forked from mindspore-Ecosystem/mindspore
modify model_zoo ssd for clould
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a2dc98f972
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@ -83,25 +83,58 @@ After installing MindSpore via the official website, you can start training and
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```bash
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```bash
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# distributed training
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# distributed training
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Usage: sh scripts/run_distribute_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
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Usage: sh scripts/run_distribute_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [RANK_TABLE_FILE] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)
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# standalone training
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# standalone training
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Usage: sh scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
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Usage: sh scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)
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# run evaluation example
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# run evaluation example
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Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
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Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATA_PATH] [CHECKPOINT_PATH]
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```
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```
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- running on CPU
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- running on CPU
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```bash
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```bash
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# standalone training
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# standalone training
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Usage: bash scripts/run_train_cpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
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Usage: bash scripts/run_train_cpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)
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# run evaluation example
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# run evaluation example
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Usage: bash scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [CHECKPOINT_PATH]
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Usage: bash scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATA_PATH] [CHECKPOINT_PATH]
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```
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```
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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 and evaluation as follows:
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```python
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# run distributed training on modelarts example
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# (1) First, Perform a or b.
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# a. Set "enable_modelarts=True" on yaml file.
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# Set other parameters on yaml file you need.
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# b. Add "enable_modelarts=True" on the website UI interface.
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# Add other parameters on the website UI interface.
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# (2) Set the config directory to "config_path=/The path of config in S3/"
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# (3) Set the Dataset directory in config file.
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# (4) Set the code directory to "/path/squeezenet" on the website UI interface.
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# (5) Set the startup file to "train.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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# run evaluation on modelarts example
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# (1) Copy or upload your trained model to S3 bucket.
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# (2) Perform a or b.
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# a. Set "enable_modelarts=True" on yaml file.
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# Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
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# Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
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# b. Add "enable_modelarts=True" on the website UI interface.
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# Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
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# Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
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# (3) Set the config directory to "config_path=/The path of config in S3/"
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# (4) Set the Dataset directory in config file.
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# (5) Set the code directory to "/path/squeezenet" on the website UI interface.
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# (6) Set the startup file to "eval.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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# [Script Description](#contents)
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# [Script Description](#contents)
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## [Script and Sample Code](#contents)
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## [Script and Sample Code](#contents)
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@ -117,14 +150,22 @@ After installing MindSpore via the official website, you can start training and
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├── run_eval.sh # launch ascend evaluation
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├── run_eval.sh # launch ascend evaluation
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├── run_infer_310.sh # shell script for 310 infer
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├── run_infer_310.sh # shell script for 310 infer
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├── src
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├── src
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├── config.py # parameter configuration
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├── dataset.py # data preprocessing
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├── dataset.py # data preprocessing
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├── CrossEntropySmooth.py # loss definition for ImageNet dataset
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├── CrossEntropySmooth.py # loss definition for ImageNet dataset
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├── lr_generator.py # generate learning rate for each step
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├── lr_generator.py # generate learning rate for each step
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└── squeezenet.py # squeezenet architecture, including squeezenet and squeezenet_residual
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└── squeezenet.py # squeezenet architecture, including squeezenet and squeezenet_residual
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├── train.py # train net
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├── model_utils
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├── eval.py # eval net
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│ ├── device_adapter.py # device adapter
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└── export.py # export checkpoint files into geir/onnx
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│ ├── local_adapter.py # local adapter
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│ ├── moxing_adapter.py # moxing adapter
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│ ├── config.py # parameter analysis
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├── squeezenet_cifar10_config.yaml # parameter configuration
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├── squeezenet_imagenet_config.yaml # parameter configuration
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├── squeezenet_residual_cifar10_config.yaml # parameter configuration
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├── squeezenet_residual_imagenet_config.yaml # parameter configuration
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├── train.py # train net
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├── eval.py # eval net
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└── export.py # export checkpoint files into geir/onnx
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├── postprocess.py # postprocess script
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├── postprocess.py # postprocess script
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├── preprocess.py # preprocess script
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├── preprocess.py # preprocess script
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```
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```
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@ -231,10 +272,10 @@ For more configuration details, please refer the script `config.py`.
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```shell
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```shell
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# distributed training
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# distributed training
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Usage: sh scripts/run_distribute_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
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Usage: sh scripts/run_distribute_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [RANK_TABLE_FILE] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)
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# standalone training
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# standalone training
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Usage: sh scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
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Usage: sh scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)
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```
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```
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For distributed training, a hccl configuration file with JSON format needs to be created in advance.
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For distributed training, a hccl configuration file with JSON format needs to be created in advance.
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@ -301,7 +342,7 @@ epoch: 5 step 5004, loss is 4.888848304748535
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```shell
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```shell
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# evaluation
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# evaluation
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Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
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Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATA_PATH] [CHECKPOINT_PATH]
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```
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```
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```shell
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```shell
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@ -344,7 +385,7 @@ result: {'top_1_accuracy': 0.6094950384122919, 'top_5_accuracy': 0.8263244238156
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### Export MindIR
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### Export MindIR
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```shell
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```shell
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python export.py --ckpt_file [CKPT_PATH] --batch_size [BATCH_SIZE] --net [NET] --dataset [DATASET] --file_format [EXPORT_FORMAT]
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python export.py --checkpoint_file_path [CKPT_PATH] --batch_size [BATCH_SIZE] --net_name [NET] --dataset [DATASET] --file_format [EXPORT_FORMAT]
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```
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```
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The ckpt_file parameter is required,
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The ckpt_file parameter is required,
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@ -604,7 +645,7 @@ If you need to use the trained model to perform inference on multiple hardware p
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device_id=device_id)
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device_id=device_id)
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# Load unseen dataset for inference
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# Load unseen dataset for inference
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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dataset = create_dataset(dataset_path=config.data_path,
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do_train=False,
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do_train=False,
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batch_size=config.batch_size,
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batch_size=config.batch_size,
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target='Ascend')
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target='Ascend')
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@ -617,7 +658,7 @@ If you need to use the trained model to perform inference on multiple hardware p
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metrics={'top_1_accuracy', 'top_5_accuracy'})
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metrics={'top_1_accuracy', 'top_5_accuracy'})
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# Load pre-trained model
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# Load pre-trained model
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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param_dict = load_checkpoint(config.checkpoint_file_path)
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load_param_into_net(net, param_dict)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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net.set_train(False)
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@ -632,7 +673,7 @@ If you need to use the trained model to perform inference on multiple hardware p
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```py
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```py
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# Load dataset
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# Load dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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dataset = create_dataset(dataset_path=config.data_path,
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do_train=True,
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do_train=True,
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repeat_num=1,
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repeat_num=1,
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batch_size=config.batch_size,
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batch_size=config.batch_size,
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@ -643,8 +684,8 @@ If you need to use the trained model to perform inference on multiple hardware p
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net = squeezenet(num_classes=config.class_num)
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net = squeezenet(num_classes=config.class_num)
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# load checkpoint
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# load checkpoint
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if args_opt.pre_trained:
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if config.pre_trained:
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param_dict = load_checkpoint(args_opt.pre_trained)
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param_dict = load_checkpoint(config.pre_trained)
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load_param_into_net(net, param_dict)
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load_param_into_net(net, param_dict)
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# init lr
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# init lr
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@ -679,7 +720,7 @@ If you need to use the trained model to perform inference on multiple hardware p
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save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
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save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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keep_checkpoint_max=config.keep_checkpoint_max)
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time_cb = TimeMonitor(data_size=step_size)
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time_cb = TimeMonitor(data_size=step_size)
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ckpt_cb = ModelCheckpoint(prefix=args_opt.net + '_' + args_opt.dataset,
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ckpt_cb = ModelCheckpoint(prefix=config.net_name + '_' + config.dataset,
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directory=ckpt_save_dir,
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directory=ckpt_save_dir,
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config=config_ck)
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config=config_ck)
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loss_cb = LossMonitor()
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loss_cb = LossMonitor()
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# ============================================================================
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# ============================================================================
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"""eval squeezenet."""
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"""eval squeezenet."""
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import os
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import os
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import argparse
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from mindspore import context
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from mindspore import context
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from mindspore.common import set_seed
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from mindspore.common import set_seed
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.train.model import Model
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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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 src.CrossEntropySmooth import CrossEntropySmooth
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from src.CrossEntropySmooth import CrossEntropySmooth
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
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help='Model.')
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parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
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args_opt = parser.parse_args()
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set_seed(1)
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set_seed(1)
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if args_opt.net == "squeezenet":
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if config.net_name == "squeezenet":
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from src.squeezenet import SqueezeNet as squeezenet
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from src.squeezenet import SqueezeNet as squeezenet
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if args_opt.dataset == "cifar10":
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if config.dataset == "cifar10":
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from src.config import config1 as config
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from src.dataset import create_dataset_cifar as create_dataset
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from src.dataset import create_dataset_cifar as create_dataset
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else:
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else:
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from src.config import config2 as config
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from src.dataset import create_dataset_imagenet as create_dataset
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from src.dataset import create_dataset_imagenet as create_dataset
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else:
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else:
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from src.squeezenet import SqueezeNet_Residual as squeezenet
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from src.squeezenet import SqueezeNet_Residual as squeezenet
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if args_opt.dataset == "cifar10":
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if config.dataset == "cifar10":
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from src.config import config3 as config
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from src.dataset import create_dataset_cifar as create_dataset
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from src.dataset import create_dataset_cifar as create_dataset
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else:
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else:
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from src.config import config4 as config
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from src.dataset import create_dataset_imagenet as create_dataset
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from src.dataset import create_dataset_imagenet as create_dataset
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if __name__ == '__main__':
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@moxing_wrapper()
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target = args_opt.device_target
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def eval_net():
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"""eval net """
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target = config.device_target
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# init context
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# init context
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device_id = os.getenv('DEVICE_ID')
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device_id = os.getenv('DEVICE_ID')
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device_id=device_id)
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device_id=device_id)
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# create dataset
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# create dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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dataset = create_dataset(dataset_path=config.data_path,
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do_train=False,
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do_train=False,
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batch_size=config.batch_size,
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batch_size=config.batch_size,
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target=target)
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target=target)
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step_size = dataset.get_dataset_size()
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# define net
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# define net
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net = squeezenet(num_classes=config.class_num)
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net = squeezenet(num_classes=config.class_num)
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# load checkpoint
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# load checkpoint
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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param_dict = load_checkpoint(config.checkpoint_file_path)
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load_param_into_net(net, param_dict)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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net.set_train(False)
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# define loss
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# define loss
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if args_opt.dataset == "imagenet":
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if config.dataset == "imagenet":
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if not config.use_label_smooth:
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if not config.use_label_smooth:
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config.label_smooth_factor = 0.0
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config.label_smooth_factor = 0.0
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loss = CrossEntropySmooth(sparse=True,
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loss = CrossEntropySmooth(sparse=True,
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# eval model
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# eval model
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res = model.eval(dataset)
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res = model.eval(dataset)
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print("result:", res, "ckpt=", args_opt.checkpoint_path)
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print("result:", res, "ckpt=", config.checkpoint_file_path)
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if __name__ == '__main__':
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eval_net()
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python export.py --net squeezenet --dataset cifar10 --checkpoint_path squeezenet_cifar10-120_1562.ckpt
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python export.py --net squeezenet --dataset cifar10 --checkpoint_path squeezenet_cifar10-120_1562.ckpt
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"""
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"""
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import argparse
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import numpy as np
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import numpy as np
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from model_utils.config import config
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from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
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from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
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parser = argparse.ArgumentParser(description='checkpoint export')
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if config.net_name == "squeezenet":
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=32, help="batch size")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument('--width', type=int, default=227, help='input width')
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parser.add_argument('--height', type=int, default=227, help='input height')
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parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
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help='Model.')
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parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
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|
||||||
parser.add_argument("--file_name", type=str, default="squeezenet", help="output file name.")
|
|
||||||
parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="AIR", help="file format")
|
|
||||||
parser.add_argument("--device_target", type=str, default="Ascend",
|
|
||||||
choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
|
|
||||||
args = parser.parse_args()
|
|
||||||
|
|
||||||
if args.net == "squeezenet":
|
|
||||||
from src.squeezenet import SqueezeNet as squeezenet
|
from src.squeezenet import SqueezeNet as squeezenet
|
||||||
else:
|
else:
|
||||||
from src.squeezenet import SqueezeNet_Residual as squeezenet
|
from src.squeezenet import SqueezeNet_Residual as squeezenet
|
||||||
if args.dataset == "cifar10":
|
if config.dataset == "cifar10":
|
||||||
num_classes = 10
|
num_classes = 10
|
||||||
else:
|
else:
|
||||||
num_classes = 1000
|
num_classes = 1000
|
||||||
|
|
||||||
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
|
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
|
||||||
if args.device_target == "Ascend":
|
if config.device_target == "Ascend":
|
||||||
context.set_context(device_id=args.device_id)
|
context.set_context(device_id=config.device_id)
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
net = squeezenet(num_classes=num_classes)
|
net = squeezenet(num_classes=num_classes)
|
||||||
|
|
||||||
param_dict = load_checkpoint(args.ckpt_file)
|
param_dict = load_checkpoint(config.checkpoint_file_path)
|
||||||
load_param_into_net(net, param_dict)
|
load_param_into_net(net, param_dict)
|
||||||
|
|
||||||
input_data = Tensor(np.zeros([args.batch_size, 3, args.height, args.width], np.float32))
|
input_data = Tensor(np.zeros([config.batch_size, 3, config.height, config.width], np.float32))
|
||||||
export(net, input_data, file_name=args.file_name, file_format=args.file_format)
|
export(net, input_data, file_name=config.file_name, file_format=config.file_format)
|
||||||
|
|
|
@ -0,0 +1,124 @@
|
||||||
|
# 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 pformat
|
||||||
|
import yaml
|
||||||
|
|
||||||
|
_config_path = "./squeezenet_cifar10_config.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="squeezenet_cifar10_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]
|
||||||
|
elif len(cfgs) == 2:
|
||||||
|
cfg, cfg_helper = cfgs
|
||||||
|
else:
|
||||||
|
raise ValueError("At most 2 docs (config and help description for help) are supported in config yaml")
|
||||||
|
print(cfg_helper)
|
||||||
|
except:
|
||||||
|
raise ValueError("Failed to parse yaml")
|
||||||
|
return cfg, cfg_helper
|
||||||
|
|
||||||
|
|
||||||
|
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, \
|
||||||
|
"../squeezenet_cifar10_config.yaml"), help="Config file path")
|
||||||
|
path_args, _ = parser.parse_known_args()
|
||||||
|
default, helper = parse_yaml(path_args.config_path)
|
||||||
|
args = parse_cli_to_yaml(parser, default, helper, 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 model_utils.config import config
|
||||||
|
|
||||||
|
if config.enable_modelarts:
|
||||||
|
from model_utils.moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
|
||||||
|
else:
|
||||||
|
from 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 +1,115 @@
|
||||||
|
# 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 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)
|
||||||
|
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_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
|
|
@ -16,7 +16,7 @@
|
||||||
|
|
||||||
if [ $# != 4 ] && [ $# != 5 ]
|
if [ $# != 4 ] && [ $# != 5 ]
|
||||||
then
|
then
|
||||||
echo "Usage: sh scripts/run_distribute_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
|
echo "Usage: sh scripts/run_distribute_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [RANK_TABLE_FILE] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)"
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
@ -74,6 +74,22 @@ export RANK_TABLE_FILE=$PATH1
|
||||||
export SERVER_ID=0
|
export SERVER_ID=0
|
||||||
rank_start=$((DEVICE_NUM * SERVER_ID))
|
rank_start=$((DEVICE_NUM * SERVER_ID))
|
||||||
|
|
||||||
|
BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
|
||||||
|
if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
|
||||||
|
else
|
||||||
|
echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
for((i=0; i<${DEVICE_NUM}; i++))
|
for((i=0; i<${DEVICE_NUM}; i++))
|
||||||
do
|
do
|
||||||
export DEVICE_ID=${i}
|
export DEVICE_ID=${i}
|
||||||
|
@ -82,17 +98,21 @@ do
|
||||||
mkdir ./train_parallel$i
|
mkdir ./train_parallel$i
|
||||||
cp ./train.py ./train_parallel$i
|
cp ./train.py ./train_parallel$i
|
||||||
cp -r ./src ./train_parallel$i
|
cp -r ./src ./train_parallel$i
|
||||||
|
cp -r ./model_utils ./train_parallel$i
|
||||||
|
cp -r ./*.yaml ./train_parallel$i
|
||||||
cd ./train_parallel$i || exit
|
cd ./train_parallel$i || exit
|
||||||
echo "start training for rank $RANK_ID, device $DEVICE_ID"
|
echo "start training for rank $RANK_ID, device $DEVICE_ID"
|
||||||
env > env.log
|
env > env.log
|
||||||
if [ $# == 4 ]
|
if [ $# == 4 ]
|
||||||
then
|
then
|
||||||
python train.py --net=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
|
python train.py --net_name=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --data_path=$PATH2 \
|
||||||
|
--config_path=$CONFIG_FILE --output_path './output' &> log &
|
||||||
fi
|
fi
|
||||||
|
|
||||||
if [ $# == 5 ]
|
if [ $# == 5 ]
|
||||||
then
|
then
|
||||||
python train.py --net=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log &
|
python train.py --net_name=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --data_path=$PATH2 \
|
||||||
|
--config_path=$CONFIG_FILE --output_path './output' &> log &
|
||||||
fi
|
fi
|
||||||
|
|
||||||
cd ..
|
cd ..
|
||||||
|
|
|
@ -16,7 +16,7 @@
|
||||||
|
|
||||||
if [ $# != 5 ]
|
if [ $# != 5 ]
|
||||||
then
|
then
|
||||||
echo "Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]"
|
echo "Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATA_PATH] [CHECKPOINT_PATH]"
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
@ -62,6 +62,22 @@ export DEVICE_ID=$3
|
||||||
export RANK_SIZE=$DEVICE_NUM
|
export RANK_SIZE=$DEVICE_NUM
|
||||||
export RANK_ID=0
|
export RANK_ID=0
|
||||||
|
|
||||||
|
BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
|
||||||
|
if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
|
||||||
|
else
|
||||||
|
echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
if [ -d "eval" ];
|
if [ -d "eval" ];
|
||||||
then
|
then
|
||||||
rm -rf ./eval
|
rm -rf ./eval
|
||||||
|
@ -69,8 +85,11 @@ fi
|
||||||
mkdir ./eval
|
mkdir ./eval
|
||||||
cp ./eval.py ./eval
|
cp ./eval.py ./eval
|
||||||
cp -r ./src ./eval
|
cp -r ./src ./eval
|
||||||
|
cp -r ./model_utils ./eval
|
||||||
|
cp -r ./*.yaml ./eval
|
||||||
cd ./eval || exit
|
cd ./eval || exit
|
||||||
env > env.log
|
env > env.log
|
||||||
echo "start evaluation for device $DEVICE_ID"
|
echo "start evaluation for device $DEVICE_ID"
|
||||||
python eval.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
|
python eval.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --checkpoint_file_path=$PATH2 \
|
||||||
|
--config_path=$CONFIG_FILE --output_path './output' &> log &
|
||||||
cd ..
|
cd ..
|
||||||
|
|
|
@ -16,7 +16,7 @@
|
||||||
|
|
||||||
if [ $# != 4 ]
|
if [ $# != 4 ]
|
||||||
then
|
then
|
||||||
echo "Usage: bash scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [CHECKPOINT_PATH]"
|
echo "Usage: bash scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATA_PATH] [CHECKPOINT_PATH]"
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
@ -56,6 +56,22 @@ then
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
|
||||||
|
if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
|
||||||
|
else
|
||||||
|
echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
if [ -d "eval" ];
|
if [ -d "eval" ];
|
||||||
then
|
then
|
||||||
rm -rf ./eval
|
rm -rf ./eval
|
||||||
|
@ -63,8 +79,11 @@ fi
|
||||||
mkdir ./eval
|
mkdir ./eval
|
||||||
cp ./eval.py ./eval
|
cp ./eval.py ./eval
|
||||||
cp -r ./src ./eval
|
cp -r ./src ./eval
|
||||||
|
cp -r ./model_utils ./eval
|
||||||
|
cp -r ./*.yaml ./eval
|
||||||
cd ./eval || exit
|
cd ./eval || exit
|
||||||
env > env.log
|
env > env.log
|
||||||
echo "start evaluation for device CPU"
|
echo "start evaluation for device CPU"
|
||||||
python eval.py --net=$1 --dataset=$2 --device_target=CPU --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
|
python eval.py --net_name=$1 --dataset=$2 --device_target=CPU --data_path=$PATH1 --checkpoint_file_path=$PATH2 \
|
||||||
|
--config_path=$CONFIG_FILE --output_path './output' &> log &
|
||||||
cd ..
|
cd ..
|
||||||
|
|
|
@ -16,7 +16,7 @@
|
||||||
|
|
||||||
if [ $# != 4 ] && [ $# != 5 ]
|
if [ $# != 4 ] && [ $# != 5 ]
|
||||||
then
|
then
|
||||||
echo "Usage: sh scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
|
echo "Usage: sh scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)"
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
@ -65,6 +65,22 @@ export DEVICE_ID=$3
|
||||||
export RANK_ID=0
|
export RANK_ID=0
|
||||||
export RANK_SIZE=1
|
export RANK_SIZE=1
|
||||||
|
|
||||||
|
BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
|
||||||
|
if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
|
||||||
|
else
|
||||||
|
echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
if [ -d "train" ];
|
if [ -d "train" ];
|
||||||
then
|
then
|
||||||
rm -rf ./train
|
rm -rf ./train
|
||||||
|
@ -72,16 +88,18 @@ fi
|
||||||
mkdir ./train
|
mkdir ./train
|
||||||
cp ./train.py ./train
|
cp ./train.py ./train
|
||||||
cp -r ./src ./train
|
cp -r ./src ./train
|
||||||
|
cp -r ./model_utils ./train
|
||||||
|
cp -r ./*.yaml ./train
|
||||||
cd ./train || exit
|
cd ./train || exit
|
||||||
echo "start training for device $DEVICE_ID"
|
echo "start training for device $DEVICE_ID"
|
||||||
env > env.log
|
env > env.log
|
||||||
if [ $# == 4 ]
|
if [ $# == 4 ]
|
||||||
then
|
then
|
||||||
python train.py --net=$1 --dataset=$2 --dataset_path=$PATH1 &> log &
|
python train.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --config_path=$CONFIG_FILE --output_path './output' &> log &
|
||||||
fi
|
fi
|
||||||
|
|
||||||
if [ $# == 5 ]
|
if [ $# == 5 ]
|
||||||
then
|
then
|
||||||
python train.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
|
python train.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --pre_trained=$PATH2 --config_path=$CONFIG_FILE --output_path './output' &> log &
|
||||||
fi
|
fi
|
||||||
cd ..
|
cd ..
|
||||||
|
|
|
@ -16,7 +16,7 @@
|
||||||
|
|
||||||
if [ $# != 3 ] && [ $# != 4 ]
|
if [ $# != 3 ] && [ $# != 4 ]
|
||||||
then
|
then
|
||||||
echo "Usage: bash scripts/run_train_cpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
|
echo "Usage: bash scripts/run_train_cpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATA_PATH] [PRETRAINED_CKPT_PATH](optional)"
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
@ -59,6 +59,22 @@ then
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
|
||||||
|
if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
|
||||||
|
elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
|
||||||
|
CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
|
||||||
|
else
|
||||||
|
echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
if [ -d "train" ];
|
if [ -d "train" ];
|
||||||
then
|
then
|
||||||
rm -rf ./train
|
rm -rf ./train
|
||||||
|
@ -66,16 +82,18 @@ fi
|
||||||
mkdir ./train
|
mkdir ./train
|
||||||
cp ./train.py ./train
|
cp ./train.py ./train
|
||||||
cp -r ./src ./train
|
cp -r ./src ./train
|
||||||
|
cp -r ./model_utils ./train
|
||||||
|
cp -r ./*.yaml ./train
|
||||||
cd ./train || exit
|
cd ./train || exit
|
||||||
echo "start training for device CPU"
|
echo "start training for device CPU"
|
||||||
env > env.log
|
env > env.log
|
||||||
if [ $# == 3 ]
|
if [ $# == 3 ]
|
||||||
then
|
then
|
||||||
python train.py --net=$1 --dataset=$2 --device_target=CPU --dataset_path=$PATH1 &> log &
|
python train.py --net_name=$1 --dataset=$2 --device_target=CPU --data_path=$PATH1 --config_path=$CONFIG_FILE --output_path './output' &> log &
|
||||||
fi
|
fi
|
||||||
|
|
||||||
if [ $# == 4 ]
|
if [ $# == 4 ]
|
||||||
then
|
then
|
||||||
python train.py --net=$1 --dataset=$2 --device_target=CPU --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
|
python train.py --net_name=$1 --dataset=$2 --device_target=CPU --data_path=$PATH1 --pre_trained=$PATH2 --config_path=$CONFIG_FILE --output_path './output' &> log &
|
||||||
fi
|
fi
|
||||||
cd ..
