# Use Configs
Configs are handy ways to define experiment parameters (models, learning rates, etc) without having to interface directly with low-level deep learning utilities. 

In MedSegPy's config structure, config types are 1-to-1 with model architectures.
This means that different architectures have different config types. For a list
of config types, see [modules/config](../modules/config). The base `Config`
class is abstract; use the config type corresponding to the architecture
you would like to build.

Configs can either be in `yaml` (recommended) or `ini` format.

### Use Configs
Some basic usage of the `Config` object is shown below. 
We use the 2D U-Net config for clarity.
```python
from medsegpy.config import UNetConfig
cfg = UNetConfig()    # obtain medsegpy's default config for 2d U-Net
cfg.xxx = yyy      # set values for data
cfg.merge_from_file("my_cfg.yaml")   # load values from a file
cfg.merge_from_file("my_cfg.ini")  # supports ini files too

cfg.merge_from_list(["USE_EARLY_STOPPING", True])  # can also load values from a list of str
cfg.summary()  # print a formatted summary
```

To see a list of example configs in medsegpy, see [configs](https://github.com/ad12/MedSegPy/tree/master/configs/).

### Adding New Fields
Existing config fields may not always service the needs for your particular project.
However, because these parameters are often project-specific, we advise against modifying the `Config` class directly.

Instead, new fields can directly be added to config files after they have been created.
```python
from medsegpy.config import UNetConfig
cfg = UNetConfig()    # obtain medsegpy's default config for 2d U-Net
cfg.MY_NEW_FIELD = VALUE
```

These new fields can also be specified in the config file and will be loaded in using `cfg.merge_from_file`.

### Best Practice with Configs

1. Keep the configs you write simple: don't include keys that do not affect the experimental setting.

2. Keep a version number in your configs (or the base config), e.g., `VERSION: 2`,
   for backward compatibility.
	 We print a warning when reading a config without version number.
   The official configs do not include version number because they are meant to
   be always up-to-date.

3. Save a full config together with a trained model, and use it to run inference.
   This is more robust to changes that may happen to the config definition
   (e.g., if a default value changed), although we will try to avoid such changes.