!18694 modify FaceQuality network for clould
Merge pull request !18694 from zhanghuiyao/FaceQuality_clould
This commit is contained in:
Gitee
commit
a97c7cfed6
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@ -85,10 +85,16 @@ We use about 122K face images as training dataset and 2K as evaluating dataset i
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The entire code structure is as following:
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```python
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```text
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.
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└─ Face Quality Assessment
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├─ README.md
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├─ model_utils
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├─ __init__.py # module init file
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├─ config.py # Parse arguments
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├─ device_adapter.py # Device adapter for ModelArts
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├─ local_adapter.py # Local adapter
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└─ moxing_adapter.py # Moxing adapter for ModelArts
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├─ scripts
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├─ run_standalone_train.sh # launch standalone training(1p) in ascend
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├─ run_distribute_train.sh # launch distributed training(8p) in ascend
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@ -102,12 +108,12 @@ The entire code structure is as following:
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├─ run_eval_cpu.sh # launch evaluating in cpu
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└─ run_export_cpu.sh # launch exporting mindir model in cpu
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├─ src
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├─ config.py # parameter configuration
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├─ dataset.py # dataset loading and preprocessing for training
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├─ face_qa.py # network backbone
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├─ log.py # log function
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├─ loss_factory.py # loss function
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└─ lr_generator.py # generate learning rate
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├─ default_config.yaml # Configurations
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├─ train.py # training scripts
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├─ eval.py # evaluation scripts
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└─ export.py # export air model
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@ -225,6 +231,95 @@ epoch[39], iter[19100], loss:2.140766, 8088.52 imgs/sec
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epoch[39], iter[19110], loss:2.111101, 8791.05 imgs/sec
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```
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- ModelArts (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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```bash
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# Train 8p on ModelArts with Ascend
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# (1) Perform a or b.
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# a. Set "enable_modelarts=True" on default_config.yaml file.
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# Set "is_distributed=1" on default_config.yaml file.
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# Set "per_batch_size=32" on default_config.yaml file.
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# Set "train_label_file='/cache/data/face_quality_dataset/qa_300W_LP_train.txt'" on default_config.yaml file.
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# (option) Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file if load pretrain.
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# (option) Set "pretrained='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file if load pretrain.
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# Set other parameters on default_config.yaml file you need.
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# b. Add "enable_modelarts=True" on the website UI interface.
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# Add "is_distributed=1" on the website UI interface.
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# Add "per_batch_size=32" on the website UI interface.
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# Add "train_label_file=/cache/data/face_quality_dataset/qa_300W_LP_train.txt" on the website UI interface.
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# (option) Add "checkpoint_url=s3://dir_to_trained_ckpt/" on the website UI interface if load pretrain.
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# (option) Add "pretrained=/cache/checkpoint_path/model.ckpt" on the website UI interface if load pretrain.
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# Add other parameters on the website UI interface.
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# (2) (option) Upload or copy your pretrained model to S3 bucket if load pretrain.
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# (3) Modify imagepath on "/dir_to_your_dataset/qa_300W_LP_train.txt" file.
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# (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
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# (5) Set the code directory to "/path/FaceQualityAssessment" on the website UI interface.
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# (6) Set the startup file to "train.py" on the website UI interface.
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# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
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# (8) Create your job.
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#
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# Train 1p on ModelArts with Ascend
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# (1) Perform a or b.
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# a. Set "enable_modelarts=True" on default_config.yaml file.
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# Set "is_distributed=0" on default_config.yaml file.
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# Set "per_batch_size=256" on default_config.yaml file.
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# Set "train_label_file='/cache/data/face_quality_dataset/qa_300W_LP_train.txt'" on default_config.yaml file.
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# (option) Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file if load pretrain.
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# (option) Set "pretrained='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file if load pretrain.
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# Set other parameters on default_config.yaml file you need.
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# b. Add "enable_modelarts=True" on the website UI interface.
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# Add "is_distributed=0" on the website UI interface.