|
cd ..
|
||||||
|
|
|
@ -0,0 +1,62 @@
|
||||||
|
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
|
||||||
|
enable_modelarts: False
|
||||||
|
# Url for modelarts
|
||||||
|
data_url: ""
|
||||||
|
train_url: ""
|
||||||
|
checkpoint_url: ""
|
||||||
|
# Path for local
|
||||||
|
run_distribute: False
|
||||||
|
enable_profiling: False
|
||||||
|
data_path: "/cache/data"
|
||||||
|
output_path: "/cache/train"
|
||||||
|
load_path: "/cache/checkpoint_path/"
|
||||||
|
device_num: 1
|
||||||
|
device_id: 0
|
||||||
|
device_target: 'Ascend'
|
||||||
|
checkpoint_path: './checkpoint/'
|
||||||
|
checkpoint_file_path: 'suqeezenet_cifar10-120_195.ckpt'
|
||||||
|
|
||||||
|
# ==============================================================================
|
||||||
|
# Training options
|
||||||
|
net_name: "suqeezenet"
|
||||||
|
dataset : "cifar10"
|
||||||
|
class_num: 10
|
||||||
|
batch_size: 32
|
||||||
|
loss_scale: 1024
|
||||||
|
momentum: 0.9
|
||||||
|
weight_decay: 0.0001
|
||||||
|
epoch_size: 120
|
||||||
|
pretrain_epoch_size: 0
|
||||||
|
save_checkpoint: True
|
||||||
|
save_checkpoint_epochs: 1
|
||||||
|
keep_checkpoint_max: 10
|
||||||
|
warmup_epochs: 5
|
||||||
|
lr_decay_mode: "poly"
|
||||||
|
lr_init: 0
|
||||||
|
lr_end: 0
|
||||||
|
lr_max: 0.01
|
||||||
|
pre_trained: ""
|
||||||
|
|
||||||
|
# export
|
||||||
|
width: 227
|
||||||
|
height: 227
|
||||||
|
file_name: "squeezenet"
|
||||||
|
file_format: "AIR"
|
||||||
|
|
||||||
|
---
|
||||||
|
# Help description for each configuration
|
||||||
|
enable_modelarts: 'Whether training on modelarts, default: False'
|
||||||
|
data_url: 'Dataset url for obs'
|
||||||
|
train_url: 'Training output url for obs'
|
||||||
|
checkpoint_url: 'The location of checkpoint for obs'
|
||||||
|
data_path: 'Dataset path for local'
|
||||||
|
output_path: 'Training output path for local'
|
||||||
|
load_path: 'The location of checkpoint for obs'
|
||||||
|
device_target: 'Target device type, available: [Ascend, GPU, CPU]'
|
||||||
|
enable_profiling: 'Whether enable profiling while training, default: False'
|
||||||
|
num_classes: 'Class for dataset'
|
||||||
|
batch_size: "Batch size for training and evaluation"
|
||||||
|
epoch_size: "Total training epochs."
|
||||||
|
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
|
||||||
|
checkpoint_path: "The location of the checkpoint file."
|
||||||
|
checkpoint_file_path: "The location of the checkpoint file."
|
|
@ -0,0 +1,64 @@
|
||||||
|
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
|
||||||
|
enable_modelarts: False
|
||||||
|
# Url for modelarts
|
||||||
|
data_url: ""
|
||||||
|
train_url: ""
|
||||||
|
checkpoint_url: ""
|
||||||
|
# Path for local
|
||||||
|
run_distribute: False
|
||||||
|
enable_profiling: False
|
||||||
|
data_path: "/cache/data"
|
||||||
|
output_path: "/cache/train"
|
||||||
|
load_path: "/cache/checkpoint_path/"
|
||||||
|
device_num: 1
|
||||||
|
device_id: 0
|
||||||
|
device_target: 'Ascend'
|
||||||
|
checkpoint_path: './checkpoint/'
|
||||||
|
checkpoint_file_path: 'suqeezenet_imagenet-200_5004.ckpt'
|
||||||
|
|
||||||
|
# ==============================================================================
|
||||||
|
# Training options
|
||||||
|
net_name: "suqeezenet"
|
||||||
|
dataset : "imagenet"
|
||||||
|
class_num: 1000
|
||||||
|
batch_size: 32
|
||||||
|
loss_scale: 1024
|
||||||
|
momentum: 0.9
|
||||||
|
weight_decay: 0.00007
|
||||||
|
epoch_size: 200
|
||||||
|
pretrain_epoch_size: 0
|
||||||
|
save_checkpoint: True
|
||||||
|
save_checkpoint_epochs: 1
|
||||||
|
keep_checkpoint_max: 10
|
||||||
|
warmup_epochs: 0
|
||||||
|
lr_decay_mode: "poly"
|
||||||
|
use_label_smooth: True
|
||||||
|
label_smooth_factor: 0.1
|
||||||
|
lr_init: 0
|
||||||
|
lr_end: 0
|
||||||
|
lr_max: 0.01
|
||||||
|
pre_trained: ""
|
||||||
|
|
||||||
|
# export
|
||||||
|
width: 227
|
||||||
|
height: 227
|
||||||
|
file_name: "squeezenet"
|
||||||
|
file_format: "AIR"
|
||||||
|
|
||||||
|
---
|
||||||
|
# Help description for each configuration
|
||||||
|
enable_modelarts: 'Whether training on modelarts, default: False'
|
||||||
|
data_url: 'Dataset url for obs'
|
||||||
|
train_url: 'Training output url for obs'
|
||||||
|
checkpoint_url: 'The location of checkpoint for obs'
|
||||||
|
data_path: 'Dataset path for local'
|
||||||
|
output_path: 'Training output path for local'
|
||||||
|
load_path: 'The location of checkpoint for obs'
|
||||||
|
device_target: 'Target device type, available: [Ascend, GPU, CPU]'
|
||||||
|
enable_profiling: 'Whether enable profiling while training, default: False'
|
||||||
|
num_classes: 'Class for dataset'
|
||||||
|
batch_size: "Batch size for training and evaluation"
|
||||||
|
epoch_size: "Total training epochs."
|
||||||
|
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
|
||||||
|
checkpoint_path: "The location of the checkpoint file."
|
||||||
|
checkpoint_file_path: "The location of the checkpoint file."
|
|
@ -0,0 +1,61 @@
|
||||||
|
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
|
||||||
|
enable_modelarts: False
|
||||||
|
# Url for modelarts
|
||||||
|
data_url: ""
|
||||||
|
train_url: ""
|
||||||
|
checkpoint_url: ""
|
||||||
|
# Path for local
|
||||||
|
run_distribute: False
|
||||||
|
enable_profiling: False
|
||||||
|
data_path: "/cache/data"
|
||||||
|
output_path: "/cache/train"
|
||||||
|
load_path: "/cache/checkpoint_path/"
|
||||||
|
device_num: 1
|
||||||
|
device_target: 'Ascend'
|
||||||
|
checkpoint_path: './checkpoint/'
|
||||||
|
checkpoint_file_path: 'suqeezenet_residual_cifar10-150_195.ckpt'
|
||||||
|
|
||||||
|
# ==============================================================================
|
||||||
|
# Training options
|
||||||
|
net_name: "suqeezenet_residual"
|
||||||
|
dataset : "cifar10"
|
||||||
|
class_num: 10
|
||||||
|
batch_size: 32
|
||||||
|
loss_scale: 1024
|
||||||
|
momentum: 0.9
|
||||||
|
weight_decay: 0.0001
|
||||||
|
epoch_size: 150
|
||||||
|
pretrain_epoch_size: 0
|
||||||
|
save_checkpoint: True
|
||||||
|
save_checkpoint_epochs: 1
|
||||||
|
keep_checkpoint_max: 10
|
||||||
|
warmup_epochs: 5
|
||||||
|
lr_decay_mode: "linear"
|
||||||
|
lr_init: 0
|
||||||
|
lr_end: 0
|
||||||
|
lr_max: 0.01
|
||||||
|
pre_trained: ""
|
||||||
|
|
||||||
|
#export
|
||||||
|
width: 227
|
||||||
|
height: 227
|
||||||
|
file_name: "squeezenet"
|
||||||
|
file_format: "AIR"
|
||||||
|
|
||||||
|
---
|
||||||
|
# Help description for each configuration
|
||||||
|
enable_modelarts: 'Whether training on modelarts, default: False'
|
||||||
|
data_url: 'Dataset url for obs'
|
||||||
|
train_url: 'Training output url for obs'
|
||||||
|
checkpoint_url: 'The location of checkpoint for obs'
|
||||||
|
data_path: 'Dataset path for local'
|
||||||
|
output_path: 'Training output path for local'
|
||||||
|
load_path: 'The location of checkpoint for obs'
|
||||||
|
device_target: 'Target device type, available: [Ascend, GPU, CPU]'
|
||||||
|
enable_profiling: 'Whether enable profiling while training, default: False'
|
||||||
|
num_classes: 'Class for dataset'
|
||||||
|
batch_size: "Batch size for training and evaluation"
|
||||||
|
epoch_size: "Total training epochs."
|
||||||
|
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
|
||||||
|
checkpoint_path: "The location of the checkpoint file."
|
||||||
|
checkpoint_file_path: "The location of the checkpoint file."
|
|
@ -0,0 +1,64 @@
|
||||||
|
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
|
||||||
|
enable_modelarts: False
|
||||||
|
# Url for modelarts
|
||||||
|
data_url: ""
|
||||||
|
train_url: ""
|
||||||
|
checkpoint_url: ""
|
||||||
|
# Path for local
|
||||||
|
run_distribute: False
|
||||||
|
enable_profiling: False
|
||||||
|
data_path: "/cache/data"
|
||||||
|
output_path: "/cache/train"
|
||||||
|
load_path: "/cache/checkpoint_path/"
|
||||||
|
device_num: 1
|
||||||
|
device_id: 0
|
||||||
|
device_target: 'Ascend'
|
||||||
|
checkpoint_path: './checkpoint/'
|
||||||
|
checkpoint_file_path: 'suqeezenet_residual_imagenet-300_5004.ckpt'
|
||||||
|
|
||||||
|
# ==============================================================================
|
||||||
|
# Training options
|
||||||
|
net_name: "suqeezenet_residual"
|
||||||
|
dataset : "imagenet"
|
||||||
|
class_num: 1000
|
||||||
|
batch_size: 32
|
||||||
|
loss_scale: 1024
|
||||||
|
momentum: 0.9
|
||||||
|
weight_decay: 0.00007
|
||||||
|
epoch_size: 300
|
||||||
|
pretrain_epoch_size: 0
|
||||||
|
save_checkpoint: True
|
||||||
|
save_checkpoint_epochs: 1
|
||||||
|
keep_checkpoint_max: 10
|
||||||
|
warmup_epochs: 0
|
||||||
|
lr_decay_mode: "cosine"
|
||||||
|
use_label_smooth: True
|
||||||
|
label_smooth_factor: 0.1
|
||||||
|
lr_init: 0
|
||||||
|
lr_end: 0
|
||||||
|
lr_max: 0.01
|
||||||
|
pre_trained: ""
|
||||||
|
|
||||||
|
#export
|
||||||
|
width: 227
|
||||||
|
height: 227
|
||||||
|
file_name: "squeezenet"
|
||||||
|
file_format: "AIR"
|
||||||
|
|
||||||
|
---
|
||||||
|
# Help description for each configuration
|
||||||
|
enable_modelarts: 'Whether training on modelarts, default: False'
|
||||||
|
data_url: 'Dataset url for obs'
|
||||||
|
train_url: 'Training output url for obs'
|
||||||
|
checkpoint_url: 'The location of checkpoint for obs'
|
||||||
|
data_path: 'Dataset path for local'
|
||||||
|
output_path: 'Training output path for local'
|
||||||
|
load_path: 'The location of checkpoint for obs'
|
||||||
|
device_target: 'Target device type, available: [Ascend, GPU, CPU]'
|
||||||
|
enable_profiling: 'Whether enable profiling while training, default: False'
|
||||||
|
num_classes: 'Class for dataset'
|
||||||
|
batch_size: "Batch size for training and evaluation"
|
||||||
|
epoch_size: "Total training epochs."
|
||||||
|
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
|
||||||
|
checkpoint_path: "The location of the checkpoint file."
|
||||||
|
checkpoint_file_path: "The location of the checkpoint file."
|
|
@ -1,102 +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 ed
|
|
||||||
|
|
||||||
# config for squeezenet, cifar10
|
|
||||||
config1 = ed({
|
|
||||||
"class_num": 10,
|
|
||||||
"batch_size": 32,
|
|
||||||
"loss_scale": 1024,
|
|
||||||
"momentum": 0.9,
|
|
||||||
"weight_decay": 1e-4,
|
|
||||||
"epoch_size": 120,
|
|
||||||
"pretrain_epoch_size": 0,
|
|
||||||
"save_checkpoint": True,
|
|
||||||
"save_checkpoint_epochs": 1,
|
|
||||||
"keep_checkpoint_max": 10,
|
|
||||||
"save_checkpoint_path": "./",
|
|
||||||
"warmup_epochs": 5,
|
|
||||||
"lr_decay_mode": "poly",
|
|
||||||
"lr_init": 0,
|
|
||||||
"lr_end": 0,
|
|
||||||
"lr_max": 0.01
|
|
||||||
})
|
|
||||||
|
|
||||||
# config for squeezenet, imagenet
|
|
||||||
config2 = ed({
|
|
||||||
"class_num": 1000,
|
|
||||||
"batch_size": 32,
|
|
||||||
"loss_scale": 1024,
|
|
||||||
"momentum": 0.9,
|
|
||||||
"weight_decay": 7e-5,
|
|
||||||
"epoch_size": 200,
|
|
||||||
"pretrain_epoch_size": 0,
|
|
||||||
"save_checkpoint": True,
|
|
||||||
"save_checkpoint_epochs": 1,
|
|
||||||
"keep_checkpoint_max": 10,
|
|
||||||
"save_checkpoint_path": "./",
|
|
||||||
"warmup_epochs": 0,
|
|
||||||
"lr_decay_mode": "poly",
|
|
||||||
"use_label_smooth": True,
|
|
||||||
"label_smooth_factor": 0.1,
|
|
||||||
"lr_init": 0,
|
|
||||||
"lr_end": 0,
|
|
||||||
"lr_max": 0.01
|
|
||||||
})
|
|
||||||
|
|
||||||
# config for squeezenet_residual, cifar10
|
|
||||||
config3 = ed({
|
|
||||||
"class_num": 10,
|
|
||||||
"batch_size": 32,
|
|
||||||
"loss_scale": 1024,
|
|
||||||
"momentum": 0.9,
|
|
||||||
"weight_decay": 1e-4,
|
|
||||||
"epoch_size": 150,
|
|
||||||
"pretrain_epoch_size": 0,
|
|
||||||
"save_checkpoint": True,
|
|
||||||
"save_checkpoint_epochs": 1,
|
|
||||||
"keep_checkpoint_max": 10,
|
|
||||||
"save_checkpoint_path": "./",
|
|
||||||
"warmup_epochs": 5,
|
|
||||||
"lr_decay_mode": "linear",
|
|
||||||
"lr_init": 0,
|
|
||||||
"lr_end": 0,
|
|
||||||
"lr_max": 0.01
|
|
||||||
})
|
|
||||||
|
|
||||||
# config for squeezenet_residual, imagenet
|
|
||||||
config4 = ed({
|
|
||||||
"class_num": 1000,
|
|
||||||
"batch_size": 32,
|
|
||||||
"loss_scale": 1024,
|
|
||||||
"momentum": 0.9,
|
|
||||||
"weight_decay": 7e-5,
|
|
||||||
"epoch_size": 300,
|
|
||||||
"pretrain_epoch_size": 0,
|
|
||||||
"save_checkpoint": True,
|
|
||||||
"save_checkpoint_epochs": 1,
|
|
||||||
"keep_checkpoint_max": 10,
|
|
||||||
"save_checkpoint_path": "./",
|
|
||||||
"warmup_epochs": 0,
|
|
||||||
"lr_decay_mode": "cosine",
|
|
||||||
"use_label_smooth": True,
|
|
||||||
"label_smooth_factor": 0.1,
|
|
||||||
"lr_init": 0,
|
|
||||||
"lr_end": 0,
|
|
||||||
"lr_max": 0.01
|
|
||||||
})
|
|
|
@ -14,7 +14,6 @@
|
||||||
# ============================================================================
|
# ============================================================================
|
||||||
"""train squeezenet."""
|
"""train squeezenet."""
|
||||||
import os
|
import os
|
||||||
import argparse
|
|
||||||
from mindspore import context
|
from mindspore import context
|
||||||
from mindspore import Tensor
|
from mindspore import Tensor
|
||||||
from mindspore.nn.optim.momentum import Momentum
|
from mindspore.nn.optim.momentum import Momentum
|
||||||
|
@ -26,53 +25,42 @@ from mindspore.train.loss_scale_manager import FixedLossScaleManager
|
||||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||||
from mindspore.communication.management import init, get_rank, get_group_size
|
from mindspore.communication.management import init, get_rank, get_group_size
|
||||||
from mindspore.common import set_seed
|
from mindspore.common import set_seed
|
||||||
|
from model_utils.config import config
|
||||||
|
from model_utils.moxing_adapter import moxing_wrapper
|
||||||
from src.lr_generator import get_lr
|
from src.lr_generator import get_lr
|
||||||
from src.CrossEntropySmooth import CrossEntropySmooth
|
from src.CrossEntropySmooth import CrossEntropySmooth
|
||||||
|
|
||||||
parser = argparse.ArgumentParser(description='Image classification')
|
|
||||||
parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
|
|
||||||
help='Model.')
|
|
||||||
parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
|
|
||||||
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
|
|
||||||
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
|
|
||||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
|
||||||
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
|
|
||||||
parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
|
|
||||||
args_opt = parser.parse_args()
|
|
||||||
|
|
||||||
set_seed(1)
|
set_seed(1)
|
||||||
|
|
||||||
if args_opt.net == "squeezenet":
|
if config.net_name == "squeezenet":
|
||||||
from src.squeezenet import SqueezeNet as squeezenet
|
from src.squeezenet import SqueezeNet as squeezenet
|
||||||
if args_opt.dataset == "cifar10":
|
if config.dataset == "cifar10":
|
||||||
from src.config import config1 as config
|
|
||||||
from src.dataset import create_dataset_cifar as create_dataset
|
from src.dataset import create_dataset_cifar as create_dataset
|
||||||
else:
|
else:
|
||||||
from src.config import config2 as config
|
|
||||||
from src.dataset import create_dataset_imagenet as create_dataset
|
from src.dataset import create_dataset_imagenet as create_dataset
|
||||||
else:
|
else:
|
||||||
from src.squeezenet import SqueezeNet_Residual as squeezenet
|
from src.squeezenet import SqueezeNet_Residual as squeezenet
|
||||||
if args_opt.dataset == "cifar10":
|
if config.dataset == "cifar10":
|
||||||
from src.config import config3 as config
|
|
||||||
from src.dataset import create_dataset_cifar as create_dataset
|
from src.dataset import create_dataset_cifar as create_dataset
|
||||||
else:
|
else:
|
||||||
from src.config import config4 as config
|
|
||||||
from src.dataset import create_dataset_imagenet as create_dataset
|
from src.dataset import create_dataset_imagenet as create_dataset
|
||||||
|
|
||||||
if __name__ == '__main__':
|
@moxing_wrapper()
|
||||||
target = args_opt.device_target
|
def train_net():
|
||||||
ckpt_save_dir = config.save_checkpoint_path
|
"""train net"""
|
||||||
|
target = config.device_target
|
||||||
|
ckpt_save_dir = config.output_path
|
||||||
|
|
||||||
# init context
|
# init context
|
||||||
context.set_context(mode=context.GRAPH_MODE,
|
context.set_context(mode=context.GRAPH_MODE,
|
||||||
device_target=target)
|
device_target=target)
|
||||||
if args_opt.run_distribute:
|
if config.run_distribute:
|
||||||
if target == "Ascend":
|
if target == "Ascend":
|
||||||
device_id = int(os.getenv('DEVICE_ID'))
|
device_id = int(os.getenv('DEVICE_ID'))
|
||||||
context.set_context(device_id=device_id,
|
context.set_context(device_id=device_id,
|
||||||
enable_auto_mixed_precision=True)
|
enable_auto_mixed_precision=True)
|
||||||
context.set_auto_parallel_context(
|
context.set_auto_parallel_context(
|
||||||
device_num=args_opt.device_num,
|
device_num=config.device_num,
|
||||||
parallel_mode=ParallelMode.DATA_PARALLEL,
|
parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||||
gradients_mean=True)
|
gradients_mean=True)
|
||||||
init()
|
init()
|
||||||
|
@ -85,11 +73,11 @@ if __name__ == '__main__':
|
||||||
device_num=get_group_size(),
|
device_num=get_group_size(),
|
||||||
parallel_mode=ParallelMode.DATA_PARALLEL,
|
parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||||
gradients_mean=True)
|
gradients_mean=True)
|
||||||
ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(
|
ckpt_save_dir = ckpt_save_dir + "/ckpt_" + str(
|
||||||
get_rank()) + "/"
|
get_rank()) + "/"
|
||||||
|
|
||||||
# create dataset
|
# create dataset
|
||||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
dataset = create_dataset(dataset_path=config.data_path,
|
||||||
do_train=True,
|
do_train=True,
|
||||||
repeat_num=1,
|
repeat_num=1,
|
||||||
batch_size=config.batch_size,
|
batch_size=config.batch_size,
|
||||||
|
@ -100,8 +88,8 @@ if __name__ == '__main__':
|
||||||
net = squeezenet(num_classes=config.class_num)
|
net = squeezenet(num_classes=config.class_num)
|
||||||
|
|
||||||
# load checkpoint
|
# load checkpoint
|
||||||
if args_opt.pre_trained:
|
if config.pre_trained:
|
||||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
param_dict = load_checkpoint(config.pre_trained)
|
||||||
load_param_into_net(net, param_dict)
|
load_param_into_net(net, param_dict)
|
||||||
|
|
||||||
# init lr
|
# init lr
|
||||||
|
@ -116,7 +104,7 @@ if __name__ == '__main__':
|
||||||
lr = Tensor(lr)
|
lr = Tensor(lr)
|
||||||
|
|
||||||
# define loss
|
# define loss
|
||||||
if args_opt.dataset == "imagenet":
|
if config.dataset == "imagenet":
|
||||||
if not config.use_label_smooth:
|
if not config.use_label_smooth:
|
||||||
config.label_smooth_factor = 0.0
|
config.label_smooth_factor = 0.0
|
||||||
loss = CrossEntropySmooth(sparse=True,
|
loss = CrossEntropySmooth(sparse=True,
|
||||||
|
@ -163,7 +151,7 @@ if __name__ == '__main__':
|
||||||
config_ck = CheckpointConfig(
|
config_ck = CheckpointConfig(
|
||||||
save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
|
save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
|
||||||
keep_checkpoint_max=config.keep_checkpoint_max)
|
keep_checkpoint_max=config.keep_checkpoint_max)
|
||||||
ckpt_cb = ModelCheckpoint(prefix=args_opt.net + '_' + args_opt.dataset,
|
ckpt_cb = ModelCheckpoint(prefix=config.net_name + '_' + config.dataset,
|
||||||
directory=ckpt_save_dir,
|
directory=ckpt_save_dir,
|
||||||
config=config_ck)
|
config=config_ck)
|
||||||
cb += [ckpt_cb]
|
cb += [ckpt_cb]
|
||||||
|
@ -172,3 +160,6 @@ if __name__ == '__main__':
|
||||||
model.train(config.epoch_size - config.pretrain_epoch_size,
|
model.train(config.epoch_size - config.pretrain_epoch_size,
|
||||||
dataset,
|
dataset,
|
||||||
callbacks=cb)
|
callbacks=cb)
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
train_net()
|
||||||
|
|
|
@ -78,7 +78,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
|
||||||
|
|
||||||
1. If coco dataset is used. **Select dataset to coco when run script.**
|
1. If coco dataset is used. **Select dataset to coco when run script.**
|
||||||
|
|
||||||
Change the `coco_root` and other settings you need in `src/config_xxx.py`. The directory structure is as follows:
|
Change the `coco_root` and other settings you need in `model_utils/ssd_xxx.yaml`. The directory structure is as follows:
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
.
|
.
