
|
||||
save_path = os.path.join(config.save_result_dir, config.run_mode)
|
||||
if not os.path.exists(save_path):
|
||||
os.makedirs(save_path)
|
||||
pred_anno_file = os.path.join(save_path, '{}_pred_result.json').format(args_opt.run_mode)
|
||||
pred_anno_file = os.path.join(save_path, '{}_pred_result.json').format(config.run_mode)
|
||||
json.dump(pred_annos, open(pred_anno_file, 'w'))
|
||||
pred_res_file = os.path.join(save_path, '{}_pred_eval.json').format(args_opt.run_mode)
|
||||
pred_res_file = os.path.join(save_path, '{}_pred_eval.json').format(config.run_mode)
|
||||
json.dump(pred_annos["annotations"], open(pred_res_file, 'w'))
|
||||
|
||||
if args_opt.run_mode != "test" and args_opt.enable_eval:
|
||||
if config.run_mode != "test" and config.enable_eval:
|
||||
run_eval(coco.annot_path, pred_res_file)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -16,21 +16,26 @@
|
|||
Export CenterNet mindir model.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import numpy as np
|
||||
from mindspore import context, Tensor
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
|
||||
|
||||
from src import CenterNetMultiPoseEval
|
||||
from src.config import net_config, eval_config, export_config
|
||||
from src.model_utils.config import config, net_config, eval_config, export_config
|
||||
from src.model_utils.moxing_adapter import moxing_wrapper
|
||||
|
||||
parser = argparse.ArgumentParser(description='centernet export')
|
||||
parser.add_argument("--device_id", type=int, default=0, help="Device id")
|
||||
args = parser.parse_args()
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
|
||||
def modelarts_pre_process():
|
||||
'''modelarts pre process function.'''
|
||||
export_config.ckpt_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), export_config.ckpt_file)
|
||||
export_config.export_name = os.path.join(config.output_path, export_config.export_name)
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@moxing_wrapper(pre_process=modelarts_pre_process)
|
||||
def run_export():
|
||||
'''export function'''
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=config.device_id)
|
||||
net = CenterNetMultiPoseEval(net_config, eval_config.K)
|
||||
net.set_train(False)
|
||||
|
||||
|
|
@ -42,3 +47,7 @@ if __name__ == '__main__':
|
|||
input_data = Tensor(np.random.uniform(-1.0, 1.0, size=input_shape).astype(np.float32))
|
||||
|
||||
export(net, input_data, file_name=export_config.export_name, file_format=export_config.export_format)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
run_export()
|
||||
|
|
|
|||
|
|
@ -29,14 +29,20 @@ PROJECT_DIR=$(cd "$(dirname "$0")" || exit; pwd)
|
|||
CUR_DIR=`pwd`
|
||||
export GLOG_log_dir=${CUR_DIR}/ms_log
|
||||
export GLOG_logtostderr=0
|
||||
export DEVICE_ID=$DEVICE_ID
|
||||
|
||||
# install nms module from third party
|
||||
if python -c "import nms" > /dev/null 2>&1
|
||||
then
|
||||
echo "NMS module already exits, no need reinstall."
|
||||
else
|
||||
if [ -f './CenterNet' ]
|
||||
then
|
||||
echo "NMS module was not found, but has been downloaded"
|
||||
else
|
||||
echo "NMS module was not found, install it now..."
|
||||
git clone https://github.com/xingyizhou/CenterNet.git
|
||||
fi
|
||||
cd CenterNet/src/lib/external/ || exit
|
||||
make
|
||||
python setup.py install
|
||||
|
|
|
|||
|
|
@ -33,9 +33,14 @@ export GLOG_logtostderr=0
|
|||
if python -c "import nms" > /dev/null 2>&1
|
||||
then
|
||||
echo "NMS module already exits, no need reinstall."
|
||||
else
|
||||
if [ -f './CenterNet' ]
|
||||
then
|
||||
echo "NMS module was not found, but has been downloaded"
|
||||
else
|
||||
echo "NMS module was not found, install it now..."