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# Add "per_batch_size=256" on the website UI interface.
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# Add "train_label_file=/cache/data/face_quality_dataset/qa_300W_LP_train.txt" on the website UI interface.
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# (option) Add "checkpoint_url=s3://dir_to_trained_ckpt/" on the website UI interface if load pretrain.
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# (option) Add "pretrained=/cache/checkpoint_path/model.ckpt" on the website UI interface if load pretrain.
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# Add other parameters on the website UI interface.
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# (2) (option) Upload or copy your pretrained model to S3 bucket if load pretrain.
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# (3) Modify imagepath on "/dir_to_your_dataset/qa_300W_LP_train.txt" file.
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# (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
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# (5) Set the code directory to "/path/FaceQualityAssessment" on the website UI interface.
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# (6) Set the startup file to "train.py" on the website UI interface.
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# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
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# (8) Create your job.
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#
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# Eval 1p on ModelArts with Ascend
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# (1) Perform a or b.
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# a. Set "enable_modelarts=True" on default_config.yaml file.
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# Set "eval_dir='/cache/data/face_quality_dataset/AFLW2000'" on default_config.yaml file.
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# Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file.
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# Set "pretrained='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
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# Set other parameters on default_config.yaml file you need.
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# b. Add "enable_modelarts=True" on the website UI interface.
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# Add "eval_dir=/cache/data/face_quality_dataset/AFLW2000" on the website UI interface.
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# Add "checkpoint_url=s3://dir_to_trained_ckpt/" on the website UI interface.
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# Add "pretrained=/cache/checkpoint_path/model.ckpt" on the website UI interface.
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# Add other parameters on the website UI interface.
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# (2) Upload or copy your trained model to S3 bucket.
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# (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
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# (4) Set the code directory to "/path/FaceQualityAssessment" on the website UI interface.
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# (5) Set the startup file to "eval.py" on the website UI interface.
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# (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
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# (7) Create your job.
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#
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# Export 1p on ModelArts with Ascend
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# (1) Perform a or b.
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# a. Set "enable_modelarts=True" on default_config.yaml file.
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# Set "batch_size=8" on default_config.yaml file.
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# Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file.
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# Set "pretrained='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
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# Set other parameters on default_config.yaml file you need.
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# b. Add "enable_modelarts=True" on the website UI interface.
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# Add "batch_size=8" on the website UI interface.
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# Add "checkpoint_url=s3://dir_to_trained_ckpt/" on the website UI interface.
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# Add "pretrained=/cache/checkpoint_path/model.ckpt" on the website UI interface.
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# Add other parameters on the website UI interface.
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# (2) Upload or copy your trained model to S3 bucket.
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# (3) Set the code directory to "/path/FaceQualityAssessment" on the website UI interface.
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# (4) Set the startup file to "export.py" on the website UI interface.
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# (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
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# (6) Create your job.
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```
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### Evaluation
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```bash
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@ -0,0 +1,64 @@
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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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need_modelarts_dataset_unzip: True
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modelarts_dataset_unzip_name: "face_quality_dataset"
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# ==============================================================================
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# options
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task: 'face_qa'
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# dataset related
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per_batch_size: 256 # if run 1p
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#per_batch_size: 32 # if run 8p
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# network structure related
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steps_per_epoch: 0
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loss_scale: 1024
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# optimizer related
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lr: 0.02
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lr_scale: 1
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lr_epochs: '10, 20, 30'
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weight_decay: 0.0005
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momentum: 0.9
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max_epoch: 40
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warmup_epochs: 0
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pretrained: ''
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# logging related
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log_interval: 10
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ckpt_path: './output'
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ckpt_interval: 500
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# train option
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is_distributed: 0
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train_label_file: ''
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# eval option
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eval_dir: ''
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# export option
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batch_size: 8
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file_name: 'FaceQualityAssessment'
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file_format: 'AIR'
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---
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# Help description for each configuration
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is_distributed: "if multi device"
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train_label_file: "image label list file, e.g. /home/label.txt"
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pretrained: "pretrained model to load"
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device_target: "device target, choices in ['Ascend', 'GPU', 'CPU']"
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eval_dir: "eval image dir, e.g. /home/test"
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batch_size: "batch size for export"
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file_name: "output file name"
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file_format: "file format, choices in ['AIR', 'ONNX', 'MINDIR']"
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@ -14,8 +14,8 @@
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# ============================================================================
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"""Face Quality Assessment eval."""