|
||||||
|
@ -91,7 +91,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
|
||||||
```
|
```
|
||||||
|
|
||||||
2. If VOC dataset is used. **Select dataset to voc when run script.**
|
2. If VOC dataset is used. **Select dataset to voc when run script.**
|
||||||
Change `classes`, `num_classes`, `voc_json` and `voc_root` in `src/config_xxx.py`. `voc_json` is the path of json file with coco format for evaluation, `voc_root` is the path of VOC dataset, the directory structure is as follows:
|
Change `classes`, `num_classes`, `voc_json` and `voc_root` in `model_utils/ssd_xxx.yaml`. `voc_json` is the path of json file with coco format for evaluation, `voc_root` is the path of VOC dataset, the directory structure is as follows:
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
.
|
.
|
||||||
|
@ -117,15 +117,15 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
|
||||||
train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
|
train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
|
||||||
```
|
```
|
||||||
|
|
||||||
Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `image_dir`(dataset directory) and the relative path in `anno_path`(the TXT file path), `image_dir` and `anno_path` are setting in `src/config_xxx.py`.
|
Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `image_dir`(dataset directory) and the relative path in `anno_path`(the TXT file path), `image_dir` and `anno_path` are setting in `model_utils/ssd_xxx.yaml`.
|
||||||
|
|
||||||
## [Quick Start](#contents)
|
## [Quick Start](#contents)
|
||||||
|
|
||||||
### Prepare the model
|
### Prepare the model
|
||||||
|
|
||||||
1. Chose the model by changing the `using_model` in `src/config.py`. The optional models are: `ssd300`, `ssd_mobilenet_v1_fpn`, `ssd_vgg16`, `ssd_resnet50_fpn`.
|
1. Chose the model by changing the `using_model` in `model_utils/ssd_xxx.yaml`. The optional models are: `ssd300`, `ssd_mobilenet_v1_fpn`, `ssd_vgg16`, `ssd_resnet50_fpn`.
|
||||||
2. Change the dataset config in the corresponding config. `src/config_xxx.py`, `xxx` is the corresponding backbone network name
|
2. Change the dataset config in the corresponding config. `model_utils/ssd_xxx.yaml`, `xxx` is the corresponding backbone network name
|
||||||
3. If you are running with `ssd_mobilenet_v1_fpn` or `ssd_resnet50_fpn`, you need a pretrained model for `mobilenet_v1` or `resnet50`. Set the checkpoint path to `feature_extractor_base_param` in `src/config_xxx.py`. For more detail about training pre-trained model, please refer to the corresponding backbone network.
|
3. If you are running with `ssd_mobilenet_v1_fpn` or `ssd_resnet50_fpn`, you need a pretrained model for `mobilenet_v1` or `resnet50`. Set the checkpoint path to `feature_extractor_base_param` in `model_utils/ssd_xxx.yaml`. For more detail about training pre-trained model, please refer to the corresponding backbone network.
|
||||||
|
|
||||||
### Run the scripts
|
### Run the scripts
|
||||||
|
|
||||||
|
@ -135,23 +135,23 @@ After installing MindSpore via the official website, you can start training and
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
# distributed training on Ascend
|
# distributed training on Ascend
|
||||||
bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE]
|
bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [CONFIG_PATH]
|
||||||
|
|
||||||
# run eval on Ascend
|
# run eval on Ascend
|
||||||
bash run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
bash run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]
|
||||||
|
|
||||||
# run inference on Ascend310, MINDIR_PATH is the mindir model which you can export from checkpoint using export.py
|
# run inference on Ascend310, MINDIR_PATH is the mindir model which you can export from checkpoint using export.py
|
||||||
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
|
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
- running on GPU
|
- running on GPU
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
# distributed training on GPU
|
# distributed training on GPU
|
||||||
bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET]
|
bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [CONFIG_PATH]
|
||||||
|
|
||||||
# run eval on GPU
|
# run eval on GPU
|
||||||
bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
- running on CPU(support Windows and Ubuntu)
|
- running on CPU(support Windows and Ubuntu)
|
||||||
|
@ -160,10 +160,10 @@ bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
# training on CPU
|
# training on CPU
|
||||||
python train.py --run_platform=CPU --lr=[LR] --dataset=[DATASET] --epoch_size=[EPOCH_SIZE] --batch_size=[BATCH_SIZE] --pre_trained=[PRETRAINED_CKPT] --filter_weight=True --save_checkpoint_epochs=1
|
python train.py --device_target=CPU --lr=[LR] --dataset=[DATASET] --epoch_size=[EPOCH_SIZE] --batch_size=[BATCH_SIZE] --config_path=[CONFIG_PATH] --pre_trained=[PRETRAINED_CKPT] --filter_weight=True --save_checkpoint_epochs=1
|
||||||
|
|
||||||
# run eval on GPU
|
# run eval on GPU
|
||||||
python eval.py --run_platform=CPU --dataset=[DATASET] --checkpoint_path=[PRETRAINED_CKPT]
|
python eval.py --device_target=CPU --dataset=[DATASET] --checkpoint_file_path=[PRETRAINED_CKPT] --config_path=[CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
- Run on docker
|
- Run on docker
|
||||||
|
@ -182,6 +182,40 @@ Create a container layer over the created image and start it
|
||||||
bash scripts/docker_start.sh ssd:20.1.0 [DATA_DIR] [MODEL_DIR]
|
bash scripts/docker_start.sh ssd:20.1.0 [DATA_DIR] [MODEL_DIR]
|
||||||
```
|
```
|
||||||
|
|
||||||
|
如果要在modelarts上进行模型的训练,可以参考modelarts的官方指导文档(https://support.huaweicloud.com/modelarts/)
|
||||||
|
开始进行模型的训练和推理,具体操作如下:
|
||||||
|
|
||||||
|
If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training and evaluation as follows:
|
||||||
|
|
||||||
|
```python
|
||||||
|
# run distributed training on modelarts example
|
||||||
|
# (1) First, Perform a or b.
|
||||||
|
# a. Set "enable_modelarts=True" on yaml file.
|
||||||
|
# Set other parameters on yaml file you need.
|
||||||
|
# b. Add "enable_modelarts=True" on the website UI interface.
|
||||||
|
# Add other parameters on the website UI interface.
|
||||||
|
# (2) Set the config directory to "config_path=/The path of config in S3/"
|
||||||
|
# (3) Set the code directory to "/path/ssd" on the website UI interface.
|
||||||
|
# (4) Set the startup file to "train.py" on the website UI interface.
|
||||||
|
# (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
|
||||||
|
# (6) Create your job.
|
||||||
|
|
||||||
|
# run evaluation on modelarts example
|
||||||
|
# (1) Copy or upload your trained model to S3 bucket.
|
||||||
|
# (2) Perform a or b.
|
||||||
|
# a. Set "enable_modelarts=True" on yaml file.
|
||||||
|
# Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
|
||||||
|
# Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
|
||||||
|
# b. Add "enable_modelarts=True" on the website UI interface.
|
||||||
|
# Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
|
||||||
|
# Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
|
||||||
|
# (3) Set the config directory to "config_path=/The path of config in S3/"
|
||||||
|
# (4) Set the code directory to "/path/ssd" on the website UI interface.
|
||||||
|
# (5) Set the startup file to "eval.py" on the website UI interface.
|
||||||
|
# (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
|
||||||
|
# (7) Create your job.
|
||||||
|
```
|
||||||
|
|
||||||
Then you can run everything just like on ascend.
|
Then you can run everything just like on ascend.
|
||||||
|
|
||||||
## [Script Description](#contents)
|
## [Script Description](#contents)
|
||||||
|
@ -220,6 +254,15 @@ Then you can run everything just like on ascend.
|
||||||
├─ resnet.py ## network definition for resnet
|
├─ resnet.py ## network definition for resnet
|
||||||
├─ ssd.py ## ssd architecture
|
├─ ssd.py ## ssd architecture
|
||||||
└─ vgg16.py ## network definition for vgg16
|
└─ vgg16.py ## network definition for vgg16
|
||||||
|
├── model_utils
|
||||||
|
│ ├── config.py ## parameter configuration
|
||||||
|
│ ├── device_adapter.py ## device adapter
|
||||||
|
│ ├── local_adapter.py ## local adapter
|
||||||
|
│ ├── moxing_adapter.py ## moxing adapter
|
||||||
|
├─ ssd_mobilenet_v1_fpn_config.yaml ## parameter configuration
|
||||||
|
├─ ssd_resnet50_fpn_config.yaml ## parameter configuration
|
||||||
|
├─ ssd_vgg16_config.yaml ## parameter configuration
|
||||||
|
├─ ssd300_config.yaml ## parameter configuration
|
||||||
├─ Dockerfile ## docker file
|
├─ Dockerfile ## docker file
|
||||||
├─ eval.py ## eval scripts
|
├─ eval.py ## eval scripts
|
||||||
├─ export.py ## export mindir script
|
├─ export.py ## export mindir script
|
||||||
|
@ -269,7 +312,7 @@ To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will ge
|
||||||
- Distribute mode
|
- Distribute mode
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [CONFIG_PATH] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
||||||
```
|
```
|
||||||
|
|
||||||
We need five or seven parameters for this scripts.
|
We need five or seven parameters for this scripts.
|
||||||
|
@ -279,6 +322,7 @@ We need five or seven parameters for this scripts.
|
||||||
- `LR`: learning rate init value for distributed train.
|
- `LR`: learning rate init value for distributed train.
|
||||||
- `DATASET`:the dataset mode for distributed train.
|
- `DATASET`:the dataset mode for distributed train.
|
||||||
- `RANK_TABLE_FILE :` the path of [rank_table.json](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools), it is better to use absolute path.
|
- `RANK_TABLE_FILE :` the path of [rank_table.json](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools), it is better to use absolute path.
|
||||||
|
- `CONFIG_PATH`: parameter configuration.
|
||||||
- `PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
|
- `PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
|
||||||
- `PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
|
- `PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
|
||||||
|
|
||||||
|
@ -306,7 +350,7 @@ epoch time: 39064.8467540741, per step time: 85.29442522723602
|
||||||
- Distribute mode
|
- Distribute mode
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [CONFIG_PATH] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
||||||
```
|
```
|
||||||
|
|
||||||
We need four or six parameters for this scripts.
|
We need four or six parameters for this scripts.
|
||||||
|
@ -315,6 +359,7 @@ We need four or six parameters for this scripts.
|
||||||
- `EPOCH_NUM`: epoch num for distributed train.
|
- `EPOCH_NUM`: epoch num for distributed train.
|
||||||
- `LR`: learning rate init value for distributed train.
|
- `LR`: learning rate init value for distributed train.
|
||||||
- `DATASET`:the dataset mode for distributed train.
|
- `DATASET`:the dataset mode for distributed train.
|
||||||
|
- `CONFIG_PATH`: parameter configuration.
|
||||||
- `PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
|
- `PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
|
||||||
- `PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
|
- `PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
|
||||||
|
|
||||||
|
@ -349,7 +394,7 @@ You can train your own model based on either pretrained classification model or
|
||||||
#### Evaluation on Ascend
|
#### Evaluation on Ascend
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
bash run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
bash run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
We need two parameters for this scripts.
|
We need two parameters for this scripts.
|
||||||
|
@ -357,6 +402,7 @@ We need two parameters for this scripts.
|
||||||
- `DATASET`:the dataset mode of evaluation dataset.
|
- `DATASET`:the dataset mode of evaluation dataset.
|
||||||
- `CHECKPOINT_PATH`: the absolute path for checkpoint file.
|
- `CHECKPOINT_PATH`: the absolute path for checkpoint file.
|
||||||
- `DEVICE_ID`: the device id for eval.
|
- `DEVICE_ID`: the device id for eval.
|
||||||
|
- `CONFIG_PATH`: parameter configuration.
|
||||||
|
|
||||||
> checkpoint can be produced in training process.
|
> checkpoint can be produced in training process.
|
||||||
|
|
||||||
|
@ -384,7 +430,7 @@ mAP: 0.23808886505483504
|
||||||
#### Evaluation on GPU
|
#### Evaluation on GPU
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
We need two parameters for this scripts.
|
We need two parameters for this scripts.
|
||||||
|
@ -392,6 +438,7 @@ We need two parameters for this scripts.
|
||||||
- `DATASET`:the dataset mode of evaluation dataset.
|
- `DATASET`:the dataset mode of evaluation dataset.
|
||||||
- `CHECKPOINT_PATH`: the absolute path for checkpoint file.
|
- `CHECKPOINT_PATH`: the absolute path for checkpoint file.
|
||||||
- `DEVICE_ID`: the device id for eval.
|
- `DEVICE_ID`: the device id for eval.
|
||||||
|
- `CONFIG_PATH`: parameter configuration.
|
||||||
|
|
||||||
> checkpoint can be produced in training process.
|
> checkpoint can be produced in training process.
|
||||||
|
|
||||||
|
@ -421,7 +468,7 @@ mAP: 0.2244936111705981
|
||||||
### [Export MindIR](#contents)
|
### [Export MindIR](#contents)
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --config_path [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
The ckpt_file parameter is required,
|
The ckpt_file parameter is required,
|
||||||
|
|
|
@ -97,7 +97,7 @@ SSD方法基于前向卷积网络,该网络产生固定大小的边界框集
|
||||||
|
|
||||||
```
|
```
|
||||||
|
|
||||||
每行是按空间分割的图像标注,第一列是图像的相对路径,其余为[xmin,ymin,xmax,ymax,class]格式的框和类信息。我们从`IMAGE_DIR`(数据集目录)和`ANNO_PATH`(TXT文件路径)的相对路径连接起来的图像路径中读取图像。在`config.py`中设置`IMAGE_DIR`和`ANNO_PATH`。
|
每行是按空间分割的图像标注,第一列是图像的相对路径,其余为[xmin,ymin,xmax,ymax,class]格式的框和类信息。我们从`IMAGE_DIR`(数据集目录)和`ANNO_PATH`(TXT文件路径)的相对路径连接起来的图像路径中读取图像。在`*yaml`中设置`IMAGE_DIR`和`ANNO_PATH`。
|
||||||
|
|
||||||
# 快速入门
|
# 快速入门
|
||||||
|
|
||||||
|
@ -107,24 +107,58 @@ SSD方法基于前向卷积网络,该网络产生固定大小的边界框集
|
||||||
|
|
||||||
```shell script
|
```shell script
|
||||||
# Ascend分布式训练
|
# Ascend分布式训练
|
||||||
sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE]
|
sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
```shell script
|
```shell script
|
||||||
# Ascend处理器环境运行eval
|
# Ascend处理器环境运行eval
|
||||||
sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
- GPU处理器环境运行
|
- GPU处理器环境运行
|
||||||
|
|
||||||
```shell script
|
```shell script
|
||||||
# GPU分布式训练
|
# GPU分布式训练
|
||||||
sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET]
|
sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
```shell script
|
```shell script
|
||||||
# GPU处理器环境运行eval
|
# GPU处理器环境运行eval
|
||||||
sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]
|
||||||
|
```
|
||||||
|
|
||||||
|
如果要在modelarts上进行模型的训练,可以参考modelarts的官方指导文档(https://support.huaweicloud.com/modelarts/)
|
||||||
|
开始进行模型的训练和推理,具体操作如下:
|
||||||
|
|
||||||
|
```python
|
||||||
|
# 在modelarts上使用分布式训练的示例:
|
||||||
|
# (1) 选址a或者b其中一种方式。
|
||||||
|
# a. 设置 "enable_modelarts=True" 。
|
||||||
|
# 在yaml文件上设置网络所需的参数。
|
||||||
|
# b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
|
||||||
|
# 在modelarts的界面上设置网络所需的参数。
|
||||||
|
# (2)设置网络配置文件的路径 "config_path=/The path of config in S3/"
|
||||||
|
# (3) 在modelarts的界面上设置代码的路径 "/path/ssd"。
|
||||||
|
# (4) 在modelarts的界面上设置模型的启动文件 "train.py" 。
|
||||||
|
# (5) 在modelarts的界面上设置模型的数据路径 "Dataset path" ,
|
||||||
|
# 模型的输出路径"Output file path" 和模型的日志路径 "Job log path" 。
|
||||||
|
# (6) 开始模型的训练。
|
||||||
|
|
||||||
|
# 在modelarts上使用模型推理的示例
|
||||||
|
# (1) 把训练好的模型地方到桶的对应位置。
|
||||||
|
# (2) 选址a或者b其中一种方式。
|
||||||
|
# a. 设置 "enable_modelarts=True"
|
||||||
|
# 设置 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt" 在 yaml 文件.
|
||||||
|
# 设置 "checkpoint_url=/The path of checkpoint in S3/" 在 yaml 文件.
|
||||||
|
# b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
|
||||||
|
# 增加 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" 参数在modearts的界面上。
|
||||||
|
# 增加 "checkpoint_url=/The path of checkpoint in S3/" 参数在modearts的界面上。
|
||||||
|
# (3) 设置网络配置文件的路径 "config_path=/The path of config in S3/"
|
||||||
|
# (4) 在modelarts的界面上设置代码的路径 "/path/ssd"。
|
||||||
|
# (5) 在modelarts的界面上设置模型的启动文件 "eval.py" 。
|
||||||
|
# (6) 在modelarts的界面上设置模型的数据路径 "Dataset path" ,
|
||||||
|
# 模型的输出路径"Output file path" 和模型的日志路径 "Job log path" 。
|
||||||
|
# (7) 开始模型的推理。
|
||||||
```
|
```
|
||||||
|
|
||||||
# 脚本说明
|
# 脚本说明
|
||||||
|
@ -163,6 +197,15 @@ sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||||
├─ resnet.py ## resnet网络定义
|
├─ resnet.py ## resnet网络定义
|
||||||
├─ ssd.py ## SSD架构
|
├─ ssd.py ## SSD架构
|
||||||
└─ vgg16.py ## vgg16网络定义
|
└─ vgg16.py ## vgg16网络定义
|
||||||
|
├── model_utils
|
||||||
|
│ ├──config.py ## 参数配置
|
||||||
|
│ ├──device_adapter.py ## 设备配置
|
||||||
|
│ ├──local_adapter.py ## 本地设备配置
|
||||||
|
│ ├──moxing_adapter.py ## modelarts设备配置
|
||||||
|
├─ ssd_mobilenet_v1_fpn_config.yaml ## 参数配置
|
||||||
|
├─ ssd_resnet50_fpn_config.yaml ## 参数配置
|
||||||
|
├─ ssd_vgg16_config.yaml ## 参数配置
|
||||||
|
├─ ssd300_config.yaml ## 参数配置
|
||||||
├─ Dockerfile ## docker文件
|
├─ Dockerfile ## docker文件
|
||||||
├─ eval.py ## 评估脚本
|
├─ eval.py ## 评估脚本
|
||||||
├─ export.py ## 导出 AIR,MINDIR模型的脚本
|
├─ export.py ## 导出 AIR,MINDIR模型的脚本
|
||||||
|
@ -205,7 +248,7 @@ sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||||
- 分布式
|
- 分布式
|
||||||
|
|
||||||
```shell script
|
```shell script
|
||||||
sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [CONFIG_PATH] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
||||||
```
|
```
|
||||||
|
|
||||||
此脚本需要五或七个参数。
|
此脚本需要五或七个参数。
|
||||||
|
@ -215,6 +258,7 @@ sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||||
- `LR`:分布式训练的学习率初始值。
|
- `LR`:分布式训练的学习率初始值。
|
||||||
- `DATASET`:分布式训练的数据集模式。
|
- `DATASET`:分布式训练的数据集模式。
|
||||||
- `RANK_TABLE_FILE`:[rank_table.json](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools)的路径。最好使用绝对路径。
|
- `RANK_TABLE_FILE`:[rank_table.json](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools)的路径。最好使用绝对路径。
|
||||||
|
- `CONFIG_PATH`: 参数配置。
|
||||||
- `PRE_TRAINED`:预训练检查点文件的路径。最好使用绝对路径。
|
- `PRE_TRAINED`:预训练检查点文件的路径。最好使用绝对路径。
|
||||||
- `PRE_TRAINED_EPOCH_SIZE`:预训练的轮次数。
|
- `PRE_TRAINED_EPOCH_SIZE`:预训练的轮次数。
|
||||||
|
|
||||||
|
@ -242,7 +286,7 @@ epoch time: 39064.8467540741, per step time: 85.29442522723602
|
||||||
- 分布式
|
- 分布式
|
||||||
|
|
||||||
```shell script
|
```shell script
|
||||||
sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [CONFIG_PATH] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
||||||
```
|
```
|
||||||
|
|
||||||
此脚本需要四或六个参数。
|
此脚本需要四或六个参数。
|
||||||
|
@ -251,6 +295,7 @@ epoch time: 39064.8467540741, per step time: 85.29442522723602
|
||||||
- `EPOCH_NUM`:分布式训练的轮次数。
|
- `EPOCH_NUM`:分布式训练的轮次数。
|
||||||
- `LR`:分布式训练的学习率初始值。
|
- `LR`:分布式训练的学习率初始值。
|
||||||
- `DATASET`:分布式训练的数据集模式。
|
- `DATASET`:分布式训练的数据集模式。
|
||||||
|
- `CONFIG_PATH`: 参数配置。
|
||||||
- `PRE_TRAINED`:预训练检查点文件的路径。最好使用绝对路径。
|
- `PRE_TRAINED`:预训练检查点文件的路径。最好使用绝对路径。
|
||||||
- `PRE_TRAINED_EPOCH_SIZE`:预训练的轮次数。
|
- `PRE_TRAINED_EPOCH_SIZE`:预训练的轮次数。
|
||||||
|
|
||||||
|
@ -272,7 +317,7 @@ epoch time: 150753.701, per step time: 329.157
|
||||||
### Ascend处理器环境评估
|
### Ascend处理器环境评估
|
||||||
|
|
||||||
```shell script
|
```shell script
|
||||||
sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
此脚本需要两个参数。
|
此脚本需要两个参数。
|
||||||
|
@ -280,6 +325,7 @@ sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||||
- `DATASET`:评估数据集的模式。
|
- `DATASET`:评估数据集的模式。
|
||||||
- `CHECKPOINT_PATH`:检查点文件的绝对路径。
|
- `CHECKPOINT_PATH`:检查点文件的绝对路径。
|
||||||
- `DEVICE_ID`: 评估的设备ID。
|
- `DEVICE_ID`: 评估的设备ID。
|
||||||
|
- `CONFIG_PATH`: 参数配置。
|
||||||
|
|
||||||
> 在训练过程中可以生成检查点。
|
> 在训练过程中可以生成检查点。
|
||||||
|
|
||||||
|
@ -307,7 +353,7 @@ mAP: 0.23808886505483504
|
||||||
### GPU处理器环境评估
|
### GPU处理器环境评估
|
||||||
|
|
||||||
```shell script
|
```shell script
|
||||||
sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
此脚本需要两个参数。
|
此脚本需要两个参数。
|
||||||
|
@ -315,6 +361,7 @@ sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||||
- `DATASET`:评估数据集的模式。
|
- `DATASET`:评估数据集的模式。
|
||||||
- `CHECKPOINT_PATH`:检查点文件的绝对路径。
|
- `CHECKPOINT_PATH`:检查点文件的绝对路径。
|
||||||
- `DEVICE_ID`: 评估的设备ID。
|
- `DEVICE_ID`: 评估的设备ID。
|
||||||
|
- `CONFIG_PATH`: 参数配置。
|
||||||
|
|
||||||
> 在训练过程中可以生成检查点。
|
> 在训练过程中可以生成检查点。
|
||||||
|
|
||||||
|
@ -344,7 +391,7 @@ mAP: 0.2244936111705981
|
||||||
### [导出MindIR](#contents)
|
### [导出MindIR](#contents)
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --config_path [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
参数ckpt_file为必填项,
|
参数ckpt_file为必填项,
|
||||||
|
@ -357,7 +404,7 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
# Ascend310 inference
|
# Ascend310 inference
|
||||||
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
|
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
- `DVPP` 为必填项,需要在["DVPP", "CPU"]选择,大小写均可。需要注意的是ssd_vgg16执行推理的图片尺寸为[300, 300],由于DVPP硬件限制宽为16整除,高为2整除,因此,这个网络需要通过CPU算子对图像进行前处理。
|
- `DVPP` 为必填项,需要在["DVPP", "CPU"]选择,大小写均可。需要注意的是ssd_vgg16执行推理的图片尺寸为[300, 300],由于DVPP硬件限制宽为16整除,高为2整除,因此,这个网络需要通过CPU算子对图像进行前处理。
|
||||||
|
|
|
@ -16,30 +16,30 @@
|
||||||
"""Evaluation for SSD"""
|
"""Evaluation for SSD"""
|
||||||
|
|
||||||
import os
|
import os
|
||||||
import argparse
|
|
||||||
from mindspore import context, Tensor
|
from mindspore import context, Tensor
|
||||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||||
from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
|
from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
|
||||||
from src.dataset import create_ssd_dataset, create_mindrecord
|
from src.dataset import create_ssd_dataset, create_mindrecord
|
||||||
from src.config import config
|
|
||||||
from src.eval_utils import apply_eval
|
from src.eval_utils import apply_eval
|
||||||
from src.box_utils import default_boxes
|
from src.box_utils import default_boxes
|
||||||
|
from src.model_utils.config import config
|
||||||
|
from src.model_utils.moxing_adapter import moxing_wrapper
|
||||||
|
|
||||||
def ssd_eval(dataset_path, ckpt_path, anno_json):
|
def ssd_eval(dataset_path, ckpt_path, anno_json):
|
||||||
"""SSD evaluation."""
