MMSkeleton provides various models, datasets, apis, operators for various applications, such as pose estimation, human detection, action recognition and dataset building. The workflow of a application is defined by a processor, which is usually a python function.
In MMSkeleton, an application is defined in a configuration file.
It is a .json, .yaml or .py file including processor_cfg field.
Here is an example:
# yaml
processor_cfg:
type: <path to processor function>
dataset:
type: <path to dataset module>
data_path: ./data
#more arguments for processor function...
argparse_cfg:
data:
bind_to: processor_cfg.dataset.data_path
help: the path of data
#more option arguments for command line...The processor_cfg specifies a processor function and its dataset module
In adittion, the data_path argument of the dataset is "./data".
The argparse_cfg create a option argument data which is bound to data_path.
Note that, mmskeleton will import processor function or modules according to the given path by the priority of local directory > system python path > mmskeleton.
With this configuration, the application can be started by:
mmskl $CONFIG_FILE [--data $DATA_PATH]