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import os
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import time
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import warnings
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import argparse
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import numpy as np
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import cv2
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from tqdm import tqdm
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@ -28,6 +28,10 @@ from mindspore import context
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from src.face_qa import FaceQABackbone
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from model_utils.config import config
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from model_utils.moxing_adapter import moxing_wrapper
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from model_utils.device_adapter import get_device_id, get_device_num
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warnings.filterwarnings('ignore')
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@ -99,11 +103,64 @@ reshape = P.Reshape()
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argmax = P.ArgMaxWithValue()
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def test_trains(args):
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'''test trains'''
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def modelarts_pre_process():
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'''modelarts pre process function.'''
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def unzip(zip_file, save_dir):
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import zipfile
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s_time = time.time()
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if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
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zip_isexist = zipfile.is_zipfile(zip_file)
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if zip_isexist:
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fz = zipfile.ZipFile(zip_file, 'r')
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data_num = len(fz.namelist())
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print("Extract Start...")
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print("unzip file num: {}".format(data_num))
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data_print = int(data_num / 100) if data_num > 100 else 1
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i = 0
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for file in fz.namelist():
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if i % data_print == 0:
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print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
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i += 1
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fz.extract(file, save_dir)
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print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
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int(int(time.time() - s_time) % 60)))
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print("Extract Done.")
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else:
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print("This is not zip.")
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else:
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print("Zip has been extracted.")
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if config.need_modelarts_dataset_unzip:
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zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
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save_dir_1 = os.path.join(config.data_path)
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sync_lock = "/tmp/unzip_sync.lock"
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# Each server contains 8 devices as most.
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if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
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print("Zip file path: ", zip_file_1)
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print("Unzip file save dir: ", save_dir_1)
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unzip(zip_file_1, save_dir_1)
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print("===Finish extract data synchronization===")
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try:
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os.mknod(sync_lock)
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except IOError:
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pass
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while True:
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if os.path.exists(sync_lock):
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break
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time.sleep(1)
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print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
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@moxing_wrapper(pre_process=modelarts_pre_process)
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def run_eval():
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'''run eval'''
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print('----eval----begin----')
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model_path = args.pretrained
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model_path = config.pretrained
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result_file = model_path.replace('.ckpt', '.txt')
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if os.path.exists(result_file):
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os.remove(result_file)
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@ -130,7 +187,7 @@ def test_trains(args):
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print('wrong model path')
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return 1
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path = args.eval_dir
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path = config.eval_dir
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kp_error_all = [[], [], [], [], []]
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eulers_error_all = [[], [], []]
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kp_ipn = []
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@ -205,17 +262,8 @@ def test_trains(args):
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Face Quality Assessment')
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parser.add_argument('--eval_dir', type=str, default='', help='eval image dir, e.g. /home/test')
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parser.add_argument('--pretrained', type=str, default='', help='pretrained model to load')
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parser.add_argument('--device_target', type=str, choices=['Ascend', 'GPU', 'CPU'], default='Ascend',
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help='device target')
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arg = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=arg.device_target, save_graphs=False)
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if arg.device_target == 'Ascend':
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context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, save_graphs=False)
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if config.device_target == 'Ascend':
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=devid)
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test_trains(arg)
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run_eval()
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@ -14,7 +14,6 @@
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# ============================================================================
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"""Convert ckpt to air/mindir."""