|
"""SSD evaluation."""
|
||||||
batch_size = 1
|
batch_size = 1
|
||||||
ds = create_ssd_dataset(dataset_path, batch_size=batch_size, repeat_num=1,
|
ds = create_ssd_dataset(dataset_path, batch_size=batch_size, repeat_num=1,
|
||||||
is_training=False, use_multiprocessing=False)
|
is_training=False, use_multiprocessing=False)
|
||||||
if config.model == "ssd300":
|
if config.model_name == "ssd300":
|
||||||
net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
|
net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
|
||||||
elif config.model == "ssd_vgg16":
|
elif config.model_name == "ssd_vgg16":
|
||||||
net = ssd_vgg16(config=config)
|
net = ssd_vgg16(config=config)
|
||||||
elif config.model == "ssd_mobilenet_v1_fpn":
|
elif config.model_name == "ssd_mobilenet_v1_fpn":
|
||||||
net = ssd_mobilenet_v1_fpn(config=config)
|
net = ssd_mobilenet_v1_fpn(config=config)
|
||||||
elif config.model == "ssd_resnet50_fpn":
|
elif config.model_name == "ssd_resnet50_fpn":
|
||||||
net = ssd_resnet50_fpn(config=config)
|
net = ssd_resnet50_fpn(config=config)
|
||||||
else:
|
else:
|
||||||
raise ValueError(f'config.model: {config.model} is not supported')
|
raise ValueError(f'config.model: {config.model_name} is not supported')
|
||||||
net = SsdInferWithDecoder(net, Tensor(default_boxes), config)
|
net = SsdInferWithDecoder(net, Tensor(default_boxes), config)
|
||||||
|
|
||||||
print("Load Checkpoint!")
|
print("Load Checkpoint!")
|
||||||
|
@ -57,27 +57,30 @@ def ssd_eval(dataset_path, ckpt_path, anno_json):
|
||||||
print("\n========================================\n")
|
print("\n========================================\n")
|
||||||
print(f"mAP: {mAP}")
|
print(f"mAP: {mAP}")
|
||||||
|
|
||||||
def get_eval_args():
|
@moxing_wrapper()
|
||||||
parser = argparse.ArgumentParser(description='SSD evaluation')
|
def eval_net():
|
||||||
parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
|
if hasattr(config, 'num_ssd_boxes') and config.num_ssd_boxes == -1:
|
||||||
parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
|
num = 0
|
||||||
parser.add_argument("--checkpoint_path", type=str, required=True, help="Checkpoint file path.")
|
h, w = config.img_shape
|
||||||
parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"),
|
for i in range(len(config.steps)):
|
||||||
help="run platform, support Ascend ,GPU and CPU.")
|
num += (h // config.steps[i]) * (w // config.steps[i]) * config.num_default[i]
|
||||||
return parser.parse_args()
|
config.num_ssd_boxes = num
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if config.dataset == "coco":
|
||||||
args_opt = get_eval_args()
|
coco_root = os.path.join(config.data_path, config.coco_root)
|
||||||
if args_opt.dataset == "coco":
|
json_path = os.path.join(coco_root, config.instances_set.format(config.val_data_type))
|
||||||
json_path = os.path.join(config.coco_root, config.instances_set.format(config.val_data_type))
|
elif config.dataset == "voc":
|
||||||
elif args_opt.dataset == "voc":
|
voc_root = os.path.join(config.data_path, config.voc_root)
|
||||||
json_path = os.path.join(config.voc_root, config.voc_json)
|
json_path = os.path.join(voc_root, config.voc_json)
|
||||||
else:
|
else:
|
||||||
raise ValueError('SSD eval only support dataset mode is coco and voc!')
|
raise ValueError('SSD eval only support dataset mode is coco and voc!')
|
||||||
|
|
||||||
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.run_platform, device_id=args_opt.device_id)
|
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=config.device_id)
|
||||||
|
|
||||||
mindrecord_file = create_mindrecord(args_opt.dataset, "ssd_eval.mindrecord", False)
|
mindrecord_file = create_mindrecord(config.dataset, "ssd_eval.mindrecord", False)
|
||||||
|
|
||||||
print("Start Eval!")
|
print("Start Eval!")
|
||||||
ssd_eval(mindrecord_file, args_opt.checkpoint_path, json_path)
|
ssd_eval(mindrecord_file, config.checkpoint_file_path, json_path)
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
eval_net()
|
||||||
|
|
|
@ -13,48 +13,37 @@
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
# ============================================================================
|
# ============================================================================
|
||||||
|
|
||||||
import argparse
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
import mindspore
|
import mindspore
|
||||||
from mindspore import context, Tensor
|
from mindspore import context, Tensor
|
||||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
|
||||||
from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
|
from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
|
||||||
from src.config import config
|
from src.model_utils.config import config
|
||||||
from src.box_utils import default_boxes
|
from src.box_utils import default_boxes
|
||||||
|
|
||||||
parser = argparse.ArgumentParser(description='SSD export')
|
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
|
||||||
parser.add_argument("--device_id", type=int, default=0, help="Device id")
|
if config.device_target == "Ascend":
|
||||||
parser.add_argument("--batch_size", type=int, default=1, help="batch size")
|
context.set_context(device_id=config.device_id)
|
||||||
parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
|
|
||||||
parser.add_argument("--file_name", type=str, default="ssd", help="output file name.")
|
|
||||||
parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format')
|
|
||||||
parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
|
|
||||||
help="device target")
|
|
||||||
args = parser.parse_args()
|
|
||||||
|
|
||||||
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
|
|
||||||
if args.device_target == "Ascend":
|
|
||||||
context.set_context(device_id=args.device_id)
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
if config.model == "ssd300":
|
if config.model_name == "ssd300":
|
||||||
net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
|
net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
|
||||||
elif config.model == "ssd_vgg16":
|
elif config.model_name == "ssd_vgg16":
|
||||||
net = ssd_vgg16(config=config)
|
net = ssd_vgg16(config=config)
|
||||||
elif config.model == "ssd_mobilenet_v1_fpn":
|
elif config.model_name == "ssd_mobilenet_v1_fpn":
|
||||||
net = ssd_mobilenet_v1_fpn(config=config)
|
net = ssd_mobilenet_v1_fpn(config=config)
|
||||||
elif config.model == "ssd_resnet50_fpn":
|
elif config.model_name == "ssd_resnet50_fpn":
|
||||||
net = ssd_resnet50_fpn(config=config)
|
net = ssd_resnet50_fpn(config=config)
|
||||||
else:
|
else:
|
||||||
raise ValueError(f'config.model: {config.model} is not supported')
|
raise ValueError(f'config.model: {config.model_name} is not supported')
|
||||||
net = SsdInferWithDecoder(net, Tensor(default_boxes), config)
|
net = SsdInferWithDecoder(net, Tensor(default_boxes), config)
|
||||||
|
|
||||||
param_dict = load_checkpoint(args.ckpt_file)
|
param_dict = load_checkpoint(config.checkpoint_file_path)
|
||||||
net.init_parameters_data()
|
net.init_parameters_data()
|
||||||
load_param_into_net(net, param_dict)
|
load_param_into_net(net, param_dict)
|
||||||
net.set_train(False)
|
net.set_train(False)
|
||||||
|
|
||||||
input_shp = [args.batch_size, 3] + config.img_shape
|
input_shp = [config.batch_size, 3] + config.img_shape
|
||||||
input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp), mindspore.float32)
|
input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp), mindspore.float32)
|
||||||
export(net, input_array, file_name=args.file_name, file_format=args.file_format)
|
export(net, input_array, file_name=config.file_name, file_format=config.file_format)
|
||||||
|
|
|
@ -21,10 +21,10 @@ echo "for example: sh run_distribute_train.sh 8 500 0.2 coco /data/hccl.json /op
|
||||||
echo "It is better to use absolute path."
|
echo "It is better to use absolute path."
|
||||||
echo "================================================================================================================="
|
echo "================================================================================================================="
|
||||||
|
|
||||||
if [ $# != 5 ] && [ $# != 7 ]
|
if [ $# != 6 ] && [ $# != 8 ]
|
||||||
then
|
then
|
||||||
echo "Usage: sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] \
|
echo "Usage: sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] \
|
||||||
[RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)"
|
[RANK_TABLE_FILE] [CONFIG_PATH] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)"
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
@ -39,8 +39,9 @@ export RANK_SIZE=$1
|
||||||
EPOCH_SIZE=$2
|
EPOCH_SIZE=$2
|
||||||
LR=$3
|
LR=$3
|
||||||
DATASET=$4
|
DATASET=$4
|
||||||
PRE_TRAINED=$6
|
PRE_TRAINED=$7
|
||||||
PRE_TRAINED_EPOCH_SIZE=$7
|
CONFIG_PATH=$6
|
||||||
|
PRE_TRAINED_EPOCH_SIZE=$8
|
||||||
export RANK_TABLE_FILE=$5
|
export RANK_TABLE_FILE=$5
|
||||||
|
|
||||||
for((i=0;i<RANK_SIZE;i++))
|
for((i=0;i<RANK_SIZE;i++))
|
||||||
|
@ -49,33 +50,38 @@ do
|
||||||
rm -rf LOG$i
|
rm -rf LOG$i
|
||||||
mkdir ./LOG$i
|
mkdir ./LOG$i
|
||||||
cp ./*.py ./LOG$i
|
cp ./*.py ./LOG$i
|
||||||
|
cp ./*.yaml ./LOG$i
|
||||||
cp -r ./src ./LOG$i
|
cp -r ./src ./LOG$i
|
||||||
cd ./LOG$i || exit
|
cd ./LOG$i || exit
|
||||||
export RANK_ID=$i
|
export RANK_ID=$i
|
||||||
echo "start training for rank $i, device $DEVICE_ID"
|
echo "start training for rank $i, device $DEVICE_ID"
|
||||||
env > env.log
|
env > env.log
|
||||||
if [ $# == 5 ]
|
if [ $# == 6 ]
|
||||||
then
|
then
|
||||||
python train.py \
|
python train.py \
|
||||||
--distribute=True \
|
--run_distribute=True \
|
||||||
--lr=$LR \
|
--lr=$LR \
|
||||||
--dataset=$DATASET \
|
--dataset=$DATASET \
|
||||||
--device_num=$RANK_SIZE \
|
--device_num=$RANK_SIZE \
|
||||||
--device_id=$DEVICE_ID \
|
--device_id=$DEVICE_ID \
|
||||||
--epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
|
--epoch_size=$EPOCH_SIZE \
|
||||||
|
--config_path=$CONFIG_PATH \
|
||||||
|
--output_path './output' > log.txt 2>&1 &
|
||||||
fi
|
fi
|
||||||
|
|
||||||
if [ $# == 7 ]
|
if [ $# == 8 ]
|
||||||
then
|
then
|
||||||
python train.py \
|
python train.py \
|
||||||
--distribute=True \
|
--run_distribute=True \
|
||||||
--lr=$LR \
|
--lr=$LR \
|
||||||
--dataset=$DATASET \
|
--dataset=$DATASET \
|
||||||
--device_num=$RANK_SIZE \
|
--device_num=$RANK_SIZE \
|
||||||
--device_id=$DEVICE_ID \
|
--device_id=$DEVICE_ID \
|
||||||
--pre_trained=$PRE_TRAINED \
|
--pre_trained=$PRE_TRAINED \
|
||||||
--pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
|
--pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
|
||||||
--epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
|
--epoch_size=$EPOCH_SIZE \
|
||||||
|
--config_path=$CONFIG_PATH \
|
||||||
|
--output_path './output' > log.txt 2>&1 &
|
||||||
fi
|
fi
|
||||||
|
|
||||||
cd ../
|
cd ../
|
||||||
|
|
|
@ -21,17 +21,17 @@ echo "for example: sh run_distribute_train_gpu.sh 8 500 0.2 coco /opt/ssd-300.ck
|
||||||
echo "It is better to use absolute path."
|
echo "It is better to use absolute path."
|
||||||
echo "================================================================================================================="
|
echo "================================================================================================================="
|
||||||
|
|
||||||
if [ $# != 4 ] && [ $# != 6 ]
|
if [ $# != 5 ] && [ $# != 7 ]
|
||||||
then
|
then
|
||||||
echo "Usage: sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] \
|
echo "Usage: sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] \
|
||||||
[PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)"
|
[CONFIG_PATH] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)"
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
# Before start distribute train, first create mindrecord files.
|
# Before start distribute train, first create mindrecord files.
|
||||||
BASE_PATH=$(cd "`dirname $0`" || exit; pwd)
|
BASE_PATH=$(cd "`dirname $0`" || exit; pwd)
|
||||||
cd $BASE_PATH/../ || exit
|
cd $BASE_PATH/../ || exit
|
||||||
python train.py --only_create_dataset=True --run_platform="GPU" --dataset=$4
|
python train.py --only_create_dataset=True --device_target="GPU" --dataset=$4
|
||||||
|
|
||||||
echo "After running the script, the network runs in the background. The log will be generated in LOG/log.txt"
|
echo "After running the script, the network runs in the background. The log will be generated in LOG/log.txt"
|
||||||
|
|
||||||
|
@ -39,39 +39,45 @@ export RANK_SIZE=$1
|
||||||
EPOCH_SIZE=$2
|
EPOCH_SIZE=$2
|
||||||
LR=$3
|
LR=$3
|
||||||
DATASET=$4
|
DATASET=$4
|
||||||
PRE_TRAINED=$5
|
CONFIG_PATH=$5
|
||||||
PRE_TRAINED_EPOCH_SIZE=$6
|
PRE_TRAINED=$6
|
||||||
|
PRE_TRAINED_EPOCH_SIZE=$7
|
||||||
|
|
||||||
rm -rf LOG
|
rm -rf LOG
|
||||||
mkdir ./LOG
|
mkdir ./LOG
|
||||||
cp ./*.py ./LOG
|
cp ./*.py ./LOG
|
||||||
|
cp ./*.yaml ./LOG
|
||||||
cp -r ./src ./LOG
|
cp -r ./src ./LOG
|
||||||
cd ./LOG || exit
|
cd ./LOG || exit
|
||||||
|
|
||||||
if [ $# == 4 ]
|
if [ $# == 5 ]
|
||||||
then
|
then
|
||||||
mpirun -allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
|
mpirun -allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
|
||||||
python train.py \
|
python train.py \
|
||||||
--distribute=True \
|
--run_distribute=True \
|
||||||
--lr=$LR \
|
--lr=$LR \
|
||||||
--dataset=$DATASET \
|
--dataset=$DATASET \
|
||||||
--device_num=$RANK_SIZE \
|
--device_num=$RANK_SIZE \
|
||||||
--loss_scale=1 \
|
--loss_scale=1 \
|
||||||
--run_platform="GPU" \
|
--device_target="GPU" \
|
||||||
--epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
|
--epoch_size=$EPOCH_SIZE \
|
||||||
|
--config_path=$CONFIG_PATH \
|
||||||
|
--output_path './output' > log.txt 2>&1 &
|
||||||
fi
|
fi
|
||||||
|
|
||||||
if [ $# == 6 ]
|
if [ $# == 7 ]
|
||||||
then
|
then
|
||||||
mpirun -allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
|
mpirun -allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
|
||||||
python train.py \
|
python train.py \
|
||||||
--distribute=True \
|
--run_distribute=True \
|
||||||
--lr=$LR \
|
--lr=$LR \
|
||||||
--dataset=$DATASET \
|
--dataset=$DATASET \
|
||||||
--device_num=$RANK_SIZE \
|
--device_num=$RANK_SIZE \
|
||||||
--pre_trained=$PRE_TRAINED \
|
--pre_trained=$PRE_TRAINED \
|
||||||
--pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
|
--pre_trained_epoch_size=$PRE_TRAINED_EPOCH_SIZE \
|
||||||
--loss_scale=1 \
|
--loss_scale=1 \
|
||||||
--run_platform="GPU" \
|
--device_target="GPU" \
|
||||||
--epoch_size=$EPOCH_SIZE > log.txt 2>&1 &
|
--epoch_size=$EPOCH_SIZE \
|
||||||
|
--config_path=$CONFIG_PATH \
|
||||||
|
--output_path './output' > log.txt 2>&1 &
|
||||||
fi
|
fi
|
||||||
|
|
|
@ -14,9 +14,9 @@
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
# ============================================================================
|
# ============================================================================
|
||||||
|
|
||||||
if [ $# != 3 ]
|
if [ $# != 4 ]
|
||||||
then
|
then
|
||||||
echo "Usage: sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]"
|
echo "Usage: sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]"
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
@ -30,7 +30,9 @@ get_real_path(){
|
||||||
|
|
||||||
DATASET=$1
|
DATASET=$1
|
||||||
CHECKPOINT_PATH=$(get_real_path $2)
|
CHECKPOINT_PATH=$(get_real_path $2)
|
||||||
|
CONFIG_PATH=$(get_real_path $4)
|
||||||
echo $DATASET
|
echo $DATASET
|
||||||
|
echo $CONFIG_PATH
|
||||||
echo $CHECKPOINT_PATH
|
echo $CHECKPOINT_PATH
|
||||||
|
|
||||||
if [ ! -f $CHECKPOINT_PATH ]
|
if [ ! -f $CHECKPOINT_PATH ]
|
||||||
|
@ -54,12 +56,14 @@ fi
|
||||||
|
|
||||||
mkdir ./eval$3
|
mkdir ./eval$3
|
||||||
cp ./*.py ./eval$3
|
cp ./*.py ./eval$3
|
||||||
|
cp ./*.yaml ./eval$3
|
||||||
cp -r ./src ./eval$3
|
cp -r ./src ./eval$3
|
||||||
cd ./eval$3 || exit
|
cd ./eval$3 || exit
|
||||||
env > env.log
|
env > env.log
|
||||||
echo "start inferring for device $DEVICE_ID"
|
echo "start inferring for device $DEVICE_ID"
|
||||||
python eval.py \
|
python eval.py \
|
||||||
--dataset=$DATASET \
|
--dataset=$DATASET \
|
||||||
--checkpoint_path=$CHECKPOINT_PATH \
|
--checkpoint_file_path=$CHECKPOINT_PATH \
|
||||||
--device_id=$3 > log.txt 2>&1 &
|
--device_id=$3 \
|
||||||
|
--config_path=$CONFIG_PATH > log.txt 2>&1 &
|
||||||
cd ..
|
cd ..
|
||||||
|
|
|
@ -14,9 +14,9 @@
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
# ============================================================================
|
# ============================================================================
|
||||||
|
|
||||||
if [ $# != 3 ]
|
if [ $# != 4 ]
|
||||||
then
|
then
|
||||||
echo "Usage: sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]"
|
echo "Usage: sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]"
|
||||||
exit 1
|
exit 1
|
||||||
fi
|
fi
|
||||||
|
|
||||||
|
@ -30,8 +30,10 @@ get_real_path(){
|
||||||
|
|
||||||
DATASET=$1
|
DATASET=$1
|
||||||
CHECKPOINT_PATH=$(get_real_path $2)
|
CHECKPOINT_PATH=$(get_real_path $2)
|
||||||
|
CONFIG_PATH=$(get_real_path $4)
|
||||||
echo $DATASET
|
echo $DATASET
|
||||||
echo $CHECKPOINT_PATH
|
echo $CHECKPOINT_PATH
|
||||||
|
echo $CONFIG_PATH
|
||||||
|
|
||||||
if [ ! -f $CHECKPOINT_PATH ]
|
if [ ! -f $CHECKPOINT_PATH ]
|
||||||
then
|
then
|
||||||
|
@ -54,13 +56,15 @@ fi
|
||||||
|
|
||||||
mkdir ./eval$3
|
mkdir ./eval$3
|
||||||
cp ./*.py ./eval$3
|
cp ./*.py ./eval$3
|
||||||
|
cp ./*.yaml ./eval$3
|
||||||
cp -r ./src ./eval$3
|
cp -r ./src ./eval$3
|
||||||
cd ./eval$3 || exit
|
cd ./eval$3 || exit
|
||||||
env > env.log
|
env > env.log
|
||||||
echo "start inferring for device $DEVICE_ID"
|
echo "start inferring for device $DEVICE_ID"
|
||||||
python eval.py \
|
python eval.py \
|
||||||
--dataset=$DATASET \
|
--dataset=$DATASET \
|
||||||
--checkpoint_path=$CHECKPOINT_PATH \
|
--checkpoint_file_path=$CHECKPOINT_PATH \
|
||||||
--run_platform="GPU" \
|
--device_target="GPU" \
|
||||||
--device_id=$3 > log.txt 2>&1 &
|
--device_id=$3 \
|
||||||
|
--config_path=$CONFIG_PATH > log.txt 2>&1 &
|
||||||
cd ..
|
cd ..
|
||||||
|
|
|
@ -18,7 +18,7 @@
|
||||||
import math
|
import math
|
||||||
import itertools as it
|
import itertools as it
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from .config import config
|
from src.model_utils.config import config
|
||||||
from .anchor_generator import GridAnchorGenerator
|
from .anchor_generator import GridAnchorGenerator
|
||||||
|
|
||||||
|
|
||||||
|
@ -62,7 +62,7 @@ class GeneratDefaultBoxes():
|
||||||
self.default_boxes_tlbr = np.array(tuple(to_tlbr(*i) for i in self.default_boxes), dtype='float32')
|
self.default_boxes_tlbr = np.array(tuple(to_tlbr(*i) for i in self.default_boxes), dtype='float32')
|
||||||
self.default_boxes = np.array(self.default_boxes, dtype='float32')
|
self.default_boxes = np.array(self.default_boxes, dtype='float32')
|
||||||
|
|
||||||
if 'use_anchor_generator' in config and config.use_anchor_generator:
|
if hasattr(config, 'use_anchor_generator') and config.use_anchor_generator:
|
||||||
generator = GridAnchorGenerator(config.img_shape, 4, 2, [1.0, 2.0, 0.5])
|
generator = GridAnchorGenerator(config.img_shape, 4, 2, [1.0, 2.0, 0.5])
|
||||||
default_boxes, default_boxes_tlbr = generator.generate_multi_levels(config.steps)
|
default_boxes, default_boxes_tlbr = generator.generate_multi_levels(config.steps)
|
||||||
else:
|
else:
|
||||||
|
|
|
@ -1,39 +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.
|
|
||||||
# ============================================================================
|
|
||||||
|
|
||||||
"""Config parameters for SSD models."""
|
|
||||||
|
|
||||||
from .config_ssd300 import config as config_ssd300
|
|
||||||
from .config_ssd_mobilenet_v1_fpn import config as config_ssd_mobilenet_v1_fpn
|
|
||||||
from .config_ssd_resnet50_fpn import config as config_ssd_resnet50_fpn
|
|
||||||
from .config_ssd_vgg16 import config as config_ssd_vgg16
|
|
||||||
|
|
||||||
using_model = "ssd300"
|
|
||||||
|
|
||||||
config_map = {
|
|
||||||
"ssd300": config_ssd300,
|
|
||||||
"ssd_vgg16": config_ssd_vgg16,
|
|
||||||
"ssd_mobilenet_v1_fpn": config_ssd_mobilenet_v1_fpn,
|
|
||||||
"ssd_resnet50_fpn": config_ssd_resnet50_fpn
|
|
||||||
}
|
|
||||||
|
|
||||||
config = config_map[using_model]
|
|
||||||
|
|
||||||
if config.num_ssd_boxes == -1:
|
|
||||||
num = 0
|
|
||||||
h, w = config.img_shape
|
|
||||||
for i in range(len(config.steps)):
|
|
||||||
num += (h // config.steps[i]) * (w // config.steps[i]) * config.num_default[i]
|
|
||||||
config.num_ssd_boxes = num
|
|
|
@ -1,82 +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.
|
|
||||||
#" ============================================================================
|
|
||||||
|
|
||||||
"""Config parameters for SSD models."""