|
||||
git clone https://github.com/xingyizhou/CenterNet.git
|
||||
fi
|
||||
cd CenterNet/src/lib/external/ || exit
|
||||
make
|
||||
python setup.py install
|
||||
|
|
|
|||
|
|
@ -35,6 +35,7 @@ PROJECT_DIR=$(cd "$(dirname "$0")" || exit; pwd)
|
|||
CUR_DIR=`pwd`
|
||||
export GLOG_log_dir=${CUR_DIR}/ms_log
|
||||
export GLOG_logtostderr=0
|
||||
export DEVICE_ID=$DEVICE_ID
|
||||
|
||||
python ${PROJECT_DIR}/../train.py \
|
||||
--distribute=false \
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ from .backbone_dla import DLASeg
|
|||
from .utils import Sigmoid, GradScale
|
||||
from .utils import FocalLoss, RegLoss, RegWeightedL1Loss
|
||||
from .decode import MultiPoseDecode
|
||||
from .config import dataset_config as data_cfg
|
||||
from .model_utils.config import dataset_config as data_cfg
|
||||
|
||||
|
||||
def _generate_feature(cin, cout, kernel_size, head_name, head_conv=0):
|
||||
|
|
|
|||
|
|
@ -1,120 +0,0 @@
|
|||
# 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 dataset.py, train.py， eval.py
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
from easydict import EasyDict as edict
|
||||
|
||||
|
||||
dataset_config = edict({
|
||||
'num_classes': 1,
|
||||
'num_joints': 17,
|
||||
'max_objs': 32,
|
||||
'input_res': [512, 512],
|
||||
'output_res': [128, 128],
|
||||
'rand_crop': False,
|
||||
'shift': 0.1,
|
||||
'scale': 0.4,
|
||||
'aug_rot': 0.0,
|
||||
'rotate': 0,
|
||||
'flip_prop': 0.5,
|
||||
'mean': np.array([0.40789654, 0.44719302, 0.47026115], dtype=np.float32),
|
||||
'std': np.array([0.28863828, 0.27408164, 0.27809835], dtype=np.float32),
|
||||
'flip_idx': [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]],
|
||||
'edges': [[0, 1], [0, 2], [1, 3], [2, 4], [4, 6], [3, 5], [5, 6],
|
||||
[5, 7], [7, 9], [6, 8], [8, 10], [6, 12], [5, 11], [11, 12],
|
||||
[12, 14], [14, 16], [11, 13], [13, 15]],
|
||||
'eig_val': np.array([0.2141788, 0.01817699, 0.00341571], dtype=np.float32),
|
||||
'eig_vec': np.array([[-0.58752847, -0.69563484, 0.41340352],
|
||||
[-0.5832747, 0.00994535, -0.81221408],
|
||||
[-0.56089297, 0.71832671, 0.41158938]], dtype=np.float32),
|
||||
'categories': [{"supercategory": "person",
|
||||
"id": 1,
|
||||
"name": "person",
|
||||
"keypoints": ["nose", "left_eye", "right_eye", "left_ear", "right_ear",
|
||||
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
|
||||
"left_wrist", "right_wrist", "left_hip", "right_hip",
|
||||
"left_knee", "right_knee", "left_ankle", "right_ankle"],
|
||||
"skeleton": [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13],
|
||||
[6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3], [1, 2], [1, 3],
|
||||
[2, 4], [3, 5], [4, 6], [5, 7]]}],
|
||||
})
|
||||
|
||||
|
||||
net_config = edict({
|
||||
'down_ratio': 4,
|
||||
'last_level': 6,
|
||||
'final_kernel': 1,
|
||||
'stage_levels': [1, 1, 1, 2, 2, 1],
|
||||
'stage_channels': [16, 32, 64, 128, 256, 512],
|
||||
'head_conv': 256,
|
||||
'dense_hp': True,
|
||||
'hm_hp': True,
|
||||
'reg_hp_offset': True,
|
||||
'reg_offset': True,
|
||||
'hm_weight': 1,
|
||||
'off_weight': 1,
|
||||
'wh_weight': 0.1,
|
||||
'hp_weight': 1,
|
||||
'hm_hp_weight': 1,
|
||||
'mse_loss': False,
|
||||
'reg_loss': 'l1',
|
||||
})
|
||||
|
||||
|
||||
train_config = edict({
|
||||
'batch_size': 32,
|
||||
'loss_scale_value': 1024,
|
||||
'optimizer': 'Adam',
|
||||
'lr_schedule': 'MultiDecay',
|
||||
'Adam': edict({
|
||||
'weight_decay': 0.0,
|
||||
'decay_filter': lambda x: x.name.endswith('.bias') or x.name.endswith('.beta') or x.name.endswith('.gamma'),
|
||||
}),
|
||||
'PolyDecay': edict({
|
||||
'learning_rate': 1.2e-4,
|
||||
'end_learning_rate': 5e-7,
|
||||
'power': 5.0,
|
||||
'eps': 1e-7,
|
||||
'warmup_steps': 2000,
|
||||
}),
|
||||
'MultiDecay': edict({
|
||||
'learning_rate': 1.2e-4,
|
||||
'eps': 1e-7,
|
||||
'warmup_steps': 2000,
|
||||
'multi_epochs': [270, 300],