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import os
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import argparse
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import numpy as np
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from mindspore import context
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@ -23,10 +22,25 @@ from mindspore.train.serialization import export, load_checkpoint, load_param_in
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from src.face_qa import FaceQABackbone
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from model_utils.config import config
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from model_utils.moxing_adapter import moxing_wrapper
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def modelarts_pre_process():
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'''modelarts pre process function.'''
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config.file_name = os.path.join(config.output_path, config.file_name)
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@moxing_wrapper(pre_process=modelarts_pre_process)
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def run_export():
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'''run export.'''
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context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, save_graphs=False)
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if config.device_target == 'Ascend':
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=devid)
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def main(args):
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network = FaceQABackbone()
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ckpt_path = args.pretrained
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ckpt_path = config.pretrained
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if os.path.isfile(ckpt_path):
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param_dict = load_checkpoint(ckpt_path)
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param_dict_new = {}
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@ -42,28 +56,12 @@ def main(args):
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else:
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print('-----------------------load model failed -----------------------')
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input_data = np.random.uniform(low=0, high=1.0, size=(args.batch_size, 3, 96, 96)).astype(np.float32)
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input_data = np.random.uniform(low=0, high=1.0, size=(config.batch_size, 3, 96, 96)).astype(np.float32)
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tensor_input_data = Tensor(input_data)
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export(network, tensor_input_data, file_name=args.file_name, file_format=args.file_format)
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export(network, tensor_input_data, file_name=config.file_name, file_format=config.file_format)
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print('-----------------------export model success-----------------------')
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Convert ckpt to air/mindir')
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parser.add_argument('--pretrained', type=str, default='', help='pretrained model to load')
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parser.add_argument('--batch_size', type=int, default=8, help='batch size')
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parser.add_argument('--device_target', type=str, choices=['Ascend', 'GPU', 'CPU'], default='Ascend',
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help='device target')
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parser.add_argument('--file_name', type=str, default='FaceQualityAssessment', help='output file name')