|
|
||||||
|
|
||||||
from easydict import EasyDict as ed
|
|
||||||
|
|
||||||
config = ed({
|
|
||||||
"model": "ssd300",
|
|
||||||
"img_shape": [300, 300],
|
|
||||||
"num_ssd_boxes": 1917,
|
|
||||||
"match_threshold": 0.5,
|
|
||||||
"nms_threshold": 0.6,
|
|
||||||
"min_score": 0.1,
|
|
||||||
"max_boxes": 100,
|
|
||||||
|
|
||||||
# learing rate settings
|
|
||||||
"lr_init": 0.001,
|
|
||||||
"lr_end_rate": 0.001,
|
|
||||||
"warmup_epochs": 2,
|
|
||||||
"momentum": 0.9,
|
|
||||||
"weight_decay": 1.5e-4,
|
|
||||||
|
|
||||||
# network
|
|
||||||
"num_default": [3, 6, 6, 6, 6, 6],
|
|
||||||
"extras_in_channels": [256, 576, 1280, 512, 256, 256],
|
|
||||||
"extras_out_channels": [576, 1280, 512, 256, 256, 128],
|
|
||||||
"extras_strides": [1, 1, 2, 2, 2, 2],
|
|
||||||
"extras_ratio": [0.2, 0.2, 0.2, 0.25, 0.5, 0.25],
|
|
||||||
"feature_size": [19, 10, 5, 3, 2, 1],
|
|
||||||
"min_scale": 0.2,
|
|
||||||
"max_scale": 0.95,
|
|
||||||
"aspect_ratios": [(), (2, 3), (2, 3), (2, 3), (2, 3), (2, 3)],
|
|
||||||
"steps": (16, 32, 64, 100, 150, 300),
|
|
||||||
"prior_scaling": (0.1, 0.2),
|
|
||||||
"gamma": 2.0,
|
|
||||||
"alpha": 0.75,
|
|
||||||
|
|
||||||
# `mindrecord_dir` and `coco_root` are better to use absolute path.
|
|
||||||
"feature_extractor_base_param": "",
|
|
||||||
"checkpoint_filter_list": ['multi_loc_layers', 'multi_cls_layers'],
|
|
||||||
"mindrecord_dir": "/data/MindRecord_COCO",
|
|
||||||
"coco_root": "/data/coco2017",
|
|
||||||
"train_data_type": "train2017",
|
|
||||||
"val_data_type": "val2017",
|
|
||||||
"instances_set": "annotations/instances_{}.json",
|
|
||||||
"classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
|
||||||
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
|
||||||
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
|
||||||
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
|
||||||
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
|
||||||
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
|
||||||
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
|
||||||
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
|
||||||
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
|
||||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
|
||||||
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
|
||||||
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
|
||||||
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
|
||||||
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
|
||||||
'teddy bear', 'hair drier', 'toothbrush'),
|
|
||||||
"num_classes": 81,
|
|
||||||
# The annotation.json position of voc validation dataset.
|
|
||||||
"voc_json": "annotations/voc_instances_val.json",
|
|
||||||
# voc original dataset.
|
|
||||||
"voc_root": "/data/voc_dataset",
|
|
||||||
# if coco or voc used, `image_dir` and `anno_path` are useless.
|
|
||||||
"image_dir": "",
|
|
||||||
"anno_path": ""
|
|
||||||
})
|
|
|
@ -1,87 +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.
|
|
||||||
#" ============================================================================
|
|
||||||
|
|
||||||
"""Config parameters for SSD models."""
|
|
||||||
|
|
||||||
from easydict import EasyDict as ed
|
|
||||||
|
|
||||||
config = ed({
|
|
||||||
"model": "ssd_mobilenet_v1_fpn",
|
|
||||||
"img_shape": [640, 640],
|
|
||||||
"num_ssd_boxes": -1,
|
|
||||||
"match_threshold": 0.5,
|
|
||||||
"nms_threshold": 0.6,
|
|
||||||
"min_score": 0.1,
|
|
||||||
"max_boxes": 100,
|
|
||||||
|
|
||||||
# learning rate settings
|
|
||||||
"global_step": 0,
|
|
||||||
"lr_init": 0.01333,
|
|
||||||
"lr_end_rate": 0.0,
|
|
||||||
"warmup_epochs": 2,
|
|
||||||
"weight_decay": 4e-5,
|
|
||||||
"momentum": 0.9,
|
|
||||||
|
|
||||||
# network
|
|
||||||
"num_default": [6, 6, 6, 6, 6],
|
|
||||||
"extras_in_channels": [256, 512, 1024, 256, 256],
|
|
||||||
"extras_out_channels": [256, 256, 256, 256, 256],
|
|
||||||
"extras_strides": [1, 1, 2, 2, 2, 2],
|
|
||||||
"extras_ratio": [0.2, 0.2, 0.2, 0.25, 0.5, 0.25],
|
|
||||||
"feature_size": [80, 40, 20, 10, 5],
|
|
||||||
"min_scale": 0.2,
|
|
||||||
"max_scale": 0.95,
|
|
||||||
"aspect_ratios": [(2, 3), (2, 3), (2, 3), (2, 3), (2, 3), (2, 3)],
|
|
||||||
"steps": (8, 16, 32, 64, 128),
|
|
||||||
"prior_scaling": (0.1, 0.2),
|
|
||||||
"gamma": 2.0,
|
|
||||||
"alpha": 0.25,
|
|
||||||
"num_addition_layers": 4,
|
|
||||||
"use_anchor_generator": True,
|
|
||||||
"use_global_norm": True,
|
|
||||||
|
|
||||||
# `mindrecord_dir` and `coco_root` are better to use absolute path.
|
|
||||||
"feature_extractor_base_param": "/ckpt/mobilenet_v1.ckpt",
|
|
||||||
"checkpoint_filter_list": ['network.multi_box.cls_layers.0.weight', 'network.multi_box.cls_layers.0.bias',
|
|
||||||
'network.multi_box.loc_layers.0.weight', 'network.multi_box.loc_layers.0.bias'],
|
|
||||||
"mindrecord_dir": "/data/MindRecord_COCO",
|
|
||||||
"coco_root": "/data/coco2017",
|
|
||||||
"train_data_type": "train2017",
|
|
||||||
"val_data_type": "val2017",
|
|
||||||
"instances_set": "annotations/instances_{}.json",
|
|
||||||
"classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
|
||||||
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
|
||||||
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
|
||||||
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
|
||||||
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
|
||||||
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
|
||||||
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
|
||||||
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
|
||||||
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
|
||||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
|
||||||
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
|
||||||
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
|
||||||
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
|
||||||
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
|
||||||
'teddy bear', 'hair drier', 'toothbrush'),
|
|
||||||
"num_classes": 81,
|
|
||||||
# The annotation.json position of voc validation dataset.
|
|
||||||
"voc_json": "annotations/voc_instances_val.json",
|
|
||||||
# voc original dataset.
|
|
||||||
"voc_root": "/data/voc_dataset",
|
|
||||||
# if coco or voc used, `image_dir` and `anno_path` are useless.
|
|
||||||
"image_dir": "",
|
|
||||||
"anno_path": ""
|
|
||||||
})
|
|
|
@ -1,88 +0,0 @@
|
||||||
# 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.
|
|
||||||
#" ============================================================================
|
|
||||||
|
|
||||||
"""Config parameters for SSD models."""
|
|
||||||
|
|
||||||
from easydict import EasyDict as ed
|
|
||||||
|
|
||||||
config = ed({
|
|
||||||
"model": "ssd_resnet50_fpn",
|
|
||||||
"img_shape": [640, 640],
|
|
||||||
"num_ssd_boxes": -1,
|
|
||||||
"match_threshold": 0.5,
|
|
||||||
"nms_threshold": 0.6,
|
|
||||||
"min_score": 0.1,
|
|
||||||
"max_boxes": 100,
|
|
||||||
|
|
||||||
# learning rate settings
|
|
||||||
"global_step": 0,
|
|
||||||
"lr_init": 0.01333,
|
|
||||||
"lr_end_rate": 0.0,
|
|
||||||
"warmup_epochs": 2,
|
|
||||||
"weight_decay": 4e-4,
|
|
||||||
"momentum": 0.9,
|
|
||||||
|
|
||||||
# network
|
|
||||||
"num_default": [6, 6, 6, 6, 6],
|
|
||||||
"extras_in_channels": [256, 512, 1024, 256, 256],
|
|
||||||
"extras_out_channels": [256, 256, 256, 256, 256],
|
|
||||||
"extras_strides": [1, 1, 2, 2, 2, 2],
|
|
||||||
"extras_ratio": [0.2, 0.2, 0.2, 0.25, 0.5, 0.25],
|
|
||||||
"feature_size": [80, 40, 20, 10, 5],
|
|
||||||
"min_scale": 0.2,
|
|
||||||
"max_scale": 0.95,
|
|
||||||
"aspect_ratios": [(2, 3), (2, 3), (2, 3), (2, 3), (2, 3), (2, 3)],
|
|
||||||
"steps": (8, 16, 32, 64, 128),
|
|
||||||
"prior_scaling": (0.1, 0.2),
|
|
||||||
"gamma": 2.0,
|
|
||||||
"alpha": 0.25,
|
|
||||||
"num_addition_layers": 4,
|
|
||||||
"use_anchor_generator": True,
|
|
||||||
"use_global_norm": True,
|
|
||||||
"use_float16": True,
|
|
||||||
|
|
||||||
# `mindrecord_dir` and `coco_root` are better to use absolute path.
|
|
||||||
"feature_extractor_base_param": "/ckpt/resnet50.ckpt",
|
|
||||||
"checkpoint_filter_list": ['network.multi_box.cls_layers.0.weight', 'network.multi_box.cls_layers.0.bias',
|
|
||||||
'network.multi_box.loc_layers.0.weight', 'network.multi_box.loc_layers.0.bias'],
|
|
||||||
"mindrecord_dir": "/data/MindRecord_COCO",
|
|
||||||
"coco_root": "/data/coco2017",
|
|
||||||
"train_data_type": "train2017",
|
|
||||||
"val_data_type": "val2017",
|
|
||||||
"instances_set": "annotations/instances_{}.json",
|
|
||||||
"classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
|
||||||
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
|
||||||
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
|
||||||
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
|
||||||
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
|
||||||
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
|
||||||
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
|
||||||
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
|
||||||
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
|
||||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
|
||||||
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
|
||||||
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
|
||||||
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
|
||||||
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
|
||||||
'teddy bear', 'hair drier', 'toothbrush'),
|
|
||||||
"num_classes": 81,
|
|
||||||
# The annotation.json position of voc validation dataset.
|
|
||||||
"voc_json": "annotations/voc_instances_val.json",
|
|
||||||
# voc original dataset.
|
|
||||||
"voc_root": "/data/voc_dataset",
|
|
||||||
# if coco or voc used, `image_dir` and `anno_path` are useless.
|
|
||||||
"image_dir": "",
|
|
||||||
"anno_path": ""
|
|
||||||
})
|
|
|
@ -1,84 +0,0 @@
|
||||||
# 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.
|
|
||||||
# ============================================================================
|
|
||||||
|
|
||||||
"""Config parameters for SSD models."""
|
|
||||||
|
|
||||||
from easydict import EasyDict as ed
|
|
||||||
|
|
||||||
config = ed({
|
|
||||||
"model": "ssd_vgg16",
|
|
||||||
"img_shape": [300, 300],
|
|
||||||
"num_ssd_boxes": 7308,
|
|
||||||
"match_threshold": 0.5,
|
|
||||||
"nms_threshold": 0.6,
|
|
||||||
"min_score": 0.1,
|
|
||||||
"max_boxes": 100,
|
|
||||||
"ssd_vgg_bn": False,
|
|
||||||
|
|
||||||
# learing rate settings
|
|
||||||
"lr_init": 0.001,
|
|
||||||
"lr_end_rate": 0.001,
|
|
||||||
"warmup_epochs": 2,
|
|
||||||
"momentum": 0.9,
|
|
||||||
"weight_decay": 1.5e-4,
|
|
||||||
|
|
||||||
# network
|
|
||||||
"num_default": [3, 6, 6, 6, 6, 6],
|
|
||||||
"extras_in_channels": [256, 512, 1024, 512, 256, 256],
|
|
||||||
"extras_out_channels": [512, 1024, 512, 256, 256, 256],
|
|
||||||
"extras_strides": [1, 1, 2, 2, 2, 2],
|
|
||||||
"extras_ratio": [0.2, 0.2, 0.2, 0.25, 0.5, 0.25],
|
|
||||||
"feature_size": [38, 19, 10, 5, 3, 1],
|
|
||||||
"min_scale": 0.2,
|
|
||||||
"max_scale": 0.95,
|
|
||||||
"aspect_ratios": [(), (2, 3), (2, 3), (2, 3), (2, 3), (2, 3)],
|
|
||||||
"steps": (8, 16, 32, 64, 100, 300),
|
|
||||||
"prior_scaling": (0.1, 0.2),
|
|
||||||
"gamma": 2.0,
|
|
||||||
"alpha": 0.75,
|
|
||||||
|
|
||||||
# `mindrecord_dir` and `coco_root` are better to use absolute path.
|
|
||||||
"feature_extractor_base_param": "",
|
|
||||||
"pretrain_vgg_bn": False,
|
|
||||||
"checkpoint_filter_list": ['multi_loc_layers', 'multi_cls_layers'],
|
|
||||||
"mindrecord_dir": "/data/MindRecord_COCO",
|
|
||||||
"coco_root": "/data/coco2017",
|
|
||||||
"train_data_type": "train2017",
|
|
||||||
"val_data_type": "val2017",
|
|
||||||
"instances_set": "annotations/instances_{}.json",
|
|
||||||
"classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
|
||||||
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
|
||||||
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
|
||||||
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
|
||||||
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
|
||||||
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
|
||||||
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
|
||||||
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
|
||||||
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
|
||||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
|
||||||
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
|
||||||
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
|
||||||
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
|
||||||
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
|
||||||
'teddy bear', 'hair drier', 'toothbrush'),
|
|
||||||
"num_classes": 81,
|
|
||||||
# The annotation.json position of voc validation dataset.
|
|
||||||
"voc_json": "annotations/voc_instances_val.json",
|
|
||||||
# voc original dataset.
|
|
||||||
"voc_root": "/data/voc_dataset",
|
|
||||||
# if coco or voc used, `image_dir` and `anno_path` are useless.
|
|
||||||
"image_dir": "",
|
|
||||||
"anno_path": ""
|
|
||||||
})
|
|
|
@ -26,7 +26,7 @@ import cv2
|
||||||
import mindspore.dataset as de
|
import mindspore.dataset as de
|
||||||
import mindspore.dataset.vision.c_transforms as C
|
import mindspore.dataset.vision.c_transforms as C
|
||||||
from mindspore.mindrecord import FileWriter
|
from mindspore.mindrecord import FileWriter
|
||||||
from .config import config
|
from src.model_utils.config import config
|
||||||
from .box_utils import jaccard_numpy, ssd_bboxes_encode
|
from .box_utils import jaccard_numpy, ssd_bboxes_encode
|
||||||
|
|
||||||
|
|
||||||
|
@ -253,7 +253,7 @@ def create_coco_label(is_training):
|
||||||
"""Get image path and annotation from COCO."""
|
"""Get image path and annotation from COCO."""
|
||||||
from pycocotools.coco import COCO
|
from pycocotools.coco import COCO
|
||||||
|
|
||||||
coco_root = config.coco_root
|
coco_root = os.path.join(config.data_path, config.coco_root)
|
||||||
data_type = config.val_data_type
|
data_type = config.val_data_type
|
||||||
if is_training:
|
if is_training:
|
||||||
data_type = config.train_data_type
|
data_type = config.train_data_type
|
||||||
|
@ -425,13 +425,14 @@ def create_mindrecord(dataset="coco", prefix="ssd.mindrecord", is_training=True)
|
||||||
# It will generate mindrecord file in config.mindrecord_dir,
|
# It will generate mindrecord file in config.mindrecord_dir,
|
||||||
# and the file name is ssd.mindrecord0, 1, ... file_num.
|
# and the file name is ssd.mindrecord0, 1, ... file_num.
|
||||||
|
|
||||||
mindrecord_dir = config.mindrecord_dir
|
mindrecord_dir = os.path.join(config.data_path, config.mindrecord_dir)
|
||||||
mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
|
mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
|
||||||
if not os.path.exists(mindrecord_file):
|
if not os.path.exists(mindrecord_file):
|
||||||
if not os.path.isdir(mindrecord_dir):
|
if not os.path.isdir(mindrecord_dir):
|
||||||
os.makedirs(mindrecord_dir)
|
os.makedirs(mindrecord_dir)
|
||||||
if dataset == "coco":
|
if dataset == "coco":
|
||||||
if os.path.isdir(config.coco_root):
|
coco_root = os.path.join(config.data_path, config.coco_root)
|
||||||
|
if os.path.isdir(coco_root):
|
||||||
print("Create Mindrecord.")
|
print("Create Mindrecord.")
|
||||||
data_to_mindrecord_byte_image("coco", is_training, prefix)
|
data_to_mindrecord_byte_image("coco", is_training, prefix)
|
||||||
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
|
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
|
||||||
|
|
|
@ -17,7 +17,7 @@
|
||||||
import json
|
import json
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from mindspore import Tensor
|
from mindspore import Tensor
|
||||||
from .config import config
|
from src.model_utils.config import config
|
||||||
|
|
||||||
def apply_eval(eval_param_dict):
|
def apply_eval(eval_param_dict):
|
||||||
net = eval_param_dict["net"]
|
net = eval_param_dict["net"]
|
||||||
|
|
|
@ -0,0 +1,124 @@
|
||||||
|
# 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 pformat
|
||||||
|
import yaml
|
||||||
|
|
||||||
|
_config_path = "./ssd300_config.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="ssd300_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]
|
||||||
|
elif len(cfgs) == 2:
|
||||||
|
cfg, cfg_helper = cfgs
|
||||||
|
else:
|
||||||
|
raise ValueError("At most 2 docs (config and help description for help) are supported in config yaml")
|
||||||
|
print(cfg_helper)
|
||||||
|
except:
|
||||||
|
raise ValueError("Failed to parse yaml")
|
||||||
|
return cfg, cfg_helper
|
||||||
|
|
||||||
|
|
||||||
|
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, \
|
||||||
|
"../../ssd300_config.yaml"), help="Config file path")
|
||||||
|
path_args, _ = parser.parse_known_args()
|
||||||
|
default, helper = parse_yaml(path_args.config_path)
|
||||||
|
args = parse_cli_to_yaml(parser, default, helper, 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 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 +1,115 @@
|
||||||
|
# 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 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)
|
||||||
|
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_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
|
|
@ -25,7 +25,6 @@ from mindspore.communication.management import get_group_size
|
||||||
from mindspore.ops import operations as P
|
from mindspore.ops import operations as P
|
||||||
from mindspore.ops import functional as F
|
from mindspore.ops import functional as F
|
||||||
from mindspore.ops import composite as C
|
from mindspore.ops import composite as C
|
||||||
|
|
||||||
from .fpn import mobilenet_v1_fpn, resnet50_fpn
|
from .fpn import mobilenet_v1_fpn, resnet50_fpn
|
||||||
from .vgg16 import vgg16
|
from .vgg16 import vgg16
|
||||||
|
|
||||||
|
|
|
@ -16,7 +16,7 @@
|
||||||
"""VGG16 backbone for SSD"""
|
"""VGG16 backbone for SSD"""
|
||||||
|
|
||||||
from mindspore import nn
|
from mindspore import nn
|
||||||
from .config_ssd_vgg16 import config
|
from src.model_utils.config import config
|
||||||
|
|
||||||
pretrain_vgg_bn = config.pretrain_vgg_bn
|
pretrain_vgg_bn = config.pretrain_vgg_bn
|
||||||
ssd_vgg_bn = config.ssd_vgg_bn
|
ssd_vgg_bn = config.ssd_vgg_bn
|
||||||
|
|
|
@ -0,0 +1,121 @@
|
||||||
|
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
|
||||||
|
enable_modelarts: False
|
||||||
|
# Url for modelarts
|
||||||
|
data_url: ""
|
||||||
|
train_url: ""
|
||||||
|
checkpoint_url: ""
|
||||||
|
# Path for local
|
||||||
|
run_distribute: False
|
||||||
|
enable_profiling: False
|
||||||
|
data_path: "/cache/data"
|
||||||
|
output_path: "/cache/train"
|
||||||
|
load_path: "/cache/checkpoint_path/"
|
||||||
|
device_target: 'Ascend'
|
||||||
|
checkpoint_path: './checkpoint/'
|
||||||
|
checkpoint_file_path: 'ssd-500_458.ckpt'
|
||||||
|
|
||||||
|
# ==============================================================================
|
||||||
|
# Training options
|
||||||
|
model_name: "ssd300"
|
||||||
|
img_shape: [300, 300]
|
||||||
|
num_ssd_boxes: 1917
|
||||||
|
match_threshold: 0.5
|
||||||
|
nms_threshold: 0.6
|
||||||
|
min_score: 0.1
|
||||||
|
max_boxes: 100
|
||||||
|
|
||||||
|
# learing rate settings
|
||||||
|
lr_init: 0.001
|
||||||
|
lr_end_rate: 0.001
|
||||||
|
warmup_epochs: 2
|
||||||
|
momentum: 0.9
|
||||||
|
weight_decay: 0.00015
|
||||||
|
ssd_vgg_bn: False
|
||||||
|
pretrain_vgg_bn: False
|
||||||
|
|
||||||
|
|
||||||
|
# network
|
||||||
|
num_default: [3, 6, 6, 6, 6, 6]
|
||||||
|
extras_in_channels: [256, 576, 1280, 512, 256, 256]
|
||||||
|
extras_out_channels: [576, 1280, 512, 256, 256, 128]
|
||||||
|
extras_strides: [1, 1, 2, 2, 2, 2]
|
||||||
|
extras_ratio: [0.2, 0.2, 0.2, 0.25, 0.5, 0.25]
|
||||||
|
feature_size: [19, 10, 5, 3, 2, 1]
|
||||||
|
min_scale: 0.2
|
||||||
|
max_scale: 0.95
|
||||||
|
aspect_ratios: [[], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]]
|
||||||
|
steps: [16, 32, 64, 100, 150, 300]
|
||||||
|
prior_scaling: [0.1, 0.2]
|
||||||
|
gamma: 2.0
|
||||||
|
alpha: 0.75
|
||||||
|
|
||||||
|
dataset: "coco"
|
||||||
|
lr: 0.05
|
||||||
|
mode_sink: "sink"
|
||||||
|
device_id: 0
|
||||||
|
device_num: 1
|
||||||
|
epoch_size: 500
|
||||||
|
batch_size: 32
|
||||||
|
loss_scale: 1024
|
||||||
|
pre_trained: ""
|
||||||
|
pre_trained_epoch_size: 0
|
||||||
|
save_checkpoint_epochs: 10
|
||||||
|
only_create_dataset: False
|
||||||
|
eval_start_epoch: 40
|
||||||
|
eval_interval: 1
|
||||||
|
run_eval: False
|
||||||
|
filter_weight: False
|
||||||
|
freeze_layer: None
|
||||||
|
save_best_ckpt: True
|
||||||
|
|
||||||
|
# `mindrecord_dir` and `coco_root` are better to use absolute path.
|
||||||
|
feature_extractor_base_param: ""
|
||||||
|
checkpoint_filter_list: ['multi_loc_layers', 'multi_cls_layers']
|
||||||
|
mindrecord_dir: "MindRecord_COCO"
|
||||||
|
coco_root: "coco_ori"
|
||||||
|
train_data_type: "train2017"
|
||||||
|
val_data_type: "val2017"
|
||||||
|
instances_set: "annotations/instances_{}.json"
|
||||||
|
classes: ['background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
||||||
|
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
||||||
|
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
||||||
|
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
||||||
|
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
||||||
|
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
||||||
|
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
||||||
|
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
||||||
|
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||||
|
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
||||||
|
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
||||||
|
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
||||||
|
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
||||||
|
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
||||||
|
'teddy bear', 'hair drier', 'toothbrush']
|
||||||
|
num_classes: 81
|
||||||
|
# The annotation.json position of voc validation dataset.
|
||||||
|
voc_json: "annotations/voc_instances_val.json"
|
||||||
|
# voc original dataset.
|
||||||
|
voc_root: "/data/voc_dataset"
|
||||||
|
# if coco or voc used, `image_dir` and `anno_path` are useless.
|
||||||
|
image_dir: ""
|
||||||
|
anno_path: ""
|
||||||
|
file_name: "ssd"
|
||||||
|
file_format: "AIR"
|
||||||
|
|
||||||
|
---
|
||||||
|
# Help description for each configuration
|
||||||
|
enable_modelarts: 'Whether training on modelarts, default: False'
|
||||||
|
data_url: 'Dataset url for obs'
|
||||||
|
train_url: 'Training output url for obs'
|
||||||
|
checkpoint_url: 'The location of checkpoint for obs'
|
||||||
|
data_path: 'Dataset path for local'
|
||||||
|
output_path: 'Training output path for local'
|
||||||
|
load_path: 'The location of checkpoint for obs'
|
||||||
|
device_target: 'Target device type, available: [Ascend, GPU, CPU]'
|
||||||
|
enable_profiling: 'Whether enable profiling while training, default: False'
|
||||||
|
num_classes: 'Class for dataset'
|
||||||
|
batch_size: "Batch size for training and evaluation"
|
||||||
|
epoch_size: "Total training epochs."