|
||||
'factor': 10,
|
||||
})
|
||||
})
|
||||
|
||||
|
||||
eval_config = edict({
|
||||
'soft_nms': True,
|
||||
'keep_res': True,
|
||||
'multi_scales': [1.0],
|
||||
'pad': 31,
|
||||
'K': 100,
|
||||
'score_thresh': 0.3
|
||||
})
|
||||
|
||||
|
||||
export_config = edict({
|
||||
'input_res': dataset_config.input_res,
|
||||
'ckpt_file': "./ckpt_file.ckpt",
|
||||
'export_format': "MINDIR",
|
||||
'export_name': "CenterNet_MultiPose",
|
||||
})
|
||||
|
|
@ -15,10 +15,9 @@
|
|||
"""
|
||||
Data operations, will be used in train.py
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
import math
|
||||
import argparse
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pycocotools.coco as coco
|
||||
|
|
@ -26,6 +25,14 @@ import pycocotools.coco as coco
|
|||
import mindspore.dataset as ds
|
||||
from mindspore import log as logger
|
||||
from mindspore.mindrecord import FileWriter
|
||||
|
||||
try:
|
||||
from src.image import get_affine_transform, affine_transform
|
||||
from src.image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian, draw_dense_reg
|
||||
from src.visual import visual_image
|
||||
except ImportError as import_error:
|
||||
print('Import Error: {}, trying append path/centernet/src/../'.format(import_error))
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
|
||||
from src.image import get_affine_transform, affine_transform
|
||||
from src.image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian, draw_dense_reg
|
||||
from src.visual import visual_image
|
||||
|
|
@ -428,14 +435,8 @@ class COCOHP(ds.Dataset):
|
|||
|
||||
if __name__ == '__main__':
|
||||
# Convert coco2017 dataset to mindrecord to improve performance on host
|
||||
from src.config import dataset_config
|
||||
from src.model_utils.config import config, dataset_config
|
||||
|
||||
parser = argparse.ArgumentParser(description='CenterNet MindRecord dataset')
|
||||
parser.add_argument("--coco_data_dir", type=str, default="", help="Coco dataset directory.")
|
||||
parser.add_argument("--mindrecord_dir", type=str, default="", help="MindRecord dataset dir.")
|
||||
parser.add_argument("--mindrecord_prefix", type=str, default="coco_hp.train.mind",
|
||||
help="Prefix of MindRecord dataset filename.")
|
||||
args_opt = parser.parse_args()
|
||||
dsc = COCOHP(dataset_config, run_mode="train")
|
||||
dsc.init(args_opt.coco_data_dir)
|
||||
dsc.transfer_coco_to_mindrecord(args_opt.mindrecord_dir, args_opt.mindrecord_prefix, shard_num=8)
|
||||
dsc.init(config.coco_data_dir)
|
||||
dsc.transfer_coco_to_mindrecord(config.mindrecord_dir, config.mindrecord_prefix, shard_num=8)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,160 @@
|
|||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
|
||||
"""Parse arguments"""
|
||||
|
||||
import os
|
||||
import ast
|
||||
import argparse
|
||||
from pprint import pprint, pformat
|
||||
import yaml
|
||||
import numpy as np
|
||||
|
||||
|
||||
class Config:
|
||||
"""
|
||||
Configuration namespace. Convert dictionary to members.
|
||||
"""
|
||||
def __init__(self, cfg_dict):
|
||||
for k, v in cfg_dict.items():
|
||||
if isinstance(v, str) and (v[:9] == 'np.array(' and v[-17:] == 'dtype=np.float32)'):
|
||||
v = np.array(ast.literal_eval(v[9:v.rfind(']') + 1]), dtype=np.float32)
|
||||
if isinstance(v, (list, tuple)):
|
||||
setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
|
||||
else:
|
||||
setattr(self, k, Config(v) if isinstance(v, dict) else v)
|
||||
|
||||
def __str__(self):
|
||||
return pformat(self.__dict__)
|
||||
|
||||
def __repr__(self):
|
||||
return self.__str__()
|
||||
|
||||
|
||||
def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="default_config.yaml"):
|
||||
"""
|
||||
Parse command line arguments to the configuration according to the default yaml.
|
||||
|
||||
Args:
|
||||
parser: Parent parser.
|
||||
cfg: Base configuration.
