Training & Evaluation in Command Line¶

LiBai provides multiple arguments for covering a variety of situations.

Training¶

LiBai provides tools/train.sh and tools/train_net.py for launching training & eval task.

You can modify tools/train_net.py according to your own needs.

Training & Evaluation¶

For completely train and test, you can run:

bash tools/train.sh \
tools/train_net.py \ 
4 \                    # number of gpus
path_to_your_config.py # config.py for your task

Training & Partial Evaluation¶

If the evaluation process is time consuming, you can set the parameter train.evaluation.eval_iter in your config.py to a smaller number such as 20, which can make the evaluation process faster by only using part of the testset. You can also set the parameter by the command line directly :

NOTE: the eval metric will be calculated by part of testing dataset

bash tools/train.sh \
tools/train_net.py \ 
4 \                             # number of gpus
path_to_your_config.py \        # config.py for your task
train.evaluation.eval_iter=20   # set eval_iter for testing

Training & No Evaluation¶

If you want to train without evaluation, you can set train.evaluation.enabled=False in your config.py or in the command line:

bash tools/train.sh \
tools/train_net.py \ 
4 \                              # number of gpus
path_to_your_config.py \         # config.py for your task
train.evaluation.enabled=False   # set no evaluation 

Resume Training¶

If you want to resume training, you should set --resume in the command line, and set train.output_dir in your config.py or in the command line

For example: your training was interrupted unexpectly, your lastest model path is output/demo/model_0000019/. you should set train.output_dir=output/demo for resume trainig.

bash tools/train.sh \
tools/train_net.py \ 
4 \                              # number of gpus
path_to_your_config.py \         # config.py for your task
--resume \                       # set resume training
train.output_dir=path/task       # set resume path, it should be parent directory of model path

Evaluation¶

If you want to evaluate your model without training, you should set --eval-only in your command line, and set train.load_weight.

Besides, train.evaluation.eval_iter=20 will be valid in --eval-only if you set it, you can set eval_iter according to your own needs.

bash tools/train.sh \
tools/train_net.py \ 
4 \                                          # number of gpus
path_to_your_config.py \                     # config.py for your task
--eval-only \                                # set eval without train
train.load_weight=path/task/model_final      # set model path

Quickly check in the respective loop¶

If you want to find out whether there are any bugs in your program, you can pass --fast-dev-run to the command line, which will change config settings to:

train.train_epoch = 0
train.train_iter = 20
train.evaluation.eval_period = 10
train.log_period = 1

Besides, train.evaluation.eval_iter=20 will be valid in --fast-dev-run if you set it, you can set eval_iter according to your own needs.

bash tools/train.sh \
tools/train_net.py \ 
4 \                                          # number of gpus
path_to_your_config.py \                     # config.py for your task
--fast-dev-run                               # set for quickly check