|
||||||
|
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
|
||||||
|
checkpoint_path: "The location of the checkpoint file."
|
||||||
|
checkpoint_file_path: "The location of the checkpoint file."
|
|
@ -0,0 +1,125 @@
|
||||||
|
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
|
||||||
|
enable_modelarts: False
|
||||||
|
# Url for modelarts
|
||||||
|
data_url: ""
|
||||||
|
train_url: ""
|
||||||
|
checkpoint_url: ""
|
||||||
|
# Path for local
|
||||||
|
run_distribute: False
|
||||||
|
enable_profiling: False
|
||||||
|
data_path: "/cache/data"
|
||||||
|
output_path: "/cache/train"
|
||||||
|
load_path: "/cache/checkpoint_path/"
|
||||||
|
device_target: 'Ascend'
|
||||||
|
checkpoint_path: './checkpoint/'
|
||||||
|
checkpoint_file_path: 'mobilenet_v1.ckpt'
|
||||||
|
|
||||||
|
# ==============================================================================
|
||||||
|
# Training options
|
||||||
|
model_name: "ssd_mobilenet_v1_fpn"
|
||||||
|
img_shape: [640, 640]
|
||||||
|
num_ssd_boxes: -1
|
||||||
|
match_threshold: 0.5
|
||||||
|
nms_threshold: 0.6
|
||||||
|
min_score: 0.1
|
||||||
|
max_boxes: 100
|
||||||
|
|
||||||
|
# learning rate settings
|
||||||
|
global_step: 0
|
||||||
|
lr_init: 0.01333
|
||||||
|
lr_end_rate: 0.0
|
||||||
|
warmup_epochs: 2
|
||||||
|
weight_decay: 0.00004
|
||||||
|
momentum: 0.9
|
||||||
|
ssd_vgg_bn: False
|
||||||
|
pretrain_vgg_bn: False
|
||||||
|
|
||||||
|
# network
|
||||||
|
num_default: [6, 6, 6, 6, 6]
|
||||||
|
extras_in_channels: [256, 512, 1024, 256, 256]
|
||||||
|
extras_out_channels: [256, 256, 256, 256, 256]
|
||||||
|
extras_strides: [1, 1, 2, 2, 2, 2]
|
||||||
|
extras_ratio: [0.2, 0.2, 0.2, 0.25, 0.5, 0.25]
|
||||||
|
feature_size: [80, 40, 20, 10, 5]
|
||||||
|
min_scale: 0.2
|
||||||
|
max_scale: 0.95
|
||||||
|
aspect_ratios: [[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]]
|
||||||
|
steps: [8, 16, 32, 64, 128]
|
||||||
|
prior_scaling: [0.1, 0.2]
|
||||||
|
gamma: 2.0
|
||||||
|
alpha: 0.25
|
||||||
|
num_addition_layers: 4
|
||||||
|
use_anchor_generator: True
|
||||||
|
use_global_norm: True
|
||||||
|
|
||||||
|
dataset: "coco"
|
||||||
|
lr: 0.05
|
||||||
|
mode_sink: "sink"
|
||||||
|
device_id: 0
|
||||||
|
device_num: 1
|
||||||
|
epoch_size: 500
|
||||||
|
batch_size: 32
|
||||||
|
loss_scale: 1024
|
||||||
|
pre_trained: ""
|
||||||
|
pre_trained_epoch_size: 0
|
||||||
|
save_checkpoint_epochs: 10
|
||||||
|
only_create_dataset: False
|
||||||
|
eval_start_epoch: 40
|
||||||
|
eval_interval: 1
|
||||||
|
run_eval: False
|
||||||
|
filter_weight: False
|
||||||
|
freeze_layer: None
|
||||||
|
save_best_ckpt: True
|
||||||
|
|
||||||
|
# `mindrecord_dir` and `coco_root` are better to use absolute path.
|
||||||
|
feature_extractor_base_param: "/ckpt/mobilenet_v1.ckpt"
|
||||||
|
checkpoint_filter_list: ['network.multi_box.cls_layers.0.weight', 'network.multi_box.cls_layers.0.bias',
|
||||||
|
'network.multi_box.loc_layers.0.weight', 'network.multi_box.loc_layers.0.bias']
|
||||||
|
mindrecord_dir: "MindRecord_COCO"
|
||||||
|
coco_root: "coco_ori"
|
||||||
|
train_data_type: "train2017"
|
||||||
|
val_data_type: "val2017"
|
||||||
|
instances_set: "annotations/instances_{}.json"
|
||||||
|
classes: ['background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
||||||
|
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
||||||
|
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
||||||
|
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
||||||
|
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
||||||
|
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
||||||
|
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
||||||
|
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
||||||
|
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||||
|
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
||||||
|
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
||||||
|
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
||||||
|
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
||||||
|
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
||||||
|
'teddy bear', 'hair drier', 'toothbrush']
|
||||||
|
num_classes: 81
|
||||||
|
# The annotation.json position of voc validation dataset.
|
||||||
|
voc_json: "annotations/voc_instances_val.json"
|
||||||
|
# voc original dataset.
|
||||||
|
voc_root: "/data/voc_dataset"
|
||||||
|
# if coco or voc used, `image_dir` and `anno_path` are useless.
|
||||||
|
image_dir: ""
|
||||||
|
anno_path: ""
|
||||||
|
file_name: "ssd"
|
||||||
|
file_format: "AIR"
|
||||||
|
|
||||||
|
---
|
||||||
|
# Help description for each configuration
|
||||||
|
enable_modelarts: 'Whether training on modelarts, default: False'
|
||||||
|
data_url: 'Dataset url for obs'
|
||||||
|
train_url: 'Training output url for obs'
|
||||||
|
checkpoint_url: 'The location of checkpoint for obs'
|
||||||
|
data_path: 'Dataset path for local'
|
||||||
|
output_path: 'Training output path for local'
|
||||||
|
load_path: 'The location of checkpoint for obs'
|
||||||
|
device_target: 'Target device type, available: [Ascend, GPU, CPU]'
|
||||||
|
enable_profiling: 'Whether enable profiling while training, default: False'
|
||||||
|
num_classes: 'Class for dataset'
|
||||||
|
batch_size: "Batch size for training and evaluation"
|
||||||
|
epoch_size: "Total training epochs."
|
||||||
|
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
|
||||||
|
checkpoint_path: "The location of the checkpoint file."
|
||||||
|
checkpoint_file_path: "The location of the checkpoint file."
|
|
@ -0,0 +1,126 @@
|
||||||
|
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
|
||||||
|
enable_modelarts: False
|
||||||
|
# Url for modelarts
|
||||||
|
data_url: ""
|
||||||
|
train_url: ""
|
||||||
|
checkpoint_url: ""
|
||||||
|
# Path for local
|
||||||
|
run_distribute: False
|
||||||
|
enable_profiling: False
|
||||||
|
data_path: "/cache/data"
|
||||||
|
output_path: "/cache/train"
|
||||||
|
load_path: "/cache/checkpoint_path/"
|
||||||
|
device_target: 'Ascend'
|
||||||
|
checkpoint_path: './checkpoint/'
|
||||||
|
checkpoint_file_path: 'resnet50.ckpt'
|
||||||
|
|
||||||
|
# ==============================================================================
|
||||||
|
# Training options
|
||||||
|
model_name: "ssd_resnet50_fpn"
|
||||||
|
img_shape: [640, 640]
|
||||||
|
num_ssd_boxes: -1
|
||||||
|
match_threshold: 0.5
|
||||||
|
nms_threshold: 0.6
|
||||||
|
min_score: 0.1
|
||||||
|
max_boxes: 100
|
||||||
|
|
||||||
|
# learning rate settings
|
||||||
|
global_step: 0
|
||||||
|
lr_init: 0.01333
|
||||||
|
lr_end_rate: 0.0
|
||||||
|
warmup_epochs: 2
|
||||||
|
weight_decay: 0.0004
|
||||||
|
momentum: 0.9
|
||||||
|
ssd_vgg_bn: False
|
||||||
|
pretrain_vgg_bn: False
|
||||||
|
|
||||||
|
# network
|
||||||
|
num_default: [6, 6, 6, 6, 6]
|
||||||
|
extras_in_channels: [256, 512, 1024, 256, 256]
|
||||||
|
extras_out_channels: [256, 256, 256, 256, 256]
|
||||||
|
extras_strides: [1, 1, 2, 2, 2, 2]
|
||||||
|
extras_ratio: [0.2, 0.2, 0.2, 0.25, 0.5, 0.25]
|
||||||
|
feature_size: [80, 40, 20, 10, 5]
|
||||||
|
min_scale: 0.2
|
||||||
|
max_scale: 0.95
|
||||||
|
aspect_ratios: [[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]]
|
||||||
|
steps: [8, 16, 32, 64, 128]
|
||||||
|
prior_scaling: [0.1, 0.2]
|
||||||
|
gamma: 2.0
|
||||||
|
alpha: 0.25
|
||||||
|
num_addition_layers: 4
|
||||||
|
use_anchor_generator: True
|
||||||
|
use_global_norm: True
|
||||||
|
use_float16: True
|
||||||
|
|
||||||
|
dataset: "coco"
|
||||||
|
lr: 0.05
|
||||||
|
mode_sink: "sink"
|
||||||
|
device_id: 0
|
||||||
|
device_num: 1
|
||||||
|
epoch_size: 500
|
||||||
|
batch_size: 32
|
||||||
|
loss_scale: 1024
|
||||||
|
pre_trained: ""
|
||||||
|
pre_trained_epoch_size: 0
|
||||||
|
save_checkpoint_epochs: 10
|
||||||
|
only_create_dataset: False
|
||||||
|
eval_start_epoch: 40
|
||||||
|
eval_interval: 1
|
||||||
|
run_eval: False
|
||||||
|
filter_weight: False
|
||||||
|
freeze_layer: None
|
||||||
|
save_best_ckpt: True
|
||||||
|
|
||||||
|
# `mindrecord_dir` and `coco_root` are better to use absolute path.
|
||||||
|
feature_extractor_base_param: "/ckpt/resnet50.ckpt"
|
||||||
|
checkpoint_filter_list: ['network.multi_box.cls_layers.0.weight', 'network.multi_box.cls_layers.0.bias',
|
||||||
|
'network.multi_box.loc_layers.0.weight', 'network.multi_box.loc_layers.0.bias']
|
||||||
|
mindrecord_dir: "MindRecord_COCO"
|
||||||
|
coco_root: "coco_ori"
|
||||||
|
train_data_type: "train2017"
|
||||||
|
val_data_type: "val2017"
|
||||||
|
instances_set: "annotations/instances_{}.json"
|
||||||
|
classes: ['background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
||||||
|
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
||||||
|
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
||||||
|
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
||||||
|
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
||||||
|
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
||||||
|
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
||||||
|
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
||||||
|
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||||
|
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
||||||
|
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
||||||
|
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
||||||
|
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
||||||
|
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
||||||
|
'teddy bear', 'hair drier', 'toothbrush']
|
||||||
|
num_classes: 81
|
||||||
|
# The annotation.json position of voc validation dataset.
|
||||||
|
voc_json: "annotations/voc_instances_val.json"
|
||||||
|
# voc original dataset.
|
||||||
|
voc_root: "/data/voc_dataset"
|
||||||
|
# if coco or voc used, `image_dir` and `anno_path` are useless.
|
||||||
|
image_dir: ""
|
||||||
|
anno_path: ""
|
||||||
|
file_name: "ssd"
|
||||||
|
file_format: "AIR"
|
||||||
|
|
||||||
|
---
|
||||||
|
# Help description for each configuration
|
||||||
|
enable_modelarts: 'Whether training on modelarts, default: False'
|
||||||
|
data_url: 'Dataset url for obs'
|
||||||
|
train_url: 'Training output url for obs'
|
||||||
|
checkpoint_url: 'The location of checkpoint for obs'
|
||||||
|
data_path: 'Dataset path for local'
|
||||||
|
output_path: 'Training output path for local'
|
||||||
|
load_path: 'The location of checkpoint for obs'
|
||||||
|
device_target: 'Target device type, available: [Ascend, GPU, CPU]'
|
||||||
|
enable_profiling: 'Whether enable profiling while training, default: False'
|
||||||
|
num_classes: 'Class for dataset'
|
||||||
|
batch_size: "Batch size for training and evaluation"
|
||||||
|
epoch_size: "Total training epochs."
|
||||||
|
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
|
||||||
|
checkpoint_path: "The location of the checkpoint file."
|
||||||
|
checkpoint_file_path: "The location of the checkpoint file."
|
|
@ -0,0 +1,120 @@
|
||||||
|
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
|
||||||
|
enable_modelarts: False
|
||||||
|
# Url for modelarts
|
||||||
|
data_url: ""
|
||||||
|
train_url: ""
|
||||||
|
checkpoint_url: ""
|
||||||
|
# Path for local
|
||||||
|
run_distribute: False
|
||||||
|
enable_profiling: False
|
||||||
|
data_path: "/cache/data"
|
||||||
|
output_path: "/cache/train"
|
||||||
|
load_path: "/cache/checkpoint_path/"
|
||||||
|
device_target: 'Ascend'
|
||||||
|
checkpoint_path: './checkpoint/'
|
||||||
|
checkpoint_file_path: 'ssd-500_458.ckpt'
|
||||||
|
|
||||||
|
# ==============================================================================
|
||||||
|
# Training options
|
||||||
|
model_name: "ssd_vgg16"
|
||||||
|
img_shape: [300, 300]
|
||||||
|
num_ssd_boxes: 7308
|
||||||
|
match_threshold: 0.5
|
||||||
|
nms_threshold: 0.6
|
||||||
|
min_score: 0.1
|
||||||
|
max_boxes: 100
|
||||||
|
|
||||||
|
# learing rate settings
|
||||||
|
lr_init: 0.001
|
||||||
|
lr_end_rate: 0.001
|
||||||
|
warmup_epochs: 2
|
||||||
|
momentum: 0.9
|
||||||
|
weight_decay: 0.00015
|
||||||
|
ssd_vgg_bn: False
|
||||||
|
pretrain_vgg_bn: False
|
||||||
|
|
||||||
|
# network
|
||||||
|
num_default: [3, 6, 6, 6, 6, 6]
|
||||||
|
extras_in_channels: [256, 512, 1024, 512, 256, 256]
|
||||||
|
extras_out_channels: [512, 1024, 512, 256, 256, 256]
|
||||||
|
extras_strides: [1, 1, 2, 2, 2, 2]
|
||||||
|
extras_ratio: [0.2, 0.2, 0.2, 0.25, 0.5, 0.25]
|
||||||
|
feature_size: [38, 19, 10, 5, 3, 1]
|
||||||
|
min_scale: 0.2
|
||||||
|
max_scale: 0.95
|
||||||
|
aspect_ratios: [[], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]]
|
||||||
|
steps: [8, 16, 32, 64, 100, 300]
|
||||||
|
prior_scaling: [0.1, 0.2]
|
||||||
|
gamma: 2.0
|
||||||
|
alpha: 0.75
|
||||||
|
|
||||||
|
dataset: "coco"
|
||||||
|
lr: 0.05
|
||||||
|
mode_sink: "sink"
|
||||||
|
device_id: 0
|
||||||
|
device_num: 1
|
||||||
|
epoch_size: 500
|
||||||
|
batch_size: 32
|
||||||
|
loss_scale: 1024
|
||||||
|
pre_trained: ""
|
||||||
|
pre_trained_epoch_size: 0
|
||||||
|
save_checkpoint_epochs: 10
|
||||||
|
only_create_dataset: False
|
||||||
|
eval_start_epoch: 40
|
||||||
|
eval_interval: 1
|
||||||
|
run_eval: False
|
||||||
|
filter_weight: False
|
||||||
|
freeze_layer: None
|
||||||
|
save_best_ckpt: True
|
||||||
|
|
||||||
|
# `mindrecord_dir` and `coco_root` are better to use absolute path.
|
||||||
|
feature_extractor_base_param: ""
|
||||||
|
checkpoint_filter_list: ['multi_loc_layers', 'multi_cls_layers']
|
||||||
|
mindrecord_dir: "MindRecord_COCO"
|
||||||
|
coco_root: "coco_ori"
|
||||||
|
train_data_type: "train2017"
|
||||||
|
val_data_type: "val2017"
|
||||||
|
instances_set: "annotations/instances_{}.json"
|
||||||
|
classes: ['background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
||||||
|
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
||||||
|
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
||||||
|
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
||||||
|
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
||||||
|
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
||||||
|
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
||||||
|
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
||||||
|
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||||
|
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
||||||
|
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
||||||
|
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
||||||
|
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
||||||
|
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
||||||
|
'teddy bear', 'hair drier', 'toothbrush']
|
||||||
|
num_classes: 81
|
||||||
|
# The annotation.json position of voc validation dataset.
|
||||||
|
voc_json: "annotations/voc_instances_val.json"
|
||||||
|
# voc original dataset.
|
||||||
|
voc_root: "/data/voc_dataset"
|
||||||
|
# if coco or voc used, `image_dir` and `anno_path` are useless.
|
||||||
|
image_dir: ""
|
||||||
|
anno_path: ""
|
||||||
|
file_name: "ssd"
|
||||||
|
file_format: "AIR"
|
||||||
|
|
||||||
|
---
|
||||||
|
# Help description for each configuration
|
||||||
|
enable_modelarts: 'Whether training on modelarts, default: False'
|
||||||
|
data_url: 'Dataset url for obs'
|
||||||
|
train_url: 'Training output url for obs'
|
||||||
|
checkpoint_url: 'The location of checkpoint for obs'
|
||||||
|
data_path: 'Dataset path for local'
|
||||||
|
output_path: 'Training output path for local'
|
||||||
|
load_path: 'The location of checkpoint for obs'
|
||||||
|
device_target: 'Target device type, available: [Ascend, GPU, CPU]'
|
||||||
|
enable_profiling: 'Whether enable profiling while training, default: False'
|
||||||
|
num_classes: 'Class for dataset'
|
||||||
|
batch_size: "Batch size for training and evaluation"
|
||||||
|
epoch_size: "Total training epochs."
|
||||||
|
keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
|
||||||
|
checkpoint_path: "The location of the checkpoint file."
|
||||||
|
checkpoint_file_path: "The location of the checkpoint file."
|
|
@ -16,8 +16,6 @@
|
||||||
"""Train SSD and get checkpoint files."""
|
"""Train SSD and get checkpoint files."""
|
||||||
|
|
||||||
import os
|
import os
|
||||||
import argparse
|
|
||||||
import ast
|
|
||||||
import mindspore.nn as nn
|
import mindspore.nn as nn
|
||||||
from mindspore import context, Tensor
|
from mindspore import context, Tensor
|
||||||
from mindspore.communication.management import init, get_rank
|
from mindspore.communication.management import init, get_rank
|
||||||
|
@ -28,60 +26,26 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||||
from mindspore.common import set_seed, dtype
|
from mindspore.common import set_seed, dtype
|
||||||
from src.ssd import SSD300, SsdInferWithDecoder, SSDWithLossCell, TrainingWrapper, ssd_mobilenet_v2,\
|
from src.ssd import SSD300, SsdInferWithDecoder, SSDWithLossCell, TrainingWrapper, ssd_mobilenet_v2,\
|
||||||
ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
|
ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
|
||||||
from src.config import config
|
|
||||||
from src.dataset import create_ssd_dataset, create_mindrecord
|
from src.dataset import create_ssd_dataset, create_mindrecord
|
||||||
from src.lr_schedule import get_lr
|
from src.lr_schedule import get_lr
|
||||||
from src.init_params import init_net_param, filter_checkpoint_parameter_by_list
|
from src.init_params import init_net_param, filter_checkpoint_parameter_by_list
|
||||||
from src.eval_callback import EvalCallBack
|
from src.eval_callback import EvalCallBack
|
||||||
from src.eval_utils import apply_eval
|
from src.eval_utils import apply_eval
|
||||||
from src.box_utils import default_boxes
|
from src.box_utils import default_boxes
|
||||||
|
from src.model_utils.config import config
|
||||||
|
from src.model_utils.moxing_adapter import moxing_wrapper
|
||||||
|
|
||||||
set_seed(1)
|
set_seed(1)
|
||||||
|
|
||||||
def get_args():
|
def ssd_model_build():
|
||||||
parser = argparse.ArgumentParser(description="SSD training")
|
if config.model_name == "ssd300":
|
||||||
parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"),
|
|
||||||
help="run platform, support Ascend, GPU and CPU.")
|
|
||||||
parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False,
|
|
||||||
help="If set it true, only create Mindrecord, default is False.")
|
|
||||||
parser.add_argument("--distribute", type=ast.literal_eval, default=False,
|
|
||||||
help="Run distribute, default is False.")
|
|
||||||
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("--lr", type=float, default=0.05, help="Learning rate, default is 0.05.")
|
|
||||||
parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink.")
|
|
||||||
parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
|
|
||||||
parser.add_argument("--epoch_size", type=int, default=500, help="Epoch size, default is 500.")
|
|
||||||
parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
|
|
||||||
parser.add_argument("--pre_trained", type=str, default=None, help="Pretrained Checkpoint file path.")
|
|
||||||
parser.add_argument("--pre_trained_epoch_size", type=int, default=0, help="Pretrained epoch size.")
|
|
||||||
parser.add_argument("--save_checkpoint_epochs", type=int, default=10, help="Save checkpoint epochs, default is 10.")
|
|
||||||
parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.")
|
|
||||||
parser.add_argument("--filter_weight", type=ast.literal_eval, default=False,
|
|
||||||
help="Filter head weight parameters, default is False.")
|
|
||||||
parser.add_argument('--freeze_layer', type=str, default="none", choices=["none", "backbone"],
|
|
||||||
help="freeze the weights of network, support freeze the backbone's weights, "
|
|
||||||
"default is not freezing.")
|
|
||||||
parser.add_argument("--run_eval", type=ast.literal_eval, default=False,
|
|
||||||
help="Run evaluation when training, default is False.")
|
|
||||||
parser.add_argument("--save_best_ckpt", type=ast.literal_eval, default=True,
|
|
||||||
help="Save best checkpoint when run_eval is True, default is True.")
|
|
||||||
parser.add_argument("--eval_start_epoch", type=int, default=40,
|
|
||||||
help="Evaluation start epoch when run_eval is True, default is 40.")
|
|
||||||
parser.add_argument("--eval_interval", type=int, default=1,
|
|
||||||
help="Evaluation interval when run_eval is True, default is 1.")