|
||||
helper: Helper description.
|
||||
cfg_path: Path to the default yaml config.
|
||||
"""
|
||||
parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]",
|
||||
parents=[parser])
|
||||
helper = {} if helper is None else helper
|
||||
choices = {} if choices is None else choices
|
||||
for item in cfg:
|
||||
if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
|
||||
help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
|
||||
choice = choices[item] if item in choices else None
|
||||
if isinstance(cfg[item], bool):
|
||||
parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice,
|
||||
help=help_description)
|
||||
else:
|
||||
parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice,
|
||||
help=help_description)
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def parse_yaml(yaml_path):
|
||||
"""
|
||||
Parse the yaml config file.
|
||||
|
||||
Args:
|
||||
yaml_path: Path to the yaml config.
|
||||
"""
|
||||
with open(yaml_path, 'r') as fin:
|
||||
try:
|
||||
cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
|
||||
cfgs = [x for x in cfgs]
|
||||
if len(cfgs) == 1:
|
||||
cfg_helper = {}
|
||||
cfg = cfgs[0]
|
||||
cfg_choices = {}
|
||||
elif len(cfgs) == 2:
|
||||
cfg, cfg_helper = cfgs
|
||||
cfg_choices = {}
|
||||
elif len(cfgs) == 3:
|
||||
cfg, cfg_helper, cfg_choices = cfgs
|
||||
else:
|
||||
raise ValueError("At most 3 docs (config, description for help, choices) are supported in config yaml")
|
||||
print(cfg_helper)
|
||||
except:
|
||||
raise ValueError("Failed to parse yaml")
|
||||
return cfg, cfg_helper, cfg_choices
|
||||
|
||||
|
||||
def merge(args, cfg):
|
||||
"""
|
||||
Merge the base config from yaml file and command line arguments.
|
||||
|
||||
Args:
|
||||
args: Command line arguments.
|
||||
cfg: Base configuration.
|
||||
"""
|
||||
args_var = vars(args)
|
||||
for item in args_var:
|
||||
cfg[item] = args_var[item]
|
||||
return cfg
|
||||
|
||||
|
||||
def extra_operations(cfg):
|
||||
"""
|
||||
Do extra work on Config object.
|
||||
|
||||
Args:
|
||||
cfg: Object after instantiation of class 'Config'.
|
||||
"""
|
||||
cfg.train_config.Adam.decay_filter = lambda x: x.name.endswith('.bias') or x.name.endswith('.beta') or x.name.endswith('.gamma')
|
||||
cfg.export_config.input_res = cfg.dataset_config.input_res
|
||||
if cfg.export_load_ckpt:
|
||||
cfg.export_config.ckpt_file = cfg.export_load_ckpt
|
||||
if cfg.export_name:
|
||||
cfg.export_config.export_name = cfg.export_name
|
||||
if cfg.export_format:
|
||||
cfg.export_config.export_format = cfg.export_format
|
||||
|
||||
|
||||
|
||||
def get_config():
|
||||
"""
|
||||
Get Config according to the yaml file and cli arguments.
|
||||
"""
|
||||
parser = argparse.ArgumentParser(description="default name", add_help=False)
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../../default_config.yaml"),
|
||||
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)
|
||||
config_obj = Config(final_config)
|
||||
extra_operations(config_obj)
|
||||
return config_obj
|
||||
|
||||
|
||||
config = get_config()
|
||||
dataset_config = config.dataset_config
|
||||
net_config = config.net_config
|
||||
train_config = config.train_config
|
||||
eval_config = config.eval_config
|
||||
export_config = config.export_config
|
||||
|
||||
if __name__ == '__main__':
|
||||
print(config)
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
|
||||
"""Device adapter for ModelArts"""
|
||||
|
||||
from src.model_utils.config import config
|
||||
|
||||
if config.enable_modelarts:
|
||||
from src.model_utils.moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
|
||||
else:
|
||||
from src.model_utils.local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
|
||||
|
||||
__all__ = [
|
||||
"get_device_id", "get_device_num", "get_rank_id", "get_job_id"
|
||||
]
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
|
||||
"""Local adapter"""
|
||||
|
||||
import os
|
||||
|
||||
def get_device_id():
|
||||
device_id = os.getenv('DEVICE_ID', '0')
|
||||
return int(device_id)
|
||||
|
||||
|
||||
def get_device_num():
|
||||
device_num = os.getenv('RANK_SIZE', '1')
|
||||
return int(device_num)
|
||||
|
||||
|
||||
def get_rank_id():
|
||||
global_rank_id = os.getenv('RANK_ID', '0')
|
||||
return int(global_rank_id)
|
||||
|
||||
|
||||
def get_job_id():
|
||||
return "Local Job"
