For this PR, the UI config handling for wavediff has been refactored using factory methods and the plotting has been enabled with plots_interface.py.
Now only a single parameter needs to be specified per run in the configs.yaml file.
---
training_conf: training_config.yaml
The respective config files for training & metrics (and shortly plotting), now contain parameters for the other configuration files which ConfigHandler will use to determine the type of commands to execute during the run.
Note, the code as is could launch multiple runs in serial with different configs files. To do this, just list all the config files in configs.yaml file with the correct key training_conf, metrics_conf or plotting_conf .
I implemented various exception handling for FileNotFoundError, TypeError and KeyError. I skipped AttributeError as I should write a function to check all the keys in the config file for typos/errors.
For this PR, the UI config handling for wavediff has been refactored using factory methods and the plotting has been enabled with
plots_interface.py
.Now only a single parameter needs to be specified per run in the
configs.yaml
file.The respective config files for
training
&metrics
(and shortly plotting), now contain parameters for the other configuration files which ConfigHandler will use to determine the type of commands to execute during the run.Note, the code as is could launch multiple runs in serial with different configs files. To do this, just list all the config files in
configs.yaml
file with the correct keytraining_conf
,metrics_conf
orplotting_conf
.I implemented various exception handling for
FileNotFoundError
,TypeError
andKeyError
. I skippedAttributeError
as I should write a function to check all the keys in the config file for typos/errors.