|
|
||||||
args_opt = parser.parse_args()
|
|
||||||
return args_opt
|
|
||||||
|
|
||||||
def ssd_model_build(args_opt):
|
|
||||||
if config.model == "ssd300":
|
|
||||||
backbone = ssd_mobilenet_v2()
|
backbone = ssd_mobilenet_v2()
|
||||||
ssd = SSD300(backbone=backbone, config=config)
|
ssd = SSD300(backbone=backbone, config=config)
|
||||||
init_net_param(ssd)
|
init_net_param(ssd)
|
||||||
if args_opt.freeze_layer == "backbone":
|
if config.freeze_layer == "backbone":
|
||||||
for param in backbone.feature_1.trainable_params():
|
for param in backbone.feature_1.trainable_params():
|
||||||
param.requires_grad = False
|
param.requires_grad = False
|
||||||
elif config.model == "ssd_mobilenet_v1_fpn":
|
elif config.model_name == "ssd_mobilenet_v1_fpn":
|
||||||
ssd = ssd_mobilenet_v1_fpn(config=config)
|
ssd = ssd_mobilenet_v1_fpn(config=config)
|
||||||
init_net_param(ssd)
|
init_net_param(ssd)
|
||||||
if config.feature_extractor_base_param != "":
|
if config.feature_extractor_base_param != "":
|
||||||
|
@ -90,7 +54,7 @@ def ssd_model_build(args_opt):
|
||||||
param_dict["network.feature_extractor.mobilenet_v1." + x] = param_dict[x]
|
param_dict["network.feature_extractor.mobilenet_v1." + x] = param_dict[x]
|
||||||
del param_dict[x]
|
del param_dict[x]
|
||||||
load_param_into_net(ssd.feature_extractor.mobilenet_v1.network, param_dict)
|
load_param_into_net(ssd.feature_extractor.mobilenet_v1.network, param_dict)
|
||||||
elif config.model == "ssd_resnet50_fpn":
|
elif config.model_name == "ssd_resnet50_fpn":
|
||||||
ssd = ssd_resnet50_fpn(config=config)
|
ssd = ssd_resnet50_fpn(config=config)
|
||||||
init_net_param(ssd)
|
init_net_param(ssd)
|
||||||
if config.feature_extractor_base_param != "":
|
if config.feature_extractor_base_param != "":
|
||||||
|
@ -99,7 +63,7 @@ def ssd_model_build(args_opt):
|
||||||
param_dict["network.feature_extractor.resnet." + x] = param_dict[x]
|
param_dict["network.feature_extractor.resnet." + x] = param_dict[x]
|
||||||
del param_dict[x]
|
del param_dict[x]
|
||||||
load_param_into_net(ssd.feature_extractor.resnet, param_dict)
|
load_param_into_net(ssd.feature_extractor.resnet, param_dict)
|
||||||
elif config.model == "ssd_vgg16":
|
elif config.model_name == "ssd_vgg16":
|
||||||
ssd = ssd_vgg16(config=config)
|
ssd = ssd_vgg16(config=config)
|
||||||
init_net_param(ssd)
|
init_net_param(ssd)
|
||||||
if config.feature_extractor_base_param != "":
|
if config.feature_extractor_base_param != "":
|
||||||
|
@ -111,76 +75,86 @@ def ssd_model_build(args_opt):
|
||||||
del param_dict[k + ".weight"]
|
del param_dict[k + ".weight"]
|
||||||
load_param_into_net(ssd.backbone, param_dict)
|
load_param_into_net(ssd.backbone, param_dict)
|
||||||
else:
|
else:
|
||||||
raise ValueError(f'config.model: {config.model} is not supported')
|
raise ValueError(f'config.model: {config.model_name} is not supported')
|
||||||
return ssd
|
return ssd
|
||||||
|
|
||||||
def set_graph_kernel_context(run_platform, model):
|
def set_graph_kernel_context(device_target, model):
|
||||||
if run_platform == "GPU" and model == "ssd300":
|
if device_target == "GPU" and model == "ssd300":
|
||||||
# Enable graph kernel for default model ssd300 on GPU back-end.
|
# Enable graph kernel for default model ssd300 on GPU back-end.
|
||||||
context.set_context(enable_graph_kernel=True, graph_kernel_flags="--enable_parallel_fusion")
|
context.set_context(enable_graph_kernel=True, graph_kernel_flags="--enable_parallel_fusion")
|
||||||
|
|
||||||
def main():
|
def set_parameter(model_name):
|
||||||
args_opt = get_args()
|
if model_name == "ssd_resnet50_fpn":
|
||||||
|
context.set_auto_parallel_context(all_reduce_fusion_config=[90, 183, 279])
|
||||||
|
if model_name == "ssd_vgg16":
|
||||||
|
context.set_auto_parallel_context(all_reduce_fusion_config=[20, 41, 62])
|
||||||
|
else:
|
||||||
|
context.set_auto_parallel_context(all_reduce_fusion_config=[29, 58, 89])
|
||||||
|
|
||||||
|
@moxing_wrapper()
|
||||||
|
def train_net():
|
||||||
|
if hasattr(config, 'num_ssd_boxes') and config.num_ssd_boxes == -1:
|
||||||
|
num = 0
|
||||||
|
h, w = config.img_shape
|
||||||
|
for i in range(len(config.steps)):
|
||||||
|
num += (h // config.steps[i]) * (w // config.steps[i]) * config.num_default[i]
|
||||||
|
config.num_ssd_boxes = num
|
||||||
|
|
||||||
rank = 0
|
rank = 0
|
||||||
device_num = 1
|
device_num = 1
|
||||||
if args_opt.run_platform == "CPU":
|
if config.device_target == "CPU":
|
||||||
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
||||||
else:
|
else:
|
||||||
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.run_platform, device_id=args_opt.device_id)
|
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=config.device_id)
|
||||||
set_graph_kernel_context(args_opt.run_platform, config.model)
|
set_graph_kernel_context(config.device_target, config.model_name)
|
||||||
if args_opt.distribute:
|
if config.run_distribute:
|
||||||
device_num = args_opt.device_num
|
device_num = config.device_num
|
||||||
context.reset_auto_parallel_context()
|
context.reset_auto_parallel_context()
|
||||||
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
|
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
|
||||||
device_num=device_num)
|
device_num=device_num)
|
||||||
init()
|
init()
|
||||||
if config.model == "ssd_resnet50_fpn":
|
set_parameter(model_name=config.model_name)
|
||||||
context.set_auto_parallel_context(all_reduce_fusion_config=[90, 183, 279])
|
|
||||||
if config.model == "ssd_vgg16":
|
|
||||||
context.set_auto_parallel_context(all_reduce_fusion_config=[20, 41, 62])
|
|
||||||
else:
|
|
||||||
context.set_auto_parallel_context(all_reduce_fusion_config=[29, 58, 89])
|
|
||||||
rank = get_rank()
|
rank = get_rank()
|
||||||
|
|
||||||
mindrecord_file = create_mindrecord(args_opt.dataset, "ssd.mindrecord", True)
|
mindrecord_file = create_mindrecord(config.dataset, "ssd.mindrecord", True)
|
||||||
|
|
||||||
if args_opt.only_create_dataset:
|
if config.only_create_dataset:
|
||||||
return
|
return
|
||||||
|
|
||||||
loss_scale = float(args_opt.loss_scale)
|
loss_scale = float(config.loss_scale)
|
||||||
if args_opt.run_platform == "CPU":
|
if config.device_target == "CPU":
|
||||||
loss_scale = 1.0
|
loss_scale = 1.0
|
||||||
|
|
||||||
# When create MindDataset, using the fitst mindrecord file, such as ssd.mindrecord0.
|
# When create MindDataset, using the fitst mindrecord file, such as ssd.mindrecord0.
|
||||||
use_multiprocessing = (args_opt.run_platform != "CPU")
|
use_multiprocessing = (config.device_target != "CPU")
|
||||||
dataset = create_ssd_dataset(mindrecord_file, repeat_num=1, batch_size=args_opt.batch_size,
|
dataset = create_ssd_dataset(mindrecord_file, repeat_num=1, batch_size=config.batch_size,
|
||||||
device_num=device_num, rank=rank, use_multiprocessing=use_multiprocessing)
|
device_num=device_num, rank=rank, use_multiprocessing=use_multiprocessing)
|
||||||
|
|
||||||
dataset_size = dataset.get_dataset_size()
|
dataset_size = dataset.get_dataset_size()
|
||||||
print(f"Create dataset done! dataset size is {dataset_size}")
|
print(f"Create dataset done! dataset size is {dataset_size}")
|
||||||
ssd = ssd_model_build(args_opt)
|
ssd = ssd_model_build()
|
||||||
if ("use_float16" in config and config.use_float16) or args_opt.run_platform == "GPU":
|
if (hasattr(config, 'use_float16') and config.use_float16) or config.device_target == "GPU":
|
||||||
ssd.to_float(dtype.float16)
|
ssd.to_float(dtype.float16)
|
||||||
net = SSDWithLossCell(ssd, config)
|
net = SSDWithLossCell(ssd, config)
|
||||||
|
|
||||||
# checkpoint
|
# checkpoint
|
||||||
ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
|
ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * config.save_checkpoint_epochs)
|
||||||
save_ckpt_path = './ckpt_' + str(rank) + '/'
|
ckpt_save_dir = config.output_path +'/ckpt_{}/'.format(rank)
|
||||||
ckpoint_cb = ModelCheckpoint(prefix="ssd", directory=save_ckpt_path, config=ckpt_config)
|
ckpoint_cb = ModelCheckpoint(prefix="ssd", directory=ckpt_save_dir, config=ckpt_config)
|
||||||
|
|
||||||
if args_opt.pre_trained:
|
if config.pre_trained:
|
||||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
param_dict = load_checkpoint(config.pre_trained)
|
||||||
if args_opt.filter_weight:
|
if config.filter_weight:
|
||||||
filter_checkpoint_parameter_by_list(param_dict, config.checkpoint_filter_list)
|
filter_checkpoint_parameter_by_list(param_dict, config.checkpoint_filter_list)
|
||||||
load_param_into_net(net, param_dict, True)
|
load_param_into_net(net, param_dict, True)
|
||||||
|
|
||||||
lr = Tensor(get_lr(global_step=args_opt.pre_trained_epoch_size * dataset_size,
|
lr = Tensor(get_lr(global_step=config.pre_trained_epoch_size * dataset_size,
|
||||||
lr_init=config.lr_init, lr_end=config.lr_end_rate * args_opt.lr, lr_max=args_opt.lr,
|
lr_init=config.lr_init, lr_end=config.lr_end_rate * config.lr, lr_max=config.lr,
|
||||||
warmup_epochs=config.warmup_epochs,
|
warmup_epochs=config.warmup_epochs,
|
||||||
total_epochs=args_opt.epoch_size,
|
total_epochs=config.epoch_size,
|
||||||
steps_per_epoch=dataset_size))
|
steps_per_epoch=dataset_size))
|
||||||
|
|
||||||
if "use_global_norm" in config and config.use_global_norm:
|
if hasattr(config, 'use_global_norm') and config.use_global_norm:
|
||||||
opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr,
|
opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr,
|
||||||
config.momentum, config.weight_decay, 1.0)
|
config.momentum, config.weight_decay, 1.0)
|
||||||
net = TrainingWrapper(net, opt, loss_scale, True)
|
net = TrainingWrapper(net, opt, loss_scale, True)
|
||||||
|
@ -190,31 +164,31 @@ def main():
|
||||||
net = TrainingWrapper(net, opt, loss_scale)
|
net = TrainingWrapper(net, opt, loss_scale)
|
||||||
|
|
||||||
callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
|
callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
|
||||||
if args_opt.run_eval:
|
if config.run_eval:
|
||||||
eval_net = SsdInferWithDecoder(ssd, Tensor(default_boxes), config)
|
eval_net = SsdInferWithDecoder(ssd, Tensor(default_boxes), config)
|
||||||
eval_net.set_train(False)
|
eval_net.set_train(False)
|
||||||
mindrecord_file = create_mindrecord(args_opt.dataset, "ssd_eval.mindrecord", False)
|
mindrecord_file = create_mindrecord(config.dataset, "ssd_eval.mindrecord", False)
|
||||||
eval_dataset = create_ssd_dataset(mindrecord_file, batch_size=args_opt.batch_size, repeat_num=1,
|
eval_dataset = create_ssd_dataset(mindrecord_file, batch_size=config.batch_size, repeat_num=1,
|
||||||
is_training=False, use_multiprocessing=False)
|
is_training=False, use_multiprocessing=False)
|
||||||
if args_opt.dataset == "coco":
|
if config.dataset == "coco":
|
||||||
anno_json = os.path.join(config.coco_root, config.instances_set.format(config.val_data_type))
|
anno_json = os.path.join(config.coco_root, config.instances_set.format(config.val_data_type))
|
||||||
elif args_opt.dataset == "voc":
|
elif config.dataset == "voc":
|
||||||
anno_json = os.path.join(config.voc_root, config.voc_json)
|
anno_json = os.path.join(config.voc_root, config.voc_json)
|
||||||
else:
|
else:
|
||||||
raise ValueError('SSD eval only support dataset mode is coco and voc!')
|
raise ValueError('SSD eval only support dataset mode is coco and voc!')
|
||||||
eval_param_dict = {"net": eval_net, "dataset": eval_dataset, "anno_json": anno_json}
|
eval_param_dict = {"net": eval_net, "dataset": eval_dataset, "anno_json": anno_json}
|
||||||
eval_cb = EvalCallBack(apply_eval, eval_param_dict, interval=args_opt.eval_interval,
|
eval_cb = EvalCallBack(apply_eval, eval_param_dict, interval=config.eval_interval,
|
||||||
eval_start_epoch=args_opt.eval_start_epoch, save_best_ckpt=True,
|
eval_start_epoch=config.eval_start_epoch, save_best_ckpt=True,
|
||||||
ckpt_directory=save_ckpt_path, besk_ckpt_name="best_map.ckpt",
|
ckpt_directory=ckpt_save_dir, besk_ckpt_name="best_map.ckpt",
|
||||||
metrics_name="mAP")
|
metrics_name="mAP")
|
||||||
callback.append(eval_cb)
|
callback.append(eval_cb)
|
||||||
model = Model(net)
|
model = Model(net)
|
||||||
dataset_sink_mode = False
|
dataset_sink_mode = False
|
||||||
if args_opt.mode == "sink" and args_opt.run_platform != "CPU":
|
if config.mode_sink == "sink" and config.device_target != "CPU":
|
||||||
print("In sink mode, one epoch return a loss.")
|
print("In sink mode, one epoch return a loss.")
|
||||||
dataset_sink_mode = True
|
dataset_sink_mode = True
|
||||||
print("Start train SSD, the first epoch will be slower because of the graph compilation.")
|
print("Start train SSD, the first epoch will be slower because of the graph compilation.")
|
||||||
model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
|
model.train(config.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
main()
|
train_net()
|
||||||
|
|
|
@ -98,7 +98,7 @@ If set `split`=1.0, you should split train dataset and val dataset by directorie
|
||||||
|
|
||||||
We support script to convert COCO and a Cell_Nuclei dataset used in used in [Unet++ original paper](https://arxiv.org/abs/1912.05074) to mulyi-class dataset format.
|
We support script to convert COCO and a Cell_Nuclei dataset used in used in [Unet++ original paper](https://arxiv.org/abs/1912.05074) to mulyi-class dataset format.
|
||||||
|
|
||||||
1. Change `cfg_unet` in `src/config.py`, you can refer to `cfg_unet_nested_cell` and `cfg_unet_simple_coco` in `src/config.py` for detail.
|
1. Select `*yaml` in `unet`.
|
||||||
|
|
||||||
2. run script to convert to mulyi-class dataset format:
|
2. run script to convert to mulyi-class dataset format:
|
||||||
|
|
||||||
|
@ -122,24 +122,24 @@ After installing MindSpore via the official website, you can start training and
|
||||||
|
|
||||||
- Select the network and dataset to use
|
- Select the network and dataset to use
|
||||||
|
|
||||||
1. Select `cfg_unet` in `src/config.py`. We support unet and unet++, and we provide some parameter configurations for quick start.
|
1. Select `yaml` in `unet/`. We support unet and unet++, and we provide some parameter configurations for quick start.
|
||||||
2. If you want other parameters, please refer to `src/config.py`. You can set `'model'` to `'unet_nested'` or `'unet_simple'` to select which net to use. We support `ISBI` and `Cell_nuclei` two dataset, you can set `'dataset'` to `'Cell_nuclei'` to use `Cell_nuclei` dataset, default is `ISBI`.
|
2. If you want other parameters, please refer to `unet/ *.yaml`. You can set `'model'` to `'unet_nested'` or `'unet_simple'` to select which net to use. We support `ISBI` and `Cell_nuclei` two dataset, you can set `'dataset'` to `'Cell_nuclei'` to use `Cell_nuclei` dataset, default is `ISBI`.
|
||||||
|
|
||||||
- Run on Ascend
|
- Run on Ascend
|
||||||
|
|
||||||
```python
|
```python
|
||||||
# run training example
|
# run training example
|
||||||
python train.py --data_url=/path/to/data/ > train.log 2>&1 &
|
python train.py --data_path=/path/to/data/ --config_path=/path/to/yaml > train.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
bash scripts/run_standalone_train.sh [DATASET]
|
bash scripts/run_standalone_train.sh [DATASET] [CONFIG_PATH]
|
||||||
|
|
||||||
# run distributed training example
|
# run distributed training example
|
||||||
bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]
|
bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET] [CONFIG_PATH]
|
||||||
|
|
||||||
# run evaluation example
|
# run evaluation example
|
||||||
python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
|
python eval.py --data_path=/path/to/data/ --checkpoint_file_path=/path/to/checkpoint/ --config_path=/path/to/yaml > eval.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
|
bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
- Run on docker
|
- Run on docker
|
||||||
|
@ -178,9 +178,11 @@ If you want to run in modelarts, please check the official documentation of [mod
|
||||||
# run evaluation on modelarts example
|
# run evaluation on modelarts example
|
||||||
# (1) Copy or upload your trained model to S3 bucket.
|
# (1) Copy or upload your trained model to S3 bucket.
|
||||||
# (2) Perform a or b.
|
# (2) Perform a or b.
|
||||||
# a. Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
|
# a. Set "enable_modelarts=True" on yaml file.
|
||||||
|
# Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
|
||||||
# Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
|
# Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
|
||||||
# b. Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
|
# b. Add "enable_modelarts=True" on the website UI interface.
|
||||||
|
# Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
|
||||||
# Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
|
# Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
|
||||||
# (3) Set the config directory to "config_path=/The path of config in S3/"
|
# (3) Set the config directory to "config_path=/The path of config in S3/"
|
||||||
# (4) Set the code directory to "/path/unet" on the website UI interface.
|
# (4) Set the code directory to "/path/unet" on the website UI interface.
|
||||||
|
@ -309,9 +311,9 @@ Parameters for both training and evaluation can be set in config.py
|
||||||
#### running on Ascend
|
#### running on Ascend
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
python train.py --data_url=/path/to/data/ > train.log 2>&1 &
|
python train.py --data_path=/path/to/data/ --config_path=/path/to/yaml > train.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
bash scripts/run_standalone_train.sh [DATASET]
|
bash scripts/run_standalone_train.sh [DATASET] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
The python command above will run in the background, you can view the results through the file `train.log`.
|
The python command above will run in the background, you can view the results through the file `train.log`.
|
||||||
|
@ -338,7 +340,7 @@ The model checkpoint will be saved in the current directory.
|
||||||
#### Distributed Training
|
#### Distributed Training
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]
|
bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
The above shell script will run distribute training in the background. You can view the results through the file `logs/device[X]/log.log`. The loss value will be achieved as follows:
|
The above shell script will run distribute training in the background. You can view the results through the file `logs/device[X]/log.log`. The loss value will be achieved as follows:
|
||||||
|
@ -365,9 +367,9 @@ You can add `run_eval` to start shell and set it True, if you want evaluation wh
|
||||||
Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/unet/ckpt_unet_medical_adam-48_600.ckpt".
|
Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/unet/ckpt_unet_medical_adam-48_600.ckpt".
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/unet.ckpt > eval.log 2>&1 &
|
python eval.py --data_path=/path/to/data/ --checkpoint_file_path=/path/to/checkpoint/ --config_path=/path/to/yaml > eval.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
|
bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
|
The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
|
||||||
|
@ -412,10 +414,10 @@ If you need to use the trained model to perform inference on multiple hardware p
|
||||||
Export MindIR
|
Export MindIR
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||||
```
|
```
|
||||||
|
|
||||||
The ckpt_file parameter is required,
|
The checkpoint_file_path parameter is required,
|
||||||
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
|
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
|
||||||
|
|
||||||
Before performing inference, the MINDIR file must be exported by export script on the 910 environment.
|
Before performing inference, the MINDIR file must be exported by export script on the 910 environment.
|
||||||
|
@ -436,11 +438,11 @@ Cross valid dice coeff is: 0.9054352151297033
|
||||||
|
|
||||||
#### Continue Training on the Pretrained Model
|
#### Continue Training on the Pretrained Model
|
||||||
|
|
||||||
Set options `resume` to True in `config.py`, and set `resume_ckpt` to the path of your checkpoint. e.g.
|
Set options `resume` to True in `*.yaml`, and set `resume_ckpt` to the path of your checkpoint. e.g.
|
||||||
|
|
||||||
```python
|
```python
|
||||||
'resume': True,
|
'resume': True,
|
||||||
'resume_ckpt': 'ckpt_0/ckpt_unet_sample_adam_1-1_600.ckpt',
|
'resume_ckpt': 'ckpt_unet_sample_adam_1-1_600.ckpt',
|
||||||
'transfer_training': False,
|
'transfer_training': False,
|
||||||
'filter_weight': ["final.weight"]
|
'filter_weight': ["final.weight"]
|
||||||
```
|
```
|
||||||
|
@ -451,7 +453,7 @@ Do the same thing as resuming traing above. In addition, set `transfer_training`
|
||||||
|
|
||||||
```python
|
```python
|
||||||
'resume': True,
|
'resume': True,
|
||||||
'resume_ckpt': 'ckpt_0/ckpt_unet_sample_adam_1-1_600.ckpt',
|
'resume_ckpt': 'ckpt_unet_sample_adam_1-1_600.ckpt',
|
||||||
'transfer_training': True,
|
'transfer_training': True,
|
||||||
'filter_weight': ["final.weight"]
|
'filter_weight': ["final.weight"]
|
||||||
```
|
```
|
||||||
|
|
|
@ -102,7 +102,7 @@ UNet++是U-Net的增强版本,使用了新的跨层链接方式和深层监督
|
||||||
|
|
||||||
我们提供了一个脚本来将 COCO 和 Cell_Nuclei 数据集([Unet++ 原论文](https://arxiv.org/abs/1912.05074) 中使用)转换为multi-class格式。
|
我们提供了一个脚本来将 COCO 和 Cell_Nuclei 数据集([Unet++ 原论文](https://arxiv.org/abs/1912.05074) 中使用)转换为multi-class格式。
|
||||||
|
|
||||||
1. 在`src/config.py`中修改`cfg_unet`,修改细节请参考`src/config.py`中的`cfg_unet_nested_cell` 和 `cfg_unet_simple_coco`。
|
1. 在`src/model_utils/`下选择对应的yaml文件。
|
||||||
|
|
||||||
2. 运行转换脚本:
|
2. 运行转换脚本:
|
||||||
|
|
||||||
|
@ -126,24 +126,24 @@ python preprocess_dataset.py -d /data/save_data_path
|
||||||
|
|
||||||
- 选择模型及数据集
|
- 选择模型及数据集
|
||||||
|
|
||||||
1. 在`src/config.py`中选择相应的配置项赋给`cfg_unet`,现在支持unet和unet++,我们在`src/config.py`预备了一些网络及数据集的参数配置用于快速体验。
|
1. 在`unet/`中选择相应的配置项,现在支持unet和unet++,我们在`unet/`预备了一些网络及数据集的参数配置用于快速体验。
|
||||||
2. 如果使用其他的参数,也可以参考`src/config.py`通过设置`'model'` 为 `'unet_nested'` 或者 `'unet_simple'` 来选择使用什么网络结构。我们支持`ISBI` 和 `Cell_nuclei`两种数据集处理,默认使用`ISBI`,可以设置`'dataset'` 为 `'Cell_nuclei'`使用`Cell_nuclei`数据集。
|
2. 如果使用其他的参数,也可以参考`unet/`下的yaml文件,通过设置`'model'` 为 `'unet_nested'` 或者 `'unet_simple'` 来选择使用什么网络结构。我们支持`ISBI` 和 `Cell_nuclei`两种数据集处理,默认使用`ISBI`,可以设置`'dataset'` 为 `'Cell_nuclei'`使用`Cell_nuclei`数据集。
|
||||||
|
|
||||||
- Ascend处理器环境运行
|
- Ascend处理器环境运行
|
||||||
|
|
||||||
```python
|
```python
|
||||||
# 训练示例
|
# 训练示例
|
||||||
python train.py --data_url=/path/to/data/ > train.log 2>&1 &
|
python train.py --data_path=/path/to/data/ --config_path=/path/to/yaml > train.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
bash scripts/run_standalone_train.sh [DATASET]
|
bash scripts/run_standalone_train.sh [DATASET] [CONFIG_PATH]
|
||||||
|
|
||||||
# 分布式训练示例
|
# 分布式训练示例
|
||||||
bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET]
|
bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATASET] [CONFIG_PATH]
|
||||||
|
|
||||||
# 评估示例
|
# 评估示例
|
||||||
python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
|
python eval.py --data_path=/path/to/data/ --checkpoint_file_path=/path/to/checkpoint/ --config_path=/path/to/yaml > eval.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
|
bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
- Docker中运行
|
- Docker中运行
|
||||||
|
@ -184,9 +184,11 @@ bash scripts/docker_start.sh unet:20.1.0 [DATA_DIR] [MODEL_DIR]