|
||||
|
|
@ -0,0 +1,123 @@
|
|||
# Copyright 2021 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.
|
||||
# ============================================================================
|
||||
|
||||
"""Moxing adapter for ModelArts"""
|
||||
|
||||
import os
|
||||
import functools
|
||||
from mindspore import context
|
||||
from mindspore.profiler import Profiler
|
||||
from src.model_utils.config import config
|
||||
|
||||
_global_sync_count = 0
|
||||
|
||||
def get_device_id():
|
||||
device_id = os.getenv('DEVICE_ID', '0')
|
||||
return int(device_id)
|
||||
|
||||
|
||||
def get_device_num():
|
||||
device_num = os.getenv('RANK_SIZE', '1')
|
||||
return int(device_num)
|
||||
|
||||
|
||||
def get_rank_id():
|
||||
global_rank_id = os.getenv('RANK_ID', '0')
|
||||
return int(global_rank_id)
|
||||
|
||||
|
||||
def get_job_id():
|
||||
job_id = os.getenv('JOB_ID')
|
||||
job_id = job_id if job_id != "" else "default"
|
||||
return job_id
|
||||
|
||||
def sync_data(from_path, to_path):
|
||||
"""
|
||||
Download data from remote obs to local directory if the first url is remote url and the second one is local path
|
||||
Upload data from local directory to remote obs in contrast.
|
||||
"""
|
||||
import moxing as mox
|
||||
import time
|
||||
global _global_sync_count
|
||||
sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count)
|
||||
_global_sync_count += 1
|
||||
|
||||
# Each server contains 8 devices as most.
|
||||
if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
|
||||
print("from path: ", from_path)
|
||||
print("to path: ", to_path)
|
||||
mox.file.copy_parallel(from_path, to_path)
|
||||
print("===finish data synchronization===")
|
||||
try:
|
||||
os.mknod(sync_lock)
|
||||
# print("os.mknod({}) success".format(sync_lock))
|
||||
except IOError:
|
||||
pass
|
||||
print("===save flag===")
|
||||
|
||||
while True:
|
||||
if os.path.exists(sync_lock):
|
||||
break
|
||||
time.sleep(1)
|
||||
|
||||
print("Finish sync data from {} to {}.".format(from_path, to_path))
|
||||
|
||||
|
||||
def moxing_wrapper(pre_process=None, post_process=None):
|
||||
"""
|
||||
Moxing wrapper to download dataset and upload outputs.
|
||||
"""
|
||||
def wrapper(run_func):
|
||||
@functools.wraps(run_func)
|
||||
def wrapped_func(*args, **kwargs):
|
||||
# Download data from data_url
|
||||
if config.enable_modelarts:
|
||||
if config.data_url:
|
||||
sync_data(config.data_url, config.data_path)
|
||||
print("Dataset downloaded: ", os.listdir(config.data_path))
|
||||
if config.checkpoint_url:
|
||||
sync_data(config.checkpoint_url, config.load_path)
|
||||
print("Preload downloaded: ", os.listdir(config.load_path))
|
||||
if config.train_url:
|
||||
sync_data(config.train_url, config.output_path)
|
||||
print("Workspace downloaded: ", os.listdir(config.output_path))
|
||||
|
||||
context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id())))
|
||||
config.device_num = get_device_num()
|
||||
config.device_id = get_device_id()
|
||||
if not os.path.exists(config.output_path):
|
||||
os.makedirs(config.output_path)
|
||||
|
||||
if pre_process:
|
||||
pre_process()
|
||||
|
||||
if config.enable_profiling:
|
||||
profiler = Profiler()
|
||||
|
||||
run_func(*args, **kwargs)
|
||||
|
||||
if config.enable_profiling:
|
||||
profiler.analyse()
|
||||
|
||||
# Upload data to train_url
|
||||
if config.enable_modelarts:
|
||||
if post_process:
|
||||
post_process()
|
||||
|
||||
if config.train_url:
|
||||
print("Start to copy output directory")
|
||||
sync_data(config.output_path, config.train_url)
|
||||
return wrapped_func
|
||||
return wrapper
|
||||
|
|
@ -17,16 +17,10 @@ Post-process functions after decoding
|
|||
"""
|
||||
|
||||
import numpy as np
|
||||
from src.config import dataset_config as config
|
||||
from src.model_utils.config import dataset_config as config
|
||||
from .image import get_affine_transform, affine_transform, transform_preds
|
||||
from .visual import coco_box_to_bbox
|
||||
|
||||
try:
|
||||
from nms import soft_nms_39
|
||||
except ImportError:
|
||||
print('NMS not installed! Do \n cd $CenterNet_ROOT/scripts/ \n'
|
||||