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parser.add_argument('--file_format', type=str, choices=['AIR', 'ONNX', 'MINDIR'], default='AIR', help='file format')
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arg = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=arg.device_target, save_graphs=False)
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if arg.device_target == 'Ascend':
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=devid)
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main(arg)
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run_export()
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@ -0,0 +1,126 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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||||
# 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 pformat
|
||||
import yaml
|
||||
|
||||
class Config:
|
||||
"""
|
||||
Configuration namespace. Convert dictionary to members.
|
||||
"""
|
||||
def __init__(self, cfg_dict):
|
||||
for k, v in cfg_dict.items():
|
||||
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 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)
|
||||
args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
|
||||
final_config = merge(args, default)
|
||||
return Config(final_config)
|
||||
|
||||
config = get_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 .config import config
|
||||
|
||||
if config.enable_modelarts:
|
||||
from .moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
|
||||
else:
|
||||
from .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 +1,116 @@
|
|||
# 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 .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)
|
||||
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()
|
||||
|
||||
# Run the main function
|
||||
run_func(*args, **kwargs)
|
||||
|
||||
# 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
|
||||
|
|
@ -80,6 +80,7 @@ do
|
|||
dev=`expr $i + 0`
|
||||
export DEVICE_ID=$dev
|
||||
python ${dirname_path}/${SCRIPT_NAME} \
|
||||
--per_batch_size=32 \
|
||||
--is_distributed=1 \
|
||||
--train_label_file=$TRAIN_LABEL_FILE \
|
||||
--pretrained=$PRETRAINED_BACKBONE > train.log 2>&1 &
|
||||
|
|
|
|||
|
|
@ -47,12 +47,14 @@ then
|
|||
mpirun -n $1 --allow-run-as-root python ${BASEPATH}/../train.py \
|
||||
--train_label_file=$3 \
|
||||
--is_distributed=1 \
|
||||
--per_batch_size=32 \
|
||||
--device_target='GPU' \
|
||||
--pretrained=$4 > train.log 2>&1 &
|
||||
else
|
||||
python ${BASEPATH}/../train.py \
|
||||
--train_label_file=$3 \
|
||||
--is_distributed=0 \
|
||||
--per_batch_size=256 \
|
||||
--device_target='GPU' \
|
||||
--pretrained=$4 > train.log 2>&1 &
|
||||
fi
|
||||
|
|
@ -62,11 +64,13 @@ else
|
|||
mpirun -n $1 --allow-run-as-root python ${BASEPATH}/../train.py \
|
||||
--train_label_file=$3 \
|
||||
--is_distributed=1 \
|
||||
--per_batch_size=32 \
|
||||
--device_target='GPU' > train.log 2>&1 &
|
||||
else
|
||||
python ${BASEPATH}/../train.py \
|
||||
--train_label_file=$3 \
|
||||
--is_distributed=0 \
|
||||
--per_batch_size=256 \
|
||||
--device_target='GPU' > train.log 2>&1 &
|
||||
fi
|
||||
fi
|
||||
|
|
|
|||
|
|
@ -77,6 +77,7 @@ dev=`expr $USE_DEVICE_ID + 0`
|
|||
export DEVICE_ID=$dev
|
||||
python ${dirname_path}/${SCRIPT_NAME} \
|
||||
--is_distributed=0 \
|
||||
--per_batch_size=256 \
|
||||
--train_label_file=$TRAIN_LABEL_FILE \
|
||||
--pretrained=$PRETRAINED_BACKBONE > train.log 2>&1 &
|
||||
|
||||
|
|
|
|||
|
|
@ -31,11 +31,13 @@ cd ${current_exec_path}/cpu || exit
|
|||
if [ $2 ] # pretrained ckpt
|
||||
then
|
||||
python ${BASEPATH}/../train.py \
|
||||
--per_batch_size=256 \
|
||||
--train_label_file=$1 \
|
||||
--device_target='CPU' \
|
||||
--pretrained=$2 > train.log 2>&1 &
|
||||
else
|
||||
python ${BASEPATH}/../train.py \
|
||||
--per_batch_size=256 \
|
||||
--train_label_file=$1 \
|
||||
--device_target='CPU' > train.log 2>&1 &
|
||||
fi
|
||||
|
|
