Training & Evaluation in Command Line¶

LiBai provides multiple arguments 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¶

To completely train and test, run:

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

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 using only 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 \ 
path_to_your_config.py \        # config.py for your task
4 \                             # number of gpus
train.evaluation.eval_iter=20   # set eval_iter for testing

Training & No Evaluation¶

To train without evaluation, set train.evaluation.enabled=False in your config.py or in the command line:

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

Resume Training¶

To resume training, set --resume in the command line, and set train.output_dir in your config.py or in the command line

For example, if your training is interrupted unexpectly, and your lastest model path is output/demo/model_0000019/, then set train.output_dir=output/demo to resume trainig:

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

Evaluation¶

To evaluate your model without training, 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 \ 
path_to_your_config.py \                     # config.py for your task
4 \                                          # number of gpus
--eval-only \                                # set eval without train
train.load_weight=path/task/model_final      # set model path

Quickly check in the respective loop¶

To find out whether there are any bugs in your program, 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 \ 
path_to_your_config.py \                     # config.py for your task
4 \                                          # number of gpus
--fast-dev-run                               # set for quickly check