'and see run_standalone_eval.sh for more details to install it\n')
|
||||
|
||||
_NUM_JOINTS = config.num_joints
|
||||
|
||||
|
||||
|
|
@ -48,6 +42,11 @@ def merge_outputs(detections, soft_nms=True):
|
|||
"""merge detections together by nms"""
|
||||
results = np.concatenate([detection for detection in detections], axis=0).astype(np.float32)
|
||||
if soft_nms:
|
||||
try:
|
||||
from nms import soft_nms_39
|
||||
except ImportError:
|
||||
print('NMS not installed! Do \n cd $CenterNet_ROOT/scripts/ \n'
|
||||
'and see run_standalone_eval.sh for more details to install it\n')
|
||||
soft_nms_39(results, Nt=0.5, threshold=0.01, method=2)
|
||||
results = results.tolist()
|
||||
return results
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ import random
|
|||
import cv2
|
||||
import numpy as np
|
||||
import pycocotools.coco as COCO
|
||||
from .config import dataset_config as data_cfg
|
||||
from .model_utils.config import dataset_config as data_cfg
|
||||
from .image import get_affine_transform, affine_transform
|
||||
|
||||
_NUM_JOINTS = data_cfg.num_joints
|
||||
|
|
|
|||
|
|
@ -17,7 +17,6 @@ Train CenterNet and get network model files(.ckpt)
|
|||
"""
|
||||
|
||||
import os
|
||||
import argparse
|
||||
import mindspore.communication.management as D
|
||||
from mindspore.communication.management import get_rank
|
||||
from mindspore import context
|
||||
|
|
@ -29,46 +28,17 @@ from mindspore.nn.optim import Adam
|
|||
from mindspore import log as logger
|
||||
from mindspore.common import set_seed
|
||||
from mindspore.profiler import Profiler
|
||||
|
||||
from src.dataset import COCOHP
|
||||
from src import CenterNetMultiPoseLossCell, CenterNetWithLossScaleCell
|
||||
from src import CenterNetWithoutLossScaleCell
|
||||
from src.utils import LossCallBack, CenterNetPolynomialDecayLR, CenterNetMultiEpochsDecayLR
|
||||
from src.config import dataset_config, net_config, train_config
|
||||
from src.model_utils.config import config, dataset_config, net_config, train_config
|
||||
from src.model_utils.moxing_adapter import moxing_wrapper
|
||||
from src.model_utils.device_adapter import get_device_id, get_rank_id, get_device_num
|
||||
|
||||
_current_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
parser = argparse.ArgumentParser(description='CenterNet training')
|
||||
parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'CPU'],
|
||||
help='device where the code will be implemented. (Default: Ascend)')
|
||||
parser.add_argument("--distribute", type=str, default="false", choices=["true", "false"],
|
||||
help="Run distribute, default is false.")
|
||||
parser.add_argument("--need_profiler", type=str, default="false", choices=["true", "false"],
|
||||
help="Profiling to parsing runtime info, default is false.")
|
||||
parser.add_argument("--profiler_path", type=str, default=" ", help="The path to save profiling data")
|
||||
parser.add_argument("--epoch_size", type=int, default="1", help="Epoch size, default is 1.")
|
||||
parser.add_argument("--train_steps", type=int, default=-1, help="Training Steps, default is -1,"
|
||||
"i.e. run all steps according to epoch number.")
|
||||
parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
|
||||
parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
|
||||
parser.add_argument("--enable_save_ckpt", type=str, default="true", choices=["true", "false"],
|
||||
help="Enable save checkpoint, default is true.")
|
||||
parser.add_argument("--do_shuffle", type=str, default="true", choices=["true", "false"],
|
||||
help="Enable shuffle for dataset, default is true.")
|
||||
parser.add_argument("--enable_data_sink", type=str, default="true", choices=["true", "false"],
|
||||
help="Enable data sink, default is true.")
|
||||
parser.add_argument("--data_sink_steps", type=int, default="1", help="Sink steps for each epoch, default is 1.")
|
||||
parser.add_argument("--save_checkpoint_path", type=str, default="", help="Save checkpoint path")
|
||||
parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path")
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parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, default is 1000.")