|
|||
|
|
@ -32,10 +32,12 @@ if [ $2 ] # pretrained ckpt
|
|||
then
|
||||
python ${BASEPATH}/../train.py \
|
||||
--train_label_file=$1 \
|
||||
--per_batch_size=256 \
|
||||
--device_target='GPU' \
|
||||
--pretrained=$2 > train.log 2>&1 &
|
||||
else
|
||||
python ${BASEPATH}/../train.py \
|
||||
--train_label_file=$1 \
|
||||
--per_batch_size=256 \
|
||||
--device_target='GPU' > train.log 2>&1 &
|
||||
fi
|
||||
|
|
|
|||
|
|
@ -1,76 +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 train.py and eval.py"""
|
||||
from easydict import EasyDict as edict
|
||||
|
||||
faceqa_1p_cfg = edict({
|
||||
'task': 'face_qa',
|
||||
|
||||
# dataset related
|
||||
'per_batch_size': 256,
|
||||
|
||||
# network structure related
|
||||
'steps_per_epoch': 0,
|
||||
'loss_scale': 1024,
|
||||
|
||||
# optimizer related
|
||||
'lr': 0.02,
|
||||
'lr_scale': 1,
|
||||
'lr_epochs': '10, 20, 30',
|
||||
'weight_decay': 0.0005,
|
||||
'momentum': 0.9,
|
||||
'max_epoch': 40,
|
||||
'warmup_epochs': 0,
|
||||
'pretrained': '',
|
||||
|
||||
'local_rank': 0,
|
||||
'world_size': 1,
|
||||
|
||||
# logging related
|
||||
'log_interval': 10,
|
||||
'ckpt_path': '../../output',
|
||||
'ckpt_interval': 500,
|
||||
|
||||
'device_id': 0,
|
||||
})
|
||||
|
||||
faceqa_8p_cfg = edict({
|
||||
'task': 'face_qa',
|
||||
|
||||
# dataset related
|
||||
'per_batch_size': 32,
|
||||
|
||||
# network structure related
|
||||
'steps_per_epoch': 0,
|
||||
'loss_scale': 1024,
|
||||
|
||||
# optimizer related
|
||||
'lr': 0.02,
|
||||
'lr_scale': 1,
|
||||
'lr_epochs': '10, 20, 30',
|
||||
'weight_decay': 0.0005,
|
||||
'momentum': 0.9,
|
||||
'max_epoch': 40,
|
||||
'warmup_epochs': 0,
|
||||
'pretrained': '',
|
||||
|
||||
'local_rank': 0,
|
||||
'world_size': 8,
|
||||
|
||||
# logging related
|
||||
'log_interval': 10, # 10
|
||||
'ckpt_path': '../../output',
|
||||
'ckpt_interval': 500,
|
||||
})
|
||||
|
|
@ -16,7 +16,6 @@
|
|||
import os
|
||||
import time
|
||||
import datetime
|
||||
import argparse
|
||||
import warnings
|
||||
import numpy as np
|
||||
|
||||
|
|
@ -31,37 +30,95 @@ from mindspore.nn.optim import Momentum
|
|||
from mindspore.communication.management import get_group_size, init, get_rank
|
||||
|
||||
from src.loss import CriterionsFaceQA
|
||||
from src.config import faceqa_1p_cfg, faceqa_8p_cfg
|
||||
from src.face_qa import FaceQABackbone, BuildTrainNetwork
|
||||
from src.lr_generator import warmup_step
|
||||
from src.dataset import faceqa_dataset
|
||||
from src.log import get_logger, AverageMeter
|
||||
|
||||
from model_utils.config import config as cfg
|
||||
from model_utils.moxing_adapter import moxing_wrapper
|
||||
from model_utils.device_adapter import get_device_id, get_device_num
|
||||
|
||||
warnings.filterwarnings('ignore')
|
||||
mindspore.common.seed.set_seed(1)
|
||||
|
||||
def main(args):
|
||||
|
||||
if args.is_distributed == 0:
|
||||
cfg = faceqa_1p_cfg
|
||||
else:
|
||||
cfg = faceqa_8p_cfg
|
||||
def modelarts_pre_process():
|
||||
'''modelarts pre process function.'''
|
||||
def unzip(zip_file, save_dir):
|
||||
import zipfile
|
||||
s_time = time.time()
|
||||
if not os.path.exists(os.path.join(save_dir, cfg.modelarts_dataset_unzip_name)):
|
||||
zip_isexist = zipfile.is_zipfile(zip_file)
|
||||
if zip_isexist:
|
||||
fz = zipfile.ZipFile(zip_file, 'r')
|
||||
data_num = len(fz.namelist())
|
||||
print("Extract Start...")
|
||||
print("unzip file num: {}".format(data_num))
|
||||
data_print = int(data_num / 100) if data_num > 100 else 1
|
||||
i = 0
|
||||
for file in fz.namelist():
|
||||
if i % data_print == 0:
|
||||
print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
|
||||
i += 1
|
||||
fz.extract(file, save_dir)
|
||||
print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
|
||||
int(int(time.time() - s_time) % 60)))
|
||||
print("Extract Done.")
|
||||
else:
|
||||
print("This is not zip.")
|
||||
else:
|
||||
print("Zip has been extracted.")