|
||||||
# 在modelarts上使用模型推理的示例
|
# 在modelarts上使用模型推理的示例
|
||||||
# (1) 把训练好的模型地方到桶的对应位置。
|
# (1) 把训练好的模型地方到桶的对应位置。
|
||||||
# (2) 选址a或者b其中一种方式。
|
# (2) 选址a或者b其中一种方式。
|
||||||
# a. 设置 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt" 在 yaml 文件.
|
# a. 设置 "enable_modelarts=True"
|
||||||
|
# 设置 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt" 在 yaml 文件.
|
||||||
# 设置 "checkpoint_url=/The path of checkpoint in S3/" 在 yaml 文件.
|
# 设置 "checkpoint_url=/The path of checkpoint in S3/" 在 yaml 文件.
|
||||||
# b. 增加 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" 参数在modearts的界面上。
|
# b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
|
||||||
|
# 增加 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" 参数在modearts的界面上。
|
||||||
# 增加 "checkpoint_url=/The path of checkpoint in S3/" 参数在modearts的界面上。
|
# 增加 "checkpoint_url=/The path of checkpoint in S3/" 参数在modearts的界面上。
|
||||||
# (3) 设置网络配置文件的路径 "config_path=/The path of config in S3/"
|
# (3) 设置网络配置文件的路径 "config_path=/The path of config in S3/"
|
||||||
# (4) 在modelarts的界面上设置代码的路径 "/path/unet"。
|
# (4) 在modelarts的界面上设置代码的路径 "/path/unet"。
|
||||||
|
@ -305,9 +307,9 @@ bash scripts/docker_start.sh unet:20.1.0 [DATA_DIR] [MODEL_DIR]
|
||||||
- Ascend处理器环境运行
|
- Ascend处理器环境运行
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
python train.py --data_url=/path/to/data/ > train.log 2>&1 &
|
python train.py --data_path=/path/to/data/ --config_path=/path/to/yaml > train.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
bash scripts/run_standalone_train.sh [DATASET]
|
bash scripts/run_standalone_train.sh [DATASET] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
上述python命令在后台运行,可通过`train.log`文件查看结果。
|
上述python命令在后台运行,可通过`train.log`文件查看结果。
|
||||||
|
@ -361,9 +363,9 @@ step: 300, loss is 0.18949677, fps is 57.63118508760329
|
||||||
在运行以下命令之前,请检查用于评估的检查点路径。将检查点路径设置为绝对全路径,如"username/unet/ckpt_unet_medical_adam-48_600.ckpt"。
|
在运行以下命令之前,请检查用于评估的检查点路径。将检查点路径设置为绝对全路径,如"username/unet/ckpt_unet_medical_adam-48_600.ckpt"。
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
python eval.py --data_url=/path/to/data/ --ckpt_path=/path/to/unet.ckpt/ > eval.log 2>&1 &
|
python eval.py --data_path=/path/to/data/ --checkpoint_file_path=/path/to/checkpoint/ --config_path=/path/to/yaml > eval.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT]
|
bash scripts/run_standalone_eval.sh [DATASET] [CHECKPOINT] [CONFIG_PATH]
|
||||||
```
|
```
|
||||||
|
|
||||||
上述python命令在后台运行。可通过"eval.log"文件查看结果。测试数据集的准确率如下:
|
上述python命令在后台运行。可通过"eval.log"文件查看结果。测试数据集的准确率如下:
|
||||||
|
@ -408,10 +410,10 @@ step: 300, loss is 0.18949677, fps is 57.63118508760329
|
||||||
导出mindir模型
|
导出mindir模型
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||||
```
|
```
|
||||||
|
|
||||||
参数`ckpt_file` 是必需的,`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。
|
参数`checkpoint_file_path` 是必需的,`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。
|
||||||
|
|
||||||
在执行推理前,MINDIR文件必须在910上通过export.py文件导出。
|
在执行推理前,MINDIR文件必须在910上通过export.py文件导出。
|
||||||
目前仅可处理batch_Size为1。
|
目前仅可处理batch_Size为1。
|
||||||
|
@ -435,7 +437,7 @@ Cross valid dice coeff is: 0.9054352151297033
|
||||||
|
|
||||||
```python
|
```python
|
||||||
'resume': True,
|
'resume': True,
|
||||||
'resume_ckpt': 'ckpt_0/ckpt_unet_medical_adam_1-1_600.ckpt',
|
'resume_ckpt': 'ckpt_unet_medical_adam_1-1_600.ckpt',
|
||||||
'transfer_training': False,
|
'transfer_training': False,
|
||||||
'filter_weight': ["final.weight"]
|
'filter_weight': ["final.weight"]
|
||||||
```
|
```
|
||||||
|
@ -446,7 +448,7 @@ Cross valid dice coeff is: 0.9054352151297033
|
||||||
|
|
||||||
```python
|
```python
|
||||||
'resume': True,
|
'resume': True,
|
||||||
'resume_ckpt': 'ckpt_0/ckpt_unet_medical_adam_1-1_600.ckpt',
|
'resume_ckpt': 'ckpt_unet_medical_adam_1-1_600.ckpt',
|
||||||
'transfer_training': True,
|
'transfer_training': True,
|
||||||
'filter_weight': ["final.weight"]
|
'filter_weight': ["final.weight"]
|
||||||
```
|
```
|
||||||
|
|
|
@ -87,7 +87,7 @@ class CellNucleiDataset:
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
if config.dataset == "Cell_nuclei":
|
if hasattr(config, 'dataset') and config.dataset == "Cell_nuclei":
|
||||||
cell_dataset = CellNucleiDataset(config.data_path, 1, config.result_path, False, 0.8)
|
cell_dataset = CellNucleiDataset(config.data_path, 1, config.result_path, False, 0.8)
|
||||||
else:
|
else:
|
||||||
preprocess_dataset(data_dir=config.data_path, cross_valid_ind=config.cross_valid_ind,
|
preprocess_dataset(data_dir=config.data_path, cross_valid_ind=config.cross_valid_ind,
|
||||||
|
|
|
@ -41,12 +41,13 @@ eval_resize: False
|
||||||
checkpoint_path: './checkpoint/'
|
checkpoint_path: './checkpoint/'
|
||||||
checkpoint_file_path: 'ckpt_unet_medical_adam-4-75.ckpt'
|
checkpoint_file_path: 'ckpt_unet_medical_adam-4-75.ckpt'
|
||||||
rst_path: './result_Files/'
|
rst_path: './result_Files/'
|
||||||
|
result_path: ""
|
||||||
|
|
||||||
# Export options
|
# Export options
|
||||||
width: 572
|
width: 572
|
||||||
height: 572
|
height: 572
|
||||||
file_name: unet
|
file_name: "unet"
|
||||||
file_format: AIR
|
file_format: "AIR"
|
||||||
|
|
||||||
---
|
---
|
||||||
# Help description for each configuration
|
# Help description for each configuration
|
||||||
|
|
|
@ -45,12 +45,13 @@ eval_resize: False
|
||||||
checkpoint_path: './checkpoint/'
|
checkpoint_path: './checkpoint/'
|
||||||
checkpoint_file_path: 'ckpt_unet_nested_adam-4-75.ckpt'
|
checkpoint_file_path: 'ckpt_unet_nested_adam-4-75.ckpt'
|
||||||
rst_path: './result_Files/'
|
rst_path: './result_Files/'
|
||||||
|
result_path: ""
|
||||||
|
|
||||||
# Export options
|
# Export options
|
||||||
width: 572
|
width: 572
|
||||||
height: 572
|
height: 572
|
||||||
file_name: unet
|
file_name: "unet"
|
||||||
file_format: AIR
|
file_format: "AIR"
|
||||||
|
|
||||||
---
|
---
|
||||||
# Help description for each configuration
|
# Help description for each configuration
|
||||||
|
|
|
@ -44,12 +44,13 @@ eval_resize: False
|
||||||
checkpoint_path: './checkpoint/'
|
checkpoint_path: './checkpoint/'
|
||||||
checkpoint_file_path: 'ckpt_unet_nested_adam-4-75.ckpt'
|
checkpoint_file_path: 'ckpt_unet_nested_adam-4-75.ckpt'
|
||||||
rst_path: './result_Files/'
|
rst_path: './result_Files/'
|
||||||
|
result_path: ""
|
||||||
|
|
||||||
# Export options
|
# Export options
|
||||||
width: 572
|
width: 572
|
||||||
height: 572
|
height: 572
|
||||||
file_name: unet
|
file_name: "unet"
|
||||||
file_format: AIR
|
file_format: "AIR"
|
||||||
|
|
||||||
---
|
---
|
||||||
# Help description for each configuration
|
# Help description for each configuration
|
||||||
|
|
|
@ -65,12 +65,13 @@ eval_resize: False
|
||||||
checkpoint_path: './checkpoint/'
|
checkpoint_path: './checkpoint/'
|
||||||
checkpoint_file_path: 'ckpt_unet_simple_adam-4-75.ckpt'
|
checkpoint_file_path: 'ckpt_unet_simple_adam-4-75.ckpt'
|
||||||
rst_path: './result_Files/'
|
rst_path: './result_Files/'
|
||||||
|
result_path: ""
|
||||||
|
|
||||||
# Export options
|
# Export options
|
||||||
width: 572
|
width: 572
|
||||||
height: 572
|
height: 572
|
||||||
file_name: unet
|
file_name: "unet"
|
||||||
file_format: AIR
|
file_format: "AIR"
|
||||||
|
|
||||||
---
|
---
|
||||||
# Help description for each configuration
|
# Help description for each configuration
|
||||||
|
|
|
@ -41,12 +41,13 @@ eval_resize: False
|
||||||
checkpoint_path: './checkpoint/'
|
checkpoint_path: './checkpoint/'
|
||||||
checkpoint_file_path: 'ckpt_unet_simple_adam-4-75.ckpt'
|
checkpoint_file_path: 'ckpt_unet_simple_adam-4-75.ckpt'
|
||||||
rst_path: './result_Files/'
|
rst_path: './result_Files/'
|
||||||
|
result_path: ""
|
||||||
|
|
||||||
# Export options
|
# Export options
|
||||||
width: 572
|
width: 572
|
||||||
height: 572
|
height: 572
|
||||||
file_name: unet
|
file_name: "unet"
|
||||||
file_format: AIR
|
file_format: "AIR"
|
||||||
|
|
||||||
---
|
---
|
||||||
# Help description for each configuration
|
# Help description for each configuration
|
||||||
|
|
|
@ -65,20 +65,20 @@ python ./src/convert_nifti.py --input_path=/path/to/input_image/ --output_path=/
|
||||||
|
|
||||||
```
|
```
|
||||||
|
|
||||||
Refer to `src/config.py`. We support some parameter configurations for quick start.
|
Refer to `default_config.yaml`. We support some parameter configurations for quick start.
|
||||||
|
|
||||||
- Run on Ascend
|
- Run on Ascend
|
||||||
|
|
||||||
```python
|
```python
|
||||||
|
|
||||||
# run training example
|
# run training example
|
||||||
python train.py --data_url=/path/to/data/ --seg_url=/path/to/segment/ > train.log 2>&1 &
|
python train.py --data_path=/path/to/data/ > train.log 2>&1 &
|
||||||
|
|
||||||
# run distributed training example
|
# run distributed training example
|
||||||
bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [IMAGE_PATH] [SEG_PATH]
|
bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [DATA_PATH]
|
||||||
|
|
||||||
# run evaluation example
|
# run evaluation example
|
||||||
python eval.py --data_url=/path/to/data/ --seg_url=/path/to/segment/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
|
python eval.py --data_path=/path/to/data/ --checkpoint_file_path=/path/to/checkpoint/ > eval.log 2>&1 &
|
||||||
|
|
||||||
```
|
```
|
||||||
|
|
||||||
|
@ -92,22 +92,22 @@ If you want to run in modelarts, please check the official documentation of [mod
|
||||||
# b. Add "enable_modelarts=True" on the website UI interface.
|
# b. Add "enable_modelarts=True" on the website UI interface.
|
||||||
# Add other parameters on the website UI interface.
|
# Add other parameters on the website UI interface.
|
||||||
# (2) Download nibabel and set pip-requirements.txt to code directory
|
# (2) Download nibabel and set pip-requirements.txt to code directory
|
||||||
# (3) Set the config directory to "config_path=/The path of config in S3/"
|
# (3) Set the code directory to "/path/unet3d" on the website UI interface.
|
||||||
# (4) Set the code directory to "/path/unet" on the website UI interface.
|
# (4) Set the startup file to "train.py" on the website UI interface.
|
||||||
# (5) Set the startup file to "train.py" on the website UI interface.
|
# (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
|
||||||
# (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
|
# (6) Create your job.
|
||||||
# (7) Create your job.
|
|
||||||
|
|
||||||
# run evaluation on modelarts example
|
# run evaluation on modelarts example
|
||||||
# (1) Copy or upload your trained model to S3 bucket.
|
# (1) Copy or upload your trained model to S3 bucket.
|
||||||
# (2) Perform a or b.
|
# (2) Perform a or b.
|
||||||
# a. Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
|
# a. Set "enable_modelarts=True" on yaml file.
|
||||||
|
# Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
|
||||||
# Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
|
# Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
|
||||||
# b. Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
|
# b. Add "enable_modelarts=True" on the website UI interface.
|
||||||
|
# Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
|
||||||
# Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
|
# Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
|
||||||
# (3) Download nibabel and set pip-requirements.txt to code directory
|
# (3) Download nibabel and set pip-requirements.txt to code directory
|
||||||
# (4) Set the config directory to "config_path=/The path of config in S3/"
|
# (5) Set the code directory to "/path/unet3d" on the website UI interface.
|
||||||
# (5) Set the code directory to "/path/unet" on the website UI interface.
|
|
||||||
# (6) Set the startup file to "eval.py" on the website UI interface.
|
# (6) Set the startup file to "eval.py" on the website UI interface.
|
||||||
# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
|
# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
|
||||||
# (8) Create your job.
|
# (8) Create your job.
|
||||||
|
@ -128,7 +128,7 @@ If you want to run in modelarts, please check the official documentation of [mod
|
||||||
│ ├──run_standalone_eval.sh // shell script for evaluation on Ascend
|
│ ├──run_standalone_eval.sh // shell script for evaluation on Ascend
|
||||||
├── src
|
├── src
|
||||||
│ ├──dataset.py // creating dataset
|
│ ├──dataset.py // creating dataset
|
||||||
│ ├──lr_schedule.py // learning rate scheduler
|
│ ├──lr_schedule.py // learning rate scheduler
|
||||||
│ ├──transform.py // handle dataset
|
│ ├──transform.py // handle dataset
|
||||||
│ ├──convert_nifti.py // convert dataset
|
│ ├──convert_nifti.py // convert dataset
|
||||||
│ ├──loss.py // loss
|
│ ├──loss.py // loss
|
||||||
|
@ -180,8 +180,7 @@ Parameters for both training and evaluation can be set in config.py
|
||||||
#### running on Ascend
|
#### running on Ascend
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
|
python train.py --data_path=/path/to/data/ > train.log 2>&1 &
|
||||||
python train.py --data_url=/path/to/data/ -seg_url=/path/to/segment/ > train.log 2>&1 &
|
|
||||||
|
|
||||||
```
|
```
|
||||||
|
|
||||||
|
@ -205,7 +204,7 @@ epoch time: 1180467.795 ms, per step time: 1380.664 ms
|
||||||
#### Distributed Training
|
#### Distributed Training
|
||||||
|
|
||||||
> Notes:
|
> Notes:
|
||||||
> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
|
> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
|
||||||
>
|
>
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
|
@ -241,8 +240,7 @@ epoch time: 140476.520 ms, per step time: 1312.865 ms
|
||||||
Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/unet3d/Unet3d-10_110.ckpt".
|
Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/unet3d/Unet3d-10_110.ckpt".
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
|
python eval.py --data_path=/path/to/data/ --checkpoint_file_path=/path/to/checkpoint/ > eval.log 2>&1 &
|
||||||
python eval.py --data_url=/path/to/data/ --seg_url=/path/to/segment/ --ckpt_path=/path/to/checkpoint/ > eval.log 2>&1 &
|
|
||||||
|
|
||||||
```
|
```
|
||||||
|
|
||||||
|
|
|
@ -57,14 +57,16 @@ After installing MindSpore via the official website, you can start training and
|
||||||
|
|
||||||
```python
|
```python
|
||||||
# run training example
|
# run training example
|
||||||
|
# need set config_path in config.py file and set data_path in yaml file
|
||||||
python train.py > train.log 2>&1 &
|
python train.py > train.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
sh scripts/run_train.sh
|
sh scripts/run_train.sh dataset
|
||||||
|
|
||||||
# run evaluation example
|
# run evaluation example
|
||||||
|
# need set config_path in config.py file and set data_path, checkpoint_file_path in yaml file
|
||||||
python eval.py > eval.log 2>&1 &
|
python eval.py > eval.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
sh scripts/run_eval.sh ckpt_path
|
sh scripts/run_eval.sh checkpoint_file_path dataset
|
||||||
```
|
```
|
||||||
|
|
||||||
If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training and evaluation as follows:
|
If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training and evaluation as follows:
|
||||||
|
@ -84,9 +86,11 @@ If you want to run in modelarts, please check the official documentation of [mod
|
||||||
# run evaluation on modelarts example
|
# run evaluation on modelarts example
|
||||||
# (1) Copy or upload your trained model to S3 bucket.
|
# (1) Copy or upload your trained model to S3 bucket.
|
||||||
# (2) Perform a or b.
|
# (2) Perform a or b.
|
||||||
# a. Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
|
# a.Set "enable_modelarts=True" on yaml file.
|
||||||
|
# Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
|
||||||
# Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
|
# Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
|
||||||
# b. Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
|
# b. Add "enable_modelarts=True" on the website UI interface.
|
||||||
|
# Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
|
||||||
# Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
|
# Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
|
||||||
# (3) Set the code directory to "/path/textcnn" on the website UI interface.
|
# (3) Set the code directory to "/path/textcnn" on the website UI interface.
|
||||||
# (4) Set the startup file to "eval.py" on the website UI interface.
|
# (4) Set the startup file to "eval.py" on the website UI interface.
|
||||||
|
@ -144,16 +148,17 @@ Parameters for both training and evaluation can be set in config.py
|
||||||
'base_lr': 1e-3 # The base learning rate
|
'base_lr': 1e-3 # The base learning rate
|
||||||
```
|
```
|
||||||
|
|
||||||
For more configuration details, please refer the script `config.py`.
|
For more configuration details, please refer the script `*.yaml`.
|
||||||
|
|
||||||
## [Training Process](#contents)
|
## [Training Process](#contents)
|
||||||
|
|
||||||
- running on Ascend
|
- running on Ascend
|
||||||
|
|
||||||
```python
|
```python
|
||||||
|
# need set config_path in config.py file and set data_path in yaml file
|
||||||
python train.py > train.log 2>&1 &
|
python train.py > train.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
sh scripts/run_train.sh
|
sh scripts/run_train.sh dataset
|
||||||
```
|
```
|
||||||
|
|
||||||
The python command above will run in the background, you can view the results through the file `train.log`.
|
The python command above will run in the background, you can view the results through the file `train.log`.
|
||||||
|
@ -176,9 +181,10 @@ For more configuration details, please refer the script `config.py`.
|
||||||
Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/textcnn/ckpt/train_textcnn.ckpt".
|
Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/textcnn/ckpt/train_textcnn.ckpt".
|
||||||
|
|
||||||
```python
|
```python
|
||||||
python eval.py --checkpoint_path=ckpt_path > eval.log 2>&1 &
|
# need set config_path in config.py file and set data_path, checkpoint_file_path in yaml file
|
||||||
|
python eval.py > eval.log 2>&1 &
|
||||||
OR
|
OR
|
||||||
sh scripts/run_eval.sh ckpt_path
|
sh scripts/run_eval.sh checkpoint_file_path dataset
|
||||||
```
|
```
|
||||||
|
|
||||||
The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
|
The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
|
||||||
|
@ -191,7 +197,7 @@ For more configuration details, please refer the script `config.py`.
|
||||||
## [Export MindIR](#contents)
|
## [Export MindIR](#contents)
|
||||||
|
|
||||||
```shell
|
```shell
|
||||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||||
```
|
```
|
||||||
|
|
||||||
The ckpt_file parameter is required,
|
The ckpt_file parameter is required,
|
||||||
|
|
|
@ -11,6 +11,7 @@ data_path: "/cache/data"
|
||||||
output_path: "/cache/train"
|
output_path: "/cache/train"
|
||||||
load_path: "/cache/checkpoint_path/"
|
load_path: "/cache/checkpoint_path/"
|
||||||
device_num: 1
|
device_num: 1
|
||||||
|
device_id: 0
|
||||||
device_target: 'Ascend'
|
device_target: 'Ascend'
|
||||||
checkpoint_path: './checkpoint/'
|
checkpoint_path: './checkpoint/'
|
||||||
checkpoint_file_path: 'suqeezenet_cifar10-120_195.ckpt'
|
checkpoint_file_path: 'suqeezenet_cifar10-120_195.ckpt'
|
||||||
|
|
|
@ -11,6 +11,7 @@ data_path: "/cache/data"
|
||||||
output_path: "/cache/train"
|
output_path: "/cache/train"
|
||||||
load_path: "/cache/checkpoint_path/"
|
load_path: "/cache/checkpoint_path/"
|
||||||
device_num: 1
|
device_num: 1
|
||||||
|
device_id: 0
|
||||||
device_target: 'Ascend'
|
device_target: 'Ascend'
|
||||||
checkpoint_path: './checkpoint/'
|
checkpoint_path: './checkpoint/'
|
||||||
checkpoint_file_path: 'suqeezenet_imagenet-200_5004.ckpt'
|
checkpoint_file_path: 'suqeezenet_imagenet-200_5004.ckpt'
|
||||||
|
|
|
@ -11,6 +11,7 @@ data_path: "/cache/data"
|
||||||
output_path: "/cache/train"
|
output_path: "/cache/train"
|
||||||
load_path: "/cache/checkpoint_path/"
|
load_path: "/cache/checkpoint_path/"
|
||||||
device_num: 1
|
device_num: 1
|
||||||
|
device_id: 0
|
||||||
device_target: 'Ascend'
|
device_target: 'Ascend'
|
||||||
checkpoint_path: './checkpoint/'
|
checkpoint_path: './checkpoint/'
|
||||||
checkpoint_file_path: 'suqeezenet_residual_imagenet-300_5004.ckpt'
|
checkpoint_file_path: 'suqeezenet_residual_imagenet-300_5004.ckpt'
|
||||||
|
|
|
@ -80,7 +80,7 @@ python train.py
|
||||||
sh run_distribute_train_ghostnet.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE]
|
sh run_distribute_train_ghostnet.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE]
|
||||||
|
|
||||||
# run eval on Ascend
|
# run eval on Ascend
|
||||||
python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.ckpt
|
python eval.py --device_id 0 --dataset coco --checkpoint_file_path LOG4/ssd-500_458.ckpt
|
||||||
```
|
```
|
||||||
|
|
||||||
If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training and evaluation as follows:
|
If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training and evaluation as follows:
|
||||||
|
|
|
@ -30,22 +30,16 @@ def test_SSD_mobilenet_v1_fpn_coco2017():
|
||||||
utils.copy_files(model_path, cur_path, model_name)
|
utils.copy_files(model_path, cur_path, model_name)
|
||||||
cur_model_path = os.path.join(cur_path, model_name)
|
cur_model_path = os.path.join(cur_path, model_name)
|
||||||
|
|
||||||
old_list = ["/data/MindRecord_COCO",
|
old_list = ["/cache/data", "MindRecord_COCO", "coco_ori", "/ckpt/mobilenet_v1.ckpt"]
|
||||||
"/ckpt/mobilenet_v1.ckpt",
|
new_list = [os.path.join(utils.data_root, "coco/coco2017"), "mindrecord_train/ssd_mindrecord", ".",
|
||||||
"/data/coco2017"]
|
os.path.join(utils.ckpt_root, "ssd_mobilenet_v1/mobilenet-v1.ckpt")]
|
||||||
new_list = [os.path.join(utils.data_root, "coco/coco2017/mindrecord_train/ssd_mindrecord"),
|
utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "ssd_mobilenet_v1_fpn_config.yaml"))
|
||||||
os.path.join(utils.ckpt_root, "ssd_mobilenet_v1/mobilenet-v1.ckpt"),
|
old_list = ["config.epoch_size", "dataset_sink_mode=dataset_sink_mode"]
|
||||||
os.path.join(utils.data_root, "coco/coco2017")]
|
|
||||||
utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "src/config_ssd_mobilenet_v1_fpn.py"))
|
|
||||||
old_list = ["ssd300"]
|
|
||||||
new_list = ["ssd_mobilenet_v1_fpn"]
|
|
||||||
utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "src/config.py"))
|
|
||||||
old_list = ["args_opt.epoch_size", "dataset_sink_mode=dataset_sink_mode"]
|
|
||||||
new_list = ["5", "dataset_sink_mode=dataset_sink_mode, sink_size=20"]
|
new_list = ["5", "dataset_sink_mode=dataset_sink_mode, sink_size=20"]
|
||||||
utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "train.py"))
|
utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "train.py"))
|
||||||
|
|
||||||
exec_network_shell = "cd {0}; sh -x scripts/run_distribute_train.sh 8 {1} 0.2 coco {2}"\
|
exec_network_shell = "cd {0}; sh -x scripts/run_distribute_train.sh 8 {1} 0.2 coco \
|
||||||
.format(model_name, 60, utils.rank_table_path)
|
{2} ssd_mobilenet_v1_fpn_config.yaml".format(model_name, 60, utils.rank_table_path)
|
||||||
os.system(exec_network_shell)
|
os.system(exec_network_shell)
|
||||||
cmd = "ps -ef | grep train.py | grep coco | grep device_num | grep device_id | grep -v grep"
|
cmd = "ps -ef | grep train.py | grep coco | grep device_num | grep device_id | grep -v grep"
|
||||||
ret = utils.process_check(120, cmd)
|
ret = utils.process_check(120, cmd)
|
||||||
|
|
Loading…
Reference in New Issue