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parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
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parser.add_argument("--mindrecord_dir", type=str, default="", help="Mindrecord dataset files directory")
|
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parser.add_argument("--mindrecord_prefix", type=str, default="coco_hp.train.mind",
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help="Prefix of MindRecord dataset filename.")
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parser.add_argument("--visual_image", type=str, default="false", help="Visulize the ground truth and predicted image")
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parser.add_argument("--save_result_dir", type=str, default="", help="The path to save the predict results")
|
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args_opt = parser.parse_args()
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def _set_parallel_all_reduce_split():
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"""set centernet all_reduce fusion split"""
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|
|
@ -102,7 +72,7 @@ def _get_optimizer(network, dataset_size):
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lr_schedule = CenterNetPolynomialDecayLR(learning_rate=train_config.PolyDecay.learning_rate,
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end_learning_rate=train_config.PolyDecay.end_learning_rate,
|
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warmup_steps=train_config.PolyDecay.warmup_steps,
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decay_steps=args_opt.train_steps,
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decay_steps=config.train_steps,
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power=train_config.PolyDecay.power)
|
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optimizer = Adam(group_params, learning_rate=lr_schedule, eps=train_config.PolyDecay.eps, loss_scale=1.0)
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elif train_config.lr_schedule == "MultiDecay":
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|
|
@ -110,7 +80,7 @@ def _get_optimizer(network, dataset_size):
|
|||
if not isinstance(multi_epochs, (list, tuple)):
|
||||
raise TypeError("multi_epochs must be list or tuple.")
|
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if not multi_epochs:
|
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multi_epochs = [args_opt.epoch_size]
|
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multi_epochs = [config.epoch_size]
|
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lr_schedule = CenterNetMultiEpochsDecayLR(learning_rate=train_config.MultiDecay.learning_rate,
|
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warmup_steps=train_config.MultiDecay.warmup_steps,
|
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multi_epochs=multi_epochs,
|
||||
|
|
@ -126,83 +96,90 @@ def _get_optimizer(network, dataset_size):
|
|||
return optimizer
|
||||
|
||||
|
||||
def modelarts_pre_process():
|
||||
'''modelarts pre process function.'''
|
||||
config.mindrecord_dir = config.data_path
|
||||
config.save_checkpoint_path = os.path.join(config.output_path, config.save_checkpoint_path)
|
||||
|
||||
|
||||
@moxing_wrapper(pre_process=modelarts_pre_process)
|
||||
def train():
|
||||
"""training CenterNet"""
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
|
||||
context.set_context(reserve_class_name_in_scope=False)
|
||||
context.set_context(save_graphs=False)
|
||||
|
||||
ckpt_save_dir = args_opt.save_checkpoint_path
|
||||
ckpt_save_dir = config.save_checkpoint_path
|
||||
rank = 0
|
||||
device_num = 1
|
||||
num_workers = 8
|
||||
if args_opt.device_target == "Ascend":
|
||||
if config.device_target == "Ascend":
|
||||
context.set_context(enable_auto_mixed_precision=False)
|
||||
context.set_context(device_id=args_opt.device_id)
|
||||
if args_opt.distribute == "true":
|
||||
context.set_context(device_id=get_device_id())
|
||||
if config.distribute == "true":
|
||||
D.init()
|
||||
device_num = args_opt.device_num
|
||||
rank = args_opt.device_id % device_num
|
||||
ckpt_save_dir = args_opt.save_checkpoint_path + 'ckpt_' + str(get_rank()) + '/'
|
||||
device_num = get_device_num()
|
||||
rank = get_rank_id()
|
||||
ckpt_save_dir = config.save_checkpoint_path + 'ckpt_' + str(get_rank()) + '/'
|
||||
|
||||
context.reset_auto_parallel_context()
|
||||
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
|
||||
device_num=device_num)
|
||||
_set_parallel_all_reduce_split()
|
||||
else:
|
||||
args_opt.distribute = "false"
|
||||
args_opt.need_profiler = "false"
|
||||
args_opt.enable_data_sink = "false"
|
||||
config.distribute = "false"
|
||||
config.need_profiler = "false"
|
||||
config.enable_data_sink = "false"