|
||||
|
||||
cfg.data_lst = args.train_label_file
|
||||
cfg.pretrained = args.pretrained
|
||||
if cfg.need_modelarts_dataset_unzip:
|
||||
zip_file_1 = os.path.join(cfg.data_path, cfg.modelarts_dataset_unzip_name + ".zip")
|
||||
save_dir_1 = os.path.join(cfg.data_path)
|
||||
|
||||
sync_lock = "/tmp/unzip_sync.lock"
|
||||
|
||||
# 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("Zip file path: ", zip_file_1)
|
||||
print("Unzip file save dir: ", save_dir_1)
|
||||
unzip(zip_file_1, save_dir_1)
|
||||
print("===Finish extract data synchronization===")
|
||||
try:
|
||||
os.mknod(sync_lock)
|
||||
except IOError:
|
||||
pass
|
||||
|
||||
while True:
|
||||
if os.path.exists(sync_lock):
|
||||
break
|
||||
time.sleep(1)
|
||||
|
||||
print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
|
||||
|
||||
cfg.ckpt_path = os.path.join(cfg.output_path, cfg.ckpt_path)
|
||||
|
||||
|
||||
@moxing_wrapper(pre_process=modelarts_pre_process)
|
||||
def run_train():
|
||||
'''run train.'''
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target, save_graphs=False)
|
||||
if cfg.device_target == 'Ascend':
|
||||
context.set_context(device_id=get_device_id())
|
||||
|
||||
cfg.data_lst = cfg.train_label_file
|
||||
|
||||
# Init distributed
|
||||
if args.is_distributed:
|
||||
if cfg.is_distributed:
|
||||
init()
|
||||
cfg.local_rank = get_rank()
|
||||
cfg.world_size = get_group_size()
|
||||
parallel_mode = ParallelMode.DATA_PARALLEL
|
||||
else:
|
||||
cfg.local_rank = 0
|
||||
cfg.world_size = 1
|
||||
parallel_mode = ParallelMode.STAND_ALONE
|
||||
|
||||
# parallel_mode 'STAND_ALONE' do not support parameter_broadcast and mirror_mean
|
||||
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=cfg.world_size,
|
||||
gradients_mean=True)
|
||||
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=cfg.world_size, gradients_mean=True)
|
||||
|
||||
mindspore.common.set_seed(1)
|
||||
|
||||
|
|
@ -104,11 +161,8 @@ def main(args):
|
|||
|
||||
# optimizer and lr scheduler
|
||||
lr = warmup_step(cfg, gamma=0.9)
|
||||
opt = Momentum(params=network.trainable_params(),
|
||||
learning_rate=lr,
|
||||
momentum=cfg.momentum,
|
||||
weight_decay=cfg.weight_decay,
|
||||
loss_scale=cfg.loss_scale)
|
||||
opt = Momentum(params=network.trainable_params(), learning_rate=lr, momentum=cfg.momentum,
|
||||
weight_decay=cfg.weight_decay, loss_scale=cfg.loss_scale)
|
||||
|
||||
# package training process, adjust lr + forward + backward + optimizer
|
||||
train_net = BuildTrainNetwork(network, criterion)
|
||||
|
|
@ -142,7 +196,6 @@ def main(args):
|
|||
loss = train_net(data, gt)
|
||||
loss_meter.update(loss.asnumpy())
|
||||
|
||||
# ckpt
|
||||
if cfg.local_rank == 0:
|
||||
cb_params.cur_step_num = i + 1 # current step number
|
||||
cb_params.batch_num = i + 2
|
||||
|
|
@ -175,18 +228,4 @@ def main(args):
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description='Face Quality Assessment')
|
||||
parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
|
||||
parser.add_argument('--train_label_file', type=str, default='', help='image label list file, e.g. /home/label.txt')
|
||||
parser.add_argument('--pretrained', type=str, default='', help='pretrained model to load')
|
||||
parser.add_argument('--device_target', type=str, choices=['Ascend', 'GPU', 'CPU'], default='Ascend',
|
||||
help='device target')
|
||||
|
||||
arg = parser.parse_args()
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=arg.device_target, save_graphs=False)
|
||||
if arg.device_target == 'Ascend':
|
||||
devid = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(device_id=devid)
|
||||
|
||||
main(arg)
|
||||
run_train()
|
||||
|
|
|
|||
Loading…
Reference in New Issue