|
||||
|
||||
# Start create dataset!
|
||||
# mindrecord files will be generated at args_opt.mindrecord_dir such as centernet.mindrecord0, 1, ... file_num.
|
||||
logger.info("Begin creating dataset for CenterNet")
|
||||
coco = COCOHP(dataset_config, run_mode="train", net_opt=net_config,
|
||||
enable_visual_image=(args_opt.visual_image == "true"), save_path=args_opt.save_result_dir)
|
||||
dataset = coco.create_train_dataset(args_opt.mindrecord_dir, args_opt.mindrecord_prefix,
|
||||
enable_visual_image=(config.visual_image == "true"), save_path=config.save_result_dir)
|
||||
dataset = coco.create_train_dataset(config.mindrecord_dir, config.mindrecord_prefix,
|
||||
batch_size=train_config.batch_size, device_num=device_num, rank=rank,
|
||||
num_parallel_workers=num_workers, do_shuffle=args_opt.do_shuffle == 'true')
|
||||
num_parallel_workers=num_workers, do_shuffle=config.do_shuffle == 'true')
|
||||
dataset_size = dataset.get_dataset_size()
|
||||
logger.info("Create dataset done!")
|
||||
|
||||
net_with_loss = CenterNetMultiPoseLossCell(net_config)
|
||||
|
||||
new_repeat_count = args_opt.epoch_size * dataset_size // args_opt.data_sink_steps
|
||||
if args_opt.train_steps > 0:
|
||||
new_repeat_count = min(new_repeat_count, args_opt.train_steps // args_opt.data_sink_steps)
|
||||
new_repeat_count = config.epoch_size * dataset_size // config.data_sink_steps
|
||||
if config.train_steps > 0:
|
||||
new_repeat_count = min(new_repeat_count, config.train_steps // config.data_sink_steps)
|
||||
else:
|
||||
args_opt.train_steps = args_opt.epoch_size * dataset_size
|
||||
logger.info("train steps: {}".format(args_opt.train_steps))
|
||||
config.train_steps = config.epoch_size * dataset_size
|
||||
logger.info("train steps: {}".format(config.train_steps))
|
||||
|
||||
optimizer = _get_optimizer(net_with_loss, dataset_size)
|
||||
|
||||
enable_static_time = args_opt.device_target == "CPU"
|
||||
callback = [TimeMonitor(args_opt.data_sink_steps), LossCallBack(dataset_size, enable_static_time)]
|
||||
if args_opt.enable_save_ckpt == "true" and args_opt.device_id % min(8, device_num) == 0:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_checkpoint_steps,
|
||||
keep_checkpoint_max=args_opt.save_checkpoint_num)
|
||||
enable_static_time = config.device_target == "CPU"
|
||||
callback = [TimeMonitor(config.data_sink_steps), LossCallBack(dataset_size, enable_static_time)]
|
||||
if config.enable_save_ckpt == "true" and get_device_id() % min(8, device_num) == 0:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
|
||||
keep_checkpoint_max=config.save_checkpoint_num)
|
||||
ckpoint_cb = ModelCheckpoint(prefix='checkpoint_centernet',
|
||||
directory=None if ckpt_save_dir == "" else ckpt_save_dir, config=config_ck)
|
||||
callback.append(ckpoint_cb)
|
||||
|
||||
if args_opt.load_checkpoint_path:
|
||||
param_dict = load_checkpoint(args_opt.load_checkpoint_path)
|
||||
if config.load_checkpoint_path:
|
||||
param_dict = load_checkpoint(config.load_checkpoint_path)
|
||||
load_param_into_net(net_with_loss, param_dict)
|
||||
if args_opt.device_target == "Ascend":
|
||||
if config.device_target == "Ascend":
|
||||
net_with_grads = CenterNetWithLossScaleCell(net_with_loss, optimizer=optimizer,
|
||||
sens=train_config.loss_scale_value)
|
||||
else:
|
||||
net_with_grads = CenterNetWithoutLossScaleCell(net_with_loss, optimizer=optimizer)
|
||||
|
||||
model = Model(net_with_grads)
|
||||
model.train(new_repeat_count, dataset, callbacks=callback, dataset_sink_mode=(args_opt.enable_data_sink == "true"),
|
||||
sink_size=args_opt.data_sink_steps)
|
||||
model.train(new_repeat_count, dataset, callbacks=callback, dataset_sink_mode=(config.enable_data_sink == "true"),
|
||||
sink_size=config.data_sink_steps)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if args_opt.need_profiler == "true":
|
||||
profiler = Profiler(output_path=args_opt.profiler_path)
|
||||
if config.need_profiler == "true":
|
||||
profiler = Profiler(output_path=config.profiler_path)
|
||||
set_seed(0)
|
||||
train()
|
||||
if args_opt.need_profiler == "true":
|
||||
if config.need_profiler == "true":
|
||||
profiler.analyse()
|
||||
|
|
|
|||
Loading…
Reference in New Issue